diff --git "a/resources/text/training_data.json" "b/resources/text/training_data.json" new file mode 100644--- /dev/null +++ "b/resources/text/training_data.json" @@ -0,0 +1,161603 @@ +{ + "2540": { + "metadata": { + "title": "Over Half Of Americans Have Had A Family Member Jailed", + "description": "Share of Americans who have had the following incarcerated*", + "main_insight": "A significant percentage of Americans have experienced the incarceration of a family member, with the highest percentage being for immediate or extended family members." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Family Member": "Immediate/extended family member", + "Percentage": 64.0 + } + ], + "score": 0.85, + "annotation": "The has a maximum value at Immediate/extended family member", + "reason": "The [{'Family Member': 'Immediate/extended family member', 'Percentage': 64.0}] of has a maximum value of 64.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Family Member": "Immediate/extended family member", + "Percentage": 64.0 + } + ], + "score": 0.74, + "annotation": "The shows a sudden jump in Percentage from Immediate family member to Immediate/extended family member.", + "reason": "The Percentage for increases from 45.0 at Immediate family member to 64.0 at Immediate/extended family member, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Family Member": "Child", + "Percentage": 12.2 + } + ], + "score": 0.54, + "annotation": "The has a minimum value at Child", + "reason": "The [{'Family Member': 'Child', 'Percentage': 12.2}] of has a mainimum value of 12.2, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Family Member": "Immediate/extended family member", + "Percentage": 64.0 + }, + { + "Family Member": "Immediate family member", + "Percentage": 45.0 + }, + { + "Family Member": "Sibling", + "Percentage": 27.5 + }, + { + "Family Member": "Parent", + "Percentage": 18.4 + }, + { + "Family Member": "Spouse or co-parent", + "Percentage": 13.5 + }, + { + "Family Member": "Child", + "Percentage": 12.2 + } + ], + "columns": [ + { + "name": "Family Member", + "importance": "primary", + "description": "Type of family member incarcerated", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Share of Americans who have had that type of family member incarcerated", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "757": { + "metadata": { + "title": "Unemployment Rate Falls for Some, Rises for Others", + "description": "U.S. unemployment rates in April and May by race and ethnicity", + "main_insight": "Unemployment rates changed between April and May, decreasing overall and for White and Black populations, while increasing for Hispanic and staying relatively the same for Asian populations." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Race/Ethnicity": "Hispanic", + "Units": 17.6, + "Month": "May" + }, + { + "Race/Ethnicity": "Hispanic", + "Units": 18.9, + "Month": "April" + } + ], + "score": 0.89, + "annotation": "The Units of May and April show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between May and April is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Race/Ethnicity": "Hispanic", + "Units": 18.9, + "Month": "April" + } + ], + "score": 0.82, + "annotation": "The April has a maximum value at Hispanic", + "reason": "The [{'Race/Ethnicity': 'Hispanic', 'Units': 18.9, 'Month': 'April'}] of April has a maximum value of 18.9, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Race/Ethnicity": "Hispanic", + "Units": 18.9, + "Month": "April" + } + ], + "score": 0.81, + "annotation": "The April shows a sudden jump in Units from Black to Hispanic.", + "reason": "The Units for April increases from 16.7 at Black to 18.9 at Hispanic, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Race/Ethnicity": "Black", + "Units": 16.8, + "Month": "May" + } + ], + "score": 0.8, + "annotation": "The May accounts for the majority in Black.", + "reason": "The Units of May in Black accounts for 0.5014925373134329 (more than 50%) of the total of Units of all Month." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Race/Ethnicity": "Asian", + "Units": 15.0, + "Month": "May" + } + ], + "score": 0.8, + "annotation": "The May accounts for the majority in Asian.", + "reason": "The Units of May in Asian accounts for 0.5084745762711864 (more than 50%) of the total of Units of all Month." + } + ], + "data": { + "data": [ + { + "Race/Ethnicity": "Total", + "Units": 13.3, + "Month": "May" + }, + { + "Race/Ethnicity": "Total", + "Units": 14.7, + "Month": "April" + }, + { + "Race/Ethnicity": "White", + "Units": 12.4, + "Month": "May" + }, + { + "Race/Ethnicity": "White", + "Units": 14.2, + "Month": "April" + }, + { + "Race/Ethnicity": "Black", + "Units": 16.8, + "Month": "May" + }, + { + "Race/Ethnicity": "Black", + "Units": 16.7, + "Month": "April" + }, + { + "Race/Ethnicity": "Asian", + "Units": 15.0, + "Month": "May" + }, + { + "Race/Ethnicity": "Asian", + "Units": 14.5, + "Month": "April" + }, + { + "Race/Ethnicity": "Hispanic", + "Units": 17.6, + "Month": "May" + }, + { + "Race/Ethnicity": "Hispanic", + "Units": 18.9, + "Month": "April" + } + ], + "columns": [ + { + "name": "Race/Ethnicity", + "importance": "primary", + "description": "Racial and ethnic groups", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding month", + "role": "y" + }, + { + "name": "Month", + "importance": "primary", + "description": "The month of the data point", + "role": "group" + } + ] + } + }, + "1909": { + "metadata": { + "title": "Continued Discrimination for LGBTQ Youth", + "description": "Percentage of LGBTQ who experienced the following related to their sexual orientation or gender identity", + "main_insight": "A significant percentage of LGBTQ youth experienced discrimination and attempts to change their identity, with a large majority feeling sad/hopeless for at least two weeks." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "Discrimination", + "Discrimination": 71.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Discrimination from Feeling Sad/Hopeless to Discrimination.", + "reason": "The Discrimination for increases from 0 at Feeling Sad/Hopeless to 71.0 at Discrimination, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Discrimination", + "Discrimination": 71.0 + } + ], + "score": 0.67, + "annotation": "The has a maximum value at Discrimination", + "reason": "The [{'Category': 'Discrimination', 'Discrimination': 71.0}] of has a maximum value of 71.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Category": "Attempted to convince to change", + "Discrimination": 0 + }, + { + "Category": "Feeling Sad/Hopeless", + "Discrimination": 0 + } + ], + "score": 0.39, + "annotation": "The has minimum values at Attempted to convince to change, Feeling Sad/Hopeless", + "reason": "The [{'Category': 'Attempted to convince to change', 'Discrimination': 0}, {'Category': 'Feeling Sad/Hopeless', 'Discrimination': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Category": "Discrimination", + "Discrimination": 71.0 + }, + { + "Category": "Attempted to convince to change", + "Discrimination": 0 + }, + { + "Category": "Feeling Sad/Hopeless", + "Discrimination": 0 + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Type of experience", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Discrimination", + "importance": "primary", + "description": "Percentage who experienced discrimination", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1650": { + "metadata": { + "title": "How Is Public Debt Evolving?", + "description": "Level of public debt as a share of GDP in selected countries in 2023 and change over 2 years (in %)", + "main_insight": "Japan has by far the highest public debt as a share of GDP in 2023 among the selected countries, at 263%. China's debt-to-GDP ratio has increased the most (+14%) between 2021 and 2023 among the countries shown, while Canada's has decreased the most (-10%)." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "JP", + "Debt-to-GDP Ratio (2023)": 263.0 + } + ], + "score": 0.98, + "annotation": "The has a maximum value at JP", + "reason": "The [{'Country': 'JP', 'Debt-to-GDP Ratio (2023)': 263.0}] of has a maximum value of 263.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "JP", + "Debt-to-GDP Ratio (2023)": 263.0 + } + ], + "score": 0.89, + "annotation": "The shows a sudden jump in Debt-to-GDP Ratio (2023) from IT to JP.", + "reason": "The Debt-to-GDP Ratio (2023) for increases from 140.0 at IT to 263.0 at JP, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "DE", + "Debt-to-GDP Ratio (2023)": 65.0 + } + ], + "score": 0.6, + "annotation": "The has a minimum value at DE", + "reason": "The [{'Country': 'DE', 'Debt-to-GDP Ratio (2023)': 65.0}] of has a mainimum value of 65.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "JP", + "Debt-to-GDP Ratio (2023)": 263.0 + }, + { + "Country": "IT", + "Debt-to-GDP Ratio (2023)": 140.0 + }, + { + "Country": "US", + "Debt-to-GDP Ratio (2023)": 133.0 + }, + { + "Country": "ES", + "Debt-to-GDP Ratio (2023)": 110.0 + }, + { + "Country": "FR", + "Debt-to-GDP Ratio (2023)": 110.0 + }, + { + "Country": "BE", + "Debt-to-GDP Ratio (2023)": 106.0 + }, + { + "Country": "CA", + "Debt-to-GDP Ratio (2023)": 104.0 + }, + { + "Country": "GB", + "Debt-to-GDP Ratio (2023)": 98.0 + }, + { + "Country": "IN", + "Debt-to-GDP Ratio (2023)": 88.0 + }, + { + "Country": "CN", + "Debt-to-GDP Ratio (2023)": 82.0 + }, + { + "Country": "DE", + "Debt-to-GDP Ratio (2023)": 65.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country abbreviation", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Debt-to-GDP Ratio (2023)", + "importance": "primary", + "description": "Public debt as a share of GDP in 2023", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2495": { + "metadata": { + "title": "Netflix Challenges TV Networks at the 2014 Emmys", + "description": "Nominations at the 2014 Primetime Emmy Awards", + "main_insight": "In 2014, Netflix received 31 Primetime Emmy nominations, demonstrating its growing presence and challenge to traditional cable and broadcast networks, though it still lagged significantly behind HBO." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Network": "HBO", + "Count": 99, + "Feature Descriptors": "Nominations" + }, + { + "Network": "HBO", + "Count": "Cable", + "Feature Descriptors": "Type" + }, + { + "Network": "CBS", + "Count": 47, + "Feature Descriptors": "Nominations" + }, + { + "Network": "CBS", + "Count": "Broadcast", + "Feature Descriptors": "Type" + }, + { + "Network": "NBC", + "Count": 46, + "Feature Descriptors": "Nominations" + }, + { + "Network": "NBC", + "Count": "Broadcast", + "Feature Descriptors": "Type" + }, + { + "Network": "FX", + "Count": 45, + "Feature Descriptors": "Nominations" + }, + { + "Network": "FX", + "Count": "Cable", + "Feature Descriptors": "Type" + }, + { + "Network": "ABC", + "Count": 37, + "Feature Descriptors": "Nominations" + }, + { + "Network": "ABC", + "Count": "Broadcast", + "Feature Descriptors": "Type" + }, + { + "Network": "PBS", + "Count": 34, + "Feature Descriptors": "Nominations" + }, + { + "Network": "PBS", + "Count": "Broadcast", + "Feature Descriptors": "Type" + }, + { + "Network": "Netflix", + "Count": 31, + "Feature Descriptors": "Nominations" + }, + { + "Network": "Netflix", + "Count": "Streaming", + "Feature Descriptors": "Type" + }, + { + "Network": "AMC", + "Count": 26, + "Feature Descriptors": "Nominations" + }, + { + "Network": "AMC", + "Count": "Cable", + "Feature Descriptors": "Type" + }, + { + "Network": "Showtime", + "Count": 24, + "Feature Descriptors": "Nominations" + }, + { + "Network": "Showtime", + "Count": "Cable", + "Feature Descriptors": "Type" + }, + { + "Network": "Comedy Central", + "Count": 21, + "Feature Descriptors": "Nominations" + }, + { + "Network": "Comedy Central", + "Count": "Cable", + "Feature Descriptors": "Type" + }, + { + "Network": "Fox", + "Count": 18, + "Feature Descriptors": "Nominations" + }, + { + "Network": "Fox", + "Count": "Broadcast", + "Feature Descriptors": "Type" + }, + { + "Network": "Lifetime", + "Count": 17, + "Feature Descriptors": "Nominations" + }, + { + "Network": "Lifetime", + "Count": "Cable", + "Feature Descriptors": "Type" + } + ], + "columns": [ + { + "name": "Network", + "importance": "primary", + "description": "Name of the TV network or streaming service", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Count", + "importance": "primary", + "description": "Count for the corresponding feature descriptors", + "role": "y" + }, + { + "name": "Feature Descriptors", + "importance": "primary", + "description": "The feature descriptors of the data point", + "role": "group" + } + ] + } + }, + "1476": { + "metadata": { + "title": "What Americans Think of Trump's Tax Plan", + "description": "Percent of respondents who think the following about the tax reform bill 2017", + "main_insight": "A majority of Americans disapprove of Trump's tax plan, believe the wealthy will profit most, and don't think jobs will increase or the economy will grow. There is a mixed response in terms of the expected personal impact of the new tax law." + }, + "chart_type": "Donut Chart", + "datafacts": [], + "data": { + "data": [ + { + "Question": "Overall tax plan", + "Response": "Disapprove" + }, + { + "Question": "Overall tax plan", + "Response": "Approve" + }, + { + "Question": "Overall tax plan", + "Response": "Don't know/No answer" + }, + { + "Question": "Will jobs increase & economy grow?", + "Response": "No" + }, + { + "Question": "Will jobs increase & economy grow?", + "Response": "Yes" + }, + { + "Question": "Will jobs increase & economy grow?", + "Response": "Don't know/No answer" + }, + { + "Question": "Who will profit most?", + "Response": "Wealthy" + }, + { + "Question": "Who will profit most?", + "Response": "Middle class" + }, + { + "Question": "Who will profit most?", + "Response": "Low income" + }, + { + "Question": "Who will profit most?", + "Response": "Don't know/No answer" + }, + { + "Question": "Effect on own tax burden", + "Response": "Not much impact" + }, + { + "Question": "Effect on own tax burden", + "Response": "Increase" + }, + { + "Question": "Effect on own tax burden", + "Response": "Reduce" + }, + { + "Question": "Effect on own tax burden", + "Response": "Don't know/No answer" + } + ], + "columns": [ + { + "name": "Question", + "importance": "primary", + "description": "Different questions asked about the tax plan", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Response", + "importance": "primary", + "description": "Possible answers to each question", + "unit": "none", + "data_type": "string", + "role": "y" + } + ] + } + }, + "2004": { + "metadata": { + "title": "Batter Up: Most and Least Consistent Hitters", + "description": "Total movement of batting averages over 5 years for select players", + "main_insight": "The visualization shows the variability in batting averages over five years for several baseball players, highlighting the most and least consistent hitters based on the total movement of their batting average." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": "Total Movement", + "Ratio": 0.009, + "Player Names": "Nori Aoki" + }, + { + "Year": "Total Movement", + "Ratio": 0.011, + "Player Names": "Tony Fernandez" + } + ], + "score": 1.0, + "annotation": "The Ratio of Nori Aoki and Tony Fernandez show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Nori Aoki and Tony Fernandez is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": "Total Movement", + "Ratio": 0.004, + "Player Names": "Khris Davis" + }, + { + "Year": "Total Movement", + "Ratio": 0.011, + "Player Names": "Tony Fernandez" + } + ], + "score": 1.0, + "annotation": "The Ratio of Khris Davis and Tony Fernandez show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Khris Davis and Tony Fernandez is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": "Total Movement", + "Ratio": 0.004, + "Player Names": "Khris Davis" + }, + { + "Year": "Total Movement", + "Ratio": 0.009, + "Player Names": "Nori Aoki" + } + ], + "score": 1.0, + "annotation": "The Ratio of Khris Davis and Nori Aoki show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Khris Davis and Nori Aoki is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Year": "Total Movement", + "Ratio": 0.004, + "Player Names": "Khris Davis" + } + ], + "score": 0.98, + "annotation": "The Khris Davis has the smallest proportion in Total Movement.", + "reason": "The Ratio of Khris Davis in Total Movement accounts for 0.0034782608695652175, smaller than all other Player Names, of the total of Ratio of all Player Names." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "Year 5", + "Ratio": 0.32, + "Player Names": "Babe Ruth" + }, + { + "Year": "Year 5", + "Ratio": 0.32, + "Player Names": "King Kelly" + } + ], + "score": 0.98, + "annotation": "The Babe Ruth, King Kelly all have the largest proportion in Year 5.", + "reason": "The Ratio of Babe Ruth, King Kelly in Year 5 all account for 0.1584158415841584, larger than all other Player Names, of the total of Ratio of all Player Names." + } + ], + "data": { + "data": [ + { + "Year": "Year 1", + "Ratio": 0.24, + "Player Names": "Khris Davis" + }, + { + "Year": "Year 1", + "Ratio": 0.29, + "Player Names": "Nori Aoki" + }, + { + "Year": "Year 1", + "Ratio": 0.29, + "Player Names": "Tony Fernandez" + }, + { + "Year": "Year 1", + "Ratio": 0.34, + "Player Names": "Babe Ruth" + }, + { + "Year": "Year 1", + "Ratio": 0.31, + "Player Names": "King Kelly" + }, + { + "Year": "Year 1", + "Ratio": 0.32, + "Player Names": "Roy Campanella_1" + }, + { + "Year": "Year 1", + "Ratio": 0.31, + "Player Names": "Roy Campanella_2" + }, + { + "Year": "Year 2", + "Ratio": 0.25, + "Player Names": "Khris Davis" + }, + { + "Year": "Year 2", + "Ratio": 0.29, + "Player Names": "Nori Aoki" + }, + { + "Year": "Year 2", + "Ratio": 0.31, + "Player Names": "Tony Fernandez" + }, + { + "Year": "Year 2", + "Ratio": 0.37, + "Player Names": "Babe Ruth" + }, + { + "Year": "Year 2", + "Ratio": 0.34, + "Player Names": "King Kelly" + }, + { + "Year": "Year 2", + "Ratio": 0.3, + "Player Names": "Roy Campanella_1" + }, + { + "Year": "Year 2", + "Ratio": 0.33, + "Player Names": "Roy Campanella_2" + }, + { + "Year": "Year 3", + "Ratio": 0.25, + "Player Names": "Khris Davis" + }, + { + "Year": "Year 3", + "Ratio": 0.28, + "Player Names": "Nori Aoki" + }, + { + "Year": "Year 3", + "Ratio": 0.29, + "Player Names": "Tony Fernandez" + }, + { + "Year": "Year 3", + "Ratio": 0.34, + "Player Names": "Babe Ruth" + }, + { + "Year": "Year 3", + "Ratio": 0.39, + "Player Names": "King Kelly" + }, + { + "Year": "Year 3", + "Ratio": 0.21, + "Player Names": "Roy Campanella_1" + }, + { + "Year": "Year 3", + "Ratio": 0.27, + "Player Names": "Roy Campanella_2" + }, + { + "Year": "Year 4", + "Ratio": 0.25, + "Player Names": "Khris Davis" + }, + { + "Year": "Year 4", + "Ratio": 0.29, + "Player Names": "Nori Aoki" + }, + { + "Year": "Year 4", + "Ratio": 0.3, + "Player Names": "Tony Fernandez" + }, + { + "Year": "Year 4", + "Ratio": 0.3, + "Player Names": "Babe Ruth" + }, + { + "Year": "Year 4", + "Ratio": 0.35, + "Player Names": "King Kelly" + }, + { + "Year": "Year 4", + "Ratio": 0.3, + "Player Names": "Roy Campanella_1" + }, + { + "Year": "Year 4", + "Ratio": 0.28, + "Player Names": "Roy Campanella_2" + }, + { + "Year": "Year 5", + "Ratio": 0.25, + "Player Names": "Khris Davis" + }, + { + "Year": "Year 5", + "Ratio": 0.29, + "Player Names": "Nori Aoki" + }, + { + "Year": "Year 5", + "Ratio": 0.3, + "Player Names": "Tony Fernandez" + }, + { + "Year": "Year 5", + "Ratio": 0.32, + "Player Names": "Babe Ruth" + }, + { + "Year": "Year 5", + "Ratio": 0.32, + "Player Names": "King Kelly" + }, + { + "Year": "Year 5", + "Ratio": 0.26, + "Player Names": "Roy Campanella_1" + }, + { + "Year": "Year 5", + "Ratio": 0.28, + "Player Names": "Roy Campanella_2" + }, + { + "Year": "Total Movement", + "Ratio": 0.004, + "Player Names": "Khris Davis" + }, + { + "Year": "Total Movement", + "Ratio": 0.009, + "Player Names": "Nori Aoki" + }, + { + "Year": "Total Movement", + "Ratio": 0.011, + "Player Names": "Tony Fernandez" + }, + { + "Year": "Total Movement", + "Ratio": 0.099, + "Player Names": "Babe Ruth" + }, + { + "Year": "Total Movement", + "Ratio": 0.331, + "Player Names": "King Kelly" + }, + { + "Year": "Total Movement", + "Ratio": 0.338, + "Player Names": "Roy Campanella_1" + }, + { + "Year": "Total Movement", + "Ratio": 0.358, + "Player Names": "Roy Campanella_2" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Represents the year in a 5-year span", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding player names", + "role": "y" + }, + { + "name": "Player Names", + "importance": "primary", + "description": "The player names of the data point", + "role": "group" + } + ] + } + }, + "631": { + "metadata": { + "title": "Study Links U.S. Anti-Trans Legislation and Suicide Attempts", + "description": "Change in the number of reported suicide attempts per trans/non-binary young person in relation to U.S. state-level laws", + "main_insight": "The number of reported suicide attempts among trans/non-binary youth, especially those aged 13-17, significantly increased after the enactment of anti-trans legislation in their states." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Time Relative to Enactment": "Year 3 after", + "Ratio": 0.28, + "Age Group": "Ages 13-17" + }, + { + "Time Relative to Enactment": "Year 3 after", + "Ratio": 0.2, + "Age Group": "Ages 13-24" + } + ], + "score": 0.95, + "annotation": "The Ratio of Ages 13-17 and Ages 13-24 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Ages 13-17 and Ages 13-24 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time Relative to Enactment": "Year 2 after", + "Ratio": 0.18, + "Age Group": "Ages 13-24" + } + ], + "score": 0.93, + "annotation": "The Ages 13-24 shows a sudden jump in Ratio from Year 2 prior to Year 2 after.", + "reason": "The Ratio for Ages 13-24 increases from 0.04 at Year 2 prior to 0.18 at Year 2 after, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time Relative to Enactment": "Year 3 after", + "Ratio": 0.28, + "Age Group": "Ages 13-17" + } + ], + "score": 0.85, + "annotation": "The Ages 13-17 shows a sudden jump in Ratio from Year 3 prior to Year 3 after.", + "reason": "The Ratio for Ages 13-17 increases from 0.08 at Year 3 prior to 0.28 at Year 3 after, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Time Relative to Enactment": "Year 2 after", + "Ratio": 0.4, + "Age Group": "Ages 13-17" + } + ], + "score": 0.84, + "annotation": "The Ages 13-17 has a maximum value at Year 2 after", + "reason": "The [{'Time Relative to Enactment': 'Year 2 after', 'Ratio': 0.4, 'Age Group': 'Ages 13-17'}] of Ages 13-17 has a maximum value of 0.4, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Time Relative to Enactment": "Year 3 after", + "Ratio": 0.28, + "Age Group": "Ages 13-17" + } + ], + "score": 0.77, + "annotation": "The Ages 13-17 accounts for the majority in Year 3 after.", + "reason": "The Ratio of Ages 13-17 in Year 3 after accounts for 0.5833333333333334 (more than 50%) of the total of Ratio of all Age Group." + } + ], + "data": { + "data": [ + { + "Time Relative to Enactment": "Year 3 prior", + "Ratio": 0.08, + "Age Group": "Ages 13-17" + }, + { + "Time Relative to Enactment": "Year 3 prior", + "Ratio": 0.04, + "Age Group": "Ages 13-24" + }, + { + "Time Relative to Enactment": "Year 2 prior", + "Ratio": 0.06, + "Age Group": "Ages 13-17" + }, + { + "Time Relative to Enactment": "Year 2 prior", + "Ratio": 0.04, + "Age Group": "Ages 13-24" + }, + { + "Time Relative to Enactment": "Year 1 prior", + "Ratio": 0.05, + "Age Group": "Ages 13-17" + }, + { + "Time Relative to Enactment": "Year 1 prior", + "Ratio": 0.02, + "Age Group": "Ages 13-24" + }, + { + "Time Relative to Enactment": "Year 1 after", + "Ratio": 0.02, + "Age Group": "Ages 13-17" + }, + { + "Time Relative to Enactment": "Year 1 after", + "Ratio": 0.01, + "Age Group": "Ages 13-24" + }, + { + "Time Relative to Enactment": "Year 2 after", + "Ratio": 0.4, + "Age Group": "Ages 13-17" + }, + { + "Time Relative to Enactment": "Year 2 after", + "Ratio": 0.18, + "Age Group": "Ages 13-24" + }, + { + "Time Relative to Enactment": "Year 3 after", + "Ratio": 0.28, + "Age Group": "Ages 13-17" + }, + { + "Time Relative to Enactment": "Year 3 after", + "Ratio": 0.2, + "Age Group": "Ages 13-24" + } + ], + "columns": [ + { + "name": "Time Relative to Enactment", + "importance": "primary", + "description": "Years before and after the enactment of anti-trans legislation", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding age group", + "role": "y" + }, + { + "name": "Age Group", + "importance": "primary", + "description": "The age group of the data point", + "role": "group" + } + ] + } + }, + "2441": { + "metadata": { + "title": "UK set to outspend European counterparts this Christmas", + "description": "Forecast Christmas spending in 2016 (in billion GBP)", + "main_insight": "The UK is forecast to have the highest Christmas spending in 2016 among major European countries, significantly exceeding other nations." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "UK", + "Spending": 75.46 + } + ], + "score": 0.76, + "annotation": "The has a maximum value at UK", + "reason": "The [{'Country': 'UK', 'Spending': 75.46}] of has a maximum value of 75.46, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "France", + "Spending": 56.49 + } + ], + "score": 0.7, + "annotation": "The shows a sudden jump in Spending from Italy to France.", + "reason": "The Spending for increases from 35.93 at Italy to 56.49 at France, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Belgium", + "Spending": 8.52 + } + ], + "score": 0.67, + "annotation": "The has a minimum value at Belgium", + "reason": "The [{'Country': 'Belgium', 'Spending': 8.52}] of has a mainimum value of 8.52, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "UK", + "Spending": 75.46 + }, + { + "Country": "Germany", + "Spending": 63.83 + }, + { + "Country": "France", + "Spending": 56.49 + }, + { + "Country": "Italy", + "Spending": 35.93 + }, + { + "Country": "Spain", + "Spending": 22.19 + }, + { + "Country": "Netherlands", + "Spending": 10.95 + }, + { + "Country": "Belgium", + "Spending": 8.52 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "European country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Spending", + "importance": "primary", + "description": "Forecast Christmas spending in billion GBP", + "unit": "Billion GBP", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1921": { + "metadata": { + "title": "Americans Couldn't Care Less About Christmas Markets", + "description": "Share of respondents who agree with the following statements about Christmas markets", + "main_insight": "Americans are much less likely to consider Christmas markets essential to the holiday season or look forward to them compared to respondents in Germany, France, and the UK." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "United States", + "Ratio": 0.13, + "Sentiment": "Christmas markets are essential" + }, + { + "Country": "United States", + "Ratio": 0.13, + "Sentiment": "Looking forward to Christmas markets" + } + ], + "score": 0.97, + "annotation": "The Ratio of Christmas markets are essential and Looking forward to Christmas markets show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Christmas markets are essential and Looking forward to Christmas markets is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United Kingdom", + "Ratio": 0.32, + "Sentiment": "Looking forward to Christmas markets" + } + ], + "score": 0.86, + "annotation": "The Looking forward to Christmas markets shows a sudden jump in Ratio from United States to United Kingdom.", + "reason": "The Ratio for Looking forward to Christmas markets increases from 0.13 at United States to 0.32 at United Kingdom, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "United States", + "Ratio": 0.13, + "Sentiment": "Christmas markets are essential" + }, + { + "Country": "United States", + "Ratio": 0.13, + "Sentiment": "Looking forward to Christmas markets" + } + ], + "score": 0.8, + "annotation": "The Christmas markets are essential, Looking forward to Christmas markets all have the largest proportion in United States.", + "reason": "The Ratio of Christmas markets are essential, Looking forward to Christmas markets in United States all account for 0.5, larger than all other Sentiment, of the total of Ratio of all Sentiment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Sentiment": "Christmas markets are essential", + "Country": "total", + "Ratio": 1.4300000000000002 + } + ], + "score": 0.79, + "annotation": "The total value of Christmas markets are essential accounts for the majority in all Sentiment.", + "reason": "The total value of Ratio of Christmas markets are essential accounts for 0.52 (more than 50%) of the total of Ratio of all Sentiment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "France", + "Ratio": 0.44, + "Sentiment": "Christmas markets are essential" + } + ], + "score": 0.79, + "annotation": "The Christmas markets are essential accounts for the majority in France.", + "reason": "The Ratio of Christmas markets are essential in France accounts for 0.5238095238095237 (more than 50%) of the total of Ratio of all Sentiment." + } + ], + "data": { + "data": [ + { + "Country": "Germany", + "Ratio": 0.58, + "Sentiment": "Christmas markets are essential" + }, + { + "Country": "Germany", + "Ratio": 0.47, + "Sentiment": "Looking forward to Christmas markets" + }, + { + "Country": "France", + "Ratio": 0.44, + "Sentiment": "Christmas markets are essential" + }, + { + "Country": "France", + "Ratio": 0.4, + "Sentiment": "Looking forward to Christmas markets" + }, + { + "Country": "United Kingdom", + "Ratio": 0.28, + "Sentiment": "Christmas markets are essential" + }, + { + "Country": "United Kingdom", + "Ratio": 0.32, + "Sentiment": "Looking forward to Christmas markets" + }, + { + "Country": "United States", + "Ratio": 0.13, + "Sentiment": "Christmas markets are essential" + }, + { + "Country": "United States", + "Ratio": 0.13, + "Sentiment": "Looking forward to Christmas markets" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of the respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "995": { + "metadata": { + "title": "Attack of the Clones", + "description": "Worldwide DAUs of Instagram Stories, WhatsApp Status, Facebook Stories and Snapchat", + "main_insight": "WhatsApp Status and Instagram Stories have quickly surpassed Snapchat in daily active users (DAUs), reaching 500 million by early 2019. Facebook stories has grown, while Snapchat DAU appears to be stable." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Points": 500.0, + "Platform": "WhatsApp Status" + }, + { + "Year": 2019, + "Points": 500.0, + "Platform": "Facebook Stories" + } + ], + "score": 0.85, + "annotation": "The Points of WhatsApp Status and Facebook Stories show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Points between WhatsApp Status and Facebook Stories is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2015, + "Points": 100.0, + "Platform": "Snapchat" + }, + { + "Year": 2016, + "Points": 100.0, + "Platform": "Instagram Stories" + }, + { + "Year": 2016, + "Points": 150.0, + "Platform": "Snapchat" + }, + { + "Year": 2017, + "Points": 250.0, + "Platform": "WhatsApp Status" + }, + { + "Year": 2017, + "Points": 300.0, + "Platform": "Instagram Stories" + }, + { + "Year": 2017, + "Points": 70.0, + "Platform": "Facebook Stories" + }, + { + "Year": 2017, + "Points": 175.0, + "Platform": "Snapchat" + }, + { + "Year": 2018, + "Points": 450.0, + "Platform": "WhatsApp Status" + }, + { + "Year": 2018, + "Points": 450.0, + "Platform": "Instagram Stories" + }, + { + "Year": 2018, + "Points": 200.0, + "Platform": "Facebook Stories" + }, + { + "Year": 2018, + "Points": 190.0, + "Platform": "Snapchat" + }, + { + "Year": 2019, + "Points": 500.0, + "Platform": "WhatsApp Status" + }, + { + "Year": 2019, + "Points": 500.0, + "Platform": "Instagram Stories" + }, + { + "Year": 2019, + "Points": 500.0, + "Platform": "Facebook Stories" + }, + { + "Year": 2019, + "Points": 190.0, + "Platform": "Snapchat" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding platform", + "role": "y" + }, + { + "name": "Platform", + "importance": "primary", + "description": "The platform of the data point", + "role": "group" + } + ] + } + }, + "213": { + "metadata": { + "title": "Surviving the Conventions: Trump vs Clinton", + "description": "Rep./Dem. leaning registered voters who think party will or won't unite behind nominee", + "main_insight": "A significantly larger percentage of Democratic voters (72%) believe their party will unite behind Hillary Clinton, compared to Republican voters (38%) who believe the same for Donald Trump." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Party": "Democrats Hillary Clinton", + "Units": 72.0, + "Support Level": "Unite solidly behind candidate" + }, + { + "Party": "Democrats Hillary Clinton", + "Units": 24.0, + "Support Level": "Refrain from support" + } + ], + "score": 1.0, + "annotation": "The Units of Unite solidly behind candidate and Refrain from support show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Unite solidly behind candidate and Refrain from support is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Party": "Democrats Hillary Clinton", + "Units": 72.0, + "Support Level": "Unite solidly behind candidate" + }, + { + "Party": "Democrats Hillary Clinton", + "Units": 4.0, + "Support Level": "Don't know" + } + ], + "score": 1.0, + "annotation": "The Units of Unite solidly behind candidate and Don't know show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Unite solidly behind candidate and Don't know is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Party": "Democrats Hillary Clinton", + "Units": 24.0, + "Support Level": "Refrain from support" + }, + { + "Party": "Democrats Hillary Clinton", + "Units": 4.0, + "Support Level": "Don't know" + } + ], + "score": 1.0, + "annotation": "The Units of Refrain from support and Don't know show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Refrain from support and Don't know is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Party": "Democrats Hillary Clinton", + "Units": 72.0, + "Support Level": "Unite solidly behind candidate" + } + ], + "score": 0.97, + "annotation": "The Unite solidly behind candidate shows a sudden jump in Units from Republicans Donald Trump to Democrats Hillary Clinton.", + "reason": "The Units for Unite solidly behind candidate increases from 38.0 at Republicans Donald Trump to 72.0 at Democrats Hillary Clinton, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Party": "Republicans Donald Trump", + "Units": 54.0, + "Support Level": "Refrain from support" + } + ], + "score": 0.97, + "annotation": "The Refrain from support shows a sudden jump in Units from Democrats Hillary Clinton to Republicans Donald Trump.", + "reason": "The Units for Refrain from support increases from 24.0 at Democrats Hillary Clinton to 54.0 at Republicans Donald Trump, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Party": "Republicans Donald Trump", + "Units": 38.0, + "Support Level": "Unite solidly behind candidate" + }, + { + "Party": "Republicans Donald Trump", + "Units": 54.0, + "Support Level": "Refrain from support" + }, + { + "Party": "Republicans Donald Trump", + "Units": 8.0, + "Support Level": "Don't know" + }, + { + "Party": "Democrats Hillary Clinton", + "Units": 72.0, + "Support Level": "Unite solidly behind candidate" + }, + { + "Party": "Democrats Hillary Clinton", + "Units": 24.0, + "Support Level": "Refrain from support" + }, + { + "Party": "Democrats Hillary Clinton", + "Units": 4.0, + "Support Level": "Don't know" + } + ], + "columns": [ + { + "name": "Party", + "importance": "primary", + "description": "Political party affiliation (Republicans or Democrats)", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding support level", + "role": "y" + }, + { + "name": "Support Level", + "importance": "primary", + "description": "The support level of the data point", + "role": "group" + } + ] + } + }, + "1179": { + "metadata": { + "title": "Sharp Increase In U.S. Border Family Apprehensions", + "description": "Apprehensions at the Southwest border from January to June", + "main_insight": "Family unit apprehensions at the U.S. Southwest border increased significantly between FY 2016 and FY 2018, while unaccompanied children apprehensions saw a smaller increase." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Fiscal Year": "2018", + "Dollars": 26260.0, + "Migrant Group": "Unaccompanied children" + } + ], + "score": 0.94, + "annotation": "The Unaccompanied children shows a sudden jump in Dollars from 2017 to 2018.", + "reason": "The Dollars for Unaccompanied children increases from 11775.0 at 2017 to 26260.0 at 2018, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Fiscal Year": "2018", + "Dollars": 48588.0, + "Migrant Group": "Family units" + } + ], + "score": 0.93, + "annotation": "The Family units shows a sudden jump in Dollars from 2016 to 2018.", + "reason": "The Dollars for Family units increases from 26081.0 at 2016 to 48588.0 at 2018, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Fiscal Year": "2016", + "Dollars": 29670.0, + "Migrant Group": "Unaccompanied children" + } + ], + "score": 0.79, + "annotation": "The Unaccompanied children accounts for the majority in 2016.", + "reason": "The Dollars of Unaccompanied children in 2016 accounts for 0.5321877634481893 (more than 50%) of the total of Dollars of all Migrant Group." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Migrant Group": "Family units", + "Fiscal Year": "total", + "Dollars": 93238.0 + } + ], + "score": 0.78, + "annotation": "The total value of Family units accounts for the majority in all Migrant Group.", + "reason": "The total value of Dollars of Family units accounts for 0.5793231143945372 (more than 50%) of the total of Dollars of all Migrant Group." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Fiscal Year": "2017", + "Dollars": 18569.0, + "Migrant Group": "Family units" + } + ], + "score": 0.77, + "annotation": "The Family units accounts for the majority in 2017.", + "reason": "The Dollars of Family units in 2017 accounts for 0.6119496440812022 (more than 50%) of the total of Dollars of all Migrant Group." + } + ], + "data": { + "data": [ + { + "Fiscal Year": "2016", + "Dollars": 26081.0, + "Migrant Group": "Family units" + }, + { + "Fiscal Year": "2016", + "Dollars": 29670.0, + "Migrant Group": "Unaccompanied children" + }, + { + "Fiscal Year": "2017", + "Dollars": 18569.0, + "Migrant Group": "Family units" + }, + { + "Fiscal Year": "2017", + "Dollars": 11775.0, + "Migrant Group": "Unaccompanied children" + }, + { + "Fiscal Year": "2018", + "Dollars": 48588.0, + "Migrant Group": "Family units" + }, + { + "Fiscal Year": "2018", + "Dollars": 26260.0, + "Migrant Group": "Unaccompanied children" + } + ], + "columns": [ + { + "name": "Fiscal Year", + "importance": "primary", + "description": "Fiscal Year (FY)", + "unit": "none", + "data_type": "time", + "discarded_count": 0, + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding migrant group", + "role": "y" + }, + { + "name": "Migrant Group", + "importance": "primary", + "description": "The migrant group of the data point", + "role": "group" + } + ] + } + }, + "73": { + "metadata": { + "title": "Refugee Numbers Remain Mostly Stable Throughout 2020", + "description": "Number of forcibly displaced people by selected countries of origin (in million people)", + "main_insight": "The number of Syrian refugees increased significantly from 2011 to 2020, while other countries showed more gradual changes, with Venezuela showing significant increase from 2017." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Ratio": 2.2, + "\"Crisis Areas\"": "South Sudan" + }, + { + "Year": 2020, + "Ratio": 1.1, + "\"Crisis Areas\"": "Rohinya" + } + ], + "score": 0.98, + "annotation": "The Ratio of South Sudan and Rohinya show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between South Sudan and Rohinya is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Ratio": 6.8, + "\"Crisis Areas\"": "Syria" + }, + { + "Year": 2020, + "Ratio": 2.2, + "\"Crisis Areas\"": "South Sudan" + } + ], + "score": 0.93, + "annotation": "The Ratio of Syria and South Sudan show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Syria and South Sudan is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Ratio": 1.1, + "\"Crisis Areas\"": "Rohinya" + }, + { + "Year": 2020, + "Ratio": 1.0, + "\"Crisis Areas\"": "DRC" + } + ], + "score": 0.9, + "annotation": "The Ratio of Rohinya and DRC show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Rohinya and DRC is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Ratio": 2.2, + "\"Crisis Areas\"": "South Sudan" + }, + { + "Year": 2020, + "Ratio": 1.0, + "\"Crisis Areas\"": "DRC" + } + ], + "score": 0.89, + "annotation": "The Ratio of South Sudan and DRC show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between South Sudan and DRC is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Ratio": 6.8, + "\"Crisis Areas\"": "Syria" + }, + { + "Year": 2020, + "Ratio": 1.1, + "\"Crisis Areas\"": "Rohinya" + } + ], + "score": 0.88, + "annotation": "The Ratio of Syria and Rohinya show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Syria and Rohinya is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2011, + "Ratio": 0.1, + "\"Crisis Areas\"": "Syria" + }, + { + "Year": 2011, + "Ratio": 2.7, + "\"Crisis Areas\"": "Afghanistan" + }, + { + "Year": 2011, + "Ratio": 0.3, + "\"Crisis Areas\"": "South Sudan" + }, + { + "Year": 2011, + "Ratio": 0.4, + "\"Crisis Areas\"": "Rohinya" + }, + { + "Year": 2011, + "Ratio": 0.4, + "\"Crisis Areas\"": "DRC" + }, + { + "Year": 2012, + "Ratio": 0.5, + "\"Crisis Areas\"": "Syria" + }, + { + "Year": 2012, + "Ratio": 2.6, + "\"Crisis Areas\"": "Afghanistan" + }, + { + "Year": 2012, + "Ratio": 0.4, + "\"Crisis Areas\"": "South Sudan" + }, + { + "Year": 2012, + "Ratio": 0.4, + "\"Crisis Areas\"": "Rohinya" + }, + { + "Year": 2012, + "Ratio": 0.4, + "\"Crisis Areas\"": "DRC" + }, + { + "Year": 2013, + "Ratio": 2.5, + "\"Crisis Areas\"": "Syria" + }, + { + "Year": 2013, + "Ratio": 2.6, + "\"Crisis Areas\"": "Afghanistan" + }, + { + "Year": 2013, + "Ratio": 0.5, + "\"Crisis Areas\"": "South Sudan" + }, + { + "Year": 2013, + "Ratio": 0.5, + "\"Crisis Areas\"": "Rohinya" + }, + { + "Year": 2013, + "Ratio": 0.5, + "\"Crisis Areas\"": "DRC" + }, + { + "Year": 2014, + "Ratio": 3.8, + "\"Crisis Areas\"": "Syria" + }, + { + "Year": 2014, + "Ratio": 2.6, + "\"Crisis Areas\"": "Afghanistan" + }, + { + "Year": 2014, + "Ratio": 0.8, + "\"Crisis Areas\"": "South Sudan" + }, + { + "Year": 2014, + "Ratio": 0.5, + "\"Crisis Areas\"": "Rohinya" + }, + { + "Year": 2014, + "Ratio": 0.5, + "\"Crisis Areas\"": "DRC" + }, + { + "Year": 2015, + "Ratio": 4.8, + "\"Crisis Areas\"": "Syria" + }, + { + "Year": 2015, + "Ratio": 0.1, + "\"Crisis Areas\"": "Venezuela" + }, + { + "Year": 2015, + "Ratio": 2.7, + "\"Crisis Areas\"": "Afghanistan" + }, + { + "Year": 2015, + "Ratio": 1.2, + "\"Crisis Areas\"": "South Sudan" + }, + { + "Year": 2015, + "Ratio": 0.6, + "\"Crisis Areas\"": "Rohinya" + }, + { + "Year": 2015, + "Ratio": 0.5, + "\"Crisis Areas\"": "DRC" + }, + { + "Year": 2016, + "Ratio": 5.5, + "\"Crisis Areas\"": "Syria" + }, + { + "Year": 2016, + "Ratio": 0.2, + "\"Crisis Areas\"": "Venezuela" + }, + { + "Year": 2016, + "Ratio": 2.5, + "\"Crisis Areas\"": "Afghanistan" + }, + { + "Year": 2016, + "Ratio": 1.4, + "\"Crisis Areas\"": "South Sudan" + }, + { + "Year": 2016, + "Ratio": 0.7, + "\"Crisis Areas\"": "Rohinya" + }, + { + "Year": 2016, + "Ratio": 0.6, + "\"Crisis Areas\"": "DRC" + }, + { + "Year": 2017, + "Ratio": 6.2, + "\"Crisis Areas\"": "Syria" + }, + { + "Year": 2017, + "Ratio": 0.6, + "\"Crisis Areas\"": "Venezuela" + }, + { + "Year": 2017, + "Ratio": 2.6, + "\"Crisis Areas\"": "Afghanistan" + }, + { + "Year": 2017, + "Ratio": 2.4, + "\"Crisis Areas\"": "South Sudan" + }, + { + "Year": 2017, + "Ratio": 1.2, + "\"Crisis Areas\"": "Rohinya" + }, + { + "Year": 2017, + "Ratio": 0.7, + "\"Crisis Areas\"": "DRC" + }, + { + "Year": 2018, + "Ratio": 6.7, + "\"Crisis Areas\"": "Syria" + }, + { + "Year": 2018, + "Ratio": 3.4, + "\"Crisis Areas\"": "Venezuela" + }, + { + "Year": 2018, + "Ratio": 2.7, + "\"Crisis Areas\"": "Afghanistan" + }, + { + "Year": 2018, + "Ratio": 2.3, + "\"Crisis Areas\"": "South Sudan" + }, + { + "Year": 2018, + "Ratio": 1.2, + "\"Crisis Areas\"": "Rohinya" + }, + { + "Year": 2018, + "Ratio": 0.8, + "\"Crisis Areas\"": "DRC" + }, + { + "Year": 2019, + "Ratio": 6.6, + "\"Crisis Areas\"": "Syria" + }, + { + "Year": 2019, + "Ratio": 4.5, + "\"Crisis Areas\"": "Venezuela" + }, + { + "Year": 2019, + "Ratio": 2.7, + "\"Crisis Areas\"": "Afghanistan" + }, + { + "Year": 2019, + "Ratio": 2.2, + "\"Crisis Areas\"": "South Sudan" + }, + { + "Year": 2019, + "Ratio": 1.1, + "\"Crisis Areas\"": "Rohinya" + }, + { + "Year": 2019, + "Ratio": 0.9, + "\"Crisis Areas\"": "DRC" + }, + { + "Year": 2020, + "Ratio": 6.8, + "\"Crisis Areas\"": "Syria" + }, + { + "Year": 2020, + "Ratio": 4.8, + "\"Crisis Areas\"": "Venezuela" + }, + { + "Year": 2020, + "Ratio": 2.8, + "\"Crisis Areas\"": "Afghanistan" + }, + { + "Year": 2020, + "Ratio": 2.2, + "\"Crisis Areas\"": "South Sudan" + }, + { + "Year": 2020, + "Ratio": 1.1, + "\"Crisis Areas\"": "Rohinya" + }, + { + "Year": 2020, + "Ratio": 1.0, + "\"Crisis Areas\"": "DRC" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 2011 to 2020", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding \"crisis areas\"", + "role": "y" + }, + { + "name": "\"Crisis Areas\"", + "importance": "primary", + "description": "The \"crisis areas\" of the data point", + "role": "group" + } + ] + } + }, + "628": { + "metadata": { + "title": "Public Teacher Pay Gap Deepens", + "description": "Percent difference in weekly wages for K-12 public school teachers compared to nonteaching, college-educated peers", + "main_insight": "The wage gap between public school teachers and their college-educated peers in other professions has generally widened since 1990, reaching -19.2% in 2019." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1980, + "Wage Gap": -7.5 + }, + { + "Year": 1990, + "Wage Gap": -7.5 + }, + { + "Year": 2000, + "Wage Gap": -22.5 + }, + { + "Year": 2010, + "Wage Gap": -12.5 + }, + { + "Year": 2020, + "Wage Gap": -19.2 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of data collection", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Wage Gap", + "importance": "primary", + "description": "Percent difference in weekly wages between K-12 public school teachers and nonteaching, college-educated peers", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1533": { + "metadata": { + "title": "Aircraft Lost During The Syrian Civil War", + "description": "Estimated number of aircraft shot down or destroyed in accidents (2011-2018)*", + "main_insight": "Syria lost the vast majority of aircraft during the Syrian Civil War, significantly more than any other involved country." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Syria", + "Aircraft Lost": 111 + } + ], + "score": 0.95, + "annotation": "The shows a sudden jump in Aircraft Lost from Russia to Syria.", + "reason": "The Aircraft Lost for increases from 17 at Russia to 111 at Syria, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Syria", + "Aircraft Lost": 111 + } + ], + "score": 0.95, + "annotation": "The has a maximum value at Syria", + "reason": "The [{'Country': 'Syria', 'Aircraft Lost': 111}] of has a maximum value of 111, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Jordan", + "Aircraft Lost": 1 + } + ], + "score": 0.35, + "annotation": "The has a minimum value at Jordan", + "reason": "The [{'Country': 'Jordan', 'Aircraft Lost': 1}] of has a mainimum value of 1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Syria", + "Aircraft Lost": 111 + }, + { + "Country": "Russia", + "Aircraft Lost": 17 + }, + { + "Country": "Israel", + "Aircraft Lost": 4 + }, + { + "Country": "United States", + "Aircraft Lost": 4 + }, + { + "Country": "Iran", + "Aircraft Lost": 3 + }, + { + "Country": "Turkey", + "Aircraft Lost": 3 + }, + { + "Country": "Jordan", + "Aircraft Lost": 1 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "The country that lost the aircraft.", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Aircraft Lost", + "importance": "primary", + "description": "Number of aircraft lost by each country.", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2075": { + "metadata": { + "title": "Approval of North Korea Talks, Doubts About Seriousness", + "description": null, + "main_insight": "A large majority of U.S. adults approve of direct U.S.-North Korea talks, but many are skeptical of North Korea's seriousness in addressing the nuclear program." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [], + "data": { + "data": [ + { + "Survey Question": "Respondents who (dis)approve of direct U.S.-North Korea talks", + "Units": 71.0, + "Sentiment": "Approve" + }, + { + "Survey Question": "Respondents who (dis)approve of direct U.S.-North Korea talks", + "Units": 21.0, + "Sentiment": "Disapprove" + }, + { + "Survey Question": "Respondents who (dis)approve of direct U.S.-North Korea talks", + "Units": 8.0, + "Sentiment": "Don't Know (Talks)" + }, + { + "Survey Question": "Respondents who think NK's leadership is (not) serious about addressing nuclear program", + "Units": 38.0, + "Sentiment": "Serious" + }, + { + "Survey Question": "Respondents who think NK's leadership is (not) serious about addressing nuclear program", + "Units": 49.0, + "Sentiment": "Not serious" + }, + { + "Survey Question": "Respondents who think NK's leadership is (not) serious about addressing nuclear program", + "Units": 13.0, + "Sentiment": "Don't Know (Seriousness)" + } + ], + "columns": [ + { + "name": "Survey Question", + "importance": "primary", + "description": "The question asked to the survey respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "1061": { + "metadata": { + "title": "Which Country Is Most Worried About Data Use Online?", + "description": "Share of internet users who are concerned about their data being misused online", + "main_insight": "Spain has the highest percentage of internet users concerned about their data being misused online, at 56%, while the US has the lowest at 27%. There is a significant variation in concern levels across different countries." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "ES", + "Concerned Users": 56.0 + } + ], + "score": 0.87, + "annotation": "The has a maximum value at ES", + "reason": "The [{'Country': 'ES', 'Concerned Users': 56.0}] of has a maximum value of 56.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "MX", + "Concerned Users": 48.0 + } + ], + "score": 0.67, + "annotation": "The shows a sudden jump in Concerned Users from FR to MX.", + "reason": "The Concerned Users for increases from 40.0 at FR to 48.0 at MX, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "US", + "Concerned Users": 27.0 + } + ], + "score": 0.61, + "annotation": "The has a minimum value at US", + "reason": "The [{'Country': 'US', 'Concerned Users': 27.0}] of has a mainimum value of 27.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "ES", + "Concerned Users": 56.0 + }, + { + "Country": "CL", + "Concerned Users": 50.0 + }, + { + "Country": "MX", + "Concerned Users": 48.0 + }, + { + "Country": "FR", + "Concerned Users": 40.0 + }, + { + "Country": "DE", + "Concerned Users": 37.0 + }, + { + "Country": "CN", + "Concerned Users": 33.0 + }, + { + "Country": "IN", + "Concerned Users": 30.0 + }, + { + "Country": "ZA", + "Concerned Users": 28.0 + }, + { + "Country": "GB", + "Concerned Users": 28.0 + }, + { + "Country": "US", + "Concerned Users": 27.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country surveyed", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Concerned Users", + "importance": "primary", + "description": "Percentage of internet users concerned about online data misuse", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1133": { + "metadata": { + "title": "Adele Vs. Beyoncé At The Grammys", + "description": "Total Grammy wins and nominations of Adele and Beyoncé as of February 2017", + "main_insight": "Beyoncé has significantly more Grammy wins and nominations than Adele as of February 2017." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Artist": "Beyoncé", + "Wins": 22.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Wins from Adele to Beyoncé.", + "reason": "The Wins for increases from 15.0 at Adele to 22.0 at Beyoncé, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Artist": "Beyoncé", + "Wins": 22.0 + } + ], + "score": 0.45, + "annotation": "The has a maximum value at Beyoncé", + "reason": "The [{'Artist': 'Beyoncé', 'Wins': 22.0}] of has a maximum value of 22.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Artist": "Adele", + "Wins": 15.0 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at Adele", + "reason": "The [{'Artist': 'Adele', 'Wins': 15.0}] of has a mainimum value of 15.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Artist": "Adele", + "Wins": 15.0 + }, + { + "Artist": "Beyoncé", + "Wins": 22.0 + } + ], + "columns": [ + { + "name": "Artist", + "importance": "primary", + "description": "The artist being compared", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Wins", + "importance": "primary", + "description": "Number of Grammy wins", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1609": { + "metadata": { + "title": "The NHS 111 calls left unanswered", + "description": "Total number of calls not answered by NHS 111 from August 2016 to January 2017*", + "main_insight": "The number of unanswered NHS 111 calls significantly increased from August 2016 to January 2017, peaking in December 2016." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Dec '16", + "Unanswered Calls": 125065 + } + ], + "score": 0.83, + "annotation": "The has a maximum value at Dec '16", + "reason": "The [{'Month': \"Dec '16\", 'Unanswered Calls': 125065}] of has a maximum value of 125065, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Oct '16", + "Unanswered Calls": 97835 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Unanswered Calls from Nov '16 to Oct '16.", + "reason": "The Unanswered Calls for increases from 80691 at Nov '16 to 97835 at Oct '16, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Aug '16", + "Unanswered Calls": 79390 + } + ], + "score": 0.52, + "annotation": "The has a minimum value at Aug '16", + "reason": "The [{'Month': \"Aug '16\", 'Unanswered Calls': 79390}] of has a mainimum value of 79390, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Month": "Aug '16", + "Unanswered Calls": 79390 + }, + { + "Month": "Sep '16", + "Unanswered Calls": 80304 + }, + { + "Month": "Oct '16", + "Unanswered Calls": 97835 + }, + { + "Month": "Nov '16", + "Unanswered Calls": 80691 + }, + { + "Month": "Dec '16", + "Unanswered Calls": 125065 + }, + { + "Month": "Jan '17", + "Unanswered Calls": 108051 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month from August 2016 to January 2017", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Unanswered Calls", + "importance": "primary", + "description": "Total number of calls not answered by NHS 111", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "482": { + "metadata": { + "title": "Windows Phone Has Stopped Losing Market Share", + "description": "Smartphone market share of Windows Phone in the United States", + "main_insight": "Windows Phone's market share in the US declined from May 2011 to February 2012, but then stabilized between February and May 2012." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Dec 2011", + "Market Share": 5.2 + } + ], + "score": 0.73, + "annotation": "The shows a sudden jump in Market Share from Jan 2012 to Dec 2011.", + "reason": "The Market Share for increases from 4.6 at Jan 2012 to 5.2 at Dec 2011, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Feb 2012", + "Market Share": 4.1 + }, + { + "Month": "Mar 2012", + "Market Share": 4.1 + }, + { + "Month": "Apr 2012", + "Market Share": 4.1 + }, + { + "Month": "May 2012", + "Market Share": 4.1 + } + ], + "score": 0.73, + "annotation": "The has minimum values at Feb 2012, Mar 2012, Apr 2012, May 2012", + "reason": "The [{'Month': 'Feb 2012', 'Market Share': 4.1}, {'Month': 'Mar 2012', 'Market Share': 4.1}, {'Month': 'Apr 2012', 'Market Share': 4.1}, {'Month': 'May 2012', 'Market Share': 4.1}] of have minimum values of 4.1, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "May 2011", + "Market Share": 5.8 + }, + { + "Month": "Jun 2011", + "Market Share": 5.8 + } + ], + "score": 0.59, + "annotation": "The has maximum values at May 2011, Jun 2011", + "reason": "The [{'Month': 'May 2011', 'Market Share': 5.8}, {'Month': 'Jun 2011', 'Market Share': 5.8}] of have maximum values of 5.8, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Month": "May 2011", + "Market Share": 5.8 + }, + { + "Month": "Jun 2011", + "Market Share": 5.8 + }, + { + "Month": "Jul 2011", + "Market Share": 5.7 + }, + { + "Month": "Aug 2011", + "Market Share": 5.7 + }, + { + "Month": "Sept 2011", + "Market Share": 5.7 + }, + { + "Month": "Oct 2011", + "Market Share": 5.6 + }, + { + "Month": "Nov 2011", + "Market Share": 5.4 + }, + { + "Month": "Dec 2011", + "Market Share": 5.2 + }, + { + "Month": "Jan 2012", + "Market Share": 4.6 + }, + { + "Month": "Feb 2012", + "Market Share": 4.1 + }, + { + "Month": "Mar 2012", + "Market Share": 4.1 + }, + { + "Month": "Apr 2012", + "Market Share": 4.1 + }, + { + "Month": "May 2012", + "Market Share": 4.1 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Market Share", + "importance": "primary", + "description": "Smartphone market share of Windows Phone", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2107": { + "metadata": { + "title": "China Now Grows More Corn Than Rice", + "description": "Share of selected cereal crops in primary cereal production in China, the U.S. and Brazil (in percent)", + "main_insight": "China's primary cereal crop production has shifted from predominantly rice in 1981 to a majority of corn in 2021, a significant change in agricultural output." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Mainland China 1981", + "Units": 21.0, + "Crop Types": "Wheat" + } + ], + "score": 0.91, + "annotation": "The Wheat shows a sudden jump in Units from United States 2021 to Mainland China 1981.", + "reason": "The Units for Wheat increases from 10.0 at United States 2021 to 21.0 at Mainland China 1981, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Mainland China 2021", + "Units": 2.0, + "Crop Types": "Others" + } + ], + "score": 0.91, + "annotation": "The Others has the smallest proportion in Mainland China 2021.", + "reason": "The Units of Others in Mainland China 2021 accounts for 0.019801980198019802, smaller than all other Crop Types, of the total of Units of all Crop Types." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Brazil 1981", + "Units": 2.0, + "Crop Types": "Others" + } + ], + "score": 0.91, + "annotation": "The Others has the smallest proportion in Brazil 1981.", + "reason": "The Units of Others in Brazil 1981 accounts for 0.019801980198019802, smaller than all other Crop Types, of the total of Units of all Crop Types." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "United States 2021", + "Units": 2.0, + "Crop Types": "Rice" + } + ], + "score": 0.91, + "annotation": "The Rice has the smallest proportion in United States 2021.", + "reason": "The Units of Rice in United States 2021 accounts for 0.02, smaller than all other Crop Types, of the total of Units of all Crop Types." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Mainland China 2021", + "Units": 43.0, + "Crop Types": "Corn" + } + ], + "score": 0.9, + "annotation": "The Corn has the largest proportion in Mainland China 2021.", + "reason": "The Units of Corn in Mainland China 2021 accounts for 0.42574257425742573, larger than all other Crop Types, of the total of Units of all Crop Types." + } + ], + "data": { + "data": [ + { + "Country": "Mainland China 1981", + "Units": 21.0, + "Crop Types": "Corn" + }, + { + "Country": "Mainland China 1981", + "Units": 51.0, + "Crop Types": "Rice" + }, + { + "Country": "Mainland China 1981", + "Units": 21.0, + "Crop Types": "Wheat" + }, + { + "Country": "Mainland China 1981", + "Units": 7.0, + "Crop Types": "Others" + }, + { + "Country": "Mainland China 2021", + "Units": 43.0, + "Crop Types": "Corn" + }, + { + "Country": "Mainland China 2021", + "Units": 34.0, + "Crop Types": "Rice" + }, + { + "Country": "Mainland China 2021", + "Units": 22.0, + "Crop Types": "Wheat" + }, + { + "Country": "Mainland China 2021", + "Units": 2.0, + "Crop Types": "Others" + }, + { + "Country": "United States 1981", + "Units": 62.0, + "Crop Types": "Corn" + }, + { + "Country": "United States 1981", + "Units": 3.0, + "Crop Types": "Rice" + }, + { + "Country": "United States 1981", + "Units": 23.0, + "Crop Types": "Wheat" + }, + { + "Country": "United States 1981", + "Units": 12.0, + "Crop Types": "Others" + }, + { + "Country": "United States 2021", + "Units": 85.0, + "Crop Types": "Corn" + }, + { + "Country": "United States 2021", + "Units": 2.0, + "Crop Types": "Rice" + }, + { + "Country": "United States 2021", + "Units": 10.0, + "Crop Types": "Wheat" + }, + { + "Country": "United States 2021", + "Units": 3.0, + "Crop Types": "Others" + }, + { + "Country": "Brazil 1981", + "Units": 66.0, + "Crop Types": "Corn" + }, + { + "Country": "Brazil 1981", + "Units": 26.0, + "Crop Types": "Rice" + }, + { + "Country": "Brazil 1981", + "Units": 7.0, + "Crop Types": "Wheat" + }, + { + "Country": "Brazil 1981", + "Units": 2.0, + "Crop Types": "Others" + }, + { + "Country": "Brazil 2021", + "Units": 79.0, + "Crop Types": "Corn" + }, + { + "Country": "Brazil 2021", + "Units": 10.0, + "Crop Types": "Rice" + }, + { + "Country": "Brazil 2021", + "Units": 7.0, + "Crop Types": "Wheat" + }, + { + "Country": "Brazil 2021", + "Units": 4.0, + "Crop Types": "Others" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country and Year", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding crop types", + "role": "y" + }, + { + "name": "Crop Types", + "importance": "primary", + "description": "The crop types of the data point", + "role": "group" + } + ] + } + }, + "180": { + "metadata": { + "title": "Japanese Household Consumption Hits Rock Bottom", + "description": "Monthly change in the consumption expenditures of Japanese households* 2018-2020 (in percent)", + "main_insight": "Japanese household consumption experienced significant volatility between 2018 and 2020, culminating in a sharp decline in 2020." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Year": "2020", + "Monthly Change": -16.2 + } + ], + "score": 0.99, + "annotation": "The has a minimum value at 2020", + "reason": "The [{'Year': '2020', 'Monthly Change': -16.2}] of has a mainimum value of -16.2, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Year": "2019", + "Monthly Change": 9.5 + } + ], + "score": 0.91, + "annotation": "The has a maximum value at 2019", + "reason": "The [{'Year': '2019', 'Monthly Change': 9.5}] of has a maximum value of 9.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Year": "2019", + "Monthly Change": -7.8 + } + ], + "score": 0.71, + "annotation": "The shows a sudden jump in Monthly Change from 2020 to 2019.", + "reason": "The Monthly Change for increases from -16.2 at 2020 to -7.8 at 2019, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Year": "2018", + "Monthly Change": -3.2 + }, + { + "Year": "2018", + "Monthly Change": -0.3 + }, + { + "Year": "2018", + "Monthly Change": 2.8 + }, + { + "Year": "2018", + "Monthly Change": 4.8 + }, + { + "Year": "2018", + "Monthly Change": -0.9 + }, + { + "Year": "2018", + "Monthly Change": 0.9 + }, + { + "Year": "2018", + "Monthly Change": -0.7 + }, + { + "Year": "2018", + "Monthly Change": 2.1 + }, + { + "Year": "2018", + "Monthly Change": 1.6 + }, + { + "Year": "2019", + "Monthly Change": 3.6 + }, + { + "Year": "2019", + "Monthly Change": 3.4 + }, + { + "Year": "2019", + "Monthly Change": 2.8 + }, + { + "Year": "2019", + "Monthly Change": 1.9 + }, + { + "Year": "2019", + "Monthly Change": 1.6 + }, + { + "Year": "2019", + "Monthly Change": 9.5 + }, + { + "Year": "2019", + "Monthly Change": -3.9 + }, + { + "Year": "2019", + "Monthly Change": -7.8 + }, + { + "Year": "2019", + "Monthly Change": -4.3 + }, + { + "Year": "2019", + "Monthly Change": -5.3 + }, + { + "Year": "2020", + "Monthly Change": -0.6 + }, + { + "Year": "2020", + "Monthly Change": 3.4 + }, + { + "Year": "2020", + "Monthly Change": -0.8 + }, + { + "Year": "2020", + "Monthly Change": -4.3 + }, + { + "Year": "2020", + "Monthly Change": -16.2 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year, representing the time periods", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Monthly Change", + "importance": "primary", + "description": "Monthly change in consumption expenditures (percent)", + "unit": "percent", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1337": { + "metadata": { + "title": "Americans Prepare for a Thanksgiving Shopping Spree", + "description": "% of Americans who plan to shop on/offline on the following dates", + "main_insight": "The percentage of Americans planning to shop during the Thanksgiving holiday period increased from 2015 to 2017, with Cyber Monday consistently being the most popular shopping day." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Shopping Day": "Cyber Monday", + "Units": 63.0, + "Year": "2016" + }, + { + "Shopping Day": "Cyber Monday", + "Units": 60.0, + "Year": "2015" + } + ], + "score": 1.0, + "annotation": "The Units of 2016 and 2015 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2016 and 2015 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Shopping Day": "Black Friday", + "Units": 61.0, + "Year": "2017" + } + ], + "score": 0.97, + "annotation": "The 2017 shows a sudden jump in Units from Thanksgiving evening to Black Friday.", + "reason": "The Units for 2017 increases from 49.0 at Thanksgiving evening to 61.0 at Black Friday, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Shopping Day": "Cyber Monday", + "Units": 61.0, + "Year": "2017" + }, + { + "Shopping Day": "Cyber Monday", + "Units": 63.0, + "Year": "2016" + } + ], + "score": 0.93, + "annotation": "The Units of 2017 and 2016 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2017 and 2016 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Shopping Day": "Cyber Monday", + "Units": 61.0, + "Year": "2017" + }, + { + "Shopping Day": "Cyber Monday", + "Units": 60.0, + "Year": "2015" + } + ], + "score": 0.92, + "annotation": "The Units of 2017 and 2015 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2017 and 2015 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Shopping Day": "Cyber Monday", + "Units": 63.0, + "Year": "2016" + } + ], + "score": 0.9, + "annotation": "The 2016 has the largest proportion in Cyber Monday.", + "reason": "The Units of 2016 in Cyber Monday accounts for 0.3423913043478261, larger than all other Year, of the total of Units of all Year." + } + ], + "data": { + "data": [ + { + "Shopping Day": "Thanksgiving evening", + "Units": 49.0, + "Year": "2017" + }, + { + "Shopping Day": "Thanksgiving evening", + "Units": 39.0, + "Year": "2016" + }, + { + "Shopping Day": "Thanksgiving evening", + "Units": 34.0, + "Year": "2015" + }, + { + "Shopping Day": "Black Friday", + "Units": 61.0, + "Year": "2017" + }, + { + "Shopping Day": "Black Friday", + "Units": 54.0, + "Year": "2016" + }, + { + "Shopping Day": "Black Friday", + "Units": 50.0, + "Year": "2015" + }, + { + "Shopping Day": "Cyber Monday", + "Units": 61.0, + "Year": "2017" + }, + { + "Shopping Day": "Cyber Monday", + "Units": 63.0, + "Year": "2016" + }, + { + "Shopping Day": "Cyber Monday", + "Units": 60.0, + "Year": "2015" + } + ], + "columns": [ + { + "name": "Shopping Day", + "importance": "primary", + "description": "The specific day during the Thanksgiving period", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "2082": { + "metadata": { + "title": "The UK's shrinking navy", + "description": "Number of regular enlisted personnel in the Royal Navy", + "main_insight": "The number of regular enlisted personnel in the Royal Navy has significantly decreased from 1945 to 2017." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1945, + "Enlisted Personnel": 861000 + }, + { + "Year": 1955, + "Enlisted Personnel": 128000 + }, + { + "Year": 1970, + "Enlisted Personnel": 86000 + }, + { + "Year": 1991, + "Enlisted Personnel": 62000 + }, + { + "Year": 2000, + "Enlisted Personnel": 38880 + }, + { + "Year": 2017, + "Enlisted Personnel": 29280 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Enlisted Personnel", + "importance": "primary", + "description": "Number of regular enlisted personnel", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1465": { + "metadata": { + "title": "How Do Consumers Feel About Self-Driving Cars?", + "description": "% of respondents who are likely/unlikely to try a self-driving car*", + "main_insight": "Consumers in India and China show the highest likelihood of trying self-driving cars, while France and Japan exhibit the lowest likelihood. There's significant variation in consumer sentiment across different countries." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Japan", + "Score": 12.0, + "Likelihood": "Very likely" + }, + { + "Country": "Japan", + "Score": 24.0, + "Likelihood": "Unlikely" + } + ], + "score": 0.96, + "annotation": "The Score of Very likely and Unlikely show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Very likely and Unlikely is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Germany", + "Score": 23.0, + "Likelihood": "Neutral" + } + ], + "score": 0.96, + "annotation": "The Neutral has the largest proportion in Germany.", + "reason": "The Score of Neutral in Germany accounts for 0.23, larger than all other Likelihood, of the total of Score of all Likelihood." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Japan", + "Score": 24.0, + "Likelihood": "Neutral" + }, + { + "Country": "Japan", + "Score": 24.0, + "Likelihood": "Unlikely" + } + ], + "score": 0.95, + "annotation": "The Neutral, Unlikely all have the largest proportion in Japan.", + "reason": "The Score of Neutral, Unlikely in Japan all account for 0.2376237623762376, larger than all other Likelihood, of the total of Score of all Likelihood." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "UK", + "Score": 25.0, + "Likelihood": "Unlikely" + } + ], + "score": 0.95, + "annotation": "The Unlikely has the largest proportion in UK.", + "reason": "The Score of Unlikely in UK accounts for 0.25, larger than all other Likelihood, of the total of Score of all Likelihood." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Likelihood": "Very likely", + "Country": "total", + "Score": 205.0 + } + ], + "score": 0.95, + "annotation": "The total value of Very likely has the largest proportion.", + "reason": "The total value of Score of Very likely accounts for 0.25625, larger than all other Likelihood, of the total of Score of all Likelihood." + } + ], + "data": { + "data": [ + { + "Country": "India", + "Score": 56.0, + "Likelihood": "Very likely" + }, + { + "Country": "India", + "Score": 29.0, + "Likelihood": "Likely" + }, + { + "Country": "India", + "Score": 10.0, + "Likelihood": "Neutral" + }, + { + "Country": "India", + "Score": 3.0, + "Likelihood": "Unlikely" + }, + { + "Country": "India", + "Score": 2.0, + "Likelihood": "Very Unlikely" + }, + { + "Country": "China", + "Score": 43.0, + "Likelihood": "Very likely" + }, + { + "Country": "China", + "Score": 32.0, + "Likelihood": "Likely" + }, + { + "Country": "China", + "Score": 16.0, + "Likelihood": "Neutral" + }, + { + "Country": "China", + "Score": 6.0, + "Likelihood": "Unlikely" + }, + { + "Country": "China", + "Score": 2.0, + "Likelihood": "Very Unlikely" + }, + { + "Country": "UAE", + "Score": 38.0, + "Likelihood": "Very likely" + }, + { + "Country": "UAE", + "Score": 32.0, + "Likelihood": "Likely" + }, + { + "Country": "UAE", + "Score": 18.0, + "Likelihood": "Neutral" + }, + { + "Country": "UAE", + "Score": 7.0, + "Likelihood": "Unlikely" + }, + { + "Country": "UAE", + "Score": 6.0, + "Likelihood": "Very Unlikely" + }, + { + "Country": "France", + "Score": 10.0, + "Likelihood": "Very likely" + }, + { + "Country": "France", + "Score": 17.0, + "Likelihood": "Likely" + }, + { + "Country": "France", + "Score": 27.0, + "Likelihood": "Neutral" + }, + { + "Country": "France", + "Score": 31.0, + "Likelihood": "Unlikely" + }, + { + "Country": "France", + "Score": 15.0, + "Likelihood": "Very Unlikely" + }, + { + "Country": "USA", + "Score": 17.0, + "Likelihood": "Very likely" + }, + { + "Country": "USA", + "Score": 25.0, + "Likelihood": "Likely" + }, + { + "Country": "USA", + "Score": 12.0, + "Likelihood": "Neutral" + }, + { + "Country": "USA", + "Score": 27.0, + "Likelihood": "Unlikely" + }, + { + "Country": "USA", + "Score": 18.0, + "Likelihood": "Very Unlikely" + }, + { + "Country": "UK", + "Score": 14.0, + "Likelihood": "Very likely" + }, + { + "Country": "UK", + "Score": 20.0, + "Likelihood": "Likely" + }, + { + "Country": "UK", + "Score": 24.0, + "Likelihood": "Neutral" + }, + { + "Country": "UK", + "Score": 25.0, + "Likelihood": "Unlikely" + }, + { + "Country": "UK", + "Score": 17.0, + "Likelihood": "Very Unlikely" + }, + { + "Country": "Germany", + "Score": 15.0, + "Likelihood": "Very likely" + }, + { + "Country": "Germany", + "Score": 20.0, + "Likelihood": "Likely" + }, + { + "Country": "Germany", + "Score": 23.0, + "Likelihood": "Neutral" + }, + { + "Country": "Germany", + "Score": 21.0, + "Likelihood": "Unlikely" + }, + { + "Country": "Germany", + "Score": 21.0, + "Likelihood": "Very Unlikely" + }, + { + "Country": "Japan", + "Score": 12.0, + "Likelihood": "Very likely" + }, + { + "Country": "Japan", + "Score": 19.0, + "Likelihood": "Likely" + }, + { + "Country": "Japan", + "Score": 24.0, + "Likelihood": "Neutral" + }, + { + "Country": "Japan", + "Score": 24.0, + "Likelihood": "Unlikely" + }, + { + "Country": "Japan", + "Score": 22.0, + "Likelihood": "Very Unlikely" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding likelihood", + "role": "y" + }, + { + "name": "Likelihood", + "importance": "primary", + "description": "The likelihood of the data point", + "role": "group" + } + ] + } + }, + "2098": { + "metadata": { + "title": "World Rugby Rankings: 2007 and today", + "description": "World Rugby awards points based on a team's performance - more recent and more significant games are more heavily weighted", + "main_insight": "The visualization compares the top 10 World Rugby rankings on August 27, 2007, and September 9, 2019. New Zealand was ranked 1st in 2007, while Ireland was ranked 1st in 2019." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Rank": 1, + "Team_2007": "New Zealand" + }, + { + "Rank": 2, + "Team_2007": "Australia" + }, + { + "Rank": 3, + "Team_2007": "France" + }, + { + "Rank": 4, + "Team_2007": "South Africa" + }, + { + "Rank": 5, + "Team_2007": "Ireland" + }, + { + "Rank": 6, + "Team_2007": "Argentina" + }, + { + "Rank": 7, + "Team_2007": "England" + }, + { + "Rank": 8, + "Team_2007": "Wales" + }, + { + "Rank": 9, + "Team_2007": "Italy" + }, + { + "Rank": 10, + "Team_2007": "Scotland" + } + ], + "columns": [ + { + "name": "Rank", + "importance": "primary", + "description": "Ranking position", + "unit": "none", + "data_type": "number", + "discarded_count": 0, + "role": "x" + }, + { + "name": "Team_2007", + "importance": "primary", + "description": "Team name in 2007", + "unit": "none", + "data_type": "string", + "discarded_count": 0, + "role": "y" + } + ] + } + }, + "1810": { + "metadata": { + "title": "Vaccine Access Is a Privilege, Not a Given", + "description": "Countries with the highest share of children not vaccinated against diphteria, tetanus and pertussis in 2023", + "main_insight": "The infographic highlights the countries with the highest percentage of unvaccinated children in 2023, showing significant disparities in vaccine access globally. North Korea and Papua New Guinea top the list with the highest rates." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "North Korea", + "Unvaccinated Children (%)": 59.0 + } + ], + "score": 0.86, + "annotation": "The has a maximum value at North Korea", + "reason": "The [{'Country': 'North Korea', 'Unvaccinated Children (%)': 59.0}] of has a maximum value of 59.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Angola", + "Unvaccinated Children (%)": 31.0 + } + ], + "score": 0.74, + "annotation": "The has a minimum value at Angola", + "reason": "The [{'Country': 'Angola', 'Unvaccinated Children (%)': 31.0}] of has a mainimum value of 31.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Papua New Guinea", + "Unvaccinated Children (%)": 55.0 + } + ], + "score": 0.65, + "annotation": "The shows a sudden jump in Unvaccinated Children (%) from Somalia to Papua New Guinea.", + "reason": "The Unvaccinated Children (%) for increases from 48.0 at Somalia to 55.0 at Papua New Guinea, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "North Korea", + "Unvaccinated Children (%)": 59.0 + }, + { + "Country": "Papua New Guinea", + "Unvaccinated Children (%)": 55.0 + }, + { + "Country": "Somalia", + "Unvaccinated Children (%)": 48.0 + }, + { + "Country": "Central African Republic", + "Unvaccinated Children (%)": 46.0 + }, + { + "Country": "Sudan", + "Unvaccinated Children (%)": 43.0 + }, + { + "Country": "Yemen", + "Unvaccinated Children (%)": 43.0 + }, + { + "Country": "Guinea", + "Unvaccinated Children (%)": 38.0 + }, + { + "Country": "Venezuela", + "Unvaccinated Children (%)": 35.0 + }, + { + "Country": "Afghanistan", + "Unvaccinated Children (%)": 33.0 + }, + { + "Country": "Angola", + "Unvaccinated Children (%)": 31.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country Name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Unvaccinated Children (%)", + "importance": "primary", + "description": "Percentage of children not vaccinated against diphteria, tetanus and pertussis in 2023", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1089": { + "metadata": { + "title": "Russia's Neighbors Ramp up Military Spending", + "description": "Change in average annual military expenditure between 2013-2017 and 2018-2022, by country", + "main_insight": "Russia's neighbors, particularly Ukraine, Lithuania, and Latvia, have significantly increased their military spending between the periods 2013-2017 and 2018-2022, compared to other nations and to Russia and South Sudan." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "UA", + "Spending Change": 52.0 + } + ], + "score": 0.95, + "annotation": "The has a maximum value at UA", + "reason": "The [{'Country': 'UA', 'Spending Change': 52.0}] of has a maximum value of 52.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "SS", + "Spending Change": -17.0 + } + ], + "score": 0.79, + "annotation": "The has a minimum value at SS", + "reason": "The [{'Country': 'SS', 'Spending Change': -17.0}] of has a mainimum value of -17.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "UA", + "Spending Change": 52.0 + } + ], + "score": 0.79, + "annotation": "The shows a sudden jump in Spending Change from LT to UA.", + "reason": "The Spending Change for increases from 22.0 at LT to 52.0 at UA, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "UA", + "Spending Change": 52.0 + }, + { + "Country": "LT", + "Spending Change": 22.0 + }, + { + "Country": "LV", + "Spending Change": 20.0 + }, + { + "Country": "TR", + "Spending Change": 8.0 + }, + { + "Country": "CN", + "Spending Change": 6.0 + }, + { + "Country": "IN", + "Spending Change": 5.0 + }, + { + "Country": "DE", + "Spending Change": 4.0 + }, + { + "Country": "US", + "Spending Change": 1.0 + }, + { + "Country": "RU", + "Spending Change": -1.0 + }, + { + "Country": "SS", + "Spending Change": -17.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country code", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Spending Change", + "importance": "primary", + "description": "Change in average annual military expenditure between 2013-2017 and 2018-2022", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1464": { + "metadata": { + "title": "Democrats Dominate Post-Election Day Twitter", + "description": "U.S. politicians with the most Twitter mentions in 2018", + "main_insight": "While Donald Trump remained the most mentioned politician on Twitter, Democrats gained significant traction in online discussions after the 2018 election, contrasting with the Republican dominance before the election." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Time Period": "January 2018 to Election Day", + "Rank": 5 + }, + { + "Time Period": "Post-Election Day 2018", + "Rank": 5 + } + ], + "score": 0.75, + "annotation": "The has maximum values at January 2018 to Election Day, Post-Election Day 2018", + "reason": "The [{'Time Period': 'January 2018 to Election Day', 'Rank': 5}, {'Time Period': 'Post-Election Day 2018', 'Rank': 5}] of have maximum values of 5, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Time Period": "January 2018 to Election Day", + "Rank": 1 + }, + { + "Time Period": "Post-Election Day 2018", + "Rank": 1 + } + ], + "score": 0.75, + "annotation": "The has minimum values at January 2018 to Election Day, Post-Election Day 2018", + "reason": "The [{'Time Period': 'January 2018 to Election Day', 'Rank': 1}, {'Time Period': 'Post-Election Day 2018', 'Rank': 1}] of have minimum values of 1, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time Period": "January 2018 to Election Day", + "Rank": 2 + }, + { + "Time Period": "January 2018 to Election Day", + "Rank": 3 + }, + { + "Time Period": "January 2018 to Election Day", + "Rank": 4 + }, + { + "Time Period": "January 2018 to Election Day", + "Rank": 5 + } + ], + "score": 0.65, + "annotation": "The exhibits multiple sudden jumps in Rank, transitioning from Post-Election Day 2018, Post-Election Day 2018, Post-Election Day 2018, Post-Election Day 2018 to January 2018 to Election Day, January 2018 to Election Day, January 2018 to Election Day, January 2018 to Election Day.", + "reason": "Across these points, the Rank for increases from 1, 2, 3, 4 at Post-Election Day 2018, Post-Election Day 2018, Post-Election Day 2018, Post-Election Day 2018 to 2, 3, 4, 5 at January 2018 to Election Day, January 2018 to Election Day, January 2018 to Election Day, January 2018 to Election Day, suggesting significant shifts in value." + } + ], + "data": { + "data": [ + { + "Time Period": "January 2018 to Election Day", + "Rank": 1 + }, + { + "Time Period": "January 2018 to Election Day", + "Rank": 2 + }, + { + "Time Period": "January 2018 to Election Day", + "Rank": 3 + }, + { + "Time Period": "January 2018 to Election Day", + "Rank": 4 + }, + { + "Time Period": "January 2018 to Election Day", + "Rank": 5 + }, + { + "Time Period": "Post-Election Day 2018", + "Rank": 1 + }, + { + "Time Period": "Post-Election Day 2018", + "Rank": 2 + }, + { + "Time Period": "Post-Election Day 2018", + "Rank": 3 + }, + { + "Time Period": "Post-Election Day 2018", + "Rank": 4 + }, + { + "Time Period": "Post-Election Day 2018", + "Rank": 5 + } + ], + "columns": [ + { + "name": "Time Period", + "importance": "primary", + "description": "The period during which Twitter mentions were measured", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Rank", + "importance": "primary", + "description": "Ranking of the politician based on Twitter mentions", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1099": { + "metadata": { + "title": "Europe's Worst Cities For Traffic Congestion", + "description": "Percentage of extra travel time due to congestion in 2019*", + "main_insight": "Moscow had the highest percentage of extra travel time due to congestion among European cities in 2019, at 59%. Istanbul and Kiev follow closely behind." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Moscow", + "Extra Travel Time (%)": 59.0 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at Moscow", + "reason": "The [{'City': 'Moscow', 'Extra Travel Time (%)': 59.0}] of has a maximum value of 59.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Moscow", + "Extra Travel Time (%)": 59.0 + } + ], + "score": 0.72, + "annotation": "The shows a sudden jump in Extra Travel Time (%) from Istanbul to Moscow.", + "reason": "The Extra Travel Time (%) for increases from 55.0 at Istanbul to 59.0 at Moscow, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "Odessa", + "Extra Travel Time (%)": 47.0 + }, + { + "City": "Lodz", + "Extra Travel Time (%)": 47.0 + } + ], + "score": 0.59, + "annotation": "The has minimum values at Odessa, Lodz", + "reason": "The [{'City': 'Odessa', 'Extra Travel Time (%)': 47.0}, {'City': 'Lodz', 'Extra Travel Time (%)': 47.0}] of have minimum values of 47.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "City": "Moscow", + "Extra Travel Time (%)": 59.0 + }, + { + "City": "Istanbul", + "Extra Travel Time (%)": 55.0 + }, + { + "City": "Kiev", + "Extra Travel Time (%)": 53.0 + }, + { + "City": "Bucharest", + "Extra Travel Time (%)": 52.0 + }, + { + "City": "Saint-Petersburg", + "Extra Travel Time (%)": 49.0 + }, + { + "City": "Dublin", + "Extra Travel Time (%)": 48.0 + }, + { + "City": "Odessa", + "Extra Travel Time (%)": 47.0 + }, + { + "City": "Lodz", + "Extra Travel Time (%)": 47.0 + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "Name of the city", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Extra Travel Time (%)", + "importance": "primary", + "description": "Percentage of extra travel time due to congestion", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2322": { + "metadata": { + "title": "The Soaring Costs of American Childbirth", + "description": "Average amount paid per childbirth in selected countries in 2012 (in U.S. dollars)", + "main_insight": "The cost of childbirth in the United States, both conventional and cesarean, far exceeds the cost in other developed countries." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "South Africa", + "Units": 3955.0, + "Delivery Method": "Caesarean" + }, + { + "Country": "South Africa", + "Units": 1862.0, + "Delivery Method": "Conventional" + } + ], + "score": 0.97, + "annotation": "The Units of Caesarean and Conventional show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Caesarean and Conventional is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United States", + "Units": 15121.0, + "Delivery Method": "Caesarean" + } + ], + "score": 0.94, + "annotation": "The Caesarean has a maximum value at United States", + "reason": "The [{'Country': 'United States', 'Units': 15121.0, 'Delivery Method': 'Caesarean'}] of Caesarean has a maximum value of 15121.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United States", + "Units": 9775.0, + "Delivery Method": "Conventional" + } + ], + "score": 0.91, + "annotation": "The Conventional has a maximum value at United States", + "reason": "The [{'Country': 'United States', 'Units': 9775.0, 'Delivery Method': 'Conventional'}] of Conventional has a maximum value of 9775.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Units": 15121.0, + "Delivery Method": "Caesarean" + } + ], + "score": 0.87, + "annotation": "The Caesarean shows a sudden jump in Units from Australia to United States.", + "reason": "The Units for Caesarean increases from 8599.0 at Australia to 15121.0 at United States, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "France", + "Units": 4415.0, + "Delivery Method": "Caesarean" + } + ], + "score": 0.78, + "annotation": "The Caesarean accounts for the majority in France.", + "reason": "The Units of Caesarean in France accounts for 0.5549270990447461 (more than 50%) of the total of Units of all Delivery Method." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Units": 15121.0, + "Delivery Method": "Caesarean" + }, + { + "Country": "United States", + "Units": 9775.0, + "Delivery Method": "Conventional" + }, + { + "Country": "Australia", + "Units": 8599.0, + "Delivery Method": "Caesarean" + }, + { + "Country": "Australia", + "Units": 6878.0, + "Delivery Method": "Conventional" + }, + { + "Country": "Switzerland", + "Units": 7751.0, + "Delivery Method": "Caesarean" + }, + { + "Country": "Switzerland", + "Units": 5761.0, + "Delivery Method": "Conventional" + }, + { + "Country": "France", + "Units": 4415.0, + "Delivery Method": "Caesarean" + }, + { + "Country": "France", + "Units": 3541.0, + "Delivery Method": "Conventional" + }, + { + "Country": "Chile", + "Units": 4059.0, + "Delivery Method": "Caesarean" + }, + { + "Country": "Chile", + "Units": 2768.0, + "Delivery Method": "Conventional" + }, + { + "Country": "Netherlands", + "Units": 5328.0, + "Delivery Method": "Caesarean" + }, + { + "Country": "Netherlands", + "Units": 3652.0, + "Delivery Method": "Conventional" + }, + { + "Country": "Britain", + "Units": 6441.0, + "Delivery Method": "Caesarean" + }, + { + "Country": "Britain", + "Units": 4343.0, + "Delivery Method": "Conventional" + }, + { + "Country": "South Africa", + "Units": 3955.0, + "Delivery Method": "Caesarean" + }, + { + "Country": "South Africa", + "Units": 1862.0, + "Delivery Method": "Conventional" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where childbirth costs are being compared", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding delivery method", + "role": "y" + }, + { + "name": "Delivery Method", + "importance": "primary", + "description": "The delivery method of the data point", + "role": "group" + } + ] + } + }, + "1359": { + "metadata": { + "title": "Are You Not Entertained?", + "description": "Entertainment industry revenue in the United States in 2018, by segment", + "main_insight": "In 2018, Video games generated the highest revenue among the entertainment industry segments in the US, followed by Home video, Cinema, and Music." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Entertainment Segment": "Video games", + "Revenue (billions)": 35.8 + } + ], + "score": 0.82, + "annotation": "The shows a sudden jump in Revenue (billions) from Home video to Video games.", + "reason": "The Revenue (billions) for increases from 23.3 at Home video to 35.8 at Video games, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Entertainment Segment": "Video games", + "Revenue (billions)": 35.8 + } + ], + "score": 0.73, + "annotation": "The has a maximum value at Video games", + "reason": "The [{'Entertainment Segment': 'Video games', 'Revenue (billions)': 35.8}] of has a maximum value of 35.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Entertainment Segment": "Music", + "Revenue (billions)": 9.9 + } + ], + "score": 0.53, + "annotation": "The has a minimum value at Music", + "reason": "The [{'Entertainment Segment': 'Music', 'Revenue (billions)': 9.9}] of has a mainimum value of 9.9, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Entertainment Segment": "Video games", + "Revenue (billions)": 35.8 + }, + { + "Entertainment Segment": "Home video", + "Revenue (billions)": 23.3 + }, + { + "Entertainment Segment": "Cinema", + "Revenue (billions)": 11.9 + }, + { + "Entertainment Segment": "Music", + "Revenue (billions)": 9.9 + } + ], + "columns": [ + { + "name": "Entertainment Segment", + "importance": "primary", + "description": "Different segments of the entertainment industry", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Revenue (billions)", + "importance": "primary", + "description": "Revenue generated by each segment in billions of US dollars", + "unit": "billions USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2017": { + "metadata": { + "title": "Lager by far the the most popular beer type in the UK", + "description": "Respondents who chose the following beer categories as their favourite", + "main_insight": "Lager is the most popular beer type in the UK, with 71% of respondents choosing it as their favorite, significantly more than any other type." + }, + "chart_type": "Proportional Icon Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Beer Type": "Lager", + "Percentage of Respondents": 71.0 + } + ], + "score": 0.88, + "annotation": "The shows a sudden jump in Percentage of Respondents from IPA to Lager.", + "reason": "The Percentage of Respondents for increases from 46.0 at IPA to 71.0 at Lager, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Beer Type": "Lager", + "Percentage of Respondents": 71.0 + } + ], + "score": 0.85, + "annotation": "The has a maximum value at Lager", + "reason": "The [{'Beer Type': 'Lager', 'Percentage of Respondents': 71.0}] of has a maximum value of 71.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Beer Type": "Stout", + "Percentage of Respondents": 30.0 + } + ], + "score": 0.58, + "annotation": "The has a minimum value at Stout", + "reason": "The [{'Beer Type': 'Stout', 'Percentage of Respondents': 30.0}] of has a mainimum value of 30.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Beer Type": "Lager", + "Percentage of Respondents": 71.0 + }, + { + "Beer Type": "IPA", + "Percentage of Respondents": 46.0 + }, + { + "Beer Type": "Pale Ale", + "Percentage of Respondents": 39.0 + }, + { + "Beer Type": "Bitter", + "Percentage of Respondents": 39.0 + }, + { + "Beer Type": "Stout", + "Percentage of Respondents": 30.0 + } + ], + "columns": [ + { + "name": "Beer Type", + "importance": "primary", + "description": "Different types of beer", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Respondents", + "importance": "primary", + "description": "Percentage of respondents who chose the beer type as their favorite", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1058": { + "metadata": { + "title": "Social Networks Provide Little Threat To Search Engines", + "description": "Social media and search engine's share of online advertising*", + "main_insight": "Search engines hold a significantly larger share of online advertising compared to social media across various countries, indicating that social networks pose a limited threat to search engine's advertising dominance." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Russia", + "Score": 68.0, + "Traffic Source": "Search engines" + } + ], + "score": 0.9, + "annotation": "The Search engines has a maximum value at Russia", + "reason": "The [{'Country': 'Russia', 'Score': 68.0, 'Traffic Source': 'Search engines'}] of Search engines has a maximum value of 68.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Brazil", + "Score": 19.0, + "Traffic Source": "Social media" + } + ], + "score": 0.87, + "annotation": "The Social media has a maximum value at Brazil", + "reason": "The [{'Country': 'Brazil', 'Score': 19.0, 'Traffic Source': 'Social media'}] of Social media has a maximum value of 19.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "China", + "Score": 4.0, + "Traffic Source": "Social media" + } + ], + "score": 0.75, + "annotation": "The Social media has the smallest proportion in China.", + "reason": "The Score of Social media in China accounts for 0.06557377049180328, smaller than all other Traffic Source, of the total of Score of all Traffic Source." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "China", + "Score": 4.0, + "Traffic Source": "Social media" + } + ], + "score": 0.74, + "annotation": "The Social media has a minimum value at China", + "reason": "The [{'Country': 'China', 'Score': 4.0, 'Traffic Source': 'Social media'}] of Social media has a mainimum value of 4.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Italy", + "Score": 36.0, + "Traffic Source": "Search engines" + }, + { + "Country": "Japan", + "Score": 36.0, + "Traffic Source": "Search engines" + } + ], + "score": 0.74, + "annotation": "The Search engines has minimum values at Italy, Japan", + "reason": "The [{'Country': 'Italy', 'Score': 36.0, 'Traffic Source': 'Search engines'}, {'Country': 'Japan', 'Score': 36.0, 'Traffic Source': 'Search engines'}] of Search engines have minimum values of 36.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Russia", + "Score": 68.0, + "Traffic Source": "Search engines" + }, + { + "Country": "Russia", + "Score": 10.0, + "Traffic Source": "Social media" + }, + { + "Country": "China", + "Score": 57.0, + "Traffic Source": "Search engines" + }, + { + "Country": "China", + "Score": 4.0, + "Traffic Source": "Social media" + }, + { + "Country": "Spain", + "Score": 54.0, + "Traffic Source": "Search engines" + }, + { + "Country": "Spain", + "Score": 14.0, + "Traffic Source": "Social media" + }, + { + "Country": "UK", + "Score": 53.0, + "Traffic Source": "Search engines" + }, + { + "Country": "UK", + "Score": 14.0, + "Traffic Source": "Social media" + }, + { + "Country": "Germany", + "Score": 51.0, + "Traffic Source": "Search engines" + }, + { + "Country": "Germany", + "Score": 5.0, + "Traffic Source": "Social media" + }, + { + "Country": "Brazil", + "Score": 51.0, + "Traffic Source": "Search engines" + }, + { + "Country": "Brazil", + "Score": 19.0, + "Traffic Source": "Social media" + }, + { + "Country": "United States", + "Score": 44.0, + "Traffic Source": "Search engines" + }, + { + "Country": "United States", + "Score": 15.0, + "Traffic Source": "Social media" + }, + { + "Country": "France", + "Score": 42.0, + "Traffic Source": "Search engines" + }, + { + "Country": "France", + "Score": 9.0, + "Traffic Source": "Social media" + }, + { + "Country": "Italy", + "Score": 36.0, + "Traffic Source": "Search engines" + }, + { + "Country": "Italy", + "Score": 9.0, + "Traffic Source": "Social media" + }, + { + "Country": "Japan", + "Score": 36.0, + "Traffic Source": "Search engines" + }, + { + "Country": "Japan", + "Score": 6.0, + "Traffic Source": "Social media" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country for which the data is shown", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding traffic source", + "role": "y" + }, + { + "name": "Traffic Source", + "importance": "primary", + "description": "The traffic source of the data point", + "role": "group" + } + ] + } + }, + "2358": { + "metadata": { + "title": "The U.S. Remains Light-Years Behind in Maternity Leave", + "description": "Total weeks of paid maternity and parental/home care leave available to mothers in 2020*", + "main_insight": "The US offers significantly less paid maternity and parental leave (0 weeks) compared to other developed countries, with Estonia offering the most at 82 weeks." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "EE", + "Weeks of Paid Leave": 82.0 + } + ], + "score": 0.91, + "annotation": "The has a maximum value at EE", + "reason": "The [{'Country': 'EE', 'Weeks of Paid Leave': 82.0}] of has a maximum value of 82.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "US", + "Weeks of Paid Leave": 0.0 + } + ], + "score": 0.73, + "annotation": "The has a minimum value at US", + "reason": "The [{'Country': 'US', 'Weeks of Paid Leave': 0.0}] of has a mainimum value of 0.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "SK", + "Weeks of Paid Leave": 69.0 + } + ], + "score": 0.59, + "annotation": "The shows a sudden jump in Weeks of Paid Leave from TR to SK.", + "reason": "The Weeks of Paid Leave for increases from 53.0 at TR to 69.0 at SK, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "EE", + "Weeks of Paid Leave": 82.0 + }, + { + "Country": "SK", + "Weeks of Paid Leave": 69.0 + }, + { + "Country": "TR", + "Weeks of Paid Leave": 53.0 + }, + { + "Country": "DE", + "Weeks of Paid Leave": 43.0 + }, + { + "Country": "JP", + "Weeks of Paid Leave": 36.0 + }, + { + "Country": "KR", + "Weeks of Paid Leave": 28.0 + }, + { + "Country": "CA", + "Weeks of Paid Leave": 26.0 + }, + { + "Country": "FR", + "Weeks of Paid Leave": 19.0 + }, + { + "Country": "ES", + "Weeks of Paid Leave": 16.0 + }, + { + "Country": "GB", + "Weeks of Paid Leave": 12.0 + }, + { + "Country": "AU", + "Weeks of Paid Leave": 8.0 + }, + { + "Country": "US", + "Weeks of Paid Leave": 0.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country or economic area", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Weeks of Paid Leave", + "importance": "primary", + "description": "Total weeks of paid maternity and parental/home care leave", + "unit": "Weeks", + "data_type": "number", + "role": "y" + } + ] + } + }, + "35": { + "metadata": { + "title": "Where Europeans Consider Corruption Widespread", + "description": "Share of EU-28 respondents considering corruption widespread in 2017", + "main_insight": "There is a significant difference in the perception of corruption across EU countries, with Greece having the highest percentage of respondents considering it widespread and Finland the lowest." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Finland", + "Perception of Corruption": 21.0 + } + ], + "score": 0.94, + "annotation": "The has a minimum value at Finland", + "reason": "The [{'Country': 'Finland', 'Perception of Corruption': 21.0}] of has a mainimum value of 21.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Greece", + "Perception of Corruption": 96.0 + } + ], + "score": 0.67, + "annotation": "The has a maximum value at Greece", + "reason": "The [{'Country': 'Greece', 'Perception of Corruption': 96.0}] of has a maximum value of 96.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Sweden", + "Perception of Corruption": 37.0 + } + ], + "score": 0.6, + "annotation": "The shows a sudden jump in Perception of Corruption from Denmark to Sweden.", + "reason": "The Perception of Corruption for increases from 22.0 at Denmark to 37.0 at Sweden, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Greece", + "Perception of Corruption": 96.0 + }, + { + "Country": "Spain", + "Perception of Corruption": 94.0 + }, + { + "Country": "Cyprus", + "Perception of Corruption": 94.0 + }, + { + "Country": "Croatia", + "Perception of Corruption": 94.0 + }, + { + "Country": "Lithuania", + "Perception of Corruption": 93.0 + }, + { + "Country": "Portugal", + "Perception of Corruption": 92.0 + }, + { + "Country": "Italy", + "Perception of Corruption": 89.0 + }, + { + "Country": "Slovenia", + "Perception of Corruption": 89.0 + }, + { + "Country": "Hungary", + "Perception of Corruption": 86.0 + }, + { + "Country": "Slovakia", + "Perception of Corruption": 85.0 + }, + { + "Country": "Latvia", + "Perception of Corruption": 84.0 + }, + { + "Country": "Czech Republic", + "Perception of Corruption": 84.0 + }, + { + "Country": "Bulgaria", + "Perception of Corruption": 83.0 + }, + { + "Country": "Romania", + "Perception of Corruption": 80.0 + }, + { + "Country": "Malta", + "Perception of Corruption": 79.0 + }, + { + "Country": "Ireland", + "Perception of Corruption": 68.0 + }, + { + "Country": "EU-28", + "Perception of Corruption": 68.0 + }, + { + "Country": "France", + "Perception of Corruption": 67.0 + }, + { + "Country": "Estonia", + "Perception of Corruption": 67.0 + }, + { + "Country": "Belgium", + "Perception of Corruption": 65.0 + }, + { + "Country": "Poland", + "Perception of Corruption": 58.0 + }, + { + "Country": "United Kingdom", + "Perception of Corruption": 55.0 + }, + { + "Country": "Germany", + "Perception of Corruption": 51.0 + }, + { + "Country": "Austria", + "Perception of Corruption": 50.0 + }, + { + "Country": "Netherlands", + "Perception of Corruption": 44.0 + }, + { + "Country": "Luxembourg", + "Perception of Corruption": 40.0 + }, + { + "Country": "Sweden", + "Perception of Corruption": 37.0 + }, + { + "Country": "Denmark", + "Perception of Corruption": 22.0 + }, + { + "Country": "Finland", + "Perception of Corruption": 21.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "EU-28 Countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Perception of Corruption", + "importance": "primary", + "description": "Percentage of respondents who consider corruption widespread", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2243": { + "metadata": { + "title": "Mark Zuckerberg Made $3.5 Billion in November", + "description": "Market value of Mark Zuckerberg's stake in Facebook (in billion U.S. dollars)", + "main_insight": "The market value of Mark Zuckerberg's stake in Facebook fluctuated significantly between May 18, 2012 and December 3, 2012, ultimately increasing by $3.5 billion in November." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "May 18, 2012", + "Market Value": 20.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Market Value from December 3, 2012 to May 18, 2012.", + "reason": "The Market Value for increases from 13.6 at December 3, 2012 to 20.0 at May 18, 2012, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "December 3, 2012", + "Market Value": 13.6 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at December 3, 2012", + "reason": "The [{'Date': 'December 3, 2012', 'Market Value': 13.6}] of has a mainimum value of 13.6, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "May 18, 2012", + "Market Value": 20.0 + } + ], + "score": 0.45, + "annotation": "The has a maximum value at May 18, 2012", + "reason": "The [{'Date': 'May 18, 2012', 'Market Value': 20.0}] of has a maximum value of 20.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Date": "May 18, 2012", + "Market Value": 20.0 + }, + { + "Date": "December 3, 2012", + "Market Value": 13.6 + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Timeline from May 18, 2012 to December 3, 2012", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Market Value", + "importance": "primary", + "description": "Market value of Mark Zuckerberg's stake in Facebook (in billion U.S. dollars)", + "unit": "Billion U.S. dollars", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1823": { + "metadata": { + "title": "Paid For The Pun: 10 Highest-Earning U.S. Comedians", + "description": "Estimated annual earning of the best-paid U.S. comedians 2017", + "main_insight": "Jerry Seinfeld was the highest-earning U.S. comedian in 2017, with estimated annual earnings of $69 million, significantly more than the other comedians listed." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Comedian": "Jerry Seinfeld", + "Annual Earnings": 69.0 + } + ], + "score": 0.86, + "annotation": "The has a maximum value at Jerry Seinfeld", + "reason": "The [{'Comedian': 'Jerry Seinfeld', 'Annual Earnings': 69.0}] of has a maximum value of 69.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Comedian": "Sebastian Maniscalco", + "Annual Earnings": 15.0 + } + ], + "score": 0.69, + "annotation": "The has a minimum value at Sebastian Maniscalco", + "reason": "The [{'Comedian': 'Sebastian Maniscalco', 'Annual Earnings': 15.0}] of has a mainimum value of 15.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Comedian": "Jim Gaffigan", + "Annual Earnings": 30.5 + }, + { + "Comedian": "Jerry Seinfeld", + "Annual Earnings": 69.0 + } + ], + "score": 0.62, + "annotation": "The exhibits multiple sudden jumps in Annual Earnings, transitioning from Terry Fator, Chris Rock to Jim Gaffigan, Jerry Seinfeld.", + "reason": "Across these points, the Annual Earnings for increases from 18.5, 57.0 at Terry Fator, Chris Rock to 30.5, 69.0 at Jim Gaffigan, Jerry Seinfeld, suggesting significant shifts in value." + } + ], + "data": { + "data": [ + { + "Comedian": "Jerry Seinfeld", + "Annual Earnings": 69.0 + }, + { + "Comedian": "Chris Rock", + "Annual Earnings": 57.0 + }, + { + "Comedian": "Louis C.K.", + "Annual Earnings": 52.0 + }, + { + "Comedian": "Dave Chappelle", + "Annual Earnings": 47.0 + }, + { + "Comedian": "Amy Schumer", + "Annual Earnings": 37.5 + }, + { + "Comedian": "Kevin Hart", + "Annual Earnings": 32.5 + }, + { + "Comedian": "Jim Gaffigan", + "Annual Earnings": 30.5 + }, + { + "Comedian": "Terry Fator", + "Annual Earnings": 18.5 + }, + { + "Comedian": "Jeff Dunham", + "Annual Earnings": 15.5 + }, + { + "Comedian": "Sebastian Maniscalco", + "Annual Earnings": 15.0 + } + ], + "columns": [ + { + "name": "Comedian", + "importance": "primary", + "description": "Name of the comedian", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Annual Earnings", + "importance": "primary", + "description": "Estimated annual earnings in millions of U.S. dollars", + "unit": "Millions USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2205": { + "metadata": { + "title": "Amazon Is The Biggest Employer in Tech", + "description": "Global workforce of selected tech companies at the end of 2018 (or most recent available)", + "main_insight": "Amazon has a significantly larger workforce than other major tech companies, employing over 600,000 people at the end of 2018." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Company": "Amazon", + "Workforce": 647500.0 + } + ], + "score": 0.98, + "annotation": "The has a maximum value at Amazon", + "reason": "The [{'Company': 'Amazon', 'Workforce': 647500.0}] of has a maximum value of 647500.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "Amazon", + "Workforce": 647500.0 + } + ], + "score": 0.94, + "annotation": "The shows a sudden jump in Workforce from Oracle to Amazon.", + "reason": "The Workforce for increases from 137000.0 at Oracle to 647500.0 at Amazon, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Company": "eBay", + "Workforce": 14000.0 + } + ], + "score": 0.43, + "annotation": "The has a minimum value at eBay", + "reason": "The [{'Company': 'eBay', 'Workforce': 14000.0}] of has a mainimum value of 14000.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Company": "Amazon", + "Workforce": 647500.0 + }, + { + "Company": "Oracle", + "Workforce": 137000.0 + }, + { + "Company": "Apple", + "Workforce": 132000.0 + }, + { + "Company": "Microsoft", + "Workforce": 131000.0 + }, + { + "Company": "Intel", + "Workforce": 107400.0 + }, + { + "Company": "Alphabet", + "Workforce": 98771.0 + }, + { + "Company": "Tesla", + "Workforce": 45000.0 + }, + { + "Company": "Facebook", + "Workforce": 35587.0 + }, + { + "Company": "PayPal", + "Workforce": 21800.0 + }, + { + "Company": "eBay", + "Workforce": 14000.0 + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Name of the tech company", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Workforce", + "importance": "primary", + "description": "Number of employees at the end of 2018", + "unit": "employees", + "data_type": "number", + "role": "y" + } + ] + } + }, + "997": { + "metadata": { + "title": "Google Play Looks Set to Overtake Apple App Store", + "description": "Total number of apps downloaded (in billions)", + "main_insight": "Google Play app downloads are rapidly increasing and are projected to surpass Apple App Store downloads by 2013." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2013, + "Units": 50.0, + "App Store": "Google Play" + }, + { + "Year": 2013, + "Units": 50.0, + "App Store": "Apple App Store" + } + ], + "score": 0.96, + "annotation": "The Units of Google Play and Apple App Store show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Google Play and Apple App Store is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2009, + "Units": 1.0, + "App Store": "Google Play" + }, + { + "Year": 2009, + "Units": 2.0, + "App Store": "Apple App Store" + }, + { + "Year": 2010, + "Units": 1.0, + "App Store": "Google Play" + }, + { + "Year": 2010, + "Units": 7.0, + "App Store": "Apple App Store" + }, + { + "Year": 2010, + "Units": 2.0, + "App Store": "Google Play" + }, + { + "Year": 2010, + "Units": 8.0, + "App Store": "Apple App Store" + }, + { + "Year": 2011, + "Units": 6.0, + "App Store": "Google Play" + }, + { + "Year": 2011, + "Units": 15.0, + "App Store": "Apple App Store" + }, + { + "Year": 2011, + "Units": 8.0, + "App Store": "Google Play" + }, + { + "Year": 2011, + "Units": 18.0, + "App Store": "Apple App Store" + }, + { + "Year": 2011, + "Units": 10.0, + "App Store": "Google Play" + }, + { + "Year": 2011, + "Units": 18.0, + "App Store": "Apple App Store" + }, + { + "Year": 2012, + "Units": 12.0, + "App Store": "Google Play" + }, + { + "Year": 2012, + "Units": 25.0, + "App Store": "Apple App Store" + }, + { + "Year": 2012, + "Units": 20.0, + "App Store": "Google Play" + }, + { + "Year": 2012, + "Units": 30.0, + "App Store": "Apple App Store" + }, + { + "Year": 2012, + "Units": 25.0, + "App Store": "Google Play" + }, + { + "Year": 2012, + "Units": 35.0, + "App Store": "Apple App Store" + }, + { + "Year": 2013, + "Units": 40.0, + "App Store": "Google Play" + }, + { + "Year": 2013, + "Units": 40.0, + "App Store": "Apple App Store" + }, + { + "Year": 2013, + "Units": 48.0, + "App Store": "Google Play" + }, + { + "Year": 2013, + "Units": 45.0, + "App Store": "Apple App Store" + }, + { + "Year": 2013, + "Units": 50.0, + "App Store": "Google Play" + }, + { + "Year": 2013, + "Units": 50.0, + "App Store": "Apple App Store" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of app downloads", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding app store", + "role": "y" + }, + { + "name": "App Store", + "importance": "primary", + "description": "The app store of the data point", + "role": "group" + } + ] + } + }, + "1856": { + "metadata": { + "title": "The Key Players in Octopus Trade", + "description": "Top 5 importers and exporters of octopus in 2021, by import value", + "main_insight": "South Korea was the largest importer of octopus in 2021, while China was the largest exporter. The total global octopus trade reached $226 million." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "South Korea", + "Import Value": 128.0 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at South Korea", + "reason": "The [{'Country': 'South Korea', 'Import Value': 128.0}] of has a maximum value of 128.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "South Korea", + "Import Value": 128.0 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in Import Value from Spain to South Korea.", + "reason": "The Import Value for increases from 34.7 at Spain to 128.0 at South Korea, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "China", + "Import Value": 0 + }, + { + "Country": "Thailand", + "Import Value": 0 + } + ], + "score": 0.43, + "annotation": "The has minimum values at China, Thailand", + "reason": "The [{'Country': 'China', 'Import Value': 0}, {'Country': 'Thailand', 'Import Value': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "South Korea", + "Import Value": 128.0 + }, + { + "Country": "Spain", + "Import Value": 34.7 + }, + { + "Country": "Italy", + "Import Value": 34.6 + }, + { + "Country": "Portugal", + "Import Value": 7.8 + }, + { + "Country": "France", + "Import Value": 4.9 + }, + { + "Country": "China", + "Import Value": 0 + }, + { + "Country": "Thailand", + "Import Value": 0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country importing or exporting octopus", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Import Value", + "importance": "primary", + "description": "Import value in millions of US dollars", + "unit": "Million USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "717": { + "metadata": { + "title": "iPhone Users Are a Loyal Bunch", + "description": "Brand retention among smartphone manufacturers in 2013", + "main_insight": "iPhone had the highest brand retention rate among smartphone manufacturers in 2013, significantly higher than Samsung and other competitors." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Smartphone Manufacturer": "Apple", + "Brand Retention": 0.76 + } + ], + "score": 0.91, + "annotation": "The has a maximum value at Apple", + "reason": "The [{'Smartphone Manufacturer': 'Apple', 'Brand Retention': 0.76}] of has a maximum value of 0.76, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Smartphone Manufacturer": "Samsung", + "Brand Retention": 0.58 + } + ], + "score": 0.76, + "annotation": "The shows a sudden jump in Brand Retention from LG to Samsung.", + "reason": "The Brand Retention for increases from 0.37 at LG to 0.58 at Samsung, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Smartphone Manufacturer": "BlackBerry", + "Brand Retention": 0.21 + } + ], + "score": 0.53, + "annotation": "The has a minimum value at BlackBerry", + "reason": "The [{'Smartphone Manufacturer': 'BlackBerry', 'Brand Retention': 0.21}] of has a mainimum value of 0.21, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Smartphone Manufacturer": "Apple", + "Brand Retention": 0.76 + }, + { + "Smartphone Manufacturer": "Samsung", + "Brand Retention": 0.58 + }, + { + "Smartphone Manufacturer": "LG", + "Brand Retention": 0.37 + }, + { + "Smartphone Manufacturer": "Nokia", + "Brand Retention": 0.33 + }, + { + "Smartphone Manufacturer": "HTC", + "Brand Retention": 0.3 + }, + { + "Smartphone Manufacturer": "Sony", + "Brand Retention": 0.24 + }, + { + "Smartphone Manufacturer": "Motorola", + "Brand Retention": 0.22 + }, + { + "Smartphone Manufacturer": "BlackBerry", + "Brand Retention": 0.21 + } + ], + "columns": [ + { + "name": "Smartphone Manufacturer", + "importance": "primary", + "description": "Name of the smartphone brand", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Brand Retention", + "importance": "primary", + "description": "Percentage of customers who replaced their smartphone with another from the same manufacturer", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "548": { + "metadata": { + "title": "Smart Assistants Are Getting Smarter", + "description": "Percentage of general knowledge questions (correctly) answered by smart assistants", + "main_insight": "Smart assistants, particularly Google Assistant, have significantly improved their ability to answer general knowledge questions correctly between 2017 and 2018." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Assistant": "Google Assistant on Google Home", + "Years": 2018, + "Test Results": "Year" + } + ], + "score": 0.97, + "annotation": "The Year shows a sudden jump in Years from Google Assistant on Smartphone to Google Assistant on Google Home.", + "reason": "The Years for Year increases from 2017 at Google Assistant on Smartphone to 2018 at Google Assistant on Google Home, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Assistant": "Amazon Alexa", + "Years": 19.8, + "Test Results": "Answered" + } + ], + "score": 0.96, + "annotation": "The Answered has the smallest proportion in Amazon Alexa.", + "reason": "The Years of Answered in Amazon Alexa accounts for 0.009290104630976398, smaller than all other Test Results, of the total of Years of all Test Results." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Assistant": "Siri", + "Years": 31.4, + "Test Results": "Answered" + } + ], + "score": 0.93, + "annotation": "The Answered has the smallest proportion in Siri.", + "reason": "The Years of Answered in Siri accounts for 0.014710705083157647, smaller than all other Test Results, of the total of Years of all Test Results." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Assistant": "Siri", + "Years": 40.8, + "Test Results": "Answered" + } + ], + "score": 0.91, + "annotation": "The Answered has the smallest proportion in Siri.", + "reason": "The Years of Answered in Siri accounts for 0.01907611744903684, smaller than all other Test Results, of the total of Years of all Test Results." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Test Results": "Year", + "Assistant": "total", + "Years": 20175 + } + ], + "score": 0.9, + "annotation": "The total value of Year has the smallest proportion.", + "reason": "The total value of Years of Year accounts for 0.022064514925721104, smaller than all other Test Results, of the total of Years of all Test Results." + } + ], + "data": { + "data": [ + { + "Assistant": "Google Assistant on Google Home", + "Years": 2017, + "Test Results": "Year" + }, + { + "Assistant": "Google Assistant on Google Home", + "Years": 65.3, + "Test Results": "Answered" + }, + { + "Assistant": "Google Assistant on Google Home", + "Years": 94.5, + "Test Results": "Thereof correct" + }, + { + "Assistant": "Google Assistant on Google Home", + "Years": 2018, + "Test Results": "Year" + }, + { + "Assistant": "Google Assistant on Google Home", + "Years": 66.2, + "Test Results": "Answered" + }, + { + "Assistant": "Google Assistant on Google Home", + "Years": 87.5, + "Test Results": "Thereof correct" + }, + { + "Assistant": "Cortana", + "Years": 2017, + "Test Results": "Year" + }, + { + "Assistant": "Cortana", + "Years": 53.9, + "Test Results": "Answered" + }, + { + "Assistant": "Cortana", + "Years": 86.0, + "Test Results": "Thereof correct" + }, + { + "Assistant": "Cortana", + "Years": 2018, + "Test Results": "Year" + }, + { + "Assistant": "Cortana", + "Years": 64.9, + "Test Results": "Answered" + }, + { + "Assistant": "Cortana", + "Years": 91.6, + "Test Results": "Thereof correct" + }, + { + "Assistant": "Siri", + "Years": 2017, + "Test Results": "Year" + }, + { + "Assistant": "Siri", + "Years": 31.4, + "Test Results": "Answered" + }, + { + "Assistant": "Siri", + "Years": 86.1, + "Test Results": "Thereof correct" + }, + { + "Assistant": "Siri", + "Years": 2018, + "Test Results": "Year" + }, + { + "Assistant": "Siri", + "Years": 40.8, + "Test Results": "Answered" + }, + { + "Assistant": "Siri", + "Years": 80.0, + "Test Results": "Thereof correct" + }, + { + "Assistant": "Amazon Alexa", + "Years": 2017, + "Test Results": "Year" + }, + { + "Assistant": "Amazon Alexa", + "Years": 19.8, + "Test Results": 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"importance": "primary", + "description": "Years for the corresponding test results", + "role": "y" + }, + { + "name": "Test Results", + "importance": "primary", + "description": "The test results of the data point", + "role": "group" + } + ] + } + }, + "494": { + "metadata": { + "title": "Von der Leyen Unlikely to Be Missed in German Politics", + "description": "Share of Germans saying they are satisfied with the work of Ursula von der Leyen", + "main_insight": "The share of Germans satisfied with Ursula von der Leyen's work has significantly decreased from March 2018 to May 2019, dropping from 38% to 28%." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Feb 2019", + "Satisfaction": 25.0 + }, + { + "Month": "Mar 2019", + "Satisfaction": 25.0 + } + ], + "score": 0.86, + "annotation": "The has minimum values at Feb 2019, Mar 2019", + "reason": "The [{'Month': 'Feb 2019', 'Satisfaction': 25.0}, {'Month': 'Mar 2019', 'Satisfaction': 25.0}] of have minimum values of 25.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Dec 2018", + "Satisfaction": 35.0 + } + ], + "score": 0.66, + "annotation": "The shows a sudden jump in Satisfaction from Jan 2019 to Dec 2018.", + "reason": "The Satisfaction for increases from 31.0 at Jan 2019 to 35.0 at Dec 2018, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Aug 2018", + "Satisfaction": 40.0 + } + ], + "score": 0.61, + "annotation": "The has a maximum value at Aug 2018", + "reason": "The [{'Month': 'Aug 2018', 'Satisfaction': 40.0}] of has a maximum value of 40.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Month": "Mar 2018", + "Satisfaction": 38.0 + }, + { + "Month": "Apr 2018", + "Satisfaction": 38.0 + }, + { + "Month": "May 2018", + "Satisfaction": 38.0 + }, + { + "Month": "Jun 2018", + "Satisfaction": 38.0 + }, + { + "Month": "Jul 2018", + "Satisfaction": 39.0 + }, + { + "Month": "Aug 2018", + "Satisfaction": 40.0 + }, + { + "Month": "Sep 2018", + "Satisfaction": 38.0 + }, + { + "Month": "Oct 2018", + "Satisfaction": 38.0 + }, + { + "Month": "Nov 2018", + "Satisfaction": 37.0 + }, + { + "Month": "Dec 2018", + "Satisfaction": 35.0 + }, + { + "Month": "Jan 2019", + "Satisfaction": 31.0 + }, + { + "Month": "Feb 2019", + "Satisfaction": 25.0 + }, + { + "Month": "Mar 2019", + "Satisfaction": 25.0 + }, + { + "Month": "Apr 2019", + "Satisfaction": 27.0 + }, + { + "Month": "May 2019", + "Satisfaction": 28.0 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the survey in 2018 and 2019", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Satisfaction", + "importance": "primary", + "description": "Percentage of Germans satisfied with Ursula von der Leyen's work", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2341": { + "metadata": { + "title": "Reduction In NSA Collection Of U.S. Phone Records", + "description": "Estimated number of records collected from phone companies by the NSA", + "main_insight": "The estimated number of phone records collected by the NSA increased significantly from 2016 to 2017, and then decreased slightly in 2018, but remained higher than 2016 levels." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2016, + "Records Collected": 151230968 + }, + { + "Year": 2017, + "Records Collected": 534396285 + }, + { + "Year": 2018, + "Records Collected": 434238543 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of phone record collection", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Records Collected", + "importance": "primary", + "description": "Estimated number of records collected", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2060": { + "metadata": { + "title": "The Top U.S. Problems For Democrats & Republicans", + "description": "\"What is the most important problem facing the U.S. today?\" (% U.S. adults)", + "main_insight": "Republicans and Democrats differ significantly on what they consider the most important problems facing the U.S. in 2016, with Terrorism being a top concern for Republicans and Economy/Environment a top concern for Democrats." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Problem": "Economy generally", + "Units": 19.0, + "Party": "Democrat" + } + ], + "score": 0.91, + "annotation": "The Democrat has a maximum value at Economy generally", + "reason": "The [{'Problem': 'Economy generally', 'Units': 19.0, 'Party': 'Democrat'}] of Democrat has a maximum value of 19.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Problem": "Terrorism/terrorist attacks", + "Units": 23.0, + "Party": "Republican" + } + ], + "score": 0.91, + "annotation": "The Republican has a maximum value at Terrorism/terrorist attacks", + "reason": "The [{'Problem': 'Terrorism/terrorist attacks', 'Units': 23.0, 'Party': 'Republican'}] of Republican has a maximum value of 23.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Problem": "Economy generally", + "Units": 19.0, + "Party": "Republican" + }, + { + "Problem": "Economy generally", + "Units": 19.0, + "Party": "Democrat" + } + ], + "score": 0.8, + "annotation": "The Republican, Democrat all have the largest proportion in Economy generally.", + "reason": "The Units of Republican, Democrat in Economy generally all account for 0.5, larger than all other Party, of the total of Units of all Party." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Problem": "Energy issues", + "Units": 1.0, + "Party": "Republican" + }, + { + "Problem": "Energy issues", + "Units": 1.0, + "Party": "Democrat" + } + ], + "score": 0.8, + "annotation": "The Republican, Democrat all have the largest proportion in Energy issues.", + "reason": "The Units of Republican, Democrat in Energy issues all account for 0.5, larger than all other Party, of the total of Units of all Party." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Party": "Republican", + "Problem": "total", + "Units": 91.0 + } + ], + "score": 0.8, + "annotation": "The total value of Republican accounts for the majority in all Party.", + "reason": "The total value of Units of Republican accounts for 0.5055555555555555 (more than 50%) of the total of Units of all Party." + } + ], + "data": { + "data": [ + { + "Problem": "Terrorism/terrorist attacks", + "Units": 23.0, + "Party": "Republican" + }, + { + "Problem": "Terrorism/terrorist attacks", + "Units": 17.0, + "Party": "Democrat" + }, + { + "Problem": "Economy generally", + "Units": 19.0, + "Party": "Republican" + }, + { + "Problem": "Economy generally", + "Units": 19.0, + "Party": "Democrat" + }, + { + "Problem": "Morality", + "Units": 15.0, + "Party": "Republican" + }, + { + "Problem": "Morality", + "Units": 5.0, + "Party": "Democrat" + }, + { + "Problem": "Immigration", + "Units": 9.0, + "Party": "Republican" + }, + { + "Problem": "Immigration", + "Units": 3.0, + "Party": "Democrat" + }, + { + "Problem": "Healthcare", + "Units": 7.0, + "Party": "Republican" + }, + { + "Problem": "Healthcare", + "Units": 10.0, + "Party": "Democrat" + }, + { + "Problem": "Unemployment/lack of jobs", + "Units": 7.0, + "Party": "Republican" + }, + { + "Problem": "Unemployment/lack of jobs", + "Units": 8.0, + "Party": "Democrat" + }, + { + "Problem": "Crime", + "Units": 4.0, + "Party": "Republican" + }, + { + "Problem": "Crime", + "Units": 8.0, + "Party": "Democrat" + }, + { + "Problem": "Education", + "Units": 3.0, + "Party": "Republican" + }, + { + "Problem": "Education", + "Units": 7.0, + "Party": "Democrat" + }, + { + "Problem": "War/foreign conflicts", + "Units": 2.0, + "Party": "Republican" + }, + { + "Problem": "War/foreign conflicts", + "Units": 5.0, + "Party": "Democrat" + }, + { + "Problem": "Environment", + "Units": 1.0, + "Party": "Republican" + }, + { + "Problem": "Environment", + "Units": 6.0, + "Party": "Democrat" + }, + { + "Problem": "Energy issues", + "Units": 1.0, + "Party": "Republican" + }, + { + "Problem": "Energy issues", + "Units": 1.0, + "Party": "Democrat" + } + ], + "columns": [ + { + "name": "Problem", + "importance": "primary", + "description": "The issue considered as the most important problem", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding party", + "role": "y" + }, + { + "name": "Party", + "importance": "primary", + "description": "The party of the data point", + "role": "group" + } + ] + } + }, + "502": { + "metadata": { + "title": "Amazon Is Popular Across Generations", + "description": "Share of respondents who bought something from the following online stores in the past 12 months (in %)", + "main_insight": "Amazon is the most popular online store across all generations, significantly outperforming Walmart, Target, and eBay. While Amazon's popularity is relatively consistent across generations, other retailers show more variation." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Online Store": "ebay", + "Score": 20.0, + "Generation": "Gen X (1965-79)" + }, + { + "Online Store": "ebay", + "Score": 20.0, + "Generation": "Baby Boomer (1946-64)" + } + ], + "score": 1.0, + "annotation": "The Score of Gen X (1965-79) and Baby Boomer (1946-64) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Gen X (1965-79) and Baby Boomer (1946-64) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Online Store": "ebay", + "Score": 17.0, + "Generation": "Gen Z (1995-2012)" + }, + { + "Online Store": "ebay", + "Score": 20.0, + "Generation": "Millennials (1980-94)" + } + ], + "score": 1.0, + "annotation": "The Score of Gen Z (1995-2012) and Millennials (1980-94) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Gen Z (1995-2012) and Millennials (1980-94) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Online Store": "ebay", + "Score": 20.0, + "Generation": "Millennials (1980-94)" + }, + { + "Online Store": "ebay", + "Score": 20.0, + "Generation": "Gen X (1965-79)" + } + ], + "score": 0.97, + "annotation": "The Score of Millennials (1980-94) and Gen X (1965-79) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Millennials (1980-94) and Gen X (1965-79) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Online Store": "ebay", + "Score": 17.0, + "Generation": "Gen Z (1995-2012)" + }, + { + "Online Store": "ebay", + "Score": 20.0, + "Generation": "Gen X (1965-79)" + } + ], + "score": 0.96, + "annotation": "The Score of Gen Z (1995-2012) and Gen X (1965-79) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Gen Z (1995-2012) and Gen X (1965-79) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Online Store": "ebay", + "Score": 20.0, + "Generation": "Millennials (1980-94)" + }, + { + "Online Store": "ebay", + "Score": 20.0, + "Generation": "Baby Boomer (1946-64)" + } + ], + "score": 0.96, + "annotation": "The Score of Millennials (1980-94) and Baby Boomer (1946-64) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Millennials (1980-94) and Baby Boomer (1946-64) is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Online Store": "amazon", + "Score": 72.0, + "Generation": "Gen Z (1995-2012)" + }, + { + "Online Store": "amazon", + "Score": 76.0, + "Generation": "Millennials (1980-94)" + }, + { + "Online Store": "amazon", + "Score": 73.0, + "Generation": "Gen X (1965-79)" + }, + { + "Online Store": "amazon", + "Score": 74.0, + "Generation": "Baby Boomer (1946-64)" + }, + { + "Online Store": "Walmart", + "Score": 44.0, + "Generation": "Gen Z (1995-2012)" + }, + { + "Online Store": "Walmart", + "Score": 51.0, + "Generation": "Millennials (1980-94)" + }, + { + "Online Store": "Walmart", + "Score": 46.0, + "Generation": "Gen X (1965-79)" + }, + { + "Online Store": "Walmart", + "Score": 45.0, + "Generation": "Baby Boomer (1946-64)" + }, + { + "Online Store": "target", + "Score": 30.0, + "Generation": "Gen Z (1995-2012)" + }, + { + "Online Store": "target", + "Score": 32.0, + "Generation": "Millennials (1980-94)" + }, + { + "Online Store": "target", + "Score": 18.0, + "Generation": "Gen X (1965-79)" + }, + { + "Online Store": "target", + "Score": 14.0, + "Generation": "Baby Boomer (1946-64)" + }, + { + "Online Store": "ebay", + "Score": 17.0, + "Generation": "Gen Z (1995-2012)" + }, + { + "Online Store": "ebay", + "Score": 20.0, + "Generation": "Millennials (1980-94)" + }, + { + "Online Store": "ebay", + "Score": 20.0, + "Generation": "Gen X (1965-79)" + }, + { + "Online Store": "ebay", + "Score": 20.0, + "Generation": "Baby Boomer (1946-64)" + } + ], + "columns": [ + { + "name": "Online Store", + "importance": "primary", + "description": "The online retailer where respondents made a purchase.", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding generation", + "role": "y" + }, + { + "name": "Generation", + "importance": "primary", + "description": "The generation of the data point", + "role": "group" + } + ] + } + }, + "124": { + "metadata": { + "title": "What Scandal? Republicans Most Likely to Turn a Blind Eye", + "description": "Democratic/Republican voters who would vote for member of party who did the following", + "main_insight": "Republican voters are more likely to overlook certain scandals like wearing blackface, cheating on their spouse, or being accused of sexual misconduct compared to Democratic voters." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Scandal": "was accused of sexual misconduct", + "Republican Voters": 0.31 + } + ], + "score": 0.89, + "annotation": "The shows a sudden jump in Republican Voters from committed a felony to was accused of sexual misconduct.", + "reason": "The Republican Voters for increases from 0.09 at committed a felony to 0.31 at was accused of sexual misconduct, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Scandal": "cheated on their spouse", + "Republican Voters": 0.43 + } + ], + "score": 0.76, + "annotation": "The has a maximum value at cheated on their spouse", + "reason": "The [{'Scandal': 'cheated on their spouse', 'Republican Voters': 0.43}] of has a maximum value of 0.43, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Scandal": "was convicted of sexual assault", + "Republican Voters": 0.07 + } + ], + "score": 0.53, + "annotation": "The has a minimum value at was convicted of sexual assault", + "reason": "The [{'Scandal': 'was convicted of sexual assault', 'Republican Voters': 0.07}] of has a mainimum value of 0.07, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Scandal": "wore blackface", + "Republican Voters": 0.39 + }, + { + "Scandal": "was accused of sexual misconduct", + "Republican Voters": 0.31 + }, + { + "Scandal": "cheated on their spouse", + "Republican Voters": 0.43 + }, + { + "Scandal": "was convicted of sexual assault", + "Republican Voters": 0.07 + }, + { + "Scandal": "misused taxpayer dollars", + "Republican Voters": 0.08 + }, + { + "Scandal": "committed tax fraud", + "Republican Voters": 0.09 + }, + { + "Scandal": "committed a felony", + "Republican Voters": 0.09 + } + ], + "columns": [ + { + "name": "Scandal", + "importance": "primary", + "description": "Type of scandal", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Republican Voters", + "importance": "primary", + "description": "Percentage of Republican voters who would vote for a Republican who committed the scandal", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "431": { + "metadata": { + "title": "Amazon Paid $0 In Federal Income Taxes Last Year", + "description": "Amazon's U.S. pretax profit and effective tax rate since 2009", + "main_insight": "Amazon's pretax profits have significantly increased in recent years, particularly in 2017 and 2018, while their federal tax paid has remained very low or even negative, resulting in a very low effective tax rate." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2018, + "Units": 10.8, + "Taxation": "Current pretax profit (after subtracting state)" + }, + { + "Year": 2018, + "Units": -1.2, + "Taxation": "Effective tax rate" + } + ], + "score": 0.99, + "annotation": "The Units of Current pretax profit (after subtracting state) and Effective tax rate show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Current pretax profit (after subtracting state) and Effective tax rate is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2009, + "Units": 8.1, + "Taxation": "Current pretax profit (after subtracting state)" + }, + { + "Year": 2009, + "Units": 8.1, + "Taxation": "Effective tax rate" + }, + { + "Year": 2010, + "Units": 5.9, + "Taxation": "Current pretax profit (after subtracting state)" + }, + { + "Year": 2010, + "Units": 5.9, + "Taxation": "Effective tax rate" + }, + { + "Year": 2011, + "Units": 4.7, + "Taxation": "Current pretax profit (after subtracting state)" + }, + { + "Year": 2011, + "Units": 4.7, + "Taxation": "Effective tax rate" + }, + { + "Year": 2012, + "Units": 5.1, + "Taxation": "Current pretax profit (after subtracting state)" + }, + { + "Year": 2012, + "Units": 5.1, + "Taxation": "Effective tax rate" + }, + { + "Year": 2013, + "Units": 15.1, + "Taxation": "Current pretax profit (after subtracting state)" + }, + { + "Year": 2013, + "Units": 15.1, + "Taxation": "Effective tax rate" + }, + { + "Year": 2014, + "Units": 91.7, + "Taxation": "Current pretax profit (after subtracting state)" + }, + { + "Year": 2014, + "Units": 91.7, + "Taxation": "Effective tax rate" + }, + { + "Year": 2015, + "Units": 5.9, + "Taxation": "Current pretax profit (after subtracting state)" + }, + { + "Year": 2015, + "Units": 5.9, + "Taxation": "Effective tax rate" + }, + { + "Year": 2015, + "Units": 0.1, + "Taxation": "Current federal tax paid" + }, + { + "Year": 2016, + "Units": 10.0, + "Taxation": "Current pretax profit (after subtracting state)" + }, + { + "Year": 2016, + "Units": 10.0, + "Taxation": "Effective tax rate" + }, + { + "Year": 2016, + "Units": 0.1, + "Taxation": "Current federal tax paid" + }, + { + "Year": 2017, + "Units": 5.6, + "Taxation": "Current pretax profit (after subtracting state)" + }, + { + "Year": 2017, + "Units": -2.5, + "Taxation": "Effective tax rate" + }, + { + "Year": 2018, + "Units": 10.8, + "Taxation": "Current pretax profit (after subtracting state)" + }, + { + "Year": 2018, + "Units": -1.2, + "Taxation": "Effective tax rate" + }, + { + "Year": 2018, + "Units": -0.13, + "Taxation": "Current federal tax paid" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year", + "unit": "none", + "data_type": "time", + "discarded_count": 0, + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding taxation", + "role": "y" + }, + { + "name": "Taxation", + "importance": "primary", + "description": "The taxation of the data point", + "role": "group" + } + ] + } + }, + "107": { + "metadata": { + "title": "Would Trump Voters Do the Same in 2020?", + "description": "If the 2020 election was held today, 2016 Trump voters would do the following", + "main_insight": "A significant majority (67%) of 2016 Trump voters surveyed in December 2018 indicated they would definitely vote to re-elect him in 2020, with an additional 17% saying they would probably vote for him." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Response": "Definitely vote to re-elect Trump", + "Definitely vote to re-elect Trump": 67.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Definitely vote to re-elect Trump from Too soon to say to Definitely vote to re-elect Trump.", + "reason": "The Definitely vote to re-elect Trump for increases from 0 at Too soon to say to 67.0 at Definitely vote to re-elect Trump, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Response": "Definitely vote to re-elect Trump", + "Definitely vote to re-elect Trump": 67.0 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at Definitely vote to re-elect Trump", + "reason": "The [{'Response': 'Definitely vote to re-elect Trump', 'Definitely vote to re-elect Trump': 67.0}] of has a maximum value of 67.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Response": "Probably vote to re-elect Trump", + "Definitely vote to re-elect Trump": 0 + }, + { + "Response": "Definitely vote for someone else", + "Definitely vote to re-elect Trump": 0 + }, + { + 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2015, while time on mobile browsers has decreased. The total time spent per day has increased as well." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Quarter": "Q1 2013", + "Percentage": 80.0, + "Engagement": "Apps" + }, + { + "Quarter": "Q1 2013", + "Percentage": 20.0, + "Engagement": "Mobile Browser" + }, + { + "Quarter": "Q1 2013", + "Percentage": "02:38", + "Engagement": "Time Spent per Day (HH:MM)" + }, + { + "Quarter": "Q2 2014", + "Percentage": 86.0, + "Engagement": "Apps" + }, + { + "Quarter": "Q2 2014", + "Percentage": 14.0, + "Engagement": "Mobile Browser" + }, + { + "Quarter": "Q2 2014", + "Percentage": "02:42", + "Engagement": "Time Spent per Day (HH:MM)" + }, + { + "Quarter": "Q2 2015", + "Percentage": 90.0, + "Engagement": "Apps" + }, + { + "Quarter": "Q2 2015", + "Percentage": 10.0, + "Engagement": "Mobile Browser" + }, + { + "Quarter": "Q2 2015", + "Percentage": "03:40", + "Engagement": "Time Spent per Day (HH:MM)" + } + ], + "columns": [ + { + "name": "Quarter", + "importance": "primary", + "description": "The quarter and year of data collection", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage for the corresponding engagement", + "role": "y" + }, + { + "name": "Engagement", + "importance": "primary", + "description": "The engagement of the data point", + "role": "group" + } + ] + } + }, + "2520": { + "metadata": { + "title": "Trump Approval at 11-Month High", + "description": "% that approve of the way President Trump is handling his job and specific issues", + "main_insight": "President Trump's approval rating reached an 11-month high in March 2018, with 42% of respondents approving of his overall performance. Approval varied across specific issues, with the economy receiving the highest approval and gun policy the lowest." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Issue": "Overall", + "Ratio": 0.42, + "Opinion": "Overall Approve" + }, + { + "Issue": "Overall", + "Ratio": 0.54, + "Opinion": "Overall Disapprove" + }, + { + "Issue": "The economy", + "Ratio": 0.48, + "Opinion": "Economy Approve" + }, + { + "Issue": "The economy", + "Ratio": 0.45, + "Opinion": "Economy Disapprove" + }, + { + "Issue": "Foreign affairs", + "Ratio": 0.39, + "Opinion": "Foreign Affairs Approve" + }, + { + "Issue": "Foreign affairs", + "Ratio": 0.53, + "Opinion": "Foreign Affairs Disapprove" + }, + { + "Issue": "Gun policy", + "Ratio": 0.36, + "Opinion": "Gun Policy Approve" + }, + { + "Issue": "Gun policy", + "Ratio": 0.54, + "Opinion": "Gun Policy Disapprove" + } + ], + "columns": [ + { + "name": "Issue", + "importance": "primary", + "description": "The issue or category being evaluated", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding opinion", + "role": "y" + }, + { + "name": "Opinion", + "importance": "primary", + "description": "The opinion of the data point", + "role": "group" + } + ] + } + }, + "424": { + "metadata": { + "title": "North Korean Economy Growing Despite Sanctions", + "description": "Estimated year-over-year GDP growth of North Korea", + "main_insight": "North Korea's GDP growth has fluctuated significantly between 2006 and 2016, but experienced a sharp increase to 3.9% in 2016." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2006, + "GDP Growth": -1.0 + }, + { + "Year": 2007, + "GDP Growth": -1.2 + }, + { + "Year": 2008, + "GDP Growth": 3.1 + }, + { + "Year": 2009, + "GDP Growth": -0.9 + }, + { + "Year": 2010, + "GDP Growth": -0.5 + }, + { + "Year": 2011, + "GDP Growth": 0.8 + }, + { + "Year": 2012, + "GDP Growth": 1.3 + }, + { + "Year": 2013, + "GDP Growth": 1.1 + }, + { + "Year": 2014, + "GDP Growth": 1.0 + }, + { + "Year": 2015, + "GDP Growth": -1.1 + }, + { + "Year": 2016, + "GDP Growth": 3.9 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of estimated GDP growth", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "GDP Growth", + "importance": "primary", + "description": "Estimated year-over-year GDP growth of North Korea", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1884": { + "metadata": { + "title": "1 In 3 Say The U.S. Spends Too Little On The Military", + "description": "% thinking the U.S. spends too little/too much on the military & defense", + "main_insight": "The percentage of Americans who think the U.S. spends too little on the military has decreased significantly since the 1970s and 1980s, and now hovers around one-third. The percentage who think the U.S. Spends too much fluctuates, crossing paths with the 'too little' view several times." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2017, + "Units": 37.0, + "Quantity": "Too little" + }, + { + "Year": 2017, + "Units": 31.0, + "Quantity": "Too much" + } + ], + "score": 0.96, + "annotation": "The Units of Too little and Too much show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Too little and Too much is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 1970, + "Units": 8.0, + "Quantity": "Too little" + }, + { + "Year": 1970, + "Units": 52.0, + "Quantity": "Too much" + }, + { + "Year": 1980, + "Units": 51.0, + "Quantity": "Too little" + }, + { + "Year": 1980, + "Units": 15.0, + "Quantity": "Too much" + }, + { + "Year": 1990, + "Units": 19.0, + "Quantity": "Too little" + }, + { + "Year": 1990, + "Units": 41.0, + "Quantity": "Too much" + }, + { + "Year": 2000, + "Units": 41.0, + "Quantity": "Too little" + }, + { + "Year": 2000, + "Units": 19.0, + "Quantity": "Too much" + }, + { + "Year": 2010, + "Units": 24.0, + "Quantity": "Too little" + }, + { + "Year": 2010, + "Units": 31.0, + "Quantity": "Too much" + }, + { + "Year": 2017, + "Units": 37.0, + "Quantity": "Too little" + }, + { + "Year": 2017, + "Units": 31.0, + "Quantity": "Too much" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding quantity", + "role": "y" + }, + { + "name": "Quantity", + "importance": "primary", + "description": "The quantity of the data point", + "role": "group" + } + ] + } + }, + "2452": { + "metadata": { + "title": "U.S. Internet Users Lag Behind In Ad Blocking", + "description": "Share of internet users using ad blocking software (Q2 2015, selected countries)", + "main_insight": "The United States had a relatively low percentage of internet users using ad blocking software in Q2 2015 compared to other countries like Greece and Germany." + }, + "chart_type": "Dot Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Greece", + "Ad Blocking Usage": 37.0 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at Greece", + "reason": "The [{'Country': 'Greece', 'Ad Blocking Usage': 37.0}] of has a maximum value of 37.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "China", + "Ad Blocking Usage": 2.0 + } + ], + "score": 0.75, + "annotation": "The has a minimum value at China", + "reason": "The [{'Country': 'China', 'Ad Blocking Usage': 2.0}] of has a mainimum value of 2.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Greece", + "Ad Blocking Usage": 37.0 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Ad Blocking Usage from Germany to Greece.", + "reason": "The Ad Blocking Usage for increases from 24.0 at Germany to 37.0 at Greece, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Greece", + "Ad Blocking Usage": 37.0 + }, + { + "Country": "Germany", + "Ad Blocking Usage": 24.0 + }, + { + "Country": "United Kingdom", + "Ad Blocking Usage": 21.0 + }, + { + "Country": "Canada", + "Ad Blocking Usage": 20.0 + }, + { + "Country": "Australia", + "Ad Blocking Usage": 18.0 + }, + { + "Country": "United States", + "Ad Blocking Usage": 15.0 + }, + { + "Country": "Mexico", + "Ad Blocking Usage": 10.0 + }, + { + "Country": "France", + "Ad Blocking Usage": 10.0 + }, + { + "Country": "Brazil", + "Ad Blocking Usage": 7.0 + }, + { + "Country": "Russia", + "Ad Blocking Usage": 6.0 + }, + { + "Country": "China", + "Ad Blocking Usage": 2.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of internet users", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ad Blocking Usage", + "importance": "primary", + "description": "Percentage of internet users using ad blocking software", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "624": { + "metadata": { + "title": "In the U.S., To Search Is To Google", + "description": "Average annual market share of Google's search engine in the United States, by device type (in %)", + "main_insight": "Google maintains a dominant market share in the US search engine market, with higher share on mobile devices compared to desktop. While mobile share has slightly increased, desktop market share has decreased from 2009 to 2024" + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Score": 95.0, + "Device Type": "Mobile" + }, + { + "Year": 2024, + "Score": 76.3, + "Device Type": "Desktop" + } + ], + "score": 0.25, + "annotation": "The Score of Mobile and Desktop show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Mobile and Desktop is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2009, + "Score": 94.7, + "Device Type": "Mobile" + }, + { + "Year": 2009, + "Score": 78.9, + "Device Type": "Desktop" + }, + { + "Year": 2014, + "Score": 87.0, + "Device Type": "Mobile" + }, + { + "Year": 2014, + "Score": 77.5, + "Device Type": "Desktop" + }, + { + "Year": 2019, + "Score": 94.5, + "Device Type": "Mobile" + }, + { + "Year": 2019, + "Score": 80.9, + "Device Type": "Desktop" + }, + { + "Year": 2024, + "Score": 95.0, + "Device Type": "Mobile" + }, + { + "Year": 2024, + "Score": 76.3, + "Device Type": "Desktop" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of market share measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding device type", + "role": "y" + }, + { + "name": "Device Type", + "importance": "primary", + "description": "The device type of the data point", + "role": "group" + } + ] + } + }, + "141": { + "metadata": { + "title": "Coup Attempts Are Mostly Unsuccessful", + "description": "Number of successful/unsuccessful coup attempts from 1946 to 2019", + "main_insight": "Most coup attempts between 1946 and 2019 were unsuccessful across the countries listed." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Sudan", + "Units": 28, + "Outcome": "Unsuccessful" + } + ], + "score": 0.9, + "annotation": "The Unsuccessful has a maximum value at Sudan", + "reason": "The [{'Country': 'Sudan', 'Units': 28, 'Outcome': 'Unsuccessful'}] of Unsuccessful has a maximum value of 28, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Thailand", + "Units": 10, + "Outcome": "Successful" + } + ], + "score": 0.88, + "annotation": "The Successful has a maximum value at Thailand", + "reason": "The [{'Country': 'Thailand', 'Units': 10, 'Outcome': 'Successful'}] of Successful has a maximum value of 10, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Syria", + "Units": 9, + "Outcome": "Unsuccessful" + } + ], + "score": 0.79, + "annotation": "The Unsuccessful accounts for the majority in Syria.", + "reason": "The Units of Unsuccessful in Syria accounts for 0.5294117647058824 (more than 50%) of the total of Units of all Outcome." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Thailand", + "Units": 10, + "Outcome": "Successful" + } + ], + "score": 0.78, + "annotation": "The Successful accounts for the majority in Thailand.", + "reason": "The Units of Successful in Thailand accounts for 0.5555555555555556 (more than 50%) of the total of Units of all Outcome." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Argentina", + "Units": 9, + "Outcome": "Unsuccessful" + } + ], + "score": 0.78, + "annotation": "The Unsuccessful accounts for the majority in Argentina.", + "reason": "The Units of Unsuccessful in Argentina accounts for 0.5625 (more than 50%) of the total of Units of all Outcome." + } + ], + "data": { + "data": [ + { + "Country": "Sudan", + "Units": 28, + "Outcome": "Unsuccessful" + }, + { + "Country": "Sudan", + "Units": 5, + "Outcome": "Successful" + }, + { + "Country": "Iraq", + "Units": 25, + "Outcome": "Unsuccessful" + }, + { + "Country": "Iraq", + "Units": 4, + "Outcome": "Successful" + }, + { + "Country": "Bolivia", + "Units": 17, + "Outcome": "Unsuccessful" + }, + { + "Country": "Bolivia", + "Units": 8, + "Outcome": "Successful" + }, + { + "Country": "Guinea-Bissau", + "Units": 16, + "Outcome": "Unsuccessful" + }, + { + "Country": "Guinea-Bissau", + "Units": 4, + "Outcome": "Successful" + }, + { + "Country": "Thailand", + "Units": 8, + "Outcome": "Unsuccessful" + }, + { + "Country": "Thailand", + "Units": 10, + "Outcome": "Successful" + }, + { + "Country": "Burundi", + "Units": 13, + "Outcome": "Unsuccessful" + }, + { + "Country": "Burundi", + "Units": 4, + "Outcome": "Successful" + }, + { + "Country": "Chad", + "Units": 14, + "Outcome": "Unsuccessful" + }, + { + "Country": "Chad", + "Units": 3, + "Outcome": 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"description": "Number of cigarettes produced in European countries in 2016*", + "main_insight": "Germany was by far the largest cigarette producer in Europe in 2016, producing almost twice as many cigarettes as the second-largest producer, Poland." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Germany", + "Cigarettes Produced (bn)": 167.7 + } + ], + "score": 0.95, + "annotation": "The has a maximum value at Germany", + "reason": "The [{'Country': 'Germany', 'Cigarettes Produced (bn)': 167.7}] of has a maximum value of 167.7, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Germany", + "Cigarettes Produced (bn)": 167.7 + } + ], + "score": 0.78, + "annotation": "The shows a sudden jump in Cigarettes Produced (bn) from Poland to Germany.", + "reason": "The Cigarettes Produced (bn) for increases from 98.5 at Poland to 167.7 at Germany, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Italy", + "Cigarettes Produced (bn)": 1.3 + } + ], + "score": 0.54, + "annotation": "The has a minimum value at Italy", + "reason": "The [{'Country': 'Italy', 'Cigarettes Produced (bn)': 1.3}] of has a mainimum value of 1.3, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Germany", + "Cigarettes Produced (bn)": 167.7 + }, + { + "Country": "Poland", + "Cigarettes Produced (bn)": 98.5 + }, + { + "Country": "Romania", + "Cigarettes Produced (bn)": 76.7 + }, + { + "Country": "Serbia", + "Cigarettes Produced (bn)": 34.6 + }, + { + "Country": "Greece", + "Cigarettes Produced (bn)": 30.5 + }, + { + "Country": "Portugal", + "Cigarettes Produced (bn)": 27.3 + }, + { + "Country": "Bulgaria", + "Cigarettes Produced (bn)": 21.0 + }, + { + "Country": "Spain", + "Cigarettes Produced (bn)": 9.8 + }, + { + "Country": "Croatia", + "Cigarettes Produced (bn)": 7.5 + }, + { + "Country": "Italy", + "Cigarettes Produced (bn)": 1.3 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "European countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Cigarettes Produced (bn)", + "importance": "primary", + "description": "Number of cigarettes produced in billions", + "unit": "bn", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1983": { + "metadata": { + "title": "Zoom Tops Weekly Download Charts", + "description": "Most downloaded apps in the U.S. from March 26 to April 1, 2020 (in million downloads)", + "main_insight": "Zoom was the most downloaded app in the U.S. between March 26 and April 1, 2020, with 3.2 million downloads, significantly more than other apps." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "App": "Zoom", + "Downloads": 3.2 + } + ], + "score": 0.96, + "annotation": "The has a maximum value at Zoom", + "reason": "The [{'App': 'Zoom', 'Downloads': 3.2}] of has a maximum value of 3.2, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "App": "Zoom", + "Downloads": 3.2 + } + ], + "score": 0.84, + "annotation": "The shows a sudden jump in Downloads from Tik Tok to Zoom.", + "reason": "The Downloads for increases from 1.9 at Tik Tok to 3.2 at Zoom, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "App": "Microsoft Teams", + "Downloads": 0.7 + } + ], + "score": 0.48, + "annotation": "The has a minimum value at Microsoft Teams", + "reason": "The [{'App': 'Microsoft Teams', 'Downloads': 0.7}] of has a mainimum value of 0.7, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "App": "Zoom", + "Downloads": 3.2 + }, + { + "App": "Tik Tok", + "Downloads": 1.9 + }, + { + "App": "Hangouts Meet", + "Downloads": 1.2 + }, + { + "App": "Houseparty", + "Downloads": 1.1 + }, + { + "App": "Google Classroom", + "Downloads": 1.1 + }, + { + "App": "Perfect Cream", + "Downloads": 0.9 + }, + { + "App": "News Break", + "Downloads": 0.8 + }, + { + "App": "Save the Girl", + "Downloads": 0.8 + }, + { + "App": "Microsoft Teams", + "Downloads": 0.7 + } + ], + "columns": [ + { + "name": "App", + "importance": "primary", + "description": "Name of the application", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Downloads", + "importance": "primary", + "description": "Number of downloads in millions", + "unit": "million", + "data_type": "number", + "role": "y" + } + ] + } + }, + "434": { + "metadata": { + "title": "Apple's Constant Search for the Next Big Thing", + "description": "Apple's revenue by operating segment since 2001 (fiscal years ending in September)", + "main_insight": "Apple's revenue has grown significantly since 2001, driven primarily by the iPhone, which became the dominant revenue source after its launch. While other product categories contribute, iPhone sales are central to Apple's overall revenue." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Fiscal Year": 2023, + "Score": 201.0, + "Product Lines": "iPhone" + }, + { + "Fiscal Year": 2023, + "Score": 83.0, + "Product Lines": "Services" + } + ], + "score": 0.87, + "annotation": "The Score of iPhone and Services show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between iPhone and Services is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Fiscal Year": 2001, + "Score": 4.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2001, + "Score": 0.4, + "Product Lines": "iPod" + }, + { + "Fiscal Year": 2002, + "Score": 5.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2002, + "Score": 0.1, + "Product Lines": "iPod" + }, + { + "Fiscal Year": 2003, + "Score": 5.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2003, + "Score": 0.6, + "Product Lines": "iPod" + }, + { + "Fiscal Year": 2004, + "Score": 6.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2004, + "Score": 2.0, + "Product Lines": "iPod" + }, + { + "Fiscal Year": 2005, + "Score": 9.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2005, + "Score": 6.0, + "Product Lines": "iPod" + }, + { + "Fiscal Year": 2006, + "Score": 11.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2006, + "Score": 8.0, + "Product Lines": "iPod" + }, + { + "Fiscal Year": 2007, + "Score": 0.1, + "Product Lines": "iPhone" + }, + { + "Fiscal Year": 2007, + "Score": 2.0, + "Product Lines": "Services" + }, + { + "Fiscal Year": 2007, + "Score": 14.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2007, + "Score": 8.0, + "Product Lines": "iPod" + }, + { + "Fiscal Year": 2008, + "Score": 7.0, + "Product Lines": "iPhone" + }, + { + "Fiscal Year": 2008, + "Score": 3.0, + "Product Lines": "Services" + }, + { + "Fiscal Year": 2008, + "Score": 18.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2008, + "Score": 9.0, + "Product Lines": "iPod" + }, + { + "Fiscal Year": 2009, + "Score": 13.0, + "Product Lines": "iPhone" + }, + { + "Fiscal Year": 2009, + "Score": 4.0, + "Product Lines": "Services" + }, + { + "Fiscal Year": 2009, + "Score": 16.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2009, + "Score": 8.0, + "Product Lines": "iPod" + }, + { + "Fiscal Year": 2010, + "Score": 25.0, + "Product Lines": "iPhone" + }, + { + "Fiscal Year": 2010, + "Score": 6.0, + "Product Lines": "Services" + }, + { + "Fiscal Year": 2010, + "Score": 22.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2010, + "Score": 5.0, + "Product Lines": "iPad" + }, + { + "Fiscal Year": 2010, + "Score": 8.0, + "Product Lines": "iPod" + }, + { + "Fiscal Year": 2011, + "Score": 47.0, + "Product Lines": "iPhone" + }, + { + "Fiscal Year": 2011, + "Score": 9.0, + "Product Lines": "Services" + }, + { + "Fiscal Year": 2011, + "Score": 29.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2011, + "Score": 20.0, + "Product Lines": "iPad" + }, + { + "Fiscal Year": 2011, + "Score": 6.0, + "Product Lines": "iPod" + }, + { + "Fiscal Year": 2012, + "Score": 80.0, + "Product Lines": "iPhone" + }, + { + "Fiscal Year": 2012, + "Score": 11.0, + "Product Lines": "Services" + }, + { + "Fiscal Year": 2012, + "Score": 29.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2012, + "Score": 31.0, + "Product Lines": "iPad" + }, + { + "Fiscal Year": 2012, + "Score": 5.0, + "Product Lines": "iPod" + }, + { + "Fiscal Year": 2013, + "Score": 91.0, + "Product Lines": "iPhone" + }, + { + "Fiscal Year": 2013, + "Score": 14.0, + "Product Lines": "Services" + }, + { + "Fiscal Year": 2013, + "Score": 27.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2013, + "Score": 32.0, + "Product Lines": "iPad" + }, + { + "Fiscal Year": 2013, + "Score": 4.0, + "Product Lines": "iPod" + }, + { + "Fiscal Year": 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"Product Lines": "Wearables, Home and Accessories" + }, + { + "Fiscal Year": 2016, + "Score": 28.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2016, + "Score": 19.0, + "Product Lines": "iPad" + }, + { + "Fiscal Year": 2017, + "Score": 141.0, + "Product Lines": "iPhone" + }, + { + "Fiscal Year": 2017, + "Score": 29.0, + "Product Lines": "Services" + }, + { + "Fiscal Year": 2017, + "Score": 17.0, + "Product Lines": "Wearables, Home and Accessories" + }, + { + "Fiscal Year": 2017, + "Score": 31.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2017, + "Score": 19.0, + "Product Lines": "iPad" + }, + { + "Fiscal Year": 2018, + "Score": 165.0, + "Product Lines": "iPhone" + }, + { + "Fiscal Year": 2018, + "Score": 36.0, + "Product Lines": "Services" + }, + { + "Fiscal Year": 2018, + "Score": 22.0, + "Product Lines": "Wearables, Home and Accessories" + }, + { + "Fiscal Year": 2018, + "Score": 32.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2018, + "Score": 19.0, + "Product 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"Fiscal Year": 2021, + "Score": 46.0, + "Product Lines": "Wearables, Home and Accessories" + }, + { + "Fiscal Year": 2021, + "Score": 43.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2021, + "Score": 32.0, + "Product Lines": "iPad" + }, + { + "Fiscal Year": 2022, + "Score": 205.0, + "Product Lines": "iPhone" + }, + { + "Fiscal Year": 2022, + "Score": 76.0, + "Product Lines": "Services" + }, + { + "Fiscal Year": 2022, + "Score": 51.0, + "Product Lines": "Wearables, Home and Accessories" + }, + { + "Fiscal Year": 2022, + "Score": 50.0, + "Product Lines": "Mac" + }, + { + "Fiscal Year": 2022, + "Score": 29.0, + "Product Lines": "iPad" + }, + { + "Fiscal Year": 2023, + "Score": 201.0, + "Product Lines": "iPhone" + }, + { + "Fiscal Year": 2023, + "Score": 83.0, + "Product Lines": "Services" + }, + { + "Fiscal Year": 2023, + "Score": 49.0, + "Product Lines": "Wearables, Home and Accessories" + }, + { + "Fiscal Year": 2023, + "Score": 35.0, + "Product Lines": "Mac" + }, + { + "Fiscal 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"How much do you know about Juneteenth?", + "Units": 12.0, + "Response": "A lot" + }, + { + "Question": "How much do you know about Juneteenth?", + "Units": 34.0, + "Response": "A little bit" + }, + { + "Question": "How much do you know about Juneteenth?", + "Units": 25.0, + "Response": "Some" + }, + { + "Question": "How much do you know about Juneteenth?", + "Units": 28.0, + "Response": "Nothing" + }, + { + "Question": "Should Juneteenth be a federal holiday?", + "Units": 35.0, + "Response": "Yes" + }, + { + "Question": "Should Juneteenth be a federal holiday?", + "Units": 25.0, + "Response": "No" + }, + { + "Question": "Should Juneteenth be a federal holiday?", + "Units": 40.0, + "Response": "Don't know" + } + ], + "columns": [ + { + "name": "Question", + "importance": "primary", + "description": "The question asked to the survey respondants", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for 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+ { + "Media Company": "facebook", + "Revenue (billions)": 11.5 + }, + { + "Media Company": "BERTELSMANN", + "Revenue (billions)": 10.0 + }, + { + "Media Company": "VIACOM", + "Revenue (billions)": 9.6 + }, + { + "Media Company": "CBS", + "Revenue (billions)": 9.6 + }, + { + "Media Company": "Baidu", + "Revenue (billions)": 7.9 + }, + { + "Media Company": "News Corp", + "Revenue (billions)": 7.1 + } + ], + "columns": [ + { + "name": "Media Company", + "importance": "primary", + "description": "Name of the media company", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Revenue (billions)", + "importance": "primary", + "description": "Media revenue in billions of US dollars", + "unit": "billions USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2294": { + "metadata": { + "title": "Cook Points to China for Apple's Lackluster Q1", + "description": "Apple Q1 revenue (in billion U.S. dollars)", + "main_insight": "Apple's Q1 revenue increased from 2013 to 2017, with a noticeable increase in revenue from China and then drop in the last bar, 2018 is estimated." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2017, + "Units": 19.0, + "Region": "Europe" + }, + { + "Year": 2017, + "Units": 19.0, + "Region": "China" + } + ], + "score": 0.99, + "annotation": "The Units of Europe and China show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Europe and China is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2017, + "Units": 7.0, + "Region": "Japan" + }, + { + "Year": 2017, + "Units": 12.0, + "Region": "Rest of the Asia-Pacific region" + } + ], + "score": 0.94, + "annotation": "The Units of Japan and Rest of the Asia-Pacific region show a positive correlation.", + "reason": "The Pearson 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+ "Year": 2017, + "Units": 19.0, + "Region": "China" + }, + { + "Year": 2017, + "Units": 7.0, + "Region": "Japan" + }, + { + "Year": 2017, + "Units": 12.0, + "Region": "Rest of the Asia-Pacific region" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of Q1 Revenue", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding region", + "role": "y" + }, + { + "name": "Region", + "importance": "primary", + "description": "The region of the data point", + "role": "group" + } + ] + } + }, + "2337": { + "metadata": { + "title": "Carillion liquidation: thousands of UK jobs at risk", + "description": "Average number of Carillion employees in the UK*", + "main_insight": "The average number of Carillion employees in the UK increased each year from 2013 to 2016." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2013, + "Average Employees": 16149 + }, + { + "Year": 2014, + "Average Employees": 17237 + }, + { + "Year": 2015, + "Average Employees": 19486 + }, + { + "Year": 2016, + "Average Employees": 19256 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Average Employees", + "importance": "primary", + "description": "Average number of Carillion employees in the UK", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1433": { + "metadata": { + "title": "Video Game Consoles Are About More Than Just Gaming", + "description": "Breakdown of weekly video game console usage in the United States, by activity", + "main_insight": "A significant portion of video game console usage in the US is dedicated to non-gaming activities such as streaming movies and TV shows. PS4 users dedicate slightly more time to non-gaming." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Entertainment": "Online gaming", + "Console": "total", + "Units": 60.0 + } + ], + "score": 0.95, + "annotation": "The total value of Online gaming has the largest proportion.", + "reason": "The total value of Units of Online gaming accounts for 0.3, larger than all other Entertainment, of the total of Units of all Entertainment." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Entertainment": "Online gaming", + "Console": "total", + "Units": 60.0 + } + ], + "score": 0.89, + "annotation": "The total value of Online gaming has the smallest proportion.", + "reason": "The total value of Units of Online gaming accounts for 0.025, smaller than all other Entertainment, of the total of Units of all Entertainment." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Console": "PS4", + "Units": 29.0, + "Entertainment": "Online gaming" + }, + { + "Console": "PS4", + "Units": 20.0, + "Entertainment": "Watching streamed movies, TV shows" + } + ], + "score": 1.0, + "annotation": "The Units of Online gaming and Watching streamed movies, TV shows show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Online gaming and Watching streamed movies, TV shows is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Console": "PS4", + "Units": 29.0, + "Entertainment": "Online gaming" + }, + { + "Console": "PS4", + "Units": 10.0, + "Entertainment": "Watching DVDs, Blu-rays" + } + ], + "score": 1.0, + "annotation": "The Units of Online gaming and Watching DVDs, Blu-rays show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Online gaming and Watching DVDs, Blu-rays is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Console": "XBOX ONE", + "Units": 31.0, + "Entertainment": "Online gaming" + }, + { + "Console": "XBOX ONE", + "Units": 19.0, + "Entertainment": "Offline gaming" + }, + { + "Console": "XBOX ONE", + "Units": 24.0, + "Entertainment": "Watching streamed movies, TV shows" + }, + { + "Console": "XBOX ONE", + "Units": 9.0, + "Entertainment": "Watching DVDs, Blu-rays" + }, + { + "Console": "XBOX ONE", + "Units": 2.0, + "Entertainment": "Watching downloaded movies, TV shows" + }, + { + "Console": "XBOX ONE", + "Units": 15.0, + "Entertainment": "Other" + }, + { + "Console": "PS4", + "Units": 29.0, + "Entertainment": "Online gaming" + }, + { + "Console": "PS4", + "Units": 19.0, + "Entertainment": "Offline gaming" + }, + { + "Console": "PS4", + "Units": 20.0, + "Entertainment": "Watching streamed movies, TV shows" + }, + { + "Console": "PS4", + "Units": 10.0, + "Entertainment": "Watching DVDs, Blu-rays" + }, + { + "Console": "PS4", + "Units": 3.0, + "Entertainment": "Watching downloaded movies, TV shows" + }, + { + "Console": "PS4", + "Units": 19.0, + "Entertainment": "Other" + } + ], + "columns": [ + { + "name": "Console", + "importance": "primary", + "description": "Video Game Console", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding entertainment", + "role": "y" + }, + { + "name": "Entertainment", + "importance": "primary", + "description": "The entertainment of the data point", + "role": "group" + } + ] + } + }, + "812": { + "metadata": { + "title": "Where Trade Union Membership Weakened the Most", + "description": "Trade union membership as a percentage of total employees", + "main_insight": "Trade union membership, as a percentage of total employees, has significantly decreased in several OECD countries between 2000 (or 2001/2002) and 2018 (or latest available). Slovakia and Slovenia experienced some of the largest drops." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Sweden", + "Units": 66.1, + "Year": "2018" + } + ], + "score": 0.97, + "annotation": "The 2018 has a maximum value at Sweden", + "reason": "The [{'Country': 'Sweden', 'Units': 66.1, 'Year': '2018'}] of 2018 has a maximum value of 66.1, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Sweden", + "Units": 80.1, + "Year": "2000" + } + ], + "score": 0.97, + "annotation": "The 2000 has a maximum value at Sweden", + "reason": "The [{'Country': 'Sweden', 'Units': 80.1, 'Year': '2000'}] of 2000 has a maximum value of 80.1, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Sweden", + "Units": 80.1, + "Year": "2000" + }, + { + "Country": "Sweden", + "Units": 66.1, + "Year": "2018" + } + ], + "score": 0.96, + "annotation": "The Units of 2000 and 2018 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2000 and 2018 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Sweden", + "Units": 66.1, + "Year": "2018" + } + ], + "score": 0.91, + "annotation": "The 2018 shows a sudden jump in Units from Austria to Sweden.", + "reason": "The Units for 2018 increases from 26.7 at Austria to 66.1 at Sweden, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Sweden", + "Units": 80.1, + "Year": "2000" + } + ], + "score": 0.9, + "annotation": "The 2000 shows a sudden jump in Units from Slovenia to Sweden.", + "reason": "The Units for 2000 increases from 41.4 at Slovenia to 80.1 at Sweden, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Turkey", + "Units": 29.4, + "Year": "2000" + }, + { + "Country": "Turkey", + "Units": 8.6, + "Year": "2018" + }, + { + "Country": "Turkey", + "Units": -20.8, + "Year": "percent change" + }, + { + "Country": "Hungary", + "Units": 19.7, + "Year": "2000" + }, + { + "Country": "Hungary", + "Units": 8.5, + "Year": "2018" + }, + { + "Country": "Hungary", + "Units": -11.2, + "Year": "percent change" + }, + { + "Country": "Czech Republic", + "Units": 27.2, + "Year": "2000" + }, + { + "Country": "Czech Republic", + "Units": 10.5, + "Year": "2018" + }, + { + "Country": "Czech Republic", + "Units": -16.7, + "Year": "percent change" + }, + { + "Country": "Slovakia", + "Units": 32.3, + "Year": "2000" + }, + { + "Country": "Slovakia", + "Units": 10.9, + "Year": "2018" + }, + { + "Country": "Slovakia", + "Units": -21.4, + "Year": "percent change" + }, + { + "Country": "Australia", + "Units": 24.7, + "Year": "2000" + }, + { + "Country": "Australia", + "Units": 13.7, + "Year": "2018" + }, + { + "Country": "Australia", + "Units": -11.0, + "Year": "percent change" + }, + { + "Country": "Slovenia", + "Units": 41.4, + "Year": "2000" + }, + { + "Country": "Slovenia", + "Units": 19.6, + "Year": "2018" + }, + { + "Country": "Slovenia", + "Units": -21.8, + "Year": "percent change" + }, + { + "Country": "Israel", + "Units": 37.7, + "Year": "2000" + }, + { + "Country": "Israel", + "Units": 22.8, + "Year": "2018" + }, + { + "Country": "Israel", + "Units": -14.9, + "Year": "percent change" + }, + { + "Country": "Ireland", + "Units": 36.0, + "Year": "2000" + }, + { + "Country": "Ireland", + "Units": 24.2, + "Year": "2018" + }, + { + "Country": "Ireland", + "Units": -11.8, + "Year": "percent change" + }, + { + "Country": "Austria", + "Units": 36.9, + "Year": "2000" + }, + { + "Country": "Austria", + "Units": 26.7, + "Year": "2018" + }, + { + "Country": "Austria", + "Units": -10.2, + "Year": "percent change" + }, + { + "Country": "Sweden", + "Units": 80.1, + "Year": "2000" + }, + { + "Country": "Sweden", + "Units": 66.1, + "Year": "2018" + }, + { + "Country": "Sweden", + "Units": -14.0, + "Year": "percent change" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "OECD Country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "1627": { + "metadata": { + "title": "The Biggest Coral Reef Regions of the World", + "description": "Size of largest reef areas by GCRMN region (in thousand km²)", + "main_insight": "The East Asian Seas and Pacific regions have the largest coral reef areas, while South Asia shows the highest mean long-term decline in live coral cover." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "East Asian Seas", + "Reef Area (thousand km²)": 78.0 + } + ], + "score": 0.84, + "annotation": "The has a maximum value at East Asian Seas", + "reason": "The [{'Region': 'East Asian Seas', 'Reef Area (thousand km²)': 78.0}] of has a maximum value of 78.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Pacific", + "Reef Area (thousand km²)": 69.0 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Reef Area (thousand km²) from Australia to Pacific.", + "reason": "The Reef Area (thousand km²) for increases from 41.0 at Australia to 69.0 at Pacific, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Region": "ROPME Sea Area", + "Reef Area (thousand km²)": 2.0 + } + ], + "score": 0.63, + "annotation": "The has a minimum value at ROPME Sea Area", + "reason": "The [{'Region': 'ROPME Sea Area', 'Reef Area (thousand km²)': 2.0}] of has a mainimum value of 2.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Region": "East Asian Seas", + "Reef Area (thousand km²)": 78.0 + }, + { + "Region": "Pacific", + "Reef Area (thousand km²)": 69.0 + }, + { + "Region": "Australia", + "Reef Area (thousand km²)": 41.0 + }, + { + "Region": "Caribbean", + "Reef Area (thousand km²)": 26.0 + }, + { + "Region": "Western Indian Ocean", + "Reef Area (thousand km²)": 15.0 + }, + { + "Region": "Red Sea and Gulf of Aden", + "Reef Area (thousand km²)": 14.0 + }, + { + "Region": "South Asia", + "Reef Area (thousand km²)": 11.0 + }, + { + "Region": "ROPME Sea Area", + "Reef Area (thousand km²)": 2.0 + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "GCRMN region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Reef Area (thousand km²)", + "importance": "primary", + "description": "Size of largest reef areas by GCRMN region", + "unit": "thousand km²", + "data_type": "number", + "role": "y" + } + ] + } + }, + "660": { + "metadata": { + "title": "Selfies Can be Deadly", + "description": "Count of deaths from selfies by cause of death between October 2011 to November 2017", + "main_insight": "Drowning is the leading cause of selfie-related deaths, followed by transportation and fall." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Cause of Death": "Fall", + "Number of Deaths": 48 + } + ], + "score": 0.84, + "annotation": "The shows a sudden jump in Number of Deaths from Electrocution to Fall.", + "reason": "The Number of Deaths for increases from 16 at Electrocution to 48 at Fall, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Cause of Death": "Drowned", + "Number of Deaths": 70 + } + ], + "score": 0.81, + "annotation": "The has a maximum value at Drowned", + "reason": "The [{'Cause of Death': 'Drowned', 'Number of Deaths': 70}] of has a maximum value of 70, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Cause of Death": "Other", + "Number of Deaths": 7 + } + ], + "score": 0.62, + "annotation": "The has a minimum value at Other", + "reason": "The [{'Cause of Death': 'Other', 'Number of Deaths': 7}] of has a mainimum value of 7, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Cause of Death": "Drowned", + "Number of Deaths": 70 + }, + { + "Cause of Death": "Transportation", + "Number of Deaths": 51 + }, + { + "Cause of Death": "Fall", + "Number of Deaths": 48 + }, + { + "Cause of Death": "Fire", + "Number of Deaths": 48 + }, + { + "Cause of Death": "Electrocution", + "Number of Deaths": 16 + }, + { + "Cause of Death": "Firearm", + "Number of Deaths": 11 + }, + { + "Cause of Death": "Animal", + "Number of Deaths": 8 + }, + { + "Cause of Death": "Other", + "Number of Deaths": 7 + } + ], + "columns": [ + { + "name": "Cause of Death", + "importance": "primary", + "description": "Different causes of selfie-related deaths", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Deaths", + "importance": "primary", + "description": "Count of deaths for each cause", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1494": { + "metadata": { + "title": "Live Music Industry to Return From Covid Slump in 2023", + "description": "Estimated gross merchandise value of online ticket sales to music events worldwide*", + "main_insight": "The live music industry experienced a significant drop in online ticket sales revenue in 2020 and 2021 due to the COVID pandemic, but is projected to rebound strongly and exceed pre-pandemic levels starting in 2023." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2017, + "Gross Merchandise Value": 17.4 + }, + { + "Year": 2018, + "Gross Merchandise Value": 19.7 + }, + { + "Year": 2019, + "Gross Merchandise Value": 22.7 + }, + { + "Year": 2020, + "Gross Merchandise Value": 3.9 + }, + { + "Year": 2021, + "Gross Merchandise Value": 5.4 + }, + { + "Year": 2022, + "Gross Merchandise Value": 20.5 + }, + { + "Year": 2023, + "Gross Merchandise Value": 33.8 + }, + { + "Year": 2024, + "Gross Merchandise Value": 36.1 + }, + { + "Year": 2025, + "Gross Merchandise Value": 37.5 + }, + { + "Year": 2026, + "Gross Merchandise Value": 38.5 + }, + { + "Year": 2027, + "Gross Merchandise Value": 39.1 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Calendar Year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Gross Merchandise Value", + "importance": "primary", + "description": "Estimated gross merchandise value of online ticket sales in billions of US dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2388": { + "metadata": { + "title": "Chinese New Year Will See Billions Of Digital Cash Gifts", + "description": "Digital red envelopes exchanged on WeChat during the Spring Festival holiday (billions)*", + "main_insight": "The number of digital red envelopes exchanged on WeChat during the Spring Festival holiday has grown significantly from 2014 to 2017, reaching 46.6 billion in 2017." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2014, + "Digital Red Envelopes (billions)": 0.2 + }, + { + "Year": 2015, + "Digital Red Envelopes (billions)": 1.0 + }, + { + "Year": 2016, + "Digital Red Envelopes (billions)": 8.0 + }, + { + "Year": 2017, + "Digital Red Envelopes (billions)": 46.6 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the Spring Festival holiday", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Digital Red Envelopes (billions)", + "importance": "primary", + "description": "Number of digital red envelopes exchanged on WeChat (in billions)", + "unit": "billions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "800": { + "metadata": { + "title": "The True Cost of Mobile Ads", + "description": "Data used to load mobile news websites (in megabytes)", + "main_insight": "The amount of data used to load advertising content on mobile news websites varies greatly and can be significantly higher than the editorial content, as exemplified by Boston.com having a high advertising-to-editorial data ratio." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Website": "boston.com", + "Units": 15.4, + "Content Metrics": "Advertising Content" + } + ], + "score": 0.98, + "annotation": "The Advertising Content has a maximum value at boston.com", + "reason": "The [{'Website': 'boston.com', 'Units': 15.4, 'Content Metrics': 'Advertising Content'}] of Advertising Content has a maximum value of 15.4, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Website": "New York Times", + "Units": 8.5, + "Content Metrics": "Editorial Content" + } + ], + "score": 0.97, + "annotation": "The Editorial Content has a maximum value at New York Times", + "reason": "The [{'Website': 'New York Times', 'Units': 8.5, 'Content Metrics': 'Editorial Content'}] of Editorial Content has a maximum value of 8.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Website": "boston.com", + "Units": 15.4, + "Content Metrics": "Advertising Content" + } + ], + "score": 0.94, + "annotation": "The Advertising Content shows a sudden jump in Units from New York Times to boston.com.", + "reason": "The Units for Advertising Content increases from 4.4 at New York Times to 15.4 at boston.com, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Website": "boston.com", + "Units": 3.85, + "Content Metrics": "Ratio Advertising/Editorial" + } + ], + "score": 0.91, + "annotation": "The Ratio Advertising/Editorial has a maximum value at boston.com", + "reason": "The [{'Website': 'boston.com', 'Units': 3.85, 'Content Metrics': 'Ratio Advertising/Editorial'}] of Ratio Advertising/Editorial has a maximum value of 3.85, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Website": "BBC", + "Units": 1.33, + "Content Metrics": "Ratio Advertising/Editorial" + } + ], + "score": 0.89, + "annotation": "The Ratio Advertising/Editorial has the largest proportion in BBC.", + "reason": "The Units of Ratio Advertising/Editorial in BBC accounts for 0.3877551020408163, larger than all other Content Metrics, of the total of Units of all Content Metrics." + } + ], + "data": { + "data": [ + { + "Website": "boston.com", + "Units": 15.4, + "Content Metrics": "Advertising Content" + }, + { + "Website": "boston.com", + "Units": 4.0, + "Content Metrics": "Editorial Content" + }, + { + "Website": "boston.com", + "Units": 3.85, + "Content Metrics": "Ratio Advertising/Editorial" + }, + { + "Website": "New York Times", + "Units": 4.4, + "Content Metrics": "Advertising Content" + }, + { + "Website": "New York Times", + "Units": 8.5, + "Content Metrics": "Editorial Content" + }, + { + "Website": "New York Times", + "Units": 0.52, + "Content Metrics": "Ratio Advertising/Editorial" + }, + { + "Website": "CNN", + "Units": 4.3, + "Content Metrics": "Advertising Content" + }, + { + "Website": "CNN", + "Units": 2.7, + "Content Metrics": "Editorial Content" + }, + { + "Website": "CNN", + "Units": 1.59, + "Content Metrics": "Ratio Advertising/Editorial" + }, + { + "Website": "Vox", + "Units": 3.2, + "Content Metrics": "Advertising Content" + }, + { + "Website": "Vox", + "Units": 1.8, + "Content Metrics": "Editorial Content" + }, + { + "Website": "Vox", + "Units": 1.78, + "Content Metrics": "Ratio Advertising/Editorial" + }, + { + "Website": "TIME", + "Units": 2.5, + "Content Metrics": "Advertising Content" + }, + { + "Website": "TIME", + "Units": 2.8, + "Content Metrics": "Editorial Content" + }, + { + "Website": "TIME", + "Units": 0.89, + "Content Metrics": "Ratio Advertising/Editorial" + }, + { + "Website": "VICE", + "Units": 2.1, + "Content Metrics": "Advertising Content" + }, + { + "Website": "VICE", + "Units": 1.3, + "Content Metrics": "Editorial Content" + }, + { + "Website": "VICE", + "Units": 1.62, + "Content Metrics": "Ratio Advertising/Editorial" + }, + { + "Website": "HUFF POST", + "Units": 2.0, + "Content Metrics": "Advertising Content" + }, + { + "Website": "HUFF POST", + "Units": 0.6, + "Content Metrics": "Editorial Content" + }, + { + "Website": "HUFF POST", + "Units": 3.33, + "Content Metrics": "Ratio Advertising/Editorial" + }, + { + "Website": "FOX NEWS", + "Units": 1.9, + "Content Metrics": "Advertising Content" + }, + { + "Website": "FOX NEWS", + "Units": 1.4, + "Content Metrics": "Editorial Content" + }, + { + "Website": "FOX NEWS", + "Units": 1.36, + "Content Metrics": "Ratio Advertising/Editorial" + }, + { + "Website": "Mashable", + "Units": 1.3, + "Content Metrics": "Advertising Content" + }, + { + "Website": "Mashable", + "Units": 1.9, + "Content Metrics": "Editorial Content" + }, + { + "Website": "Mashable", + "Units": 0.68, + "Content Metrics": "Ratio Advertising/Editorial" + }, + { + "Website": "BBC", + "Units": 1.2, + "Content Metrics": "Advertising Content" + }, + { + "Website": "BBC", + "Units": 0.9, + "Content Metrics": "Editorial Content" + }, + { + "Website": "BBC", + "Units": 1.33, + "Content Metrics": "Ratio Advertising/Editorial" + } + ], + "columns": [ + { + "name": "Website", + "importance": "primary", + "description": "Name of the news website", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding content metrics", + "role": "y" + }, + { + "name": "Content Metrics", + "importance": "primary", + "description": "The content metrics of the data point", + "role": "group" + } + ] + } + }, + "1250": { + "metadata": { + "title": "Would you pick up your phone at the wheel?", + "description": "Attitudes of drivers in the UK to handheld mobile phone use while driving", + "main_insight": "The percentage of UK drivers admitting to using their phone to make/receive calls while driving increased significantly from 8% in 2014 to 31% in 2016. In 2016, a substantial portion of drivers admitted to or thought various phone usages were acceptable while driving." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2014, + "Units": 8, + "Survey Questions": "Admitted_Phone_Use_Driving" + }, + { + "Year": 2016, + "Units": 31, + "Survey Questions": "Admitted_Phone_Use_Driving" + }, + { + "Year": 2016, + "Units": 48, + "Survey Questions": "Admitted_Phone_Use_Traffic_2016" + }, + { + "Year": 2016, + "Units": 20, + "Survey Questions": "Acceptable_Social_Media_Traffic_2016" + }, + { + "Year": 2016, + "Units": 14, + "Survey Questions": "Used_Phone_Photos_Videos_2016" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of survey data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding survey questions", + "role": "y" + }, + { + "name": "Survey Questions", + "importance": "primary", + "description": "The survey questions of the data point", + "role": "group" + } + ] + } + }, + "2278": { + "metadata": { + "title": "The UK has the biggest thirst for champagne", + "description": "Biggest importers of champagne from France in 2016 (in 75cl bottles)", + "main_insight": "The UK imported the most champagne from France in 2016, significantly more than other major importers like the United States and Germany." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "UK", + "Imported Bottles (Millions)": 31.2 + } + ], + "score": 0.8, + "annotation": "The has a maximum value at UK", + "reason": "The [{'Country': 'UK', 'Imported Bottles (Millions)': 31.2}] of has a maximum value of 31.2, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "UK", + "Imported Bottles (Millions)": 31.2 + } + ], + "score": 0.78, + "annotation": "The shows a sudden jump in Imported Bottles (Millions) from United States to UK.", + "reason": "The Imported Bottles (Millions) for increases from 21.8 at United States to 31.2 at UK, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Belgium", + "Imported Bottles (Millions)": 8.3 + } + ], + "score": 0.56, + "annotation": "The has a minimum value at Belgium", + "reason": "The [{'Country': 'Belgium', 'Imported Bottles (Millions)': 8.3}] of has a mainimum value of 8.3, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "UK", + "Imported Bottles (Millions)": 31.2 + }, + { + "Country": "United States", + "Imported Bottles (Millions)": 21.8 + }, + { + "Country": "Germany", + "Imported Bottles (Millions)": 12.5 + }, + { + "Country": "Japan", + "Imported Bottles (Millions)": 11.0 + }, + { + "Country": "Belgium", + "Imported Bottles (Millions)": 8.3 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Importing country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Imported Bottles (Millions)", + "importance": "primary", + "description": "Number of 75cl champagne bottles imported in 2016", + "unit": "Millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "959": { + "metadata": { + "title": "Christmas Trees: The Battle Between Pine & Plastic", + "description": "Estimated number of real & fake Christmas trees purchased in the U.S. by year (millions)", + "main_insight": "Real Christmas tree purchases have generally been higher than fake tree purchases in the U.S. from 2012 to 2018, with real tree purchases peaking in 2013 and fake tree purchases increasing over time." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2018, + "Units": 32.8, + "Authenticity": "Real" + }, + { + "Year": 2018, + "Units": 23.6, + "Authenticity": "Fake" + } + ], + "score": 0.56, + "annotation": "The Units of Real and Fake show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Real and Fake is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2012, + "Units": 24.5, + "Authenticity": "Real" + }, + { + "Year": 2012, + "Units": 10.9, + "Authenticity": "Fake" + }, + { + "Year": 2013, + "Units": 33.0, + "Authenticity": "Real" + }, + { + "Year": 2013, + "Units": 14.7, + "Authenticity": "Fake" + }, + { + "Year": 2014, + "Units": 26.3, + "Authenticity": "Real" + }, + { + "Year": 2014, + "Units": 13.9, + "Authenticity": "Fake" + }, + { + "Year": 2015, + "Units": 25.9, + "Authenticity": "Real" + }, + { + "Year": 2015, + "Units": 12.5, + "Authenticity": "Fake" + }, + { + "Year": 2016, + "Units": 27.4, + "Authenticity": "Real" + }, + { + "Year": 2016, + "Units": 18.6, + "Authenticity": "Fake" + }, + { + "Year": 2017, + "Units": 27.4, + "Authenticity": "Real" + }, + { + "Year": 2017, + "Units": 21.1, + "Authenticity": "Fake" + }, + { + "Year": 2018, + "Units": 32.8, + "Authenticity": "Real" + }, + { + "Year": 2018, + "Units": 23.6, + "Authenticity": "Fake" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of purchase", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding authenticity", + "role": "y" + }, + { + "name": "Authenticity", + "importance": "primary", + "description": "The authenticity of the data point", + "role": "group" + } + ] + } + }, + "234": { + "metadata": { + "title": "Inflation Woes Affect Thanksgiving Dinner", + "description": "Average price of a ten-person Thanksgiving dinner in the U.S. from 2000 to 2022", + "main_insight": "The average price of a Thanksgiving dinner has increased significantly from 2000 to 2022, with a sharp rise between 2020 and 2022." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2000, + "Average Price": 32.37 + }, + { + "Year": 2001, + "Average Price": 35.31 + }, + { + "Year": 2002, + "Average Price": 34.56 + }, + { + "Year": 2003, + "Average Price": 35.88 + }, + { + "Year": 2004, + "Average Price": 36.06 + }, + { + "Year": 2005, + "Average Price": 36.78 + }, + { + "Year": 2006, + "Average Price": 38.99 + }, + { + "Year": 2007, + "Average Price": 42.26 + }, + { + "Year": 2008, + "Average Price": 44.43 + }, + { + "Year": 2009, + "Average Price": 42.91 + }, + { + "Year": 2010, + "Average Price": 43.47 + }, + { + "Year": 2011, + "Average Price": 49.2 + }, + { + "Year": 2012, + "Average Price": 49.48 + }, + { + "Year": 2013, + "Average Price": 49.04 + }, + { + "Year": 2014, + "Average Price": 49.41 + }, + { + "Year": 2015, + "Average Price": 50.11 + }, + { + "Year": 2016, + "Average Price": 49.87 + }, + { + "Year": 2017, + "Average Price": 49.12 + }, + { + "Year": 2018, + "Average Price": 48.9 + }, + { + "Year": 2019, + "Average Price": 48.91 + }, + { + "Year": 2020, + "Average Price": 46.9 + }, + { + "Year": 2021, + "Average Price": 53.31 + }, + { + "Year": 2022, + "Average Price": 64.05 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 2000 to 2022", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Average Price", + "importance": "primary", + "description": "Average price of a ten-person Thanksgiving dinner in the U.S.", + "unit": "USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1054": { + "metadata": { + "title": "Samsung Bumps Up U.S. Advertising Efforts", + "description": "Fastest growing ad spenders among Top 100 advertisers in the United States in 2012", + "main_insight": "Samsung had the largest increase in advertising spending in the US in 2012 among the top 100 advertisers, with a 58% growth." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Company": "Samsung", + "Ad Spend Growth": 58.0 + } + ], + "score": 0.91, + "annotation": "The has a maximum value at Samsung", + "reason": "The [{'Company': 'Samsung', 'Ad Spend Growth': 58.0}] of has a maximum value of 58.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "AbbVie", + "Ad Spend Growth": 41.0 + }, + { + "Company": "Samsung", + "Ad Spend Growth": 58.0 + } + ], + "score": 0.73, + "annotation": "The exhibits multiple sudden jumps in Ad Spend Growth, transitioning from Discover, Amazon to AbbVie, Samsung.", + "reason": "Across these points, the Ad Spend Growth for increases from 30.0, 47.0 at Discover, Amazon to 41.0, 58.0 at AbbVie, Samsung, suggesting significant shifts in value." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Company": "Kia Motors", + "Ad Spend Growth": 27.0 + }, + { + "Company": "T-Mobile", + "Ad Spend Growth": 27.0 + } + ], + "score": 0.59, + "annotation": "The has minimum values at Kia Motors, T-Mobile", + "reason": "The [{'Company': 'Kia Motors', 'Ad Spend Growth': 27.0}, {'Company': 'T-Mobile', 'Ad Spend Growth': 27.0}] of have minimum values of 27.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Company": "Samsung", + "Ad Spend Growth": 58.0 + }, + { + "Company": "Amazon", + "Ad Spend Growth": 47.0 + }, + { + "Company": "IAC", + "Ad Spend Growth": 43.0 + }, + { + "Company": "LIONSGATE", + "Ad Spend Growth": 43.0 + }, + { + "Company": "AbbVie", + "Ad Spend Growth": 41.0 + }, + { + "Company": "Discover", + "Ad Spend Growth": 30.0 + }, + { + "Company": "Dish", + "Ad Spend Growth": 29.0 + }, + { + "Company": "Honda", + "Ad Spend Growth": 29.0 + }, + { + "Company": "Kia Motors", + "Ad Spend Growth": 27.0 + }, + { + "Company": "T-Mobile", + "Ad Spend Growth": 27.0 + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Name of the company", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ad Spend Growth", + "importance": "primary", + "description": "Percentage growth of ad spending in 2012", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2053": { + "metadata": { + "title": "Most National Emergencies Are for International Crises", + "description": "Active U.S. national emergencies by category and president who declared them*", + "main_insight": "The vast majority of active national emergencies are related to international crises, and this has been consistent across multiple presidencies." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "President": "Trump", + "Score": 4.0, + "Emergency Scope": "International emergency" + } + ], + "score": 0.84, + "annotation": "The International emergency accounts for the majority in Trump.", + "reason": "The Score of International emergency in Trump accounts for 0.5714285714285714 (more than 50%) of the total of Score of all Emergency Scope." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Emergency Scope": "International emergency", + "President": "total", + "Score": 27.0 + } + ], + "score": 0.79, + "annotation": "The total value of International emergency accounts for the majority in all Emergency Scope.", + "reason": "The total value of Score of International emergency accounts for 0.8181818181818182 (more than 50%) of the total of Score of all Emergency Scope." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "President": "Obama", + "Score": 10.0, + "Emergency Scope": "International emergency" + } + ], + "score": 0.78, + "annotation": "The International emergency accounts for the majority in Obama.", + "reason": "The Score of International emergency in Obama accounts for 0.8333333333333334 (more than 50%) of the total of Score of all Emergency Scope." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "President": "G. W. Bush", + "Score": 1.0, + "Emergency Scope": "Domestic emergency" + }, + { + "President": "Carter", + "Score": 1.0, + "Emergency Scope": "Foreign emergency" + } + ], + "score": 0.71, + "annotation": "The Domestic emergency, Foreign emergency all have the smallest proportion in Obama.", + "reason": "The Score of Domestic emergency, Foreign emergency in Obama all account for 0.08333333333333333, smaller than all other Emergency Scope, of the total of Score of all Emergency Scope." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Emergency Scope": "International emergency", + "President": "total", + "Score": 27.0 + } + ], + "score": 0.69, + "annotation": "The total value of International emergency has the smallest proportion.", + "reason": "The total value of Score of International emergency accounts for 0.09090909090909091, smaller than all other Emergency Scope, of the total of Score of all Emergency Scope." + } + ], + "data": { + "data": [ + { + "President": "Trump", + "Score": 4.0, + "Emergency Scope": "International emergency" + }, + { + "President": "Trump", + "Score": 1.0, + "Emergency Scope": "Domestic emergency" + }, + { + "President": "Obama", + "Score": 10.0, + "Emergency Scope": "International emergency" + }, + { + "President": "G. W. Bush", + "Score": 8.0, + "Emergency Scope": "International emergency" + }, + { + "President": "G. W. Bush", + "Score": 1.0, + "Emergency Scope": "Domestic emergency" + }, + { + "President": "G. W. Bush", + "Score": 2.0, + "Emergency Scope": "Foreign emergency" + }, + { + "President": "Clinton", + "Score": 5.0, + "Emergency Scope": "International emergency" + }, + { + "President": "Clinton", + "Score": 1.0, + "Emergency Scope": "Domestic emergency" + }, + { + "President": "Carter", + "Score": 1.0, + "Emergency Scope": "Foreign emergency" + } + ], + "columns": [ + { + "name": "President", + "importance": "primary", + "description": "The U.S. president who declared the emergency", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding emergency scope", + "role": "y" + }, + { + "name": "Emergency Scope", + "importance": "primary", + "description": "The emergency scope of the data point", + "role": "group" + } + ] + } + }, + "589": { + "metadata": { + "title": "How Many Consumers Own Cryptocurrency?", + "description": "Share of respondents who own Bitcoin or a similar digital currency in selected countries in 2018", + "main_insight": "Turkey had the highest percentage of respondents (18%) who owned cryptocurrency in 2018, while Luxembourg had the lowest at 4%. There is significant variation in cryptocurrency ownership across different countries." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Luxembourg", + "Score": 4.0, + "Response": "Yes" + }, + { + "Country": "Luxembourg", + "Score": 96.0, + "Response": "No" + } + ], + "score": 1.0, + "annotation": "The Score of Yes and No show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Yes and No is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Turkey", + "Score": 18.0, + "Response": "Yes" + } + ], + "score": 0.98, + "annotation": "The Yes has a maximum value at Turkey", + "reason": "The [{'Country': 'Turkey', 'Score': 18.0, 'Response': 'Yes'}] of Yes has a maximum value of 18.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Turkey", + "Score": 82.0, + "Response": "No" + } + ], + "score": 0.98, + "annotation": "The No has a minimum value at Turkey", + "reason": "The [{'Country': 'Turkey', 'Score': 82.0, 'Response': 'No'}] of No has a mainimum value of 82.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Luxembourg", + "Score": 4.0, + "Response": "Yes" + } + ], + "score": 0.83, + "annotation": "The Yes has the smallest proportion in Luxembourg.", + "reason": "The Score of Yes in Luxembourg accounts for 0.04, smaller than all other Response, of the total of Score of all Response." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Belgium", + "Score": 5.0, + "Response": "Yes" + } + ], + "score": 0.8, + "annotation": "The Yes has the smallest proportion in Belgium.", + "reason": "The Score of Yes in Belgium accounts for 0.05, smaller than all other Response, of the total of Score of all Response." + } + ], + "data": { + "data": [ + { + "Country": "Turkey", + "Score": 18.0, + "Response": "Yes" + }, + { + "Country": "Turkey", + "Score": 82.0, + "Response": "No" + }, + { + "Country": "Romania", + "Score": 12.0, + "Response": "Yes" + }, + { + "Country": "Romania", + "Score": 88.0, + "Response": "No" + }, + { + "Country": "Poland", + "Score": 11.0, + "Response": "Yes" + }, + { + "Country": "Poland", + "Score": 89.0, + "Response": "No" + }, + { + "Country": "Spain", + "Score": 10.0, + "Response": "Yes" + }, + { + "Country": "Spain", + "Score": 90.0, + "Response": "No" + }, + { + "Country": "Czechia", + "Score": 9.0, + "Response": "Yes" + }, + { + "Country": "Czechia", + "Score": 91.0, + "Response": "No" + }, + { + "Country": "USA", + "Score": 8.0, + "Response": "Yes" + }, + { + "Country": "USA", + "Score": 92.0, + "Response": "No" + }, + { + "Country": "Austria", + "Score": 8.0, + "Response": "Yes" + }, + { + "Country": "Austria", + "Score": 92.0, + "Response": "No" + }, + { + "Country": "Germany", + "Score": 8.0, + "Response": "Yes" + }, + { + "Country": "Germany", + "Score": 92.0, + "Response": "No" + }, + { + "Country": "Italy", + "Score": 8.0, + "Response": "Yes" + }, + { + "Country": "Italy", + "Score": 92.0, + "Response": "No" + }, + { + "Country": "Australia", + "Score": 7.0, + "Response": "Yes" + }, + { + "Country": "Australia", + "Score": 93.0, + "Response": "No" + }, + { + "Country": "Netherlands", + "Score": 7.0, + "Response": "Yes" + }, + { + "Country": "Netherlands", + "Score": 93.0, + "Response": "No" + }, + { + "Country": "United Kingdom", + "Score": 6.0, + "Response": "Yes" + }, + { + "Country": "United Kingdom", + "Score": 94.0, + "Response": "No" + }, + { + "Country": "France", + "Score": 6.0, + "Response": "Yes" + }, + { + "Country": "France", + "Score": 94.0, + "Response": "No" + }, + { + "Country": "Belgium", + "Score": 5.0, + "Response": "Yes" + }, + { + "Country": "Belgium", + "Score": 95.0, + "Response": "No" + }, + { + "Country": "Luxembourg", + "Score": 4.0, + "Response": "Yes" + }, + { + "Country": "Luxembourg", + "Score": 96.0, + "Response": "No" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding response", + "role": "y" + }, + { + "name": "Response", + "importance": "primary", + "description": "The response of the data point", + "role": "group" + } + ] + } + }, + "1184": { + "metadata": { + "title": "New Coronavirus Cases Ebbing in Big Outbreak Centers", + "description": "Two-week rolling averages of daily new cases of COVID-19 in the U.S., the EU, India and Russia", + "main_insight": "Daily new COVID-19 cases peaked and subsequently decreased in the EU, U.S., India, and Russia between 2020 and early 2021. The timing and magnitude of the peaks varied among the regions." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Time": "'21", + "Ratio": 50000.0, + "Region": "U.S." + }, + { + "Time": "'21", + "Ratio": 20000.0, + "Region": "India" + } + ], + "score": 0.96, + "annotation": "The Ratio of U.S. and India show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between U.S. and India is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time": "Late 2020", + "Ratio": 200000.0, + "Region": "U.S." + } + ], + "score": 0.95, + "annotation": "The U.S. shows a sudden jump in Ratio from Mid 2020 to Late 2020.", + "reason": "The Ratio for U.S. increases from 75000.0 at Mid 2020 to 200000.0 at Late 2020, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time": "Mid 2020", + "Ratio": 20000.0, + "Region": "Russia" + } + ], + "score": 0.93, + "annotation": "The Russia shows a sudden jump in Ratio from Early 2020 to Mid 2020.", + "reason": "The Ratio for Russia increases from 5000.0 at Early 2020 to 20000.0 at Mid 2020, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "EU", + "Time": "total", + "Ratio": 375000.0 + }, + { + "Region": "U.S.", + "Time": "total", + "Ratio": 375000.0 + } + ], + "score": 0.91, + "annotation": "The total value of EU, U.S. all have the largest proportion.", + "reason": "The total value of Ratio of EU, U.S. all account for 0.375, larger than all other Region, of the total of Ratio of all Region." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Time": "Late 2020", + "Ratio": 200000.0, + "Region": "EU" + }, + { + "Time": "Late 2020", + "Ratio": 200000.0, + "Region": "U.S." + } + ], + "score": 0.91, + "annotation": "The EU, U.S. all have the largest proportion in Late 2020.", + "reason": "The Ratio of EU, U.S. in Late 2020 all account for 0.38095238095238093, larger than all other Region, of the total of Ratio of all Region." + } + ], + "data": { + "data": [ + { + "Time": "Early 2020", + "Ratio": 50000.0, + "Region": "EU" + }, + { + "Time": "Early 2020", + "Ratio": 50000.0, + "Region": "U.S." + }, + { + "Time": "Early 2020", + "Ratio": 10000.0, + "Region": "India" + }, + { + "Time": "Early 2020", + "Ratio": 5000.0, + "Region": "Russia" + }, + { + "Time": "Mid 2020", + "Ratio": 25000.0, + "Region": "EU" + }, + { + "Time": "Mid 2020", + "Ratio": 75000.0, + "Region": "U.S." + }, + { + "Time": "Mid 2020", + "Ratio": 50000.0, + "Region": "India" + }, + { + "Time": "Mid 2020", + "Ratio": 20000.0, + "Region": "Russia" + }, + { + "Time": "Late 2020", + "Ratio": 200000.0, + "Region": "EU" + }, + { + "Time": "Late 2020", + "Ratio": 200000.0, + "Region": "U.S." + }, + { + "Time": "Late 2020", + "Ratio": 100000.0, + "Region": "India" + }, + { + "Time": "Late 2020", + "Ratio": 25000.0, + "Region": "Russia" + }, + { + "Time": "'21", + "Ratio": 100000.0, + "Region": "EU" + }, + { + "Time": "'21", + "Ratio": 50000.0, + "Region": "U.S." + }, + { + "Time": "'21", + "Ratio": 20000.0, + "Region": "India" + }, + { + "Time": "'21", + "Ratio": 20000.0, + "Region": "Russia" + } + ], + "columns": [ + { + "name": "Time", + "importance": "primary", + "description": "Time period from early 2020 to early 2021", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding region", + "role": "y" + }, + { + "name": "Region", + "importance": "primary", + "description": "The region of the data point", + "role": "group" + } + ] + } + }, + "74": { + "metadata": { + "title": "Racial Divide in Coronavirus Impact", + "description": "Percentage who say they or someone in their household has lost a job or taken a paycut due to COVID-19", + "main_insight": "Hispanic households were significantly more likely to report job loss or pay cuts due to COVID-19 in both March and April compared to Black and White households. All groups saw an increase in reported job loss or pay cuts between March and April." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "April", + "Units": 61.0, + "Race/Ethnicity": "Hispanic" + }, + { + "Month": "April", + "Units": 44.0, + "Race/Ethnicity": "Black" + } + ], + "score": 1.0, + "annotation": "The Units of Hispanic and Black show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Hispanic and Black is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "April", + "Units": 61.0, + "Race/Ethnicity": "Hispanic" + }, + { + "Month": "April", + "Units": 38.0, + "Race/Ethnicity": "White" + } + ], + "score": 1.0, + "annotation": "The Units of Hispanic and White show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Hispanic and White is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "April", + "Units": 44.0, + "Race/Ethnicity": "Black" + }, + { + "Month": "April", + "Units": 38.0, + "Race/Ethnicity": "White" + } + ], + "score": 1.0, + "annotation": "The Units of Black and White show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Black and White is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "April", + "Units": 61.0, + "Race/Ethnicity": "Hispanic" + } + ], + "score": 0.97, + "annotation": "The Hispanic shows a sudden jump in Units from March to April.", + "reason": "The Units for Hispanic increases from 49.0 at March to 61.0 at April, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "April", + "Units": 44.0, + "Race/Ethnicity": "Black" + } + ], + "score": 0.97, + "annotation": "The Black shows a sudden jump in Units from March to April.", + "reason": "The Units for Black increases from 36.0 at March to 44.0 at April, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Month": "March", + "Units": 49.0, + "Race/Ethnicity": "Hispanic" + }, + { + "Month": "March", + "Units": 36.0, + "Race/Ethnicity": "Black" + }, + { + "Month": "March", + "Units": 29.0, + "Race/Ethnicity": "White" + }, + { + "Month": "April", + "Units": 61.0, + "Race/Ethnicity": "Hispanic" + }, + { + "Month": "April", + "Units": 44.0, + "Race/Ethnicity": "Black" + }, + { + "Month": "April", + "Units": 38.0, + "Race/Ethnicity": "White" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Time period of the survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding race/ethnicity", + "role": "y" + }, + { + "name": "Race/Ethnicity", + "importance": "primary", + "description": "The race/ethnicity of the data point", + "role": "group" + } + ] + } + }, + "51": { + "metadata": { + "title": "PC Growth Choked by the iPad's Arrival", + "description": "Year-over-year growth of global PC shipments", + "main_insight": "Global PC shipment growth significantly declined after the release of the iPad in April 2010, shifting from positive growth to negative growth by 2012." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Quarter": "Q1 2010", + "PC Shipment Growth": 0.28 + } + ], + "score": 0.95, + "annotation": "The has a maximum value at Q1 2010", + "reason": "The [{'Quarter': 'Q1 2010', 'PC Shipment Growth': 0.28}] of has a maximum value of 0.28, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Quarter": "Q1 2013", + "PC Shipment Growth": -0.13 + } + ], + "score": 0.75, + "annotation": "The has a minimum value at Q1 2013", + "reason": "The [{'Quarter': 'Q1 2013', 'PC Shipment Growth': -0.13}] of has a mainimum value of -0.13, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Quarter": "Q2 2010", + "PC Shipment Growth": 0.21 + } + ], + "score": 0.7, + "annotation": "The shows a sudden jump in PC Shipment Growth from Q3 2011 to Q2 2010.", + "reason": "The PC Shipment Growth for increases from 0.08 at Q3 2011 to 0.21 at Q2 2010, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Quarter": "Q1 2010", + "PC Shipment Growth": 0.28 + }, + { + "Quarter": "Q2 2010", + "PC Shipment Growth": 0.21 + }, + { + "Quarter": "Q3 2010", + "PC Shipment Growth": 0.08 + }, + { + "Quarter": "Q4 2010", + "PC Shipment Growth": 0.03 + }, + { + "Quarter": "Q1 2011", + "PC Shipment Growth": 0.02 + }, + { + "Quarter": "Q2 2011", + "PC Shipment Growth": 0.05 + }, + { + "Quarter": "Q3 2011", + "PC Shipment Growth": 0.08 + }, + { + "Quarter": "Q4 2011", + "PC Shipment Growth": 0.01 + }, + { + "Quarter": "Q1 2012", + "PC Shipment Growth": 0.02 + }, + { + "Quarter": "Q2 2012", + "PC Shipment Growth": -0.02 + }, + { + "Quarter": "Q3 2012", + "PC Shipment Growth": -0.08 + }, + { + "Quarter": "Q4 2012", + "PC Shipment Growth": -0.05 + }, + { + "Quarter": "Q1 2013", + "PC Shipment Growth": -0.13 + }, + { + "Quarter": "Q2 2013", + "PC Shipment Growth": -0.11 + }, + { + "Quarter": "Q3 2013", + "PC Shipment Growth": -0.08 + } + ], + "columns": [ + { + "name": "Quarter", + "importance": "primary", + "description": "Quarter of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "PC Shipment Growth", + "importance": "primary", + "description": "Year-over-year growth of global PC shipments", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2461": { + "metadata": { + "title": "Americans Split on Corporate Activism", + "description": "Percentage of U.S. adults who believe it's important for companies to publicly address political/social issues", + "main_insight": "Americans are divided on the importance of corporate activism, with a slight majority believing it's at least somewhat important for companies to publicly address political/social issues, but a significant portion believing it's not important at all." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Response": "Very", + "Percentage": 19.0 + } + ], + "score": 0.87, + "annotation": "The shows a sudden jump in Percentage from No opinion to Very.", + "reason": "The Percentage for increases from 1.0 at No opinion to 19.0 at Very, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Response": "No opinion", + "Percentage": 1.0 + } + ], + "score": 0.84, + "annotation": "The has a minimum value at No opinion", + "reason": "The [{'Response': 'No opinion', 'Percentage': 1.0}] of has a mainimum value of 1.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Response": "Somewhat", + "Percentage": 32.0 + } + ], + "score": 0.61, + "annotation": "The has a maximum value at Somewhat", + "reason": "The [{'Response': 'Somewhat', 'Percentage': 32.0}] of has a maximum value of 32.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Response": "Not at all", + "Percentage": 25.0 + }, + { + "Response": "Not very", + "Percentage": 23.0 + }, + { + "Response": "Somewhat", + "Percentage": 32.0 + }, + { + "Response": "Very", + "Percentage": 19.0 + }, + { + "Response": "No opinion", + "Percentage": 1.0 + } + ], + "columns": [ + { + "name": "Response", + "importance": "primary", + "description": "Survey response categories", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents for each category", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1722": { + "metadata": { + "title": "Gaming Subscriptions: Xbox on the March", + "description": "Number of subscribers to the biggest gaming subscription services (in millions)", + "main_insight": "Xbox Game Pass experienced the largest growth in subscribers between 2020 and 2021 compared to PlayStation Plus and Nintendo Switch Online." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Gaming Service": "Xbox Game Pass", + "Units": 15.0, + "Subscriber Metrics": "2020 Subscribers" + }, + { + "Gaming Service": "Xbox Game Pass", + "Units": 25.0, + "Subscriber Metrics": "2021 Subscribers" + } + ], + "score": 1.0, + "annotation": "The Units of 2020 Subscribers and 2021 Subscribers show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2020 Subscribers and 2021 Subscribers is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Gaming Service": "Xbox Game Pass", + "Units": 15.0, + "Subscriber Metrics": "2020 Subscribers" + }, + { + "Gaming Service": "Xbox Game Pass", + "Units": 67.0, + "Subscriber Metrics": "Change (in %)" + } + ], + "score": 0.93, + "annotation": "The Units of 2020 Subscribers and Change (in %) show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2020 Subscribers and Change (in %) is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Gaming Service": "Xbox Game Pass", + "Units": 25.0, + "Subscriber Metrics": "2021 Subscribers" + }, + { + "Gaming Service": "Xbox Game Pass", + "Units": 67.0, + "Subscriber Metrics": "Change (in %)" + } + ], + "score": 0.92, + "annotation": "The Units of 2021 Subscribers and Change (in %) show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2021 Subscribers and Change (in %) is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Gaming Service": "PlayStation Plus", + "Units": 47.0, + "Subscriber Metrics": "2021 Subscribers" + } + ], + "score": 0.91, + "annotation": "The 2021 Subscribers shows a sudden jump in Units from Nintendo Switch Online to PlayStation Plus.", + "reason": "The Units for 2021 Subscribers increases from 32.0 at Nintendo Switch Online to 47.0 at PlayStation Plus, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Gaming Service": "Xbox Game Pass", + "Units": 67.0, + "Subscriber Metrics": "Change (in %)" + } + ], + "score": 0.91, + "annotation": "The Change (in %) shows a sudden jump in Units from Nintendo Switch Online to Xbox Game Pass.", + "reason": "The Units for Change (in %) increases from 23.0 at Nintendo Switch Online to 67.0 at Xbox Game Pass, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Gaming Service": "PlayStation Plus", + "Units": 46.0, + "Subscriber Metrics": "2020 Subscribers" + }, + { + "Gaming Service": "PlayStation Plus", + "Units": 47.0, + "Subscriber Metrics": "2021 Subscribers" + }, + { + "Gaming Service": "PlayStation Plus", + "Units": 2.0, + "Subscriber Metrics": "Change (in %)" + }, + { + "Gaming Service": "Nintendo Switch Online", + "Units": 26.0, + "Subscriber Metrics": "2020 Subscribers" + }, + { + "Gaming Service": "Nintendo Switch Online", + "Units": 32.0, + "Subscriber Metrics": "2021 Subscribers" + }, + { + "Gaming Service": "Nintendo Switch Online", + "Units": 23.0, + "Subscriber Metrics": "Change (in %)" + }, + { + "Gaming Service": "Xbox Game Pass", + "Units": 15.0, + "Subscriber Metrics": "2020 Subscribers" + }, + { + "Gaming Service": "Xbox Game Pass", + "Units": 25.0, + "Subscriber Metrics": "2021 Subscribers" + }, + { + "Gaming Service": "Xbox Game Pass", + "Units": 67.0, + "Subscriber Metrics": "Change (in %)" + } + ], + "columns": [ + { + "name": "Gaming Service", + "importance": "primary", + "description": "Name of the gaming subscription service", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding subscriber metrics", + "role": "y" + }, + { + "name": "Subscriber Metrics", + "importance": "primary", + "description": "The subscriber metrics of the data point", + "role": "group" + } + ] + } + }, + "1016": { + "metadata": { + "title": "The Massive Scale of Russian Election Trolling", + "description": "Average monthly volume of posts by Russia's Internet Research Agency", + "main_insight": "Russian election trolling activity significantly increased in 2016, especially on Instagram, and continued at high levels in 2017 across various social media platforms." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Platform": "Twitter Posts", + "Units": 59126, + "Year": "2016" + }, + { + "Platform": "Twitter Posts", + "Units": 59634, + "Year": "2017" + } + ], + "score": 1.0, + "annotation": "The Units of 2016 and 2017 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2016 and 2017 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Year": "2017", + "Platform": "total", + "Units": 67020 + } + ], + "score": 0.99, + "annotation": "The total value of 2017 has the smallest proportion.", + "reason": "The total value of Units of 2017 accounts for 0.0020905183848888417, smaller than all other Year, of the total of Units of all Year." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Platform": "Instagram Posts", + "Units": 238, + "Year": "Increase in activity" + } + ], + "score": 0.98, + "annotation": "The Increase in activity has the smallest proportion in Twitter Posts.", + "reason": "The Units of Increase in activity in Twitter Posts accounts for 0.0038885076626474528, smaller than all other Year, of the total of Units of all Year." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Platform": "Twitter Posts", + "Units": 56247, + "Year": "2015" + } + ], + "score": 0.97, + "annotation": "The 2015 shows a sudden jump in Units from Facebook Posts to Twitter Posts.", + "reason": "The Units for 2015 increases from 360 at Facebook Posts to 56247 at Twitter Posts, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Platform": "Twitter Posts", + "Units": 59126, + "Year": "2016" + } + ], + "score": 0.97, + "annotation": "The 2016 shows a sudden jump in Units from Facebook Posts to Twitter Posts.", + "reason": "The Units for 2016 increases from 2442 at Facebook Posts to 59126 at Twitter Posts, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Platform": "Facebook Ads", + "Units": 207, + "Year": "2015" + }, + { + "Platform": "Facebook Ads", + "Units": 564, + "Year": "2016" + }, + { + "Platform": "Facebook Ads", + "Units": 541, + "Year": "2017" + }, + { + "Platform": "Facebook Ads", + "Units": 45, + "Year": "Increase in activity" + }, + { + "Platform": "Facebook Posts", + "Units": 360, + "Year": "2015" + }, + { + "Platform": "Facebook Posts", + "Units": 2442, + "Year": "2016" + }, + { + "Platform": "Facebook Posts", + "Units": 4234, + "Year": "2017" + }, + { + "Platform": "Facebook Posts", + "Units": 59, + "Year": "Increase in activity" + }, + { + "Platform": "Instagram Posts", + "Units": 2110, + "Year": "2016" + }, + { + "Platform": "Instagram Posts", + "Units": 2611, + "Year": "2017" + }, + { + "Platform": "Instagram Posts", + "Units": 238, + "Year": "Increase in activity" + }, + { + "Platform": "Twitter Posts", + "Units": 56247, + "Year": "2015" + }, + { + "Platform": "Twitter Posts", + "Units": 59126, + "Year": "2016" + }, + { + "Platform": "Twitter Posts", + "Units": 59634, + "Year": "2017" + }, + { + "Platform": "Twitter Posts", + "Units": 52, + "Year": "Increase in activity" + } + ], + "columns": [ + { + "name": "Platform", + "importance": "primary", + "description": "Social media platform", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "1617": { + "metadata": { + "title": "North Indians Agree With the CAA Most", + "description": "Share of people in different regions of India supporting the Citizenship Amendment Act", + "main_insight": "Support for the Citizenship Amendment Act (CAA) varies significantly across different regions of India, with the highest support in the North and the lowest in Assam." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Region": "Assam", + "Support": 31.0 + } + ], + "score": 0.89, + "annotation": "The has a minimum value at Assam", + "reason": "The [{'Region': 'Assam', 'Support': 31.0}] of has a mainimum value of 31.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Northeast", + "Support": 50.6 + } + ], + "score": 0.85, + "annotation": "The shows a sudden jump in Support from Assam to Northeast.", + "reason": "The Support for increases from 31.0 at Assam to 50.6 at Northeast, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "North", + "Support": 67.7 + } + ], + "score": 0.59, + "annotation": "The has a maximum value at North", + "reason": "The [{'Region': 'North', 'Support': 67.7}] of has a maximum value of 67.7, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Region": "North", + "Support": 67.7 + }, + { + "Region": "West", + "Support": 64.2 + }, + { + "Region": "South", + "Support": 58.5 + }, + { + "Region": "East", + "Support": 57.3 + }, + { + "Region": "Northeast", + "Support": 50.6 + }, + { + "Region": "Assam", + "Support": 31.0 + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Region of India", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Support", + "importance": "primary", + "description": "Percentage of people supporting the CAA", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "319": { + "metadata": { + "title": "Global Confidence In Trump & Putin", + "description": "Share confident in Trump/Putin doing the right thing regarding world affairs (2017)", + "main_insight": "Across several countries, confidence in Trump doing the right thing in world affairs is generally higher than confidence in Putin, although confidence levels vary significantly by country." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "South Africa", + "Ratio": 0.33, + "Approval Rating": "Putin" + } + ], + "score": 0.89, + "annotation": "The Putin has a maximum value at South Africa", + "reason": "The [{'Country': 'South Africa', 'Ratio': 0.33, 'Approval Rating': 'Putin'}] of Putin has a maximum value of 0.33, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "India", + "Ratio": 0.4, + "Approval Rating": "Trump" + } + ], + "score": 0.87, + "annotation": "The Trump has a maximum value at India", + "reason": "The [{'Country': 'India', 'Ratio': 0.4, 'Approval Rating': 'Trump'}] of Trump has a maximum value of 0.4, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Australia", + "Ratio": 0.29, + "Approval Rating": "Trump" + } + ], + "score": 0.79, + "annotation": "The Trump accounts for the majority in Australia.", + "reason": "The Ratio of Trump in Australia accounts for 0.5178571428571428 (more than 50%) of the total of Ratio of all Approval Rating." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Approval Rating": "Putin", + "Country": "total", + "Ratio": 2.83 + } + ], + "score": 0.79, + "annotation": "The total value of Putin accounts for the majority in all Approval Rating.", + "reason": "The total value of Ratio of Putin accounts for 0.5329566854990584 (more than 50%) of the total of Ratio of all Approval Rating." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Canada", + "Ratio": 0.22, + "Approval Rating": "Trump" + } + ], + "score": 0.79, + "annotation": "The Trump accounts for the majority in Canada.", + "reason": "The Ratio of Trump in Canada accounts for 0.5365853658536585 (more than 50%) of the total of Ratio of all Approval Rating." + } + ], + "data": { + "data": [ + { + "Country": "South Africa", + "Ratio": 0.39, + "Approval Rating": "Trump" + }, + { + "Country": "South Africa", + "Ratio": 0.33, + "Approval Rating": "Putin" + }, + { + "Country": "India", + "Ratio": 0.4, + "Approval Rating": "Trump" + }, + { + "Country": "India", + "Ratio": 0.29, + "Approval Rating": "Putin" + }, + { + "Country": "Japan", + "Ratio": 0.24, + "Approval Rating": "Trump" + }, + { + "Country": "Japan", + "Ratio": 0.28, + "Approval Rating": "Putin" + }, + { + "Country": "Australia", + "Ratio": 0.29, + "Approval Rating": "Trump" + }, + { + "Country": "Australia", + "Ratio": 0.27, + "Approval Rating": "Putin" + }, + { + "Country": "South Korea", + "Ratio": 0.17, + "Approval Rating": "Trump" + }, + { + "Country": "South Korea", + "Ratio": 0.27, + "Approval Rating": "Putin" + }, + { + "Country": "Germany", + "Ratio": 0.11, + "Approval Rating": "Trump" + }, + { + "Country": "Germany", + "Ratio": 0.25, + "Approval Rating": "Putin" + }, + { + "Country": "Turkey", + "Ratio": 0.11, + "Approval Rating": "Trump" + }, + { + "Country": "Turkey", + "Ratio": 0.2, + "Approval Rating": "Putin" + }, + { + "Country": "Brazil", + "Ratio": 0.14, + "Approval Rating": "Trump" + }, + { + "Country": "Brazil", + "Ratio": 0.19, + "Approval Rating": "Putin" + }, + { + "Country": "Canada", + "Ratio": 0.22, + "Approval Rating": "Trump" + }, + { + "Country": "Canada", + "Ratio": 0.19, + "Approval Rating": "Putin" + }, + { + "Country": "Mexico", + "Ratio": 0.05, + "Approval Rating": "Trump" + }, + { + "Country": "Mexico", + "Ratio": 0.19, + "Approval Rating": "Putin" + }, + { + "Country": "United Kingdom", + "Ratio": 0.22, + "Approval Rating": "Trump" + }, + { + "Country": "United Kingdom", + "Ratio": 0.19, + "Approval Rating": "Putin" + }, + { + "Country": "France", + "Ratio": 0.14, + "Approval Rating": "Trump" + }, + { + "Country": "France", + "Ratio": 0.18, + "Approval Rating": "Putin" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding approval rating", + "role": "y" + }, + { + "name": "Approval Rating", + "importance": "primary", + "description": "The approval rating of the data point", + "role": "group" + } + ] + } + }, + "583": { + "metadata": { + "title": "European Banks: Hiring or Firing?", + "description": "Change in number of employees of selected leading banks in Europe", + "main_insight": "Between 2014 and 2017, there was a wide range of employee number changes among leading European banks. Some, like BBVA, significantly increased their workforce, while others, like Royal Bank of Scotland, significantly reduced theirs." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Bank": "Royal Bank of Scotland", + "Units": 71200, + "Metric": "2017" + }, + { + "Bank": "Royal Bank of Scotland", + "Units": 108700, + "Metric": "2014" + } + ], + "score": 0.96, + "annotation": "The Units of 2017 and 2014 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2017 and 2014 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Bank": "СБЕРБАНК", + "Units": 329566, + "Metric": "2014" + } + ], + "score": 0.94, + "annotation": "The 2014 has a maximum value at СБЕРБАНК", + "reason": "The [{'Bank': 'СБЕРБАНК', 'Units': 329566, 'Metric': '2014'}] of 2014 has a maximum value of 329566, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Bank": "СБЕРБАНК", + "Units": 310300, + "Metric": "2017" + } + ], + "score": 0.92, + "annotation": "The 2017 has a maximum value at СБЕРБАНК", + "reason": "The [{'Bank': 'СБЕРБАНК', 'Units': 310300, 'Metric': '2017'}] of 2017 has a maximum value of 310300, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Bank": "Royal Bank of Scotland", + "Units": -34.5, + "Metric": "Percentage change" + } + ], + "score": 0.91, + "annotation": "The Percentage change has a minimum value at Royal Bank of Scotland", + "reason": "The [{'Bank': 'Royal Bank of Scotland', 'Units': -34.5, 'Metric': 'Percentage change'}] of Percentage change has a mainimum value of -34.5, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Bank": "BBVA", + "Units": 20.6, + "Metric": "Percentage change" + } + ], + "score": 0.84, + "annotation": "The Percentage change has a maximum value at BBVA", + "reason": "The [{'Bank': 'BBVA', 'Units': 20.6, 'Metric': 'Percentage change'}] of Percentage change has a maximum value of 20.6, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Bank": "BBVA", + "Units": 20.6, + "Metric": "Percentage change" + }, + { + "Bank": "BBVA", + "Units": 131784, + "Metric": "2017" + }, + { + "Bank": "BBVA", + "Units": 109239, + "Metric": "2014" + }, + { + "Bank": "Santander", + "Units": 9.1, + "Metric": "Percentage change" + }, + { + "Bank": "Santander", + "Units": 202251, + "Metric": "2017" + }, + { + "Bank": "Santander", + "Units": 185405, + "Metric": "2014" + }, + { + "Bank": "BNP PARIBAS", + "Units": 5.4, + "Metric": "Percentage change" + }, + { + "Bank": "BNP PARIBAS", + "Units": 198011, + "Metric": "2017" + }, + { + "Bank": "BNP PARIBAS", + "Units": 187903, + "Metric": "2014" + }, + { + "Bank": "BARCLAYS", + "Units": -1.7, + "Metric": "Percentage change" + }, + { + "Bank": "BARCLAYS", + "Units": 130000, + "Metric": "2017" + }, + { + "Bank": "BARCLAYS", + "Units": 132300, + "Metric": "2014" + }, + { + "Bank": "SOCIETE GENERALE", + "Units": -1.8, + "Metric": "Percentage change" + }, + { + "Bank": "SOCIETE GENERALE", + "Units": 145700, + "Metric": "2017" + }, + { + "Bank": "SOCIETE GENERALE", + "Units": 148322, + "Metric": "2014" + }, + { + "Bank": "ВТБ", + "Units": -4.9, + "Metric": "Percentage change" + }, + { + "Bank": "ВТБ", + "Units": 96167, + "Metric": "2017" + }, + { + "Bank": "ВТБ", + "Units": 101072, + "Metric": "2014" + }, + { + "Bank": "СБЕРБАНК", + "Units": -5.8, + "Metric": "Percentage change" + }, + { + "Bank": "СБЕРБАНК", + "Units": 310300, + "Metric": "2017" + }, + { + "Bank": "СБЕРБАНК", + "Units": 329566, + "Metric": "2014" + }, + { + "Bank": "HSBC", + "Units": -11.1, + "Metric": "Percentage change" + }, + { + "Bank": "HSBC", + "Units": 229000, + "Metric": "2017" + }, + { + "Bank": "HSBC", + "Units": 257063, + "Metric": "2014" + }, + { + "Bank": "UniCredit", + "Units": -19.6, + "Metric": "Percentage change" + }, + { + "Bank": "UniCredit", + "Units": 103771, + "Metric": "2017" + }, + { + "Bank": "UniCredit", + "Units": 129021, + "Metric": "2014" + }, + { + "Bank": "Royal Bank of Scotland", + "Units": -34.5, + "Metric": "Percentage change" + }, + { + "Bank": "Royal Bank of Scotland", + "Units": 71200, + "Metric": "2017" + }, + { + "Bank": "Royal Bank of Scotland", + "Units": 108700, + "Metric": "2014" + } + ], + "columns": [ + { + "name": "Bank", + "importance": "primary", + "description": "Name of the bank", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding metric", + "role": "y" + }, + { + "name": "Metric", + "importance": "primary", + "description": "The metric of the data point", + "role": "group" + } + ] + } + }, + "1887": { + "metadata": { + "title": "The Steady Rise Of U.S. Gun Deaths", + "description": "Number of U.S. gun deaths due to firearms (1999-2017)", + "main_insight": "Gun deaths in the U.S. have been steadily rising from 1999 to 2017, with a significant increase in the later years." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1999, + "Gun Deaths": 28874 + }, + { + "Year": 2000, + "Gun Deaths": 28683 + }, + { + "Year": 2001, + "Gun Deaths": 29573 + }, + { + "Year": 2002, + "Gun Deaths": 30242 + }, + { + "Year": 2003, + "Gun Deaths": 30419 + }, + { + "Year": 2004, + "Gun Deaths": 29569 + }, + { + "Year": 2005, + "Gun Deaths": 30692 + }, + { + "Year": 2006, + "Gun Deaths": 30896 + }, + { + "Year": 2007, + "Gun Deaths": 31224 + }, + { + "Year": 2008, + "Gun Deaths": 31593 + }, + { + "Year": 2009, + "Gun Deaths": 31347 + }, + { + "Year": 2010, + "Gun Deaths": 31672 + }, + { + "Year": 2011, + "Gun Deaths": 32351 + }, + { + "Year": 2012, + "Gun Deaths": 33698 + }, + { + "Year": 2013, + "Gun Deaths": 33636 + }, + { + "Year": 2014, + "Gun Deaths": 33599 + }, + { + "Year": 2015, + "Gun Deaths": 36252 + }, + { + "Year": 2016, + "Gun Deaths": 38658 + }, + { + "Year": 2017, + "Gun Deaths": 39773 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of recorded gun deaths", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Gun Deaths", + "importance": "primary", + "description": "Number of gun deaths due to firearms", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "9": { + "metadata": { + "title": "The Rise of the Robo-Advisors", + "description": "Estimated worldwide assets under management of robo-advisors (in billion U.S. dollars)", + "main_insight": "The assets under management by robo-advisors have grown significantly from 2017 to 2023 and are projected to grow even more by 2027. The growth from 2017 to 2023 alone demonstrates a massive increase." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2017, + "Assets Under Management": 186.9 + }, + { + "Year": 2023, + "Assets Under Management": 2761.0 + }, + { + "Year": 2027, + "Assets Under Management": 4664.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the estimated assets", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Assets Under Management", + "importance": "primary", + "description": "Estimated worldwide assets under management of robo-advisors in billion U.S. dollars", + "unit": "Billion U.S. Dollars", + "data_type": "number", + "role": "y" + } + ] + } + }, + "89": { + "metadata": { + "title": "Saudi Arabia's Devastating Aerial Bombardment In Yemen", + "description": "Estimated number of airstrikes by the Saudi-led coalition in Yemen (Mar 2015 - Apr 2018)", + "main_insight": "The number of Saudi-led airstrikes in Yemen fluctuated significantly between March 2015 and April 2018, with peaks in 2015 and 2016, and a total estimate of 17,243 airstrikes." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Oct 2015", + "Airstrikes": 918 + } + ], + "score": 0.96, + "annotation": "The has a maximum value at Oct 2015", + "reason": "The [{'Month': 'Oct 2015', 'Airstrikes': 918}] of has a maximum value of 918, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Jul 2016", + "Airstrikes": 127 + } + ], + "score": 0.9, + "annotation": "The has a minimum value at Jul 2016", + "reason": "The [{'Month': 'Jul 2016', 'Airstrikes': 127}] of has a mainimum value of 127, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Apr 2016", + "Airstrikes": 785 + } + ], + "score": 0.54, + "annotation": "The shows a sudden jump in Airstrikes from Nov 2016 to Apr 2016.", + "reason": "The Airstrikes for increases from 675 at Nov 2016 to 785 at Apr 2016, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Month": "Mar 2015", + "Airstrikes": 202 + }, + { + "Month": "Apr 2015", + "Airstrikes": 635 + }, + { + "Month": "May 2015", + "Airstrikes": 599 + }, + { + "Month": "Jun 2015", + "Airstrikes": 592 + }, + { + "Month": "Jul 2015", + "Airstrikes": 592 + }, + { + "Month": "Aug 2015", + "Airstrikes": 503 + }, + { + "Month": "Sep 2015", + "Airstrikes": 647 + }, + { + "Month": "Oct 2015", + "Airstrikes": 918 + }, + { + "Month": "Nov 2015", + "Airstrikes": 795 + }, + { + "Month": "Dec 2015", + "Airstrikes": 377 + }, + { + "Month": "Jan 2016", + "Airstrikes": 404 + }, + { + "Month": "Feb 2016", + "Airstrikes": 647 + }, + { + "Month": "Mar 2016", + "Airstrikes": 849 + }, + { + "Month": "Apr 2016", + "Airstrikes": 785 + }, + { + "Month": "May 2016", + "Airstrikes": 424 + }, + { + "Month": "Jun 2016", + "Airstrikes": 218 + }, + { + "Month": "Jul 2016", + "Airstrikes": 127 + }, + { + "Month": "Aug 2016", + "Airstrikes": 265 + }, + { + "Month": "Sep 2016", + "Airstrikes": 486 + }, + { + "Month": "Oct 2016", + "Airstrikes": 637 + }, + { + "Month": "Nov 2016", + "Airstrikes": 675 + }, + { + "Month": "Dec 2016", + "Airstrikes": 439 + }, + { + "Month": "Jan 2017", + "Airstrikes": 317 + }, + { + "Month": "Feb 2017", + "Airstrikes": 381 + }, + { + "Month": "Mar 2017", + "Airstrikes": 389 + }, + { + "Month": "Apr 2017", + "Airstrikes": 410 + }, + { + "Month": "May 2017", + "Airstrikes": 368 + }, + { + "Month": "Jun 2017", + "Airstrikes": 365 + }, + { + "Month": "Jul 2017", + "Airstrikes": 303 + }, + { + "Month": "Aug 2017", + "Airstrikes": 364 + }, + { + "Month": "Sep 2017", + "Airstrikes": 284 + }, + { + "Month": "Oct 2017", + "Airstrikes": 517 + }, + { + "Month": "Nov 2017", + "Airstrikes": 307 + }, + { + "Month": "Dec 2017", + "Airstrikes": 552 + }, + { + "Month": "Jan 2018", + "Airstrikes": 397 + }, + { + "Month": "Feb 2018", + "Airstrikes": 358 + }, + { + "Month": "Mar 2018", + "Airstrikes": 450 + }, + { + "Month": "Apr 2018", + "Airstrikes": 396 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the airstrike from March 2015 to April 2018", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Airstrikes", + "importance": "primary", + "description": "Estimated number of airstrikes by the Saudi-led coalition in Yemen", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "993": { + "metadata": { + "title": "Searches for 'Contagion Movie' Spike on Google", + "description": "Number of Google index searches for \"Contagion movie\" amid coronavirus outbreak", + "main_insight": "Google searches for the movie 'Contagion' spiked dramatically in early 2020, coinciding with the coronavirus outbreak, compared to relatively low and stable search interest throughout 2019." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time": "Mar 1, 2020", + "Search Index": 100.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Search Index from Mar 10, 2019 to Mar 1, 2020.", + "reason": "The Search Index for increases from 3.0 at Mar 10, 2019 to 100.0 at Mar 1, 2020, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Time": "Mar 1, 2020", + "Search Index": 100.0 + } + ], + "score": 0.45, + "annotation": "The has a maximum value at Mar 1, 2020", + "reason": "The [{'Time': 'Mar 1, 2020', 'Search Index': 100.0}] of has a maximum value of 100.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Time": "Mar 10, 2019", + "Search Index": 3.0 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at Mar 10, 2019", + "reason": "The [{'Time': 'Mar 10, 2019', 'Search Index': 3.0}] of has a mainimum value of 3.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Time": "Mar 10, 2019", + "Search Index": 3.0 + }, + { + "Time": "Mar 1, 2020", + "Search Index": 100.0 + } + ], + "columns": [ + { + "name": "Time", + "importance": "primary", + "description": "Represents the time period of the search index.", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Search Index", + "importance": "primary", + "description": "Represents the Google search index for 'Contagion movie'. Index ranges from 0-100, where 100 is the peak popularity of the term.", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "297": { + "metadata": { + "title": "The German Military Is Woefully Unprepared For Action", + "description": "Number of weapons systems ready for action in the German military in 2018", + "main_insight": "A significantly small portion of German military weapon systems were ready for action in 2018 across various categories." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Weapon System": "Leopard 2 tanks", + "Ready for action": 105.0 + } + ], + "score": 0.97, + "annotation": "The has a maximum value at Leopard 2 tanks", + "reason": "The [{'Weapon System': 'Leopard 2 tanks', 'Ready for action': 105.0}] of has a maximum value of 105.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Weapon System": "Leopard 2 tanks", + "Ready for action": 105.0 + } + ], + "score": 0.89, + "annotation": "The shows a sudden jump in Ready for action from Eurofighter jets to Leopard 2 tanks.", + "reason": "The Ready for action for increases from 39.0 at Eurofighter jets to 105.0 at Leopard 2 tanks, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Weapon System": "Submarines", + "Ready for action": 0.0 + } + ], + "score": 0.47, + "annotation": "The has a minimum value at Submarines", + "reason": "The [{'Weapon System': 'Submarines', 'Ready for action': 0.0}] of has a mainimum value of 0.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Weapon System": "Eurofighter jets", + "Ready for action": 39.0 + }, + { + "Weapon System": "Tornado jets", + "Ready for action": 26.0 + }, + { + "Weapon System": "A400M transport aircraft", + "Ready for action": 3.0 + }, + { + "Weapon System": "CH-53 transport helicopters", + "Ready for action": 16.0 + }, + { + "Weapon System": "NH-90 transport helicopters", + "Ready for action": 13.0 + }, + { + "Weapon System": "Tiger attack helicopters", + "Ready for action": 12.0 + }, + { + "Weapon System": "Leopard 2 tanks", + "Ready for action": 105.0 + }, + { + "Weapon System": "Frigates", + "Ready for action": 5.0 + }, + { + "Weapon System": "Submarines", + "Ready for action": 0.0 + } + ], + "columns": [ + { + "name": "Weapon System", + "importance": "primary", + "description": "Type of military weapon system", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ready for action", + "importance": "primary", + "description": "Number of weapon systems ready for deployment", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "850": { + "metadata": { + "title": "Which Working Benefits Do Millennials Value Most?", + "description": "% of global millennials ranking each of the following benefits first", + "main_insight": "Training and development is the most valued working benefit by millennials, followed by flexible working hours. Other benefits like free childcare and maternity/paternity benefits are valued by a very small percentage of millennials." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Working Benefit": "Training and Development", + "Percentage": 22.0 + } + ], + "score": 0.93, + "annotation": "The has a maximum value at Training and Development", + "reason": "The [{'Working Benefit': 'Training and Development', 'Percentage': 22.0}] of has a maximum value of 22.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Working Benefit": "Cash Bonuses", + "Percentage": 14.0 + } + ], + "score": 0.69, + "annotation": "The shows a sudden jump in Percentage from Free Private Healthcare to Cash Bonuses.", + "reason": "The Percentage for increases from 8.0 at Free Private Healthcare to 14.0 at Cash Bonuses, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Working Benefit": "Maternity/Paternity Benefits", + "Percentage": 2.0 + }, + { + "Working Benefit": "Free Child Care", + "Percentage": 2.0 + } + ], + "score": 0.54, + "annotation": "The has minimum values at Maternity/Paternity Benefits, Free Child Care", + "reason": "The [{'Working Benefit': 'Maternity/Paternity Benefits', 'Percentage': 2.0}, {'Working Benefit': 'Free Child Care', 'Percentage': 2.0}] of have minimum values of 2.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Working Benefit": "Training and Development", + "Percentage": 22.0 + }, + { + "Working Benefit": "Flexible Working Hours", + "Percentage": 19.0 + }, + { + "Working Benefit": "Cash Bonuses", + "Percentage": 14.0 + }, + { + "Working Benefit": "Free Private Healthcare", + "Percentage": 8.0 + }, + { + "Working Benefit": "Pension Scheme/Other Retirement Funding", + "Percentage": 6.0 + }, + { + "Working Benefit": "Greater Vacation Allowance", + "Percentage": 6.0 + }, + { + "Working Benefit": "Financial Assistance With Housing", + "Percentage": 5.0 + }, + { + "Working Benefit": "Company Car", + "Percentage": 4.0 + }, + { + "Working Benefit": "No Benefits & Higher Wages", + "Percentage": 4.0 + }, + { + "Working Benefit": "Assistance Clearing Study Debts", + "Percentage": 3.0 + }, + { + "Working Benefit": "Maternity/Paternity Benefits", + "Percentage": 2.0 + }, + { + "Working Benefit": "Free Child Care", + "Percentage": 2.0 + } + ], + "columns": [ + { + "name": "Working Benefit", + "importance": "primary", + "description": "Type of working benefit", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of global millennials ranking the benefit first", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "190": { + "metadata": { + "title": "Vaccine Race Lifts Biotech Shares As Pharma Giants Trot Along", + "description": "Year-to-date stock performance of frontrunners in the race to develop a COVID-19 vaccine (as of Nov. 18, 2020)", + "main_insight": "Biotech companies Moderna and BioNTech experienced significant stock growth in 2020, while larger pharmaceutical companies AstraZeneca and Pfizer showed minimal change." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Feb", + "Ratio": -5.0, + "Vaccine": "AstraZeneca" + } + ], + "score": 0.92, + "annotation": "The AstraZeneca has a minimum value at Feb", + "reason": "The [{'Month': 'Feb', 'Ratio': -5.0, 'Vaccine': 'AstraZeneca'}] of AstraZeneca has a mainimum value of -5.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Feb", + "Ratio": 25.0, + "Vaccine": "Moderna" + } + ], + "score": 0.87, + "annotation": "The Moderna has a minimum value at Feb", + "reason": "The [{'Month': 'Feb', 'Ratio': 25.0, 'Vaccine': 'Moderna'}] of Moderna has a mainimum value of 25.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Feb", + "Ratio": 10.0, + "Vaccine": "BioNTech" + } + ], + "score": 0.84, + "annotation": "The BioNTech has a minimum value at Feb", + "reason": "The [{'Month': 'Feb', 'Ratio': 10.0, 'Vaccine': 'BioNTech'}] of BioNTech has a mainimum value of 10.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Jul", + "Ratio": 35.0, + "Vaccine": "AstraZeneca" + } + ], + "score": 0.83, + "annotation": "The AstraZeneca has a maximum value at Jul", + "reason": "The [{'Month': 'Jul', 'Ratio': 35.0, 'Vaccine': 'AstraZeneca'}] of AstraZeneca has a maximum value of 35.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Nov", + "Ratio": 167.0, + "Vaccine": "BioNTech" + } + ], + "score": 0.82, + "annotation": "The BioNTech has a maximum value at Nov", + "reason": "The [{'Month': 'Nov', 'Ratio': 167.0, 'Vaccine': 'BioNTech'}] of BioNTech has a maximum value of 167.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Month": "Feb", + "Ratio": 25.0, + "Vaccine": "Moderna" + }, + { + "Month": "Feb", + "Ratio": 10.0, + "Vaccine": "BioNTech" + }, + { + "Month": "Feb", + "Ratio": -5.0, + "Vaccine": "AstraZeneca" + }, + { + "Month": "Feb", + "Ratio": -10.0, + "Vaccine": "Pfizer" + }, + { + "Month": "Mar", + "Ratio": 50.0, + "Vaccine": "Moderna" + }, + { + "Month": "Mar", + "Ratio": 100.0, + "Vaccine": "BioNTech" + }, + { + "Month": "Mar", + "Ratio": 10.0, + "Vaccine": "AstraZeneca" + }, + { + "Month": "Mar", + "Ratio": -5.0, + "Vaccine": "Pfizer" + }, + { + "Month": "Apr", + "Ratio": 150.0, + "Vaccine": "Moderna" + }, + { + "Month": "Apr", + "Ratio": 40.0, + "Vaccine": "BioNTech" + }, + { + "Month": "Apr", + "Ratio": 20.0, + "Vaccine": "AstraZeneca" + }, + { + "Month": "Apr", + "Ratio": 5.0, + "Vaccine": "Pfizer" + }, + { + "Month": "May", + "Ratio": 250.0, + "Vaccine": "Moderna" + }, + { + "Month": "May", + "Ratio": 50.0, + "Vaccine": "BioNTech" + }, + { + "Month": "May", + "Ratio": 25.0, + "Vaccine": "AstraZeneca" + }, + { + "Month": "Jun", + "Ratio": 200.0, + "Vaccine": "Moderna" + }, + { + "Month": "Jun", + "Ratio": 60.0, + "Vaccine": "BioNTech" + }, + { + "Month": "Jun", + "Ratio": 25.0, + "Vaccine": "AstraZeneca" + }, + { + "Month": "Jul", + "Ratio": 300.0, + "Vaccine": "Moderna" + }, + { + "Month": "Jul", + "Ratio": 110.0, + "Vaccine": "BioNTech" + }, + { + "Month": "Jul", + "Ratio": 35.0, + "Vaccine": "AstraZeneca" + }, + { + "Month": "Jul", + "Ratio": 5.0, + "Vaccine": "Pfizer" + }, + { + "Month": "Aug", + "Ratio": 250.0, + "Vaccine": "Moderna" + }, + { + "Month": "Aug", + "Ratio": 100.0, + "Vaccine": "BioNTech" + }, + { + "Month": "Aug", + "Ratio": 20.0, + "Vaccine": "AstraZeneca" + }, + { + "Month": "Sep", + "Ratio": 250.0, + "Vaccine": "Moderna" + }, + { + "Month": "Sep", + "Ratio": 110.0, + "Vaccine": "BioNTech" + }, + { + "Month": "Sep", + "Ratio": 15.0, + "Vaccine": "AstraZeneca" + }, + { + "Month": "Oct", + "Ratio": 225.0, + "Vaccine": "Moderna" + }, + { + "Month": "Oct", + "Ratio": 150.0, + "Vaccine": "BioNTech" + }, + { + "Month": "Oct", + "Ratio": 20.0, + "Vaccine": "AstraZeneca" + }, + { + "Month": "Oct", + "Ratio": -5.0, + "Vaccine": "Pfizer" + }, + { + "Month": "Nov", + "Ratio": 354.0, + "Vaccine": "Moderna" + }, + { + "Month": "Nov", + "Ratio": 167.0, + "Vaccine": "BioNTech" + }, + { + "Month": "Nov", + "Ratio": 8.0, + "Vaccine": "AstraZeneca" + }, + { + "Month": "Nov", + "Ratio": -2.0, + "Vaccine": "Pfizer" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year 2020", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding vaccine", + "role": "y" + }, + { + "name": "Vaccine", + "importance": "primary", + "description": "The vaccine of the data point", + "role": "group" + } + ] + } + }, + "1896": { + "metadata": { + "title": "Apple Leads the Race to $1 Trillion", + "description": "Market capitalization of the world's largest tech companies on May 29, 2009-2018", + "main_insight": "Apple's market capitalization consistently increased from 2009 to 2018, surpassing its competitors and approaching $1 trillion." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": "2018", + "Units": 782.6, + "Companies": "Amazon" + }, + { + "Year": "2018", + "Units": 753.0, + "Companies": "Microsoft" + } + ], + "score": 0.99, + "annotation": "The Units of Amazon and Microsoft show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Amazon and Microsoft is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Year": "2018", + "Units": 782.6, + "Companies": "Amazon" + } + ], + "score": 0.96, + "annotation": "The Amazon has a maximum value at 2018", + "reason": "The [{'Year': '2018', 'Units': 782.6, 'Companies': 'Amazon'}] of Amazon has a maximum value of 782.6, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Year": "2018", + "Units": 753.0, + "Companies": "Microsoft" + } + ], + "score": 0.95, + "annotation": "The Microsoft has a maximum value at 2018", + "reason": "The [{'Year': '2018', 'Units': 753.0, 'Companies': 'Microsoft'}] of Microsoft has a maximum value of 753.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "2017", + "Units": 796.4, + "Companies": "Apple" + } + ], + "score": 0.93, + "annotation": "The Apple has the largest proportion in 2017.", + "reason": "The Units of Apple in 2017 accounts for 0.31561843617485036, larger than all other Companies, of the total of Units of all Companies." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "2017", + "Units": 623.7, + "Companies": "Alphabet" + } + ], + "score": 0.92, + "annotation": "The Alphabet has the largest proportion in 2016.", + "reason": "The Units of Alphabet in 2016 accounts for 0.32826315789473687, larger than all other Companies, of the total of Units of all Companies." + } + ], + "data": { + "data": [ + { + "Year": "2009", + "Units": 121.2, + "Companies": "Apple" + }, + { + "Year": "2009", + "Units": 33.5, + "Companies": "Amazon" + }, + { + "Year": "2009", + "Units": 185.9, + "Companies": "Microsoft" + }, + { + "Year": "2010", + "Units": 240.6, + "Companies": "Apple" + }, + { + "Year": "2010", + "Units": 53.5, + "Companies": "Amazon" + }, + { + "Year": "2010", + "Units": 211.7, + "Companies": "Microsoft" + }, + { + "Year": "2010", + "Units": 131.8, + "Companies": "Alphabet" + }, + { + "Year": "2011", + "Units": 319.6, + "Companies": "Apple" + }, + { + "Year": "2011", + "Units": 92.5, + "Companies": "Amazon" + }, + { + "Year": "2011", + "Units": 217.9, + "Companies": "Microsoft" + }, + { + "Year": "2011", + "Units": 174.6, + "Companies": "Alphabet" + }, + { + "Year": "2012", + "Units": 548.2, + "Companies": "Apple" + }, + { + "Year": "2012", + "Units": 103.5, + "Companies": "Amazon" + }, + { + "Year": "2012", + "Units": 249.5, + "Companies": "Microsoft" + }, + { + "Year": "2012", + "Units": 197.4, + "Companies": "Alphabet" + }, + { + "Year": "2013", + "Units": 418.9, + "Companies": "Apple" + }, + { + "Year": "2013", + "Units": 127.7, + "Companies": "Amazon" + }, + { + "Year": "2013", + "Units": 254.3, + "Companies": "Microsoft" + }, + { + "Year": "2013", + "Units": 234.3, + "Companies": "Alphabet" + }, + { + "Year": "2014", + "Units": 552.4, + "Companies": "Apple" + }, + { + "Year": "2014", + "Units": 147.1, + "Companies": "Amazon" + }, + { + "Year": "2014", + "Units": 296.4, + "Companies": "Microsoft" + }, + { + "Year": "2014", + "Units": 350.7, + "Companies": "Alphabet" + }, + { + "Year": "2015", + "Units": 727.8, + "Companies": "Apple" + }, + { + "Year": "2015", + "Units": 184.0, + "Companies": "Amazon" + }, + { + "Year": "2015", + "Units": 349.0, + "Companies": "Microsoft" + }, + { + "Year": "2015", + "Units": 358.8, + "Companies": "Alphabet" + }, + { + "Year": "2016", + "Units": 534.7, + "Companies": "Apple" + }, + { + "Year": "2016", + "Units": 334.3, + "Companies": "Amazon" + }, + { + "Year": "2016", + "Units": 407.3, + "Companies": "Microsoft" + }, + { + "Year": "2016", + "Units": 493.2, + "Companies": "Alphabet" + }, + { + "Year": "2017", + "Units": 796.4, + "Companies": "Apple" + }, + { + "Year": "2017", + "Units": 455.8, + "Companies": "Amazon" + }, + { + "Year": "2017", + "Units": 529.3, + "Companies": "Microsoft" + }, + { + "Year": "2017", + "Units": 623.7, + "Companies": "Alphabet" + }, + { + "Year": "2018", + "Units": 923.6, + "Companies": "Apple" + }, + { + "Year": "2018", + "Units": 782.6, + "Companies": "Amazon" + }, + { + "Year": "2018", + "Units": 753.0, + "Companies": "Microsoft" + }, + { + "Year": "2018", + "Units": 741.8, + "Companies": "Alphabet" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from May 2009 to May 2018", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding companies", + "role": "y" + }, + { + "name": "Companies", + "importance": "primary", + "description": "The companies of the data point", + "role": "group" + } + ] + } + }, + "450": { + "metadata": { + "title": "The Extent of Global Coral Bleaching Events", + "description": "Share of coral reefs worldwide that experienced heat stress causing bleaching, by declared event", + "main_insight": "The percentage of coral reefs worldwide experiencing heat stress causing bleaching increased significantly between the first and third global bleaching events, with a slight decrease during the current fourth event." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Event": "2023", + "Bleached Reefs (%)": 54.0 + } + ], + "score": 0.85, + "annotation": "The shows a sudden jump in Bleached Reefs (%) from 2010 to 2023.", + "reason": "The Bleached Reefs (%) for increases from 35.0 at 2010 to 54.0 at 2023, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Event": "1998", + "Bleached Reefs (%)": 20.0 + } + ], + "score": 0.71, + "annotation": "The has a minimum value at 1998", + "reason": "The [{'Event': '1998', 'Bleached Reefs (%)': 20.0}] of has a mainimum value of 20.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Event": "2014", + "Bleached Reefs (%)": 56.0 + } + ], + "score": 0.53, + "annotation": "The has a maximum value at 2014", + "reason": "The [{'Event': '2014', 'Bleached Reefs (%)': 56.0}] of has a maximum value of 56.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Event": "1998", + "Bleached Reefs (%)": 20.0 + }, + { + "Event": "2010", + "Bleached Reefs (%)": 35.0 + }, + { + "Event": "2014", + "Bleached Reefs (%)": 56.0 + }, + { + "Event": "2023", + "Bleached Reefs (%)": 54.0 + } + ], + "columns": [ + { + "name": "Event", + "importance": "primary", + "description": "The declared global coral bleaching event number and year(s)", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Bleached Reefs (%)", + "importance": "primary", + "description": "Percentage of coral reefs worldwide experiencing heat stress causing bleaching", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1501": { + "metadata": { + "title": "Beer Still The No.1 Alcoholic Beverage For Americans", + "description": "American drinkers' beverage of choice from 1992 to 2016", + "main_insight": "Beer has remained the most popular alcoholic beverage for American drinkers from 1992 to 2016, although its popularity has decreased, while wine and liquor have seen more stable preferences." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2016, + "Units": 42.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2016, + "Units": 32.0, + "Alcohol Type": "Wine" + } + ], + "score": 0.86, + "annotation": "The Units of Beer and Wine show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Beer and Wine is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2016, + "Units": 42.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2016, + "Units": 20.0, + "Alcohol Type": "Liquor" + } + ], + "score": 0.59, + "annotation": "The Units of Beer and Liquor show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Beer and Liquor is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2016, + "Units": 32.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2016, + "Units": 20.0, + "Alcohol Type": "Liquor" + } + ], + "score": 0.43, + "annotation": "The Units of Wine and Liquor show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Wine and Liquor is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 1992, + "Units": 47.0, + "Alcohol Type": "Beer" + }, + { + "Year": 1992, + "Units": 27.0, + "Alcohol Type": "Wine" + }, + { + "Year": 1992, + "Units": 19.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 1994, + "Units": 44.0, + "Alcohol Type": "Beer" + }, + { + "Year": 1994, + "Units": 28.0, + "Alcohol Type": "Wine" + }, + { + "Year": 1994, + "Units": 19.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 1996, + "Units": 44.0, + "Alcohol Type": "Beer" + }, + { + "Year": 1996, + "Units": 31.0, + "Alcohol Type": "Wine" + }, + { + "Year": 1996, + "Units": 18.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 1997, + "Units": 46.0, + "Alcohol Type": "Beer" + }, + { + "Year": 1997, + "Units": 30.0, + "Alcohol Type": "Wine" + }, + { + "Year": 1997, + "Units": 19.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 1999, + "Units": 43.0, + "Alcohol Type": "Beer" + }, + { + "Year": 1999, + "Units": 33.0, + "Alcohol Type": "Wine" + }, + { + "Year": 1999, + "Units": 19.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2001, + "Units": 45.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2001, + "Units": 30.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2001, + "Units": 23.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2002, + "Units": 45.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2002, + "Units": 30.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2002, + "Units": 20.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2003, + "Units": 43.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2003, + "Units": 32.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2003, + "Units": 21.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2004, + "Units": 40.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2004, + "Units": 33.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2004, + "Units": 22.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2005, + "Units": 39.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2005, + "Units": 36.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2005, + "Units": 21.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2006, + "Units": 41.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2006, + "Units": 33.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2006, + "Units": 21.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2008, + "Units": 41.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2008, + "Units": 31.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2008, + "Units": 22.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2009, + "Units": 40.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2009, + "Units": 34.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2009, + "Units": 21.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2010, + "Units": 40.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2010, + "Units": 34.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2010, + "Units": 22.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2011, + "Units": 36.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2011, + "Units": 35.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2011, + "Units": 23.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2012, + "Units": 38.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2012, + "Units": 35.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2012, + "Units": 21.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2013, + "Units": 41.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2013, + "Units": 35.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2013, + "Units": 20.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2014, + "Units": 41.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2014, + "Units": 34.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2014, + "Units": 21.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2015, + "Units": 43.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2015, + "Units": 32.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2015, + "Units": 21.0, + "Alcohol Type": "Liquor" + }, + { + "Year": 2016, + "Units": 42.0, + "Alcohol Type": "Beer" + }, + { + "Year": 2016, + "Units": 32.0, + "Alcohol Type": "Wine" + }, + { + "Year": 2016, + "Units": 20.0, + "Alcohol Type": "Liquor" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding alcohol type", + "role": "y" + }, + { + "name": "Alcohol Type", + "importance": "primary", + "description": "The alcohol type of the data point", + "role": "group" + } + ] + } + }, + "1334": { + "metadata": { + "title": "Climate Change: Record Tree Losses in Northern Regions", + "description": "Global tree cover loss from boreal forests from 2001 to 2021 (in million hectares)", + "main_insight": "Tree cover loss in boreal forests has generally increased from 2001 to 2021, reaching a record high of 8.55 million hectares in 2021." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2001, + "Tree Cover Loss": 3.0 + }, + { + "Year": 2002, + "Tree Cover Loss": 4.0 + }, + { + "Year": 2003, + "Tree Cover Loss": 5.0 + }, + { + "Year": 2004, + "Tree Cover Loss": 4.5 + }, + { + "Year": 2005, + "Tree Cover Loss": 6.0 + }, + { + "Year": 2006, + "Tree Cover Loss": 4.5 + }, + { + "Year": 2007, + "Tree Cover Loss": 4.5 + }, + { + "Year": 2008, + "Tree Cover Loss": 3.8 + }, + { + "Year": 2009, + "Tree Cover Loss": 4.8 + }, + { + "Year": 2010, + "Tree Cover Loss": 3.2 + }, + { + "Year": 2011, + "Tree Cover Loss": 7.5 + }, + { + "Year": 2012, + "Tree Cover Loss": 7.8 + }, + { + "Year": 2013, + "Tree Cover Loss": 7.5 + }, + { + "Year": 2014, + "Tree Cover Loss": 5.0 + }, + { + "Year": 2015, + "Tree Cover Loss": 7.0 + }, + { + "Year": 2016, + "Tree Cover Loss": 7.5 + }, + { + "Year": 2017, + "Tree Cover Loss": 7.5 + }, + { + "Year": 2018, + "Tree Cover Loss": 5.8 + }, + { + "Year": 2019, + "Tree Cover Loss": 6.0 + }, + { + "Year": 2020, + "Tree Cover Loss": 7.5 + }, + { + "Year": 2021, + "Tree Cover Loss": 8.55 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 2001 to 2021", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Tree Cover Loss", + "importance": "primary", + "description": "Global tree cover loss from boreal forests (in million hectares)", + "unit": "million hectares", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1273": { + "metadata": { + "title": "The World's Biggest Exporters of Rice", + "description": "Biggest exporters of rice worldwide in 2022 (in million tons)", + "main_insight": "India was by far the world's largest exporter of rice in 2022, with exports totaling 22.5 million tons, more than double the next largest exporter, Thailand." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "India", + "Export Volume (Million Tons)": 22.5 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at India", + "reason": "The [{'Country': 'India', 'Export Volume (Million Tons)': 22.5}] of has a maximum value of 22.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "India", + "Export Volume (Million Tons)": 22.5 + } + ], + "score": 0.86, + "annotation": "The shows a sudden jump in Export Volume (Million Tons) from Others to India.", + "reason": "The Export Volume (Million Tons) for increases from 11.4 at Others to 22.5 at India, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "U.S.", + "Export Volume (Million Tons)": 2.1 + } + ], + "score": 0.59, + "annotation": "The has a minimum value at U.S.", + "reason": "The [{'Country': 'U.S.', 'Export Volume (Million Tons)': 2.1}] of has a mainimum value of 2.1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "India", + "Export Volume (Million Tons)": 22.5 + }, + { + "Country": "Thailand", + "Export Volume (Million Tons)": 8.5 + }, + { + "Country": "Vietnam", + "Export Volume (Million Tons)": 7.5 + }, + { + "Country": "Pakistan", + "Export Volume (Million Tons)": 3.6 + }, + { + "Country": "U.S.", + "Export Volume (Million Tons)": 2.1 + }, + { + "Country": "Others", + "Export Volume (Million Tons)": 11.4 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country or category of rice exporter", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Export Volume (Million Tons)", + "importance": "primary", + "description": "Volume of rice exported in million tons", + "unit": "Million Tons", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2030": { + "metadata": { + "title": "How Economies Reacted to Covid Varies Substantially", + "description": "Annual change to quarterly GDP in selected countries in 2020 compared to the OECD average", + "main_insight": "The impact of COVID-19 on GDP varied significantly across countries in 2020. China experienced a strong recovery after Q1, while the UK and South Korea saw significant declines, with the US remaining close to OECD average." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Quarter": "Q4", + "Score": -3.0, + "Geography": "United States" + }, + { + "Quarter": "Q4", + "Score": -4.0, + "Geography": "OECD average" + } + ], + "score": 1.0, + "annotation": "The Score of United States and OECD average show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between United States and OECD average is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Quarter": "Q4", + "Score": -4.0, + "Geography": "OECD average" + }, + { + "Quarter": "Q4", + "Score": -7.0, + "Geography": "United Kingdom" + } + ], + "score": 0.98, + "annotation": "The Score of OECD average and United Kingdom show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between OECD average and United Kingdom is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Quarter": "Q4", + "Score": -3.0, + "Geography": "United States" + }, + { + "Quarter": "Q4", + "Score": -7.0, + "Geography": "United Kingdom" + } + ], + "score": 0.97, + "annotation": "The Score of United States and United Kingdom show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between United States and United Kingdom is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Quarter": "Q3", + "Score": -3.0, + "Geography": "United States" + } + ], + "score": 0.95, + "annotation": "The United States shows a sudden jump in Score from Q2 to Q3.", + "reason": "The Score for United States increases from -9.0 at Q2 to -3.0 at Q3, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Quarter": "Q3", + "Score": -4.0, + "Geography": "OECD average" + } + ], + "score": 0.94, + "annotation": "The OECD average shows a sudden jump in Score from Q2 to Q3.", + "reason": "The Score for OECD average increases from -11.0 at Q2 to -4.0 at Q3, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Quarter": "Q1", + "Score": -2.0, + "Geography": "United States" + }, + { + "Quarter": "Q1", + "Score": -2.0, + "Geography": "OECD average" + }, + { + "Quarter": "Q1", + "Score": -7.0, + "Geography": "China" + }, + { + "Quarter": "Q1", + "Score": -2.0, + "Geography": "United Kingdom" + }, + { + "Quarter": "Q1", + "Score": 1.0, + "Geography": "South Korea" + }, + { + "Quarter": "Q2", + "Score": -9.0, + "Geography": "United States" + }, + { + "Quarter": "Q2", + "Score": -11.0, + "Geography": "OECD average" 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"Quarter", + "importance": "primary", + "description": "Quarter of the year 2020", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding geography", + "role": "y" + }, + { + "name": "Geography", + "importance": "primary", + "description": "The geography of the data point", + "role": "group" + } + ] + } + }, + "2010": { + "metadata": { + "title": "What Words Won't Say", + "description": "Share of total emoji use made up of happy/sad faces and hearts, by language (2015)", + "main_insight": "French speakers use heart emojis significantly more than speakers of other languages, while all other languages use happy face emojis much more frequently than sad face or heart emojis." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Language": "Spanish (US)", + "Happy Face Emoji": 40.0 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in Happy Face Emoji from French to Spanish (US).", + "reason": "The Happy Face Emoji for increases from 25.0 at French to 40.0 at Spanish (US), indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Language": "French", + "Happy Face Emoji": 25.0 + } + ], + "score": 0.91, + "annotation": "The has a minimum value at French", + "reason": "The [{'Language': 'French', 'Happy Face Emoji': 25.0}] of has a mainimum value of 25.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Language": "English (US)", + "Happy Face Emoji": 45.0 + }, + { + "Language": "Portuguese (BR)", + "Happy Face Emoji": 45.0 + }, + { + "Language": "German", + "Happy Face Emoji": 45.0 + }, + { + "Language": "Italian", + "Happy Face Emoji": 45.0 + } + ], + "score": 0.36, + "annotation": "The has maximum values at English (US), Portuguese (BR), German, Italian", + "reason": "The [{'Language': 'English (US)', 'Happy Face Emoji': 45.0}, {'Language': 'Portuguese (BR)', 'Happy Face Emoji': 45.0}, {'Language': 'German', 'Happy Face Emoji': 45.0}, {'Language': 'Italian', 'Happy Face Emoji': 45.0}] of have maximum values of 45.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Language": "English (US)", + "Happy Face Emoji": 45.0 + }, + { + "Language": "Portuguese (BR)", + "Happy Face Emoji": 45.0 + }, + { + "Language": "German", + "Happy Face Emoji": 45.0 + }, + { + "Language": "Italian", + "Happy Face Emoji": 45.0 + }, + { + "Language": "Spanish (US)", + "Happy Face Emoji": 40.0 + }, + { + "Language": "French", + "Happy Face Emoji": 25.0 + } + ], + "columns": [ + { + "name": "Language", + "importance": "primary", + "description": "Language of emoji usage", + "unit": "none", + "data_type": "string", + "discarded_count": 0, + "role": "x" + }, + { + "name": "Happy Face Emoji", + "importance": "primary", + "description": "Percentage of total emoji usage", + "unit": "%", + "data_type": "number", + "discarded_count": 0, + "role": "y" + } + ] + } + }, + "894": { + "metadata": { + "title": "Toshiba in Trouble", + "description": "Net profit/loss attributable to shareholders of Toshiba 2014 to 2017 (in billion U.S. dollars)", + "main_insight": "Toshiba's financial performance significantly worsened from 2014 to 2017, transitioning from a small profit to substantial losses, with a negative outlook continuing into the 2017 forecast." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2014, + "Standard Deviations": -0.58, + "Profitability": "Net Profit/Loss" + }, + { + "Year": 2015, + "Standard Deviations": -0.32, + "Profitability": "Net Profit/Loss" + }, + { + "Year": 2016, + "Standard Deviations": -4.07, + "Profitability": "Net Profit/Loss" + }, + { + "Year": 2017, + "Standard Deviations": -4.6, + "Profitability": "Net Profit/Loss" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of reported financial performance", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Standard Deviations", + "importance": "primary", + "description": "Standard Deviations for the corresponding profitability", + "role": "y" + }, + { + "name": "Profitability", + "importance": "primary", + "description": "The profitability of the data point", + "role": "group" + } + ] + } + }, + "1584": { + "metadata": { + "title": "Generational Differences in U.S. Sexual Orientation", + "description": "Share of U.S. adults who described their sexuality as follows, by generation (in percent)", + "main_insight": "The percentage of people identifying as heterosexual decreases across generations, from Baby Boomers to Gen Z, while identification with other sexual orientations, especially bisexual, increases." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Generation": "Gen Z", + "Score": 79.0, + "Sexual Orientation": "Heterosexual" + }, + { + "Generation": "Gen Z", + "Score": 11.0, + "Sexual Orientation": "Bisexual" + } + ], + "score": 1.0, + "annotation": "The Score of Heterosexual and Bisexual show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Heterosexual and Bisexual is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Generation": "Gen Z", + "Score": 11.0, + "Sexual Orientation": "Bisexual" + }, + { + "Generation": "Gen Z", + "Score": 3.0, + "Sexual Orientation": "Homosexual" + } + ], + "score": 0.15, + "annotation": "The Score of Bisexual and Homosexual show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Bisexual and Homosexual is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Generation": "Gen Z", + "Score": 79.0, + "Sexual Orientation": "Heterosexual" + }, + { + "Generation": "Gen Z", + "Score": 3.0, + "Sexual Orientation": "Homosexual" + } + ], + "score": 0.11, + "annotation": "The Score of Heterosexual and Homosexual show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Heterosexual and Homosexual is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Generation": "Baby Boomers", + "Score": 96.0, + "Sexual Orientation": "Heterosexual" + }, + { + "Generation": "Baby Boomers", + "Score": 1.0, + "Sexual Orientation": "Bisexual" + }, + { + "Generation": "Baby Boomers", + "Score": 3.0, + "Sexual Orientation": "Homosexual" + }, + { + "Generation": "Generation X", + "Score": 94.0, + "Sexual Orientation": "Heterosexual" + }, + { + "Generation": "Generation X", + "Score": 2.0, + "Sexual Orientation": "Bisexual" + }, + { + "Generation": "Generation X", + "Score": 3.0, + "Sexual Orientation": "Homosexual" + }, + { + "Generation": "Millennials", + "Score": 88.0, + "Sexual Orientation": "Heterosexual" + }, + { + "Generation": "Millennials", + "Score": 6.0, + "Sexual Orientation": "Bisexual" + }, + { + "Generation": "Millennials", + "Score": 1.0, + "Sexual Orientation": "Homosexual" + }, + { + "Generation": "Millennials", + "Score": 1.0, + "Sexual Orientation": "Other" + }, + { + "Generation": "Millennials", + "Score": 3.0, + "Sexual Orientation": "Asexual" + }, + { + "Generation": "Gen Z", + "Score": 79.0, + "Sexual Orientation": "Heterosexual" + }, + { + "Generation": "Gen Z", + "Score": 11.0, + "Sexual Orientation": "Bisexual" + }, + { + "Generation": "Gen Z", + "Score": 3.0, + "Sexual Orientation": "Homosexual" + }, + { + "Generation": "Gen Z", + "Score": 1.0, + "Sexual Orientation": "Other" + }, + { + "Generation": "Gen Z", + "Score": 4.0, + "Sexual Orientation": "Asexual" + }, + { + "Generation": "Gen Z", + "Score": 1.0, + "Sexual Orientation": "Pansexual" + }, + { + "Generation": "Gen Z", + "Score": 1.0, + "Sexual Orientation": "Queer" + } + ], + "columns": [ + { + "name": "Generation", + "importance": "primary", + "description": "The generation group", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding sexual orientation", + "role": "y" + }, + { + "name": "Sexual Orientation", + "importance": "primary", + "description": "The sexual orientation of the data point", + "role": "group" + } + ] + } + }, + "1814": { + "metadata": { + "title": "Japan Trails in Vaccine Race As the Olympics Loom", + "description": "Share of people who are partly/fully vaccinated against COVID-19 (as of June 22)", + "main_insight": "Japan has a significantly lower vaccination rate compared to other countries and the world average as of June 22nd, with only 8.2% of the population fully vaccinated." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country/Region": "China*", + "Score": 43.2, + "Vaccination Status": "Received at least one dose" + } + ], + "score": 0.89, + "annotation": "The Received at least one dose shows a sudden jump in Score from World to China*.", + "reason": "The Score for Received at least one dose increases from 22.2 at World to 43.2 at China*, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country/Region": "Japan", + "Score": 19.0, + "Vaccination Status": "Received at least one dose" + }, + { + "Country/Region": "Japan", + "Score": 8.2, + "Vaccination Status": "Fully vaccinated" + } + ], + "score": 0.87, + "annotation": "The Score of Received at least one dose and Fully vaccinated show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Received at least one dose and Fully vaccinated is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country/Region": "United States", + "Score": 45.0, + "Vaccination Status": "Fully vaccinated" + } + ], + "score": 0.81, + "annotation": "The Fully vaccinated has a maximum value at United States", + "reason": "The [{'Country/Region': 'United States', 'Score': 45.0, 'Vaccination Status': 'Fully vaccinated'}] of Fully vaccinated has a maximum value of 45.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country/Region": "United States", + "Score": 45.0, + "Vaccination Status": "Fully vaccinated" + } + ], + "score": 0.8, + "annotation": "The Fully vaccinated shows a sudden jump in Score from European Union to United States.", + "reason": "The Score for Fully vaccinated increases from 28.8 at European Union to 45.0 at United States, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country/Region": "United States", + "Score": 53.1, + "Vaccination Status": "Received at least one dose" + } + ], + "score": 0.79, + "annotation": "The Received at least one dose accounts for the majority in United States.", + "reason": "The Score of Received at least one dose in United States accounts for 0.5412844036697249 (more than 50%) of the total of Score of all Vaccination Status." + } + ], + "data": { + "data": [ + { + "Country/Region": "United States", + "Score": 53.1, + "Vaccination Status": "Received at least one dose" + }, + { + "Country/Region": "United States", + "Score": 45.0, + "Vaccination Status": "Fully vaccinated" + }, + { + "Country/Region": "European Union", + "Score": 47.8, + "Vaccination Status": "Received at least one dose" + }, + { + "Country/Region": "European Union", + "Score": 28.8, + "Vaccination Status": "Fully vaccinated" + }, + { + "Country/Region": "China*", + "Score": 43.2, + "Vaccination Status": "Received at least one dose" + }, + { + "Country/Region": "China*", + "Score": 15.5, + "Vaccination Status": "Fully vaccinated" + }, + { + "Country/Region": "World", + "Score": 22.2, + "Vaccination Status": "Received at least one dose" + }, + { + "Country/Region": "World", + "Score": 10.2, + "Vaccination Status": "Fully vaccinated" + }, + { + "Country/Region": "Japan", + "Score": 19.0, + "Vaccination Status": "Received at least one dose" + }, + { + "Country/Region": "Japan", + "Score": 8.2, + "Vaccination Status": "Fully vaccinated" + } + ], + "columns": [ + { + "name": "Country/Region", + "importance": "primary", + "description": "Country or region for the vaccination data", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding vaccination status", + "role": "y" + }, + { + "name": "Vaccination Status", + "importance": "primary", + "description": "The vaccination status of the data point", + "role": "group" + } + ] + } + }, + "1224": { + "metadata": { + "title": "NATO capability reductions & cost cutting. A step too far?", + "description": "Armed forces reductions among selected NATO members 2000/2013", + "main_insight": "Selected NATO members (Spain, Italy, Germany, France, UK) significantly reduced their military capabilities, including manpower, combat aircraft, principal surface combatants, and main battle tanks, between 2000 and 2013." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2013, + "Units": 136.0, + "Military Assets": "Spain_Manpower" + }, + { + "Year": 2013, + "Units": 181.0, + "Military Assets": "Italy_Manpower" + } + ], + "score": 1.0, + "annotation": "The Units of Spain_Manpower and Italy_Manpower show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Spain_Manpower and Italy_Manpower is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2013, + "Units": 136.0, + "Military Assets": "Spain_Manpower" + }, + { + "Year": 2013, + "Units": 181.0, + "Military Assets": "Germany_Manpower" + } + ], + "score": 1.0, + "annotation": "The Units of Spain_Manpower and Germany_Manpower show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Spain_Manpower and Germany_Manpower is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2013, + "Units": 136.0, + "Military Assets": "Spain_Manpower" + }, + { + "Year": 2013, + "Units": 229.0, + "Military Assets": "France_Manpower" + } + ], + "score": 1.0, + "annotation": "The Units of Spain_Manpower and France_Manpower show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Spain_Manpower and France_Manpower is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2013, + "Units": 136.0, + "Military Assets": "Spain_Manpower" + }, + { + "Year": 2013, + "Units": 166.0, + "Military Assets": "UK_Manpower" + } + ], + "score": 1.0, + "annotation": "The Units of Spain_Manpower and UK_Manpower show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Spain_Manpower and UK_Manpower is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2013, + "Units": 136.0, + "Military Assets": "Spain_Manpower" + }, + { + "Year": 2013, + "Units": 173.0, + "Military Assets": "Spain_Aircraft" + } + ], + "score": 1.0, + "annotation": "The Units of Spain_Manpower and Spain_Aircraft show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Spain_Manpower and Spain_Aircraft is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2000, + "Units": 187.0, + "Military Assets": "Spain_Manpower" + }, + { + "Year": 2000, + "Units": 266.0, + "Military Assets": "Italy_Manpower" + }, + { + "Year": 2000, + "Units": 333.0, + "Military Assets": "Germany_Manpower" + }, + { + "Year": 2000, + "Units": 317.0, + "Military Assets": "France_Manpower" + }, + { + "Year": 2000, + "Units": 212.0, + "Military Assets": "UK_Manpower" + }, + { + "Year": 2000, + "Units": 208.0, + "Military Assets": "Spain_Aircraft" + }, + { + "Year": 2000, + "Units": 321.0, + "Military Assets": "Italy_Aircraft" + }, + { + "Year": 2000, + "Units": 451.0, + "Military Assets": "Germany_Aircraft" + }, + { + "Year": 2000, + "Units": 531.0, + "Military Assets": "France_Aircraft" + }, + { + "Year": 2000, + "Units": 462.0, + "Military Assets": "UK_Aircraft" + }, + { + "Year": 2000, + "Units": 18.0, + "Military Assets": "Spain_Combatants" + }, + { + "Year": 2000, + "Units": 30.0, + "Military Assets": "Italy_Combatants" + }, + { + "Year": 2000, + "Units": 14.0, + "Military Assets": "Germany_Combatants" + }, + { + "Year": 2000, + "Units": 41.0, + "Military Assets": "France_Combatants" + }, + { + "Year": 2000, + "Units": 34.0, + "Military Assets": "UK_Combatants" + }, + { + "Year": 2000, + "Units": 660.0, + "Military Assets": "Spain_Tanks" + }, + { + "Year": 2000, + "Units": 1322.0, + "Military Assets": "Italy_Tanks" + }, + { + "Year": 2000, + "Units": 2809.0, + "Military Assets": "Germany_Tanks" + }, + { + "Year": 2000, + "Units": 545.0, + "Military Assets": "France_Tanks" + }, + { + "Year": 2000, + "Units": 542.0, + "Military Assets": "UK_Tanks" + }, + { + "Year": 2013, + "Units": 136.0, + "Military Assets": "Spain_Manpower" + }, + { + "Year": 2013, + "Units": 181.0, + "Military Assets": "Italy_Manpower" + }, + { + "Year": 2013, + "Units": 181.0, + "Military Assets": "Germany_Manpower" + }, + { + "Year": 2013, + "Units": 229.0, + "Military Assets": "France_Manpower" + }, + { + "Year": 2013, + "Units": 166.0, + "Military Assets": "UK_Manpower" + }, + { + "Year": 2013, + "Units": 173.0, + "Military Assets": "Spain_Aircraft" + }, + { + "Year": 2013, + "Units": 234.0, + "Military Assets": "Italy_Aircraft" + }, + { + "Year": 2013, + "Units": 209.0, + "Military Assets": "Germany_Aircraft" + }, + { + "Year": 2013, + "Units": 336.0, + "Military Assets": "France_Aircraft" + }, + { + "Year": 2013, + "Units": 318.0, + "Military Assets": "UK_Aircraft" + }, + { + "Year": 2013, + "Units": 12.0, + "Military Assets": "Spain_Combatants" + }, + { + "Year": 2013, + "Units": 18.0, + "Military Assets": "Italy_Combatants" + }, + { + "Year": 2013, + "Units": 18.0, + "Military Assets": "Germany_Combatants" + }, + { + "Year": 2013, + "Units": 19.0, + "Military Assets": "France_Combatants" + }, + { + "Year": 2013, + "Units": 18.0, + "Military Assets": "UK_Combatants" + }, + { + "Year": 2013, + "Units": 324.0, + "Military Assets": "Spain_Tanks" + }, + { + "Year": 2013, + "Units": 322.0, + "Military Assets": "Italy_Tanks" + }, + { + "Year": 2013, + "Units": 1447.0, + "Military Assets": "Germany_Tanks" + }, + { + "Year": 2013, + "Units": 542.0, + "Military Assets": "France_Tanks" + }, + { + "Year": 2013, + "Units": 227.0, + "Military Assets": "UK_Tanks" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "The year the data point corresponds to (either 2000 or 2013)", + "unit": "none", + "data_type": "time", + "discarded_count": 0, + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding military assets", + "role": "y" + }, + { + "name": "Military Assets", + "importance": "primary", + "description": "The military assets of the data point", + "role": "group" + } + ] + } + }, + "708": { + "metadata": { + "title": "Platinum Rappers", + "description": "Rap artists with the most certified album sales in the United States", + "main_insight": "Eminem has the most certified album sales among rap artists in the United States, with 61.5 million sales, significantly more than the next highest, 2Pac with 36.5 million." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Artist": "Eminem", + "Certified Album Sales": 61.5 + } + ], + "score": 0.96, + "annotation": "The has a maximum value at Eminem", + "reason": "The [{'Artist': 'Eminem', 'Certified Album Sales': 61.5}] of has a maximum value of 61.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Artist": "Eminem", + "Certified Album Sales": 61.5 + } + ], + "score": 0.9, + "annotation": "The shows a sudden jump in Certified Album Sales from 2Pac to Eminem.", + "reason": "The Certified Album Sales for increases from 36.5 at 2Pac to 61.5 at Eminem, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Artist": "Beastie Boys", + "Certified Album Sales": 23.0 + }, + { + "Artist": "Nelly", + "Certified Album Sales": 23.0 + }, + { + "Artist": "Outkast", + "Certified Album Sales": 23.0 + } + ], + "score": 0.4, + "annotation": "The has minimum values at Beastie Boys, Nelly, Outkast", + "reason": "The [{'Artist': 'Beastie Boys', 'Certified Album Sales': 23.0}, {'Artist': 'Nelly', 'Certified Album Sales': 23.0}, {'Artist': 'Outkast', 'Certified Album Sales': 23.0}] of have minimum values of 23.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Artist": "Eminem", + "Certified Album Sales": 61.5 + }, + { + "Artist": "2Pac", + "Certified Album Sales": 36.5 + }, + { + "Artist": "Jay-Z", + "Certified Album Sales": 27.5 + }, + { + "Artist": "Kanye West", + "Certified Album Sales": 26.5 + }, + { + "Artist": "Drake", + "Certified Album Sales": 25.5 + }, + { + "Artist": "Beastie Boys", + "Certified Album Sales": 23.0 + }, + { + "Artist": "Nelly", + "Certified Album Sales": 23.0 + }, + { + "Artist": "Outkast", + "Certified Album Sales": 23.0 + } + ], + "columns": [ + { + "name": "Artist", + "importance": "primary", + "description": "Name of the rap artist", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Certified Album Sales", + "importance": "primary", + "description": "Number of certified album sales in millions", + "unit": "Million", + "data_type": "number", + "role": "y" + } + ] + } + }, + "574": { + "metadata": { + "title": "U.S. Retail Sales Dip Unexpectedly", + "description": "Percent change in monthly U.S. retail sales growth between 2018 and 2019", + "main_insight": "U.S. retail sales growth experienced significant fluctuations between 2018 and 2019, with a notable dip in late 2018." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "2018_7", + "Sales Growth": -2.0 + } + ], + "score": 0.97, + "annotation": "The has a minimum value at 2018_7", + "reason": "The [{'Month': '2018_7', 'Sales Growth': -2.0}] of has a mainimum value of -2.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "2019_1", + "Sales Growth": 1.8 + } + ], + "score": 0.87, + "annotation": "The has a maximum value at 2019_1", + "reason": "The [{'Month': '2019_1', 'Sales Growth': 1.8}] of has a maximum value of 1.8, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "2018_9", + "Sales Growth": -0.5 + } + ], + "score": 0.76, + "annotation": "The shows a sudden jump in Sales Growth from 2018_7 to 2018_9.", + "reason": "The Sales Growth for increases from -2.0 at 2018_7 to -0.5 at 2018_9, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Month": "2018_1", + "Sales Growth": 0.1 + }, + { + "Month": "2018_2", + "Sales Growth": 0.5 + }, + { + "Month": "2018_3", + "Sales Growth": -0.2 + }, + { + "Month": "2018_4", + "Sales Growth": 0.0 + }, + { + "Month": "2018_5", + "Sales Growth": 1.1 + }, + { + "Month": "2018_6", + "Sales Growth": -0.1 + }, + { + "Month": "2018_7", + "Sales Growth": -2.0 + }, + { + "Month": "2018_8", + "Sales Growth": 1.5 + }, + { + "Month": "2018_9", + "Sales Growth": -0.5 + }, + { + "Month": "2019_1", + "Sales Growth": 1.8 + }, + { + "Month": "2019_2", + "Sales Growth": 0.4 + }, + { + "Month": "2019_3", + "Sales Growth": 0.5 + }, + { + "Month": "2019_4", + "Sales Growth": 0.4 + }, + { + "Month": "2019_5", + "Sales Growth": 0.7 + }, + { + "Month": "2019_6", + "Sales Growth": 0.6 + }, + { + "Month": "2019_7", + "Sales Growth": -0.3 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month in 2018 or 2019", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Sales Growth", + "importance": "primary", + "description": "Percent change in monthly U.S. retail sales growth", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1995": { + "metadata": { + "title": "Christmas Trees By The Numbers: Real vs Fake", + "description": "Real and fake christmas trees sold in the U.S. from 2007 to 2013", + "main_insight": "The number of real Christmas trees sold in the US has decreased from 2007 to 2013, but is still more than double the amount of fake trees." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2007, + "Real Trees": 31.3 + }, + { + "Year": 2009, + "Real Trees": 28.2 + }, + { + "Year": 2011, + "Real Trees": 30.8 + }, + { + "Year": 2013, + "Real Trees": 33.02 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of sale", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Real Trees", + "importance": "primary", + "description": "Number of real trees sold (millions)", + "unit": "Millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "78": { + "metadata": { + "title": "Millions of Americans Fear Eviction as Moratorium Expires", + "description": "Estimated number of U.S. adults in rental housing behind on rent and their perceived eviction risk (as of July 2021)", + "main_insight": "As of July 2021, 7.43 million US adults were behind on rent, and a significant number of them (3.65m) believed they were somewhat or very likely to be evicted in the next two months." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Likelihood of eviction in next two months": "Behind on rent", + "Number of Adults (millions)": 7.43 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in Number of Adults (millions) from Somewhat likely to Behind on rent.", + "reason": "The Number of Adults (millions) for increases from 2.25 at Somewhat likely to 7.43 at Behind on rent, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Likelihood of eviction in next two months": "Behind on rent", + "Number of Adults (millions)": 7.43 + } + ], + "score": 0.91, + "annotation": "The has a maximum value at Behind on rent", + "reason": "The [{'Likelihood of eviction in next two months': 'Behind on rent', 'Number of Adults (millions)': 7.43}] of has a maximum value of 7.43, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Likelihood of eviction in next two months": "Did not report", + "Number of Adults (millions)": 0.09 + } + ], + "score": 0.57, + "annotation": "The has a minimum value at Did not report", + "reason": "The [{'Likelihood of eviction in next two months': 'Did not report', 'Number of Adults (millions)': 0.09}] of has a mainimum value of 0.09, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Likelihood of eviction in next two months": "Behind on rent", + "Number of Adults (millions)": 7.43 + }, + { + "Likelihood of eviction in next two months": "Very likely", + "Number of Adults (millions)": 1.4 + }, + { + "Likelihood of eviction in next two months": "Somewhat likely", + "Number of Adults (millions)": 2.25 + }, + { + "Likelihood of eviction in next two months": "Not very likely", + "Number of Adults (millions)": 2.18 + }, + { + "Likelihood of eviction in next two months": "Not likely at all", + "Number of Adults (millions)": 1.52 + }, + { + "Likelihood of eviction in next two months": "Did not report", + "Number of Adults (millions)": 0.09 + } + ], + "columns": [ + { + "name": "Likelihood of eviction in next two months", + "importance": "primary", + "description": "Categories of perceived eviction risk", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Adults (millions)", + "importance": "primary", + "description": "Estimated number of U.S. adults in each category", + "unit": "millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1046": { + "metadata": { + "title": "Working Women Can't Get in the Picture", + "description": "% of women appearing in an online image search for job vs actual % in each job", + "main_insight": "Online image searches for various jobs often misrepresent the actual percentage of women in those roles, with some jobs showing a significant overrepresentation and others an underrepresentation." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Job": "Medical records technician", + "Points": 57.0, + "Search Performance": "Image Search" + } + ], + "score": 0.91, + "annotation": "The Image Search shows a sudden jump in Points from Bartender to Medical records technician.", + "reason": "The Points for Image Search increases from 29.0 at Bartender to 57.0 at Medical records technician, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Job": "Mechanic", + "Points": 22.0, + "Search Performance": "Difference" + } + ], + "score": 0.91, + "annotation": "The Difference shows a sudden jump in Points from Bartender to Mechanic.", + "reason": "The Points for Difference increases from -28.0 at Bartender to 22.0 at Mechanic, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Job": "Bill collector", + "Points": 71.0, + "Search Performance": "Actual" + }, + { + "Job": "Bill collector", + "Points": -51.0, + "Search Performance": "Difference" + } + ], + "score": 0.84, + "annotation": "The Points of Actual and Difference show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Points between Actual and Difference is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Job": "Mechanic", + "Points": 2.0, + "Search Performance": "Actual" + } + ], + "score": 0.79, + "annotation": "The Actual has a minimum value at Mechanic", + "reason": "The [{'Job': 'Mechanic', 'Points': 2.0, 'Search Performance': 'Actual'}] of Actual has a mainimum value of 2.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Job": "Singer", + "Points": 38.0, + "Search Performance": "Actual" + } + ], + "score": 0.77, + "annotation": "The Actual shows a sudden jump in Points from Mechanic to Singer.", + "reason": "The Points for Actual increases from 2.0 at Mechanic to 38.0 at Singer, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Job": "Singer", + "Points": 38.0, + "Search Performance": "Actual" + }, + { + "Job": "Singer", + "Points": 62.0, + "Search Performance": "Image Search" + }, + { + "Job": "Singer", + "Points": 24.0, + "Search Performance": "Difference" + }, + { + "Job": "Mechanic", + "Points": 2.0, + "Search Performance": "Actual" + }, + { + "Job": "Mechanic", + "Points": 24.0, + "Search Performance": "Image Search" + }, + { + "Job": "Mechanic", + "Points": 22.0, + "Search Performance": "Difference" + }, + { + "Job": "Bartender", + "Points": 57.0, + "Search Performance": "Actual" + }, + { + "Job": "Bartender", + "Points": 29.0, + "Search Performance": "Image Search" + }, + { + "Job": "Bartender", + "Points": -28.0, + "Search Performance": "Difference" + }, + { + "Job": "Medical records technician", + "Points": 92.0, + "Search Performance": "Actual" + }, + { + "Job": "Medical records technician", + "Points": 57.0, + "Search Performance": "Image Search" + }, + { + "Job": "Medical records technician", + "Points": -35.0, + "Search Performance": "Difference" + }, + { + "Job": "Bill collector", + "Points": 71.0, + "Search Performance": "Actual" + }, + { + "Job": "Bill collector", + "Points": 20.0, + "Search Performance": "Image Search" + }, + { + "Job": "Bill collector", + "Points": -51.0, + "Search Performance": "Difference" + } + ], + "columns": [ + { + "name": "Job", + "importance": "primary", + "description": "The job title", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding search performance", + "role": "y" + }, + { + "name": "Search Performance", + "importance": "primary", + "description": "The search performance of the data point", + "role": "group" + } + ] + } + }, + "1825": { + "metadata": { + "title": "Starbucks Holds More Cash Than Many Banks", + "description": "Consumer cash held by institutions in 2016 (billion U.S. dollars)", + "main_insight": "Starbucks held significantly more cash in 2016 ($13.02 billion) than many banks and even more than PayPal, demonstrating the massive scale of its stored value card system." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Institution": "PayPal", + "Cash Held (Billion USD)": 13.02 + } + ], + "score": 0.95, + "annotation": "The has a maximum value at PayPal", + "reason": "The [{'Institution': 'PayPal', 'Cash Held (Billion USD)': 13.02}] of has a maximum value of 13.02, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Institution": "PayPal", + "Cash Held (Billion USD)": 13.02 + } + ], + "score": 0.91, + "annotation": "The shows a sudden jump in Cash Held (Billion USD) from SVB to PayPal.", + "reason": "The Cash Held (Billion USD) for increases from 4.55 at SVB to 13.02 at PayPal, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Institution": "Green Dot", + "Cash Held (Billion USD)": 0.56 + } + ], + "score": 0.49, + "annotation": "The has a minimum value at Green Dot", + "reason": "The [{'Institution': 'Green Dot', 'Cash Held (Billion USD)': 0.56}] of has a mainimum value of 0.56, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Institution": "PayPal", + "Cash Held (Billion USD)": 13.02 + }, + { + "Institution": "SVB", + "Cash Held (Billion USD)": 4.55 + }, + { + "Institution": "SunTrust", + "Cash Held (Billion USD)": 3.36 + }, + { + "Institution": "American Express", + "Cash Held (Billion USD)": 3.32 + }, + { + "Institution": "The Bancorp", + "Cash Held (Billion USD)": 2.68 + }, + { + "Institution": "Starbucks", + "Cash Held (Billion USD)": 1.2 + }, + { + "Institution": "Customers Bank", + "Cash Held (Billion USD)": 0.78 + }, + { + "Institution": "Green Dot", + "Cash Held (Billion USD)": 0.56 + } + ], + "columns": [ + { + "name": "Institution", + "importance": "primary", + "description": "Name of the financial institution or company", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Cash Held (Billion USD)", + "importance": "primary", + "description": "Amount of consumer cash held in billions of US dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1727": { + "metadata": { + "title": "Trump Has Played 138% More Golf Than Obama", + "description": "Rounds of golf during first two years in office", + "main_insight": "President Trump played significantly more rounds of golf than President Obama during their first two years in office." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "President and Year": "2018 (Trump)", + "Trump Rounds": 63 + } + ], + "score": 0.95, + "annotation": "The shows a sudden jump in Trump Rounds from First Two Years to 2018 (Trump).", + "reason": "The Trump Rounds for increases from 0 at First Two Years to 63 at 2018 (Trump), indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "President and Year": "2017 (Trump)", + "Trump Rounds": 75 + } + ], + "score": 0.71, + "annotation": "The has a maximum value at 2017 (Trump)", + "reason": "The [{'President and Year': '2017 (Trump)', 'Trump Rounds': 75}] of has a maximum value of 75, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "President and Year": "2009 (Obama)", + "Trump Rounds": 0 + }, + { + "President and Year": "2010 (Obama)", + "Trump Rounds": 0 + }, + { + "President and Year": "First Two Years", + "Trump Rounds": 0 + } + ], + "score": 0.46, + "annotation": "The has minimum values at 2009 (Obama), 2010 (Obama), First Two Years", + "reason": "The [{'President and Year': '2009 (Obama)', 'Trump Rounds': 0}, {'President and Year': '2010 (Obama)', 'Trump Rounds': 0}, {'President and Year': 'First Two Years', 'Trump Rounds': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "President and Year": "2009 (Obama)", + "Trump Rounds": 0 + }, + { + "President and Year": "2017 (Trump)", + "Trump Rounds": 75 + }, + { + "President and Year": "2010 (Obama)", + "Trump Rounds": 0 + }, + { + "President and Year": "2018 (Trump)", + "Trump Rounds": 63 + }, + { + "President and Year": "First Two Years", + "Trump Rounds": 0 + } + ], + "columns": [ + { + "name": "President and Year", + "importance": "primary", + "description": "President and the year of their term", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Trump Rounds", + "importance": "primary", + "description": "Number of rounds of golf played by Trump", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1333": { + "metadata": { + "title": "Samsung's Galaxy S8 Is Expensive to Build", + "description": "Estimated material and manufacturing costs of selected high-end smartphones*", + "main_insight": "The Samsung Galaxy S8 has the highest estimated material and manufacturing costs among the selected high-end smartphones, exceeding $300." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Smartphone Model": "Samsung Galaxy S8 2017", + "Units": 307.5, + "Document Type": "Bill of Materials" + } + ], + "score": 0.8, + "annotation": "The Bill of Materials has a maximum value at Samsung Galaxy S8 2017", + "reason": "The [{'Smartphone Model': 'Samsung Galaxy S8 2017', 'Units': 307.5, 'Document Type': 'Bill of Materials'}] of Bill of Materials has a maximum value of 307.5, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Smartphone Model": "Apple iPhone 6s 2015", + "Units": 187.9, + "Document Type": "Bill of Materials" + } + ], + "score": 0.78, + "annotation": "The Bill of Materials has a minimum value at Apple iPhone 6s 2015", + "reason": "The [{'Smartphone Model': 'Apple iPhone 6s 2015', 'Units': 187.9, 'Document Type': 'Bill of Materials'}] of Bill of Materials has a mainimum value of 187.9, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Smartphone Model": "Samsung Galaxy S8 2017", + "Units": 307.5, + "Document Type": "Bill of Materials" + } + ], + "score": 0.58, + "annotation": "The Bill of Materials shows a sudden jump in Units from Google Pixel XL 2016 to Samsung Galaxy S8 2017.", + "reason": "The Units for Bill of Materials increases from 285.8 at Google Pixel XL 2016 to 307.5 at Samsung Galaxy S8 2017, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Smartphone Model": "Apple iPhone 6s 2015", + "Units": 187.9, + "Document Type": "Bill of Materials" + }, + { + "Smartphone Model": "Apple iPhone 6s Plus 2015", + "Units": 236.0, + "Document Type": "Bill of Materials" + }, + { + "Smartphone Model": "Samsung Galaxy S7 2016", + "Units": 255.1, + "Document Type": "Bill of Materials" + }, + { + "Smartphone Model": "Samsung Galaxy S7 Edge 2016", + "Units": 271.2, + "Document Type": "Bill of Materials" + }, + { + "Smartphone Model": "Huawei P9 2016", + "Units": 205.3, + "Document Type": "Bill of Materials" + }, + { + "Smartphone Model": "Apple iPhone 7 2016", + "Units": 224.8, + "Document Type": "Bill of Materials" + }, + { + "Smartphone Model": "Google Pixel XL 2016", + "Units": 285.8, + "Document Type": "Bill of Materials" + }, + { + "Smartphone Model": "Samsung Galaxy S8 2017", + "Units": 307.5, + "Document Type": "Bill of Materials" + } + ], + "columns": [ + { + "name": "Smartphone Model", + "importance": "primary", + "description": "Name and release year of the smartphone", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding document type", + "role": "y" + }, + { + "name": "Document Type", + "importance": "primary", + "description": "The document type of the data point", + "role": "group" + } + ] + } + }, + "1191": { + "metadata": { + "title": "Cocaine Cultivation Reaches New High in 2021", + "description": "Estimated illicit coca bush cultivation in main producing countries (in hectares)*", + "main_insight": "Cocaine cultivation reached a new high in 2021, driven primarily by a significant increase in cultivation in Colombia and Peru." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Dollars": 80000, + "Country": "Peru" + }, + { + "Year": 2021, + "Dollars": 30000, + "Country": "Bolivia" + } + ], + "score": 0.87, + "annotation": "The Dollars of Peru and Bolivia show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between Peru and Bolivia is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Dollars": 204000, + "Country": "Colombia" + }, + { + "Year": 2021, + "Dollars": 30000, + "Country": "Bolivia" + } + ], + "score": 0.38, + "annotation": "The Dollars of Colombia and Bolivia show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between Colombia and Bolivia is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Dollars": 204000, + "Country": "Colombia" + }, + { + "Year": 2021, + "Dollars": 80000, + "Country": "Peru" + } + ], + "score": 0.32, + "annotation": "The Dollars of Colombia and Peru show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between Colombia and Peru is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2011, + "Dollars": 63000, + "Country": "Colombia" + }, + { + "Year": 2011, + "Dollars": 61000, + "Country": "Peru" + }, + { + "Year": 2011, + "Dollars": 26000, + "Country": "Bolivia" + }, + { + "Year": 2012, + "Dollars": 48000, + "Country": "Colombia" + }, + { + "Year": 2012, + "Dollars": 62000, + "Country": "Peru" + }, + { + "Year": 2012, + "Dollars": 25000, + "Country": "Bolivia" + }, + { + "Year": 2013, + "Dollars": 48000, + "Country": "Colombia" + }, + { + "Year": 2013, + "Dollars": 48000, + "Country": "Peru" + }, + { + "Year": 2013, + "Dollars": 23000, + "Country": "Bolivia" + }, + { + "Year": 2014, + "Dollars": 69000, + "Country": "Colombia" + }, + { + "Year": 2014, + "Dollars": 41000, + "Country": "Peru" + }, + { + "Year": 2014, + "Dollars": 23000, + "Country": "Bolivia" + }, + { + "Year": 2015, + "Dollars": 96000, + "Country": "Colombia" + }, + { + "Year": 2015, + "Dollars": 41000, + "Country": "Peru" + }, + { + "Year": 2015, + "Dollars": 20000, + "Country": "Bolivia" + }, + { + "Year": 2016, + "Dollars": 146000, + "Country": "Colombia" + }, + { + "Year": 2016, + "Dollars": 42000, + "Country": "Peru" + }, + { + "Year": 2016, + "Dollars": 23000, + "Country": "Bolivia" + }, + { + "Year": 2017, + "Dollars": 171000, + "Country": "Colombia" + }, + { + "Year": 2017, + "Dollars": 49000, + "Country": "Peru" + }, + { + "Year": 2017, + "Dollars": 24000, + "Country": "Bolivia" + }, + { + "Year": 2018, + "Dollars": 169000, + "Country": "Colombia" + }, + { + "Year": 2018, + "Dollars": 53000, + "Country": "Peru" + }, + { + "Year": 2018, + "Dollars": 23000, + "Country": "Bolivia" + }, + { + "Year": 2019, + "Dollars": 154000, + "Country": "Colombia" + }, + { + "Year": 2019, + "Dollars": 53000, + "Country": "Peru" + }, + { + "Year": 2019, + "Dollars": 25000, + "Country": "Bolivia" + }, + { + "Year": 2020, + "Dollars": 143000, + "Country": "Colombia" + }, + { + "Year": 2020, + "Dollars": 60000, + "Country": "Peru" + }, + { + "Year": 2020, + "Dollars": 29000, + "Country": "Bolivia" + }, + { + "Year": 2021, + "Dollars": 204000, + "Country": "Colombia" + }, + { + "Year": 2021, + "Dollars": 80000, + "Country": "Peru" + }, + { + "Year": 2021, + "Dollars": 30000, + "Country": "Bolivia" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of cultivation", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding country", + "role": "y" + }, + { + "name": "Country", + "importance": "primary", + "description": "The country of the data point", + "role": "group" + } + ] + } + }, + "323": { + "metadata": { + "title": "Do Brits support Harry and Meghan's \"step back\"?", + "description": "Share of UK adults who support/oppose the move (Jan 9, 2020)", + "main_insight": "A plurality of UK adults (45%) supported Harry and Meghan's decision to step back from royal duties on January 9, 2020, while 26% opposed and 29% didn't know." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Opinion": "Support", + "Percentage": 45.0 + } + ], + "score": 0.95, + "annotation": "The shows a sudden jump in Percentage from Don't know to Support.", + "reason": "The Percentage for increases from 29.0 at Don't know to 45.0 at Support, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Opinion": "Support", + "Percentage": 45.0 + } + ], + "score": 0.66, + "annotation": "The has a maximum value at Support", + "reason": "The [{'Opinion': 'Support', 'Percentage': 45.0}] of has a maximum value of 45.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Opinion": "Oppose", + "Percentage": 26.0 + } + ], + "score": 0.46, + "annotation": "The has a minimum value at Oppose", + "reason": "The [{'Opinion': 'Oppose', 'Percentage': 26.0}] of has a mainimum value of 26.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Opinion": "Support", + "Percentage": 45.0 + }, + { + "Opinion": "Oppose", + "Percentage": 26.0 + }, + { + "Opinion": "Don't know", + "Percentage": 29.0 + } + ], + "columns": [ + { + "name": "Opinion", + "importance": "primary", + "description": "Categories of opinion regarding Harry and Meghan's step back", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of UK adults holding each opinion", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "742": { + "metadata": { + "title": "American Muslims Concerned About Islamist Extremism", + "description": "U.S. Muslims and general public by degree of concern about worldwide Islamist extremism", + "main_insight": "American Muslims show a higher degree of concern about Islamist extremism compared to the general public, with a larger percentage being 'Very' concerned." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Group": "General Public", + "Units": 49.0, + "Agreement Level": "Very" + }, + { + "Group": "General Public", + "Units": 33.0, + "Agreement Level": "Somewhat" + } + ], + "score": 1.0, + "annotation": "The Units of Very and Somewhat show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Very and Somewhat is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Group": "General Public", + "Units": 49.0, + "Agreement Level": "Very" + }, + { + "Group": "General Public", + "Units": 15.0, + "Agreement Level": "Not too/not at all" + } + ], + "score": 1.0, + "annotation": "The Units of Very and Not too/not at all show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Very and Not too/not at all is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Group": "U.S. Muslims", + "Units": 2.0, + "Agreement Level": "Don't know" + } + ], + "score": 0.91, + "annotation": "The Don't know has the smallest proportion in U.S. Muslims.", + "reason": "The Units of Don't know in U.S. Muslims accounts for 0.019801980198019802, smaller than all other Agreement Level, of the total of Units of all Agreement Level." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Agreement Level": "Very", + "Group": "total", + "Units": 115.0 + } + ], + "score": 0.91, + "annotation": "The total value of Very has the smallest proportion.", + "reason": "The total value of Units of Very accounts for 0.02, smaller than all other Agreement Level, of the total of Units of all Agreement Level." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Group": "General Public", + "Units": 2.0, + "Agreement Level": "Don't know" + } + ], + "score": 0.91, + "annotation": "The Don't know has the smallest proportion in General Public.", + "reason": "The Units of Don't know in General Public accounts for 0.020202020202020204, smaller than all other Agreement Level, of the total of Units of all Agreement Level." + } + ], + "data": { + "data": [ + { + "Group": "U.S. Muslims", + "Units": 66.0, + "Agreement Level": "Very" + }, + { + "Group": "U.S. Muslims", + "Units": 16.0, + "Agreement Level": "Somewhat" + }, + { + "Group": "U.S. Muslims", + "Units": 17.0, + "Agreement Level": "Not too/not at all" + }, + { + "Group": "U.S. Muslims", + "Units": 2.0, + "Agreement Level": "Don't know" + }, + { + "Group": "General Public", + "Units": 49.0, + "Agreement Level": "Very" + }, + { + "Group": "General Public", + "Units": 33.0, + "Agreement Level": "Somewhat" + }, + { + "Group": "General Public", + "Units": 15.0, + "Agreement Level": "Not too/not at all" + }, + { + "Group": "General Public", + "Units": 2.0, + "Agreement Level": "Don't know" + } + ], + "columns": [ + { + "name": "Group", + "importance": "primary", + "description": "The group being surveyed", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding agreement level", + "role": "y" + }, + { + "name": "Agreement Level", + "importance": "primary", + "description": "The agreement level of the data point", + "role": "group" + } + ] + } + }, + "1800": { + "metadata": { + "title": "The Tropical Rainforest Is Disappearing at an Alarming Rate", + "description": "Countries with the highest primary tropical forest losses in 2023, in million hectares", + "main_insight": "Brazil experienced the highest primary tropical forest loss in 2023 at 1.14 million hectares, significantly more than other countries. The total worldwide losses amounted to 3.7 Mha." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Annual worldwide losses", + "Forest Loss (Mha)": 3.7 + } + ], + "score": 0.97, + "annotation": "The has a maximum value at Annual worldwide losses", + "reason": "The [{'Country': 'Annual worldwide losses', 'Forest Loss (Mha)': 3.7}] of has a maximum value of 3.7, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Annual worldwide losses", + "Forest Loss (Mha)": 3.7 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in Forest Loss (Mha) from Brazil to Annual worldwide losses.", + "reason": "The Forest Loss (Mha) for increases from 1.14 at Brazil to 3.7 at Annual worldwide losses, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Madagascar", + "Forest Loss (Mha)": 0.08 + } + ], + "score": 0.38, + "annotation": "The has a minimum value at Madagascar", + "reason": "The [{'Country': 'Madagascar', 'Forest Loss (Mha)': 0.08}] of has a mainimum value of 0.08, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Brazil", + "Forest Loss (Mha)": 1.14 + }, + { + "Country": "DR Congo", + "Forest Loss (Mha)": 0.53 + }, + { + "Country": "Bolivia", + "Forest Loss (Mha)": 0.49 + }, + { + "Country": "Indonesia", + "Forest Loss (Mha)": 0.29 + }, + { + "Country": "Peru", + "Forest Loss (Mha)": 0.15 + }, + { + "Country": "Laos", + "Forest Loss (Mha)": 0.14 + }, + { + "Country": "Cameroon", + "Forest Loss (Mha)": 0.1 + }, + { + "Country": "Madagascar", + "Forest Loss (Mha)": 0.08 + }, + { + "Country": "Annual worldwide losses", + "Forest Loss (Mha)": 3.7 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country with the highest primary tropical forest loss", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Forest Loss (Mha)", + "importance": "primary", + "description": "Primary tropical forest loss in million hectares", + "unit": "Mha", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2235": { + "metadata": { + "title": "If the Millennium Falcon was a car...", + "description": "Projected Annual Insurance Cost of selected Star Wars Spaceships/Vehicles (in GBP)", + "main_insight": "The CR90 Corvette has a significantly higher projected annual insurance cost compared to other Star Wars vehicles, including the Millennium Falcon." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Spaceship/Vehicle": "CR90 Corvette", + "Insurance Cost (GBP)": 4643460.0 + } + ], + "score": 0.97, + "annotation": "The has a maximum value at CR90 Corvette", + "reason": "The [{'Spaceship/Vehicle': 'CR90 Corvette', 'Insurance Cost (GBP)': 4643460.0}] of has a maximum value of 4643460.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Spaceship/Vehicle": "CR90 Corvette", + "Insurance Cost (GBP)": 4643460.0 + } + ], + "score": 0.96, + "annotation": "The shows a sudden jump in Insurance Cost (GBP) from Millennium Falcon to CR90 Corvette.", + "reason": "The Insurance Cost (GBP) for increases from 414533.0 at Millennium Falcon to 4643460.0 at CR90 Corvette, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Spaceship/Vehicle": "X-34 Landspeeder", + "Insurance Cost (GBP)": 544.0 + } + ], + "score": 0.33, + "annotation": "The has a minimum value at X-34 Landspeeder", + "reason": "The [{'Spaceship/Vehicle': 'X-34 Landspeeder', 'Insurance Cost (GBP)': 544.0}] of has a mainimum value of 544.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Spaceship/Vehicle": "CR90 Corvette", + "Insurance Cost (GBP)": 4643460.0 + }, + { + "Spaceship/Vehicle": "Millennium Falcon", + "Insurance Cost (GBP)": 414533.0 + }, + { + "Spaceship/Vehicle": "T-65 X-Wing Starfighter", + "Insurance Cost (GBP)": 217323.0 + }, + { + "Spaceship/Vehicle": "Slave I", + "Insurance Cost (GBP)": 169624.0 + }, + { + "Spaceship/Vehicle": "Tie Advanced X1", + "Insurance Cost (GBP)": 112932.0 + }, + { + "Spaceship/Vehicle": "Sebulba's Podracer", + "Insurance Cost (GBP)": 9545.0 + }, + { + "Spaceship/Vehicle": "Khetanna", + "Insurance Cost (GBP)": 5483.0 + }, + { + "Spaceship/Vehicle": "X-34 Landspeeder", + "Insurance Cost (GBP)": 544.0 + } + ], + "columns": [ + { + "name": "Spaceship/Vehicle", + "importance": "primary", + "description": "Name of the Star Wars spaceship or vehicle", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Insurance Cost (GBP)", + "importance": "primary", + "description": "Projected annual insurance cost in British Pounds", + "unit": "GBP", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1638": { + "metadata": { + "title": "Urban mobility", + "description": "Mobility providers 2021", + "main_insight": "Berlin has the highest number of mobility providers among the listed German cities, with a total of 46 providers. The number of providers generally decreases from Berlin down to Essen." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Berlin", + "Providers": 46 + } + ], + "score": 0.86, + "annotation": "The has a maximum value at Berlin", + "reason": "The [{'City': 'Berlin', 'Providers': 46}] of has a maximum value of 46, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "Essen", + "Providers": 26 + } + ], + "score": 0.68, + "annotation": "The has a minimum value at Essen", + "reason": "The [{'City': 'Essen', 'Providers': 26}] of has a mainimum value of 26, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Frankfurt", + "Providers": 34 + } + ], + "score": 0.65, + "annotation": "The shows a sudden jump in Providers from Stuttgart to Frankfurt.", + "reason": "The Providers for increases from 29 at Stuttgart to 34 at Frankfurt, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "City": "Berlin", + "Providers": 46 + }, + { + "City": "Munich", + "Providers": 42 + }, + { + "City": "Düsseldorf", + "Providers": 39 + }, + { + "City": "Hamburg", + "Providers": 37 + }, + { + "City": "Cologne", + "Providers": 35 + }, + { + "City": "Frankfurt", + "Providers": 34 + }, + { + "City": "Stuttgart", + "Providers": 29 + }, + { + "City": "Bonn", + "Providers": 27 + }, + { + "City": "Dortmund", + "Providers": 27 + }, + { + "City": "Essen", + "Providers": 26 + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "German cities ranked by number of mobility providers", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Providers", + "importance": "primary", + "description": "Total number of mobility providers in each city", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "466": { + "metadata": { + "title": "Electric Mobility Has a Long Way to Go", + "description": "Estimated plug-in electric vehicle and total light vehicles sales in the U.S. in 2018", + "main_insight": "Plug-in electric vehicles accounted for a small percentage (2.1%) of total light vehicle sales in the U.S. in 2018." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Vehicle Type": "Total light vehicle sales", + "Sales": 17247250.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Sales from Plug-in electric vehicle sales to Total light vehicle sales.", + "reason": "The Sales for increases from 361307.0 at Plug-in electric vehicle sales to 17247250.0 at Total light vehicle sales, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Vehicle Type": "Total light vehicle sales", + "Sales": 17247250.0 + } + ], + "score": 0.45, + "annotation": "The has a maximum value at Total light vehicle sales", + "reason": "The [{'Vehicle Type': 'Total light vehicle sales', 'Sales': 17247250.0}] of has a maximum value of 17247250.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Vehicle Type": "Plug-in electric vehicle sales", + "Sales": 361307.0 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at Plug-in electric vehicle sales", + "reason": "The [{'Vehicle Type': 'Plug-in electric vehicle sales', 'Sales': 361307.0}] of has a mainimum value of 361307.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Vehicle Type": "Plug-in electric vehicle sales", + "Sales": 361307.0 + }, + { + "Vehicle Type": "Total light vehicle sales", + "Sales": 17247250.0 + } + ], + "columns": [ + { + "name": "Vehicle Type", + "importance": "primary", + "description": "Category of vehicle sales", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Sales", + "importance": "primary", + "description": "Number of vehicles sold", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2389": { + "metadata": { + "title": "Brady Jerseys Still in High Demand", + "description": "Highest-selling NFL jerseys in the final week of the 2021/2022 regular season (Jan 8 - Jan 15 2022)", + "main_insight": "Tom Brady's jersey was the highest-selling NFL jersey in the final week of the 2021/2022 regular season, and increased its ranking by 1 position from the prior week." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Rank": 1.0, + "Player Name": "Tom Brady" + }, + { + "Rank": 2.0, + "Player Name": "Ben Roethlisberger" + }, + { + "Rank": 3.0, + "Player Name": "Dak Prescott" + }, + { + "Rank": 4.0, + "Player Name": "George Kittle" + }, + { + "Rank": 5.0, + "Player Name": "Micah Parsons" + }, + { + "Rank": 6.0, + "Player Name": "Aaron Rodgers" + }, + { + "Rank": 7.0, + "Player Name": "Ja'marr Chase" + }, + { + "Rank": 8.0, + "Player Name": "Josh Allen" + } + ], + "columns": [ + { + "name": "Rank", + "importance": "primary", + "description": "Ranking of the player's jersey sales", + "unit": "none", + "data_type": "number", + "role": "x" + }, + { + "name": "Player Name", + "importance": "primary", + "description": "Name of the NFL player", + "unit": "none", + "data_type": "string", + "role": "y" + } + ] + } + }, + "2387": { + "metadata": { + "title": "China Is Beating The U.S. In The Renewables Race", + "description": "Estimated direct and indirect jobs in renewable energy in China and the U.S.", + "main_insight": "China significantly leads the U.S. in renewable energy jobs across various sectors, and this gap widened between 2014 and 2016." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2014, + "China Total": 2940241 + }, + { + "Year": 2016, + "China Total": 3498000 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of data collection", + "unit": "none", + "data_type": "time", + "discarded_count": 0, + "role": "x" + }, + { + "name": "China Total", + "importance": "primary", + "description": "Total renewable energy jobs in China", + "unit": "none", + "data_type": "number", + "discarded_count": 0, + "role": "y" + } + ] + } + }, + "1910": { + "metadata": { + "title": "Swift or the Super Bowl: Who is the Real MVP?", + "description": "Selected metrics of the Super Bowl and Taylor Swift's Eras Tour", + "main_insight": "Taylor Swift's Eras Tour demonstrates significantly higher attendance and net worth compared to the Super Bowl and its top players, suggesting a greater economic impact." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Metric": "Number of attendees (in Glendale, Arizona/2023)", + "Super Bowl": 60000.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Super Bowl from Average spend per U.S. attendee (2023) to Number of attendees (in Glendale, Arizona/2023).", + "reason": "The Super Bowl for increases from 2500.0 at Average spend per U.S. attendee (2023) to 60000.0 at Number of attendees (in Glendale, Arizona/2023), indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Metric": "Number of attendees (in Glendale, Arizona/2023)", + "Super Bowl": 60000.0 + } + ], + "score": 0.67, + "annotation": "The has a maximum value at Number of attendees (in Glendale, Arizona/2023)", + "reason": "The [{'Metric': 'Number of attendees (in Glendale, Arizona/2023)', 'Super Bowl': 60000.0}] of has a maximum value of 60000.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Metric": "Highest net worth (2024)", + "Super Bowl": 0 + } + ], + "score": 0.41, + "annotation": "The has a minimum value at Highest net worth (2024)", + "reason": "The [{'Metric': 'Highest net worth (2024)', 'Super Bowl': 0}] of has a mainimum value of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Metric": "Average spend per U.S. attendee (2023)", + "Super Bowl": 2500.0 + }, + { + "Metric": "Number of attendees (in Glendale, Arizona/2023)", + "Super Bowl": 60000.0 + }, + { + "Metric": "Highest net worth (2024)", + "Super Bowl": 0 + } + ], + "columns": [ + { + "name": "Metric", + "importance": "primary", + "description": "The different metrics compared", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Super Bowl", + "importance": "primary", + "description": "Values related to the Super Bowl", + "unit": "USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "702": { + "metadata": { + "title": "Chemical Weapons Attacks In Syria", + "description": "Number of attacks in Syria involving chemical weapons", + "main_insight": "The number of chemical weapon attacks in Syria has decreased since 2014, with a significant drop between 2016 and 2018." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2014, + "Attacks": 30 + }, + { + "Year": 2015, + "Attacks": 28 + }, + { + "Year": 2016, + "Attacks": 23 + }, + { + "Year": 2017, + "Attacks": 17 + }, + { + "Year": 2018, + "Attacks": 8 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the attacks", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Attacks", + "importance": "primary", + "description": "Number of chemical weapon attacks", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1244": { + "metadata": { + "title": "Inflation Hits the Grill: The Price of a July 4th Cookout", + "description": "Average cost of a July 4th cookout for 10 people in 2024", + "main_insight": "The average cost of a July 4th cookout in 2024 is $71.22, which is 30% higher than in 2019, showcasing the impact of inflation on food prices." + }, + "chart_type": "Proportional Icon Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Food Item": "Porkchops", + "Price": 15.49 + } + ], + "score": 0.95, + "annotation": "The has a maximum value at Porkchops", + "reason": "The [{'Food Item': 'Porkchops', 'Price': 15.49}] of has a maximum value of 15.49, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Food Item": "Ground beef", + "Price": 12.77 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Price from Chicken breast to Ground beef.", + "reason": "The Price for increases from 7.83 at Chicken breast to 12.77 at Ground beef, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Food Item": "Hamburger buns", + "Price": 2.41 + } + ], + "score": 0.53, + "annotation": "The has a minimum value at Hamburger buns", + "reason": "The [{'Food Item': 'Hamburger buns', 'Price': 2.41}] of has a mainimum value of 2.41, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Food Item": "Hamburger buns", + "Price": 2.41 + }, + { + "Food Item": "Cheese", + "Price": 3.57 + }, + { + "Food Item": "Ground beef", + "Price": 12.77 + }, + { + "Food Item": "Chicken breast", + "Price": 7.83 + }, + { + "Food Item": "Porkchops", + "Price": 15.49 + }, + { + "Food Item": "Pork and beans", + "Price": 2.49 + }, + { + "Food Item": "Chips", + "Price": 4.9 + }, + { + "Food Item": "Potato salad", + "Price": 3.32 + }, + { + "Food Item": "Ice cream", + "Price": 5.65 + }, + { + "Food Item": "Cookies", + "Price": 3.99 + }, + { + "Food Item": "Strawberries", + "Price": 4.61 + }, + { + "Food Item": "Lemonade", + "Price": 4.19 + } + ], + "columns": [ + { + "name": "Food Item", + "importance": "primary", + "description": "Name of the food item", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Price", + "importance": "primary", + "description": "Price of the food item in 2024", + "unit": "USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1838": { + "metadata": { + "title": "How Brits see Londoners (and how they see themselves)", + "description": "Which of the following words do you think best describe Londoners?", + "main_insight": "London-based adults are more likely to describe themselves as 'Diverse' than British adults are to describe them that way. Both groups use a variety of words, with no single descriptor dominating." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Word": "Diverse", + "Units": 60.0, + "Location": "London-based adults" + } + ], + "score": 0.94, + "annotation": "The London-based adults shows a sudden jump in Units from Normal to Diverse.", + "reason": "The Units for London-based adults increases from 25.0 at Normal to 60.0 at Diverse, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Word": "Different", + "Units": 12.0, + "Location": "British adults" + }, + { + "Word": "Friendly", + "Units": 18.0, + "Location": "London-based adults" + } + ], + "score": 0.91, + "annotation": "The Units of British adults and London-based adults show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between British adults and London-based adults is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Word": "Diverse", + "Units": 60.0, + "Location": "London-based adults" + } + ], + "score": 0.87, + "annotation": "The London-based adults has a maximum value at Diverse", + "reason": "The [{'Word': 'Diverse', 'Units': 60.0, 'Location': 'London-based adults'}] of London-based adults has a maximum value of 60.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Word": "Diverse", + "Units": 41.0, + "Location": "British adults" + } + ], + "score": 0.81, + "annotation": "The British adults has a maximum value at Diverse", + "reason": "The [{'Word': 'Diverse', 'Units': 41.0, 'Location': 'British adults'}] of British adults has a maximum value of 41.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Word": "Insular", + "Units": 18.0, + "Location": "British adults" + } + ], + "score": 0.79, + "annotation": "The British adults accounts for the majority in Insular.", + "reason": "The Units of British adults in Insular accounts for 0.5294117647058824 (more than 50%) of the total of Units of all Location." + } + ], + "data": { + "data": [ + { + "Word": "Diverse", + "Units": 41.0, + "Location": "British adults" + }, + { + "Word": "Diverse", + "Units": 60.0, + "Location": "London-based adults" + }, + { + "Word": "Arrogant", + "Units": 29.0, + "Location": "British adults" + }, + { + "Word": "Insular", + "Units": 18.0, + "Location": "British adults" + }, + { + "Word": "Normal", + "Units": 17.0, + "Location": "British adults" + }, + { + "Word": "Normal", + "Units": 25.0, + "Location": "London-based adults" + }, + { + "Word": "Different", + "Units": 12.0, + "Location": "British adults" + }, + { + "Word": "Different", + "Units": 16.0, + "Location": "London-based adults" + }, + { + "Word": "Liberal", + "Units": 21.0, + "Location": "London-based adults" + }, + { + "Word": "Friendly", + "Units": 18.0, + "Location": "London-based adults" + } + ], + "columns": [ + { + "name": "Word", + "importance": "primary", + "description": "Word describing Londoners", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding location", + "role": "y" + }, + { + "name": "Location", + "importance": "primary", + "description": "The location of the data point", + "role": "group" + } + ] + } + }, + "440": { + "metadata": { + "title": "Where the Most Money is Burned on Cigars", + "description": "Per capita revenue from cigars in selected countries in 2022 (in U.S. dollars)", + "main_insight": "Lebanon and the United States have significantly higher per capita revenue from cigars than other countries, exceeding $35, while others range from around $10 to $27." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Lebanon", + "Per Capita Revenue (USD)": 36.7 + } + ], + "score": 0.86, + "annotation": "The has a maximum value at Lebanon", + "reason": "The [{'Country': 'Lebanon', 'Per Capita Revenue (USD)': 36.7}] of has a maximum value of 36.7, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Per Capita Revenue (USD)": 35.6 + } + ], + "score": 0.7, + "annotation": "The shows a sudden jump in Per Capita Revenue (USD) from Qatar to United States.", + "reason": "The Per Capita Revenue (USD) for increases from 27.4 at Qatar to 35.6 at United States, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Finland", + "Per Capita Revenue (USD)": 10.8 + } + ], + "score": 0.56, + "annotation": "The has a minimum value at Finland", + "reason": "The [{'Country': 'Finland', 'Per Capita Revenue (USD)': 10.8}] of has a mainimum value of 10.8, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Lebanon", + "Per Capita Revenue (USD)": 36.7 + }, + { + "Country": "United States", + "Per Capita Revenue (USD)": 35.6 + }, + { + "Country": "Qatar", + "Per Capita Revenue (USD)": 27.4 + }, + { + "Country": "Luxembourg", + "Per Capita Revenue (USD)": 20.2 + }, + { + "Country": "Iceland", + "Per Capita Revenue (USD)": 15.1 + }, + { + "Country": "Switzerland", + "Per Capita Revenue (USD)": 14.7 + }, + { + "Country": "United Kingdom", + "Per Capita Revenue (USD)": 13.7 + }, + { + "Country": "Bahrain", + "Per Capita Revenue (USD)": 12.4 + }, + { + "Country": "Netherlands", + "Per Capita Revenue (USD)": 12.1 + }, + { + "Country": "Finland", + "Per Capita Revenue (USD)": 10.8 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Selected countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Per Capita Revenue (USD)", + "importance": "primary", + "description": "Per capita revenue from cigars in 2022 in U.S. dollars", + "unit": "USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1980": { + "metadata": { + "title": "U.S. Shoppers Plan to Increase Holiday Spending in 2019", + "description": "Planned holiday spending in November and December per U.S. consumer from 2015 to 2019", + "main_insight": "Planned holiday spending by U.S. consumers has generally increased from 2015 to 2019, reaching $1,048 in 2019." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2015, + "Planned Spending": 953.0 + }, + { + "Year": 2016, + "Planned Spending": 937.0 + }, + { + "Year": 2017, + "Planned Spending": 968.0 + }, + { + "Year": 2018, + "Planned Spending": 1002.0 + }, + { + "Year": 2019, + "Planned Spending": 1048.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of planned holiday spending", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Planned Spending", + "importance": "primary", + "description": "Planned holiday spending in US dollars", + "unit": "USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2340": { + "metadata": { + "title": "Labour Secures Landslide Victory in UK Election", + "description": "Distribution of seats in the United Kingdom's general election on July 4, 2024", + "main_insight": "The Labour party won a majority in the 2024 UK general election with 412 seats, significantly surpassing the 326 seats required for a majority. The Conservative party experienced a major loss, retaining only 121 seats." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Party": "Labour", + "Seats": 412 + } + ], + "score": 0.98, + "annotation": "The has a maximum value at Labour", + "reason": "The [{'Party': 'Labour', 'Seats': 412}] of has a maximum value of 412, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Party": "Labour", + "Seats": 412 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in Seats from Conservatives to Labour.", + "reason": "The Seats for increases from 121 at Conservatives to 412 at Labour, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Party": "Reform UK", + "Seats": 4 + }, + { + "Party": "Green Party", + "Seats": 4 + }, + { + "Party": "Plaid Cymru", + "Seats": 4 + } + ], + "score": 0.33, + "annotation": "The has minimum values at Reform UK, Green Party, Plaid Cymru", + "reason": "The [{'Party': 'Reform UK', 'Seats': 4}, {'Party': 'Green Party', 'Seats': 4}, {'Party': 'Plaid Cymru', 'Seats': 4}] of have minimum values of 4, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Party": "Labour", + "Seats": 412 + }, + { + "Party": "Conservatives", + "Seats": 121 + }, + { + "Party": "Liberal Democrats", + "Seats": 71 + }, + { + "Party": "Scottish National Party", + "Seats": 9 + }, + { + "Party": "Sinn Féin", + "Seats": 7 + }, + { + "Party": "Independent", + "Seats": 6 + }, + { + "Party": "Democratic Unionist Party", + "Seats": 5 + }, + { + "Party": "Other", + "Seats": 5 + }, + { + "Party": "Reform UK", + "Seats": 4 + }, + { + "Party": "Green Party", + "Seats": 4 + }, + { + "Party": "Plaid Cymru", + "Seats": 4 + } + ], + "columns": [ + { + "name": "Party", + "importance": "primary", + "description": "Political party", + "unit": 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"difference", + "subtype": "sudden_change", + "data_points": [ + { + "Magician": "David Copperfield", + "Earnings": 61.5 + } + ], + "score": 0.86, + "annotation": "The shows a sudden jump in Earnings from Penn & Teller to David Copperfield.", + "reason": "The Earnings for increases from 30.5 at Penn & Teller to 61.5 at David Copperfield, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Magician": "David Blaine", + "Earnings": 6.0 + } + ], + "score": 0.42, + "annotation": "The has a minimum value at David Blaine", + "reason": "The [{'Magician': 'David Blaine', 'Earnings': 6.0}] of has a mainimum value of 6.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Magician": "David Copperfield", + "Earnings": 61.5 + }, + { + "Magician": "Penn & Teller", + "Earnings": 30.5 + }, + { + "Magician": "Criss Angel", + "Earnings": 14.5 + }, + { + "Magician": "The Illusionists", + "Earnings": 11.5 + }, + { + "Magician": "Dynamo", + "Earnings": 9.0 + }, + { + "Magician": "Derren Brown", + "Earnings": 7.0 + }, + { + "Magician": "Michael Carbonaro", + "Earnings": 7.0 + }, + { + "Magician": "David Blaine", + "Earnings": 6.0 + } + ], + "columns": [ + { + "name": "Magician", + "importance": "primary", + "description": "Name of the magician or magic act", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Earnings", + "importance": "primary", + "description": "Earnings in millions of US dollars in 2017", + "unit": "Million USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "421": { + "metadata": { + "title": "Heroin Deaths In The U.S. Are Skyrocketing", + "description": "Number of drug-poisoning deaths involving heroin in the U.S. (2000-2013)", + "main_insight": "Heroin-related deaths have dramatically increased in the U.S. between 2000 and 2013, with a much steeper increase for men than for women." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2013, + "Count": 6525, + "Gender": "Men" + }, + { + "Year": 2013, + "Count": 1732, + "Gender": "Women" + } + ], + "score": 1.0, + "annotation": "The Count of Men and Women show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Count between Men and Women is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2000, + "Count": 1379, + "Gender": "Men" + }, + { + "Year": 2000, + "Count": 195, + "Gender": "Women" + }, + { + "Year": 2001, + "Count": 1394, + "Gender": "Men" + }, + { + "Year": 2001, + "Count": 200, + "Gender": "Women" + }, + { + "Year": 2002, + "Count": 1639, + "Gender": "Men" + }, + { + "Year": 2002, + "Count": 295, + "Gender": "Women" + }, + { + "Year": 2003, + "Count": 1670, + "Gender": "Men" + }, + { + "Year": 2003, + "Count": 295, + "Gender": "Women" + }, + { + "Year": 2004, + "Count": 1697, + "Gender": "Men" + }, + { + "Year": 2004, + "Count": 257, + "Gender": "Women" + }, + { + "Year": 2005, + "Count": 1764, + "Gender": "Men" + }, + { + "Year": 2005, + "Count": 316, + "Gender": "Women" + }, + { + "Year": 2006, + "Count": 1569, + "Gender": "Men" + }, + { + "Year": 2006, + "Count": 325, + "Gender": "Women" + }, + { + "Year": 2007, + "Count": 1779, + "Gender": "Men" + }, + { + "Year": 2007, + "Count": 349, + "Gender": "Women" + }, + { + "Year": 2008, + "Count": 2367, + "Gender": "Men" + }, + { + "Year": 2008, + "Count": 419, + "Gender": "Women" + }, + { + "Year": 2009, + "Count": 2388, + "Gender": "Men" + }, + { + "Year": 2009, + "Count": 473, + "Gender": "Women" + }, + { + "Year": 2010, + "Count": 2440, + "Gender": "Men" + }, + { + "Year": 2010, + "Count": 590, + "Gender": "Women" + }, + { + "Year": 2011, + "Count": 3482, + "Gender": "Men" + }, + { + "Year": 2011, + "Count": 817, + "Gender": "Women" + }, + { + "Year": 2012, + "Count": 4651, + "Gender": "Men" + }, + { + "Year": 2012, + "Count": 1246, + "Gender": "Women" + }, + { + "Year": 2013, + "Count": 6525, + "Gender": "Men" + }, + { + "Year": 2013, + "Count": 1732, + "Gender": "Women" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 2000 to 2013", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Count", + "importance": "primary", + "description": "Count for the corresponding gender", + "role": "y" + }, + { + "name": "Gender", + "importance": "primary", + "description": "The gender of the data point", + "role": "group" + } + ] + } + }, + "2134": { + "metadata": { + "title": "The most divided states in the US", + "description": "% chance of winning by candidate in US states (4 Nov 2016)", + "main_insight": "Florida and North Carolina were the most divided states in the US, with Trump having a slightly greater chance of winning in both. Other states like Nevada were also divided, but states like Ohio and Iowa displayed strong preferences for one candidate over the other." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "State": "Iowa", + "Units": 28.3, + "Vote Share": "Clinton" + }, + { + "State": "Iowa", + "Units": 71.6, + "Vote Share": "Trump" + } + ], + "score": 1.0, + "annotation": "The Units of Clinton and Trump show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Clinton and Trump is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "State": "North Carolina", + "Units": 49.5, + "Vote Share": "Clinton" + } + ], + "score": 0.82, + "annotation": "The Clinton shows a sudden jump in Units from Ohio to North Carolina.", + "reason": "The Units for Clinton increases from 32.7 at Ohio to 49.5 at North Carolina, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "State": "Ohio", + "Units": 67.3, + "Vote Share": "Trump" + } + ], + "score": 0.82, + "annotation": "The Trump shows a sudden jump in Units from North Carolina to Ohio.", + "reason": "The Units for Trump increases from 50.5 at North Carolina to 67.3 at Ohio, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "State": "Florida", + "Units": 50.4, + "Vote Share": "Trump" + } + ], + "score": 0.8, + "annotation": "The Trump accounts for the majority in Florida.", + "reason": "The Units of Trump in Florida accounts for 0.504 (more than 50%) of the total of Units of all Vote Share." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "State": "North Carolina", + "Units": 50.5, + "Vote Share": "Trump" + } + ], + "score": 0.8, + "annotation": "The Trump accounts for the majority in North Carolina.", + "reason": "The Units of Trump in North Carolina accounts for 0.505 (more than 50%) of the total of Units of all Vote Share." + } + ], + "data": { + "data": [ + { + "State": "Florida", + "Units": 49.6, + "Vote Share": "Clinton" + }, + { + "State": "Florida", + "Units": 50.4, + "Vote Share": "Trump" + }, + { + "State": "North Carolina", + "Units": 49.5, + "Vote Share": "Clinton" + }, + { + "State": "North Carolina", + "Units": 50.5, + "Vote Share": "Trump" + }, + { + "State": "Nevada", + "Units": 51.8, + "Vote Share": "Clinton" + }, + { + "State": "Nevada", + "Units": 48.2, + "Vote Share": "Trump" + }, + { + "State": "New Hampshire", + "Units": 63.4, + "Vote Share": "Clinton" + }, + { + "State": "New Hampshire", + "Units": 36.5, + "Vote Share": "Trump" + }, + { + "State": "Ohio", + "Units": 32.7, + "Vote Share": "Clinton" + }, + { + "State": "Ohio", + "Units": 67.3, + "Vote Share": "Trump" + }, + { + "State": "Iowa", + "Units": 28.3, + "Vote Share": "Clinton" + }, + { + "State": "Iowa", + "Units": 71.6, + "Vote Share": "Trump" + } + ], + "columns": [ + { + "name": "State", + "importance": "primary", + "description": "US State", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding vote share", + "role": "y" + }, + { + "name": "Vote Share", + "importance": "primary", + "description": "The vote share of the data point", + "role": "group" + } + ] + } + }, + "54": { + "metadata": { + "title": "Google Dethrones Apple as Most Valuable Company", + "description": "Market capitalization (in billion U.S. dollars)", + "main_insight": "Google/Alphabet's market capitalization surpassed Apple's in early 2016, making it the most valuable company." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Jan. 2016", + "Units": 553.07, + "Company": "Apple" + } + ], + "score": 0.86, + "annotation": "The Apple has a minimum value at Jan. 2016", + "reason": "The [{'Month': 'Jan. 2016', 'Units': 553.07, 'Company': 'Apple'}] of Apple has a mainimum value of 553.07, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Month": "Jan. 2016", + "Units": 528.01, + "Company": "Google/Alphabet" + }, + { + "Month": "Jan. 2016", + "Units": 553.07, + "Company": "Apple" + } + ], + "score": 0.81, + "annotation": "The Units of Google/Alphabet and Apple show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Google/Alphabet and Apple is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Jan. 2016", + "Units": 553.07, + "Company": "Apple" + } + ], + "score": 0.8, + "annotation": "The Apple accounts for the majority in Jan. 2016.", + "reason": "The Units of Apple in Jan. 2016 accounts for 0.5115902615902617 (more than 50%) of the total of Units of all Company." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Dec. 2015", + "Units": 600.0, + "Company": "Apple" + } + ], + "score": 0.79, + "annotation": "The Apple accounts for the majority in Dec. 2015.", + "reason": "The Units of Apple in Dec. 2015 accounts for 0.5309734513274337 (more than 50%) of the total of Units of all Company." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Nov. 2015", + "Units": 650.0, + "Company": "Apple" + } + ], + "score": 0.79, + "annotation": "The Apple accounts for the majority in Nov. 2015.", + "reason": "The Units of Apple in Nov. 2015 accounts for 0.5462184873949579 (more than 50%) of the total of Units of all Company." + } + ], + "data": { + "data": [ + { + "Month": "Feb. 2015", + "Units": 370.0, + "Company": "Google/Alphabet" + }, + { + "Month": "Feb. 2015", + "Units": 720.0, + "Company": "Apple" + }, + { + "Month": "Mar. 2015", + "Units": 380.0, + "Company": "Google/Alphabet" + }, + { + "Month": "Mar. 2015", + "Units": 750.0, + "Company": "Apple" + }, + { + "Month": "Apr. 2015", + "Units": 360.0, + "Company": "Google/Alphabet" + }, + { + "Month": "Apr. 2015", + "Units": 730.0, + "Company": "Apple" + }, + { + "Month": "May 2015", + "Units": 370.0, + "Company": "Google/Alphabet" + }, + { + "Month": "May 2015", + "Units": 740.0, + "Company": "Apple" + }, + { + "Month": "Jun. 2015", + "Units": 380.0, + "Company": "Google/Alphabet" + }, + { + "Month": "Jun. 2015", + "Units": 700.0, + "Company": "Apple" + }, + { + "Month": "Jul. 2015", + "Units": 450.0, + "Company": "Google/Alphabet" + }, + { + "Month": "Jul. 2015", + "Units": 750.0, + "Company": "Apple" + }, + { + "Month": "Aug. 2015", + "Units": 450.0, + "Company": "Google/Alphabet" + }, + { + "Month": "Aug. 2015", + "Units": 620.0, + "Company": "Apple" + }, + { + "Month": "Sep. 2015", + "Units": 470.0, + "Company": "Google/Alphabet" + }, + { + "Month": "Sep. 2015", + "Units": 610.0, + "Company": "Apple" + }, + { + "Month": "Oct. 2015", + "Units": 500.0, + "Company": "Google/Alphabet" + }, + { + "Month": "Oct. 2015", + "Units": 630.0, + "Company": "Apple" + }, + { + "Month": "Nov. 2015", + "Units": 540.0, + "Company": "Google/Alphabet" + }, + { + "Month": "Nov. 2015", + "Units": 650.0, + "Company": "Apple" + }, + { + "Month": "Dec. 2015", + "Units": 530.0, + "Company": "Google/Alphabet" + }, + { + "Month": "Dec. 2015", + "Units": 600.0, + "Company": "Apple" + }, + { + "Month": "Jan. 2016", + "Units": 528.01, + "Company": "Google/Alphabet" + }, + { + "Month": "Jan. 2016", + "Units": 553.07, + "Company": "Apple" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding company", + "role": "y" + }, + { + "name": "Company", + "importance": "primary", + "description": "The company of the data point", + "role": "group" + } + ] + } + }, + "1187": { + "metadata": { + "title": "U.S. Remains Hot Spot for Semiconductor Design", + "description": "Semiconductor industry value added by activity and region in 2021 (in percent)", + "main_insight": "The U.S. leads significantly in semiconductor design, contributing the highest percentage of value added in this activity compared to other regions in 2021." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Process Steps": "Equipment", + "Region": "total", + "Units": 121.0 + } + ], + "score": 0.96, + "annotation": "The total value of Equipment has the largest proportion.", + "reason": "The total value of Units of Equipment accounts for 0.2274436090225564, larger than all other Process Steps, of the total of Units of all Process Steps." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "South Korea", + "Units": 20.0, + "Process Steps": "Design" + } + ], + "score": 0.95, + "annotation": "The Design has the largest proportion in South Korea.", + "reason": "The Units of Design in South Korea accounts for 0.2898550724637681, larger than all other Process Steps, of the total of Units of all Process Steps." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Taiwan", + "Units": 24.0, + "Process Steps": "Wafer fabrication" + } + ], + "score": 0.94, + "annotation": "The Wafer fabrication has the largest proportion in Taiwan.", + "reason": "The Units of Wafer fabrication in Taiwan accounts for 0.3, larger than all other Process Steps, of the total of Units of all Process Steps." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "U.S.", + "Units": 52.0, + "Process Steps": "Design" + } + ], + "score": 0.94, + "annotation": "The Design has a maximum value at U.S.", + "reason": "The [{'Region': 'U.S.', 'Units': 52.0, 'Process Steps': 'Design'}] of Design has a maximum value of 52.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Others", + "Units": 19.0, + "Process Steps": "Packaging, assembly & test" + } + ], + "score": 0.94, + "annotation": "The Packaging, assembly & test has the largest proportion in Others.", + "reason": "The Units of Packaging, assembly & test in Others accounts for 0.3392857142857143, larger than all other Process Steps, of the total of Units of all Process Steps." + } + ], + "data": { + "data": [ + { + "Region": "U.S.", + "Units": 52.0, + "Process Steps": "Design" + }, + { + "Region": "U.S.", + "Units": 42.0, + "Process Steps": "Equipment" + }, + { + "Region": "U.S.", + "Units": 12.0, + "Process Steps": "Materials" + }, + { + "Region": "U.S.", + "Units": 9.0, + "Process Steps": "Wafer fabrication" + }, + { + "Region": "U.S.", + "Units": 4.0, + "Process Steps": "Packaging, assembly & test" + }, + { + "Region": "South Korea", + "Units": 20.0, + "Process Steps": "Design" + }, + { + "Region": "South Korea", + "Units": 4.0, + "Process Steps": "Equipment" + }, + { + "Region": "South Korea", + "Units": 18.0, + "Process Steps": "Materials" + }, + { + "Region": "South Korea", + "Units": 18.0, + "Process Steps": "Wafer fabrication" + }, + { + "Region": "South Korea", + "Units": 9.0, + "Process Steps": "Packaging, assembly & test" + }, + { + "Region": "Japan", + "Units": 9.0, + "Process Steps": "Design" + }, + { + "Region": "Japan", + "Units": 28.0, + "Process Steps": "Equipment" + }, + { + "Region": "Japan", + "Units": 17.0, + "Process Steps": "Materials" + }, + { + "Region": "Japan", + "Units": 13.0, + "Process Steps": "Wafer fabrication" + }, + { + "Region": "Japan", + "Units": 6.0, + "Process Steps": "Packaging, assembly & test" + }, + { + "Region": "Europe", + "Units": 9.0, + "Process Steps": "Design" + }, + { + "Region": "Europe", + "Units": 21.0, + "Process Steps": "Equipment" + }, + { + "Region": "Europe", + "Units": 8.0, + "Process Steps": "Materials" + }, + { + "Region": "Europe", + "Units": 8.0, + "Process Steps": "Wafer fabrication" + }, + { + "Region": "Europe", + "Units": 5.0, + "Process Steps": "Packaging, assembly & test" + }, + { + "Region": "Taiwan", + "Units": 6.0, + "Process Steps": "Design" + }, + { + "Region": "Taiwan", + "Units": 9.0, + "Process Steps": "Equipment" + }, + { + "Region": "Taiwan", + "Units": 22.0, + "Process Steps": "Materials" + }, + { + "Region": "Taiwan", + "Units": 24.0, + "Process Steps": "Wafer fabrication" + }, + { + "Region": "Taiwan", + "Units": 19.0, + "Process Steps": "Packaging, assembly & test" + }, + { + "Region": "China", + "Units": 3.0, + "Process Steps": "Design" + }, + { + "Region": "China", + "Units": 4.0, + "Process Steps": "Equipment" + }, + { + "Region": "China", + "Units": 18.0, + "Process Steps": "Materials" + }, + { + "Region": "China", + "Units": 21.0, + "Process Steps": "Wafer fabrication" + }, + { + "Region": "China", + "Units": 38.0, + "Process Steps": "Packaging, assembly & test" + }, + { + "Region": "Others", + "Units": 6.0, + "Process Steps": "Design" + }, + { + "Region": "Others", + "Units": 13.0, + "Process Steps": "Equipment" + }, + { + "Region": "Others", + "Units": 9.0, + "Process Steps": "Materials" + }, + { + "Region": "Others", + "Units": 9.0, + "Process Steps": "Wafer fabrication" + }, + { + "Region": "Others", + "Units": 19.0, + "Process Steps": "Packaging, assembly & test" + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Geographic region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding process steps", + "role": "y" + }, + { + "name": "Process Steps", + "importance": "primary", + "description": "The process steps of the data point", + "role": "group" + } + ] + } + }, + "1514": { + "metadata": { + "title": "The World's Leading Soybean Producers", + "description": "Harvests of the world's main soybean-producing countries by year (in millions of tonnes)", + "main_insight": "Brazil's soybean production has dramatically increased since 2010/11, surpassing the U.S. as the world's leading producer. The U.S. production has also increased, albeit at a slower rate." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": "2023/24", + "Score": 163.0, + "Country": "Brazil" + }, + { + "Year": "2023/24", + "Score": 122.7, + "Country": "U.S." + } + ], + "score": 0.96, + "annotation": "The Score of Brazil and U.S. show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Brazil and U.S. is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "2010/11", + "Score": 90.0, + "Country": "U.S." + } + ], + "score": 0.91, + "annotation": "The U.S. has the largest proportion in 2010/11.", + "reason": "The Score of U.S. in 2010/11 accounts for 0.391304347826087, larger than all other Country, of the total of Score of all Country." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "U.S.", + "Year": "total", + "Score": 727.7 + } + ], + "score": 0.91, + "annotation": "The total value of U.S. has the largest proportion.", + "reason": "The total value of Score of U.S. accounts for 0.395231370845101, larger than all other Country, of the total of Score of all Country." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "2005/06", + "Score": 90.0, + "Country": "U.S." + } + ], + "score": 0.91, + "annotation": "The U.S. has the largest proportion in 2005/06.", + "reason": "The Score of U.S. in 2005/06 accounts for 0.3964757709251101, larger than all other Country, of the total of Score of all Country." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "2015/16", + "Score": 110.0, + "Country": "U.S." + } + ], + "score": 0.91, + "annotation": "The U.S. has the largest proportion in 2015/16.", + "reason": "The Score of U.S. in 2015/16 accounts for 0.3971119133574007, larger than all other Country, of the total of Score of all Country." + } + ], + "data": { + "data": [ + { + "Year": "1980/82", + "Score": 15.0, + "Country": "Brazil" + }, + { + "Year": "1980/82", + "Score": 60.0, + "Country": "U.S." + }, + { + "Year": "1980/82", + "Score": 40.0, + "Country": "Argentina" + }, + { + "Year": "1980/82", + "Score": 10.0, + "Country": "China" + }, + { + "Year": "1990/92", + "Score": 20.0, + "Country": "Brazil" + }, + { + "Year": "1990/92", + "Score": 60.0, + "Country": "U.S." + }, + { + "Year": "1990/92", + "Score": 40.0, + "Country": "Argentina" + }, + { + "Year": "1990/92", + "Score": 10.0, + "Country": "China" + }, + { + "Year": "2000/02", + "Score": 40.0, + "Country": "Brazil" + }, + { + "Year": "2000/02", + "Score": 80.0, + "Country": "U.S." + }, + { + "Year": "2000/02", + "Score": 50.0, + "Country": "Argentina" + }, + { + "Year": "2000/02", + "Score": 15.0, + "Country": "China" + }, + { + "Year": "2005/06", + "Score": 60.0, + "Country": "Brazil" + }, + { + "Year": "2005/06", + "Score": 90.0, + "Country": "U.S." + }, + { + "Year": "2005/06", + "Score": 60.0, + "Country": "Argentina" + }, + { + "Year": "2005/06", + "Score": 17.0, + "Country": "China" + }, + { + "Year": "2010/11", + "Score": 75.0, + "Country": "Brazil" + }, + { + "Year": "2010/11", + "Score": 90.0, + "Country": "U.S." + }, + { + "Year": "2010/11", + "Score": 50.0, + "Country": "Argentina" + }, + { + "Year": "2010/11", + "Score": 15.0, + "Country": "China" + }, + { + "Year": "2015/16", + "Score": 95.0, + "Country": "Brazil" + }, + { + "Year": "2015/16", + "Score": 110.0, + "Country": "U.S." + }, + { + "Year": "2015/16", + "Score": 60.0, + "Country": "Argentina" + }, + { + "Year": "2015/16", + "Score": 12.0, + "Country": "China" + }, + { + "Year": "2020/21", + "Score": 135.0, + "Country": "Brazil" + }, + { + "Year": "2020/21", + "Score": 115.0, + "Country": "U.S." + }, + { + "Year": "2020/21", + "Score": 45.0, + "Country": "Argentina" + }, + { + "Year": "2020/21", + "Score": 18.0, + "Country": "China" + }, + { + "Year": "2023/24", + "Score": 163.0, + "Country": "Brazil" + }, + { + "Year": "2023/24", + "Score": 122.7, + "Country": "U.S." + }, + { + "Year": "2023/24", + "Score": 48.0, + "Country": "Argentina" + }, + { + "Year": "2023/24", + "Score": 20.5, + "Country": "China" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Biennial periods representing soybean harvest years", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding country", + "role": "y" + }, + { + "name": "Country", + "importance": "primary", + "description": "The country of the data point", + "role": "group" + } + ] + } + }, + "208": { + "metadata": { + "title": "Investments and tools used by Australian hoteliers to attract guests", + "description": "Based on a survey of 250 executives/managers in the Australian accommodation industry, asking: What are the most effective tools/investment priorities to maximize occupancy at your property?", + "main_insight": "Australian hoteliers prioritize offering targeted discounts, promoting on social media, and investments in comfort to attract guests. Less emphasis is given to selling inventory to wholesalers and promotion using traditional media" + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Investment/Tool": "Selling inventory to wholesalers", + "Percentage": 24.0 + } + ], + "score": 0.9, + "annotation": "The has a minimum value at Selling inventory to wholesalers", + "reason": "The [{'Investment/Tool': 'Selling inventory to wholesalers', 'Percentage': 24.0}] of has a mainimum value of 24.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Investment/Tool": "Promotion on traditional media", + "Percentage": 32.0 + } + ], + "score": 0.72, + "annotation": "The shows a sudden jump in Percentage from Selling inventory to wholesalers to Promotion on traditional media.", + "reason": "The Percentage for increases from 24.0 at Selling inventory to wholesalers to 32.0 at Promotion on traditional media, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Investment/Tool": "Offer targeted discounts", + "Percentage": 48.0 + } + ], + "score": 0.69, + "annotation": "The has a maximum value at Offer targeted discounts", + "reason": "The [{'Investment/Tool': 'Offer targeted discounts', 'Percentage': 48.0}] of has a maximum value of 48.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Investment/Tool": "Offer targeted discounts", + "Percentage": 48.0 + }, + { + "Investment/Tool": "Promotion on social media/influencer marketing", + "Percentage": 47.0 + }, + { + "Investment/Tool": "Investments in comfort", + "Percentage": 46.0 + }, + { + "Investment/Tool": "Investments in amenities", + "Percentage": 44.0 + }, + { + "Investment/Tool": "Listing on digital platforms", + "Percentage": 41.0 + }, + { + "Investment/Tool": "Investments in style/aesthetics", + "Percentage": 36.0 + }, + { + "Investment/Tool": "Using revenue/channel management software", + "Percentage": 35.0 + }, + { + "Investment/Tool": "Investment in staff training", + "Percentage": 35.0 + }, + { + "Investment/Tool": "Promotion on traditional media", + "Percentage": 32.0 + }, + { + "Investment/Tool": "Selling inventory to wholesalers", + "Percentage": 24.0 + } + ], + "columns": [ + { + "name": "Investment/Tool", + "importance": "primary", + "description": "The specific investment or tool used by hoteliers", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents who selected the investment/tool as effective", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1849": { + "metadata": { + "title": "0.7% Of The World's Population Control 41% Of Its Wealth", + "description": "How the world's wealth is shared amongst its population, by income group", + "main_insight": "The visualization highlights the extreme disparity in global wealth distribution, with a very small percentage of the population controlling a disproportionately large share of the world's wealth." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Income Group": ">1 million USD", + "Arbitrary Units": 0.7, + "Attribute": "Share of the world's population" + } + ], + "score": 0.92, + "annotation": "The Share of the world's population has the smallest proportion in >1 million USD.", + "reason": "The Arbitrary Units of Share of the world's population in >1 million USD accounts for 0.016786570743405272, smaller than all other Attribute, of the total of Arbitrary Units of all Attribute." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Income Group": ">1 million USD", + "Arbitrary Units": 41.0, + "Attribute": "% of the world's wealth" + } + ], + "score": 0.92, + "annotation": "The % of the world's wealth shows a sudden jump in Arbitrary Units from 10,000-100,000 USD to >1 million USD.", + "reason": "The Arbitrary Units for % of the world's wealth increases from 13.7 at 10,000-100,000 USD to 41.0 at >1 million USD, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Income Group": "<10,000 USD", + "Arbitrary Units": 68.7, + "Attribute": "Share of the world's population" + } + ], + "score": 0.91, + "annotation": "The Share of the world's population shows a sudden jump in Arbitrary Units from 10,000-100,000 USD to <10,000 USD.", + "reason": "The Arbitrary Units for Share of the world's population increases from 22.9 at 10,000-100,000 USD to 68.7 at <10,000 USD, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Income Group": ">1 million USD", + "Arbitrary Units": 41.0, + "Attribute": "% of the world's wealth" + }, + { + "Income Group": ">1 million USD", + "Arbitrary Units": 0.7, + "Attribute": "Share of the world's population" + } + ], + "score": 0.9, + "annotation": "The Arbitrary Units of % of the world's wealth and Share of the world's population show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Arbitrary Units between % of the world's wealth and Share of the world's population is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Income Group": "<10,000 USD", + "Arbitrary Units": 3.0, + "Attribute": "% of the world's wealth" + } + ], + "score": 0.83, + "annotation": "The % of the world's wealth has the smallest proportion in <10,000 USD.", + "reason": "The Arbitrary Units of % of the world's wealth in <10,000 USD accounts for 0.04184100418410042, smaller than all other Attribute, of the total of Arbitrary Units of all Attribute." + } + ], + "data": { + "data": [ + { + "Income Group": "<10,000 USD", + "Arbitrary Units": 3.0, + "Attribute": "% of the world's wealth" + }, + { + "Income Group": "<10,000 USD", + "Arbitrary Units": 68.7, + "Attribute": "Share of the world's population" + }, + { + "Income Group": "10,000-100,000 USD", + "Arbitrary Units": 13.7, + "Attribute": "% of the world's wealth" + }, + { + "Income Group": "10,000-100,000 USD", + "Arbitrary Units": 22.9, + "Attribute": "Share of the world's population" + }, + { + "Income Group": "100,000-1 million USD", + "Arbitrary Units": 42.3, + "Attribute": "% of the world's wealth" + }, + { + "Income Group": "100,000-1 million USD", + "Arbitrary Units": 7.7, + "Attribute": "Share of the world's population" + }, + { + "Income Group": ">1 million USD", + "Arbitrary Units": 41.0, + "Attribute": "% of the world's wealth" + }, + { + "Income Group": ">1 million USD", + "Arbitrary Units": 0.7, + "Attribute": "Share of the world's population" + } + ], + "columns": [ + { + "name": "Income Group", + "importance": "primary", + "description": "Income brackets in USD", + "unit": "USD", + "data_type": "string", + "role": "x" + }, + { + "name": "Arbitrary Units", + "importance": "primary", + "description": "Arbitrary Units for the corresponding attribute", + "role": "y" + }, + { + "name": "Attribute", + "importance": "primary", + "description": "The attribute of the data point", + "role": "group" + } + ] + } + }, + "1687": { + "metadata": { + "title": "Maharashtra Counts One Third of COVID-19 Deaths in India", + "description": "Confirmed COVID-19 cases and deaths in India, by state/city (in percent)", + "main_insight": "Maharashtra, particularly areas outside of Mumbai, accounts for a significantly larger proportion of COVID-19 deaths compared to its proportion of cases, representing one third of all deaths in India, as in December 21, 2020." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Category": "Deaths", + "Score": 8.0, + "Regions": "Mumbai" + }, + { + "Category": "Deaths", + "Score": 26.0, + "Regions": "Other Maharashtra" + } + ], + "score": 1.0, + "annotation": "The Score of Mumbai and Other Maharashtra show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Mumbai and Other Maharashtra is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Category": "Deaths", + "Score": 8.0, + "Regions": "Mumbai" + }, + { + "Category": "Deaths", + "Score": 8.0, + "Regions": "Karnataka" + } + ], + "score": 1.0, + "annotation": "The Score of Mumbai and Karnataka show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Mumbai and Karnataka is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Category": "Deaths", + "Score": 8.0, + "Regions": "Mumbai" + }, + { + "Category": "Deaths", + "Score": 5.0, + "Regions": "Andhra Pradesh" + } + ], + "score": 1.0, + "annotation": "The Score of Mumbai and Andhra Pradesh show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Mumbai and Andhra Pradesh is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Category": "Cases", + "Score": 3.0, + "Regions": "Mumbai" + } + ], + "score": 0.87, + "annotation": "The Mumbai has the smallest proportion in Cases.", + "reason": "The Score of Mumbai in Cases accounts for 0.029411764705882353, smaller than all other Regions, of the total of Score of all Regions." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Category": "Deaths", + "Score": 5.0, + "Regions": "Andhra Pradesh" + } + ], + "score": 0.81, + "annotation": "The Andhra Pradesh has the smallest proportion in Deaths.", + "reason": "The Score of Andhra Pradesh in Deaths accounts for 0.046296296296296294, smaller than all other Regions, of the total of Score of all Regions." + } + ], + "data": { + "data": [ + { + "Category": "Cases", + "Score": 3.0, + "Regions": "Mumbai" + }, + { + "Category": "Cases", + "Score": 16.0, + "Regions": "Other Maharashtra" + }, + { + "Category": "Cases", + "Score": 9.0, + "Regions": "Karnataka" + }, + { + "Category": "Cases", + "Score": 8.0, + "Regions": "Andhra Pradesh" + }, + { + "Category": "Cases", + "Score": 8.0, + "Regions": "Tamil Nadu" + }, + { + "Category": "Cases", + "Score": 58.0, + "Regions": "Others" + }, + { + "Category": "Deaths", + "Score": 8.0, + "Regions": "Mumbai" + }, + { + "Category": "Deaths", + "Score": 26.0, + "Regions": "Other Maharashtra" + }, + { + "Category": "Deaths", + "Score": 8.0, + "Regions": "Karnataka" + }, + { + "Category": "Deaths", + "Score": 5.0, + "Regions": "Andhra Pradesh" + }, + { + "Category": "Deaths", + "Score": 8.0, + "Regions": "Tamil Nadu" + }, + { + "Category": "Deaths", + "Score": 53.0, + "Regions": "Others" + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Type of data: Cases or Deaths", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding regions", + "role": "y" + }, + { + "name": "Regions", + "importance": "primary", + "description": "The regions of the data point", + "role": "group" + } + ] + } + }, + "334": { + "metadata": { + "title": "The U.S. Has Only Admitted 11 Syrian Refugees This Year", + "description": "Number of Syrian refugees resettled in the U.S. by year*", + "main_insight": "The number of Syrian refugees admitted to the U.S. peaked in 2016 and has drastically decreased in 2017 and 2018. As of April 18, 2018, only 11 refugees were admitted." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2011, + "Refugees Resettled": 23 + }, + { + "Year": 2012, + "Refugees Resettled": 41 + }, + { + "Year": 2013, + "Refugees Resettled": 45 + }, + { + "Year": 2014, + "Refugees Resettled": 249 + }, + { + "Year": 2015, + "Refugees Resettled": 2192 + }, + { + "Year": 2016, + "Refugees Resettled": 15479 + }, + { + "Year": 2017, + "Refugees Resettled": 3024 + }, + { + "Year": 2018, + "Refugees Resettled": 11 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of refugee resettlement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Refugees Resettled", + "importance": "primary", + "description": "Number of Syrian refugees resettled in the U.S.", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "623": { + "metadata": { + "title": "Americans See Foreign Trade as Positive", + "description": "Percentage of Americans who say foreign trade is an opportunity for economic growth", + "main_insight": "Both Democrats and Republicans increasingly view foreign trade as positive, with Democrats showing significantly more positive views since 2016." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 82.0, + "Party Affiliation": "Democrats" + }, + { + "Year": 2020, + "Units": 78.0, + "Party Affiliation": "Republicans" + } + ], + "score": 0.82, + "annotation": "The Units of Democrats and Republicans show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Democrats and Republicans is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 1992, + "Units": 36.0, + "Party Affiliation": "Democrats" + }, + { + "Year": 1992, + "Units": 49.0, + "Party Affiliation": "Republicans" + }, + { + "Year": 2002, + "Units": 52.0, + "Party Affiliation": "Democrats" + }, + { + "Year": 2002, + "Units": 57.0, + "Party Affiliation": "Republicans" + }, + { + "Year": 2016, + "Units": 68.0, + "Party Affiliation": "Democrats" + }, + { + "Year": 2016, + "Units": 51.0, + "Party Affiliation": "Republicans" + }, + { + "Year": 2019, + "Units": 80.0, + "Party Affiliation": "Democrats" + }, + { + "Year": 2019, + "Units": 72.0, + "Party Affiliation": "Republicans" + }, + { + "Year": 2020, + "Units": 82.0, + "Party Affiliation": "Democrats" + }, + { + "Year": 2020, + "Units": 78.0, + "Party Affiliation": "Republicans" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding party affiliation", + "role": "y" + }, + { + "name": "Party Affiliation", + "importance": "primary", + "description": "The party affiliation of the data point", + "role": "group" + } + ] + } + }, + "437": { + "metadata": { + "title": "Gold Outpacing Dollar", + "description": "Correlation between gold and U.S. dollar in 2020", + "main_insight": "The correlation between gold and the U.S. dollar fluctuated throughout the first half of 2020, ending with a strong negative correlation in July, indicating that gold and the dollar were moving in opposite directions." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Jun", + "Correlation": 0.1 + } + ], + "score": 0.8, + "annotation": "The has a maximum value at Jun", + "reason": "The [{'Month': 'Jun', 'Correlation': 0.1}] of has a maximum value of 0.1, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Jul", + "Correlation": -0.43 + } + ], + "score": 0.79, + "annotation": "The has a minimum value at Jul", + "reason": "The [{'Month': 'Jul', 'Correlation': -0.43}] of has a mainimum value of -0.43, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Jun", + "Correlation": 0.1 + } + ], + "score": 0.68, + "annotation": "The shows a sudden jump in Correlation from May to Jun.", + "reason": "The Correlation for increases from -0.05 at May to 0.1 at Jun, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Month": "Jan", + "Correlation": -0.35 + }, + { + "Month": "Feb", + "Correlation": -0.15 + }, + { + "Month": "Mar", + "Correlation": -0.1 + }, + { + "Month": "Apr", + "Correlation": -0.2 + }, + { + "Month": "May", + "Correlation": -0.05 + }, + { + "Month": "Jun", + "Correlation": 0.1 + }, + { + "Month": "Jul", + "Correlation": -0.43 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month in 2020", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Correlation", + "importance": "primary", + "description": "30-day moving correlation between percent changes in gold futures and ICE U.S. Dollar index", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "189": { + "metadata": { + "title": "The U.S. Has a Jails Suicide Crisis on Its Hands", + "description": "U.S. suicide rates in jails/prisons and the general population (per 100,000 persons)", + "main_insight": "Suicide rates in local jails are significantly higher than in state prisons, federal prisons, and the overall U.S. population. While rates fluctuated over time, local jail suicide rates remain consistently the highest." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Score": 27.0, + "Population": "State Prisons" + }, + { + "Year": 2019, + "Score": 20.0, + "Population": "Federal Prisons" + } + ], + "score": 0.66, + "annotation": "The Score of State Prisons and Federal Prisons show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between State Prisons and Federal Prisons is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Score": 27.0, + "Population": "State Prisons" + }, + { + "Year": 2019, + "Score": 13.9, + "Population": "Overall U.S. Population" + } + ], + "score": 0.66, + "annotation": "The Score of State Prisons and Overall U.S. Population show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between State Prisons and Overall U.S. Population is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Score": 20.0, + "Population": "Federal Prisons" + }, + { + "Year": 2019, + "Score": 13.9, + "Population": "Overall U.S. Population" + } + ], + "score": 0.56, + "annotation": "The Score of Federal Prisons and Overall U.S. Population show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Federal Prisons and Overall U.S. Population is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Score": 49.0, + "Population": "Local Jails" + }, + { + "Year": 2019, + "Score": 20.0, + "Population": "Federal Prisons" + } + ], + "score": 0.55, + "annotation": "The Score of Local Jails and Federal Prisons show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Local Jails and Federal Prisons is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Score": 49.0, + "Population": "Local Jails" + }, + { + "Year": 2019, + "Score": 27.0, + "Population": "State Prisons" + } + ], + "score": 0.24, + "annotation": "The Score of Local Jails and State Prisons show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Local Jails and State Prisons is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2000, + "Score": 47.0, + "Population": "Local Jails" + }, + { + "Year": 2000, + "Score": 14.0, + "Population": "State Prisons" + }, + { + "Year": 2000, + "Score": 11.0, + "Population": "Federal Prisons" + }, + { + "Year": 2000, + "Score": 10.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2001, + "Score": 48.0, + "Population": "Local Jails" + }, + { + "Year": 2001, + "Score": 15.0, + "Population": "State Prisons" + }, + { + "Year": 2001, + "Score": 13.0, + "Population": "Federal Prisons" + }, + { + "Year": 2001, + "Score": 10.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2002, + "Score": 45.0, + "Population": "Local Jails" + }, + { + "Year": 2002, + "Score": 14.0, + "Population": "State Prisons" + }, + { + "Year": 2002, + "Score": 5.0, + "Population": "Federal Prisons" + }, + { + "Year": 2002, + "Score": 11.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2003, + "Score": 43.0, + "Population": "Local Jails" + }, + { + "Year": 2003, + "Score": 15.0, + "Population": "State Prisons" + }, + { + "Year": 2003, + "Score": 7.0, + "Population": "Federal Prisons" + }, + { + "Year": 2003, + "Score": 11.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2004, + "Score": 41.0, + "Population": "Local Jails" + }, + { + "Year": 2004, + "Score": 15.0, + "Population": "State Prisons" + }, + { + "Year": 2004, + "Score": 5.0, + "Population": "Federal Prisons" + }, + { + "Year": 2004, + "Score": 11.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2005, + "Score": 42.0, + "Population": "Local Jails" + }, + { + "Year": 2005, + "Score": 16.0, + "Population": "State Prisons" + }, + { + "Year": 2005, + "Score": 9.0, + "Population": "Federal Prisons" + }, + { + "Year": 2005, + "Score": 11.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2006, + "Score": 39.0, + "Population": "Local Jails" + }, + { + "Year": 2006, + "Score": 16.0, + "Population": "State Prisons" + }, + { + "Year": 2006, + "Score": 9.0, + "Population": "Federal Prisons" + }, + { + "Year": 2006, + "Score": 11.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2007, + "Score": 36.0, + "Population": "Local Jails" + }, + { + "Year": 2007, + "Score": 16.0, + "Population": "State Prisons" + }, + { + "Year": 2007, + "Score": 11.0, + "Population": "Federal Prisons" + }, + { + "Year": 2007, + "Score": 11.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2008, + "Score": 36.0, + "Population": "Local Jails" + }, + { + "Year": 2008, + "Score": 16.0, + "Population": "State Prisons" + }, + { + "Year": 2008, + "Score": 8.0, + "Population": "Federal Prisons" + }, + { + "Year": 2008, + "Score": 12.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2009, + "Score": 29.0, + "Population": "Local Jails" + }, + { + "Year": 2009, + "Score": 14.0, + "Population": "State Prisons" + }, + { + "Year": 2009, + "Score": 4.0, + "Population": "Federal Prisons" + }, + { + "Year": 2009, + "Score": 12.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2010, + "Score": 40.0, + "Population": "Local Jails" + }, + { + "Year": 2010, + "Score": 17.0, + "Population": "State Prisons" + }, + { + "Year": 2010, + "Score": 11.0, + "Population": "Federal Prisons" + }, + { + "Year": 2010, + "Score": 12.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2011, + "Score": 40.0, + "Population": "Local Jails" + }, + { + "Year": 2011, + "Score": 14.0, + "Population": "State Prisons" + }, + { + "Year": 2011, + "Score": 9.0, + "Population": "Federal Prisons" + }, + { + "Year": 2011, + "Score": 12.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2012, + "Score": 43.0, + "Population": "Local Jails" + }, + { + "Year": 2012, + "Score": 15.0, + "Population": "State Prisons" + }, + { + "Year": 2012, + "Score": 14.0, + "Population": "Federal Prisons" + }, + { + "Year": 2012, + "Score": 13.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2013, + "Score": 47.0, + "Population": "Local Jails" + }, + { + "Year": 2013, + "Score": 13.0, + "Population": "State Prisons" + }, + { + "Year": 2013, + "Score": 15.0, + "Population": "Federal Prisons" + }, + { + "Year": 2013, + "Score": 13.0, + "Population": "Overall U.S. 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Jails" + }, + { + "Year": 2017, + "Score": 22.0, + "Population": "State Prisons" + }, + { + "Year": 2017, + "Score": 13.0, + "Population": "Federal Prisons" + }, + { + "Year": 2017, + "Score": 14.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2018, + "Score": 47.0, + "Population": "Local Jails" + }, + { + "Year": 2018, + "Score": 24.0, + "Population": "State Prisons" + }, + { + "Year": 2018, + "Score": 16.0, + "Population": "Federal Prisons" + }, + { + "Year": 2018, + "Score": 14.0, + "Population": "Overall U.S. Population" + }, + { + "Year": 2019, + "Score": 49.0, + "Population": "Local Jails" + }, + { + "Year": 2019, + "Score": 27.0, + "Population": "State Prisons" + }, + { + "Year": 2019, + "Score": 20.0, + "Population": "Federal Prisons" + }, + { + "Year": 2019, + "Score": 13.9, + "Population": "Overall U.S. Population" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the recorded suicide rate.", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding population", + "role": "y" + }, + { + "name": "Population", + "importance": "primary", + "description": "The population of the data point", + "role": "group" + } + ] + } + }, + "1121": { + "metadata": { + "title": "Agriculture Is the Biggest Employer in India", + "description": "Share of workers in India, by broad industry of work (2021-22)", + "main_insight": "Agriculture is the largest employer in India, employing 45.5% of workers in 2021-22, significantly more than any other sector." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Industry": "Agriculture", + "Share of Workers": 45.5 + } + ], + "score": 0.95, + "annotation": "The has a maximum value at Agriculture", + "reason": "The [{'Industry': 'Agriculture', 'Share of Workers': 45.5}] of has a maximum value of 45.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Industry": "Agriculture", + "Share of Workers": 45.5 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in Share of Workers from Construction to Agriculture.", + "reason": "The Share of Workers for increases from 12.6 at Construction to 45.5 at Agriculture, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Industry": "Mining & quarrying", + "Share of Workers": 0.3 + } + ], + "score": 0.53, + "annotation": "The has a minimum value at Mining & quarrying", + "reason": "The [{'Industry': 'Mining & quarrying', 'Share of Workers': 0.3}] of has a mainimum value of 0.3, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Industry": "Agriculture", + "Share of Workers": 45.5 + }, + { + "Industry": "Construction", + "Share of Workers": 12.6 + }, + { + "Industry": "Trade, hotel & restaurant", + "Share of Workers": 12.1 + }, + { + "Industry": "Other services", + "Share of Workers": 11.9 + }, + { + "Industry": "Manufacturing", + "Share of Workers": 11.6 + }, + { + "Industry": "Transport, storage & communications", + "Share of Workers": 5.6 + }, + { + "Industry": "Electricity, water, etc.", + "Share of Workers": 0.6 + }, + { + "Industry": "Mining & quarrying", + "Share of Workers": 0.3 + } + ], + "columns": [ + { + "name": "Industry", + "importance": "primary", + "description": "Broad industry of work", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Share of Workers", + "importance": "primary", + "description": "Percentage of total workers in India employed in each industry", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1546": { + "metadata": { + "title": "54% of the money goes to Berlin start-ups", + "description": "Berlin's share of VC investment in Germany and Total VC investments 1st half of 2021 (bn euros)", + "main_insight": "Berlin startups received 54% of the total VC investment in Germany in the first half of 2021, amounting to 4.1 billion euros." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Fintech/Insurtech", + "Berlin's share": 0.83 + } + ], + "score": 0.81, + "annotation": "The has a maximum value at Fintech/Insurtech", + "reason": "The [{'Category': 'Fintech/Insurtech', \"Berlin's share\": 0.83}] of has a maximum value of 0.83, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "E-Commerce", + "Berlin's share": 0.82 + } + ], + "score": 0.72, + "annotation": "The shows a sudden jump in Berlin's share from Total to E-Commerce.", + "reason": "The Berlin's share for increases from 0.54 at Total to 0.82 at E-Commerce, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Category": "Berlin", + "Berlin's share": 0 + }, + { + "Category": "Bavaria", + "Berlin's share": 0 + }, + { + "Category": "Baden-Württemberg", + "Berlin's share": 0 + }, + { + "Category": "North Rhine-Westphalia", + "Berlin's share": 0 + } + ], + "score": 0.59, + "annotation": "The has minimum values at Berlin, Bavaria, Baden-Württemberg, North Rhine-Westphalia", + "reason": "The [{'Category': 'Berlin', \"Berlin's share\": 0}, {'Category': 'Bavaria', \"Berlin's share\": 0}, {'Category': 'Baden-Württemberg', \"Berlin's share\": 0}, {'Category': 'North Rhine-Westphalia', \"Berlin's share\": 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Category": "Fintech/Insurtech", + "Berlin's share": 0.83 + }, + { + "Category": "E-Commerce", + "Berlin's share": 0.82 + }, + { + "Category": "Health", + "Berlin's share": 0.46 + }, + { + "Category": "Mobility", + "Berlin's share": 0.34 + }, + { + "Category": "Software & Analytics", + "Berlin's share": 0.24 + }, + { + "Category": "Total", + "Berlin's share": 0.54 + }, + { + "Category": "Berlin", + "Berlin's share": 0 + }, + { + "Category": "Bavaria", + "Berlin's share": 0 + }, + { + "Category": "Baden-Württemberg", + "Berlin's share": 0 + }, + { + "Category": "North Rhine-Westphalia", + "Berlin's share": 0 + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Category of VC investment or German state", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Berlin's share", + "importance": "primary", + "description": "Percentage of total VC investment in Germany for each category", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "324": { + "metadata": { + "title": "Don't Take The Kids", + "description": "Respondents in favor or against separating children from asylum seekers in the U.S.", + "main_insight": "A significant majority (66%) of U.S. voters surveyed opposed the policy of separating children from asylum-seeking parents, while 27% supported it." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Response": "Oppose", + "Percentage": 66.0 + } + ], + "score": 0.91, + "annotation": "The shows a sudden jump in Percentage from Support to Oppose.", + "reason": "The Percentage for increases from 27.0 at Support to 66.0 at Oppose, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Response": "Oppose", + "Percentage": 66.0 + } + ], + "score": 0.64, + "annotation": "The has a maximum value at Oppose", + "reason": "The [{'Response': 'Oppose', 'Percentage': 66.0}] of has a maximum value of 66.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Response": "Don't know / No answer", + "Percentage": 8.0 + } + ], + "score": 0.53, + "annotation": "The has a minimum value at Don't know / No answer", + "reason": "The [{'Response': \"Don't know / No answer\", 'Percentage': 8.0}] of has a mainimum value of 8.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Response": "Oppose", + "Percentage": 66.0 + }, + { + "Response": "Support", + "Percentage": 27.0 + }, + { + "Response": "Don't know / No answer", + "Percentage": 8.0 + } + ], + "columns": [ + { + "name": "Response", + "importance": "primary", + "description": "The different responses to the survey question", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents giving each response", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1092": { + "metadata": { + "title": "The Most Frequently Elected UN Security Council Members", + "description": "Non-permanent UN Security Council members with the most terms since 1946*", + "main_insight": "Japan has been elected the most frequently as a non-permanent member of the UN Security Council, with 11 terms, followed by Brazil with 10 terms." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Japan", + "Terms": 11 + } + ], + "score": 0.85, + "annotation": "The has a maximum value at Japan", + "reason": "The [{'Country': 'Japan', 'Terms': 11}] of has a maximum value of 11, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Canada", + "Terms": 6 + } + ], + "score": 0.7, + "annotation": "The has a minimum value at Canada", + "reason": "The [{'Country': 'Canada', 'Terms': 6}] of has a mainimum value of 6, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Pakistan", + "Terms": 7 + }, + { + "Country": "India", + "Terms": 8 + }, + { + "Country": "Argentina", + "Terms": 9 + }, + { + "Country": "Brazil", + "Terms": 10 + }, + { + "Country": "Japan", + "Terms": 11 + } + ], + "score": 0.6, + "annotation": "The exhibits multiple sudden jumps in Terms, transitioning from Canada, Italy, India, Argentina, Brazil to Pakistan, India, Argentina, Brazil, Japan.", + "reason": "Across these points, the Terms for increases from 6, 7, 8, 9, 10 at Canada, Italy, India, Argentina, Brazil to 7, 8, 9, 10, 11 at Pakistan, India, Argentina, Brazil, Japan, suggesting significant shifts in value." + } + ], + "data": { + "data": [ + { + "Country": "Japan", + "Terms": 11 + }, + { + "Country": "Brazil", + "Terms": 10 + }, + { + "Country": "Argentina", + "Terms": 9 + }, + { + "Country": "India", + "Terms": 8 + }, + { + "Country": "Pakistan", + "Terms": 7 + }, + { + "Country": "Colombia", + "Terms": 7 + }, + { + "Country": "Italy", + "Terms": 7 + }, + { + "Country": "Canada", + "Terms": 6 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Non-permanent member country of the UN Security Council", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Terms", + "importance": "primary", + "description": "Number of terms served on the UN Security Council", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2522": { + "metadata": { + "title": "Champions of Europe", + "description": "Football clubs with the most UEFA Champions League titles since 1955/56*", + "main_insight": "Real Madrid has the most UEFA Champions League titles, with 14 wins, significantly more than any other club. AC Milan is second with 7 wins." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Club": "Real Madrid", + "Titles": 14 + } + ], + "score": 0.95, + "annotation": "The has a maximum value at Real Madrid", + "reason": "The [{'Club': 'Real Madrid', 'Titles': 14}] of has a maximum value of 14, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Club": "Real Madrid", + "Titles": 14 + } + ], + "score": 0.9, + "annotation": "The shows a sudden jump in Titles from AC Milan to Real Madrid.", + "reason": "The Titles for increases from 7 at AC Milan to 14 at Real Madrid, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Club": "Inter", + "Titles": 3 + }, + { + "Club": "Manchester United", + "Titles": 3 + } + ], + "score": 0.52, + "annotation": "The has minimum values at Inter, Manchester United", + "reason": "The [{'Club': 'Inter', 'Titles': 3}, {'Club': 'Manchester United', 'Titles': 3}] of have minimum values of 3, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Club": "Real Madrid", + "Titles": 14 + }, + { + "Club": "AC Milan", + "Titles": 7 + }, + { + "Club": "Bayern Munich", + "Titles": 6 + }, + { + "Club": "Liverpool", + "Titles": 6 + }, + { + "Club": "Barcelona", + "Titles": 5 + }, + { + "Club": "Ajax", + "Titles": 4 + }, + { + "Club": "Inter", + "Titles": 3 + }, + { + "Club": "Manchester United", + "Titles": 3 + } + ], + "columns": [ + { + "name": "Club", + "importance": "primary", + "description": "Football club name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Titles", + "importance": "primary", + "description": "Number of UEFA Champions League titles won", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1266": { + "metadata": { + "title": "One In Eight Bird Species Threatened With Extinction", + "description": "Global conservation status of total extant bird species in 2018*", + "main_insight": "A significant portion of bird species are facing conservation challenges, with 13% classified as either vulnerable, endangered, or critically endangered, and another 9% classified as near threatened." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Conservation Status": "Least concern", + "Number of Species": 8417 + } + ], + "score": 0.96, + "annotation": "The shows a sudden jump in Number of Species from Near threatened to Least concern.", + "reason": "The Number of Species for increases from 1017 at Near threatened to 8417 at Least concern, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Conservation Status": "Least concern", + "Number of Species": 8417 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at Least concern", + "reason": "The [{'Conservation Status': 'Least concern', 'Number of Species': 8417}] of has a maximum value of 8417, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Conservation Status": "Critically endangered", + "Number of Species": 222 + } + ], + "score": 0.38, + "annotation": "The has a minimum value at Critically endangered", + "reason": "The [{'Conservation Status': 'Critically endangered', 'Number of Species': 222}] of has a mainimum value of 222, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Conservation Status": "Least concern", + "Number of Species": 8417 + }, + { + "Conservation Status": "Near threatened", + "Number of Species": 1017 + }, + { + "Conservation Status": "Vulnerable", + "Number of Species": 786 + }, + { + "Conservation Status": "Endangered", + "Number of Species": 461 + }, + { + "Conservation Status": "Critically endangered", + "Number of Species": 222 + } + ], + "columns": [ + { + "name": "Conservation Status", + "importance": "primary", + "description": "Category of conservation concern", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Species", + "importance": "primary", + "description": "The number of bird species in each conservation status category", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "479": { + "metadata": { + "title": "Brits and Americans Differ Hugely on the Origins of Life", + "description": "How do you personally think life on Earth began originally? (%)", + "main_insight": "There are significant differences between British and American adults in their beliefs about the origin of life, particularly regarding the role of God and the idea of spontaneous generation." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Origin Belief": "It was created by God", + "Points": 53.0, + "Location": "U.S. adults" + } + ], + "score": 0.94, + "annotation": "The U.S. adults shows a sudden jump in Points from No idea to It was created by God.", + "reason": "The Points for U.S. adults increases from 16.0 at No idea to 53.0 at It was created by God, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Origin Belief": "It was created by God", + "Points": 53.0, + "Location": "U.S. adults" + } + ], + "score": 0.87, + "annotation": "The U.S. adults has a maximum value at It was created by God", + "reason": "The [{'Origin Belief': 'It was created by God', 'Points': 53.0, 'Location': 'U.S. adults'}] of U.S. adults has a maximum value of 53.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Origin Belief": "None of these things: it happened because conditions here on Earth happened to be suitable", + "Points": 40.0, + "Location": "G.B. adults" + } + ], + "score": 0.83, + "annotation": "The G.B. adults shows a sudden jump in Points from No idea to None of these things: it happened because conditions here on Earth happened to be suitable.", + "reason": "The Points for G.B. adults increases from 22.0 at No idea to 40.0 at None of these things: it happened because conditions here on Earth happened to be suitable, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Origin Belief": "None of these things: it happened because conditions here on Earth happened to be suitable", + "Points": 40.0, + "Location": "G.B. adults" + } + ], + "score": 0.81, + "annotation": "The G.B. adults has a maximum value at None of these things: it happened because conditions here on Earth happened to be suitable", + "reason": "The [{'Origin Belief': 'None of these things: it happened because conditions here on Earth happened to be suitable', 'Points': 40.0, 'Location': 'G.B. adults'}] of G.B. adults has a maximum value of 40.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Location": "G.B. adults", + "Origin Belief": "total", + "Points": 100.0 + } + ], + "score": 0.8, + "annotation": "The total value of G.B. adults accounts for the majority in all Location.", + "reason": "The total value of Points of G.B. adults accounts for 0.5025125628140703 (more than 50%) of the total of Points of all Location." + } + ], + "data": { + "data": [ + { + "Origin Belief": "It was created by God", + "Points": 15.0, + "Location": "G.B. adults" + }, + { + "Origin Belief": "It was created by God", + "Points": 53.0, + "Location": "U.S. adults" + }, + { + "Origin Belief": "It was brought here originally by an older, alien civilisation from elsewhere in the universe", + "Points": 4.0, + "Location": "G.B. adults" + }, + { + "Origin Belief": "It was brought here originally by an older, alien civilisation from elsewhere in the universe", + "Points": 5.0, + "Location": "U.S. adults" + }, + { + "Origin Belief": "It started with organic compounds brought here by comets", + "Points": 19.0, + "Location": "G.B. adults" + }, + { + "Origin Belief": "It started with organic compounds brought here by comets", + "Points": 10.0, + "Location": "U.S. adults" + }, + { + "Origin Belief": "None of these things: it happened because conditions here on Earth happened to be suitable", + "Points": 40.0, + "Location": "G.B. adults" + }, + { + "Origin Belief": "None of these things: it happened because conditions here on Earth happened to be suitable", + "Points": 15.0, + "Location": "U.S. adults" + }, + { + "Origin Belief": "No idea", + "Points": 22.0, + "Location": "G.B. adults" + }, + { + "Origin Belief": "No idea", + "Points": 16.0, + "Location": "U.S. adults" + } + ], + "columns": [ + { + "name": "Origin Belief", + "importance": "primary", + "description": "Different beliefs about the origin of life", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding location", + "role": "y" + }, + { + "name": "Location", + "importance": "primary", + "description": "The location of the data point", + "role": "group" + } + ] + } + }, + "2228": { + "metadata": { + "title": "Where Lethal Violence is Fueled By Firearms", + "description": "Share of violent deaths attributable to guns or other means in 2016*", + "main_insight": "The chart shows the proportion of violent deaths caused by firearms versus other means across several countries. A significant majority of violent deaths in the listed countries are attributable to firearms, with Puerto Rico and Venezuela having the highest percentages." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "United States", + "Score": 63.0, + "Weapon Type": "Guns" + }, + { + "Country": "United States", + "Score": 37.0, + "Weapon Type": "Other Means" + } + ], + "score": 1.0, + "annotation": "The Score of Guns and Other Means show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Guns and Other Means is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Puerto Rico", + "Score": 79.0, + "Weapon Type": "Guns" + } + ], + "score": 0.88, + "annotation": "The Guns has a maximum value at Puerto Rico", + "reason": "The [{'Country': 'Puerto Rico', 'Score': 79.0, 'Weapon Type': 'Guns'}] of Guns has a maximum value of 79.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Puerto Rico", + "Score": 21.0, + "Weapon Type": "Other Means" + } + ], + "score": 0.88, + "annotation": "The Other Means has a minimum value at Puerto Rico", + "reason": "The [{'Country': 'Puerto Rico', 'Score': 21.0, 'Weapon Type': 'Other Means'}] of Other Means has a mainimum value of 21.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Brazil", + "Score": 63.0, + "Weapon Type": "Guns" + } + ], + "score": 0.76, + "annotation": "The Guns accounts for the majority in Brazil.", + "reason": "The Score of Guns in Brazil accounts for 0.63 (more than 50%) of the total of Score of all Weapon Type." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Colombia", + "Score": 63.0, + "Weapon Type": "Guns" + } + ], + "score": 0.76, + "annotation": "The Guns accounts for the majority in Colombia.", + "reason": "The Score of Guns in Colombia accounts for 0.63 (more than 50%) of the total of Score of all Weapon Type." + } + ], + "data": { + "data": [ + { + "Country": "Puerto Rico", + "Score": 79.0, + "Weapon Type": "Guns" + }, + { + "Country": "Puerto Rico", + "Score": 21.0, + "Weapon Type": "Other Means" + }, + { + "Country": "Venezuela", + "Score": 78.0, + "Weapon Type": "Guns" + }, + { + "Country": "Venezuela", + "Score": 22.0, + "Weapon Type": "Other Means" + }, + { + "Country": "El Salvador", + "Score": 73.0, + "Weapon Type": "Guns" + }, + { + "Country": "El Salvador", + "Score": 27.0, + "Weapon Type": "Other Means" + }, + { + "Country": "Guatemala", + "Score": 71.0, + "Weapon Type": "Guns" + }, + { + "Country": "Guatemala", + "Score": 29.0, + "Weapon Type": "Other Means" + }, + { + "Country": "Paraguay", + "Score": 70.0, + "Weapon Type": "Guns" + }, + { + "Country": "Paraguay", + "Score": 30.0, + "Weapon Type": "Other Means" + }, + { + "Country": "Trinidad and Tobago", + "Score": 69.0, + "Weapon Type": "Guns" + }, + { + "Country": "Trinidad and Tobago", + "Score": 31.0, + "Weapon Type": "Other Means" + }, + { + "Country": "Bolivia", + "Score": 68.0, + "Weapon Type": "Guns" + }, + { + "Country": "Bolivia", + "Score": 32.0, + "Weapon Type": "Other Means" + }, + { + "Country": "Honduras", + "Score": 66.0, + "Weapon Type": "Guns" + }, + { + "Country": "Honduras", + "Score": 34.0, + "Weapon Type": "Other Means" + }, + { + "Country": "Albania", + "Score": 65.0, + "Weapon Type": "Guns" + }, + { + "Country": "Albania", + "Score": 35.0, + "Weapon Type": "Other Means" + }, + { + "Country": "Brazil", + "Score": 63.0, + "Weapon Type": "Guns" + }, + { + "Country": "Brazil", + "Score": 37.0, + "Weapon Type": "Other Means" + }, + { + "Country": "Colombia", + "Score": 63.0, + "Weapon Type": "Guns" + }, + { + "Country": "Colombia", + "Score": 37.0, + "Weapon Type": "Other Means" + }, + { + "Country": "United States", + "Score": 63.0, + "Weapon Type": "Guns" + }, + { + "Country": "United States", + "Score": 37.0, + "Weapon Type": "Other Means" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where violent deaths occurred", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding weapon type", + "role": "y" + }, + { + "name": "Weapon Type", + "importance": "primary", + "description": "The weapon type of the data point", + "role": "group" + } + ] + } + }, + "1040": { + "metadata": { + "title": "Who Pays The Most Tax On Their Income?", + "description": "% taxes on wages in selected OECD countries in 2015*", + "main_insight": "Single earners with no children generally pay a higher percentage of their income in taxes compared to earners married with two children, across most of the listed OECD countries." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Chile", + "Score": 7.0, + "Household Type": "Single earner with no children" + }, + { + "Country": "Chile", + "Score": 7.0, + "Household Type": "One earner (married with two children)" + } + ], + "score": 0.93, + "annotation": "The Score of Single earner with no children and One earner (married with two children) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Single earner with no children and One earner (married with two children) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Chile", + "Score": 7.0, + "Household Type": "Single earner with no children" + } + ], + "score": 0.93, + "annotation": "The Single earner with no children has a minimum value at Chile", + "reason": "The [{'Country': 'Chile', 'Score': 7.0, 'Household Type': 'Single earner with no children'}] of Single earner with no children has a mainimum value of 7.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Belgium", + "Score": 45.8, + "Household Type": "One earner (married with two children)" + } + ], + "score": 0.83, + "annotation": "The One earner (married with two children) has a maximum value at Belgium", + "reason": "The [{'Country': 'Belgium', 'Score': 45.8, 'Household Type': 'One earner (married with two children)'}] of One earner (married with two children) has a maximum value of 45.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Belgium", + "Score": 55.8, + "Household Type": "Single earner with no children" + } + ], + "score": 0.83, + "annotation": "The Single earner with no children has a maximum value at Belgium", + "reason": "The [{'Country': 'Belgium', 'Score': 55.8, 'Household Type': 'Single earner with no children'}] of Single earner with no children has a maximum value of 55.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Chile", + "Score": 7.0, + "Household Type": "One earner (married with two children)" + } + ], + "score": 0.82, + "annotation": "The One earner (married with two children) has a minimum value at Chile", + "reason": "The [{'Country': 'Chile', 'Score': 7.0, 'Household Type': 'One earner (married with two children)'}] of One earner (married with two children) has a mainimum value of 7.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Belgium", + "Score": 55.8, + "Household Type": "Single earner with no children" + }, + { + "Country": "Belgium", + "Score": 45.8, + "Household Type": "One earner (married with two children)" + }, + { + "Country": "Germany", + "Score": 49.7, + "Household Type": "Single earner with no children" + }, + { + "Country": "Germany", + "Score": 34.4, + "Household Type": "One earner (married with two children)" + }, + { + "Country": "France", + "Score": 48.4, + "Household Type": "Single earner with no children" + }, + { + "Country": "France", + "Score": 39.8, + "Household Type": "One earner (married with two children)" + }, + { + "Country": "Spain", + "Score": 40.1, + "Household Type": "Single earner with no children" + }, + { + "Country": "Spain", + "Score": 34.6, + "Household Type": "One earner (married with two children)" + }, + { + "Country": "Greece", + "Score": 40.0, + "Household Type": "Single earner with no children" + }, + { + "Country": "Greece", + "Score": 36.6, + "Household Type": "One earner (married with two children)" + }, + { + "Country": "Japan", + "Score": 32.4, + "Household Type": "Single earner with no children" + }, + { + "Country": "Japan", + "Score": 24.9, + "Household Type": "One earner (married with two children)" + }, + { + "Country": "United States", + "Score": 32.2, + "Household Type": "Single earner with no children" + }, + { + "Country": "United States", + "Score": 21.8, + "Household Type": "One earner (married with two children)" + }, + { + "Country": "Canada", + "Score": 31.3, + "Household Type": "Single earner with no children" + }, + { + "Country": "Canada", + "Score": 15.7, + "Household Type": "One earner (married with two children)" + }, + { + "Country": "United Kingdom", + "Score": 30.9, + "Household Type": "Single earner with no children" + }, + { + "Country": "United Kingdom", + "Score": 25.5, + "Household Type": "One earner (married with two children)" + }, + { + "Country": "Australia", + "Score": 28.8, + "Household Type": "Single earner with no children" + }, + { + "Country": "Australia", + "Score": 16.9, + "Household Type": "One earner (married with two children)" + }, + { + "Country": "Switzerland", + "Score": 21.8, + "Household Type": "Single earner with no children" + }, + { + "Country": "Switzerland", + "Score": 9.8, + "Household Type": "One earner (married with two children)" + }, + { + "Country": "Chile", + "Score": 7.0, + "Household Type": "Single earner with no children" + }, + { + "Country": "Chile", + "Score": 7.0, + "Household Type": "One earner (married with two children)" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "OECD countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding household type", + "role": "y" + }, + { + "name": "Household Type", + "importance": "primary", + "description": "The household type of the data point", + "role": "group" + } + ] + } + }, + "271": { + "metadata": { + "title": "Wordle Holding Interest After Initial Hype", + "description": "Google Search interest in the term 'Wordle' in the US from December 2021 to August 2022", + "main_insight": "Google Search interest in 'Wordle' peaked in February 2022 and has since declined, stabilizing around 40% of its peak interest." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Dec", + "Search Interest": 1.0 + } + ], + "score": 0.86, + "annotation": "The has a minimum value at Dec", + "reason": "The [{'Month': 'Dec', 'Search Interest': 1.0}] of has a mainimum value of 1.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Feb", + "Search Interest": 95.0 + } + ], + "score": 0.76, + "annotation": "The has a maximum value at Feb", + "reason": "The [{'Month': 'Feb', 'Search Interest': 95.0}] of has a maximum value of 95.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Aug", + "Search Interest": 40.0 + } + ], + "score": 0.63, + "annotation": "The shows a sudden jump in Search Interest from Jan to Aug.", + "reason": "The Search Interest for increases from 18.0 at Jan to 40.0 at Aug, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Month": "Dec", + "Search Interest": 1.0 + }, + { + "Month": "Jan", + "Search Interest": 18.0 + }, + { + "Month": "Feb", + "Search Interest": 95.0 + }, + { + "Month": "Mar", + "Search Interest": 80.0 + }, + { + "Month": "Apr", + "Search Interest": 78.0 + }, + { + "Month": "May", + "Search Interest": 65.0 + }, + { + "Month": "Jun", + "Search Interest": 55.0 + }, + { + "Month": "Jul", + "Search Interest": 45.0 + }, + { + "Month": "Aug", + "Search Interest": 40.0 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year, from December 2021 to August 2022", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Search Interest", + "importance": "primary", + "description": "Relative search interest for 'Wordle' on Google (US), scaled to peak interest (100)", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2497": { + "metadata": { + "title": "Falling For a Knock-Off?", + "description": "Share of respondents in selected countries who have bought a counterfeit luxury product before (in percent)", + "main_insight": "A significant portion of respondents across various countries admit to having purchased counterfeit luxury goods, either intentionally or unintentionally. China shows the lowest percentage of intentional counterfeit purchases." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "China", + "Units": 37.0, + "Intentionality": "Maybe/not sure" + } + ], + "score": 0.94, + "annotation": "The Maybe/not sure shows a sudden jump in Units from Germany to China.", + "reason": "The Units for Maybe/not sure increases from 23.0 at Germany to 37.0 at China, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "China", + "Units": 37.0, + "Intentionality": "Maybe/not sure" + } + ], + "score": 0.92, + "annotation": "The Maybe/not sure has a maximum value at China", + "reason": "The [{'Country': 'China', 'Units': 37.0, 'Intentionality': 'Maybe/not sure'}] of Maybe/not sure has a maximum value of 37.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Italy", + "Units": 25.0, + "Intentionality": "Yes, but not on purpose" + } + ], + "score": 0.87, + "annotation": "The Yes, but not on purpose has the largest proportion in Italy.", + "reason": "The Units of Yes, but not on purpose in Italy accounts for 0.44642857142857145, larger than all other Intentionality, of the total of Units of all Intentionality." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Intentionality": "Yes, but not on purpose", + "Country": "total", + "Units": 197.0 + } + ], + "score": 0.86, + "annotation": "The total value of Yes, but not on purpose has the largest proportion.", + "reason": "The total value of Units of Yes, but not on purpose accounts for 0.4758454106280193, larger than all other Intentionality, of the total of Units of all Intentionality." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "U.S.", + "Units": 40.0, + "Intentionality": "Yes, but not on purpose" + } + ], + "score": 0.86, + "annotation": "The Yes, but not on purpose has the largest proportion in U.S..", + "reason": "The Units of Yes, but not on purpose in U.S. accounts for 0.49382716049382713, larger than all other Intentionality, of the total of Units of all Intentionality." + } + ], + "data": { + "data": [ + { + "Country": "U.S.", + "Units": 20.0, + "Intentionality": "Yes, on purpose" + }, + { + "Country": "U.S.", + "Units": 40.0, + "Intentionality": "Yes, but not on purpose" + }, + { + "Country": "U.S.", + "Units": 21.0, + "Intentionality": "Maybe/not sure" + }, + { + "Country": "UK", + "Units": 14.0, + "Intentionality": "Yes, on purpose" + }, + { + "Country": "UK", + "Units": 36.0, + "Intentionality": "Yes, but not on purpose" + }, + { + "Country": "UK", + "Units": 22.0, + "Intentionality": "Maybe/not sure" + }, + { + "Country": "Germany", + "Units": 12.0, + "Intentionality": "Yes, on purpose" + }, + { + "Country": "Germany", + "Units": 35.0, + "Intentionality": "Yes, but not on purpose" + }, + { + "Country": "Germany", + "Units": 23.0, + "Intentionality": "Maybe/not sure" + }, + { + "Country": "France", + "Units": 13.0, + "Intentionality": "Yes, on purpose" + }, + { + "Country": "France", + "Units": 32.0, + "Intentionality": "Yes, but not on purpose" + }, + { + "Country": "France", + "Units": 19.0, + "Intentionality": "Maybe/not sure" + }, + { + "Country": "China", + "Units": 5.0, + "Intentionality": "Yes, on purpose" + }, + { + "Country": "China", + "Units": 29.0, + "Intentionality": "Yes, but not on purpose" + }, + { + "Country": "China", + "Units": 37.0, + "Intentionality": "Maybe/not sure" + }, + { + "Country": "Italy", + "Units": 10.0, + "Intentionality": "Yes, on purpose" + }, + { + "Country": "Italy", + "Units": 25.0, + "Intentionality": "Yes, but not on purpose" + }, + { + "Country": "Italy", + "Units": 21.0, + "Intentionality": "Maybe/not sure" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of the respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding intentionality", + "role": "y" + }, + { + "name": "Intentionality", + "importance": "primary", + "description": "The intentionality of the data point", + "role": "group" + } + ] + } + }, + "372": { + "metadata": { + "title": "The Scale Of The U.S. Election In Comparison", + "description": "Number of registered voters in recent national elections*", + "main_insight": "The number of registered voters in India's 2019 election far exceeds that of the United States and other major countries, highlighting the vast scale of India's electorate." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "India (2019)", + "Registered Voters": 910512091 + } + ], + "score": 0.96, + "annotation": "The has a maximum value at India (2019)", + "reason": "The [{'Country': 'India (2019)', 'Registered Voters': 910512091}] of has a maximum value of 910512091, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "India (2019)", + "Registered Voters": 910512091 + } + ], + "score": 0.95, + "annotation": "The shows a sudden jump in Registered Voters from United States (2016) to India (2019).", + "reason": "The Registered Voters for increases from 214109367 at United States (2016) to 910512091 at India (2019), indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Japan (2014)", + "Registered Voters": 101280758 + } + ], + "score": 0.35, + "annotation": "The has a minimum value at Japan (2014)", + "reason": "The [{'Country': 'Japan (2014)', 'Registered Voters': 101280758}] of has a mainimum value of 101280758, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "India (2019)", + "Registered Voters": 910512091 + }, + { + "Country": "United States (2016)", + "Registered Voters": 214109367 + }, + { + "Country": "Indonesia (2019)", + "Registered Voters": 192866254 + }, + { + "Country": "Brazil (2018)", + "Registered Voters": 146750529 + }, + { + "Country": "Russian Federation (2018)", + "Registered Voters": 110061200 + }, + { + "Country": "Pakistan (2018)", + "Registered Voters": 105955409 + }, + { + "Country": "Bangladesh (2018)", + "Registered Voters": 104190480 + }, + { + "Country": "Japan (2014)", + "Registered Voters": 101280758 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country and year of election", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Registered Voters", + "importance": "primary", + "description": "Number of registered voters", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1716": { + "metadata": { + "title": "Eight Years Later: #oscarsstillowhite?", + "description": "Big Five Oscar nominations with participation of the following races/ethnic backgrounds between 2016 and 2023", + "main_insight": "The chart shows the number of Oscar nominations in the Big Five categories (Best Picture, Best Director, Best Actor, Best Actress, Best Screenplay) between 2016 and 2023, broken down by race/ethnicity. It reveals a significant disparity, with the 'Other' category (presumably representing white nominees) receiving the vast majority of nominations compared to Black, Asian/Pacific Islander, and Latin American nominees." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Category": "Best Actress", + "Count": 1, + "Race/Ethnicity": "Asian/Pacific Islander" + } + ], + "score": 0.89, + "annotation": "The Asian/Pacific Islander has the smallest proportion in Best Actress.", + "reason": "The Count of Asian/Pacific Islander in Best Actress accounts for 0.023809523809523808, smaller than all other Race/Ethnicity, of the total of Count of all Race/Ethnicity." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Category": "Best Screenplay (Adapted & Original)", + "Count": 58, + "Race/Ethnicity": "Other" + }, + { + "Category": "Best Screenplay (Adapted & Original)", + "Count": 8, + "Race/Ethnicity": "Black" + } + ], + "score": 0.87, + "annotation": "The Count of Other and Black show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Count between Other and Black is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "Best Screenplay (Adapted & Original)", + "Count": 10, + "Race/Ethnicity": "Asian/Pacific Islander" + } + ], + "score": 0.86, + "annotation": "The Asian/Pacific Islander shows a sudden jump in Count from Best Director to Best Screenplay (Adapted & Original).", + "reason": "The Count for Asian/Pacific Islander increases from 5 at Best Director to 10 at Best Screenplay (Adapted & Original), indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Category": "Best Director", + "Count": 29, + "Race/Ethnicity": "Other" + } + ], + "score": 0.85, + "annotation": "The Other accounts for the majority in Best Director.", + "reason": "The Count of Other in Best Director accounts for 0.725 (more than 50%) of the total of Count of all Race/Ethnicity." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Category": "Best Picture", + "Count": 46, + "Race/Ethnicity": "Other" + } + ], + "score": 0.85, + "annotation": "The Other accounts for the majority in Best Picture.", + "reason": "The Count of Other in Best Picture accounts for 0.7301587301587301 (more than 50%) of the total of Count of all Race/Ethnicity." + } + ], + "data": { + "data": [ + { + "Category": "Best Picture", + "Count": 46, + "Race/Ethnicity": "Other" + }, + { + "Category": "Best Picture", + "Count": 7, + "Race/Ethnicity": "Black" + }, + { + "Category": "Best Picture", + "Count": 5, + "Race/Ethnicity": "Asian/Pacific Islander" + }, + { + "Category": "Best Picture", + "Count": 5, + "Race/Ethnicity": "Latin American" + }, + { + "Category": "Best Director", + "Count": 29, + "Race/Ethnicity": "Other" + }, + { + "Category": "Best Director", + "Count": 3, + "Race/Ethnicity": "Black" + }, + { + "Category": "Best Director", + "Count": 5, + "Race/Ethnicity": "Asian/Pacific Islander" + }, + { + "Category": "Best Director", + "Count": 3, + "Race/Ethnicity": "Latin American" + }, + { + "Category": "Best Actor", + "Count": 32, + "Race/Ethnicity": "Other" + }, + { + "Category": "Best Actor", + "Count": 6, + "Race/Ethnicity": "Black" + }, + { + "Category": "Best Actor", + "Count": 2, + "Race/Ethnicity": "Asian/Pacific Islander" + }, + { + "Category": "Best Actress", + "Count": 33, + "Race/Ethnicity": "Other" + }, + { + "Category": "Best Actress", + "Count": 4, + "Race/Ethnicity": "Black" + }, + { + "Category": "Best Actress", + "Count": 1, + "Race/Ethnicity": "Asian/Pacific Islander" + }, + { + "Category": "Best Actress", + "Count": 2, + "Race/Ethnicity": "Latin American" + }, + { + "Category": "Best Screenplay (Adapted & Original)", + "Count": 58, + "Race/Ethnicity": "Other" + }, + { + "Category": "Best Screenplay (Adapted & Original)", + "Count": 8, + "Race/Ethnicity": "Black" + }, + { + "Category": "Best Screenplay (Adapted & Original)", + "Count": 10, + "Race/Ethnicity": "Asian/Pacific Islander" + }, + { + "Category": "Best Screenplay (Adapted & Original)", + "Count": 4, + "Race/Ethnicity": "Latin American" + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Oscar nomination category", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Count", + "importance": "primary", + "description": "Count for the corresponding race/ethnicity", + "role": "y" + }, + { + "name": "Race/Ethnicity", + "importance": "primary", + "description": "The race/ethnicity of the data point", + "role": "group" + } + ] + } + }, + "796": { + "metadata": { + "title": "Arming Teachers: What Do the Teachers Think?", + "description": "U.S. teachers' responses to a March 2018 survey on arming teachers with guns", + "main_insight": "A majority of teachers strongly oppose the proposal to arm teachers, believe it would not be effective in limiting shooting victims, and think it would make schools less safe." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Question": "What effect, if any, do you think it would have on schools in general if some teachers/staff members were armed?", + "Units": 20.0, + "Sentiment": "Strongly Favor/Very Effective/Safer" + }, + { + "Question": "What effect, if any, do you think it would have on schools in general if some teachers/staff members were armed?", + "Units": 22.0, + "Sentiment": "Somewhat Favor/Somewhat Effective/About as safe" + } + ], + "score": 0.94, + "annotation": "The Units of Strongly Favor/Very Effective/Safer and Somewhat Favor/Somewhat Effective/About as safe show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Strongly Favor/Very Effective/Safer and Somewhat Favor/Somewhat Effective/About as safe is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Question": "What effect, if any, do you think it would have on schools in general if some teachers/staff members were armed?", + "Units": 20.0, + "Sentiment": "Strongly Favor/Very Effective/Safer" + }, + { + "Question": "What effect, if any, do you think it would have on schools in general if some teachers/staff members were armed?", + "Units": 58.0, + "Sentiment": "Strongly Oppose/Not too Effective/Less Safe" + } + ], + "score": 0.19, + "annotation": "The Units of Strongly Favor/Very Effective/Safer and Strongly Oppose/Not too Effective/Less Safe show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Strongly Favor/Very Effective/Safer and Strongly Oppose/Not too Effective/Less Safe is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Question": "What effect, if any, do you think it would have on schools in general if some teachers/staff members were armed?", + "Units": 22.0, + "Sentiment": "Somewhat Favor/Somewhat Effective/About as safe" + }, + { + "Question": "What effect, if any, do you think it would have on schools in general if some teachers/staff members were armed?", + "Units": 58.0, + "Sentiment": "Strongly Oppose/Not too Effective/Less Safe" + } + ], + "score": 0.16, + "annotation": "The Units of Somewhat Favor/Somewhat Effective/About as safe and Strongly Oppose/Not too Effective/Less Safe show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Somewhat Favor/Somewhat Effective/About as safe and Strongly Oppose/Not too Effective/Less Safe is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Question": "What is your view of the proposal to give some teachers special training to carry guns at school?", + "Units": 11.0, + "Sentiment": "Strongly Favor/Very Effective/Safer" + }, + { + "Question": "What is your view of the proposal to give some teachers special training to carry guns at school?", + "Units": 9.0, + "Sentiment": "Somewhat Favor/Somewhat Effective/About as safe" + }, + { + "Question": "What is your view of the proposal to give some teachers special training to carry guns at school?", + "Units": 7.0, + "Sentiment": "Neutral" + }, + { + "Question": "What is your view of the proposal to give some teachers special training to carry guns at school?", + "Units": 10.0, + "Sentiment": "Somewhat Oppose" + }, + { + "Question": "What is your view of the proposal to give some teachers special training to carry guns at school?", + "Units": 63.0, + "Sentiment": "Strongly Oppose/Not too Effective/Less Safe" + }, + { + "Question": "How effective do you think teachers carrying guns would be in limiting the number of shooting victims?", + "Units": 13.0, + "Sentiment": "Strongly Favor/Very Effective/Safer" + }, + { + "Question": "How effective do you think teachers carrying guns would be in limiting the number of shooting victims?", + "Units": 16.0, + "Sentiment": "Somewhat Favor/Somewhat Effective/About as safe" + }, + { + "Question": "How effective do you think teachers carrying guns would be in limiting the number of shooting victims?", + "Units": 24.0, + "Sentiment": "Strongly Oppose/Not too Effective/Less Safe" + }, + { + "Question": "How effective do you think teachers carrying guns would be in limiting the number of shooting victims?", + "Units": 47.0, + "Sentiment": "Not Effective at all" + }, + { + "Question": "What effect, if any, do you think it would have on schools in general if some teachers/staff members were armed?", + "Units": 20.0, + "Sentiment": "Strongly Favor/Very Effective/Safer" + }, + { + "Question": "What effect, if any, do you think it would have on schools in general if some teachers/staff members were armed?", + "Units": 22.0, + "Sentiment": "Somewhat Favor/Somewhat Effective/About as safe" + }, + { + "Question": "What effect, if any, do you think it would have on schools in general if some teachers/staff members were armed?", + "Units": 58.0, + "Sentiment": "Strongly Oppose/Not too Effective/Less Safe" + } + ], + "columns": [ + { + "name": "Question", + "importance": "primary", + "description": "The survey question asked to teachers", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "2203": { + "metadata": { + "title": "Earth or Economy: A Global Snapshot", + "description": "Share of respondents who feel more closely aligned to the following viewpoints, by country (in percent)", + "main_insight": "Globally, there's a wide range of opinions on prioritizing the environment versus the economy. While the average leans slightly towards economic growth, individual countries show significant variations, with some, like Vietnam and China, strongly favoring the economy, and others like Russia showing a more balanced view." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Lebanon", + "Units": 60.5, + "Priority Areas": "Economy" + }, + { + "Country": "Lebanon", + "Units": 37.3, + "Priority Areas": "Environment" + } + ], + "score": 0.84, + "annotation": "The Units of Economy and Environment show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Economy and Environment is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Lebanon", + "Units": 37.3, + "Priority Areas": "Environment" + } + ], + "score": 0.83, + "annotation": "The Environment shows a sudden jump in Units from Great Britain to Lebanon.", + "reason": "The Units for Environment increases from 29.5 at Great Britain to 37.3 at Lebanon, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Russia", + "Units": 44.5, + "Priority Areas": "Economy" + } + ], + "score": 0.8, + "annotation": "The Economy accounts for the majority in Russia.", + "reason": "The Units of Economy in Russia accounts for 0.5138568129330254 (more than 50%) of the total of Units of all Priority Areas." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Argentina", + "Units": 43.2, + "Priority Areas": "Economy" + } + ], + "score": 0.8, + "annotation": "The Economy accounts for the majority in Argentina.", + "reason": "The Units of Economy in Argentina accounts for 0.5155131264916467 (more than 50%) of the total of Units of all Priority Areas." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Argentina", + "Units": 43.2, + "Priority Areas": "Economy" + } + ], + "score": 0.79, + "annotation": "The Economy has a minimum value at Argentina", + "reason": "The [{'Country': 'Argentina', 'Units': 43.2, 'Priority Areas': 'Economy'}] of Economy has a mainimum value of 43.2, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Vietnam", + "Units": 71.7, + "Priority Areas": "Economy" + }, + { + "Country": "Vietnam", + "Units": 26.2, + "Priority Areas": "Environment" + }, + { + "Country": "China", + "Units": 68.2, + "Priority Areas": "Economy" + }, + { + "Country": "China", + "Units": 26.2, + "Priority Areas": "Environment" + }, + { + "Country": "Great Britain", + "Units": 65.0, + "Priority Areas": "Economy" + }, + { + "Country": "Great Britain", + "Units": 29.5, + "Priority Areas": "Environment" + }, + { + "Country": "Germany", + "Units": 63.4, + "Priority Areas": "Economy" + }, + { + "Country": "Germany", + "Units": 27.4, + "Priority Areas": "Environment" + }, + { + "Country": "Average", + "Units": 54.5, + "Priority Areas": "Economy" + }, + { + "Country": "Average", + "Units": 38.2, + "Priority Areas": "Environment" + }, + { + "Country": "Brazil", + "Units": 54.4, + "Priority Areas": "Economy" + }, + { + "Country": "Brazil", + "Units": 27.5, + "Priority Areas": "Environment" + }, + { + "Country": "United States", + "Units": 50.4, + "Priority Areas": "Economy" + }, + { + "Country": "United States", + "Units": 38.6, + "Priority Areas": "Environment" + }, + { + "Country": "Russia", + "Units": 44.5, + "Priority Areas": "Economy" + }, + { + "Country": "Russia", + "Units": 42.1, + "Priority Areas": "Environment" + }, + { + "Country": "Argentina", + "Units": 43.2, + "Priority Areas": "Economy" + }, + { + "Country": "Argentina", + "Units": 40.6, + "Priority Areas": "Environment" + }, + { + "Country": "Lebanon", + "Units": 60.5, + "Priority Areas": "Economy" + }, + { + "Country": "Lebanon", + "Units": 37.3, + "Priority Areas": "Environment" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country or Average", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding priority areas", + "role": "y" + }, + { + "name": "Priority Areas", + "importance": "primary", + "description": "The priority areas of the data point", + "role": "group" + } + ] + } + }, + "1262": { + "metadata": { + "title": "Gaming: The Real Money Is in Software", + "description": "U.S. video game industry revenue", + "main_insight": "Software revenue significantly outweighs hardware revenue in the U.S. video game industry, and this trend continued from 2016 to 2017." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2016, + "Software Revenue": 24.6 + }, + { + "Year": 2017, + "Software Revenue": 29.1 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of revenue", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Software Revenue", + "importance": "primary", + "description": "Revenue from software, including in-game purchases and subscriptions, in billions of U.S. dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "127": { + "metadata": { + "title": "Analysis: Meghan Gets More Negative Media Coverage", + "description": "Share of headlines about Kate & Meghan that were positive/negative (May '18-Jan '19)", + "main_insight": "Meghan Markle received a significantly higher share of negative media coverage (43%) compared to Kate Middleton (8%) between May 2018 and January 2019." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Person": "Kate (Duchess of Cambridge)", + "Points": 8.0, + "Sentiment": "Negative" + }, + { + "Person": "Kate (Duchess of Cambridge)", + "Points": 47.0, + "Sentiment": "Neutral" + } + ], + "score": 1.0, + "annotation": "The Points of Negative and Neutral show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Points between Negative and Neutral is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Person": "Kate (Duchess of Cambridge)", + "Points": 8.0, + "Sentiment": "Negative" + }, + { + "Person": "Kate (Duchess of Cambridge)", + "Points": 45.0, + "Sentiment": "Positive" + } + ], + "score": 1.0, + "annotation": "The Points of Negative and Positive show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Points between Negative and Positive is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Person": "Kate (Duchess of Cambridge)", + "Points": 47.0, + "Sentiment": "Neutral" + }, + { + "Person": "Kate (Duchess of Cambridge)", + "Points": 45.0, + "Sentiment": "Positive" + } + ], + "score": 1.0, + "annotation": "The Points of Neutral and Positive show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Points between Neutral and Positive is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Person": "Meghan (Duchess of Sussex)", + "Points": 43.0, + "Sentiment": "Negative" + } + ], + "score": 0.97, + "annotation": "The Negative shows a sudden jump in Points from Kate (Duchess of Cambridge) to Meghan (Duchess of Sussex).", + "reason": "The Points for Negative increases from 8.0 at Kate (Duchess of Cambridge) to 43.0 at Meghan (Duchess of Sussex), indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Person": "Kate (Duchess of Cambridge)", + "Points": 47.0, + "Sentiment": "Neutral" + } + ], + "score": 0.97, + "annotation": "The Neutral shows a sudden jump in Points from Meghan (Duchess of Sussex) to Kate (Duchess of Cambridge).", + "reason": "The Points for Neutral increases from 36.0 at Meghan (Duchess of Sussex) to 47.0 at Kate (Duchess of Cambridge), indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Person": "Meghan (Duchess of Sussex)", + "Points": 43.0, + "Sentiment": "Negative" + }, + { + "Person": "Meghan (Duchess of Sussex)", + "Points": 36.0, + "Sentiment": "Neutral" + }, + { + "Person": "Meghan (Duchess of Sussex)", + "Points": 20.0, + "Sentiment": "Positive" + }, + { + "Person": "Kate (Duchess of Cambridge)", + "Points": 8.0, + "Sentiment": "Negative" + }, + { + "Person": "Kate (Duchess of Cambridge)", + "Points": 47.0, + "Sentiment": "Neutral" + }, + { + "Person": "Kate (Duchess of Cambridge)", + "Points": 45.0, + "Sentiment": "Positive" + } + ], + "columns": [ + { + "name": "Person", + "importance": "primary", + "description": "The person the headlines are about", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "782": { + "metadata": { + "title": "Where Afghan Refugees Are Located", + "description": "Selected countries by number of Afghan refugees they are hosting (2020)", + "main_insight": "Pakistan and Iran host the vast majority of Afghan refugees, significantly more than any other listed country." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Pakistan", + "Number of Refugees": 1438000 + } + ], + "score": 0.97, + "annotation": "The has a maximum value at Pakistan", + "reason": "The [{'Country': 'Pakistan', 'Number of Refugees': 1438000}] of has a maximum value of 1438000, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Pakistan", + "Number of Refugees": 1438000 + } + ], + "score": 0.81, + "annotation": "The shows a sudden jump in Number of Refugees from Iran to Pakistan.", + "reason": "The Number of Refugees for increases from 780000 at Iran to 1438000 at Pakistan, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "U.S.", + "Number of Refugees": 2000 + } + ], + "score": 0.36, + "annotation": "The has a minimum value at U.S.", + "reason": "The [{'Country': 'U.S.', 'Number of Refugees': 2000}] of has a mainimum value of 2000, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Pakistan", + "Number of Refugees": 1438000 + }, + { + "Country": "Iran", + "Number of Refugees": 780000 + }, + { + "Country": "Germany", + "Number of Refugees": 148000 + }, + { + "Country": "Austria", + "Number of Refugees": 40000 + }, + { + "Country": "France", + "Number of Refugees": 32000 + }, + { + "Country": "Sweden", + "Number of Refugees": 30000 + }, + { + "Country": "Australia", + "Number of Refugees": 11000 + }, + { + "Country": "UK", + "Number of Refugees": 9000 + }, + { + "Country": "India", + "Number of Refugees": 8000 + }, + { + "Country": "U.S.", + "Number of Refugees": 2000 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country hosting Afghan refugees", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Refugees", + "importance": "primary", + "description": "Number of Afghan refugees hosted by the country in 2020", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "841": { + "metadata": { + "title": "The U.S. Fine Dining Capitals", + "description": "U.S. cities with the highest number of Michelin-starred restaurants", + "main_insight": "New York City has the highest number of Michelin-starred restaurants among U.S. cities, significantly outnumbering other major metropolitan areas. The distribution shows a mix of 1, 2, and 3-star restaurants across these cities." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "New York City", + "Units": 58, + "Star Rating": "1 star" + } + ], + "score": 0.91, + "annotation": "The 1 star has a maximum value at New York City", + "reason": "The [{'City': 'New York City', 'Units': 58, 'Star Rating': '1 star'}] of 1 star has a maximum value of 58, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "New York City", + "Units": 58, + "Star Rating": "1 star" + } + ], + "score": 0.91, + "annotation": "The 1 star shows a sudden jump in Units from San Francisco & Bay Area to New York City.", + "reason": "The Units for 1 star increases from 26 at San Francisco & Bay Area to 58 at New York City, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "New York City", + "Units": 5, + "Star Rating": "3 stars" + } + ], + "score": 0.91, + "annotation": "The 3 stars shows a sudden jump in Units from Washington D.C. to New York City.", + "reason": "The Units for 3 stars increases from 3 at Washington D.C. to 5 at New York City, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "City": "Miami Metro", + "Units": 10, + "Star Rating": "1 star" + }, + { + "City": "Miami Metro", + "Units": 1, + "Star Rating": "2 stars" + } + ], + "score": 0.89, + "annotation": "The Units of 1 star and 2 stars show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 1 star and 2 stars is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "City": "San Francisco & Bay Area", + "Units": 26, + "Star Rating": "1 star" + } + ], + "score": 0.81, + "annotation": "The 1 star accounts for the majority in San Francisco & Bay Area.", + "reason": "The Units of 1 star in San Francisco & Bay Area accounts for 0.6842105263157895 (more than 50%) of the total of Units of all Star Rating." + } + ], + "data": { + "data": [ + { + "City": "New York City", + "Units": 58, + "Star Rating": "1 star" + }, + { + "City": "New York City", + "Units": 9, + "Star Rating": "2 stars" + }, + { + "City": "New York City", + "Units": 5, + "Star Rating": "3 stars" + }, + { + "City": "San Francisco & Bay Area", + "Units": 26, + "Star Rating": "1 star" + }, + { + "City": "San Francisco & Bay Area", + "Units": 7, + "Star Rating": "2 stars" + }, + { + "City": "San Francisco & Bay Area", + "Units": 5, + "Star Rating": "3 stars" + }, + { + "City": "Greater Los Angeles", + "Units": 19, + "Star Rating": "1 star" + }, + { + "City": "Greater Los Angeles", + "Units": 5, + "Star Rating": "2 stars" + }, + { + "City": "Washington D.C.", + "Units": 17, + "Star Rating": "1 star" + }, + { + "City": "Washington D.C.", + "Units": 4, + "Star Rating": "2 stars" + }, + { + "City": "Washington D.C.", + "Units": 3, + "Star Rating": "3 stars" + }, + { + "City": "Chicago", + "Units": 16, + "Star Rating": "1 star" + }, + { + "City": "Chicago", + "Units": 4, + "Star Rating": "2 stars" + }, + { + "City": "Chicago", + "Units": 2, + "Star Rating": "3 stars" + }, + { + "City": "Miami Metro", + "Units": 10, + "Star Rating": "1 star" + }, + { + "City": "Miami Metro", + "Units": 1, + "Star Rating": "2 stars" + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "U.S. City", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding star rating", + "role": "y" + }, + { + "name": "Star Rating", + "importance": "primary", + "description": "The star rating of the data point", + "role": "group" + } + ] + } + }, + "1319": { + "metadata": { + "title": "Then & Now India's Tiger Population Rebounds", + "description": "Number of wild tigers documented in India", + "main_insight": "India's tiger population has significantly increased from 1,411 in 2006 to 2,967 in 2018, though it is still far below the estimated 40,000 in 1947." + }, + "chart_type": "Proportional Icon Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1947, + "Tiger Population": 40000 + }, + { + "Year": 2006, + "Tiger Population": 1411 + }, + { + "Year": 2018, + "Tiger Population": 2967 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of tiger population estimate", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Tiger Population", + "importance": "primary", + "description": "Estimated number of wild tigers in India", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "655": { + "metadata": { + "title": "Earth Experiences Steady Stream of Heat Records", + "description": "Share of the Earth surface reaching mean monthly temperature records (in percent)", + "main_insight": "The chart shows a significant difference between the percentage of the Earth's surface experiencing record heat versus record cold, with heat records consistently higher and showing considerable fluctuation over time." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Month": "Jul 2021", + "Value": 0.01, + "Temperature Extremes": "Cold record" + } + ], + "score": 0.99, + "annotation": "The Cold record has the smallest proportion in Jul 2020.", + "reason": "The Value of Cold record in Jul 2020 accounts for 0.0012610340479192938, smaller than all other Temperature Extremes, of the total of Value of all Temperature Extremes." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Month": "Aug 2019", + "Value": 0.02, + "Temperature Extremes": "Cold record" + } + ], + "score": 0.99, + "annotation": "The Cold record has the smallest proportion in Aug 2019.", + "reason": "The Value of Cold record in Aug 2019 accounts for 0.001707941929974381, smaller than all other Temperature Extremes, of the total of Value of all Temperature Extremes." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Month": "Sep 2019", + "Value": 0.02, + "Temperature Extremes": "Cold record" + } + ], + "score": 0.99, + "annotation": "The Cold record has the smallest proportion in Sep 2019.", + "reason": "The Value of Cold record in Sep 2019 accounts for 0.0024570024570024574, smaller than all other Temperature Extremes, of the total of Value of all Temperature Extremes." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Temperature Extremes": "Heat record", + "Month": "total", + "Value": 177.12000000000003 + } + ], + "score": 0.99, + "annotation": "The total value of Heat record has the smallest proportion.", + "reason": "The total value of Value of Heat record accounts for 0.0026465454135931074, smaller than all other Temperature Extremes, of the total of Value of all Temperature Extremes." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Month": "May 2021", + "Value": 0.02, + "Temperature Extremes": "Cold record" + } + ], + "score": 0.99, + "annotation": "The Cold record has the smallest proportion in Jun 2020.", + "reason": "The Value of Cold record in Jun 2020 accounts for 0.0029455081001472758, smaller than all other Temperature Extremes, of the total of Value of all Temperature Extremes." + } + ], + "data": { + "data": [ + { + "Month": "Aug 2019", + "Value": 11.69, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Aug 2019", + "Value": 0.02, + "Temperature Extremes": "Cold record" + }, + { + "Month": "Sep 2019", + "Value": 8.12, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Sep 2019", + "Value": 0.02, + "Temperature Extremes": "Cold record" + }, + { + "Month": "Oct 2019", + "Value": 6.28, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Oct 2019", + "Value": 0.03, + "Temperature Extremes": "Cold record" + }, + { + "Month": "Nov 2019", + "Value": 8.86, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Nov 2019", + "Value": 0.03, + "Temperature Extremes": "Cold record" + }, + { + "Month": "Jan 2020", + "Value": 6.83, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Jan 2020", + "Value": 0.03, + "Temperature Extremes": "Cold record" + }, + { + "Month": "Feb 2020", + "Value": 8.13, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Mar 2020", + "Value": 6.97, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Apr 2020", + "Value": 8.15, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Apr 2020", + "Value": 0.04, + "Temperature Extremes": "Cold record" + }, + { + "Month": "May 2020", + "Value": 8.7, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Jun 2020", + "Value": 6.77, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Jul 2020", + "Value": 7.92, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Aug 2020", + "Value": 7.61, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Sep 2020", + "Value": 6.15, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Oct 2020", + "Value": 5.7, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Oct 2020", + "Value": 0.07, + "Temperature Extremes": "Cold record" + }, + { + "Month": "Nov 2020", + "Value": 2.49, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Nov 2020", + "Value": 0.09, + "Temperature Extremes": "Cold record" + }, + { + "Month": "Jan 2021", + "Value": 5.71, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Feb 2021", + "Value": 4.07, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Mar 2021", + "Value": 4.43, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Mar 2021", + "Value": 0.06, + "Temperature Extremes": "Cold record" + }, + { + "Month": "Apr 2021", + "Value": 2.12, + "Temperature Extremes": "Heat record" + }, + { + "Month": "May 2021", + "Value": 3.22, + "Temperature Extremes": "Heat record" + }, + { + "Month": "May 2021", + "Value": 0.02, + "Temperature Extremes": "Cold record" + }, + { + "Month": "Jun 2021", + "Value": 5.16, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Jul 2021", + "Value": 9.91, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Jul 2021", + "Value": 0.01, + "Temperature Extremes": "Cold record" + }, + { + "Month": "Jan 2022", + "Value": 2.66, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Feb 2022", + "Value": 3.98, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Mar 2022", + "Value": 5.33, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Mar 2022", + "Value": 0.05, + "Temperature Extremes": "Cold record" + }, + { + "Month": "Apr 2022", + "Value": 7.68, + "Temperature Extremes": "Heat record" + }, + { + "Month": "May 2022", + "Value": 4.8, + "Temperature Extremes": "Heat record" + }, + { + "Month": "Jun 2022", + "Value": 7.68, + "Temperature Extremes": "Heat record" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month and year of the record", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding temperature extremes", + "role": "y" + }, + { + "name": "Temperature Extremes", + "importance": "primary", + "description": "The temperature extremes of the data point", + "role": "group" + } + ] + } + }, + "2121": { + "metadata": { + "title": "2022: Big Tech's Year of Reckoning", + "description": "Annual stock price change of selected U.S. tech companies", + "main_insight": "Major U.S. tech companies experienced significant stock price declines in 2022 compared to their growth in 2020 and 2021." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Company": "Apple", + "Units": -27.0, + "Company Performance": "Apple_2022" + }, + { + "Company": "Apple", + "Units": -29.0, + "Company Performance": "Microsoft_2022" + }, + { + "Company": "Apple", + "Units": -39.0, + "Company Performance": "Alphabet_2022" + }, + { + "Company": "Apple", + "Units": -50.0, + "Company Performance": "Amazon_2022" + }, + { + "Company": "Apple", + "Units": -64.0, + "Company Performance": "Meta_2022" + }, + { + "Company": "Apple", + "Units": 81.0, + "Company Performance": "Apple_2020" + }, + { + "Company": "Apple", + "Units": 41.0, + "Company Performance": "Microsoft_2020" + }, + { + "Company": "Apple", + "Units": 31.0, + "Company Performance": "Alphabet_2020" + }, + { + "Company": "Apple", + "Units": 76.0, + "Company Performance": "Amazon_2020" + }, + { + "Company": "Apple", + "Units": 33.0, + "Company Performance": "Meta_2020" + }, + { + "Company": "Apple", + "Units": 34.0, + "Company Performance": "Apple_2021" + }, + { + "Company": "Apple", + "Units": 51.0, + "Company Performance": "Microsoft_2021" + }, + { + "Company": "Apple", + "Units": 65.0, + "Company Performance": "Alphabet_2021" + }, + { + "Company": "Apple", + "Units": 2.0, + "Company Performance": "Amazon_2021" + }, + { + "Company": "Apple", + "Units": 23.0, + "Company Performance": "Meta_2021" + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Name of the tech company", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding company performance", + "role": "y" + }, + { + "name": "Company Performance", + "importance": "primary", + "description": "The company performance of the data point", + "role": "group" + } + ] + } + }, + "1772": { + "metadata": { + "title": "Americans Didn't Let Inflation Spoil Their Appetite", + "description": "Monthly sales of food services and drinking places in the United States (seasonally adjusted)", + "main_insight": "Despite a brief dip during the 2020 recession, sales of food services and drinking places in the US have grown significantly since 2020, reaching $97.3 billion by October 2024." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2017, + "Sales": 58.0 + }, + { + "Year": 2018, + "Sales": 62.0 + }, + { + "Year": 2019, + "Sales": 67.0 + }, + { + "Year": 2020, + "Sales": 35.0 + }, + { + "Year": 2020, + "Sales": 65.0 + }, + { + "Year": 2021, + "Sales": 75.0 + }, + { + "Year": 2022, + "Sales": 85.0 + }, + { + "Year": 2023, + "Sales": 92.0 + }, + { + "Year": 2024, + "Sales": 97.3 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year, with some specific months labeled", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Sales", + "importance": "primary", + "description": "Monthly sales of food services and drinking places in billions of dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1690": { + "metadata": { + "title": "Who is the Government Aid Package Benefiting?", + "description": "Indian COVID-19 government aid package and beneficiaries as well as estimated economic impact of outbreak", + "main_insight": "The Indian government's COVID-19 relief package of Rs. 1.7 trillion is targeted towards various groups, while the estimated economic losses are significantly higher at Rs. 8.8 trillion." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Beneficiary": "Estimated economic losses", + "Aid/Losses": 8.8 + } + ], + "score": 0.96, + "annotation": "The has a maximum value at Estimated economic losses", + "reason": "The [{'Beneficiary': 'Estimated economic losses', 'Aid/Losses': 8.8}] of has a maximum value of 8.8, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Beneficiary": "Estimated economic losses", + "Aid/Losses": 8.8 + } + ], + "score": 0.94, + "annotation": "The shows a sudden jump in Aid/Losses from Relief package to Estimated economic losses.", + "reason": "The Aid/Losses for increases from 1.7 at Relief package to 8.8 at Estimated economic losses, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Beneficiary": "People receiving free cereals, pulses", + "Aid/Losses": 0 + }, + { + "Beneficiary": "Female Jan Dhan account holders", + "Aid/Losses": 0 + }, + { + "Beneficiary": "Farmers", + "Aid/Losses": 0 + }, + { + "Beneficiary": "Seniors, widows, disabled", + "Aid/Losses": 0 + }, + { + "Beneficiary": "Health care workers", + "Aid/Losses": 0 + } + ], + "score": 0.33, + "annotation": "The has minimum values at People receiving free cereals, pulses, Female Jan Dhan account holders, Farmers, Seniors, widows, disabled, Health care workers", + "reason": "The [{'Beneficiary': 'People receiving free cereals, pulses', 'Aid/Losses': 0}, {'Beneficiary': 'Female Jan Dhan account holders', 'Aid/Losses': 0}, {'Beneficiary': 'Farmers', 'Aid/Losses': 0}, {'Beneficiary': 'Seniors, widows, disabled', 'Aid/Losses': 0}, {'Beneficiary': 'Health care workers', 'Aid/Losses': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Beneficiary": "Relief package", + "Aid/Losses": 1.7 + }, + { + "Beneficiary": "Estimated economic losses", + "Aid/Losses": 8.8 + }, + { + "Beneficiary": "People receiving free cereals, pulses", + "Aid/Losses": 0 + }, + { + "Beneficiary": "Female Jan Dhan account holders", + "Aid/Losses": 0 + }, + { + "Beneficiary": "Farmers", + "Aid/Losses": 0 + }, + { + "Beneficiary": "Construction workers", + "Aid/Losses": 0.52 + }, + { + "Beneficiary": "Seniors, widows, disabled", + "Aid/Losses": 0 + }, + { + "Beneficiary": "Health care workers", + "Aid/Losses": 0 + } + ], + "columns": [ + { + "name": "Beneficiary", + "importance": "primary", + "description": "Group receiving aid", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Aid/Losses", + "importance": "primary", + "description": "Amount of aid or estimated losses", + "unit": "Rs. Trillion", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2179": { + "metadata": { + "title": "Germans Are More Satisfied Than Other Europeans", + "description": "Are you satisfied with the direction your country is taking?", + "main_insight": "Germans are significantly more satisfied with the direction their country is taking compared to other European countries, with 59% responding 'Yes'. Other European countries show much lower satisfaction levels." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country/Region": "Italy", + "Points": 13.0, + "Response": "Yes" + }, + { + "Country/Region": "Italy", + "Points": 87.0, + "Response": "No" + } + ], + "score": 1.0, + "annotation": "The Points of Yes and No show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Points between Yes and No is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country/Region": "Germany", + "Points": 59.0, + "Response": "Yes" + } + ], + "score": 0.88, + "annotation": "The Yes has a maximum value at Germany", + "reason": "The [{'Country/Region': 'Germany', 'Points': 59.0, 'Response': 'Yes'}] of Yes has a maximum value of 59.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country/Region": "Germany", + "Points": 41.0, + "Response": "No" + } + ], + "score": 0.88, + "annotation": "The No has a minimum value at Germany", + "reason": "The [{'Country/Region': 'Germany', 'Points': 41.0, 'Response': 'No'}] of No has a mainimum value of 41.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country/Region": "Germany", + "Points": 59.0, + "Response": "Yes" + } + ], + "score": 0.83, + "annotation": "The Yes shows a sudden jump in Points from Poland to Germany.", + "reason": "The Points for Yes increases from 36.0 at Poland to 59.0 at Germany, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country/Region": "France", + "Points": 64.0, + "Response": "No" + } + ], + "score": 0.83, + "annotation": "The No shows a sudden jump in Points from Germany to France.", + "reason": "The Points for No increases from 41.0 at Germany to 64.0 at France, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country/Region": "Germany", + "Points": 59.0, + "Response": "Yes" + }, + { + "Country/Region": "Germany", + "Points": 41.0, + "Response": "No" + }, + { + "Country/Region": "France", + "Points": 36.0, + "Response": "Yes" + }, + { + "Country/Region": "France", + "Points": 64.0, + "Response": "No" + }, + { + "Country/Region": "EU-28", + "Points": 36.0, + "Response": "Yes" + }, + { + "Country/Region": "EU-28", + "Points": 64.0, + "Response": "No" + }, + { + "Country/Region": "Poland", + "Points": 36.0, + "Response": "Yes" + }, + { + "Country/Region": "Poland", + "Points": 64.0, + "Response": "No" + }, + { + "Country/Region": "United Kingdom", + "Points": 31.0, + "Response": "Yes" + }, + { + "Country/Region": "United Kingdom", + "Points": 69.0, + "Response": "No" + }, + { + "Country/Region": "Spain", + "Points": 27.0, + "Response": "Yes" + }, + { + "Country/Region": "Spain", + "Points": 73.0, + "Response": "No" + }, + { + "Country/Region": "Italy", + "Points": 13.0, + "Response": "Yes" + }, + { + "Country/Region": "Italy", + "Points": 87.0, + "Response": "No" + } + ], + "columns": [ + { + "name": "Country/Region", + "importance": "primary", + "description": "Country or region of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding response", + "role": "y" + }, + { + "name": "Response", + "importance": "primary", + "description": "The response of the data point", + "role": "group" + } + ] + } + }, + "1518": { + "metadata": { + "title": "Long Term Development of the U.S. Trade Balance", + "description": "U.S. trade balance in goods and services in percent of GDP (1960-2016)", + "main_insight": "The U.S. trade balance has generally declined since 1980, representing a growing trade deficit as a percentage of GDP." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Time Period": "2000-2009", + "Trade Balance (% of GDP)": -4.0 + } + ], + "score": 0.79, + "annotation": "The has a minimum value at 2000-2009", + "reason": "The [{'Time Period': '2000-2009', 'Trade Balance (% of GDP)': -4.0}] of has a mainimum value of -4.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Time Period": "1960-1969", + "Trade Balance (% of GDP)": 0.65 + } + ], + "score": 0.72, + "annotation": "The has a maximum value at 1960-1969", + "reason": "The [{'Time Period': '1960-1969', 'Trade Balance (% of GDP)': 0.65}] of has a maximum value of 0.65, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time Period": "1970-1979", + "Trade Balance (% of GDP)": 0.0 + } + ], + "score": 0.71, + "annotation": "The shows a sudden jump in Trade Balance (% of GDP) from 1990-1999 to 1970-1979.", + "reason": "The Trade Balance (% of GDP) for increases from -1.5 at 1990-1999 to 0.0 at 1970-1979, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Time Period": "1960-1969", + "Trade Balance (% of GDP)": 0.65 + }, + { + "Time Period": "1970-1979", + "Trade Balance (% of GDP)": 0.0 + }, + { + "Time Period": "1980-1989", + "Trade Balance (% of GDP)": -1.5 + }, + { + "Time Period": "1990-1999", + "Trade Balance (% of GDP)": -1.5 + }, + { + "Time Period": "2000-2009", + "Trade Balance (% of GDP)": -4.0 + }, + { + "Time Period": "2010-2016", + "Trade Balance (% of GDP)": -2.72 + } + ], + "columns": [ + { + "name": "Time Period", + "importance": "primary", + "description": "Represents the time periods in ranges of years.", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Trade Balance (% of GDP)", + "importance": "primary", + "description": "U.S. trade balance as a percentage of GDP", + "unit": "% of GDP", + "data_type": "number", + "role": "y" + } + ] + } + }, + "130": { + "metadata": { + "title": "Online Still Hasn't Conquered Physical Grocery Stores", + "description": "Share of Americans and their families who do the following...", + "main_insight": "A significant majority of Americans still shop for groceries in person at a store more than once a week or once a week, far surpassing other methods of obtaining food." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Activity": "Order a meal preparation kit with fresh food to be delivered to your home", + "Score": 1.0, + "Frequency": "More than once a week" + }, + { + "Activity": "Order a meal preparation kit with fresh food to be delivered to your home", + "Score": 1.0, + "Frequency": "Once a week" + } + ], + "score": 0.95, + "annotation": "The More than once a week, Once a week all have the smallest proportion in Order a meal preparation kit with fresh food to be delivered to your home.", + "reason": "The Score of More than once a week, Once a week in Order a meal preparation kit with fresh food to be delivered to your home all account for 0.01, smaller than all other Frequency, of the total of Score of all Frequency." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Activity": "Order food for take-out/delivery from a restaurant", + "Score": 31.0, + "Frequency": "Once a month" + } + ], + "score": 0.94, + "annotation": "The Once a month has the largest proportion in Order food for take-out/delivery from a restaurant.", + "reason": "The Score of Once a month in Order food for take-out/delivery from a restaurant accounts for 0.31, larger than all other Frequency, of the total of Score of all Frequency." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Activity": "Order a meal preparation kit with fresh food to be delivered to your home", + "Score": 1.0, + "Frequency": "Once a week" + }, + { + "Activity": "Order a meal preparation kit with fresh food to be delivered to your home", + "Score": 84.0, + "Frequency": "Never" + } + ], + "score": 0.94, + "annotation": "The Score of Once a week and Never show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Once a week and Never is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Activity": "Eat a meal out at a restaurant", + "Score": 32.0, + "Frequency": "Once a week" + } + ], + "score": 0.94, + "annotation": "The Once a week has the largest proportion in Eat a meal out at a restaurant.", + "reason": "The Score of Once a week in Eat a meal out at a restaurant accounts for 0.32323232323232326, larger than all other Frequency, of the total of Score of all Frequency." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Frequency": "Never", + "Activity": "total", + "Score": 165.0 + } + ], + "score": 0.94, + "annotation": "The total value of Never has the largest proportion.", + "reason": "The total value of Score of Never accounts for 0.3319919517102616, larger than all other Frequency, of the total of Score of all Frequency." + } + ], + "data": { + "data": [ + { + "Activity": "Shop for groceries in person at a store", + "Score": 45.0, + "Frequency": "More than once a week" + }, + { + "Activity": "Shop for groceries in person at a store", + "Score": 39.0, + "Frequency": "Once a week" + }, + { + "Activity": "Shop for groceries in person at a store", + "Score": 8.0, + "Frequency": "Once a month" + }, + { + "Activity": "Shop for groceries in person at a store", + "Score": 5.0, + "Frequency": "Less often" + }, + { + "Activity": "Shop for groceries in person at a store", + "Score": 2.0, + "Frequency": "Never" + }, + { + "Activity": "Eat a meal out at a restaurant", + "Score": 18.0, + "Frequency": "More than once a week" + }, + { + "Activity": "Eat a meal out at a restaurant", + "Score": 32.0, + "Frequency": "Once a week" + }, + { + "Activity": "Eat a meal out at a restaurant", + "Score": 28.0, + "Frequency": "Once a month" + }, + { + "Activity": "Eat a meal out at a restaurant", + "Score": 17.0, + "Frequency": "Less often" + }, + { + "Activity": "Eat a meal out at a restaurant", + "Score": 4.0, + "Frequency": "Never" + }, + { + "Activity": "Order food for take-out/delivery from a restaurant", + "Score": 8.0, + "Frequency": "More than once a week" + }, + { + "Activity": "Order food for take-out/delivery from a restaurant", + "Score": 20.0, + "Frequency": "Once a week" + }, + { + "Activity": "Order food for take-out/delivery from a restaurant", + "Score": 31.0, + "Frequency": "Once a month" + }, + { + "Activity": "Order food for take-out/delivery from a restaurant", + "Score": 30.0, + "Frequency": "Less often" + }, + { + "Activity": "Order food for take-out/delivery from a restaurant", + "Score": 11.0, + "Frequency": "Never" + }, + { + "Activity": "Order groceries online for pickup or delivery", + "Score": 2.0, + "Frequency": "More than once a week" + }, + { + "Activity": "Order groceries online for pickup or delivery", + "Score": 5.0, + "Frequency": "Once a week" + }, + { + "Activity": "Order groceries online for pickup or delivery", + "Score": 8.0, + "Frequency": "Once a month" + }, + { + "Activity": "Order groceries online for pickup or delivery", + "Score": 20.0, + "Frequency": "Less often" + }, + { + "Activity": "Order groceries online for pickup or delivery", + "Score": 64.0, + "Frequency": "Never" + }, + { + "Activity": "Order a meal preparation kit with fresh food to be delivered to your home", + "Score": 1.0, + "Frequency": "More than once a week" + }, + { + "Activity": "Order a meal preparation kit with fresh food to be delivered to your home", + "Score": 1.0, + "Frequency": "Once a week" + }, + { + "Activity": "Order a meal preparation kit with fresh food to be delivered to your home", + "Score": 4.0, + "Frequency": "Once a month" + }, + { + "Activity": "Order a meal preparation kit with fresh food to be delivered to your home", + "Score": 10.0, + "Frequency": "Less often" + }, + { + "Activity": "Order a meal preparation kit with fresh food to be delivered to your home", + "Score": 84.0, + "Frequency": "Never" + } + ], + "columns": [ + { + "name": "Activity", + "importance": "primary", + "description": "Different activities related to food and grocery shopping", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding frequency", + "role": "y" + }, + { + "name": "Frequency", + "importance": "primary", + "description": "The frequency of the data point", + "role": "group" + } + ] + } + }, + "397": { + "metadata": { + "title": "In Europe, France Benefits Most From Online Travel Platforms", + "description": "Cumulative GDP impact attributable to online travel platforms in Europe by country, 2019-2021 (in billion euros)", + "main_insight": "France had the largest cumulative GDP impact attributable to online travel platforms among individual European countries between 2019 and 2021, at 11.8 billion euros. The combined 'Rest of Europe' had a slightly greater impact than France, with 12.7 billion euros." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Rest of Europe", + "GDP Impact (Billion Euros)": 12.7 + } + ], + "score": 0.9, + "annotation": "The has a maximum value at Rest of Europe", + "reason": "The [{'Country': 'Rest of Europe', 'GDP Impact (Billion Euros)': 12.7}] of has a maximum value of 12.7, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "France", + "GDP Impact (Billion Euros)": 11.8 + } + ], + "score": 0.74, + "annotation": "The shows a sudden jump in GDP Impact (Billion Euros) from Spain to France.", + "reason": "The GDP Impact (Billion Euros) for increases from 7.8 at Spain to 11.8 at France, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Croatia", + "GDP Impact (Billion Euros)": 1.7 + } + ], + "score": 0.56, + "annotation": "The has a minimum value at Croatia", + "reason": "The [{'Country': 'Croatia', 'GDP Impact (Billion Euros)': 1.7}] of has a mainimum value of 1.7, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "France", + "GDP Impact (Billion Euros)": 11.8 + }, + { + "Country": "Spain", + "GDP Impact (Billion Euros)": 7.8 + }, + { + "Country": "Italy", + "GDP Impact (Billion Euros)": 6.7 + }, + { + "Country": "Germany", + "GDP Impact (Billion Euros)": 4.6 + }, + { + "Country": "UK", + "GDP Impact (Billion Euros)": 2.9 + }, + { + "Country": "Austria", + "GDP Impact (Billion Euros)": 2.8 + }, + { + "Country": "Greece", + "GDP Impact (Billion Euros)": 2.6 + }, + { + "Country": "Netherlands", + "GDP Impact (Billion Euros)": 2.5 + }, + { + "Country": "Sweden", + "GDP Impact (Billion Euros)": 2.3 + }, + { + "Country": "Croatia", + "GDP Impact (Billion Euros)": 1.7 + }, + { + "Country": "Rest of Europe", + "GDP Impact (Billion Euros)": 12.7 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "European countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "GDP Impact (Billion Euros)", + "importance": "primary", + "description": "Cumulative GDP impact attributable to online travel platforms, 2019-2021", + "unit": "Billion Euros", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2038": { + "metadata": { + "title": "Germans Hesitant To Label Their Troops Heroes", + "description": "Views on whether their armed forces personnel should be described as heroes (2018)", + "main_insight": "Germans are significantly less likely to label all armed forces personnel as heroes compared to the United States and the United Kingdom, with a much larger percentage believing that only those who performed particularly brave acts or no members at all should be called heroes." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Opinion": "All armed forces personal should be called heroes", + "Units": 50.0, + "Country": "United States" + } + ], + "score": 0.93, + "annotation": "The United States shows a sudden jump in Units from Only those who performed particularly brave acts to All armed forces personal should be called heroes.", + "reason": "The Units for United States increases from 17.0 at Only those who performed particularly brave acts to 50.0 at All armed forces personal should be called heroes, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Opinion": "Only those who performed particularly brave acts", + "Units": 31.0, + "Country": "United Kingdom" + } + ], + "score": 0.93, + "annotation": "The United Kingdom shows a sudden jump in Units from Don't know to Only those who performed particularly brave acts.", + "reason": "The Units for United Kingdom increases from 12.0 at Don't know to 31.0 at Only those who performed particularly brave acts, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Opinion": "All armed forces personal should be called heroes", + "Units": 50.0, + "Country": "United States" + } + ], + "score": 0.91, + "annotation": "The United States has a maximum value at All armed forces personal should be called heroes", + "reason": "The [{'Opinion': 'All armed forces personal should be called heroes', 'Units': 50.0, 'Country': 'United States'}] of United States has a maximum value of 50.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "United States", + "Opinion": "total", + "Units": 100.0 + }, + { + "Country": "Germany", + "Opinion": "total", + "Units": 100.0 + } + ], + "score": 0.9, + "annotation": "The total value of United States, Germany all have the largest proportion.", + "reason": "The total value of Units of United States, Germany all account for 0.33444816053511706, larger than all other Country, of the total of Units of all Country." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Opinion": "Only those armed forces serving in combat roles", + "Units": 9.0, + "Country": "United States" + } + ], + "score": 0.88, + "annotation": "The United States has the largest proportion in Only those armed forces serving in combat roles.", + "reason": "The Units of United States in Only those armed forces serving in combat roles accounts for 0.391304347826087, larger than all other Country, of the total of Units of all Country." + } + ], + "data": { + "data": [ + { + "Opinion": "All armed forces personal should be called heroes", + "Units": 50.0, + "Country": "United States" + }, + { + "Opinion": "All armed forces personal should be called heroes", + "Units": 32.0, + "Country": "United Kingdom" + }, + { + "Opinion": "All armed forces personal should be called heroes", + "Units": 15.0, + "Country": "Germany" + }, + { + "Opinion": "Only those armed forces serving in combat roles", + "Units": 9.0, + "Country": "United States" + }, + { + "Opinion": "Only those armed forces serving in combat roles", + "Units": 7.0, + "Country": "United Kingdom" + }, + { + "Opinion": "Only those armed forces serving in combat roles", + "Units": 7.0, + "Country": "Germany" + }, + { + "Opinion": "Only those who have seen combat", + "Units": 7.0, + "Country": "United States" + }, + { + "Opinion": "Only those who have seen combat", + "Units": 11.0, + "Country": "United Kingdom" + }, + { + "Opinion": "Only those who have seen combat", + "Units": 12.0, + "Country": "Germany" + }, + { + "Opinion": "Only those who performed particularly brave acts", + "Units": 17.0, + "Country": "United States" + }, + { + "Opinion": "Only those who performed particularly brave acts", + "Units": 31.0, + "Country": "United Kingdom" + }, + { + "Opinion": "Only those who performed particularly brave acts", + "Units": 30.0, + "Country": "Germany" + }, + { + "Opinion": "No members of the armed forces should be called heroes", + "Units": 5.0, + "Country": "United States" + }, + { + "Opinion": "No members of the armed forces should be called heroes", + "Units": 6.0, + "Country": "United Kingdom" + }, + { + "Opinion": "No members of the armed forces should be called heroes", + "Units": 16.0, + "Country": "Germany" + }, + { + "Opinion": "Don't know", + "Units": 12.0, + "Country": "United States" + }, + { + "Opinion": "Don't know", + "Units": 12.0, + "Country": "United Kingdom" + }, + { + "Opinion": "Don't know", + "Units": 20.0, + "Country": "Germany" + } + ], + "columns": [ + { + "name": "Opinion", + "importance": "primary", + "description": "Different views on who should be considered a hero", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding country", + "role": "y" + }, + { + "name": "Country", + "importance": "primary", + "description": "The country of the data point", + "role": "group" + } + ] + } + }, + "2135": { + "metadata": { + "title": "From Fringe to Mainstream: The Rise of the BJP", + "description": "Indian parliament lower house seat distribution by party since independence", + "main_insight": "The BJP has dramatically increased its seat share in the Indian parliament's lower house since independence, becoming the dominant party, while the Congress party's share has significantly declined." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2019, + "Units": 52, + "Parties": "Congress" + }, + { + "Year": 2019, + "Units": 550, + "Parties": "Total Seats" + } + ], + "score": 0.85, + "annotation": "The Units of Congress and Total Seats show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Congress and Total Seats is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Units": 195, + "Parties": "Others" + }, + { + "Year": 2019, + "Units": 550, + "Parties": "Total Seats" + } + ], + "score": 0.71, + "annotation": "The Units of Others and Total Seats show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Others and Total Seats is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2019, + "Units": 52, + "Parties": "Congress" + }, + { + "Year": 2019, + "Units": 195, + "Parties": "Others" + } + ], + "score": 0.7, + "annotation": "The Units of Congress and Others show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Congress and Others is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 1952, + "Units": 3, + "Parties": "BJP" + }, + { + "Year": 1952, + "Units": 364, + "Parties": "Congress" + }, + { + "Year": 1952, + "Units": 122, + "Parties": "Others" + }, + { + "Year": 1952, + "Units": 489, + "Parties": "Total Seats" + }, + { + "Year": 1957, + "Units": 4, + "Parties": "BJP" + }, + { + "Year": 1957, + "Units": 371, + "Parties": "Congress" + }, + { + "Year": 1957, + "Units": 119, + "Parties": "Others" + }, + { + "Year": 1957, + "Units": 494, + "Parties": "Total Seats" + }, + { + "Year": 1962, + "Units": 14, + "Parties": "BJP" + }, + { + "Year": 1962, + "Units": 361, + "Parties": "Congress" + }, + { + "Year": 1962, + "Units": 119, + "Parties": "Others" + }, + { + "Year": 1962, + "Units": 494, + "Parties": "Total Seats" + }, + { + "Year": 1967, + "Units": 35, + "Parties": "BJP" + }, + { + "Year": 1967, + "Units": 283, 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"Year": 1984, + "Units": 514, + "Parties": "Total Seats" + }, + { + "Year": 1989, + "Units": 85, + "Parties": "BJP" + }, + { + "Year": 1989, + "Units": 197, + "Parties": "Congress" + }, + { + "Year": 1989, + "Units": 247, + "Parties": "Others" + }, + { + "Year": 1989, + "Units": 529, + "Parties": "Total Seats" + }, + { + "Year": 1991, + "Units": 120, + "Parties": "BJP" + }, + { + "Year": 1991, + "Units": 244, + "Parties": "Congress" + }, + { + "Year": 1991, + "Units": 170, + "Parties": "Others" + }, + { + "Year": 1991, + "Units": 534, + "Parties": "Total Seats" + }, + { + "Year": 1996, + "Units": 161, + "Parties": "BJP" + }, + { + "Year": 1996, + "Units": 140, + "Parties": "Congress" + }, + { + "Year": 1996, + "Units": 242, + "Parties": "Others" + }, + { + "Year": 1996, + "Units": 543, + "Parties": "Total Seats" + }, + { + "Year": 1998, + "Units": 182, + "Parties": "BJP" + }, + { + "Year": 1998, + "Units": 141, + "Parties": "Congress" + }, + { + "Year": 1998, + "Units": 220, + "Parties": "Others" + }, + { + "Year": 1998, + "Units": 543, + "Parties": "Total Seats" + }, + { + "Year": 1999, + "Units": 182, + "Parties": "BJP" + }, + { + "Year": 1999, + "Units": 114, + "Parties": "Congress" + }, + { + "Year": 1999, + "Units": 247, + "Parties": "Others" + }, + { + "Year": 1999, + "Units": 543, + "Parties": "Total Seats" + }, + { + "Year": 2004, + "Units": 138, + "Parties": "BJP" + }, + { + "Year": 2004, + "Units": 145, + "Parties": "Congress" + }, + { + "Year": 2004, + "Units": 260, + "Parties": "Others" + }, + { + "Year": 2004, + "Units": 543, + "Parties": "Total Seats" + }, + { + "Year": 2009, + "Units": 116, + "Parties": "BJP" + }, + { + "Year": 2009, + "Units": 206, + "Parties": "Congress" + }, + { + "Year": 2009, + "Units": 221, + "Parties": "Others" + }, + { + "Year": 2009, + "Units": 543, + "Parties": "Total Seats" + }, + { + "Year": 2014, + "Units": 282, + "Parties": "BJP" + }, + { + "Year": 2014, + "Units": 44, + "Parties": "Congress" + }, + { + "Year": 2014, + "Units": 217, + "Parties": "Others" + }, + { + "Year": 2014, + "Units": 543, + "Parties": "Total Seats" + }, + { + "Year": 2019, + "Units": 303, + "Parties": "BJP" + }, + { + "Year": 2019, + "Units": 52, + "Parties": "Congress" + }, + { + "Year": 2019, + "Units": 195, + "Parties": "Others" + }, + { + "Year": 2019, + "Units": 550, + "Parties": "Total Seats" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Election year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding parties", + "role": "y" + }, + { + "name": "Parties", + "importance": "primary", + "description": "The parties of the data point", + "role": "group" + } + ] + } + }, + "477": { + "metadata": { + "title": "Canada's Best Employers 2018", + "description": "Top Canadian employers as rated by 8,000 employees on a scale from 0-10", + "main_insight": "Google is ranked as the best employer in Canada in 2018, followed by Kruger Products and Hydro Quebec. Several educational institutions also make the top 10 list." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Rank": 1, + "Employer": "Google" + }, + { + "Rank": 2, + "Employer": "Kruger Products" + }, + { + "Rank": 3, + "Employer": "Hydro Quebec" + }, + { + "Rank": 4, + "Employer": "SAP" + }, + { + "Rank": 5, + "Employer": "Vitalite" + }, + { + "Rank": 6, + "Employer": "Queen's University" + }, + { + "Rank": 7, + "Employer": "Concordia University" + }, + { + "Rank": 8, + "Employer": "Siemens" + }, + { + "Rank": 9, + "Employer": "SaskTel" + }, + { + "Rank": 10, + "Employer": "Ottawa-Carleton District School Board" + } + ], + "columns": [ + { + "name": "Rank", + "importance": "primary", + "description": "Ranking of the employer", + "unit": "none", + "data_type": "number", + "role": "x" + }, + { + "name": "Employer", + "importance": "primary", + "description": "Name of the employer", + "unit": "none", + "data_type": "string", + "role": "y" + } + ] + } + }, + "2118": { + "metadata": { + "title": "Internet Shutdowns Spike in Russia", + "description": "Cost of major internet shutdowns in selected countries (in million U.S. dollars)", + "main_insight": "The cost of internet shutdowns in Russia dramatically increased in 2022, surpassing other countries significantly by March 22." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2019, + "Units": 1300.0, + "Country": "India" + }, + { + "Year": 2020, + "Units": 2800.0, + "Country": "India" + }, + { + "Year": 2021, + "Units": 1000.0, + "Country": "Russia" + }, + { + "Year": 2021, + "Units": 500.0, + "Country": "India" + }, + { + "Year": 2021, + "Units": 2800.0, + "Country": "Myanmar" + }, + { + "Year": 2021, + "Units": 1500.0, + "Country": "Nigeria" + }, + { + "Year": 2022, + "Units": 1500.0, + "Country": "Russia" + }, + { + "Year": 2022, + "Units": 100.0, + "Country": "India" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of internet shutdown", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding country", + "role": "y" + }, + { + "name": "Country", + "importance": "primary", + "description": "The country of the data point", + "role": "group" + } + ] + } + }, + "911": { + "metadata": { + "title": "Under Threat: The World's Reef-Building Corals", + "description": "Conservation status of the world's reef-building corals in 2022", + "main_insight": "A significant portion of the world's reef-building corals are under threat, with 3% Critically Endangered, 3% Endangered and 24% Vulnerable. Only 33% of corals are in the category of least concern." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Conservation Status": "Data deficient", + "Percentage": 16.0 + } + ], + "score": 0.79, + "annotation": "The shows a sudden jump in Percentage from Endangered to Data deficient.", + "reason": "The Percentage for increases from 3.0 at Endangered to 16.0 at Data deficient, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Conservation Status": "Least concern", + "Percentage": 33.0 + } + ], + "score": 0.76, + "annotation": "The has a maximum value at Least concern", + "reason": "The [{'Conservation Status': 'Least concern', 'Percentage': 33.0}] of has a maximum value of 33.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Conservation Status": "Critically endangered", + "Percentage": 3.0 + }, + { + "Conservation Status": "Endangered", + "Percentage": 3.0 + } + ], + "score": 0.67, + "annotation": "The has minimum values at Critically endangered, Endangered", + "reason": "The [{'Conservation Status': 'Critically endangered', 'Percentage': 3.0}, {'Conservation Status': 'Endangered', 'Percentage': 3.0}] of have minimum values of 3.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Conservation Status": "Critically endangered", + "Percentage": 3.0 + }, + { + "Conservation Status": "Endangered", + "Percentage": 3.0 + }, + { + "Conservation Status": "Vulnerable", + "Percentage": 24.0 + }, + { + "Conservation Status": "Near threatened", + "Percentage": 21.0 + }, + { + "Conservation Status": "Least concern", + "Percentage": 33.0 + }, + { + "Conservation Status": "Data deficient", + "Percentage": 16.0 + } + ], + "columns": [ + { + "name": "Conservation Status", + "importance": "primary", + "description": "Category of 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68.7}] of has a maximum value of 68.7, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Buyer": "Microsoft", + "Acquisition Cost (Billion USD)": 68.7 + } + ], + "score": 0.95, + "annotation": "The shows a sudden jump in Acquisition Cost (Billion USD) from Take-Two Interactive to Microsoft.", + "reason": "The Acquisition Cost (Billion USD) for increases from 12.7 at Take-Two Interactive to 68.7 at Microsoft, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Buyer": "Sony Interactive", + "Acquisition Cost (Billion USD)": 3.6 + } + ], + "score": 0.35, + "annotation": "The has a minimum value at Sony Interactive", + "reason": "The [{'Buyer': 'Sony Interactive', 'Acquisition Cost (Billion USD)': 3.6}] of has a mainimum value of 3.6, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Buyer": "Microsoft", + "Acquisition Cost 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"metadata": { + "title": "The World's Largest Arms Suppliers", + "description": "Share of global arms exports by country from 2014 to 2018", + "main_insight": "The USA and Russia were by far the largest arms exporters between 2014 and 2018, accounting for 36% and 21% of global exports, respectively. The remaining countries each accounted for less than 10% of the share." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "USA", + "USA": 36.0 + } + ], + "score": 0.99, + "annotation": "The has a maximum value at USA", + "reason": "The [{'Country': 'USA', 'USA': 36.0}] of has a maximum value of 36.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "USA", + "USA": 36.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in USA from Others to USA.", + "reason": "The USA for increases from 0 at Others to 36.0 at USA, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Russia", + "USA": 0 + }, + { + "Country": "France", + "USA": 0 + }, + { + "Country": "Germany", + "USA": 0 + }, + { + "Country": "China", + "USA": 0 + }, + { + "Country": "United 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"Country": "Germany", + "USA": 0 + }, + { + "Country": "China", + "USA": 0 + }, + { + "Country": "United Kingdom", + "USA": 0 + }, + { + "Country": "Spain", + "USA": 0 + }, + { + "Country": "Israel", + "USA": 0 + }, + { + "Country": "Italy", + "USA": 0 + }, + { + "Country": "Netherlands", + "USA": 0 + }, + { + "Country": "Others", + "USA": 0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "USA", + "importance": "primary", + "description": "Share of global arms exports", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1195": { + "metadata": { + "title": "IMF Downgrades US and UK Growth Forecasts", + "description": "Year-on-year output growth estimates and projections as of July 2017 (in %)", + "main_insight": "The IMF downgraded its growth forecasts for the US and UK between April and July 2017, while global growth projections remained relatively stable." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2016, + "Score": 1.6, + "Scenario": "US Actual" + }, + { + "Year": 2016, + "Score": 1.8, + "Scenario": "UK Actual" + } + ], + "score": 1.0, + "annotation": "The Score of US Actual and UK Actual show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between US Actual and UK Actual is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2016, + "Score": 1.6, + "Scenario": "US Actual" + }, + { + "Year": 2016, + "Score": 3.2, + "Scenario": "World Actual" + } + ], + "score": 1.0, + "annotation": "The Score of US Actual and World Actual show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between US Actual and World Actual is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2016, + "Score": 1.6, + "Scenario": "US Actual" + }, + { + "Year": 2018, + "Score": -0.4, + "Scenario": "US Revision" + } + ], + "score": 1.0, + "annotation": "The Score of US Actual and US Revision show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between US Actual and US Revision is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2016, + "Score": 1.6, + "Scenario": "US Actual" + }, + { + "Year": 2018, + "Score": 1.5, + "Scenario": "UK Projected" + } + ], + "score": 1.0, + "annotation": "The Score of US Actual and UK Projected show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between US Actual and UK Projected is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2015, + "Score": 2.6, + "Scenario": "US Actual" + }, + { + "Year": 2015, + "Score": 2.2, + "Scenario": "UK Actual" + }, + { + "Year": 2015, + "Score": 3.4, + "Scenario": "World Actual" + }, + { + "Year": 2016, + "Score": 1.6, + "Scenario": "US Actual" + }, + { + "Year": 2016, + "Score": 1.8, + "Scenario": "UK Actual" + }, + { + "Year": 2016, + "Score": 3.2, + "Scenario": "World Actual" + }, + { + "Year": 2017, + "Score": 2.1, + "Scenario": "US Projected" + }, + { + "Year": 2017, + "Score": -0.2, + "Scenario": "US Revision" + }, + { + "Year": 2017, + "Score": 1.7, + "Scenario": "UK Projected" + }, + { + "Year": 2017, + "Score": -0.3, + "Scenario": "UK Revision" + }, + { + "Year": 2017, + "Score": 3.5, + "Scenario": "World Projected" + }, + { + "Year": 2018, + "Score": 2.1, + "Scenario": "US Projected" + }, + { + "Year": 2018, + "Score": -0.4, + "Scenario": "US Revision" + }, + { + "Year": 2018, + "Score": 1.5, + "Scenario": "UK Projected" + }, + { + "Year": 2018, + "Score": 3.6, + "Scenario": "World Projected" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of growth estimate/projection", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding scenario", + "role": "y" + }, + { + "name": "Scenario", + "importance": "primary", + "description": "The scenario of the data point", + "role": "group" + } + ] + } + }, + "730": { + "metadata": { + "title": "Where the Rich Park Their Money", + "description": "Countries/territories holding the highest amounts of private offshore wealth in 2020", + "main_insight": "Switzerland and Hong Kong held the largest amounts of private offshore wealth in 2020, and Hong Kong is projected to have higher growth compared to Switzerland in the following 5 years." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country/Territory": "Switzerland", + "Trillion U.S. dollars": 2.4 + } + ], + "score": 0.86, + "annotation": "The has a maximum value at Switzerland", + "reason": "The [{'Country/Territory': 'Switzerland', 'Trillion U.S. dollars': 2.4}] of has a maximum value of 2.4, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country/Territory": "Hong Kong", + "Trillion U.S. dollars": 2.1 + } + ], + "score": 0.79, + "annotation": "The shows a sudden jump in Trillion U.S. dollars from Singapore to Hong Kong.", + "reason": "The Trillion U.S. dollars for increases from 1.2 at Singapore to 2.1 at Hong Kong, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country/Territory": "UK", + "Trillion U.S. dollars": 0.3 + } + ], + "score": 0.56, + "annotation": "The has a minimum value at UK", + "reason": "The [{'Country/Territory': 'UK', 'Trillion U.S. dollars': 0.3}] of has a mainimum value of 0.3, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country/Territory": "Switzerland", + "Trillion U.S. dollars": 2.4 + }, + { + "Country/Territory": "Hong Kong", + "Trillion U.S. dollars": 2.1 + }, + { + "Country/Territory": "Singapore", + "Trillion U.S. dollars": 1.2 + }, + { + "Country/Territory": "U.S.", + "Trillion U.S. dollars": 0.9 + }, + { + "Country/Territory": "Channel Islands/Isle of Man", + "Trillion U.S. dollars": 0.5 + }, + { + "Country/Territory": "United Arab Emirates", + "Trillion U.S. dollars": 0.5 + }, + { + "Country/Territory": "Luxembourg", + "Trillion U.S. dollars": 0.4 + }, + { + "Country/Territory": "UK", + "Trillion U.S. dollars": 0.3 + } + ], + "columns": [ + { + "name": "Country/Territory", + "importance": "primary", + "description": "List of countries or territories", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Trillion U.S. dollars", + "importance": "primary", + "description": "Amount of private offshore wealth held in 2020, in trillions of US dollars", + "unit": "Trillion U.S. dollars", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1085": { + "metadata": { + "title": "Where Air Pollution Is Cutting Lives Short", + "description": "Average life expectancy gains in years if WHO guidelines on air quality were met", + "main_insight": "Air pollution significantly reduces life expectancy across the globe, with the most substantial impact seen in Bangladesh, where meeting WHO guidelines could increase life expectancy by 6.8 years. The US also shows room for improvement." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "BD", + "Life Expectancy Gain (Years)": 6.8 + } + ], + "score": 0.89, + "annotation": "The has a maximum value at BD", + "reason": "The [{'Country': 'BD', 'Life Expectancy Gain (Years)': 6.8}] of has a maximum value of 6.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "US", + "Life Expectancy Gain (Years)": 0.3 + } + ], + "score": 0.88, + "annotation": "The has a minimum value at US", + "reason": "The [{'Country': 'US', 'Life Expectancy Gain (Years)': 0.3}] of has a mainimum value of 0.3, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "BI", + "Life Expectancy Gain (Years)": 2.6 + } + ], + "score": 0.73, + "annotation": "The shows a sudden jump in Life Expectancy Gain (Years) from US to BI.", + "reason": "The Life Expectancy Gain (Years) for increases from 0.3 at US to 2.6 at BI, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "BD", + "Life Expectancy Gain (Years)": 6.8 + }, + { + "Country": "IN", + "Life Expectancy Gain (Years)": 5.3 + }, + { + "Country": "NP", + "Life Expectancy Gain (Years)": 4.6 + }, + { + "Country": "PK", + "Life Expectancy Gain (Years)": 3.9 + }, + { + "Country": "CD", + "Life Expectancy Gain (Years)": 2.9 + }, + { + "Country": "MM", + "Life Expectancy Gain (Years)": 2.9 + }, + { + "Country": "CG", + "Life Expectancy Gain (Years)": 2.7 + }, + { + "Country": "RW", + "Life Expectancy Gain (Years)": 2.7 + }, + { + "Country": "BI", + "Life Expectancy Gain (Years)": 2.6 + }, + { + "Country": "US", + "Life Expectancy Gain (Years)": 0.3 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country abbreviation and flag", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Life Expectancy Gain (Years)", + "importance": "primary", + "description": "Average life expectancy gains in years if WHO guidelines on air quality were met", + "unit": "Years", + "data_type": "number", + "role": "y" + } + ] + } + }, + "106": { + "metadata": { + "title": "UK crime: keeping it in the family?", + "description": "If a family member had committed one of these crimes, would you report them to the police?* *% saying 'definitely not' or 'probably not'", + "main_insight": "A significant percentage of people in the UK would not report family members to the police for less severe crimes like tax evasion, shoplifting, and drug dealing (cannabis). However, for serious crimes like murder and rape almost all said they would report." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Crime": "Tax evasion", + "Percentage": 48.0 + } + ], + "score": 0.77, + "annotation": "The has a maximum value at Tax evasion", + "reason": "The [{'Crime': 'Tax evasion', 'Percentage': 48.0}] of has a maximum value of 48.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Crime": "Rape", + "Percentage": 2.0 + }, + { + "Crime": "Murder", + "Percentage": 2.0 + } + ], + "score": 0.69, + "annotation": "The has minimum values at Rape, Murder", + "reason": "The [{'Crime': 'Rape', 'Percentage': 2.0}, {'Crime': 'Murder', 'Percentage': 2.0}] of have minimum values of 2.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Crime": "Drug dealing (cannabis)", + "Percentage": 41.0 + } + ], + "score": 0.66, + "annotation": "The shows a sudden jump in Percentage from Assault to Drug dealing (cannabis).", + "reason": "The Percentage for increases from 29.0 at Assault to 41.0 at Drug dealing (cannabis), indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Crime": "Tax evasion", + "Percentage": 48.0 + }, + { + "Crime": "Shoplifting", + "Percentage": 43.0 + }, + { + "Crime": "Drug dealing (cannabis)", + "Percentage": 41.0 + }, + { + "Crime": "Assault", + "Percentage": 29.0 + }, + { + "Crime": "Drunk driving", + "Percentage": 19.0 + }, + { + "Crime": "Possession of a firearm", + "Percentage": 15.0 + }, + { + "Crime": "Drug dealing (heroin)", + "Percentage": 11.0 + }, + { + "Crime": "Rape", + "Percentage": 2.0 + }, + { + "Crime": "Murder", + "Percentage": 2.0 + } + ], + "columns": [ + { + "name": "Crime", + "importance": "primary", + "description": "Type of crime committed", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents who would 'definitely not' or 'probably not' report a family member to the police", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2444": { + "metadata": { + "title": "Germany has never seen so many traffic jams", + "description": "Number of reported traffic jams on the German autobahn network", + "main_insight": "The number of reported traffic jams on the German autobahn network has increased significantly from 2011 to 2017, more than tripling in that time." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2011, + "Traffic Jams": 189000 + }, + { + "Year": 2012, + "Traffic Jams": 285000 + }, + { + "Year": 2013, + "Traffic Jams": 415000 + }, + { + "Year": 2014, + "Traffic Jams": 475000 + }, + { + "Year": 2015, + "Traffic Jams": 568000 + }, + { + "Year": 2016, + "Traffic Jams": 694000 + }, + { + "Year": 2017, + "Traffic Jams": 723000 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of reported traffic jams", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Traffic Jams", + "importance": "primary", + "description": "Number of reported traffic jams", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "611": { + "metadata": { + "title": "Room to Grow for Premium Streaming in Southeast Asia", + "description": "Minutes of video streaming watched in Southeast Asia. Two charts are presented, one by type of service and other by content origin.", + "main_insight": "In Southeast Asia, user-generated content dominates video streaming minutes, but there's significant potential for premium video services. Additionally, content from South Korea and North America leads by origin." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Type of Service (Q3 2021)": "User-Generated Content", + "Index": 63.0, + "Content": "Percentage of Minutes Watched" + }, + { + "Type of Service (Q3 2021)": "User-Generated Content", + "Index": "South Korea", + "Content": "Content Origin (2020)" + }, + { + "Type of Service (Q3 2021)": "User-Generated Content", + "Index": 34.0, + "Content": "Percentage of Content Origin" + }, + { + "Type of Service (Q3 2021)": "Premium Video", + "Index": 26.0, + "Content": "Percentage of Minutes Watched" + }, + { + "Type of Service (Q3 2021)": "Premium Video", + "Index": "North America", + "Content": "Content Origin (2020)" + }, + { + "Type of Service (Q3 2021)": "Premium Video", + "Index": 30.0, + "Content": "Percentage of Content Origin" + }, + { + "Type of Service (Q3 2021)": "Live Streaming", + "Index": 10.0, + "Content": "Percentage of Minutes Watched" + }, + { + "Type of Service (Q3 2021)": "Live Streaming", + "Index": "Local", + "Content": "Content Origin (2020)" + }, + { + "Type of Service (Q3 2021)": "Live Streaming", + "Index": 13.0, + "Content": "Percentage of Content Origin" + }, + { + "Type of Service (Q3 2021)": "Others", + "Index": 2.0, + "Content": "Percentage of Minutes Watched" + }, + { + "Type of Service (Q3 2021)": "Others", + "Index": "Europe", + "Content": "Content Origin (2020)" + }, + { + "Type of Service (Q3 2021)": "Others", + "Index": 9.0, + "Content": "Percentage of Content Origin" + } + ], + "columns": [ + { + "name": "Type of Service (Q3 2021)", + "importance": "primary", + "description": "Categories of video streaming services", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Index", + "importance": "primary", + "description": "Index for the corresponding content", + "role": "y" + }, + { + "name": "Content", + "importance": "primary", + "description": "The content of the data point", + "role": "group" + } + ] + } + }, + "1236": { + "metadata": { + "title": "Decline in Halloween Costumes for 2020", + "description": "Percentage of U.S. adults who plan to wear Halloween costumes this year compared to those who dressed up last year", + "main_insight": "There is a general decline in the percentage of US adults planning to wear Halloween costumes in 2020 compared to 2019 across all age groups." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Age Group": "Boomers", + "Units": 12, + "Year": "2019" + }, + { + "Age Group": "Boomers", + "Units": 8, + "Year": "2020" + } + ], + "score": 0.99, + "annotation": "The Units of 2019 and 2020 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2019 and 2020 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Age Group": "All", + "Units": 31, + "Year": "2019" + } + ], + "score": 0.83, + "annotation": "The 2019 shows a sudden jump in Units from Boomers to All.", + "reason": "The Units for 2019 increases from 12 at Boomers to 31 at All, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Age Group": "Millenials", + "Units": 43, + "Year": "2020" + } + ], + "score": 0.82, + "annotation": "The 2020 shows a sudden jump in Units from All to Millenials.", + "reason": "The Units for 2020 increases from 25 at All to 43 at Millenials, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "Gen Z", + "Units": 50, + "Year": "2019" + } + ], + "score": 0.79, + "annotation": "The 2019 accounts for the majority in Gen Z.", + "reason": "The Units of 2019 in Gen Z accounts for 0.5208333333333334 (more than 50%) of the total of Units of all Year." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "Millenials", + "Units": 50, + "Year": "2019" + } + ], + "score": 0.79, + "annotation": "The 2019 accounts for the majority in Millenials.", + "reason": "The Units of 2019 in Millenials accounts for 0.5376344086021505 (more than 50%) of the total of Units of all Year." + } + ], + "data": { + "data": [ + { + "Age Group": "All", + "Units": 31, + "Year": "2019" + }, + { + "Age Group": "All", + "Units": 25, + "Year": "2020" + }, + { + "Age Group": "Gen Z", + "Units": 50, + "Year": "2019" + }, + { + "Age Group": "Gen Z", + "Units": 46, + "Year": "2020" + }, + { + "Age Group": "Millenials", + "Units": 50, + "Year": "2019" + }, + { + "Age Group": "Millenials", + "Units": 43, + "Year": "2020" + }, + { + "Age Group": "Gen X", + "Units": 32, + "Year": "2019" + }, + { + "Age Group": "Gen X", + "Units": 23, + "Year": "2020" + }, + { + "Age Group": "Boomers", + "Units": 12, + "Year": "2019" + }, + { + "Age Group": "Boomers", + "Units": 8, + "Year": "2020" + } + ], + "columns": [ + { + "name": "Age Group", + "importance": "primary", + "description": "US adult age demographics", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "1240": { + "metadata": { + "title": "Who Will Be the Next UK PM?", + "description": "Implied probability based on bookmaker odds for the next permanent leader of the Conservative Party", + "main_insight": "As of October 21, 2022, Rishi Sunak had the highest implied probability of becoming the next UK PM, based on bookmaker odds, significantly ahead of other candidates like Boris Johnson and Penny Mordaunt." + }, + "chart_type": "Multiple Semi Circle Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Candidate": "Rishi Sunak", + "Ratio": 0.6, + "Probability Type": "Implied Probability" + } + ], + "score": 0.87, + "annotation": "The Implied Probability has a maximum value at Rishi Sunak", + "reason": "The [{'Candidate': 'Rishi Sunak', 'Ratio': 0.6, 'Probability Type': 'Implied Probability'}] of Implied Probability has a maximum value of 0.6, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Candidate": "Rishi Sunak", + "Ratio": 0.6, + "Probability Type": "Implied Probability" + } + ], + "score": 0.83, + "annotation": "The Implied Probability shows a sudden jump in Ratio from Boris Johnson to Rishi Sunak.", + "reason": "The Ratio for Implied Probability increases from 0.31 at Boris Johnson to 0.6 at Rishi Sunak, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Candidate": "Jeremy Hunt", + "Ratio": 0.02, + "Probability Type": "Implied Probability" + }, + { + "Candidate": "Kemi Badenoch", + "Ratio": 0.02, + "Probability Type": "Implied Probability" + } + ], + "score": 0.49, + "annotation": "The Implied Probability has minimum values at Jeremy Hunt, Kemi Badenoch", + "reason": "The [{'Candidate': 'Jeremy Hunt', 'Ratio': 0.02, 'Probability Type': 'Implied Probability'}, {'Candidate': 'Kemi Badenoch', 'Ratio': 0.02, 'Probability Type': 'Implied Probability'}] of Implied Probability have minimum values of 0.02, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Candidate": "Rishi Sunak", + "Ratio": 0.6, + "Probability Type": "Implied Probability" + }, + { + "Candidate": "Boris Johnson", + "Ratio": 0.31, + "Probability Type": "Implied Probability" + }, + { + "Candidate": "Penny Mordaunt", + "Ratio": 0.18, + "Probability Type": "Implied Probability" + }, + { + "Candidate": "Ben Wallace", + "Ratio": 0.05, + "Probability Type": "Implied Probability" + }, + { + "Candidate": "Jeremy Hunt", + "Ratio": 0.02, + "Probability Type": "Implied Probability" + }, + { + "Candidate": "Kemi Badenoch", + "Ratio": 0.02, + "Probability Type": "Implied Probability" + } + ], + "columns": [ + { + "name": "Candidate", + "importance": "primary", + "description": "Name of the potential candidate for the next UK PM", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding probability type", + "role": "y" + }, + { + "name": "Probability Type", + "importance": "primary", + "description": "The probability type of the data point", + "role": "group" + } + ] + } + }, + "349": { + "metadata": { + "title": "Women Projected to Win Big on State Ballots", + "description": "Percent of women serving in statehouses in the U.S., 1971-2019", + "main_insight": "The percentage of women serving in U.S. statehouses has significantly increased since 1971 and is projected to reach 38% in 2019." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1971, + "Percent of Women": 4.5 + }, + { + "Year": 1979, + "Percent of Women": 10.3 + }, + { + "Year": 1981, + "Percent of Women": 12.1 + }, + { + "Year": 1989, + "Percent of Women": 17.0 + }, + { + "Year": 1991, + "Percent of Women": 18.3 + }, + { + "Year": 1999, + "Percent of Women": 22.4 + }, + { + "Year": 2001, + "Percent of Women": 22.4 + }, + { + "Year": 2009, + "Percent of Women": 24.3 + }, + { + "Year": 2009, + "Percent of Women": 23.7 + }, + { + "Year": 2019, + "Percent of Women": 38.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Percent of Women", + "importance": "primary", + "description": "Percentage of women serving in statehouses", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1710": { + "metadata": { + "title": "Asia Leading the Cashless Revolution", + "description": "Number of non-cash transactions worldwide from 2016 to 2022 (in billions)", + "main_insight": "Asia has seen a dramatic increase in non-cash transactions from 2016 to 2022, surpassing all other regions. Europe and North America have also increased, but at a slower rate." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 201.8, + "Region": "North America" + }, + { + "Year": 2022, + "Units": 57.7, + "Region": "Latin America" + } + ], + "score": 1.0, + "annotation": "The Units of North America and Latin America show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between North America and Latin America is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 352.8, + "Region": "Asia" + }, + { + "Year": 2022, + "Units": 139.3, + "Region": "Middle East/Africa" + } + ], + "score": 1.0, + "annotation": "The Units of Asia and Middle East/Africa show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Asia and Middle East/Africa is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 203.6, + "Region": "Europe" + }, + { + "Year": 2022, + "Units": 201.8, + "Region": "North America" + } + ], + "score": 1.0, + "annotation": "The Units of Europe and North America show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Europe and North America is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 203.6, + "Region": "Europe" + }, + { + "Year": 2022, + "Units": 57.7, + "Region": "Latin America" + } + ], + "score": 1.0, + "annotation": "The Units of Europe and Latin America show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Europe and Latin America is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 203.6, + "Region": "Europe" + }, + { + "Year": 2022, + "Units": 139.3, + "Region": "Middle East/Africa" + } + ], + "score": 1.0, + "annotation": "The Units of Europe and Middle East/Africa show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Europe and Middle East/Africa is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2016, + "Units": 72.6, + "Region": "Asia" + }, + { + "Year": 2016, + "Units": 124.4, + "Region": "Europe" + }, + { + "Year": 2016, + "Units": 152.9, + "Region": "North America" + }, + { + "Year": 2016, + "Units": 41.3, + "Region": "Middle East/Africa" + }, + { + "Year": 2016, + "Units": 39.8, + "Region": "Latin America" + }, + { + "Year": 2017, + "Units": 96.2, + "Region": "Asia" + }, + { + "Year": 2017, + "Units": 133.8, + "Region": "Europe" + }, + { + "Year": 2017, + "Units": 160.6, + "Region": "North America" + }, + { + "Year": 2017, + "Units": 49.3, + "Region": "Middle East/Africa" + }, + { + "Year": 2017, + "Units": 43.1, + "Region": "Latin America" + }, + { + "Year": 2018, + "Units": 123.7, + "Region": "Asia" + }, + { + "Year": 2018, + "Units": 144.2, + "Region": "Europe" + }, + { + "Year": 2018, + "Units": 168.2, + "Region": "North America" + }, + { + "Year": 2018, + "Units": 61.9, + "Region": "Middle East/Africa" + }, + { + "Year": 2018, + "Units": 45.5, + "Region": "Latin America" + }, + { + "Year": 2019, + "Units": 161.1, + "Region": "Asia" + }, + { + "Year": 2019, + "Units": 156.3, + "Region": "Europe" + }, + { + "Year": 2019, + "Units": 176.3, + "Region": "North America" + }, + { + "Year": 2019, + "Units": 74.4, + "Region": "Middle East/Africa" + }, + { + "Year": 2019, + "Units": 48.4, + "Region": "Latin America" + }, + { + "Year": 2020, + "Units": 208.7, + "Region": "Asia" + }, + { + "Year": 2020, + "Units": 170.4, + "Region": "Europe" + }, + { + "Year": 2020, + "Units": 184.5, + "Region": "North America" + }, + { + "Year": 2020, + "Units": 90.2, + "Region": "Middle East/Africa" + }, + { + "Year": 2020, + "Units": 51.1, + "Region": "Latin America" + }, + { + "Year": 2021, + "Units": 269.5, + "Region": "Asia" + }, + { + "Year": 2021, + "Units": 185.9, + "Region": "Europe" + }, + { + "Year": 2021, + "Units": 192.8, + "Region": "North America" + }, + { + "Year": 2021, + "Units": 111.2, + "Region": "Middle East/Africa" + }, + { + "Year": 2021, + "Units": 54.3, + "Region": "Latin America" + }, + { + "Year": 2022, + "Units": 352.8, + "Region": "Asia" + }, + { + "Year": 2022, + "Units": 203.6, + "Region": "Europe" + }, + { + "Year": 2022, + "Units": 201.8, + "Region": "North America" + }, + { + "Year": 2022, + "Units": 139.3, + "Region": "Middle East/Africa" + }, + { + "Year": 2022, + "Units": 57.7, + "Region": "Latin America" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of transaction", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding region", + "role": "y" + }, + { + "name": "Region", + "importance": "primary", + "description": "The region of the data point", + "role": "group" + } + ] + } + }, + "1885": { + "metadata": { + "title": "Supreme Court: Opposition to Kavanaugh Growing", + "description": "\"Do you support or oppose Brett Kavanaugh's Supreme Court nomination?\"", + "main_insight": "Opposition to Brett Kavanaugh's Supreme Court nomination increased between September 4th and September 17th, while support remained relatively stable." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "Sep 16", + "Units": 32.8, + "Sentiment": "Support" + } + ], + "score": 0.98, + "annotation": "The Support has a maximum value at Sep 16", + "reason": "The [{'Date': 'Sep 16', 'Units': 32.8, 'Sentiment': 'Support'}] of Support has a maximum value of 32.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "Sep 4", + "Units": 30.1, + "Sentiment": "Oppose" + } + ], + "score": 0.84, + "annotation": "The Oppose has a minimum value at Sep 4", + "reason": "The [{'Date': 'Sep 4', 'Units': 30.1, 'Sentiment': 'Oppose'}] of Oppose has a mainimum value of 30.1, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "Sep 17", + "Units": 35.7, + "Sentiment": "Oppose" + } + ], + "score": 0.81, + "annotation": "The Oppose has a maximum value at Sep 17", + "reason": "The [{'Date': 'Sep 17', 'Units': 35.7, 'Sentiment': 'Oppose'}] of Oppose has a maximum value of 35.7, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Date": "Sep 5", + "Units": 30.6, + "Sentiment": "Oppose" + } + ], + "score": 0.8, + "annotation": "The Oppose accounts for the majority in Sep 5.", + "reason": "The Units of Oppose in Sep 5 accounts for 0.5008183306055647 (more than 50%) of the total of Units of all Sentiment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Date": "Sep 4", + "Units": 30.5, + "Sentiment": "Support" + } + ], + "score": 0.8, + "annotation": "The Support accounts for the majority in Sep 4.", + "reason": "The Units of Support in Sep 4 accounts for 0.5033003300330033 (more than 50%) of the total of Units of all Sentiment." + } + ], + "data": { + "data": [ + { + "Date": "Sep 4", + "Units": 30.1, + "Sentiment": "Oppose" + }, + { + "Date": "Sep 4", + "Units": 30.5, + "Sentiment": "Support" + }, + { + "Date": "Sep 5", + "Units": 30.6, + "Sentiment": "Oppose" + }, + { + "Date": "Sep 5", + "Units": 30.5, + "Sentiment": "Support" + }, + { + "Date": "Sep 6", + "Units": 31.1, + "Sentiment": "Oppose" + }, + { + "Date": "Sep 6", + "Units": 30.3, + "Sentiment": "Support" + }, + { + "Date": "Sep 7", + "Units": 31.5, + "Sentiment": "Oppose" + }, + { + "Date": "Sep 7", + "Units": 30.1, + "Sentiment": "Support" + }, + { + "Date": "Sep 8", + "Units": 32.1, + "Sentiment": "Oppose" + }, + { + "Date": "Sep 8", + "Units": 31.0, + "Sentiment": "Support" + }, + { + "Date": "Sep 9", + "Units": 32.0, + "Sentiment": "Oppose" + }, + { + "Date": "Sep 9", + "Units": 30.5, + "Sentiment": "Support" + }, + { + "Date": "Sep 10", + "Units": 32.9, + "Sentiment": "Oppose" + }, + { + "Date": "Sep 10", + "Units": 31.2, + "Sentiment": "Support" + }, + { + "Date": "Sep 11", + "Units": 33.6, + "Sentiment": "Oppose" + }, + { + "Date": "Sep 11", + "Units": 30.6, + "Sentiment": "Support" + }, + { + "Date": "Sep 12", + "Units": 33.7, + "Sentiment": "Oppose" + }, + { + "Date": "Sep 12", + "Units": 31.8, + "Sentiment": "Support" + }, + { + "Date": "Sep 13", + "Units": 34.7, + "Sentiment": "Oppose" + }, + { + "Date": "Sep 13", + "Units": 31.0, + "Sentiment": "Support" + }, + { + "Date": "Sep 14", + "Units": 34.7, + "Sentiment": "Oppose" + }, + { + "Date": "Sep 14", + "Units": 30.9, + "Sentiment": "Support" + }, + { + "Date": "Sep 15", + "Units": 34.0, + "Sentiment": "Oppose" + }, + { + "Date": "Sep 15", + "Units": 30.8, + "Sentiment": "Support" + }, + { + "Date": "Sep 16", + "Units": 35.1, + "Sentiment": "Oppose" + }, + { + "Date": "Sep 16", + "Units": 32.8, + "Sentiment": "Support" + }, + { + "Date": "Sep 17", + "Units": 35.7, + "Sentiment": "Oppose" + }, + { + "Date": "Sep 17", + "Units": 31.0, + "Sentiment": "Support" + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Date of poll in September", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "304": { + "metadata": { + "title": "UK real estate transaction volume on the wane", + "description": "Breakdown of real estate transaction volume share in Europe H1 2015 to H1 2016", + "main_insight": "The UK had the largest share of European real estate transaction volume in H1 2016, followed by Germany and France. However, the UK's share decreased by 7.5 percentage points compared to H1 2015, while most other countries saw increases." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "UK", + "Transaction Volume Share": 27.4 + } + ], + "score": 0.93, + "annotation": "The has a maximum value at UK", + "reason": "The [{'Country': 'UK', 'Transaction Volume Share': 27.4}] of has a maximum value of 27.4, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "UK", + "Transaction Volume Share": 27.4 + } + ], + "score": 0.7, + "annotation": "The shows a sudden jump in Transaction Volume Share from Germany to UK.", + "reason": "The Transaction Volume Share for increases from 19.5 at Germany to 27.4 at UK, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Denmark", + "Transaction Volume Share": 2.0 + } + ], + "score": 0.58, + "annotation": "The has a minimum value at Denmark", + "reason": "The [{'Country': 'Denmark', 'Transaction Volume Share': 2.0}] of has a mainimum value of 2.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "UK", + "Transaction Volume Share": 27.4 + }, + { + "Country": "Germany", + "Transaction Volume Share": 19.5 + }, + { + "Country": "France", + "Transaction Volume Share": 9.0 + }, + { + "Country": "Sweden", + "Transaction Volume Share": 7.9 + }, + { + "Country": "Netherlands", + "Transaction Volume Share": 5.9 + }, + { + "Country": "Spain", + "Transaction Volume Share": 5.3 + }, + { + "Country": "Italy", + "Transaction Volume Share": 4.9 + }, + { + "Country": "Finland", + "Transaction Volume Share": 2.5 + }, + { + "Country": "Denmark", + "Transaction Volume Share": 2.0 + }, + { + "Country": "Other", + "Transaction Volume Share": 15.6 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "European countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Transaction Volume Share", + "importance": "primary", + "description": "Percentage share of real estate transaction volume in Europe in H1 2016", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1901": { + "metadata": { + "title": "Financial Woes on the Rise in the U.S.", + "description": "Share of U.S. adults that were very/moderately worried about the following in 2019 and 2022", + "main_insight": "Americans' financial worries increased across all surveyed categories between 2019 and 2022, with the largest increase in concern related to maintaining their standard of living." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Financial Concern": "Paying rent/mortgage/other housing costs", + "Points": 35.0, + "Yearly Sales": "2022" + }, + { + "Financial Concern": "Paying rent/mortgage/other housing costs", + "Points": 30.0, + "Yearly Sales": "2019" + } + ], + "score": 0.98, + "annotation": "The Points of 2022 and 2019 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Points between 2022 and 2019 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Financial Concern": "Not having enough money for retirement", + "Points": 9.0, + "Yearly Sales": "p.p. increase" + } + ], + "score": 0.91, + "annotation": "The p.p. increase shows a sudden jump in Points from Paying rent/mortgage/other housing costs to Not having enough money for retirement.", + "reason": "The Points for p.p. increase increases from 5.0 at Paying rent/mortgage/other housing costs to 9.0 at Not having enough money for retirement, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Financial Concern": "Not having enough money for retirement", + "Points": 63.0, + "Yearly Sales": "2022" + } + ], + "score": 0.86, + "annotation": "The 2022 has the largest proportion in Not having enough money for retirement.", + "reason": "The Points of 2022 in Not having enough money for retirement accounts for 0.5, larger than all other Yearly Sales, of the total of Points of all Yearly Sales." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Financial Concern": "Paying medical costs for serious illness/accident", + "Points": 56.0, + "Yearly Sales": "2022" + } + ], + "score": 0.86, + "annotation": "The 2022 has the largest proportion in Paying medical costs for serious illness/accident.", + "reason": "The Points of 2022 in Paying medical costs for serious illness/accident accounts for 0.5, larger than all other Yearly Sales, of the total of Points of all Yearly Sales." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Financial Concern": "Maintaining standard of living", + "Points": 52.0, + "Yearly Sales": "2022" + } + ], + "score": 0.86, + "annotation": "The 2022 has the largest proportion in Maintaining standard of living.", + "reason": "The Points of 2022 in Maintaining standard of living accounts for 0.5, larger than all other Yearly Sales, of the total of Points of all Yearly Sales." + } + ], + "data": { + "data": [ + { + "Financial Concern": "Not having enough money for retirement", + "Points": 63.0, + "Yearly Sales": "2022" + }, + { + "Financial Concern": "Not having enough money for retirement", + "Points": 54.0, + "Yearly Sales": "2019" + }, + { + "Financial Concern": "Not having enough money for retirement", + "Points": 9.0, + "Yearly Sales": "p.p. increase" + }, + { + "Financial Concern": "Paying medical costs for serious illness/accident", + "Points": 56.0, + "Yearly Sales": "2022" + }, + { + "Financial Concern": "Paying medical costs for serious illness/accident", + "Points": 51.0, + "Yearly Sales": "2019" + }, + { + "Financial Concern": "Paying medical costs for serious illness/accident", + "Points": 5.0, + "Yearly Sales": "p.p. increase" + }, + { + "Financial Concern": "Maintaining standard of living", + "Points": 52.0, + "Yearly Sales": "2022" + }, + { + "Financial Concern": "Maintaining standard of living", + "Points": 42.0, + "Yearly Sales": "2019" + }, + { + "Financial Concern": "Maintaining standard of living", + "Points": 10.0, + "Yearly Sales": "p.p. increase" + }, + { + "Financial Concern": "Not having enough to pay monthly bills", + "Points": 40.0, + "Yearly Sales": "2022" + }, + { + "Financial Concern": "Not having enough to pay monthly bills", + "Points": 36.0, + "Yearly Sales": "2019" + }, + { + "Financial Concern": "Not having enough to pay monthly bills", + "Points": 4.0, + "Yearly Sales": "p.p. increase" + }, + { + "Financial Concern": "Paying rent/mortgage/other housing costs", + "Points": 35.0, + "Yearly Sales": "2022" + }, + { + "Financial Concern": "Paying rent/mortgage/other housing costs", + "Points": 30.0, + "Yearly Sales": "2019" + }, + { + "Financial Concern": "Paying rent/mortgage/other housing costs", + "Points": 5.0, + "Yearly Sales": "p.p. increase" + } + ], + "columns": [ + { + "name": "Financial Concern", + "importance": "primary", + "description": "Type of financial concern", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding yearly sales", + "role": "y" + }, + { + "name": "Yearly Sales", + "importance": "primary", + "description": "The yearly sales of the data point", + "role": "group" + } + ] + } + }, + "777": { + "metadata": { + "title": "How Green Are The Capitals Of Europe?", + "description": "Tree cover rate in a selection of European capitals, in %", + "main_insight": "Oslo has by far the highest tree cover rate among the selected European capitals at 72%, while Nicosia has the lowest at 4%." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Oslo", + "Tree Cover Rate (%)": 72.0 + } + ], + "score": 0.89, + "annotation": "The has a maximum value at Oslo", + "reason": "The [{'City': 'Oslo', 'Tree Cover Rate (%)': 72.0}] of has a maximum value of 72.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "Nicosia", + "Tree Cover Rate (%)": 4.0 + } + ], + "score": 0.83, + "annotation": "The has a minimum value at Nicosia", + "reason": "The [{'City': 'Nicosia', 'Tree Cover Rate (%)': 4.0}] of has a mainimum value of 4.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Oslo", + "Tree Cover Rate (%)": 72.0 + } + ], + "score": 0.67, + "annotation": "The shows a sudden jump in Tree Cover Rate (%) from Bern to Oslo.", + "reason": "The Tree Cover Rate (%) for increases from 53.0 at Bern to 72.0 at Oslo, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "City": "Oslo", + "Tree Cover Rate (%)": 72.0 + }, + { + "City": "Bern", + "Tree Cover Rate (%)": 53.0 + }, + { + "City": "Ljubljana", + "Tree Cover Rate (%)": 50.0 + }, + { + "City": "Berlin", + "Tree Cover Rate (%)": 44.0 + }, + { + "City": "Madrid", + "Tree Cover Rate (%)": 39.0 + }, + { + "City": "Brussels", + "Tree Cover Rate (%)": 37.0 + }, + { + "City": "Vienna", + "Tree Cover Rate (%)": 34.0 + }, + { + "City": "Rome", + "Tree Cover Rate (%)": 24.0 + }, + { + "City": "Paris", + "Tree Cover Rate (%)": 20.0 + }, + { + "City": "Athens", + "Tree Cover Rate (%)": 11.0 + }, + { + "City": "Nicosia", + "Tree Cover Rate (%)": 4.0 + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "European capital city", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Tree Cover Rate (%)", + "importance": "primary", + "description": "Percentage of the city covered by tree crowns", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1857": { + "metadata": { + "title": "What India and China Spend on Russian Oil", + "description": "Spending on Russian oil in India and China in Mar-May 2021 vs. 2022 (in billion U.S. dollars)", + "main_insight": "Both India and China significantly increased their spending on Russian oil between March-May 2021 and March-May 2022, with India's spending increasing by a much larger percentage." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "China", + "Units": 8.8, + "Timeframe": "Mar-May 2021" + }, + { + "Country": "China", + "Units": 15.7, + "Timeframe": "Mar-May 2022" + } + ], + "score": 1.0, + "annotation": "The Units of Mar-May 2021 and Mar-May 2022 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Mar-May 2021 and Mar-May 2022 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "China", + "Units": 8.8, + "Timeframe": "Mar-May 2021" + }, + { + "Country": "China", + "Units": 78.0, + "Timeframe": "Change" + } + ], + "score": 1.0, + "annotation": "The Units of Mar-May 2021 and Change show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Mar-May 2021 and Change is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "China", + "Units": 15.7, + "Timeframe": "Mar-May 2022" + }, + { + "Country": "China", + "Units": 78.0, + "Timeframe": "Change" + } + ], + "score": 1.0, + "annotation": "The Units of Mar-May 2022 and Change show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Mar-May 2022 and Change is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "India", + "Units": 0.5, + "Timeframe": "Mar-May 2021" + } + ], + "score": 1.0, + "annotation": "The Mar-May 2021 has the smallest proportion in India.", + "reason": "The Units of Mar-May 2021 in India accounts for 0.0009225092250922509, smaller than all other Timeframe, of the total of Units of all Timeframe." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "China", + "Units": 8.8, + "Timeframe": "Mar-May 2021" + } + ], + "score": 0.97, + "annotation": "The Mar-May 2021 shows a sudden jump in Units from India to China.", + "reason": "The Units for Mar-May 2021 increases from 0.5 at India to 8.8 at China, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "India", + "Units": 0.5, + "Timeframe": "Mar-May 2021" + }, + { + "Country": "India", + "Units": 3.5, + "Timeframe": "Mar-May 2022" + }, + { + "Country": "India", + "Units": 538.0, + "Timeframe": "Change" + }, + { + "Country": "China", + "Units": 8.8, + "Timeframe": "Mar-May 2021" + }, + { + "Country": "China", + "Units": 15.7, + "Timeframe": "Mar-May 2022" + }, + { + "Country": "China", + "Units": 78.0, + "Timeframe": "Change" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country spending on Russian Oil", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding timeframe", + "role": "y" + }, + { + "name": "Timeframe", + "importance": "primary", + "description": "The timeframe of the data point", + "role": "group" + } + ] + } + }, + "2467": { + "metadata": { + "title": "International Travel Expands Faster Than Domestic in the Growing Market", + "description": "Share of worldwide outbound overnight stays, by source", + "main_insight": "International travel (Interregional and Intraregional) is projected to grow as a share of worldwide outbound overnight stays from 2023 to 2030, while domestic travel's share is expected to decrease." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2030, + "Score": 19.0, + "Travel Type": "Interregional" + }, + { + "Year": 2030, + "Score": 30.0, + "Travel Type": "Intraregional" + } + ], + "score": 1.0, + "annotation": "The Score of Interregional and Intraregional show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Interregional and Intraregional is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2030, + "Score": 19.0, + "Travel Type": "Interregional" + }, + { + "Year": 2030, + "Score": 51.0, + "Travel Type": "Domestic" + } + ], + "score": 1.0, + "annotation": "The Score of Interregional and Domestic show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Interregional and Domestic is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2030, + "Score": 19.0, + "Travel Type": "Interregional" + }, + { + "Year": 2030, + "Score": 38.7, + "Travel Type": "Total" + } + ], + "score": 1.0, + "annotation": "The Score of Interregional and Total show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Interregional and Total is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2030, + "Score": 30.0, + "Travel Type": "Intraregional" + }, + { + "Year": 2030, + "Score": 51.0, + "Travel Type": "Domestic" + } + ], + "score": 1.0, + "annotation": "The Score of Intraregional and Domestic show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Intraregional and Domestic is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2030, + "Score": 30.0, + "Travel Type": "Intraregional" + }, + { + "Year": 2030, + "Score": 38.7, + "Travel Type": "Total" + } + ], + "score": 1.0, + "annotation": "The Score of Intraregional and Total show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Intraregional and Total is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2023, + "Score": 16.0, + "Travel Type": "Interregional" + }, + { + "Year": 2023, + "Score": 25.0, + "Travel Type": "Intraregional" + }, + { + "Year": 2023, + "Score": 59.0, + "Travel Type": "Domestic" + }, + { + "Year": 2023, + "Score": 27.4, + "Travel Type": "Total" + }, + { + "Year": 2030, + "Score": 19.0, + "Travel Type": "Interregional" + }, + { + "Year": 2030, + "Score": 30.0, + "Travel Type": "Intraregional" + }, + { + "Year": 2030, + "Score": 51.0, + "Travel Type": "Domestic" + }, + { + "Year": 2030, + "Score": 38.7, + "Travel Type": "Total" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding travel type", + "role": "y" + }, + { + "name": "Travel Type", + "importance": "primary", + "description": "The travel type of the data point", + "role": "group" + } + ] + } + }, + "1886": { + "metadata": { + "title": "U.S. is Stepping up Crude Oil Exports", + "description": "Monthly crude oil exports in the biggest exporting countries (in million barrels per day)", + "main_insight": "U.S. crude oil exports have significantly increased since 2012, surpassing those of Canada, Iraq, and Russia by 2019." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Ratio": 4.0, + "Producers": "Iraq" + }, + { + "Year": 2019, + "Ratio": 3.4, + "Producers": "Canada" + } + ], + "score": 0.91, + "annotation": "The Ratio of Iraq and Canada show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Iraq and Canada is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Ratio": 3.3, + "Producers": "U.S." + }, + { + "Year": 2019, + "Ratio": 4.0, + "Producers": "Iraq" + } + ], + "score": 0.9, + "annotation": "The Ratio of U.S. and Iraq show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between U.S. and Iraq is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Ratio": 3.3, + "Producers": "U.S." + }, + { + "Year": 2019, + "Ratio": 3.4, + "Producers": "Canada" + } + ], + "score": 0.78, + "annotation": "The Ratio of U.S. and Canada show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between U.S. and Canada is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2019, + "Ratio": 3.3, + "Producers": "U.S." + }, + { + "Year": 2019, + "Ratio": 6.7, + "Producers": "Saudi Arabia" + } + ], + "score": 0.75, + "annotation": "The Ratio of U.S. and Saudi Arabia show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between U.S. and Saudi Arabia is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2019, + "Ratio": 6.7, + "Producers": "Saudi Arabia" + }, + { + "Year": 2019, + "Ratio": 4.0, + "Producers": "Iraq" + } + ], + "score": 0.66, + "annotation": "The Ratio of Saudi Arabia and Iraq show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Saudi Arabia and Iraq is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2012, + "Ratio": 0.1, + "Producers": "U.S." + }, + { + "Year": 2012, + "Ratio": 7.5, + "Producers": "Saudi Arabia" + }, + { + "Year": 2012, + "Ratio": 4.8, + "Producers": "Russia" + }, + { + "Year": 2012, + "Ratio": 2.4, + "Producers": "Iraq" + }, + { + "Year": 2012, + "Ratio": 2.2, + "Producers": "Canada" + }, + { + "Year": 2013, + "Ratio": 0.3, + "Producers": "U.S." + }, + { + "Year": 2013, + "Ratio": 7.5, + "Producers": "Saudi Arabia" + }, + { + "Year": 2013, + "Ratio": 4.8, + "Producers": "Russia" + }, + { + "Year": 2013, + "Ratio": 2.4, + "Producers": "Iraq" + }, + { + "Year": 2013, + "Ratio": 2.5, + "Producers": "Canada" + }, + { + "Year": 2014, + "Ratio": 0.4, + "Producers": "U.S." + }, + { + "Year": 2014, + "Ratio": 7.1, + "Producers": "Saudi Arabia" + }, + { + "Year": 2014, + "Ratio": 4.5, + "Producers": "Russia" + }, + { + "Year": 2014, + "Ratio": 2.5, + "Producers": "Iraq" + }, + { + "Year": 2014, + "Ratio": 2.8, + "Producers": "Canada" + }, + { + "Year": 2015, + "Ratio": 0.5, + "Producers": "U.S." + }, + { + "Year": 2015, + "Ratio": 7.2, + "Producers": "Saudi Arabia" + }, + { + "Year": 2015, + "Ratio": 5.2, + "Producers": "Russia" + }, + { + "Year": 2015, + "Ratio": 3.0, + "Producers": "Iraq" + }, + { + "Year": 2015, + "Ratio": 3.0, + "Producers": "Canada" + }, + { + "Year": 2016, + "Ratio": 0.5, + "Producers": "U.S." + }, + { + "Year": 2016, + "Ratio": 7.5, + "Producers": "Saudi Arabia" + }, + { + "Year": 2016, + "Ratio": 5.0, + "Producers": "Russia" + }, + { + "Year": 2016, + "Ratio": 3.2, + "Producers": "Iraq" + }, + { + "Year": 2016, + "Ratio": 3.0, + "Producers": "Canada" + }, + { + "Year": 2017, + "Ratio": 1.1, + "Producers": "U.S." + }, + { + "Year": 2017, + "Ratio": 7.0, + "Producers": "Saudi Arabia" + }, + { + "Year": 2017, + "Ratio": 5.1, + "Producers": "Russia" + }, + { + "Year": 2017, + "Ratio": 3.3, + "Producers": "Iraq" + }, + { + "Year": 2017, + "Ratio": 3.2, + "Producers": "Canada" + }, + { + "Year": 2018, + "Ratio": 2.0, + "Producers": "U.S." + }, + { + "Year": 2018, + "Ratio": 7.3, + "Producers": "Saudi Arabia" + }, + { + "Year": 2018, + "Ratio": 5.0, + "Producers": "Russia" + }, + { + "Year": 2018, + "Ratio": 3.5, + "Producers": "Iraq" + }, + { + "Year": 2018, + "Ratio": 3.4, + "Producers": "Canada" + }, + { + "Year": 2019, + "Ratio": 3.3, + "Producers": "U.S." + }, + { + "Year": 2019, + "Ratio": 6.7, + "Producers": "Saudi Arabia" + }, + { + "Year": 2019, + "Ratio": 4.5, + "Producers": "Russia" + }, + { + "Year": 2019, + "Ratio": 4.0, + "Producers": "Iraq" + }, + { + "Year": 2019, + "Ratio": 3.4, + "Producers": "Canada" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of crude oil export data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding producers", + "role": "y" + }, + { + "name": "Producers", + "importance": "primary", + "description": "The producers of the data point", + "role": "group" + } + ] + } + }, + "1323": { + "metadata": { + "title": "India Tries to Reign in Ballooning Military Pensions", + "description": "Share of Indian military pensions in total military budget, by fiscal year", + "main_insight": "The share of Indian military pensions in the total military budget increased from 23% in 2016-2017 to a peak of 27% in 2019-2021, before decreasing to 23% in 2022-2023." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Fiscal Year": "2022-2023", + "Units": 23.0, + "Budget Items": "Pension allocation" + }, + { + "Fiscal Year": "2022-2023", + "Units": 77.0, + "Budget Items": "Other defense budget" + } + ], + "score": 1.0, + "annotation": "The Units of Pension allocation and Other defense budget show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Pension allocation and Other defense budget is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Fiscal Year": "2019-2020", + "Units": 27.0, + "Budget Items": "Pension allocation" + } + ], + "score": 0.83, + "annotation": "The Pension allocation shows a sudden jump in Units from 2018-2019 to 2019-2020.", + "reason": "The Units for Pension allocation increases from 25.0 at 2018-2019 to 27.0 at 2019-2020, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Fiscal Year": "2018-2019", + "Units": 75.0, + "Budget Items": "Other defense budget" + } + ], + "score": 0.83, + "annotation": "The Other defense budget shows a sudden jump in Units from 2020-2021 to 2018-2019.", + "reason": "The Units for Other defense budget increases from 73.0 at 2020-2021 to 75.0 at 2018-2019, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Fiscal Year": "2019-2020", + "Units": 73.0, + "Budget Items": "Other defense budget" + }, + { + "Fiscal Year": "2020-2021", + "Units": 73.0, + "Budget Items": "Other defense budget" + } + ], + "score": 0.75, + "annotation": "The Other defense budget has minimum values at 2019-2020, 2020-2021", + "reason": "The [{'Fiscal Year': '2019-2020', 'Units': 73.0, 'Budget Items': 'Other defense budget'}, {'Fiscal Year': '2020-2021', 'Units': 73.0, 'Budget Items': 'Other defense budget'}] of Other defense budget have minimum values of 73.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Fiscal Year": "2019-2020", + "Units": 27.0, + "Budget Items": "Pension allocation" + }, + { + "Fiscal Year": "2020-2021", + "Units": 27.0, + "Budget Items": "Pension allocation" + } + ], + "score": 0.75, + "annotation": "The Pension allocation has maximum values at 2019-2020, 2020-2021", + "reason": "The [{'Fiscal Year': '2019-2020', 'Units': 27.0, 'Budget Items': 'Pension allocation'}, {'Fiscal Year': '2020-2021', 'Units': 27.0, 'Budget Items': 'Pension allocation'}] of Pension allocation have maximum values of 27.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Fiscal Year": "2016-2017", + "Units": 23.0, + "Budget Items": "Pension allocation" + }, + { + "Fiscal Year": "2016-2017", + "Units": 77.0, + "Budget Items": "Other defense budget" + }, + { + "Fiscal Year": "2017-2018", + "Units": 24.0, + "Budget Items": "Pension allocation" + }, + { + "Fiscal Year": "2017-2018", + "Units": 76.0, + "Budget Items": "Other defense budget" + }, + { + "Fiscal Year": "2018-2019", + "Units": 25.0, + "Budget Items": "Pension allocation" + }, + { + "Fiscal Year": "2018-2019", + "Units": 75.0, + "Budget Items": "Other defense budget" + }, + { + "Fiscal Year": "2019-2020", + "Units": 27.0, + "Budget Items": "Pension allocation" + }, + { + "Fiscal Year": "2019-2020", + "Units": 73.0, + "Budget Items": "Other defense budget" + }, + { + "Fiscal Year": "2020-2021", + "Units": 27.0, + "Budget Items": "Pension allocation" + }, + { + "Fiscal Year": "2020-2021", + "Units": 73.0, + "Budget Items": "Other defense budget" + }, + { + "Fiscal Year": "2021-2022", + "Units": 24.0, + "Budget Items": "Pension allocation" + }, + { + "Fiscal Year": "2021-2022", + "Units": 76.0, + "Budget Items": "Other defense budget" + }, + { + "Fiscal Year": "2022-2023", + "Units": 23.0, + "Budget Items": "Pension allocation" + }, + { + "Fiscal Year": "2022-2023", + "Units": 77.0, + "Budget Items": "Other defense budget" + } + ], + "columns": [ + { + "name": "Fiscal Year", + "importance": "primary", + "description": "Fiscal year of the budget", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding budget items", + "role": "y" + }, + { + "name": "Budget Items", + "importance": "primary", + "description": "The budget items of the data point", + "role": "group" + } + ] + } + }, + "1787": { + "metadata": { + "title": "Overregulated Lifeline? Agriculture in India", + "description": "Facts about agriculture in India", + "main_insight": "Agriculture in India constitutes a significant portion of employment and government procurement, despite a smaller share of GDP, with price moderation contributing to household savings." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Metric": "Average annual household food savings due to price moderation (2019/20)", + "Value": 217 + } + ], + "score": 0.9, + "annotation": "The shows a sudden jump in Value from Government procurement of wheat and rice surplus in Haryana/Punjab (2018/19) to Average annual household food savings due to price moderation (2019/20).", + "reason": "The Value for increases from 85 at Government procurement of wheat and rice surplus in Haryana/Punjab (2018/19) to 217 at Average annual household food savings due to price moderation (2019/20), indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Metric": "Average annual household food savings due to price moderation (2019/20)", + "Value": 217 + } + ], + "score": 0.86, + "annotation": "The has a maximum value at Average annual household food savings due to price moderation (2019/20)", + "reason": "The [{'Metric': 'Average annual household food savings due to price moderation (2019/20)', 'Value': 217}] of has a maximum value of 217, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Metric": "Share of GDP (2019)", + "Value": 16 + } + ], + "score": 0.5, + "annotation": "The has a minimum value at Share of GDP (2019)", + "reason": "The [{'Metric': 'Share of GDP (2019)', 'Value': 16}] of has a mainimum value of 16, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Metric": "Share of GDP (2019)", + "Value": 16 + }, + { + "Metric": "Share of employment (2020)", + "Value": 41 + }, + { + "Metric": "Government procurement of wheat and rice surplus (2018/19)", + "Value": 45 + }, + { + "Metric": "Government procurement of wheat and rice surplus in Haryana/Punjab (2018/19)", + "Value": 85 + }, + { + "Metric": "Average annual household food savings due to price moderation (2019/20)", + "Value": 217 + } + ], + "columns": [ + { + "name": "Metric", + "importance": "primary", + "description": "The aspect of Indian agriculture being measured", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "The corresponding percentage or value for each metric", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "472": { + "metadata": { + "title": "Investors Focus on Vacant and Independent Hotels", + "description": "Acquisition priorities for European investors by property type", + "main_insight": "European investors primarily prioritize vacant properties and independent hotels, with these two categories accounting for the majority of acquisition interest." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Property Type": "Hotels that are soft branded, connected to the globally recognized brand families but maintain an independent brand", + "Investor Preference": 0.22 + } + ], + "score": 0.81, + "annotation": "The shows a sudden jump in Investor Preference from Operational lease agreement to Hotels that are soft branded, connected to the globally recognized brand families but maintain an independent brand.", + "reason": "The Investor Preference for increases from 0.08 at Operational lease agreement to 0.22 at Hotels that are soft branded, connected to the globally recognized brand families but maintain an independent brand, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Property Type": "Vacant properties", + "Investor Preference": 0.36 + } + ], + "score": 0.71, + "annotation": "The has a maximum value at Vacant properties", + "reason": "The [{'Property Type': 'Vacant properties', 'Investor Preference': 0.36}] of has a maximum value of 0.36, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Property Type": "Hotels affiliated with a globally recognized brand family", + "Investor Preference": 0.06 + } + ], + "score": 0.64, + "annotation": "The has a minimum value at Hotels affiliated with a globally recognized brand family", + "reason": "The [{'Property Type': 'Hotels affiliated with a globally recognized brand family', 'Investor Preference': 0.06}] of has a mainimum value of 0.06, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Property Type": "Vacant properties", + "Investor Preference": 0.36 + }, + { + "Property Type": "Independent hotels", + "Investor Preference": 0.28 + }, + { + "Property Type": "Hotels that are soft branded, connected to the globally recognized brand families but maintain an independent brand", + "Investor Preference": 0.22 + }, + { + "Property Type": "Operational lease agreement", + "Investor Preference": 0.08 + }, + { + "Property Type": "Hotels affiliated with a globally recognized brand family", + "Investor Preference": 0.06 + } + ], + "columns": [ + { + "name": "Property Type", + "importance": "primary", + "description": "Type of hotel property", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Investor Preference", + "importance": "primary", + "description": "Percentage of investors prioritizing the property type", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "459": { + "metadata": { + "title": "Juventus Sold $60 Million Of Ronaldo Jerseys In 24 Hours", + "description": "Ronaldo's transfer fee and jersey sales in first 24 hours since merchandise release", + "main_insight": "Juventus sold $62.4 million worth of Ronaldo jerseys in the first 24 hours, which is nearly half of his $129.3 million transfer fee." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "Ronaldo's transfer fee", + "Amount ($ millions)": 129.3 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Amount ($ millions) from Jersey sales in the first 24 hours to Ronaldo's transfer fee.", + "reason": "The Amount ($ millions) for increases from 62.4 at Jersey sales in the first 24 hours to 129.3 at Ronaldo's transfer fee, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Category": "Jersey sales in the first 24 hours", + "Amount ($ millions)": 62.4 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at Jersey sales in the first 24 hours", + "reason": "The [{'Category': 'Jersey sales in the first 24 hours', 'Amount ($ millions)': 62.4}] of has a mainimum value of 62.4, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Ronaldo's transfer fee", + "Amount ($ millions)": 129.3 + } + ], + "score": 0.45, + "annotation": "The has a maximum value at Ronaldo's transfer fee", + "reason": "The [{'Category': \"Ronaldo's transfer fee\", 'Amount ($ millions)': 129.3}] of has a maximum value of 129.3, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Category": "Jersey sales in the first 24 hours", + "Amount ($ millions)": 62.4 + }, + { + "Category": "Ronaldo's transfer fee", + "Amount ($ millions)": 129.3 + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "The category of financial transaction", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Amount ($ millions)", + "importance": "primary", + "description": "The monetary value in millions of US dollars", + "unit": "$ millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "984": { + "metadata": { + "title": "Free Apps are More Likely to Invade User Privacy", + "description": "Percentage of apps in the Google Play Store that have access to the following functions", + "main_insight": "Free apps on the Google Play Store are consistently more likely to request access to various user functions than paid apps, especially tracking user location." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Function": "Send text messages", + "Units": 2.6, + "App Type": "Free Apps" + }, + { + "Function": "Send text messages", + "Units": 1.5, + "App Type": "Paid Apps" + } + ], + "score": 1.0, + "annotation": "The Units of Free Apps and Paid Apps show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Free Apps and Paid Apps is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Function": "Track user location", + "Units": 6.0, + "App Type": "Paid Apps" + } + ], + "score": 0.96, + "annotation": "The Paid Apps shows a sudden jump in Units from Access the camera to Track user location.", + "reason": "The Units for Paid Apps increases from 2.1 at Access the camera to 6.0 at Track user location, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Function": "Track user location", + "Units": 24.1, + "App Type": "Free Apps" + } + ], + "score": 0.94, + "annotation": "The Free Apps shows a sudden jump in Units from Access address books to Track user location.", + "reason": "The Units for Free Apps increases from 6.7 at Access address books to 24.1 at Track user location, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Function": "Track user location", + "Units": 6.0, + "App Type": "Paid Apps" + } + ], + "score": 0.88, + "annotation": "The Paid Apps has a maximum value at Track user location", + "reason": "The [{'Function': 'Track user location', 'Units': 6.0, 'App Type': 'Paid Apps'}] of Paid Apps has a maximum value of 6.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Function": "Track user location", + "Units": 24.1, + "App Type": "Free Apps" + } + ], + "score": 0.87, + "annotation": "The Free Apps has a maximum value at Track user location", + "reason": "The [{'Function': 'Track user location', 'Units': 24.1, 'App Type': 'Free Apps'}] of Free Apps has a maximum value of 24.1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Function": "Track user location", + "Units": 24.1, + "App Type": "Free Apps" + }, + { + "Function": "Track user location", + "Units": 6.0, + "App Type": "Paid Apps" + }, + { + "Function": "Access address books", + "Units": 6.7, + "App Type": "Free Apps" + }, + { + "Function": "Access address books", + "Units": 2.1, + "App Type": "Paid Apps" + }, + { + "Function": "Initiate calls", + "Units": 6.4, + "App Type": "Free Apps" + }, + { + "Function": "Initiate calls", + "Units": 1.9, + "App Type": "Paid Apps" + }, + { + "Function": "Access the camera", + "Units": 5.5, + "App Type": "Free Apps" + }, + { + "Function": "Access the camera", + "Units": 2.1, + "App Type": "Paid Apps" + }, + { + "Function": "Send text messages", + "Units": 2.6, + "App Type": "Free Apps" + }, + { + "Function": "Send text messages", + "Units": 1.5, + "App Type": "Paid Apps" + } + ], + "columns": [ + { + "name": "Function", + "importance": "primary", + "description": "Specific user function the app requests access to", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding app type", + "role": "y" + }, + { + "name": "App Type", + "importance": "primary", + "description": "The app type of the data point", + "role": "group" + } + ] + } + }, + "2108": { + "metadata": { + "title": "Where Teachers Spend The Most Time In The Classroom", + "description": "Annual hours of instruction time in primary education in selected countries*", + "main_insight": "Teachers in Chile and Australia spend the most time in the classroom, exceeding 1000 hours annually, while China has the lowest instruction time at 612 hours." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "China", + "Annual Hours": 612.0 + } + ], + "score": 0.78, + "annotation": "The has a minimum value at China", + "reason": "The [{'Country': 'China', 'Annual Hours': 612.0}] of has a mainimum value of 612.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Chile", + "Annual Hours": 1049.0 + } + ], + "score": 0.76, + "annotation": "The has a maximum value at Chile", + "reason": "The [{'Country': 'Chile', 'Annual Hours': 1049.0}] of has a maximum value of 1049.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "England", + "Annual Hours": 861.0 + } + ], + "score": 0.65, + "annotation": "The shows a sudden jump in Annual Hours from Italy to England.", + "reason": "The Annual Hours for increases from 752.0 at Italy to 861.0 at England, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Chile", + "Annual Hours": 1049.0 + }, + { + "Country": "Australia", + "Annual Hours": 1010.0 + }, + { + "Country": "United States", + "Annual Hours": 967.0 + }, + { + "Country": "Ireland", + "Annual Hours": 915.0 + }, + { + "Country": "France", + "Annual Hours": 864.0 + }, + { + "Country": "England", + "Annual Hours": 861.0 + }, + { + "Country": "Italy", + "Annual Hours": 752.0 + }, + { + "Country": "Germany", + "Annual Hours": 683.0 + }, + { + "Country": "Poland", + "Annual Hours": 635.0 + }, + { + "Country": "China", + "Annual Hours": 612.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Annual Hours", + "importance": "primary", + "description": "Annual hours of instruction time", + "unit": "hours", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2189": { + "metadata": { + "title": "Facts on Flags: Most Countries Fly the Red, White & Blue", + "description": "Most frequent colors and # of colors per flag used in national flags worldwide*", + "main_insight": "Red, white, and blue are the most common colors used in national flags, and most flags contain 2-4 colors." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Color": "Red", + "Color Percentage": 30.3 + } + ], + "score": 0.85, + "annotation": "The has a maximum value at Red", + "reason": "The [{'Color': 'Red', 'Color Percentage': 30.3}] of has a maximum value of 30.3, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Color": "Red", + "Color Percentage": 30.3 + } + ], + "score": 0.69, + "annotation": "The shows a sudden jump in Color Percentage from Blue to Red.", + "reason": "The Color Percentage for increases from 21.2 at Blue to 30.3 at Red, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Color": "Brown", + "Color Percentage": 0.1 + } + ], + "score": 0.68, + "annotation": "The has a minimum value at Brown", + "reason": "The [{'Color': 'Brown', 'Color Percentage': 0.1}] of has a mainimum value of 0.1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Color": "Brown", + "Color Percentage": 0.1 + }, + { + "Color": "Orange", + "Color Percentage": 1.2 + }, + { + "Color": "Black", + "Color Percentage": 5.1 + }, + { + "Color": "Yellow/Gold", + "Color Percentage": 8.6 + }, + { + "Color": "Green", + "Color Percentage": 15.3 + }, + { + "Color": "White", + "Color Percentage": 19.1 + }, + { + "Color": "Blue", + "Color Percentage": 21.2 + }, + { + "Color": "Red", + "Color Percentage": 30.3 + } + ], + "columns": [ + { + "name": "Color", + "importance": "primary", + "description": "Color of flag", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Color Percentage", + "importance": "primary", + "description": "Percentage of flags containing the color", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1975": { + "metadata": { + "title": "Drones: A Tech Growth Market in the United States", + "description": "Sales of consumer drones to dealers in the U.S. from 2013 to 2017 (in million U.S. dollars)", + "main_insight": "Consumer drone sales in the US experienced significant growth between 2013 and 2017, with sales projected to almost triple between 2015 and 2017." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2013, + "Sales (Million USD)": 44.0 + }, + { + "Year": 2014, + "Sales (Million USD)": 204.0 + }, + { + "Year": 2015, + "Sales (Million USD)": 443.0 + }, + { + "Year": 2016, + "Sales (Million USD)": 799.0 + }, + { + "Year": 2017, + "Sales (Million USD)": 1296.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of drone sales", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Sales (Million USD)", + "importance": "primary", + "description": "Sales of consumer drones to dealers in million U.S. dollars", + "unit": "Million USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2215": { + "metadata": { + "title": "How Vaccines Helped All But Eradicate Diseases", + "description": "Annual 20th century morbidity and 2021 morbidity for vaccine-preventable diseases in the U.S.", + "main_insight": "Vaccines have drastically reduced the morbidity of several diseases in the U.S., with some diseases like Smallpox, Diphtheria and Polio being completely eradicated." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Disease": "Measles", + "Units": 9.0, + "Morbidity Type": "2021 Morbidity" + } + ], + "score": 1.0, + "annotation": "The 2021 Morbidity has the smallest proportion in Measles.", + "reason": "The Units of 2021 Morbidity in Measles accounts for 1.6970725498515063e-05, smaller than all other Morbidity Type, of the total of Units of all Morbidity Type." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Disease": "Rubella", + "Units": 3.0, + "Morbidity Type": "2021 Morbidity" + } + 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accounts for 0.0006088560885608856, smaller than all other Morbidity Type, of the total of Units of all Morbidity Type." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Morbidity Type": "20th Century Morbidity", + "Disease": "total", + "Units": 1007432.0 + } + ], + "score": 1.0, + "annotation": "The total value of 20th Century Morbidity has the smallest proportion.", + "reason": "The total value of Units of 20th Century Morbidity accounts for 0.0006891730517493707, smaller than all other Morbidity Type, of the total of Units of all Morbidity Type." + } + ], + "data": { + "data": [ + { + "Disease": "Measles", + "Units": 530217.0, + "Morbidity Type": "20th Century Morbidity" + }, + { + "Disease": "Measles", + "Units": 9.0, + "Morbidity Type": "2021 Morbidity" + }, + { + "Disease": "Measles", + "Units": 99.0, + "Morbidity Type": "Decrease" + }, + { + "Disease": "Pertussis", + "Units": 200752.0, + "Morbidity Type": "20th Century Morbidity" + }, + { 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"Diphtheria", + "Units": 100.0, + "Morbidity Type": "Decrease" + }, + { + "Disease": "Polio", + "Units": 16316.0, + "Morbidity Type": "20th Century Morbidity" + }, + { + "Disease": "Polio", + "Units": 100.0, + "Morbidity Type": "Decrease" + } + ], + "columns": [ + { + "name": "Disease", + "importance": "primary", + "description": "Name of the disease", + "unit": "none", + "data_type": "string", + "discarded_count": 0, + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding morbidity type", + "role": "y" + }, + { + "name": "Morbidity Type", + "importance": "primary", + "description": "The morbidity type of the data point", + "role": "group" + } + ] + } + }, + "495": { + "metadata": { + "title": "Governments Are Shutting Down the Internet More", + "description": "Number of government internet shutdowns/disruptions worldwide between 2011 and 2018", + "main_insight": "The number of government-imposed internet shutdowns worldwide has significantly increased from 2011 to 2018, particularly accelerating after 2015." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2011, + "Shutdowns": 3 + }, + { + "Year": 2012, + "Shutdowns": 12 + }, + { + "Year": 2013, + "Shutdowns": 22 + }, + { + "Year": 2014, + "Shutdowns": 17 + }, + { + "Year": 2015, + "Shutdowns": 33 + }, + { + "Year": 2016, + "Shutdowns": 75 + }, + { + "Year": 2017, + "Shutdowns": 109 + }, + { + "Year": 2018, + "Shutdowns": 188 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Calendar year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Shutdowns", + "importance": "primary", + "description": "Number of government internet shutdowns/disruptions", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2460": { + "metadata": { + "title": "The Rise of the Asian Middle Class", + "description": "Share of the global middle class by region (in percent)", + "main_insight": "The Asian middle class, specifically in the Asia Pacific region, is projected to grow significantly, increasing its share of the global middle class from 54% in 2020 to 65% in 2030." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2030, + "Score": 65.0, + "Regions": "Asia Pacific" + }, + { + "Year": 2030, + "Score": 14.0, + "Regions": "Europe" + } + ], + "score": 1.0, + "annotation": "The Score of Asia Pacific and Europe show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Asia Pacific and Europe is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2030, + "Score": 65.0, + "Regions": "Asia Pacific" + }, + { + "Year": 2030, + "Score": 13.0, + "Regions": "Americas" + } + ], + "score": 1.0, + "annotation": "The Score of Asia Pacific and Americas show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Asia Pacific and Americas is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2030, + "Score": 65.0, + "Regions": "Asia Pacific" + }, + { + "Year": 2030, + "Score": 5.0, + "Regions": "Middle East and North Africa" + } + ], + "score": 1.0, + "annotation": "The Score of Asia Pacific and Middle East and North Africa show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Asia Pacific and Middle East and North Africa is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2030, + "Score": 14.0, + "Regions": "Europe" + }, + { + "Year": 2030, + "Score": 13.0, + "Regions": "Americas" + } + ], + "score": 1.0, + "annotation": "The Score of Europe and Americas show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Europe and Americas is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2020, + "Score": 54.0, + "Regions": "Asia Pacific" + }, + { + "Year": 2020, + "Score": 20.0, + "Regions": "Europe" + }, + { + "Year": 2020, + "Score": 17.0, + "Regions": "Americas" + }, + { + "Year": 2020, + "Score": 6.0, + "Regions": "Middle East and North Africa" + }, + { + "Year": 2020, + "Score": 4.0, + "Regions": "Sub-Saharan Africa" + }, + { + "Year": 2030, + "Score": 65.0, + "Regions": "Asia Pacific" + }, + { + "Year": 2030, + "Score": 14.0, + "Regions": "Europe" + }, + { + "Year": 2030, + "Score": 13.0, + "Regions": "Americas" + }, + { + "Year": 2030, + "Score": 5.0, + "Regions": "Middle East and North Africa" + }, + { + "Year": 2030, + "Score": 4.0, + "Regions": "Sub-Saharan Africa" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding regions", + "role": "y" + }, + { + "name": "Regions", + "importance": "primary", + "description": "The regions of the data point", + "role": "group" + } + ] + } + }, + "988": { + "metadata": { + "title": "Electric Vehicle Buyers Have the Agony of Choice", + "description": "Number of electric vehicle models available to consumers in North America*", + "main_insight": "The number of electric vehicle models available in North America has significantly increased from 2008 to 2018, with battery electric vehicles consistently offering the most choices." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2018, + "Count": 32, + "Vehicle Type": "Plug-in Hybrids" + }, + { + "Year": 2018, + "Count": 4, + "Vehicle Type": "Fuel Cell Electric Vehicles" + } + ], + "score": 0.95, + "annotation": "The Count of Plug-in Hybrids and Fuel Cell Electric Vehicles show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Count between Plug-in Hybrids and Fuel Cell Electric Vehicles is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2008, + "Count": 4, + "Vehicle Type": "Battery Electric Vehicles" + }, + { + "Year": 2009, + "Count": 6, + "Vehicle Type": "Battery Electric Vehicles" + }, + { + "Year": 2010, + "Count": 6, + "Vehicle Type": "Battery Electric Vehicles" + }, + { + "Year": 2011, + "Count": 10, + "Vehicle Type": "Battery Electric Vehicles" + }, + { + "Year": 2012, + "Count": 17, + "Vehicle Type": "Battery Electric Vehicles" + }, + { + "Year": 2013, + "Count": 17, + "Vehicle Type": "Battery Electric Vehicles" + }, + { + "Year": 2013, + "Count": 6, + "Vehicle Type": "Plug-in 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+ }, + { + "Year": 2017, + "Count": 1, + "Vehicle Type": "Fuel Cell Electric Vehicles" + }, + { + "Year": 2018, + "Count": 54, + "Vehicle Type": "Battery Electric Vehicles" + }, + { + "Year": 2018, + "Count": 9, + "Vehicle Type": "Plug-in Hybrids" + }, + { + "Year": 2018, + "Count": 2, + "Vehicle Type": "Fuel Cell Electric Vehicles" + }, + { + "Year": 2018, + "Count": 29, + "Vehicle Type": "Battery Electric Vehicles" + }, + { + "Year": 2018, + "Count": 32, + "Vehicle Type": "Plug-in Hybrids" + }, + { + "Year": 2018, + "Count": 4, + "Vehicle Type": "Fuel Cell Electric Vehicles" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of data, all data as of Q4 of the respective year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Count", + "importance": "primary", + "description": "Count for the corresponding vehicle type", + "role": "y" + }, + { + "name": "Vehicle Type", + "importance": "primary", + "description": "The vehicle type of the data point", + "role": "group" + } + ] + } + }, + "1561": { + "metadata": { + "title": "Covid-19 patients in UK hospitals", + "description": "Confirmed Covid-19 patients in hospital in the United Kingdom", + "main_insight": "The number of Covid-19 patients in UK hospitals saw two significant peaks, one in April 2020 and a larger one in January 2021, with a period of low hospitalizations during the summer of 2020." + }, + "chart_type": "Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Jan 2021", + "Patients": 40000 + } + ], + "score": 0.96, + "annotation": "The has a maximum value at Jan 2021", + "reason": "The [{'Month': 'Jan 2021', 'Patients': 40000}] of has a maximum value of 40000, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Jan 2021", + "Patients": 40000 + } + ], + "score": 0.84, + "annotation": "The shows a sudden jump in Patients from Dec 2020 to Jan 2021.", + "reason": "The Patients for increases from 20000 at Dec 2020 to 40000 at Jan 2021, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Aug 2020", + "Patients": 1000 + } + ], + "score": 0.6, + "annotation": "The has a minimum value at Aug 2020", + "reason": "The [{'Month': 'Aug 2020', 'Patients': 1000}] of has a mainimum value of 1000, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Month": "Mar 2020", + "Patients": 10000 + }, + { + "Month": "Apr 2020", + "Patients": 20000 + }, + { + "Month": "May 2020", + "Patients": 10000 + }, + { + "Month": "Jun 2020", + "Patients": 5000 + }, + { + "Month": "Jul 2020", + "Patients": 2000 + }, + { + "Month": "Aug 2020", + "Patients": 1000 + }, + { + "Month": "Sep 2020", + "Patients": 2000 + }, + { + "Month": "Oct 2020", + "Patients": 10000 + }, + { + "Month": "Nov 2020", + "Patients": 18000 + }, + { + "Month": "Dec 2020", + "Patients": 20000 + }, + { + "Month": "Jan 2021", + "Patients": 40000 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Patients", + "importance": "primary", + "description": "Number of confirmed Covid-19 patients in UK hospitals", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1380": { + "metadata": { + "title": "Viagra Sales Went Soft as Exclusivity Expired", + "description": "Global sales of Viagra since its launch in 1998", + "main_insight": "Viagra's global sales peaked around 2012 and declined significantly after its exclusivity rights expired in major markets, starting with Europe in 2013." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1998, + "Global Sales": 0.8 + }, + { + "Year": 1999, + "Global Sales": 1.0 + }, + { + "Year": 2000, + "Global Sales": 1.2 + }, + { + "Year": 2001, + "Global Sales": 1.4 + }, + { + "Year": 2002, + "Global Sales": 1.6 + }, + { + "Year": 2003, + "Global Sales": 1.8 + }, + { + "Year": 2004, + "Global Sales": 1.8 + }, + { + "Year": 2005, + "Global Sales": 1.8 + }, + { + "Year": 2006, + "Global Sales": 1.7 + }, + { + "Year": 2007, + "Global Sales": 1.8 + }, + { + "Year": 2008, + "Global Sales": 1.9 + }, + { + "Year": 2009, + "Global Sales": 1.9 + }, + { + "Year": 2010, + "Global Sales": 1.9 + }, + { + "Year": 2011, + "Global Sales": 2.0 + }, + { + "Year": 2012, + "Global Sales": 2.0 + }, + { + "Year": 2013, + "Global Sales": 1.8 + }, + { + "Year": 2014, + "Global Sales": 1.7 + }, + { + "Year": 2015, + "Global Sales": 1.7 + }, + { + "Year": 2016, + "Global Sales": 1.6 + }, + { + "Year": 2017, + "Global Sales": 1.2 + }, + { + "Year": 2018, + "Global Sales": 0.7 + }, + { + "Year": 2019, + "Global Sales": 0.5 + }, + { + "Year": 2020, + "Global Sales": 0.4 + }, + { + "Year": 2021, + "Global Sales": 0.6 + }, + { + "Year": 2022, + "Global Sales": 0.5 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of Viagra sales", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Global Sales", + "importance": "primary", + "description": "Global sales of Viagra in billions of US dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1676": { + "metadata": { + "title": "How Powerful Was The Explosion In Beirut?", + "description": "Estimated kiloton yield of selected explosions/weapons", + "main_insight": "The 2020 Beirut Port explosion, with an estimated yield of 1.5kt, was significantly more powerful than several other explosions but less powerful than the Hiroshima atomic bomb." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Event": "\"Little Boy\" (Hiroshima)", + "Yield (kt)": 15.0 + } + ], + "score": 0.94, + "annotation": "The shows a sudden jump in Yield (kt) from 1917 Halifax Explosion to \"Little Boy\" (Hiroshima).", + "reason": "The Yield (kt) for increases from 2.9 at 1917 Halifax Explosion to 15.0 at \"Little Boy\" (Hiroshima), indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Event": "\"Little Boy\" (Hiroshima)", + "Yield (kt)": 15.0 + } + ], + "score": 0.92, + "annotation": "The has a maximum value at \"Little Boy\" (Hiroshima)", + "reason": "The [{'Event': '\"Little Boy\" (Hiroshima)', 'Yield (kt)': 15.0}] of has a maximum value of 15.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Event": "1995 Oklahoma City Bombing", + "Yield (kt)": 0.002 + } + ], + "score": 0.38, + "annotation": "The has a minimum value at 1995 Oklahoma City Bombing", + "reason": "The [{'Event': '1995 Oklahoma City Bombing', 'Yield (kt)': 0.002}] of has a mainimum value of 0.002, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Event": "1995 Oklahoma City Bombing", + "Yield (kt)": 0.002 + }, + { + "Event": "GBU-43 MOAB", + "Yield (kt)": 0.011 + }, + { + "Event": "2015 Tianjin Explosions", + "Yield (kt)": 0.34 + }, + { + "Event": "2020 Beirut Port Explosion", + "Yield (kt)": 1.5 + }, + { + "Event": "1917 Halifax Explosion", + "Yield (kt)": 2.9 + }, + { + "Event": "\"Little Boy\" (Hiroshima)", + "Yield (kt)": 15.0 + } + ], + "columns": [ + { + "name": "Event", + "importance": "primary", + "description": "Name and year of the explosion event", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Yield (kt)", + "importance": "primary", + "description": "Estimated kiloton yield of the explosion", + "unit": "kt", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1357": { + "metadata": { + "title": "What a Future Hurricane Florence Might Look Like", + "description": "Total storm surge impact modelled for states hit by Hurricane Florence", + "main_insight": "The number of homes impacted by storm surge in North Carolina, South Carolina, and Virginia is projected to significantly increase by 2050 compared to 1970 and 2018 levels, due to higher storm surge levels." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1970, + "Homes Impacted": 40000 + }, + { + "Year": 2018, + "Homes Impacted": 51000 + }, + { + "Year": 2050, + "Homes Impacted": 102000 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of storm surge measurement or model", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Homes Impacted", + "importance": "primary", + "description": "Number of homes impacted by the storm surge", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "71": { + "metadata": { + "title": "Smartphone Shipments to Top 1 Billion in 2014", + "description": "Global connected device shipment forecast (in million units)", + "main_insight": "Smartphone shipments surpassed 1 billion units in 2014 and continued to grow, significantly outpacing shipments of other connected devices like tablets, desktop PCs, and portable PCs." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2017, + "Units": 1516.0, + "Device Type": "Smartphone" + }, + { + "Year": 2017, + "Units": 352.0, + "Device Type": "Tablet" + } + ], + "score": 1.0, + "annotation": "The Units of Smartphone and Tablet show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Smartphone and Tablet is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2017, + "Units": 1516.0, + "Device Type": "Smartphone" + }, + { + "Year": 2017, + "Units": 241.0, + "Device Type": "Portable PC" + } + ], + "score": 0.97, + "annotation": "The Units of Smartphone and Portable PC show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Smartphone and Portable PC is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2017, + "Units": 352.0, + "Device Type": "Tablet" + }, + { + "Year": 2017, + "Units": 241.0, + "Device Type": "Portable PC" + } + ], + "score": 0.96, + "annotation": "The Units of Tablet and Portable PC show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Tablet and Portable PC is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2017, + "Units": 352.0, + "Device Type": "Tablet" + }, + { + "Year": 2017, + "Units": 141.0, + "Device Type": "Desktop PC" + } + ], + "score": 0.77, + "annotation": "The Units of Tablet and Desktop PC show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Tablet and Desktop PC is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2017, + "Units": 1516.0, + "Device Type": "Smartphone" + }, + { + "Year": 2017, + "Units": 141.0, + "Device Type": "Desktop PC" + } + ], + "score": 0.75, + "annotation": "The Units of Smartphone and Desktop PC show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Smartphone and Desktop PC is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2012, + "Units": 722.0, + "Device Type": "Smartphone" + }, + { + "Year": 2012, + "Units": 128.0, + "Device Type": "Tablet" + }, + { + "Year": 2012, + "Units": 202.0, + "Device Type": "Portable PC" + }, + { + "Year": 2012, + "Units": 148.0, + "Device Type": "Desktop PC" + }, + { + "Year": 2013, + "Units": 919.0, + "Device Type": "Smartphone" + }, + { + "Year": 2013, + "Units": 191.0, + "Device Type": "Tablet" + }, + { + "Year": 2013, + "Units": 204.0, + "Device Type": "Portable PC" + }, + { + "Year": 2013, + "Units": 142.0, + "Device Type": "Desktop PC" + }, + { + "Year": 2014, + "Units": 1078.0, + "Device Type": "Smartphone" + }, + { + "Year": 2014, + "Units": 236.0, + "Device Type": "Tablet" + }, + { + "Year": 2014, + "Units": 211.0, + "Device Type": "Portable PC" + }, + { + "Year": 2014, + "Units": 142.0, + "Device Type": "Desktop PC" + }, + { + "Year": 2015, + "Units": 1237.0, + "Device Type": "Smartphone" + }, + { + "Year": 2015, + "Units": 281.0, + "Device Type": "Tablet" + }, + { + "Year": 2015, + "Units": 223.0, + "Device Type": "Portable PC" + }, + { + "Year": 2015, + "Units": 143.0, + "Device Type": "Desktop PC" + }, + { + "Year": 2016, + "Units": 1380.0, + "Device Type": "Smartphone" + }, + { + "Year": 2016, + "Units": 319.0, + "Device Type": "Tablet" + }, + { + "Year": 2016, + "Units": 232.0, + "Device Type": "Portable PC" + }, + { + "Year": 2016, + "Units": 142.0, + "Device Type": "Desktop PC" + }, + { + "Year": 2017, + "Units": 1516.0, + "Device Type": "Smartphone" + }, + { + "Year": 2017, + "Units": 352.0, + "Device Type": "Tablet" + }, + { + "Year": 2017, + "Units": 241.0, + "Device Type": "Portable PC" + }, + { + "Year": 2017, + "Units": 141.0, + "Device Type": "Desktop PC" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of shipment", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding device type", + "role": "y" + }, + { + "name": "Device Type", + "importance": "primary", + "description": "The device type of the data point", + "role": "group" + } + ] + } + }, + "968": { + "metadata": { + "title": "Misplaced Faith? The U.S. And NATO", + "description": "\"Do you think the U.S. would use military force to defend a NATO ally?\" *If Russia entered military conflict with a neighboring country (which is a NATO ally) in 2015", + "main_insight": "A majority of respondents in most NATO countries surveyed believe the U.S. would use military force to defend a NATO ally, but the percentage varies significantly across countries. Poland had the lowest confidence in the U.S. response." + }, + "chart_type": "Multiple Semi Circle Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Poland", + "Points": 49.0, + "Response": "Would" + } + ], + "score": 0.94, + "annotation": "The Would has a minimum value at Poland", + "reason": "The [{'Country': 'Poland', 'Points': 49.0, 'Response': 'Would'}] of Would has a mainimum value of 49.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "France", + "Points": 65.0, + "Response": "Would" + } + ], + "score": 0.92, + "annotation": "The Would shows a sudden jump in Points from Poland to France.", + "reason": "The Points for Would increases from 49.0 at Poland to 65.0 at France, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "France", + "Points": 34.0, + "Response": "Would not" + } + ], + "score": 0.82, + "annotation": "The Would not has a maximum value at France", + "reason": "The [{'Country': 'France', 'Points': 34.0, 'Response': 'Would not'}] of Would not has a maximum value of 34.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Poland", + "Points": 49.0, + "Response": "Would" + } + ], + "score": 0.77, + "annotation": "The Would accounts for the majority in Poland.", + "reason": "The Points of Would in Poland accounts for 0.6125 (more than 50%) of the total of Points of all Response." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "France", + "Points": 65.0, + "Response": "Would" + } + ], + "score": 0.75, + "annotation": "The Would accounts for the majority in France.", + "reason": "The Points of Would in France accounts for 0.6565656565656566 (more than 50%) of the total of Points of all Response." + } + ], + "data": { + "data": [ + { + "Country": "Canada", + "Points": 72.0, + "Response": "Would" + }, + { + "Country": "Canada", + "Points": 18.0, + "Response": "Would not" + }, + { + "Country": "Spain", + "Points": 70.0, + "Response": "Would" + }, + { + "Country": "Spain", + "Points": 21.0, + "Response": "Would not" + }, + { + "Country": "Italy", + "Points": 68.0, + "Response": "Would" + }, + { + "Country": "Italy", + "Points": 23.0, + "Response": "Would not" + }, + { + "Country": "Germany", + "Points": 68.0, + "Response": "Would" + }, + { + "Country": "Germany", + "Points": 26.0, + "Response": "Would not" + }, + { + "Country": "United Kingdom", + "Points": 66.0, + "Response": "Would" + }, + { + "Country": "United Kingdom", + "Points": 24.0, + "Response": "Would not" + }, + { + "Country": "France", + "Points": 65.0, + "Response": "Would" + }, + { + "Country": "France", + "Points": 34.0, + "Response": "Would not" + }, + { + "Country": "Poland", + "Points": 49.0, + "Response": "Would" + }, + { + "Country": "Poland", + "Points": 31.0, + "Response": "Would not" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "NATO member country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding response", + "role": "y" + }, + { + "name": "Response", + "importance": "primary", + "description": "The response of the data point", + "role": "group" + } + ] + } + }, + "1594": { + "metadata": { + "title": "OBGYNs Worry About Legal Risk, Standard of Care post-Dobbs", + "description": "Share of U.S. OBGYNs agreeing with the following statements on their work after the overturning of Roe v. Wade (in %)", + "main_insight": "OBGYNs in states where abortion is banned or restricted express greater concern about legal risks and a decline in their ability to provide standard care compared to those in states where abortion is legal." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Statement": "I have a very good understanding of legal aspects of abortion", + "Score": 79.0, + "Access Level": "Legal" + } + ], + "score": 0.92, + "annotation": "The Legal shows a sudden jump in Score from I am concerned about my legal risk in regards to abortion to I have a very good understanding of legal aspects of abortion.", + "reason": "The Score for Legal increases from 27.0 at I am concerned about my legal risk in regards to abortion to 79.0 at I have a very good understanding of legal aspects of abortion, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Statement": "I have a very good understanding of legal aspects of abortion", + "Score": 79.0, + "Access Level": "Legal" + } + ], + "score": 0.88, + "annotation": "The Legal has the largest proportion in I have a very good understanding of legal aspects of abortion.", + "reason": "The Score of Legal in I have a very good understanding of legal aspects of abortion accounts for 0.4114583333333333, larger than all other Access Level, of the total of Score of all Access Level." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Statement": "I am concerned about my legal risk in regards to abortion", + "Score": 61.0, + "Access Level": "Banned" + } + ], + "score": 0.88, + "annotation": "The Banned has the largest proportion in I am concerned about my legal risk in regards to abortion.", + "reason": "The Score of Banned in I am concerned about my legal risk in regards to abortion accounts for 0.41496598639455784, larger than all other Access Level, of the total of Score of all Access Level." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Access Level": "Banned", + "Statement": "total", + "Score": 261.0 + } + ], + "score": 0.88, + "annotation": "The total value of Banned has the largest proportion.", + "reason": "The total value of Score of Banned accounts for 0.4237012987012987, larger than all other Access Level, of the total of Score of all Access Level." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Statement": "My ability to provide the standard of care has worsened", + "Score": 55.0, + "Access Level": "Banned" + } + ], + "score": 0.87, + "annotation": "The Banned has the largest proportion in My ability to provide the standard of care has worsened.", + "reason": "The Score of Banned in My ability to provide the standard of care has worsened accounts for 0.44, larger than all other Access Level, of the total of Score of all Access Level." + } + ], + "data": { + "data": [ + { + "Statement": "I have a very good understanding of legal aspects of abortion", + "Score": 79.0, + "Access Level": "Legal" + }, + { + "Statement": "I have a very good understanding of legal aspects of abortion", + "Score": 45.0, + "Access Level": "Restricted" + }, + { + "Statement": "I have a very good understanding of legal aspects of abortion", + "Score": 68.0, + "Access Level": "Banned" + }, + { + "Statement": "I am concerned about my legal risk in regards to abortion", + "Score": 27.0, + "Access Level": "Legal" + }, + { + "Statement": "I am concerned about my legal risk in regards to abortion", + "Score": 59.0, + "Access Level": "Restricted" + }, + { + "Statement": "I am concerned about my legal risk in regards to abortion", + "Score": 61.0, + "Access Level": "Banned" + }, + { + "Statement": "My ability to provide the standard of care has worsened", + "Score": 23.0, + "Access Level": "Legal" + }, + { + "Statement": "My ability to provide the standard of care has worsened", + "Score": 47.0, + "Access Level": "Restricted" + }, + { + "Statement": "My ability to provide the standard of care has worsened", + "Score": 55.0, + "Access Level": "Banned" + }, + { + "Statement": "... concerning management of miscarriages", + "Score": 6.0, + "Access Level": "Legal" + }, + { + "Statement": "... concerning management of miscarriages", + "Score": 34.0, + "Access Level": "Restricted" + }, + { + "Statement": "... concerning management of miscarriages", + "Score": 40.0, + "Access Level": "Banned" + }, + { + "Statement": "... concerning pregnancy-related emergencies", + "Score": 8.0, + "Access Level": "Legal" + }, + { + "Statement": "... concerning pregnancy-related emergencies", + "Score": 27.0, + "Access Level": "Restricted" + }, + { + "Statement": "... concerning pregnancy-related emergencies", + "Score": 37.0, + "Access Level": "Banned" + } + ], + "columns": [ + { + "name": "Statement", + "importance": "primary", + "description": "Survey statements presented to OBGYNs", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding access level", + "role": "y" + }, + { + "name": "Access Level", + "importance": "primary", + "description": "The access level of the data point", + "role": "group" + } + ] + } + }, + "292": { + "metadata": { + "title": "The UK's Fastest Mobile Networks", + "description": "Average 4G download speed by provider in the UK (Sept '17)", + "main_insight": "EE has the fastest average 4G download speed among UK mobile providers, significantly outpacing its competitors." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Provider": "EE", + "Average 4G Download Speed (Mbps)": 28.99 + } + ], + "score": 0.82, + "annotation": "The shows a sudden jump in Average 4G Download Speed (Mbps) from 3 to EE.", + "reason": "The Average 4G Download Speed (Mbps) for increases from 22.31 at 3 to 28.99 at EE, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Provider": "EE", + "Average 4G Download Speed (Mbps)": 28.99 + } + ], + "score": 0.73, + "annotation": "The has a maximum value at EE", + "reason": "The [{'Provider': 'EE', 'Average 4G Download Speed (Mbps)': 28.99}] of has a maximum value of 28.99, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Provider": "O2", + "Average 4G Download Speed (Mbps)": 15.06 + } + ], + "score": 0.63, + "annotation": "The has a minimum value at O2", + "reason": "The [{'Provider': 'O2', 'Average 4G Download Speed (Mbps)': 15.06}] of has a mainimum value of 15.06, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Provider": "EE", + "Average 4G Download Speed (Mbps)": 28.99 + }, + { + "Provider": "3", + "Average 4G Download Speed (Mbps)": 22.31 + }, + { + "Provider": "Vodafone", + "Average 4G Download Speed (Mbps)": 18.94 + }, + { + "Provider": "O2", + "Average 4G Download Speed (Mbps)": 15.06 + } + ], + "columns": [ + { + "name": "Provider", + "importance": "primary", + "description": "Mobile network provider", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Average 4G Download Speed (Mbps)", + "importance": "primary", + "description": "Average download speed in megabits per second", + "unit": "Mbps", + "data_type": "number", + "role": "y" + } + ] + } + }, + "390": { + "metadata": { + "title": "Where Small Scale Mining Is Most Prevalent", + "description": "Countries with the highest estimated number of people working in artisanal & small scale mining", + "main_insight": "India has the highest number of people working in artisanal and small-scale mining, with 15 million workers, significantly more than China, which has 9 million." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "India", + "Number of People (Millions)": 15.0 + } + ], + "score": 0.9, + "annotation": "The has a maximum value at India", + "reason": "The [{'Country': 'India', 'Number of People (Millions)': 15.0}] of has a maximum value of 15.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "India", + "Number of People (Millions)": 15.0 + } + ], + "score": 0.79, + "annotation": "The shows a sudden jump in Number of People (Millions) from China to India.", + "reason": "The Number of People (Millions) for increases from 9.0 at China to 15.0 at India, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Burkina Faso", + "Number of People (Millions)": 1.0 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at Burkina Faso", + "reason": "The [{'Country': 'Burkina Faso', 'Number of People (Millions)': 1.0}] of has a mainimum value of 1.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "India", + "Number of People (Millions)": 15.0 + }, + { + "Country": "China", + "Number of People (Millions)": 9.0 + }, + { + "Country": "Indonesia", + "Number of People (Millions)": 3.6 + }, + { + "Country": "DR Congo", + "Number of People (Millions)": 2.0 + }, + { + "Country": "Ethiopia", + "Number of People (Millions)": 1.3 + }, + { + "Country": "Ghana", + "Number of People (Millions)": 1.1 + }, + { + "Country": "Burkina Faso", + "Number of People (Millions)": 1.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country with the highest number of small scale miners", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of People (Millions)", + "importance": "primary", + "description": "Estimated number of people working in artisanal & small scale mining", + "unit": "Millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "277": { + "metadata": { + "title": "Americans Divided on Athlete Protests", + "description": "% of U.S. adults saying kneeling during the national anthem to protest racial discrimination is...", + "main_insight": "Americans are divided on whether kneeling during the national anthem is an acceptable form of protest, with significant differences across age groups and political affiliations. Democrats are overwhelmingly supportive, while Republicans are largely opposed." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Category": "Rep.", + "Units": 23.0, + "Status": "Acceptable" + }, + { + "Category": "Rep.", + "Units": 77.0, + "Status": "Unacceptable" + } + ], + "score": 1.0, + "annotation": "The Units of Acceptable and Unacceptable show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Acceptable and Unacceptable is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Category": "Rep.", + "Units": 23.0, + "Status": "Acceptable" + } + ], + "score": 0.88, + "annotation": "The Acceptable has a minimum value at Rep.", + "reason": "The [{'Category': 'Rep.', 'Units': 23.0, 'Status': 'Acceptable'}] of Acceptable has a mainimum value of 23.0, which is the largest 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"Category": "65+", + "Units": 43.0, + "Status": "Unacceptable" + }, + { + "Category": "Dem.", + "Units": 88.0, + "Status": "Acceptable" + }, + { + "Category": "Dem.", + "Units": 12.0, + "Status": "Unacceptable" + }, + { + "Category": "Rep.", + "Units": 23.0, + "Status": "Acceptable" + }, + { + "Category": "Rep.", + "Units": 77.0, + "Status": "Unacceptable" + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Demographic and political groups", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding status", + "role": "y" + }, + { + "name": "Status", + "importance": "primary", + "description": "The status of the data point", + "role": "group" + } + ] + } + }, + "1789": { + "metadata": { + "title": "Unemployment Benefits Expiring", + "description": "Percentage of U.S. adults whose unemployment benefits end during each time frame", + 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+ "subtype": "max", + "data_points": [ + { + "Time Frame": "December", + "Percentage": 47.0 + } + ], + "score": 0.59, + "annotation": "The has a maximum value at December", + "reason": "The [{'Time Frame': 'December', 'Percentage': 47.0}] of has a maximum value of 47.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Time Frame": "November", + "Percentage": 19.0 + }, + { + "Time Frame": "December", + "Percentage": 47.0 + }, + { + "Time Frame": "Sometime in 2021", + "Percentage": 34.0 + } + ], + "columns": [ + { + "name": "Time Frame", + "importance": "primary", + "description": "The time period when unemployment benefits expire", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of U.S. adults whose unemployment benefits end during each time frame", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1816": { + "metadata": { + "title": "Three Million Years to Become the Next Elon Musk", + "description": "Time needed working at average annual wage to match country's richest billionaire (in million years)", + "main_insight": "It would take an extraordinarily long time, millions of years in some cases, for an average worker to accumulate the wealth of their country's richest billionaire, highlighting the vast wealth disparity." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "CH", + "Years (millions)": 15.3 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in Years (millions) from FR to CH.", + "reason": "The Years (millions) for increases from 3.5 at FR to 15.3 at CH, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "IN", + "Years (millions)": 17.4 + } + ], + "score": 0.78, + "annotation": "The has a maximum value at IN", + "reason": "The [{'Country': 'IN', 'Years (millions)': 17.4}] of has a maximum value of 17.4, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "CA", + "Years (millions)": 1.2 + } + ], + "score": 0.5, + "annotation": "The has a minimum value at CA", + "reason": "The [{'Country': 'CA', 'Years (millions)': 1.2}] of has a mainimum value of 1.2, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "IN", + "Years (millions)": 17.4 + }, + { + "Country": "CH", + "Years (millions)": 15.3 + }, + { + "Country": "FR", + "Years (millions)": 3.5 + }, + { + "Country": "US", + "Years (millions)": 3.1 + }, + { + "Country": "ES", + "Years (millions)": 1.6 + }, + { + "Country": "CA", + "Years (millions)": 1.2 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of the richest billionaire", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Years (millions)", + "importance": "primary", + "description": "Time needed in million years to match the billionaire's wealth", + "unit": "millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1365": { + "metadata": { + "title": "Strong Support For An Independent Catalonia", + "description": "Do you want Catalonia to become an independent state? *voting intentions for the Nov 9th Catalan independence vote as of June 2014", + "main_insight": "A plurality of respondents (44.1%) indicated they would vote 'Yes' for Catalonia to become an independent state, while 33.9% would vote 'No'." + }, + "chart_type": "Multiple Semi Circle Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Response": "No", + "Units": 33.9, + "Percentage Type": "Percentage" + } + ], + "score": 0.81, + "annotation": "The Percentage shows a sudden jump in Units from Other to No.", + "reason": "The Units for Percentage increases from 16.4 at Other to 33.9 at No, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Response": "Abstain", + "Units": 5.6, + "Percentage Type": "Percentage" + } + ], + "score": 0.66, + "annotation": "The Percentage has a minimum value at Abstain", + "reason": "The [{'Response': 'Abstain', 'Units': 5.6, 'Percentage Type': 'Percentage'}] of Percentage has a mainimum value of 5.6, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Response": "Yes", + "Units": 44.1, + "Percentage Type": "Percentage" + } + ], + "score": 0.65, + "annotation": "The Percentage has a maximum value at Yes", + "reason": "The [{'Response': 'Yes', 'Units': 44.1, 'Percentage Type': 'Percentage'}] of Percentage has a maximum value of 44.1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Response": "Yes", + "Units": 44.1, + "Percentage Type": "Percentage" + }, + { + "Response": "No", + "Units": 33.9, + "Percentage Type": "Percentage" + }, + { + "Response": "Abstain", + "Units": 5.6, + "Percentage Type": "Percentage" + }, + { + "Response": "Other", + "Units": 16.4, + "Percentage Type": "Percentage" + } + ], + "columns": [ + { + "name": "Response", + "importance": "primary", + "description": "The possible answers to the question", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding percentage type", + "role": "y" + }, + { + "name": "Percentage Type", + "importance": "primary", + "description": "The percentage type of the data point", + "role": "group" + } + ] + } + }, + "2064": { + "metadata": { + "title": "The UK's Extraordinary Imbalance in Transport Expenditure", + "description": "Spending per head of the English population on transport infrastructure by region (in GBP)", + "main_insight": "Transport expenditure per capita in London is significantly higher than in any other region of England, highlighting a major imbalance in infrastructure investment." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "London", + "Spending per capita (GBP)": 2595.68 + } + ], + "score": 0.96, + "annotation": "The has a maximum value at London", + "reason": "The [{'Region': 'London', 'Spending per capita (GBP)': 2595.68}] of has a maximum value of 2595.68, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "London", + "Spending per capita (GBP)": 2595.68 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in Spending per capita (GBP) from South East to London.", + "reason": "The Spending per capita (GBP) for increases from 714.08 at South East to 2595.68 at London, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Region": "North East", + "Spending per capita (GBP)": 5.01 + } + ], + "score": 0.39, + "annotation": "The has a minimum value at North East", + "reason": "The [{'Region': 'North East', 'Spending per capita (GBP)': 5.01}] of has a mainimum value of 5.01, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Region": "London", + "Spending per capita (GBP)": 2595.68 + }, + { + "Region": "South East", + "Spending per capita (GBP)": 714.08 + }, + { + "Region": "East Midlands", + "Spending per capita (GBP)": 218.94 + }, + { + "Region": "West Midlands", + "Spending per capita (GBP)": 184.96 + }, + { + "Region": "Yorkshire and the Humber", + "Spending per capita (GBP)": 160.29 + }, + { + "Region": "North West", + "Spending per capita (GBP)": 99.19 + }, + { + "Region": "East of England", + "Spending per capita (GBP)": 30.07 + }, + { + "Region": "North East", + "Spending per capita (GBP)": 5.01 + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "English regions", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Spending per capita (GBP)", + "importance": "primary", + "description": "Spending per head on transport infrastructure in GBP", + "unit": "GBP", + "data_type": "number", + "role": "y" + } + ] + } + }, + "432": { + "metadata": { + "title": "Work-sharing Soars in U.S.", + "description": "Number of work-sharing continued weeks claims in 2020", + "main_insight": "Work-sharing claims in the US increased dramatically in the first half of 2020, peaking in July, before declining in the later part of the year." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Jul", + "Claims": 1700000 + } + ], + "score": 0.83, + "annotation": "The has a maximum value at Jul", + "reason": "The [{'Month': 'Jul', 'Claims': 1700000}] of has a maximum value of 1700000, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Sep", + "Claims": 869000 + } + ], + "score": 0.72, + "annotation": "The shows a sudden jump in Claims from Apr to Sep.", + "reason": "The Claims for increases from 300000 at Apr to 869000 at Sep, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Jan", + "Claims": 0 + }, + { + "Month": "Feb", + "Claims": 0 + }, + { + "Month": "Mar", + "Claims": 0 + } + ], + "score": 0.68, + "annotation": "The has minimum values at Jan, Feb, Mar", + "reason": "The [{'Month': 'Jan', 'Claims': 0}, {'Month': 'Feb', 'Claims': 0}, {'Month': 'Mar', 'Claims': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Month": "Jan", + "Claims": 0 + }, + { + "Month": "Feb", + "Claims": 0 + }, + { + "Month": "Mar", + "Claims": 0 + }, + { + "Month": "Apr", + "Claims": 300000 + }, + { + "Month": "May", + "Claims": 900000 + }, + { + "Month": "Jun", + "Claims": 1300000 + }, + { + "Month": "Jul", + "Claims": 1700000 + }, + { + "Month": "Aug", + "Claims": 1300000 + }, + { + "Month": "Sep", + "Claims": 869000 + }, + { + "Month": "Oct", + "Claims": 869000 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month in 2020", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Claims", + "importance": "primary", + "description": "Number of work-sharing continued weeks claims", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1354": { + "metadata": { + "title": "Trick-or-Treaters Likely to Get Snubbed in the UK", + "description": "How respondents would most likely act towards trick-or-treaters", + "main_insight": "A large proportion of UK residents (43%) are likely to not answer the door or pretend not to be home when trick-or-treaters come, while 45% will give them sweets." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Response": "Not answer the door/pretend not be home", + "Percentage": 43.0 + } + ], + "score": 0.96, + "annotation": "The shows a sudden jump in Percentage from Don't know to Not answer the door/pretend not be home.", + "reason": "The Percentage for increases from 6.0 at Don't know to 43.0 at Not answer the door/pretend not be home, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Response": "Give them some sweets", + "Percentage": 45.0 + } + ], + "score": 0.66, + "annotation": "The has a maximum value at Give them some sweets", + "reason": "The [{'Response': 'Give them some sweets', 'Percentage': 45.0}] of has a maximum value of 45.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Response": "Give them something else", + "Percentage": 3.0 + }, + { + "Response": "Answer the door but give them nothing", + "Percentage": 3.0 + } + ], + "score": 0.49, + "annotation": "The has minimum values at Give them something else, Answer the door but give them nothing", + "reason": "The [{'Response': 'Give them something else', 'Percentage': 3.0}, {'Response': 'Answer the door but give them nothing', 'Percentage': 3.0}] of have minimum values of 3.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Response": "Give them some sweets", + "Percentage": 45.0 + }, + { + "Response": "Not answer the door/pretend not be home", + "Percentage": 43.0 + }, + { + "Response": "Don't know", + "Percentage": 6.0 + }, + { + "Response": "Give them something else", + "Percentage": 3.0 + }, + { + "Response": "Answer the door but give them nothing", + "Percentage": 3.0 + } + ], + "columns": [ + { + "name": "Response", + "importance": "primary", + "description": "The action respondents would take", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "The percentage of respondents choosing that action", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "792": { + "metadata": { + "title": "Asia, the Globe's Smoking Stronghold", + "description": "Countries with the biggest share of the world's smokers and the world's population in 2020*", + "main_insight": "Asia has a disproportionately high share of the world's smokers compared to its share of the world's population. China, in particular, has the largest share of smokers (28%) and population (20%)." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Others", + "Units": 42.0, + "Metric": "Share of world's population" + } + ], + "score": 0.96, + "annotation": "The Share of world's population has a maximum value at Others", + "reason": "The [{'Country': 'Others', 'Units': 42.0, 'Metric': \"Share of world's population\"}] of Share of world's population has a maximum value of 42.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Others", + "Units": 37.0, + "Metric": "Share of world's smokers" + } + ], + "score": 0.94, + "annotation": "The Share of world's smokers has a maximum value at Others", + "reason": "The [{'Country': 'Others', 'Units': 37.0, 'Metric': \"Share of world's smokers\"}] of Share of world's smokers has a maximum value of 37.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Others", + "Units": 37.0, + "Metric": "Share of world's smokers" + }, + { + "Country": "Others", + "Units": 42.0, + "Metric": "Share of world's population" + } + ], + "score": 0.93, + "annotation": "The Units of Share of world's smokers and Share of world's population show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Share of world's smokers and Share of world's population is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Others", + "Units": 42.0, + "Metric": "Share of world's population" + } + ], + "score": 0.86, + "annotation": "The Share of world's population shows a sudden jump in Units from India to Others.", + "reason": "The Units for Share of world's population increases from 20.0 at India to 42.0 at Others, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "China", + "Units": 28.0, + "Metric": "Share of world's smokers" + } + ], + "score": 0.84, + "annotation": "The Share of world's smokers shows a sudden jump in Units from India to China.", + "reason": "The Units for Share of world's smokers increases from 10.0 at India to 28.0 at China, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "China", + "Units": 28.0, + "Metric": "Share of world's smokers" + }, + { + "Country": "China", + "Units": 20.0, + "Metric": "Share of world's population" + }, + { + "Country": "India", + "Units": 10.0, + "Metric": "Share of world's smokers" + }, + { + "Country": "India", + "Units": 20.0, + "Metric": "Share of world's population" + }, + { + "Country": "Indonesia", + "Units": 8.0, + "Metric": "Share of world's smokers" + }, + { + "Country": "Indonesia", + "Units": 5.0, + "Metric": "Share of world's population" + }, + { + "Country": "U.S.", + "Units": 5.0, + "Metric": "Share of world's smokers" + }, + { + "Country": "U.S.", + "Units": 4.0, + "Metric": "Share of world's population" + }, + { + "Country": "Russia", + "Units": 3.0, + "Metric": "Share of world's smokers" + }, + { + "Country": "Russia", + "Units": 3.0, + "Metric": "Share of world's population" + }, + { + "Country": "Brazil", + "Units": 3.0, + "Metric": "Share of world's smokers" + }, + { + "Country": "Brazil", + "Units": 3.0, + "Metric": "Share of world's population" + }, + { + "Country": "Bangladesh", + "Units": 2.0, + "Metric": "Share of world's smokers" + }, + { + "Country": "Bangladesh", + "Units": 2.0, + "Metric": "Share of world's population" + }, + { + "Country": "Pakistan", + "Units": 2.0, + "Metric": "Share of world's smokers" + }, + { + "Country": "Pakistan", + "Units": 2.0, + "Metric": "Share of world's population" + }, + { + "Country": "Japan", + "Units": 2.0, + "Metric": "Share of world's smokers" + }, + { + "Country": "Japan", + "Units": 2.0, + "Metric": "Share of world's population" + }, + { + "Country": "Others", + "Units": 37.0, + "Metric": "Share of world's smokers" + }, + { + "Country": "Others", + "Units": 42.0, + "Metric": "Share of world's population" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country or 'Others' category", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding metric", + "role": "y" + }, + { + "name": "Metric", + "importance": "primary", + "description": "The metric of the data point", + "role": "group" + } + ] + } + }, + "2513": { + "metadata": { + "title": "World Cup Humiliations Are a Rare Event", + "description": "The biggest World Cup wins in history", + "main_insight": "The chart lists the biggest wins in World Cup history, showing that large goal differences are uncommon. Hungary's 10-1 victory over El Salvador in 1982 is the largest margin of victory." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Winning Team": "Hungary", + "Score": "10:1" + }, + { + "Winning Team": "Yugoslavia", + "Score": "9:0" + }, + { + "Winning Team": "Hungary", + "Score": "9:0" + }, + { + "Winning Team": "Germany", + "Score": "8:0" + }, + { + "Winning Team": "Uruguay", + "Score": "8:0" + }, + { + "Winning Team": "Sweden", + "Score": "8:0" + }, + { + "Winning Team": "Portugal", + "Score": "7:0" + }, + { + "Winning Team": "Poland", + "Score": "7:0" + }, + { + "Winning Team": "Uruguay", + "Score": "7:0" + }, + { + "Winning Team": "Turkey", + "Score": "7:0" + }, + { + "Winning Team": "Germany", + "Score": "7:1" + } + ], + "columns": [ + { + "name": "Winning Team", + "importance": "primary", + "description": "The team that won the match", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "The final score of the match", + "unit": "none", + "data_type": "string", + "role": "y" + } + ] + } + }, + "2343": { + "metadata": { + "title": "Game Over for Crypto And NFTs in Games?", + "description": "Interest of video game studios in implementing crypto payments and NFTs (in %)", + "main_insight": "The majority of game studios are not interested in implementing cryptocurrency payments or NFTs, with a significantly high percentage expressing no interest." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Category": "Cryptocurrency payments", + "Score": 1.0, + "Interest Level": "Already in use" + } + ], + "score": 0.95, + "annotation": "The Already in use has the smallest proportion in Cryptocurrency payments.", + "reason": "The Score of Already in use in Cryptocurrency payments accounts for 0.01, smaller than all other Interest Level, of the total of Score of all Interest Level." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Interest Level": "Not interested", + "Category": "total", + "Score": 142.0 + } + ], + "score": 0.95, + "annotation": "The total value of Not interested has the smallest proportion.", + "reason": "The total value of Score of Not interested accounts for 0.010050251256281407, smaller than all other Interest Level, of the total of Score of all Interest Level." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Interest Level": "Not interested", + "Category": "total", + "Score": 142.0 + } + ], + "score": 0.85, + "annotation": "The total value of Not interested accounts for the majority in all Interest Level.", + "reason": "The total value of Score of Not interested accounts for 0.7135678391959799 (more than 50%) of the total of Score of all Interest Level." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Cryptocurrency payments", + "Score": 72.0, + "Interest Level": "Not interested" + } + ], + "score": 0.45, + "annotation": "The Not interested has a maximum value at Cryptocurrency payments", + "reason": "The [{'Category': 'Cryptocurrency payments', 'Score': 72.0, 'Interest Level': 'Not interested'}] of Not interested has a maximum value of 72.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Category": "NFTs", + "Score": 70.0, + "Interest Level": "Not interested" + } + ], + "score": 0.45, + "annotation": "The Not interested has a minimum value at NFTs", + "reason": "The [{'Category': 'NFTs', 'Score': 70.0, 'Interest Level': 'Not interested'}] of Not interested has a mainimum value of 70.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Category": "Cryptocurrency payments", + "Score": 72.0, + "Interest Level": "Not interested" + }, + { + "Category": "Cryptocurrency payments", + "Score": 21.0, + "Interest Level": "Somewhat interested" + }, + { + "Category": "Cryptocurrency payments", + "Score": 6.0, + "Interest Level": "Very interested" + }, + { + "Category": "Cryptocurrency payments", + "Score": 1.0, + "Interest Level": "Already in use" + }, + { + "Category": "NFTs", + "Score": 70.0, + "Interest Level": "Not interested" + }, + { + "Category": "NFTs", + "Score": 21.0, + "Interest Level": "Somewhat interested" + }, + { + "Category": "NFTs", + "Score": 7.0, + "Interest Level": "Very interested" + }, + { + "Category": "NFTs", + "Score": 1.0, + "Interest Level": "Already in use" + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Type of technology: Cryptocurrency payments or NFTs", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding interest level", + "role": "y" + }, + { + "name": "Interest Level", + "importance": "primary", + "description": "The interest level of the data point", + "role": "group" + } + ] + } + }, + "1637": { + "metadata": { + "title": "Thousands of Flights Canceled & Delayed Over July 4 Break", + "description": "Canceled and delayed flights landing at/departing from U.S. airports between July 2, 5am and July 4, 4am 2022", + "main_insight": "There were significant flight disruptions over the July 4th holiday in 2022, with two distinct peaks in delays and cancellations, one around the start of the holiday period and one towards the end." + }, + "chart_type": "Stacked Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Time": "July 2, 5am", + "Score": 50, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 3, 5am", + "Score": 50, + "Flight Disruptions": "Delays" + } + ], + "score": 0.85, + "annotation": "The Delays has minimum values at July 2, 5am, July 3, 5am", + "reason": "The [{'Time': 'July 2, 5am', 'Score': 50, 'Flight Disruptions': 'Delays'}, {'Time': 'July 3, 5am', 'Score': 50, 'Flight Disruptions': 'Delays'}] of Delays have minimum values of 50, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Time": "July 4, 4am", + "Score": 5, + "Flight Disruptions": "Cancellations" + } + ], + "score": 0.83, + "annotation": "The Cancellations has a minimum value at July 4, 4am", + "reason": "The [{'Time': 'July 4, 4am', 'Score': 5, 'Flight Disruptions': 'Cancellations'}] of Cancellations has a mainimum value of 5, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Time": "July 4, 4am", + "Score": 5, + "Flight Disruptions": "Cancellations" + } + ], + "score": 0.81, + "annotation": "The Cancellations has the smallest proportion in July 4, 4am.", + "reason": "The Score of Cancellations in July 4, 4am accounts for 0.047619047619047616, smaller than all other Flight Disruptions, of the total of Score of all Flight Disruptions." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Time": "July 2, 8pm", + "Score": 650, + "Flight Disruptions": "Delays" + } + ], + "score": 0.8, + "annotation": "The Delays has a maximum value at July 2, 8pm", + "reason": "The [{'Time': 'July 2, 8pm', 'Score': 650, 'Flight Disruptions': 'Delays'}] of Delays has a maximum value of 650, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Time": "July 2, 2pm", + "Score": 60, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 2, 5pm", + "Score": 60, + "Flight Disruptions": "Cancellations" + } + ], + "score": 0.8, + "annotation": "The Cancellations has maximum values at July 2, 2pm, July 2, 5pm", + "reason": "The [{'Time': 'July 2, 2pm', 'Score': 60, 'Flight Disruptions': 'Cancellations'}, {'Time': 'July 2, 5pm', 'Score': 60, 'Flight Disruptions': 'Cancellations'}] of Cancellations have maximum values of 60, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Time": "July 2, 5am", + "Score": 50, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 2, 5am", + "Score": 10, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 2, 8am", + "Score": 300, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 2, 8am", + "Score": 40, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 2, 11am", + "Score": 500, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 2, 11am", + "Score": 30, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 2, 2pm", + "Score": 510, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 2, 2pm", + "Score": 60, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 2, 5pm", + "Score": 450, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 2, 5pm", + "Score": 60, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 2, 8pm", + "Score": 650, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 2, 8pm", + "Score": 50, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 2, 11pm", + "Score": 580, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 2, 11pm", + "Score": 40, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 3, 2am", + "Score": 300, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 3, 2am", + "Score": 30, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 3, 5am", + "Score": 50, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 3, 5am", + "Score": 10, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 3, 8am", + "Score": 200, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 3, 8am", + "Score": 15, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 3, 11am", + "Score": 300, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 3, 11am", + "Score": 20, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 3, 2pm", + "Score": 350, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 3, 2pm", + "Score": 40, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 3, 5pm", + "Score": 450, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 3, 5pm", + "Score": 55, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 3, 8pm", + "Score": 580, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 3, 8pm", + "Score": 40, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 3, 11pm", + "Score": 450, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 3, 11pm", + "Score": 30, + "Flight Disruptions": "Cancellations" + }, + { + "Time": "July 4, 4am", + "Score": 100, + "Flight Disruptions": "Delays" + }, + { + "Time": "July 4, 4am", + "Score": 5, + "Flight Disruptions": "Cancellations" + } + ], + "columns": [ + { + "name": "Time", + "importance": "primary", + "description": "Time intervals from July 2nd to July 4th, 2022", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding flight disruptions", + "role": "y" + }, + { + "name": "Flight Disruptions", + "importance": "primary", + "description": "The flight disruptions of the data point", + "role": "group" + } + ] + } + }, + "1341": { + "metadata": { + "title": "Snapshot of a Mexican Body Count", + "description": "Civilians killed by military and soldiers killed in Mexico between 2007 and 2012", + "main_insight": "The infographic highlights a significant disparity between civilian and military deaths in Mexico between 2007 and 2012, with a far greater number of civilians killed." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "Civilians", + "Number Killed": 3000 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Number Killed from Military to Civilians.", + "reason": "The Number Killed for increases from 158 at Military to 3000 at Civilians, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Civilians", + "Number Killed": 3000 + } + ], + "score": 0.45, + "annotation": "The has a maximum value at Civilians", + "reason": "The [{'Category': 'Civilians', 'Number Killed': 3000}] of has a maximum value of 3000, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Category": "Military", + "Number Killed": 158 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at Military", + "reason": "The [{'Category': 'Military', 'Number Killed': 158}] of has a mainimum value of 158, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Category": "Civilians", + "Number Killed": 3000 + }, + { + "Category": "Military", + "Number Killed": 158 + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "The category of individuals killed, either Civilians or Military", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number Killed", + "importance": "primary", + "description": "The number of individuals killed within each category", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2422": { + "metadata": { + "title": "The Rise of Chinese Smartphone Vendors", + "description": "Global quarterly market share of the top 5 smartphone vendors, by shipped devices (in %)", + "main_insight": "Chinese smartphone vendors have significantly increased their global market share over the past decade, collectively surpassing the market share of established leaders like Samsung and Apple." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Quarter": "Q2 '14", + "Score": 5.0, + "Smartphone Brands": "Lenovo" + } + ], + "score": 0.97, + "annotation": "The Lenovo shows a sudden jump in Score from Q2 '22 to Q2 '14.", + "reason": "The Score for Lenovo increases from 3.0 at Q2 '22 to 5.0 at Q2 '14, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Smartphone Brands": "Samsung", + "Quarter": "total", + "Score": 240.0 + } + ], + "score": 0.97, + "annotation": "The total value of Samsung has the largest proportion.", + "reason": "The total value of Score of Samsung accounts for 0.2853745541022592, larger than all other Smartphone Brands, of the total of Score of all Smartphone Brands." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q2 '15", + "Score": 22.0, + "Smartphone Brands": "Samsung" + } + ], + "score": 0.97, + "annotation": "The Samsung has the largest proportion in Q2 '15.", + "reason": "The Score of Samsung in Q2 '15 accounts for 0.3055555555555556, larger than all other Smartphone Brands, of the total of Score of all Smartphone Brands." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q2 '16", + "Score": 23.0, + "Smartphone Brands": "Samsung" + } + ], + "score": 0.97, + "annotation": "The Samsung has the largest proportion in Q2 '16.", + "reason": "The Score of Samsung in Q2 '16 accounts for 0.30666666666666664, larger than all other Smartphone Brands, of the total of Score of all Smartphone Brands." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q2 '14", + "Score": 25.0, + "Smartphone Brands": "Samsung" + } + ], + "score": 0.96, + "annotation": "The Samsung has the largest proportion in Q2 '14.", + "reason": "The Score of Samsung in Q2 '14 accounts for 0.36764705882352944, larger than all other Smartphone Brands, of the total of Score of all Smartphone Brands." + } + ], + "data": { + "data": [ + { + "Quarter": "Q2 '14", + "Score": 25.0, + "Smartphone Brands": "Samsung" + }, + { + "Quarter": "Q2 '14", + "Score": 12.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q2 '14", + "Score": 5.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q2 '14", + "Score": 7.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q2 '14", + "Score": 2.0, + "Smartphone Brands": "Oppo" + }, + { + "Quarter": "Q2 '14", + "Score": 3.0, + "Smartphone Brands": "Vivo" + }, + { + "Quarter": "Q2 '14", + "Score": 5.0, + "Smartphone Brands": "Lenovo" + }, + { + "Quarter": "Q2 '14", + "Score": 4.0, + "Smartphone Brands": "LG" + }, + { + "Quarter": "Q2 '15", + "Score": 22.0, + "Smartphone Brands": "Samsung" + }, + { + "Quarter": "Q2 '15", + "Score": 14.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q2 '15", + "Score": 5.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q2 '15", + "Score": 9.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q2 '15", + "Score": 2.0, + "Smartphone Brands": "Oppo" + }, + { + "Quarter": "Q2 '15", + "Score": 4.0, + "Smartphone Brands": "Vivo" + }, + { + "Quarter": "Q2 '15", + "Score": 5.0, + "Smartphone Brands": "Lenovo" + }, + { + "Quarter": "Q2 '15", + "Score": 4.0, + "Smartphone Brands": "LG" + }, + { + "Quarter": "Q2 '16", + "Score": 23.0, + "Smartphone Brands": "Samsung" + }, + { + "Quarter": "Q2 '16", + "Score": 13.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q2 '16", + "Score": 4.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q2 '16", + "Score": 9.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q2 '16", + "Score": 6.0, + "Smartphone Brands": "Oppo" + }, + { + "Quarter": "Q2 '16", + "Score": 5.0, + "Smartphone Brands": "Vivo" + }, + { + "Quarter": "Q2 '16", + "Score": 3.0, + "Smartphone Brands": "Lenovo" + }, + { + "Quarter": "Q2 '16", + "Score": 3.0, + "Smartphone Brands": "LG" + }, + { + "Quarter": "Q2 '17", + "Score": 23.0, + "Smartphone Brands": "Samsung" + }, + { + "Quarter": "Q2 '17", + "Score": 12.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q2 '17", + "Score": 6.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q2 '17", + "Score": 11.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q2 '17", + "Score": 8.0, + "Smartphone Brands": "Oppo" + }, + { + "Quarter": "Q2 '17", + "Score": 7.0, + "Smartphone Brands": "Vivo" + }, + { + "Quarter": "Q2 '17", + "Score": 3.0, + "Smartphone Brands": "Lenovo" + }, + { + "Quarter": "Q2 '17", + "Score": 3.0, + "Smartphone Brands": "LG" + }, + { + "Quarter": "Q2 '18", + "Score": 21.0, + "Smartphone Brands": "Samsung" + }, + { + "Quarter": "Q2 '18", + "Score": 12.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q2 '18", + "Score": 9.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q2 '18", + "Score": 16.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q2 '18", + "Score": 8.0, + "Smartphone Brands": "Oppo" + }, + { + "Quarter": "Q2 '18", + "Score": 7.0, + "Smartphone Brands": "Vivo" + }, + { + "Quarter": "Q2 '18", + "Score": 3.0, + "Smartphone Brands": "Lenovo" + }, + { + "Quarter": "Q2 '18", + "Score": 2.0, + "Smartphone Brands": "LG" + }, + { + "Quarter": "Q2 '19", + "Score": 23.0, + "Smartphone Brands": "Samsung" + }, + { + "Quarter": "Q2 '19", + "Score": 11.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q2 '19", + "Score": 9.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q2 '19", + "Score": 18.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q2 '19", + "Score": 9.0, + "Smartphone Brands": "Oppo" + }, + { + "Quarter": "Q2 '19", + "Score": 8.0, + "Smartphone Brands": "Vivo" + }, + { + "Quarter": "Q2 '19", + "Score": 3.0, + "Smartphone Brands": "Lenovo" + }, + { + "Quarter": "Q2 '19", + "Score": 2.0, + "Smartphone Brands": "LG" + }, + { + "Quarter": "Q2 '20", + "Score": 20.0, + "Smartphone Brands": "Samsung" + }, + { + "Quarter": "Q2 '20", + "Score": 14.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q2 '20", + "Score": 10.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q2 '20", + "Score": 20.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q2 '20", + "Score": 8.0, + "Smartphone Brands": "Oppo" + }, + { + "Quarter": "Q2 '20", + "Score": 9.0, + "Smartphone Brands": "Vivo" + }, + { + "Quarter": "Q2 '20", + "Score": 3.0, + "Smartphone Brands": "Lenovo" + }, + { + "Quarter": "Q2 '20", + "Score": 2.0, + "Smartphone Brands": "LG" + }, + { + "Quarter": "Q2 '21", + "Score": 19.0, + "Smartphone Brands": "Samsung" + }, + { + "Quarter": "Q2 '21", + "Score": 17.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q2 '21", + "Score": 17.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q2 '21", + "Score": 4.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q2 '21", + "Score": 10.0, + "Smartphone Brands": "Oppo" + }, + { + "Quarter": "Q2 '21", + "Score": 10.0, + "Smartphone Brands": "Vivo" + }, + { + "Quarter": "Q2 '21", + "Score": 3.0, + "Smartphone Brands": "Lenovo" + }, + { + "Quarter": "Q2 '22", + "Score": 22.0, + "Smartphone Brands": "Samsung" + }, + { + "Quarter": "Q2 '22", + "Score": 17.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q2 '22", + "Score": 14.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q2 '22", + "Score": 2.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q2 '22", + "Score": 9.0, + "Smartphone Brands": "Oppo" + }, + { + "Quarter": "Q2 '22", + "Score": 9.0, + "Smartphone Brands": "Vivo" + }, + { + "Quarter": "Q2 '22", + "Score": 5.0, + "Smartphone Brands": "Transsion" + }, + { + "Quarter": "Q2 '22", + "Score": 3.0, + "Smartphone Brands": "Lenovo" + }, + { + "Quarter": "Q2 '23", + "Score": 22.0, + "Smartphone Brands": "Samsung" + }, + { + "Quarter": "Q2 '23", + "Score": 17.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q2 '23", + "Score": 13.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q2 '23", + "Score": 6.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q2 '23", + "Score": 10.0, + "Smartphone Brands": "Oppo" + }, + { + "Quarter": "Q2 '23", + "Score": 8.0, + "Smartphone Brands": "Vivo" + }, + { + "Quarter": "Q2 '23", + "Score": 7.0, + "Smartphone Brands": "Transsion" + }, + { + "Quarter": "Q2 '24", + "Score": 20.0, + "Smartphone Brands": "Samsung" + }, + { + "Quarter": "Q2 '24", + "Score": 16.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q2 '24", + "Score": 14.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q2 '24", + "Score": 9.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q2 '24", + "Score": 8.0, + "Smartphone Brands": "Oppo" + }, + { + "Quarter": "Q2 '24", + "Score": 7.0, + "Smartphone Brands": "Vivo" + }, + { + "Quarter": "Q2 '24", + "Score": 9.0, + "Smartphone Brands": "Transsion" + } + ], + "columns": [ + { + "name": "Quarter", + "importance": "primary", + "description": "Time period in quarter-year format", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding smartphone brands", + "role": "y" + }, + { + "name": "Smartphone Brands", + "importance": "primary", + "description": "The smartphone brands of the data point", + "role": "group" + } + ] + } + }, + "115": { + "metadata": { + "title": "Nearly 1 in 5 Kids Use Mobile Devices Every Day", + "description": "% of 0- to 8-year-olds in the U.S. who engage in the following media activities at least once per day*", + "main_insight": "A significant percentage of young children (0-8 years old) engage with various media daily, with reading/being read to and watching TV being the most prevalent activities." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Media Activity": "Read or are read to", + "Read or are read to": 0.6 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Read or are read to from Read an ebook to Read or are read to.", + "reason": "The Read or are read to for increases from 0 at Read an ebook to 0.6 at Read or are read to, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Media Activity": "Read or are read to", + "Read or are read to": 0.6 + } + ], + "score": 0.97, + "annotation": "The has a maximum value at Read or are read to", + "reason": "The [{'Media Activity': 'Read or are read to', 'Read or are read to': 0.6}] of has a maximum value of 0.6, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Media Activity": "Watch TV", + "Read or are read to": 0 + }, + { + "Media Activity": "Watch DVDs", + "Read or are read to": 0 + }, + { + "Media Activity": "Use a mobile device", + "Read or are read to": 0 + }, + { + "Media Activity": "Use a computer", + "Read or are read to": 0 + }, + { + "Media Activity": "Use handheld video game", + "Read or are read to": 0 + }, + { + "Media Activity": "Use video game console", + "Read or are read to": 0 + }, + { + "Media Activity": "Read an ebook", + "Read or are read to": 0 + } + ], + "score": 0.29, + "annotation": "The has minimum values at Watch TV, Watch DVDs, Use a mobile device, Use a computer, Use handheld video game, Use video game console, Read an ebook", + "reason": "The [{'Media Activity': 'Watch TV', 'Read or are read to': 0}, {'Media Activity': 'Watch DVDs', 'Read or are read to': 0}, {'Media Activity': 'Use a mobile device', 'Read or are read to': 0}, {'Media Activity': 'Use a computer', 'Read or are read to': 0}, {'Media Activity': 'Use handheld video game', 'Read or are read to': 0}, {'Media Activity': 'Use video game console', 'Read or are read to': 0}, {'Media Activity': 'Read an ebook', 'Read or are read to': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Media Activity": "Read or are read to", + "Read or are read to": 0.6 + }, + { + "Media Activity": "Watch TV", + "Read or are read to": 0 + }, + { + "Media Activity": "Watch DVDs", + "Read or are read to": 0 + }, + { + "Media Activity": "Use a mobile device", + "Read or are read to": 0 + }, + { + "Media Activity": "Use a computer", + "Read or are read to": 0 + }, + { + "Media Activity": "Use handheld video game", + "Read or are read to": 0 + }, + { + "Media Activity": "Use video game console", + "Read or are read to": 0 + }, + { + "Media Activity": "Read an ebook", + "Read or are read to": 0 + } + ], + "columns": [ + { + "name": "Media Activity", + "importance": "primary", + "description": "Type of media activity", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Read or are read to", + "importance": "primary", + "description": "Percentage of children who read or are read to daily", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1593": { + "metadata": { + "title": "High Support for Hollywood, Auto Workers Strikes", + "description": "Share of U.S. respondents who sympathize with workers/employers in currently ongoing labor disputes", + "main_insight": "A significant majority of U.S. respondents sympathize with workers rather than employers in the ongoing labor disputes involving auto workers, television/film writers, and television/film actors." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Labor Dispute": "United Auto Workers vs. U.S. auto companies", + "Score": 1.0, + "Stakeholder": "Both equally" + } + ], + "score": 0.95, + "annotation": "The Both equally has the smallest proportion in United Auto Workers vs. U.S. auto companies.", + "reason": "The Score of Both equally in United Auto Workers vs. U.S. auto companies accounts for 0.010526315789473684, smaller than all other Stakeholder, of the total of Score of all Stakeholder." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Stakeholder": "Side with workers", + "Labor Dispute": "total", + "Score": 214.0 + } + ], + "score": 0.95, + "annotation": "The total value of Side with workers has the smallest proportion.", + "reason": "The total value of Score of Side with workers accounts for 0.010752688172043012, smaller than all other Stakeholder, of the total of Score of all Stakeholder." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Labor Dispute": "Television & film writers vs. television & film studios", + "Score": 1.0, + "Stakeholder": "Both equally" + } + ], + "score": 0.95, + "annotation": "The Both equally has the smallest proportion in Television & film writers vs. television & film studios.", + "reason": "The Score of Both equally in Television & film writers vs. television & film studios accounts for 0.010869565217391304, smaller than all other Stakeholder, of the total of Score of all Stakeholder." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Labor Dispute": "Television & film actors vs. television & film studios", + "Score": 1.0, + "Stakeholder": "Both equally" + } + ], + "score": 0.95, + "annotation": "The Both equally has the smallest proportion in Television & film actors vs. television & film studios.", + "reason": "The Score of Both equally in Television & film actors vs. television & film studios accounts for 0.010869565217391304, smaller than all other Stakeholder, of the total of Score of all Stakeholder." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Labor Dispute": "Television & film actors vs. television & film studios", + "Score": 67.0, + "Stakeholder": "Side with workers" + }, + { + "Labor Dispute": "Television & film actors vs. television & film studios", + "Score": 24.0, + "Stakeholder": "Side with employer" + } + ], + "score": 0.93, + "annotation": "The Score of Side with workers and Side with employer show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Side with workers and Side with employer is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Labor Dispute": "United Auto Workers vs. U.S. auto companies", + "Score": 75.0, + "Stakeholder": "Side with workers" + }, + { + "Labor Dispute": "United Auto Workers vs. U.S. auto companies", + "Score": 19.0, + "Stakeholder": "Side with employer" + }, + { + "Labor Dispute": "United Auto Workers vs. U.S. auto companies", + "Score": 1.0, + "Stakeholder": "Both equally" + }, + { + "Labor Dispute": "Television & film writers vs. television & film studios", + "Score": 72.0, + "Stakeholder": "Side with workers" + }, + { + "Labor Dispute": "Television & film writers vs. television & film studios", + "Score": 19.0, + "Stakeholder": "Side with employer" + }, + { + "Labor Dispute": "Television & film writers vs. television & film studios", + "Score": 1.0, + "Stakeholder": "Both equally" + }, + { + "Labor Dispute": "Television & film actors vs. television & film studios", + "Score": 67.0, + "Stakeholder": "Side with workers" + }, + { + "Labor Dispute": "Television & film actors vs. television & film studios", + "Score": 24.0, + "Stakeholder": "Side with employer" + }, + { + "Labor Dispute": "Television & film actors vs. television & film studios", + "Score": 1.0, + "Stakeholder": "Both equally" + } + ], + "columns": [ + { + "name": "Labor Dispute", + "importance": "primary", + "description": "The specific labor dispute group", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding stakeholder", + "role": "y" + }, + { + "name": "Stakeholder", + "importance": "primary", + "description": "The stakeholder of the data point", + "role": "group" + } + ] + } + }, + "1384": { + "metadata": { + "title": "The Rise of Premium Audio Subscriptions", + "description": "Share of U.S. respondents who spent/didn't spend money on digital music (in %)*", + "main_insight": "The percentage of U.S. respondents who spent money on a digital music subscription has increased significantly from 2020 to 2023, while the share of respondents only using free/ad-financed services has dropped during that time." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2023, + "Score": 43.0, + "Listening Habits": "On a subscription" + }, + { + "Year": 2023, + "Score": 31.0, + "Listening Habits": "Usage of free/ad-financed services only" + } + ], + "score": 0.98, + "annotation": "The Score of On a subscription and Usage of free/ad-financed services only show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between On a subscription and Usage of free/ad-financed services only is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2023, + "Score": 31.0, + "Listening Habits": "Usage of free/ad-financed services only" + }, + { + "Year": 2023, + "Score": 16.0, + "Listening Habits": "Didn't listen to digital music" + } + ], + "score": 0.95, + "annotation": "The Score of Usage of free/ad-financed services only and Didn't listen to digital music show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Usage of free/ad-financed services only and Didn't listen to digital music is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Score": 43.0, + "Listening Habits": "On a subscription" + }, + { + "Year": 2023, + "Score": 16.0, + "Listening Habits": "Didn't listen to digital music" + } + ], + "score": 0.9, + "annotation": "The Score of On a subscription and Didn't listen to digital music show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between On a subscription and Didn't listen to digital music is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2023, + "Score": 18.0, + "Listening Habits": "On downloads" + }, + { + "Year": 2023, + "Score": 16.0, + "Listening Habits": "Didn't listen to digital music" + } + ], + "score": 0.58, + "annotation": "The Score of On downloads and Didn't listen to digital music show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between On downloads and Didn't listen to digital music is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Score": 31.0, + "Listening Habits": "Usage of free/ad-financed services only" + }, + { + "Year": 2023, + "Score": 18.0, + "Listening Habits": "On downloads" + } + ], + "score": 0.31, + "annotation": "The Score of Usage of free/ad-financed services only and On downloads show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Usage of free/ad-financed services only and On downloads is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2020, + "Score": 35.0, + "Listening Habits": "On a subscription" + }, + { + "Year": 2020, + "Score": 40.0, + "Listening Habits": "Usage of free/ad-financed services only" + }, + { + "Year": 2020, + "Score": 18.0, + "Listening Habits": "On downloads" + }, + { + "Year": 2020, + "Score": 15.0, + "Listening Habits": "Didn't listen to digital music" + }, + { + "Year": 2021, + "Score": 35.0, + "Listening Habits": "On a subscription" + }, + { + "Year": 2021, + "Score": 39.0, + "Listening Habits": "Usage of free/ad-financed services only" + }, + { + "Year": 2021, + "Score": 18.0, + "Listening Habits": "On downloads" + }, + { + "Year": 2021, + "Score": 15.0, + "Listening Habits": "Didn't listen to digital music" + }, + { + "Year": 2022, + "Score": 39.0, + "Listening Habits": "On a subscription" + }, + { + "Year": 2022, + "Score": 34.0, + "Listening Habits": "Usage of free/ad-financed services only" + }, + { + "Year": 2022, + "Score": 17.0, + "Listening Habits": "On downloads" + }, + { + "Year": 2022, + "Score": 16.0, + "Listening Habits": "Didn't listen to digital music" + }, + { + "Year": 2023, + "Score": 43.0, + "Listening Habits": "On a subscription" + }, + { + "Year": 2023, + "Score": 31.0, + "Listening Habits": "Usage of free/ad-financed services only" + }, + { + "Year": 2023, + "Score": 18.0, + "Listening Habits": "On downloads" + }, + { + "Year": 2023, + "Score": 16.0, + "Listening Habits": "Didn't listen to digital music" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding listening habits", + "role": "y" + }, + { + "name": "Listening Habits", + "importance": "primary", + "description": "The listening habits of the data point", + "role": "group" + } + ] + } + }, + "1551": { + "metadata": { + "title": "Pine & Plastic: Christmas Tree Sales In The U.S.", + "description": "Real and fake christmas trees sold in the U.S. from 2008 to 2015", + "main_insight": "Sales of both real and fake Christmas trees fluctuated between 2009 and 2015. Fake tree sales consistently exceeded real tree sales, with the largest difference observed in 2013." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2009, + "Fake": 28.2 + }, + { + "Year": 2010, + "Fake": 27.0 + }, + { + "Year": 2011, + "Fake": 30.8 + }, + { + "Year": 2012, + "Fake": 24.5 + }, + { + "Year": 2013, + "Fake": 33.0 + }, + { + "Year": 2014, + "Fake": 26.3 + }, + { + "Year": 2015, + "Fake": 25.9 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of sales", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Fake", + "importance": "primary", + "description": "Number of fake Christmas trees sold (in millions)", + "unit": "Millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1072": { + "metadata": { + "title": "The Countries Pulling Their Weight in Ukraine Aid", + "description": "Governments committing the most aid to Ukraine as a share of own GDP* (in %)", + "main_insight": "Latvia and Estonia committed the most aid to Ukraine as a percentage of their GDP, significantly more than other nations like the US and UK." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "LV", + "Aid as % of GDP": 1.01 + } + ], + "score": 0.91, + "annotation": "The has a maximum value at LV", + "reason": "The [{'Country': 'LV', 'Aid as % of GDP': 1.01}] of has a maximum value of 1.01, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "EE", + "Aid as % of GDP": 0.95 + } + ], + "score": 0.8, + "annotation": "The shows a sudden jump in Aid as % of GDP from PL to EE.", + "reason": "The Aid as % of GDP for increases from 0.6 at PL to 0.95 at EE, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "DK", + "Aid as % of GDP": 0.22 + } + ], + "score": 0.5, + "annotation": "The has a minimum value at DK", + "reason": "The [{'Country': 'DK', 'Aid as % of GDP': 0.22}] of has a mainimum value of 0.22, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "LV", + "Aid as % of GDP": 1.01 + }, + { + "Country": "EE", + "Aid as % of GDP": 0.95 + }, + { + "Country": "PL", + "Aid as % of GDP": 0.6 + }, + { + "Country": "LT", + "Aid as % of GDP": 0.52 + }, + { + "Country": "NO", + "Aid as % of GDP": 0.38 + }, + { + "Country": "SK", + "Aid as % of GDP": 0.33 + }, + { + "Country": "CZ", + "Aid as % of GDP": 0.25 + }, + { + "Country": "US", + "Aid as % of GDP": 0.25 + }, + { + "Country": "GB", + "Aid as % of GDP": 0.24 + }, + { + "Country": "AT", + "Aid as % of GDP": 0.23 + }, + { + "Country": "DK", + "Aid as % of GDP": 0.22 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Two-letter country code", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Aid as % of GDP", + "importance": "primary", + "description": "Aid to Ukraine as a percentage of the country's GDP", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "307": { + "metadata": { + "title": "Roku Holds Its Ground Against Tech Heavyweights", + "description": "U.S. household penetration of TV streaming boxes/sticks in April 2017*", + "main_insight": "Roku had the highest household penetration among TV streaming devices in the U.S. in April 2017, exceeding that of major competitors like Amazon Fire TV, Chromecast, and Apple TV." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Device": "Amazon Fire TV", + "Penetration": 14.0 + } + ], + "score": 0.88, + "annotation": "The shows a sudden jump in Penetration from Chromecast to Amazon Fire TV.", + "reason": "The Penetration for increases from 8.0 at Chromecast to 14.0 at Amazon Fire TV, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Device": "Roku", + "Penetration": 16.0 + } + ], + "score": 0.62, + "annotation": "The has a maximum value at Roku", + "reason": "The [{'Device': 'Roku', 'Penetration': 16.0}] of has a maximum value of 16.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Device": "Apple TV", + "Penetration": 6.0 + } + ], + "score": 0.62, + "annotation": "The has a minimum value at Apple TV", + "reason": "The [{'Device': 'Apple TV', 'Penetration': 6.0}] of has a mainimum value of 6.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Device": "Roku", + "Penetration": 16.0 + }, + { + "Device": "Amazon Fire TV", + "Penetration": 14.0 + }, + { + "Device": "Chromecast", + "Penetration": 8.0 + }, + { + "Device": "Apple TV", + "Penetration": 6.0 + } + ], + "columns": [ + { + "name": "Device", + "importance": "primary", + "description": "Name of the streaming device", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Penetration", + "importance": "primary", + "description": "Percentage of U.S. household penetration", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1981": { + "metadata": { + "title": "What Happens When Apple Releases a New iOS Version", + "description": "iOS crash rates by device (% of app launches resulting in a crash)*", + "main_insight": "App crash rates for all iPhone models tend to spike immediately after a new iOS release (iOS 8 launch on September 17, 2014), and they mostly stabilize or decrease after the initial spike." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Sep 2014", + "Value": 2.1, + "iPhone Model": "iPhone 5S" + }, + { + "Month": "Sep 2014", + "Value": 2.1, + "iPhone Model": "iPhone 5" + }, + { + "Month": "Sep 2014", + "Value": 2.1, + "iPhone Model": "iPhone 4S" + } + ], + "score": 0.98, + "annotation": "The iPhone 5S, iPhone 5, iPhone 4S all have the largest proportion in Sep 2014.", + "reason": "The Value of iPhone 5S, iPhone 5, iPhone 4S in Sep 2014 all account for 0.14893617021276598, larger than all other iPhone Model, of the total of Value of all iPhone Model." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "May 2014", + "Value": 2.2, + "iPhone Model": "iPhone 4S" + }, + { + "Month": "Oct 2014", + "Value": 2.2, + "iPhone Model": "iPhone 6" + } + ], + "score": 0.98, + "annotation": "The iPhone 4S, iPhone 6 all have the largest proportion in May 2014.", + "reason": "The Value of iPhone 4S, iPhone 6 in May 2014 all account for 0.15492957746478875, larger than all other iPhone Model, of the total of Value of all iPhone Model." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Aug 2014", + "Value": 2.1, + "iPhone Model": "iPhone 4" + }, + { + "Month": "Jan 2015", + "Value": 2.1, + "iPhone Model": "iPhone 6 Plus" + } + ], + "score": 0.98, + "annotation": "The iPhone 4, iPhone 6 Plus all have the largest proportion in Aug 2014.", + "reason": "The Value of iPhone 4, iPhone 6 Plus in Aug 2014 all account for 0.16279069767441862, larger than all other iPhone Model, of the total of Value of all iPhone Model." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Jul 2014", + "Value": 2.2, + "iPhone Model": "iPhone 4" + }, + { + "Month": "Dec 2014", + "Value": 2.2, + "iPhone Model": "iPhone 6 Plus" + } + ], + "score": 0.98, + "annotation": "The iPhone 4, iPhone 6 Plus all have the largest proportion in Jul 2014.", + "reason": "The Value of iPhone 4, iPhone 6 Plus in Jul 2014 all account for 0.16296296296296295, larger than all other iPhone Model, of the total of Value of all iPhone Model." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Jun 2014", + "Value": 2.3, + "iPhone Model": "iPhone 4" + } + ], + "score": 0.98, + "annotation": "The iPhone 4 has the largest proportion in Jun 2014.", + "reason": "The Value of iPhone 4 in Jun 2014 accounts for 0.16546762589928057, larger than all other iPhone Model, of the total of Value of all iPhone Model." + } + ], + "data": { + "data": [ + { + "Month": "Apr 2014", + "Value": 2.1, + "iPhone Model": "iPhone 5S" + }, + { + "Month": "Apr 2014", + "Value": 1.8, + "iPhone Model": "iPhone 5C" + }, + { + "Month": "Apr 2014", + "Value": 2.0, + "iPhone Model": "iPhone 5" + }, + { + "Month": "Apr 2014", + "Value": 2.3, + "iPhone Model": "iPhone 4S" + }, + { + "Month": "Apr 2014", + "Value": 2.2, + "iPhone Model": "iPhone 4" + }, + { + "Month": "May 2014", + "Value": 2.0, + "iPhone Model": "iPhone 5S" + }, + { + "Month": "May 2014", + "Value": 1.7, + "iPhone Model": "iPhone 5C" + }, + { + "Month": "May 2014", + "Value": 1.9, + "iPhone Model": "iPhone 5" + }, + { + "Month": "May 2014", + "Value": 2.2, + "iPhone Model": "iPhone 4S" + }, + { + "Month": "May 2014", + "Value": 2.1, + "iPhone Model": "iPhone 4" + }, + { + "Month": "Jun 2014", + "Value": 1.9, + "iPhone Model": "iPhone 5S" + }, + { + "Month": "Jun 2014", + "Value": 1.6, + "iPhone Model": "iPhone 5C" + }, + { + "Month": "Jun 2014", + "Value": 1.8, + "iPhone Model": "iPhone 5" + }, + { + "Month": "Jun 2014", + "Value": 2.1, + "iPhone Model": "iPhone 4S" + }, + { + "Month": "Jun 2014", + "Value": 2.3, + "iPhone Model": "iPhone 4" + }, + { + "Month": "Jul 2014", + "Value": 1.8, + "iPhone Model": "iPhone 5S" + }, + { + "Month": "Jul 2014", + "Value": 1.6, + "iPhone Model": "iPhone 5C" + }, + { + "Month": "Jul 2014", + "Value": 1.8, + "iPhone Model": "iPhone 5" + }, + { + "Month": "Jul 2014", + "Value": 2.0, + "iPhone Model": "iPhone 4S" + }, + { + "Month": "Jul 2014", + "Value": 2.2, + "iPhone Model": "iPhone 4" + }, + { + "Month": "Aug 2014", + "Value": 1.7, + "iPhone Model": "iPhone 5S" + }, + { + "Month": "Aug 2014", + "Value": 1.5, + "iPhone Model": "iPhone 5C" + }, + { + "Month": "Aug 2014", + "Value": 1.8, + "iPhone Model": "iPhone 5" + }, + { + "Month": "Aug 2014", + "Value": 1.9, + "iPhone Model": "iPhone 4S" + }, + { + "Month": "Aug 2014", + "Value": 2.1, + "iPhone Model": "iPhone 4" + }, + { + "Month": "Sep 2014", + "Value": 1.2, + "iPhone Model": "iPhone 6 Plus" + }, + { + "Month": "Sep 2014", + "Value": 2.0, + "iPhone Model": "iPhone 6" + }, + { + "Month": "Sep 2014", + "Value": 2.1, + "iPhone Model": "iPhone 5S" + }, + { + "Month": "Sep 2014", + "Value": 2.0, + "iPhone Model": "iPhone 5C" + }, + { + "Month": "Sep 2014", + "Value": 2.1, + "iPhone Model": "iPhone 5" + }, + { + "Month": "Sep 2014", + "Value": 2.1, + "iPhone Model": "iPhone 4S" + }, + { + "Month": "Sep 2014", + "Value": 2.0, + "iPhone Model": "iPhone 4" + }, + { + "Month": "Oct 2014", + "Value": 2.1, + "iPhone Model": "iPhone 6 Plus" + }, + { + "Month": "Oct 2014", + "Value": 2.2, + "iPhone Model": "iPhone 6" + }, + { + "Month": "Oct 2014", + "Value": 1.9, + "iPhone Model": "iPhone 5S" + }, + { + "Month": "Oct 2014", + "Value": 1.8, + "iPhone Model": "iPhone 5C" + }, + { + "Month": "Oct 2014", + "Value": 1.9, + "iPhone Model": "iPhone 5" + }, + { + "Month": "Oct 2014", + "Value": 2.0, + "iPhone Model": "iPhone 4S" + }, + { + "Month": "Oct 2014", + "Value": 2.0, + "iPhone Model": "iPhone 4" + }, + { + "Month": "Nov 2014", + "Value": 2.2, + "iPhone Model": "iPhone 6 Plus" + }, + { + "Month": "Nov 2014", + "Value": 2.0, + "iPhone Model": "iPhone 6" + }, + { + "Month": "Nov 2014", + "Value": 1.9, + "iPhone Model": "iPhone 5S" + }, + { + "Month": "Nov 2014", + "Value": 1.9, + "iPhone Model": "iPhone 5C" + }, + { + "Month": "Nov 2014", + "Value": 1.9, + "iPhone Model": "iPhone 5" + }, + { + "Month": "Nov 2014", + "Value": 1.9, + "iPhone Model": "iPhone 4S" + }, + { + "Month": "Nov 2014", + "Value": 2.0, + "iPhone Model": "iPhone 4" + }, + { + "Month": "Dec 2014", + "Value": 2.2, + "iPhone Model": "iPhone 6 Plus" + }, + { + "Month": "Dec 2014", + "Value": 1.9, + "iPhone Model": "iPhone 6" + }, + { + "Month": "Dec 2014", + "Value": 1.8, + "iPhone Model": "iPhone 5S" + }, + { + "Month": "Dec 2014", + "Value": 1.9, + "iPhone Model": "iPhone 5C" + }, + { + "Month": "Dec 2014", + "Value": 1.9, + "iPhone Model": "iPhone 5" + }, + { + "Month": "Dec 2014", + "Value": 2.0, + "iPhone Model": "iPhone 4S" + }, + { + "Month": "Dec 2014", + "Value": 2.1, + "iPhone Model": "iPhone 4" + }, + { + "Month": "Jan 2015", + "Value": 2.1, + "iPhone Model": "iPhone 6 Plus" + }, + { + "Month": "Jan 2015", + "Value": 1.8, + "iPhone Model": "iPhone 6" + }, + { + "Month": "Jan 2015", + "Value": 1.8, + "iPhone Model": "iPhone 5S" + }, + { + "Month": "Jan 2015", + "Value": 1.9, + "iPhone Model": "iPhone 5C" + }, + { + "Month": "Jan 2015", + "Value": 2.0, + "iPhone Model": "iPhone 5" + }, + { + "Month": "Jan 2015", + "Value": 2.1, + "iPhone Model": "iPhone 4S" + }, + { + "Month": "Jan 2015", + "Value": 2.2, + "iPhone Model": "iPhone 4" + }, + { + "Month": "Feb 2015", + "Value": 2.0, + "iPhone Model": "iPhone 6 Plus" + }, + { + "Month": "Feb 2015", + "Value": 1.8, + "iPhone Model": "iPhone 6" + }, + { + "Month": "Feb 2015", + "Value": 1.7, + "iPhone Model": "iPhone 5S" + }, + { + "Month": "Feb 2015", + "Value": 1.8, + "iPhone Model": "iPhone 5C" + }, + { + "Month": "Feb 2015", + "Value": 1.8, + "iPhone Model": "iPhone 5" + }, + { + "Month": "Feb 2015", + "Value": 2.1, + "iPhone Model": "iPhone 4S" + }, + { + "Month": "Feb 2015", + "Value": 2.2, + "iPhone Model": "iPhone 4" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding iphone model", + "role": "y" + }, + { + "name": "iPhone Model", + "importance": "primary", + "description": "The iphone model of the data point", + "role": "group" + } + ] + } + }, + "425": { + "metadata": { + "title": "Regional Bank Rollercoaster", + "description": "Stock price change of U.S. regional banks since March 1, 2023", + "main_insight": "The stock prices of U.S. regional banks experienced significant declines after the collapse of Silicon Valley Bank, with First Republic experiencing the largest drop." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Date": "20", + "Ratio": -61.1, + "Bank Stocks": "Western Alliance" + }, + { + "Date": "20", + "Ratio": -40.2, + "Bank Stocks": "Zions" + } + ], + "score": 0.99, + "annotation": "The Ratio of Western Alliance and Zions show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Western Alliance and Zions is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Date": "20", + "Ratio": -90.1, + "Bank Stocks": "First Republic" + }, + { + "Date": "20", + "Ratio": -61.1, + "Bank Stocks": "Western Alliance" + } + ], + "score": 0.96, + "annotation": "The Ratio of First Republic and Western Alliance show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between First Republic and Western Alliance is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Date": "20", + "Ratio": -90.1, + "Bank Stocks": "First Republic" + }, + { + "Date": "20", + "Ratio": -40.2, + "Bank Stocks": "Zions" + } + ], + "score": 0.96, + "annotation": "The Ratio of First Republic and Zions show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between First Republic and Zions is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "6", + "Ratio": -5.0, + "Bank Stocks": "PacWest" + } + ], + "score": 0.95, + "annotation": "The PacWest shows a sudden jump in Ratio from 15 to 6.", + "reason": "The Ratio for PacWest increases from -55.0 at 15 to -5.0 at 6, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "10", + "Ratio": -20.0, + "Bank Stocks": "Western Alliance" + } + ], + "score": 0.92, + "annotation": "The Western Alliance shows a sudden jump in Ratio from 15 to 10.", + "reason": "The Ratio for Western Alliance increases from -60.0 at 15 to -20.0 at 10, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Date": "6", + "Ratio": -5.0, + "Bank Stocks": "First Republic" + }, + { + "Date": "6", + "Ratio": -5.0, + "Bank Stocks": "PacWest" + }, + { + "Date": "6", + "Ratio": -5.0, + "Bank Stocks": "Western Alliance" + }, + { + "Date": "6", + "Ratio": -5.0, + "Bank Stocks": "Zions" + }, + { + "Date": "10", + "Ratio": -30.0, + "Bank Stocks": "First Republic" + }, + { + "Date": "10", + "Ratio": -55.0, + "Bank Stocks": "PacWest" + }, + { + "Date": "10", + "Ratio": -20.0, + "Bank Stocks": "Western Alliance" + }, + { + "Date": "10", + "Ratio": -20.0, + "Bank Stocks": "Zions" + }, + { + "Date": "15", + "Ratio": -65.0, + "Bank Stocks": "First Republic" + }, + { + "Date": "15", + "Ratio": -55.0, + "Bank Stocks": "PacWest" + }, + { + "Date": "15", + "Ratio": -60.0, + "Bank Stocks": "Western Alliance" + }, + { + "Date": "15", + "Ratio": -40.0, + "Bank Stocks": "Zions" + }, + { + "Date": "20", + "Ratio": -90.1, + "Bank Stocks": "First Republic" + }, + { + "Date": "20", + "Ratio": -63.3, + "Bank Stocks": "PacWest" + }, + { + "Date": "20", + "Ratio": -61.1, + "Bank Stocks": "Western Alliance" + }, + { + "Date": "20", + "Ratio": -40.2, + "Bank Stocks": "Zions" + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Date in March 2023", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding bank stocks", + "role": "y" + }, + { + "name": "Bank Stocks", + "importance": "primary", + "description": "The bank stocks of the data point", + "role": "group" + } + ] + } + }, + "910": { + "metadata": { + "title": "Where Cash is King in Europe", + "description": "Share of point of sale purchases using cash by households in selected European countries", + "main_insight": "Cash usage varies significantly across European countries, with Southern European countries like Greece, Spain, and Italy showing the highest reliance on cash, while Scandinavian countries like Sweden show the lowest." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Sweden", + "Cash_Usage": 15.0 + } + ], + "score": 0.88, + "annotation": "The has a minimum value at Sweden", + "reason": "The [{'Country': 'Sweden', 'Cash_Usage': 15.0}] of has a mainimum value of 15.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Belgium", + "Cash_Usage": 63.0 + } + ], + "score": 0.64, + "annotation": "The shows a sudden jump in Cash_Usage from Netherlands to Belgium.", + "reason": "The Cash_Usage for increases from 45.0 at Netherlands to 63.0 at Belgium, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Greece", + "Cash_Usage": 88.0 + } + ], + "score": 0.58, + "annotation": "The has a maximum value at Greece", + "reason": "The [{'Country': 'Greece', 'Cash_Usage': 88.0}] of has a maximum value of 88.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Greece", + "Cash_Usage": 88.0 + }, + { + "Country": "Spain", + "Cash_Usage": 87.0 + }, + { + "Country": "Italy", + "Cash_Usage": 86.0 + }, + { + "Country": "Portugal", + "Cash_Usage": 81.0 + }, + { + "Country": "Germany", + "Cash_Usage": 80.0 + }, + { + "Country": "Ireland", + "Cash_Usage": 79.0 + }, + { + "Country": "France", + "Cash_Usage": 68.0 + }, + { + "Country": "Belgium", + "Cash_Usage": 63.0 + }, + { + "Country": "Netherlands", + "Cash_Usage": 45.0 + }, + { + "Country": "United Kingdom", + "Cash_Usage": 34.0 + }, + { + "Country": "Denmark", + "Cash_Usage": 23.0 + }, + { + "Country": "Sweden", + "Cash_Usage": 15.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Selected European countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Cash_Usage", + "importance": "primary", + "description": "Percentage of point-of-sale purchases made using cash", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1984": { + "metadata": { + "title": "Robust Spending, Investment Fuel 5.2% GDP Growth in Q3 '23", + "description": "U.S. gross domestic product and its components in Q3 2023 (seasonally adjusted at annual rates)", + "main_insight": "US GDP grew by 5.2% in Q3 2023, driven primarily by personal consumption expenditure and gross private domestic investment. The chart breaks down the contribution of each component to the overall GDP growth." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "GDP Component": "Gross private domestic investment", + "Value": 10.5, + "GDP Metrics": "% Change" + } + ], + "score": 0.92, + "annotation": "The % Change shows a sudden jump in Value from Government consumption expenditures & gross investment to Gross private domestic investment.", + "reason": "The Value for % Change increases from 5.5 at Government consumption expenditures & gross investment to 10.5 at Gross private domestic investment, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "GDP Component": "Personal consumption expenditure", + "Value": 18712.0, + "GDP Metrics": "GDP Value (Billions)" + } + ], + "score": 0.82, + "annotation": "The GDP Value (Billions) shows a sudden jump in Value from Gross private domestic investment to Personal consumption expenditure.", + "reason": "The Value for GDP Value (Billions) increases from 4922.0 at Gross private domestic investment to 18712.0 at Personal consumption expenditure, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "GDP Component": "Gross private domestic investment", + "Value": 10.5, + "GDP Metrics": "% Change" + } + ], + "score": 0.78, + "annotation": "The % Change has a maximum value at Gross private domestic investment", + "reason": "The [{'GDP Component': 'Gross private domestic investment', 'Value': 10.5, 'GDP Metrics': '% Change'}] of % Change has a maximum value of 10.5, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "GDP Component": "Gross domestic product", + "Value": 27645.0, + "GDP Metrics": "GDP Value (Billions)" + } + ], + "score": 0.77, + "annotation": "The GDP Value (Billions) has a maximum value at Gross domestic product", + "reason": "The [{'GDP Component': 'Gross domestic product', 'Value': 27645.0, 'GDP Metrics': 'GDP Value (Billions)'}] of GDP Value (Billions) has a maximum value of 27645.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "GDP Component": "Government consumption expenditures & gross investment", + "Value": 0.94, + "GDP Metrics": "Contribution to GDP Growth" + } + ], + "score": 0.77, + "annotation": "The Contribution to GDP Growth shows a sudden jump in Value from Net exports of goods and services to Government consumption expenditures & gross investment.", + "reason": "The Value for Contribution to GDP Growth increases from -0.04 at Net exports of goods and services to 0.94 at Government consumption expenditures & gross investment, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "GDP Component": "Gross domestic product", + "Value": 27645.0, + "GDP Metrics": "GDP Value (Billions)" + }, + { + "GDP Component": "Gross domestic product", + "Value": 5.2, + "GDP Metrics": "% Change" + }, + { + "GDP Component": "Personal consumption expenditure", + "Value": 18712.0, + "GDP Metrics": "GDP Value (Billions)" + }, + { + "GDP Component": "Personal consumption expenditure", + "Value": 3.6, + "GDP Metrics": "% Change" + }, + { + "GDP Component": "Personal consumption expenditure", + "Value": 2.44, + "GDP Metrics": "Contribution to GDP Growth" + }, + { + "GDP Component": "Gross private domestic investment", + "Value": 4922.0, + "GDP Metrics": "GDP Value (Billions)" + }, + { + "GDP Component": "Gross private domestic investment", + "Value": 10.5, + "GDP Metrics": "% Change" + }, + { + "GDP Component": "Gross private domestic investment", + "Value": 1.82, + "GDP Metrics": "Contribution to GDP Growth" + }, + { + "GDP Component": "Government consumption expenditures & gross investment", + "Value": 4793.0, + "GDP Metrics": "GDP Value (Billions)" + }, + { + "GDP Component": "Government consumption expenditures & gross investment", + "Value": 5.5, + "GDP Metrics": "% Change" + }, + { + "GDP Component": "Government consumption expenditures & gross investment", + "Value": 0.94, + "GDP Metrics": "Contribution to GDP Growth" + }, + { + "GDP Component": "Net exports of goods and services", + "Value": -782.0, + "GDP Metrics": "GDP Value (Billions)" + }, + { + "GDP Component": "Net exports of goods and services", + "Value": -0.04, + "GDP Metrics": "Contribution to GDP Growth" + } + ], + "columns": [ + { + "name": "GDP Component", + "importance": "primary", + "description": "Categories of GDP components", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding gdp metrics", + "role": "y" + }, + { + "name": "GDP Metrics", + "importance": "primary", + "description": "The gdp metrics of the data point", + "role": "group" + } + ] + } + }, + "1389": { + "metadata": { + "title": "Trudeau's Snap Election Reaps Little Reward", + "description": "Seats won in Canada federal elections, by party", + "main_insight": "The 2021 Canadian federal election resulted in a very similar distribution of seats compared to the 2019 election, with the Liberal party, led by Trudeau, gaining a small amount of seats, but not enough to win a majority." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Party": "Conservative", + "Index": 121, + "Election Years": "Seats won in 2019" + }, + { + "Party": "Conservative", + "Index": 119, + "Election Years": "Seats won in 2021" + } + ], + "score": 1.0, + "annotation": "The Index of Seats won in 2019 and Seats won in 2021 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Index between Seats won in 2019 and Seats won in 2021 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Party": "Conservative", + "Index": 121, + "Election Years": "Seats won in 2019" + } + ], + "score": 0.87, + "annotation": "The Seats won in 2019 shows a sudden jump in Index from Bloc Québécois to Conservative.", + "reason": "The Index for Seats won in 2019 increases from 32 at Bloc Québécois to 121 at Conservative, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Party": "Conservative", + "Index": 119, + "Election Years": "Seats won in 2021" + } + ], + "score": 0.86, + "annotation": "The Seats won in 2021 shows a sudden jump in Index from Bloc Québécois to Conservative.", + "reason": "The Index for Seats won in 2021 increases from 32 at Bloc Québécois to 119 at Conservative, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Party": "Bloc Québécois", + "Index": 32, + "Election Years": "Seats won in 2019" + }, + { + "Party": "Bloc Québécois", + "Index": 32, + "Election Years": "Seats won in 2021" + } + ], + "score": 0.8, + "annotation": "The Seats won in 2019, Seats won in 2021 all have the largest proportion in Bloc Québécois.", + "reason": "The Index of Seats won in 2019, Seats won in 2021 in Bloc Québécois all account for 0.5, larger than all other Election Years, of the total of Index of all Election Years." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Election Years": "Seats won in 2019", + "Party": "total", + "Index": 338 + } + ], + "score": 0.8, + "annotation": "The total value of Seats won in 2019 accounts for the majority in all Election Years.", + "reason": "The total value of Index of Seats won in 2019 accounts for 0.5007407407407407 (more than 50%) of the total of Index of all Election Years." + } + ], + "data": { + "data": [ + { + "Party": "Liberal", + "Index": 158, + "Election Years": "Seats won in 2019" + }, + { + "Party": "Liberal", + "Index": 159, + "Election Years": "Seats won in 2021" + }, + { + "Party": "New Democratic", + "Index": 24, + "Election Years": "Seats won in 2019" + }, + { + "Party": "New Democratic", + "Index": 25, + "Election Years": "Seats won in 2021" + }, + { + "Party": "Green", + "Index": 3, + "Election Years": "Seats won in 2019" + }, + { + "Party": "Green", + "Index": 2, + "Election Years": "Seats won in 2021" + }, + { + "Party": "Bloc Québécois", + "Index": 32, + "Election Years": "Seats won in 2019" + }, + { + "Party": "Bloc Québécois", + "Index": 32, + "Election Years": "Seats won in 2021" + }, + { + "Party": "Conservative", + "Index": 121, + "Election Years": "Seats won in 2019" + }, + { + "Party": "Conservative", + "Index": 119, + "Election Years": "Seats won in 2021" + } + ], + "columns": [ + { + "name": "Party", + "importance": "primary", + "description": "Political party in the Canadian federal election", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Index", + "importance": "primary", + "description": "Index for the corresponding election years", + "role": "y" + }, + { + "name": "Election Years", + "importance": "primary", + "description": "The election years of the data point", + "role": "group" + } + ] + } + }, + "639": { + "metadata": { + "title": "U.S. Poverty Rate Jumps up as Covid-Era Tax Credits Expire", + "description": "Share of Americans living below the poverty line", + "main_insight": "The U.S. poverty rate, particularly the Supplemental Poverty Measure, increased significantly in 2022, coinciding with the expiration of Covid-era tax credits." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 12.4, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2022, + "Units": 11.5, + "Poverty Measure": "Official poverty rate" + } + ], + "score": 0.8, + "annotation": "The Units of Supplemental Poverty Measure and Official poverty rate show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Supplemental Poverty Measure and Official poverty rate is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2009, + "Units": 14.8, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2009, + "Units": 14.3, + "Poverty Measure": "Official poverty rate" + }, + { + "Year": 2010, + "Units": 15.8, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2010, + "Units": 15.1, + "Poverty Measure": "Official poverty rate" + }, + { + "Year": 2011, + "Units": 16.1, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2011, + "Units": 15.0, + "Poverty Measure": "Official poverty rate" + }, + { + "Year": 2012, + "Units": 16.0, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2012, + "Units": 15.0, + "Poverty Measure": "Official poverty rate" + }, + { + "Year": 2013, + "Units": 15.9, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2013, + "Units": 14.8, + "Poverty Measure": "Official poverty rate" + }, + { + "Year": 2014, + "Units": 15.6, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2014, + "Units": 14.8, + "Poverty Measure": "Official poverty rate" + }, + { + "Year": 2015, + "Units": 14.7, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2015, + "Units": 13.5, + "Poverty Measure": "Official poverty rate" + }, + { + "Year": 2016, + "Units": 14.0, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2016, + "Units": 12.7, + "Poverty Measure": "Official poverty rate" + }, + { + "Year": 2017, + "Units": 13.9, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2017, + "Units": 12.3, + "Poverty Measure": "Official poverty rate" + }, + { + "Year": 2018, + "Units": 13.1, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2018, + "Units": 11.8, + "Poverty Measure": "Official poverty rate" + }, + { + "Year": 2019, + "Units": 12.8, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2019, + "Units": 10.5, + "Poverty Measure": "Official poverty rate" + }, + { + "Year": 2020, + "Units": 11.4, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2020, + "Units": 11.5, + "Poverty Measure": "Official poverty rate" + }, + { + "Year": 2021, + "Units": 8.0, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2021, + "Units": 11.6, + "Poverty Measure": "Official poverty rate" + }, + { + "Year": 2022, + "Units": 12.4, + "Poverty Measure": "Supplemental Poverty Measure" + }, + { + "Year": 2022, + "Units": 11.5, + "Poverty Measure": "Official poverty rate" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of poverty rate measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding poverty measure", + "role": "y" + }, + { + "name": "Poverty Measure", + "importance": "primary", + "description": "The poverty measure of the data point", + "role": "group" + } + ] + } + }, + "1720": { + "metadata": { + "title": "Half of Urban Koreans Skip Chuseok With the Family", + "description": "Share of office workers who said they would not visit their hometowns for the Chuseok fall festival", + "main_insight": "The percentage of urban Korean office workers who said they would not visit their hometowns for Chuseok increased significantly between 2016 and 2020, peaking in 2020, and then saw a drop in 2021." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2016, + "Percentage": 37.3 + }, + { + "Year": 2017, + "Percentage": 39.4 + }, + { + "Year": 2018, + "Percentage": 37.1 + }, + { + "Year": 2019, + "Percentage": 39.7 + }, + { + "Year": 2020, + "Percentage": 57.7 + }, + { + "Year": 2021, + "Percentage": 51.9 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of office workers who said they would not visit their hometowns for Chuseok", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "439": { + "metadata": { + "title": "Mortgage Rates Drop Below 7% But Remain Elevated", + "description": "Average 30-year fixed mortgage rate in the United States", + "main_insight": "The average 30-year fixed mortgage rate in the US has recently dropped below 7%, but remains elevated compared to rates between 2009 and 2021. The rates significantly increased beginning in 2022." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2002, + "Mortgage Rate": 7.0 + }, + { + "Year": 2004, + "Mortgage Rate": 6.0 + }, + { + "Year": 2006, + "Mortgage Rate": 6.5 + }, + { + "Year": 2008, + "Mortgage Rate": 6.0 + }, + { + "Year": 2010, + "Mortgage Rate": 5.0 + }, + { + "Year": 2012, + "Mortgage Rate": 4.0 + }, + { + "Year": 2014, + "Mortgage Rate": 4.2 + }, + { + "Year": 2016, + "Mortgage Rate": 4.0 + }, + { + "Year": 2018, + "Mortgage Rate": 4.5 + }, + { + "Year": 2020, + "Mortgage Rate": 3.5 + }, + { + "Year": 2022, + "Mortgage Rate": 6.0 + }, + { + "Year": 2024, + "Mortgage Rate": 7.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 2002 to 2024", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Mortgage Rate", + "importance": "primary", + "description": "Average 30-year fixed mortgage rate", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "682": { + "metadata": { + "title": "The Majority of Business Trips are in the Home Market", + "description": "Share of business trips made internationally or domestically", + "main_insight": "For the US, Germany, and the UK, the majority of business trips are domestic rather than international, with the US having the highest percentage of domestic business trips." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "UK", + "Percentage": 68.9, + "Market Segment": "Domestic" + }, + { + "Country": "UK", + "Percentage": 31.1, + "Market Segment": "International" + } + ], + "score": 1.0, + "annotation": "The Percentage of Domestic and International show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Percentage between Domestic and International is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "US", + "Percentage": 83.6, + "Market Segment": "Domestic" + } + ], + "score": 0.92, + "annotation": "The Domestic shows a sudden jump in Percentage from Germany to US.", + "reason": "The Percentage for Domestic increases from 73.3 at Germany to 83.6 at US, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Germany", + "Percentage": 26.7, + "Market Segment": "International" + } + ], + "score": 0.92, + "annotation": "The International shows a sudden jump in Percentage from US to Germany.", + "reason": "The Percentage for International increases from 16.4 at US to 26.7 at Germany, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "UK", + "Percentage": 68.9, + "Market Segment": "Domestic" + } + ], + "score": 0.74, + "annotation": "The Domestic accounts for the majority in UK.", + "reason": "The Percentage of Domestic in UK accounts for 0.6890000000000001 (more than 50%) of the total of Percentage of all Market Segment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Germany", + "Percentage": 73.3, + "Market Segment": "Domestic" + } + ], + "score": 0.73, + "annotation": "The Domestic accounts for the majority in Germany.", + "reason": "The Percentage of Domestic in Germany accounts for 0.733 (more than 50%) of the total of Percentage of all Market Segment." + } + ], + "data": { + "data": [ + { + "Country": "US", + "Percentage": 83.6, + "Market Segment": "Domestic" + }, + { + "Country": "US", + "Percentage": 16.4, + "Market Segment": "International" + }, + { + "Country": "Germany", + "Percentage": 73.3, + "Market Segment": "Domestic" + }, + { + "Country": "Germany", + "Percentage": 26.7, + "Market Segment": "International" + }, + { + "Country": "UK", + "Percentage": 68.9, + "Market Segment": "Domestic" + }, + { + "Country": "UK", + "Percentage": 31.1, + "Market Segment": "International" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of origin for business trips", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage for the corresponding market segment", + "role": "y" + }, + { + "name": "Market Segment", + "importance": "primary", + "description": "The market segment of the data point", + "role": "group" + } + ] + } + }, + "665": { + "metadata": { + "title": "The Most Common New Year's Resolutions for 2018", + "description": "\"Which of the following are you planning to include among your New Year's resolutions?\"", + "main_insight": "Eating healthier, getting more exercise, and saving money were the most common New Year's resolutions for 2018, all tied at 37%. A significant portion of respondents (32%) did not plan on making any resolutions." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "New Year's Resolution": "I don't plan on making new year's resolutions", + "Percentage of Respondents": 32.0 + } + ], + "score": 0.72, + "annotation": "The shows a sudden jump in Percentage of Respondents from Focus on self-care (e.g. get more sleep) to I don't plan on making new year's resolutions.", + "reason": "The Percentage of Respondents for increases from 24.0 at Focus on self-care (e.g. get more sleep) to 32.0 at I don't plan on making new year's resolutions, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "New Year's Resolution": "Eat healthier", + "Percentage of Respondents": 37.0 + }, + { + "New Year's Resolution": "Get more exercise", + "Percentage of Respondents": 37.0 + }, + { + "New Year's Resolution": "Save (more) money", + "Percentage of Respondents": 37.0 + } + ], + "score": 0.7, + "annotation": "The has maximum values at Eat healthier, Get more exercise, Save (more) money", + "reason": "The [{\"New Year's Resolution\": 'Eat healthier', 'Percentage of Respondents': 37.0}, {\"New Year's Resolution\": 'Get more exercise', 'Percentage of Respondents': 37.0}, {\"New Year's Resolution\": 'Save (more) money', 'Percentage of Respondents': 37.0}] of have maximum values of 37.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "New Year's Resolution": "Take up a new hobby", + "Percentage of Respondents": 13.0 + } + ], + "score": 0.63, + "annotation": "The has a minimum value at Take up a new hobby", + "reason": "The [{\"New Year's Resolution\": 'Take up a new hobby', 'Percentage of Respondents': 13.0}] of has a mainimum value of 13.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "New Year's Resolution": "Eat healthier", + "Percentage of Respondents": 37.0 + }, + { + "New Year's Resolution": "Get more exercise", + "Percentage of Respondents": 37.0 + }, + { + "New Year's Resolution": "Save (more) money", + "Percentage of Respondents": 37.0 + }, + { + "New Year's Resolution": "Focus on self-care (e.g. get more sleep)", + "Percentage of Respondents": 24.0 + }, + { + "New Year's Resolution": "Read more", + "Percentage of Respondents": 18.0 + }, + { + "New Year's Resolution": "Make new friends", + "Percentage of Respondents": 15.0 + }, + { + "New Year's Resolution": "Learn a new skill", + "Percentage of Respondents": 15.0 + }, + { + "New Year's Resolution": "Get a (new) job", + "Percentage of Respondents": 14.0 + }, + { + "New Year's Resolution": "Take up a new hobby", + "Percentage of Respondents": 13.0 + }, + { + "New Year's Resolution": "I don't plan on making new year's resolutions", + "Percentage of Respondents": 32.0 + } + ], + "columns": [ + { + "name": "New Year's Resolution", + "importance": "primary", + "description": "List of resolutions", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Respondents", + "importance": "primary", + "description": "Percentage of respondents who selected each resolution", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1963": { + "metadata": { + "title": "Erdogan's Crackdown On Turkish Media", + "description": "Media organisations closed by the Turkish government", + "main_insight": "The Turkish government has closed a significant number of media organizations, with newspapers being the most affected category, followed by publishing houses and radio stations." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Media Type": "Newspapers", + "Number of Organizations Closed": 45 + } + ], + "score": 0.84, + "annotation": "The has a maximum value at Newspapers", + "reason": "The [{'Media Type': 'Newspapers', 'Number of Organizations Closed': 45}] of has a maximum value of 45, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Media Type": "Newspapers", + "Number of Organizations Closed": 45 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Number of Organizations Closed from Publishing houses to Newspapers.", + "reason": "The Number of Organizations Closed for increases from 29 at Publishing houses to 45 at Newspapers, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Media Type": "News agencies", + "Number of Organizations Closed": 3 + } + ], + "score": 0.74, + "annotation": "The has a minimum value at News agencies", + "reason": "The [{'Media Type': 'News agencies', 'Number of Organizations Closed': 3}] of has a mainimum value of 3, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Media Type": "Newspapers", + "Number of Organizations Closed": 45 + }, + { + "Media Type": "Publishing houses", + "Number of Organizations Closed": 29 + }, + { + "Media Type": "Radio stations", + "Number of Organizations Closed": 23 + }, + { + "Media Type": "TV stations", + "Number of Organizations Closed": 16 + }, + { + "Media Type": "Magazines", + "Number of Organizations Closed": 15 + }, + { + "Media Type": "News agencies", + "Number of Organizations Closed": 3 + } + ], + "columns": [ + { + "name": "Media Type", + "importance": "primary", + "description": "Type of media organization", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Organizations Closed", + "importance": "primary", + "description": "Number of media organizations closed by the Turkish government", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2277": { + "metadata": { + "title": "Boris Johnson loses his majority", + "description": "Composition of the House of Commons as of 3 September 2019", + "main_insight": "As of September 3, 2019, the government, led by Boris Johnson had 319 seats and lost their majority, against 320 opposition seats. And 11 do not vote." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Party": "Conservative", + "Seats": 298 + } + ], + "score": 0.97, + "annotation": "The has a maximum value at Conservative", + "reason": "The [{'Party': 'Conservative', 'Seats': 298}] of has a maximum value of 298, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Party": "Labour", + "Seats": 247 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in Seats from SNP to Labour.", + "reason": "The Seats for increases from 35 at SNP to 247 at Labour, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Party": "Green", + "Seats": 1 + } + ], + "score": 0.35, + "annotation": "The has a minimum value at Green", + "reason": "The [{'Party': 'Green', 'Seats': 1}] of has a mainimum value of 1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Party": "Conservative", + "Seats": 298 + }, + { + "Party": "DUP", + "Seats": 10 + }, + { + "Party": "Speaker/deputies", + "Seats": 4 + }, + { + "Party": "Labour", + "Seats": 247 + }, + { + "Party": "SNP", + "Seats": 35 + }, + { + "Party": "Lib Dem", + "Seats": 17 + }, + { + "Party": "Independent", + "Seats": 9 + }, + { + "Party": "Plaid Cymru", + "Seats": 4 + }, + { + "Party": "Green", + "Seats": 1 + }, + { + "Party": "Change UK", + "Seats": 5 + }, + { + "Party": "The Brexit Party", + "Seats": 2 + }, + { + "Party": "Sinn Féin", + "Seats": 7 + }, + { + "Party": "Speaker/deputies", + "Seats": 4 + } + ], + "columns": [ + { + "name": "Party", + "importance": "primary", + "description": "Political party", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Seats", + "importance": "primary", + "description": "Number of seats in the House of Commons", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "198": { + "metadata": { + "title": "Americans Don't See Inflation Returning to 2% Anytime Soon", + "description": "Median expected inflation rate one year and three years ahead according to U.S. consumers", + "main_insight": "Americans expect inflation to remain above 2% in both the one-year and three-year timeframes, with the one-year expectation being higher and more volatile." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Ratio": 3.0, + "Forecast Horizon": "One year ahead" + }, + { + "Year": 2024, + "Ratio": 3.0, + "Forecast Horizon": "Three years ahead" + } + ], + "score": 0.9, + "annotation": "The Ratio of One year ahead and Three years ahead show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between One year ahead and Three years ahead is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2018, + "Ratio": 2.8, + "Forecast Horizon": "One year ahead" + }, + { + "Year": 2018, + "Ratio": 2.9, + "Forecast Horizon": "Three years ahead" + }, + { + "Year": 2019, + "Ratio": 2.7, + "Forecast Horizon": "One year ahead" + }, + { + "Year": 2019, + "Ratio": 2.6, + "Forecast Horizon": "Three years ahead" + }, + { + "Year": 2020, + "Ratio": 3.0, + "Forecast Horizon": "One year ahead" + }, + { + "Year": 2020, + "Ratio": 2.8, + "Forecast Horizon": "Three years ahead" + }, + { + "Year": 2021, + "Ratio": 5.0, + "Forecast Horizon": "One year ahead" + }, + { + "Year": 2021, + "Ratio": 3.5, + "Forecast Horizon": "Three years ahead" + }, + { + "Year": 2022, + "Ratio": 5.8, + "Forecast Horizon": "One year ahead" + }, + { + "Year": 2022, + "Ratio": 3.5, + "Forecast Horizon": "Three years ahead" + }, + { + "Year": 2023, + "Ratio": 4.0, + "Forecast Horizon": "One year ahead" + }, + { + "Year": 2023, + "Ratio": 2.9, + "Forecast Horizon": "Three years ahead" 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Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Organization Type": "The government", + "Units": 30.0, + "Trust Level": "Neutral" + } + ], + "score": 0.93, + "annotation": "The Neutral has a minimum value at The government", + "reason": "The [{'Organization Type': 'The government', 'Units': 30.0, 'Trust Level': 'Neutral'}] of Neutral has a mainimum value of 30.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Organization Type": "Banking companies", + "Units": 40.0, + "Trust Level": "Neutral" + } + ], + "score": 0.88, + "annotation": "The Neutral has the largest proportion in Banking companies.", + "reason": "The Units of Neutral in Banking companies accounts for 0.4, larger than all other Trust Level, of the total of Units of all Trust Level." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Organization 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the largest proportion in The media.", + "reason": "The Units of Untrustworthy in The media accounts for 0.43, larger than all other Trust Level, of the total of Units of all Trust Level." + } + ], + "data": { + "data": [ + { + "Organization Type": "Technology companies", + "Units": 34.0, + "Trust Level": "Trustworthy" + }, + { + "Organization Type": "Technology companies", + "Units": 47.0, + "Trust Level": "Neutral" + }, + { + "Organization Type": "Technology companies", + "Units": 19.0, + "Trust Level": "Untrustworthy" + }, + { + "Organization Type": "Pharmaceutical companies", + "Units": 31.0, + "Trust Level": "Trustworthy" + }, + { + "Organization Type": "Pharmaceutical companies", + "Units": 41.0, + "Trust Level": "Neutral" + }, + { + "Organization Type": "Pharmaceutical companies", + "Units": 28.0, + "Trust Level": "Untrustworthy" + }, + { + "Organization Type": "Banking companies", + "Units": 28.0, + "Trust Level": "Trustworthy" + }, + { + "Organization Type": "Banking companies", + "Units": 40.0, + "Trust Level": "Neutral" + }, + { + "Organization Type": "Banking companies", + "Units": 32.0, + "Trust Level": "Untrustworthy" + }, + { + "Organization Type": "Car/automotive companies", + "Units": 27.0, + "Trust Level": "Trustworthy" + }, + { + "Organization Type": "Car/automotive companies", + "Units": 50.0, + "Trust Level": "Neutral" + }, + { + "Organization Type": "Car/automotive companies", + "Units": 23.0, + "Trust Level": "Untrustworthy" + }, + { + "Organization Type": "Public services", + "Units": 27.0, + "Trust Level": "Trustworthy" + }, + { + "Organization Type": "Public services", + "Units": 44.0, + "Trust Level": "Neutral" + }, + { + "Organization Type": "Public services", + "Units": 29.0, + "Trust Level": "Untrustworthy" + }, + { + "Organization Type": "Oil and gas companies", + "Units": 22.0, + "Trust Level": "Trustworthy" + }, + { + "Organization Type": "Oil and gas companies", + "Units": 44.0, + "Trust Level": "Neutral" + }, + { + "Organization Type": "Oil and gas companies", + "Units": 34.0, + "Trust Level": "Untrustworthy" + }, + { + "Organization Type": "The government", + "Units": 20.0, + "Trust Level": "Trustworthy" + }, + { + "Organization Type": "The government", + "Units": 30.0, + "Trust Level": "Neutral" + }, + { + "Organization Type": "The government", + "Units": 50.0, + "Trust Level": "Untrustworthy" + }, + { + "Organization Type": "The media", + "Units": 19.0, + "Trust Level": "Trustworthy" + }, + { + "Organization Type": "The media", + "Units": 38.0, + "Trust Level": "Neutral" + }, + { + "Organization Type": "The media", + "Units": 43.0, + "Trust Level": "Untrustworthy" + }, + { + "Organization Type": "Social media companies", + "Units": 17.0, + "Trust Level": "Trustworthy" + }, + { + "Organization Type": "Social media companies", + "Units": 44.0, + "Trust Level": "Neutral" + }, + { + "Organization Type": "Social media companies", + "Units": 39.0, + "Trust Level": "Untrustworthy" + } + ], + "columns": [ + { + "name": "Organization Type", + "importance": "primary", + "description": "Type of organization", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding trust level", + "role": "y" + }, + { + "name": "Trust Level", + "importance": "primary", + "description": "The trust level of the data point", + "role": "group" + } + ] + } + }, + "1412": { + "metadata": { + "title": "German Election 2017: Far-Right Makes Biggest Gains", + "description": "Share of votes received in 2017 German Bundestag election and change on 2013*", + "main_insight": "The Alternative for Germany (AfD) party saw the largest increase in vote share in the 2017 German election compared to 2013, while the CDU/CSU and SPD saw significant decreases." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Party": "CDU/CSU (Merkel)", + "Vote Share 2017": 33.0 + } + ], + "score": 0.91, + "annotation": "The has a maximum value at CDU/CSU (Merkel)", + "reason": "The [{'Party': 'CDU/CSU (Merkel)', 'Vote Share 2017': 33.0}] of has a maximum value of 33.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Party": "CDU/CSU (Merkel)", + "Vote Share 2017": 33.0 + } + ], + "score": 0.8, + "annotation": "The shows a sudden jump in Vote Share 2017 from SPD (Schulz) to CDU/CSU (Merkel).", + "reason": "The Vote Share 2017 for increases from 20.5 at SPD (Schulz) to 33.0 at CDU/CSU (Merkel), indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Party": "Others", + "Vote Share 2017": 5.0 + } + ], + "score": 0.59, + "annotation": "The has a minimum value at Others", + "reason": "The [{'Party': 'Others', 'Vote Share 2017': 5.0}] of has a mainimum value of 5.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Party": "CDU/CSU (Merkel)", + "Vote Share 2017": 33.0 + }, + { + "Party": "SPD (Schulz)", + "Vote Share 2017": 20.5 + }, + { + "Party": "Alternative for Germany", + "Vote Share 2017": 12.6 + }, + { + "Party": "Free Democratic Party", + "Vote Share 2017": 10.7 + }, + { + "Party": "The Left", + "Vote Share 2017": 9.2 + }, + { + "Party": "The Greens", + "Vote Share 2017": 8.9 + }, + { + "Party": "Others", + "Vote Share 2017": 5.0 + } + ], + "columns": [ + { + "name": "Party", + "importance": "primary", + "description": "Political party", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Vote Share 2017", + "importance": "primary", + "description": "Percentage of votes received in the 2017 election", + "unit": "Percentage", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1777": { + "metadata": { + "title": "2 in 3 Americans Think Trump Will Get Reelected", + "description": "Percentage of registered voters who think President Donald Trump will get reelected in November", + "main_insight": "A significant majority (66%) of registered voters in February 2020 believed that Donald Trump would be reelected, with 27% believing he would definitely be reelected and 39% believing he would probably be reelected." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Opinion": "Probably get reelected", + "Percentage": 39.0 + } + ], + "score": 0.8, + "annotation": "The has a maximum value at Probably get reelected", + "reason": "The [{'Opinion': 'Probably get reelected', 'Percentage': 39.0}] of has a maximum value of 39.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Opinion": "Probably lose to the Democrat", + "Percentage": 22.0 + } + ], + "score": 0.78, + "annotation": "The shows a sudden jump in Percentage from Definitely lose to the Democrat to Probably lose to the Democrat.", + "reason": "The Percentage for increases from 6.0 at Definitely lose to the Democrat to 22.0 at Probably lose to the Democrat, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Opinion": "Depends on the Democrat", + "Percentage": 1.0 + } + ], + "score": 0.61, + "annotation": "The has a minimum value at Depends on the Democrat", + "reason": "The [{'Opinion': 'Depends on the Democrat', 'Percentage': 1.0}] of has a mainimum value of 1.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Opinion": "Definitely get reelected", + "Percentage": 27.0 + }, + { + "Opinion": "Probably get reelected", + "Percentage": 39.0 + }, + { + "Opinion": "Probably lose to the Democrat", + "Percentage": 22.0 + }, + { + "Opinion": "Definitely lose to the Democrat", + "Percentage": 6.0 + }, + { + "Opinion": "Depends on the Democrat", + "Percentage": 1.0 + }, + { + "Opinion": "Don't know", + "Percentage": 4.0 + } + ], + "columns": [ + { + "name": "Opinion", + "importance": "primary", + "description": "Voter opinions on Trump's reelection chances", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of registered voters holding each opinion", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "651": { + "metadata": { + "title": "Indian Goods Exports Are Slumping", + "description": "Monthly trade deficit/surplus* of merchandise and services in India (in billion U.S. dollars)", + "main_insight": "India's merchandise trade deficit has significantly widened between April 2021 and September 2022, while the trade balance for services has remained relatively stable and positive." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Feb 2022", + "Value": 14.2, + "Economic Activity": "Services" + } + ], + "score": 0.95, + "annotation": "The Services has a maximum value at Feb 2022", + "reason": "The [{'Month': 'Feb 2022', 'Value': 14.2, 'Economic Activity': 'Services'}] of Services has a maximum value of 14.2, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "May 2021", + "Value": -7.1, + "Economic Activity": "Merchandise" + } + ], + "score": 0.88, + "annotation": "The Merchandise has a maximum value at May 2021", + "reason": "The [{'Month': 'May 2021', 'Value': -7.1, 'Economic Activity': 'Merchandise'}] of Merchandise has a maximum value of -7.1, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Jul 2022", + "Value": -26.7, + "Economic Activity": "Merchandise" + } + ], + "score": 0.78, + "annotation": "The Merchandise has a minimum value at Jul 2022", + "reason": "The [{'Month': 'Jul 2022', 'Value': -26.7, 'Economic Activity': 'Merchandise'}] of Merchandise has a mainimum value of -26.7, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Apr 2021", + "Value": 8.5, + "Economic Activity": "Services" + } + ], + "score": 0.71, + "annotation": "The Services has a minimum value at Apr 2021", + "reason": "The [{'Month': 'Apr 2021', 'Value': 8.5, 'Economic Activity': 'Services'}] of Services has a mainimum value of 8.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Feb 2022", + "Value": 14.2, + "Economic Activity": "Services" + } + ], + "score": 0.61, + "annotation": "The Services shows a sudden jump in Value from Jun 2022 to Feb 2022.", + "reason": "The Value for Services increases from 13.0 at Jun 2022 to 14.2 at Feb 2022, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Month": "Apr 2021", + "Value": -7.8, + "Economic Activity": "Merchandise" + }, + { + "Month": "Apr 2021", + "Value": 8.5, + "Economic Activity": "Services" + }, + { + "Month": "May 2021", + "Value": -7.1, + "Economic Activity": "Merchandise" + }, + { + "Month": "May 2021", + "Value": 9.5, + "Economic Activity": "Services" + }, + { + "Month": "Jun 2021", + "Value": -11.6, + "Economic Activity": "Merchandise" + }, + { + "Month": "Jun 2021", + "Value": 9, + "Economic Activity": "Services" + }, + { + "Month": "Jul 2021", + "Value": -10.8, + "Economic Activity": "Merchandise" + }, + { + "Month": "Jul 2021", + "Value": 10.5, + "Economic Activity": "Services" + }, + { + "Month": "Aug 2021", + "Value": -14.8, + "Economic Activity": "Merchandise" + }, + { + "Month": "Aug 2021", + "Value": 9.3, + "Economic Activity": "Services" + }, + { + "Month": "Sep 2021", + "Value": -22.0, + "Economic Activity": "Merchandise" + }, + { + "Month": "Sep 2021", + "Value": 10.0, + "Economic Activity": "Services" + }, + { + "Month": "Oct 2021", + "Value": -18.6, + "Economic Activity": "Merchandise" + }, + { + "Month": "Oct 2021", + "Value": 9.0, + "Economic Activity": "Services" + }, + { + "Month": "Nov 2021", + "Value": -21.3, + "Economic Activity": "Merchandise" + }, + { + "Month": "Nov 2021", + "Value": 11.2, + "Economic Activity": "Services" + }, + { + "Month": "Dec 2021", + "Value": -19.4, + "Economic Activity": "Merchandise" + }, + { + "Month": "Dec 2021", + "Value": 12.5, + "Economic Activity": "Services" + }, + { + "Month": "Jan 2022", + "Value": -18.2, + "Economic Activity": "Merchandise" + }, + { + "Month": "Jan 2022", + "Value": 9.4, + "Economic Activity": "Services" + }, + { + "Month": "Feb 2022", + "Value": -19.9, + "Economic Activity": "Merchandise" + }, + { + "Month": "Feb 2022", + "Value": 14.2, + "Economic Activity": "Services" + }, + { + "Month": "Mar 2022", + "Value": -18.0, + "Economic Activity": "Merchandise" + }, + { + "Month": "Mar 2022", + "Value": 9.6, + "Economic Activity": "Services" + }, + { + "Month": "Apr 2022", + "Value": -21.8, + "Economic Activity": "Merchandise" + }, + { + "Month": "Apr 2022", + "Value": 10.7, + "Economic Activity": "Services" + }, + { + "Month": "May 2022", + "Value": -24.3, + "Economic Activity": "Merchandise" + }, + { + "Month": "May 2022", + "Value": 11.9, + "Economic Activity": "Services" + }, + { + "Month": "Jun 2022", + "Value": -26.0, + "Economic Activity": "Merchandise" + }, + { + "Month": "Jun 2022", + "Value": 13.0, + "Economic Activity": "Services" + }, + { + "Month": "Jul 2022", + "Value": -26.7, + "Economic Activity": "Merchandise" + }, + { + "Month": "Jul 2022", + "Value": 10.3, + "Economic Activity": "Services" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Time period in months", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding economic activity", + "role": "y" + }, + { + "name": "Economic Activity", + "importance": "primary", + "description": "The economic activity of the data point", + "role": "group" + } + ] + } + }, + "468": { + "metadata": { + "title": "Majority of Turkish Expats Vote for President Erdoğan", + "description": "Voting of Turkish expatriates in Turkey's presidential elections (in percent)", + "main_insight": "President Erdoğan has consistently received the majority of votes from Turkish expatriates in the 2014, 2018, and 2023 elections." + }, + "chart_type": "Multiple Semi Circle Donut Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2014, + "Units": 62.3, + "Candidates": "Recep Tayyip Erdoğan" + }, + { + "Year": 2014, + "Units": 27.9, + "Candidates": "Ekmeleddin İhsanoğlu" + }, + { + "Year": 2014, + "Units": 9.8, + "Candidates": "Selahattin Demirtaş" + }, + { + "Year": 2018, + "Units": 60.2, + "Candidates": "Recep Tayyip Erdoğan" + }, + { + "Year": 2018, + "Units": 11.7, + "Candidates": "Selahattin Demirtaş" + }, + { + "Year": 2018, + "Units": 24.5, + "Candidates": "Muharrem İnce" + }, + { + "Year": 2018, + "Units": 2.8, + "Candidates": "Meral Akşener" + }, + { + "Year": 2018, + "Units": 0.2, + "Candidates": "Doğu Perinçek" + }, + { + "Year": 2018, + "Units": 0.6, + "Candidates": "Temel Karamollaoğlu" + }, + { + "Year": 2023, + "Units": 59.6, + "Candidates": "Recep Tayyip Erdoğan" + }, + { + "Year": 2023, + "Units": 40.4, + "Candidates": "Kemal Kılıçdaroğlu" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Election year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding candidates", + "role": "y" + }, + { + "name": "Candidates", + "importance": "primary", + "description": "The candidates of the data point", + "role": "group" + } + ] + } + }, + "325": { + "metadata": { + "title": "Halloween Canceled for Third of Americans", + "description": "Percentage of U.S. adults who plan to allow their children to trick-or-treat this Halloween", + "main_insight": "A significant portion of American adults (38%) plan to allow their children to trick-or-treat, while 31% do not, and 31% don't know." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Response": "Yes", + "Percentage": 38.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Percentage from Don't know to Yes.", + "reason": "The Percentage for increases from 31.0 at Don't know to 38.0 at Yes, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Response": "Yes", + "Percentage": 38.0 + } + ], + "score": 0.67, + "annotation": "The has a maximum value at Yes", + "reason": "The [{'Response': 'Yes', 'Percentage': 38.0}] of has a maximum value of 38.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Response": "No", + "Percentage": 31.0 + }, + { + "Response": "Don't know", + "Percentage": 31.0 + } + ], + "score": 0.39, + "annotation": "The has minimum values at No, Don't know", + "reason": "The [{'Response': 'No', 'Percentage': 31.0}, {'Response': \"Don't know\", 'Percentage': 31.0}] of have minimum values of 31.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Response": "Yes", + "Percentage": 38.0 + }, + { + "Response": "No", + "Percentage": 31.0 + }, + { + "Response": "Don't know", + "Percentage": 31.0 + } + ], + "columns": [ + { + "name": "Response", + "importance": "primary", + "description": "Survey response categories", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of U.S. adults for each response", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2286": { + "metadata": { + "title": "Over 50 Lawsuits Have Already Been Filed Against Trump", + "description": "Number of lawsuits files against US administrations in their first 14 days", + "main_insight": "Donald Trump faced significantly more lawsuits in the first 14 days of his administration compared to Bill Clinton, George W. Bush, and Barack Obama." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "US President": "Donald Trump", + "Lawsuits Filed": 55 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Lawsuits Filed from Barack Obama to Donald Trump.", + "reason": "The Lawsuits Filed for increases from 5 at Barack Obama to 55 at Donald Trump, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "US President": "Donald Trump", + "Lawsuits Filed": 55 + } + ], + "score": 0.8, + "annotation": "The has a maximum value at Donald Trump", + "reason": "The [{'US President': 'Donald Trump', 'Lawsuits Filed': 55}] of has a maximum value of 55, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "US President": "George W. Bush", + "Lawsuits Filed": 4 + } + ], + "score": 0.37, + "annotation": "The has a minimum value at George W. Bush", + "reason": "The [{'US President': 'George W. Bush', 'Lawsuits Filed': 4}] of has a mainimum value of 4, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "US President": "Bill Clinton", + "Lawsuits Filed": 5 + }, + { + "US President": "George W. Bush", + "Lawsuits Filed": 4 + }, + { + "US President": "Barack Obama", + "Lawsuits Filed": 5 + }, + { + "US President": "Donald Trump", + "Lawsuits Filed": 55 + } + ], + "columns": [ + { + "name": "US President", + "importance": "primary", + "description": "The US President the lawsuits were filed against", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Lawsuits Filed", + "importance": "primary", + "description": "Number of lawsuits filed in the first 14 days of their administration", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "403": { + "metadata": { + "title": "iPhone 6 Helps Apple Reclaim the Smartphone Throne", + "description": "Top 5 smartphone vendors based on worldwide unit sales to end users in Q4 2014", + "main_insight": "Apple's Q4 2014 smartphone sales increased significantly compared to Q4 2013, surpassing Samsung's sales, which decreased." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Vendor": "Samsung", + "Units": 73.0, + "Sales Quarter": "Q4 2014 Sales" + } + ], + "score": 0.96, + "annotation": "The Q4 2014 Sales shows a sudden jump in Units from Lenovo to Samsung.", + "reason": "The Units for Q4 2014 Sales increases from 24.3 at Lenovo to 73.0 at Samsung, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Vendor": "Xiaomi", + "Units": 18.6, + "Sales Quarter": "Q4 2014 Sales" + }, + { + "Vendor": "Xiaomi", + "Units": 5.6, + "Sales Quarter": "Q4 2013 Sales" + } + ], + "score": 0.92, + "annotation": "The Units of Q4 2014 Sales and Q4 2013 Sales show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Q4 2014 Sales and Q4 2013 Sales is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Vendor": "Samsung", + "Units": 83.3, + "Sales Quarter": "Q4 2013 Sales" + } + ], + "score": 0.81, + "annotation": "The Q4 2013 Sales has a maximum value at Samsung", + "reason": "The [{'Vendor': 'Samsung', 'Units': 83.3, 'Sales Quarter': 'Q4 2013 Sales'}] of Q4 2013 Sales has a maximum value of 83.3, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Vendor": "Apple", + "Units": 50.2, + "Sales Quarter": "Q4 2013 Sales" + } + ], + "score": 0.79, + "annotation": "The Q4 2013 Sales shows a sudden jump in Units from Lenovo to Apple.", + "reason": "The Units for Q4 2013 Sales increases from 16.5 at Lenovo to 50.2 at Apple, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Vendor": "Samsung", + "Units": 83.3, + "Sales Quarter": "Q4 2013 Sales" + } + ], + "score": 0.79, + "annotation": "The Q4 2013 Sales accounts for the majority in Samsung.", + "reason": "The Units of Q4 2013 Sales in Samsung accounts for 0.5329494561740242 (more than 50%) of the total of Units of all Sales Quarter." + } + ], + "data": { + "data": [ + { + "Vendor": "Apple", + "Units": 74.8, + "Sales Quarter": "Q4 2014 Sales" + }, + { + "Vendor": "Apple", + "Units": 50.2, + "Sales Quarter": "Q4 2013 Sales" + }, + { + "Vendor": "Samsung", + "Units": 73.0, + "Sales Quarter": "Q4 2014 Sales" + }, + { + "Vendor": "Samsung", + "Units": 83.3, + "Sales Quarter": "Q4 2013 Sales" + }, + { + "Vendor": "Lenovo", + "Units": 24.3, + "Sales Quarter": "Q4 2014 Sales" + }, + { + "Vendor": "Lenovo", + "Units": 16.5, + "Sales Quarter": "Q4 2013 Sales" + }, + { + "Vendor": "Huawei", + "Units": 21.0, + "Sales Quarter": "Q4 2014 Sales" + }, + { + "Vendor": "Huawei", + "Units": 16.1, + "Sales Quarter": "Q4 2013 Sales" + }, + { + "Vendor": "Xiaomi", + "Units": 18.6, + "Sales Quarter": "Q4 2014 Sales" + }, + { + "Vendor": "Xiaomi", + "Units": 5.6, + "Sales Quarter": "Q4 2013 Sales" + } + ], + "columns": [ + { + "name": "Vendor", + "importance": "primary", + "description": "Smartphone vendor", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sales quarter", + "role": "y" + }, + { + "name": "Sales Quarter", + "importance": "primary", + "description": "The sales quarter of the data point", + "role": "group" + } + ] + } + }, + "1435": { + "metadata": { + "title": "From Top to Flop: Tumbling Down the League in 2021", + "description": "League positions of selected teams which were competition leaders at the end of 2020", + "main_insight": "Several football teams that were leading their respective leagues at the end of 2020 experienced significant drops in their rankings by March 5, 2021." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Team": "Carlisle United (League 2)", + "End of 2020 Position": 1 + }, + { + "Team": "Liverpool FC (Premier League)", + "End of 2020 Position": 1 + }, + { + "Team": "Bayer Leverkusen (1. Bundesliga)", + "End of 2020 Position": 1 + }, + { + "Team": "Hamburger SV (2. Bundesliga)", + "End of 2020 Position": 1 + }, + { + "Team": "Grenoble Foot 38 (Ligue 2)", + "End of 2020 Position": 1 + } + ], + "columns": [ + { + "name": "Team", + "importance": "primary", + "description": "Football team and their league", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "End of 2020 Position", + "importance": "primary", + "description": "League position at the end of 2020", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "646": { + "metadata": { + "title": "Belgium and the Russian Diamonds", + "description": "Monthly import value of diamonds from Russia to Belgium (in million euros)", + "main_insight": "Belgium's diamond imports from Russia peaked in June 2022 at €393.8 million, but drastically decreased to €35.9 million by August 2022." + }, + "chart_type": "Spline Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Jun 2022", + "Import Value (Million Euros)": 393.8 + } + ], + "score": 0.99, + "annotation": "The has a maximum value at Jun 2022", + "reason": "The [{'Month': 'Jun 2022', 'Import Value (Million Euros)': 393.8}] of has a maximum value of 393.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Aug 2022", + "Import Value (Million Euros)": 35.9 + } + ], + "score": 0.83, + "annotation": "The has a minimum value at Aug 2022", + "reason": "The [{'Month': 'Aug 2022', 'Import Value (Million Euros)': 35.9}] of has a mainimum value of 35.9, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Jun 2022", + "Import Value (Million Euros)": 393.8 + } + ], + "score": 0.82, + "annotation": "The shows a sudden jump in Import Value (Million Euros) from Oct 2021 to Jun 2022.", + "reason": "The Import Value (Million Euros) for increases from 220.0 at Oct 2021 to 393.8 at Jun 2022, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Month": "Jan 2021", + "Import Value (Million Euros)": 120.0 + }, + { + "Month": "Feb 2021", + "Import Value (Million Euros)": 130.0 + }, + { + "Month": "Mar 2021", + "Import Value (Million Euros)": 180.0 + }, + { + "Month": "Apr 2021", + "Import Value (Million Euros)": 140.0 + }, + { + "Month": "May 2021", + "Import Value (Million Euros)": 130.0 + }, + { + "Month": "Jun 2021", + "Import Value (Million Euros)": 150.0 + }, + { + "Month": "Jul 2021", + "Import Value (Million Euros)": 100.0 + }, + { + "Month": "Aug 2021", + "Import Value (Million Euros)": 215.4 + }, + { + "Month": "Sep 2021", + "Import Value (Million Euros)": 140.0 + }, + { + "Month": "Oct 2021", + "Import Value (Million Euros)": 220.0 + }, + { + "Month": "Nov 2021", + "Import Value (Million Euros)": 110.0 + }, + { + "Month": "Dec 2021", + "Import Value (Million Euros)": 100.0 + }, + { + "Month": "Jan 2022", + "Import Value (Million Euros)": 130.0 + }, + { + "Month": "Feb 2022", + "Import Value (Million Euros)": 130.0 + }, + { + "Month": "Mar 2022", + "Import Value (Million Euros)": 150.0 + }, + { + "Month": "Apr 2022", + "Import Value (Million Euros)": 200.0 + }, + { + "Month": "May 2022", + "Import Value (Million Euros)": 170.0 + }, + { + "Month": "Jun 2022", + "Import Value (Million Euros)": 393.8 + }, + { + "Month": "Jul 2022", + "Import Value (Million Euros)": 80.0 + }, + { + "Month": "Aug 2022", + "Import Value (Million Euros)": 35.9 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year, from January 2021 to August 2022", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Import Value (Million Euros)", + "importance": "primary", + "description": "Monthly import value of diamonds from Russia to Belgium", + "unit": "Million Euros", + "data_type": "number", + "role": "y" + } + ] + } + }, + "90": { + "metadata": { + "title": "U.S. Support For Legalizing Marijuana Hits All-Time High", + "description": "\"Should the use of marijuana be made legal or not?\"", + "main_insight": "Support for legalizing marijuana in the U.S. has steadily increased since 1969, reaching an all-time high of 64% in 2017, while opposition to legalization has decreased." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2017, + "Units": 64.0, + "Legality": "Yes, legal" + }, + { + "Year": 2017, + "Units": 34.0, + "Legality": "No, illegal" + } + ], + "score": 1.0, + "annotation": "The Units of Yes, legal and No, illegal show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Yes, legal and No, illegal is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 1969, + "Units": 12.0, + "Legality": "Yes, legal" + }, + { + "Year": 1969, + "Units": 84.0, + "Legality": "No, illegal" + }, + { + "Year": 1973, + "Units": 19.0, + "Legality": "Yes, legal" + }, + { + "Year": 1973, + "Units": 78.0, + "Legality": "No, illegal" + }, + { + "Year": 1977, + "Units": 28.0, + "Legality": "Yes, legal" + }, + { + "Year": 1977, + "Units": 69.0, + "Legality": "No, illegal" + }, + { + "Year": 1980, + "Units": 25.0, + "Legality": "Yes, legal" + }, + { + "Year": 1980, + "Units": 73.0, + "Legality": "No, illegal" + }, + { + "Year": 1985, + "Units": 23.0, + "Legality": "Yes, legal" + }, + { + "Year": 1985, + "Units": 73.0, + "Legality": "No, illegal" + }, + { + "Year": 1990, + "Units": 24.0, + "Legality": "Yes, legal" + }, + { + "Year": 1990, + "Units": 73.0, + "Legality": "No, illegal" + }, + { + "Year": 1995, + "Units": 24.0, + "Legality": "Yes, legal" + }, + { + "Year": 1995, + "Units": 73.0, + "Legality": "No, illegal" + }, + { + "Year": 2000, + "Units": 31.0, + "Legality": "Yes, legal" + }, + { + "Year": 2000, + "Units": 64.0, + "Legality": "No, illegal" + }, + { + "Year": 2005, + "Units": 36.0, + "Legality": "Yes, legal" + }, + { + "Year": 2005, + "Units": 60.0, + "Legality": "No, illegal" + }, + { + "Year": 2010, + "Units": 46.0, + "Legality": "Yes, legal" + }, + { + "Year": 2010, + "Units": 50.0, + "Legality": "No, illegal" + }, + { + "Year": 2011, + "Units": 50.0, + "Legality": "Yes, legal" + }, + { + "Year": 2011, + "Units": 46.0, + "Legality": "No, illegal" + }, + { + "Year": 2012, + "Units": 48.0, + "Legality": "Yes, legal" + }, + { + "Year": 2012, + "Units": 50.0, + "Legality": "No, illegal" + }, + { + "Year": 2013, + "Units": 58.0, + "Legality": "Yes, legal" + }, + { + "Year": 2013, + "Units": 39.0, + "Legality": "No, illegal" + }, + { + "Year": 2014, + "Units": 54.0, + "Legality": "Yes, legal" + }, + { + "Year": 2014, + "Units": 42.0, + "Legality": "No, illegal" + }, + { + "Year": 2015, + "Units": 53.0, + "Legality": "Yes, legal" + }, + { + "Year": 2015, + "Units": 44.0, + "Legality": "No, illegal" + }, + { + "Year": 2016, + "Units": 60.0, + "Legality": "Yes, legal" + }, + { + "Year": 2016, + "Units": 37.0, + "Legality": "No, illegal" + }, + { + "Year": 2017, + "Units": 64.0, + "Legality": "Yes, legal" + }, + { + "Year": 2017, + "Units": 34.0, + "Legality": "No, illegal" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding legality", + "role": "y" + }, + { + "name": "Legality", + "importance": "primary", + "description": "The legality of the data point", + "role": "group" + } + ] + } + }, + "1159": { + "metadata": { + "title": "World Cup: The Quarter Final Winners (According to Google)", + "description": "Win probability of teams in the World Cup 2018 quarter final matches", + "main_insight": "The chart shows the win probabilities for each team in the 2018 World Cup quarter-finals, along with the probability of a draw. England had the highest win probability at 50%." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Match": "Russia vs Croatia", + "Units": 24.0, + "Outcome": "Team1_Win" + }, + { + "Match": "Russia vs Croatia", + "Units": 44.0, + "Outcome": "Team2_Win" + } + ], + "score": 0.99, + "annotation": "The Units of Team1_Win and Team2_Win show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Team1_Win and Team2_Win is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Match": "Russia vs Croatia", + "Units": 32.0, + "Outcome": "Draw" + } + ], + "score": 0.97, + "annotation": "The Draw shows a sudden jump in Units from Sweden vs England to Russia vs Croatia.", + "reason": "The Units for Draw increases from 29.0 at Sweden vs England to 32.0 at Russia vs Croatia, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Match": "Brazil vs Belgium", + "Units": 43.0, + "Outcome": "Team1_Win" + } + ], + "score": 0.95, + "annotation": "The Team1_Win shows a sudden jump in Units from Uruguay vs France to Brazil vs Belgium.", + "reason": "The Units for Team1_Win increases from 25.0 at Uruguay vs France to 43.0 at Brazil vs Belgium, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Match": "Russia vs Croatia", + "Units": 44.0, + "Outcome": "Team2_Win" + } + ], + "score": 0.92, + "annotation": "The Team2_Win shows a sudden jump in Units from Brazil vs Belgium to Russia vs Croatia.", + "reason": "The Units for Team2_Win increases from 28.0 at Brazil vs Belgium to 44.0 at Russia vs Croatia, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Outcome": "Team2_Win", + "Match": "total", + "Units": 168.0 + } + ], + "score": 0.88, + "annotation": "The total value of Team2_Win has the largest proportion.", + "reason": "The total value of Units of Team2_Win accounts for 0.42, larger than all other Outcome, of the total of Units of all Outcome." + } + ], + "data": { + "data": [ + { + "Match": "Uruguay vs France", + "Units": 25.0, + "Outcome": "Team1_Win" + }, + { + "Match": "Uruguay vs France", + "Units": 29.0, + "Outcome": "Draw" + }, + { + "Match": "Uruguay vs France", + "Units": 46.0, + "Outcome": "Team2_Win" + }, + { + "Match": "Brazil vs Belgium", + "Units": 43.0, + "Outcome": "Team1_Win" + }, + { + "Match": "Brazil vs Belgium", + "Units": 29.0, + "Outcome": "Draw" + }, + { + "Match": "Brazil vs Belgium", + "Units": 28.0, + "Outcome": "Team2_Win" + }, + { + "Match": "Sweden vs England", + "Units": 21.0, + "Outcome": "Team1_Win" + }, + { + "Match": "Sweden vs England", + "Units": 29.0, + "Outcome": "Draw" + }, + { + "Match": "Sweden vs England", + "Units": 50.0, + "Outcome": "Team2_Win" + }, + { + "Match": "Russia vs Croatia", + "Units": 24.0, + "Outcome": "Team1_Win" + }, + { + "Match": "Russia vs Croatia", + "Units": 32.0, + "Outcome": "Draw" + }, + { + "Match": "Russia vs Croatia", + "Units": 44.0, + "Outcome": "Team2_Win" + } + ], + "columns": [ + { + "name": "Match", + "importance": "primary", + "description": "The quarter-final match pairings", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding outcome", + "role": "y" + }, + { + "name": "Outcome", + "importance": "primary", + "description": "The outcome of the data point", + "role": "group" + } + ] + } + }, + "2270": { + "metadata": { + "title": "How Common Businesses Score On Customer Service", + "description": "% of Americans considering customer service at the following...", + "main_insight": "Bank branches and Pharmacies receive the highest customer service ratings, while fast food restaurants receive the lowest." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Business Type": "Fast food restaurant", + "Units": 15.0, + "Ratings": "Excellent" + }, + { + "Business Type": "Fast food restaurant", + "Units": 22.0, + "Ratings": "Only fair" + } + ], + "score": 0.96, + "annotation": "The Units of Excellent and Only fair show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Excellent and Only fair is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Business Type": "Grocery store", + "Units": 2.0, + "Ratings": "Poor" + } + ], + "score": 0.91, + "annotation": "The Poor has the smallest proportion in Grocery store.", + "reason": "The Units of Poor in Grocery store accounts for 0.019801980198019802, smaller than all other Ratings, of the total of Units of all Ratings." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Business Type": "Clothing or retail store", + "Units": 2.0, + "Ratings": "Poor" + } + ], + "score": 0.91, + "annotation": "The Poor has the smallest proportion in Clothing or retail store.", + "reason": "The Units of Poor in Clothing or retail store accounts for 0.02, smaller than all other Ratings, of the total of Units of all Ratings." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Business Type": "Pharmacy", + "Units": 2.0, + "Ratings": "Poor" + } + ], + "score": 0.91, + "annotation": "The Poor has the smallest proportion in Pharmacy.", + "reason": "The Units of Poor in Pharmacy accounts for 0.020202020202020204, smaller than all other Ratings, of the total of Units of all Ratings." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Business Type": "Post 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"Units": 30.0, + "Ratings": "Excellent" + }, + { + "Business Type": "Post office", + "Units": 49.0, + "Ratings": "Good" + }, + { + "Business Type": "Post office", + "Units": 15.0, + "Ratings": "Only fair" + }, + { + "Business Type": "Post office", + "Units": 6.0, + "Ratings": "Poor" + }, + { + "Business Type": "Grocery store", + "Units": 29.0, + "Ratings": "Excellent" + }, + { + "Business Type": "Grocery store", + "Units": 58.0, + "Ratings": "Good" + }, + { + "Business Type": "Grocery store", + "Units": 12.0, + "Ratings": "Only fair" + }, + { + "Business Type": "Grocery store", + "Units": 2.0, + "Ratings": "Poor" + }, + { + "Business Type": "Convenience store", + "Units": 20.0, + "Ratings": "Excellent" + }, + { + "Business Type": "Convenience store", + "Units": 57.0, + "Ratings": "Good" + }, + { + "Business Type": "Convenience store", + "Units": 20.0, + "Ratings": "Only fair" + }, + { + "Business Type": "Convenience store", + "Units": 3.0, + "Ratings": "Poor" + }, + { + "Business Type": "Clothing or retail store", + "Units": 19.0, + "Ratings": "Excellent" + }, + { + "Business Type": "Clothing or retail store", + "Units": 61.0, + "Ratings": "Good" + }, + { + "Business Type": "Clothing or retail store", + "Units": 18.0, + "Ratings": "Only fair" + }, + { + "Business Type": "Clothing or retail store", + "Units": 2.0, + "Ratings": "Poor" + }, + { + "Business Type": "Fast food restaurant", + "Units": 15.0, + "Ratings": "Excellent" + }, + { + "Business Type": "Fast food restaurant", + "Units": 54.0, + "Ratings": "Good" + }, + { + "Business Type": "Fast food restaurant", + "Units": 22.0, + "Ratings": "Only fair" + }, + { + "Business Type": "Fast food restaurant", + "Units": 8.0, + "Ratings": "Poor" + } + ], + "columns": [ + { + "name": "Business Type", + "importance": "primary", + "description": "Type of business", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding ratings", + "role": "y" + }, + { + "name": "Ratings", + "importance": "primary", + "description": "The ratings of the data point", + "role": "group" + } + ] + } + }, + "1696": { + "metadata": { + "title": "WHEN A VIDEO CALL WON'T CUT IT", + "description": "Leading reasons for business travel", + "main_insight": "Conferences/events and client engagement are the top two reasons for business travel, indicating that in-person interactions remain crucial for certain business activities." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Reason": "Conferences/events", + "Percentage": 79.0 + } + ], + "score": 0.79, + "annotation": "The has a maximum value at Conferences/events", + "reason": "The [{'Reason': 'Conferences/events', 'Percentage': 79.0}] of has a maximum value of 79.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Reason": "Visits to other offices", + "Percentage": 42.0 + } + ], + "score": 0.75, + "annotation": "The has a minimum value at Visits to other offices", + "reason": "The [{'Reason': 'Visits to other offices', 'Percentage': 42.0}] of has a mainimum value of 42.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Reason": "Conferences/events", + "Percentage": 79.0 + } + ], + "score": 0.66, + "annotation": "The shows a sudden jump in Percentage from Client engagement to Conferences/events.", + "reason": "The Percentage for increases from 69.0 at Client engagement to 79.0 at Conferences/events, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Reason": "Conferences/events", + "Percentage": 79.0 + }, + { + "Reason": "Client engagement", + "Percentage": 69.0 + }, + { + "Reason": "Trade show visits", + "Percentage": 62.0 + }, + { + "Reason": "On-site projects", + "Percentage": 56.0 + }, + { + "Reason": "Visits to manufacturers/suppliers", + "Percentage": 51.0 + }, + { + "Reason": "Visits to other offices", + "Percentage": 42.0 + } + ], + "columns": [ + { + "name": "Reason", + "importance": "primary", + "description": "The reason for business travel", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents who selected this reason", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2451": { + "metadata": { + "title": "England's World Cup squad: where they ply their trade", + "description": "England's 23-man World Cup 2018 squad, by domestic club played for in 2017/18", + "main_insight": "Tottenham Hotspur contributed the most players (5) to England's 2018 World Cup squad, followed by Manchester City and Manchester United (4 each)." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Club": "Tottenham Hotspur", + "Players": 5 + } + ], + "score": 0.9, + "annotation": "The has a maximum value at Tottenham Hotspur", + "reason": "The [{'Club': 'Tottenham Hotspur', 'Players': 5}] of has a maximum value of 5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Club": "Manchester City", + "Players": 4 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Players from Liverpool to Manchester City.", + "reason": "The Players for increases from 2 at Liverpool to 4 at Manchester City, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Club": "Arsenal", + "Players": 1 + }, + { + "Club": "Burnley", + "Players": 1 + }, + { + "Club": "Chelsea", + "Players": 1 + }, + { + "Club": "Crystal Palace", + "Players": 1 + }, + { + "Club": "Everton", + "Players": 1 + }, + { + "Club": "Stoke City", + "Players": 1 + } + ], + "score": 0.46, + "annotation": "The has minimum values at Arsenal, Burnley, Chelsea, Crystal Palace, Everton, Stoke City", + "reason": "The [{'Club': 'Arsenal', 'Players': 1}, {'Club': 'Burnley', 'Players': 1}, {'Club': 'Chelsea', 'Players': 1}, {'Club': 'Crystal Palace', 'Players': 1}, {'Club': 'Everton', 'Players': 1}, {'Club': 'Stoke City', 'Players': 1}] of have minimum values of 1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Club": "Tottenham Hotspur", + "Players": 5 + }, + { + "Club": "Manchester City", + "Players": 4 + }, + { + "Club": "Manchester United", + "Players": 4 + }, + { + "Club": "Leicester City", + "Players": 2 + }, + { + "Club": "Liverpool", + "Players": 2 + }, + { + "Club": "Arsenal", + "Players": 1 + }, + { + "Club": "Burnley", + "Players": 1 + }, + { + "Club": "Chelsea", + "Players": 1 + }, + { + "Club": "Crystal Palace", + "Players": 1 + }, + { + "Club": "Everton", + "Players": 1 + }, + { + "Club": "Stoke City", + "Players": 1 + } + ], + "columns": [ + { + "name": "Club", + "importance": "primary", + "description": "Domestic club", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Players", + "importance": "primary", + "description": "Number of players from each club in the squad", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1310": { + "metadata": { + "title": "May's snap election gamble backfires", + "description": "Seats won and share of votes received in the 2017 UK general election", + "main_insight": "The Conservative party won the most seats in the 2017 UK general election but failed to secure a majority, despite receiving the highest vote share." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Party": "Labour", + "Seats": 261 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in Seats from SNP to Labour.", + "reason": "The Seats for increases from 35 at SNP to 261 at Labour, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Party": "Conservative", + "Seats": 317 + } + ], + "score": 0.72, + "annotation": "The has a maximum value at Conservative", + "reason": "The [{'Party': 'Conservative', 'Seats': 317}] of has a maximum value of 317, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Party": "Liberal Democrats", + "Seats": 12 + } + ], + "score": 0.5, + "annotation": "The has a minimum value at Liberal Democrats", + "reason": "The [{'Party': 'Liberal Democrats', 'Seats': 12}] of has a mainimum value of 12, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Party": "Conservative", + "Seats": 317 + }, + { + "Party": "Labour", + "Seats": 261 + }, + { + "Party": "SNP", + "Seats": 35 + }, + { + "Party": "Liberal Democrats", + "Seats": 12 + }, + { + "Party": "Other parties", + "Seats": 24 + } + ], + "columns": [ + { + "name": "Party", + "importance": "primary", + "description": "Political party", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Seats", + "importance": "primary", + "description": "Number of seats won in the election", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2346": { + "metadata": { + "title": "Exit Strategy: Most Startups Are Hoping For An Acquisition", + "description": "Long-term goal of tech and healthcare startups as of November 2016", + "main_insight": "A majority (53%) of tech and healthcare startups in 2016 had acquisition as their long-term goal, significantly more than any other exit strategy." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Exit Strategy": "Acquisition", + "Units": 53.0, + "Percentage": "Percentage" + } + ], + "score": 0.96, + "annotation": "The Percentage shows a sudden jump in Units from Stay private to Acquisition.", + "reason": "The Units for Percentage increases from 18.0 at Stay private to 53.0 at Acquisition, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Exit Strategy": "Acquisition", + "Units": 53.0, + "Percentage": "Percentage" + } + ], + "score": 0.8, + "annotation": "The Percentage has a maximum value at Acquisition", + "reason": "The [{'Exit Strategy': 'Acquisition', 'Units': 53.0, 'Percentage': 'Percentage'}] of Percentage has a maximum value of 53.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Exit Strategy": "Don't know", + "Units": 13.0, + "Percentage": "Percentage" + } + ], + "score": 0.42, + "annotation": "The Percentage has a minimum value at Don't know", + "reason": "The [{'Exit Strategy': \"Don't know\", 'Units': 13.0, 'Percentage': 'Percentage'}] of Percentage has a mainimum value of 13.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Exit Strategy": "Acquisition", + "Units": 53.0, + "Percentage": "Percentage" + }, + { + "Exit Strategy": "Stay private", + "Units": 18.0, + "Percentage": "Percentage" + }, + { + "Exit Strategy": "IPO", + "Units": 16.0, + "Percentage": "Percentage" + }, + { + "Exit Strategy": "Don't know", + "Units": 13.0, + "Percentage": "Percentage" + } + ], + "columns": [ + { + "name": "Exit Strategy", + "importance": "primary", + "description": "Different exit strategies for startups", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding percentage", + "role": "y" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "The percentage of the data point", + "role": "group" + } + ] + } + }, + "2325": { + "metadata": { + "title": "Germany Has the Highest Winter Olympic Medal Count", + "description": "All-time medal count of the most successful nations at the Winter Olympic Games since 1924", + "main_insight": "Germany has the highest total medal count in Winter Olympic history, with a significant lead in Gold and Silver medals. Russia and Norway are close contenders." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Italy", + "Units": 106, + "Medals": "Total amount" + }, + { + "Country": "Italy", + "Units": 32, + "Medals": "Silver" + } + ], + "score": 0.98, + "annotation": "The Units of Total amount and Silver show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total amount and Silver is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Italy", + "Units": 106, + "Medals": "Total amount" + }, + { + "Country": "Italy", + "Units": 37, + "Medals": "Bronze" + } + ], + "score": 0.98, + "annotation": "The Units of Total amount and Bronze show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total amount and Bronze is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Italy", + "Units": 106, + "Medals": "Total amount" + }, + { + "Country": "Italy", + "Units": 37, + "Medals": "Gold" + } + ], + "score": 0.97, + "annotation": "The Units of Total amount and Gold show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total amount and Gold is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Italy", + "Units": 32, + "Medals": "Silver" + }, + { + "Country": "Italy", + "Units": 37, + "Medals": "Bronze" + } + ], + "score": 0.95, + "annotation": "The Units of Silver and Bronze show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Silver and Bronze is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Italy", + "Units": 37, + "Medals": "Gold" + }, + { + "Country": "Italy", + "Units": 37, + "Medals": "Bronze" + } + ], + "score": 0.91, + "annotation": "The Units of Gold and Bronze show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Gold and Bronze is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Country": "Germany", + "Units": 358, + "Medals": "Total amount" + }, + { + "Country": "Germany", + "Units": 128, + "Medals": "Gold" + }, + { + "Country": "Germany", + "Units": 129, + "Medals": "Silver" + }, + { + "Country": "Germany", + "Units": 101, + "Medals": "Bronze" + }, + { + "Country": "Russia", + "Units": 308, + "Medals": "Total amount" + }, + { + "Country": "Russia", + "Units": 123, + "Medals": "Gold" + }, + { + "Country": "Russia", + "Units": 92, + "Medals": "Silver" + }, + { + "Country": "Russia", + "Units": 93, + "Medals": "Bronze" + }, + { + "Country": "Norway", + "Units": 303, + "Medals": "Total amount" + }, + { + "Country": "Norway", + "Units": 107, + "Medals": "Gold" + }, + { + "Country": "Norway", + "Units": 106, + "Medals": "Silver" + }, + { + "Country": "Norway", + "Units": 90, + "Medals": "Bronze" + }, + { + "Country": "United States", + "Units": 253, + "Medals": "Total amount" + }, + { + "Country": "United States", + "Units": 87, + "Medals": "Gold" + }, + { + "Country": "United States", + "Units": 95, + "Medals": "Silver" + }, + { + "Country": "United States", + "Units": 71, + "Medals": "Bronze" + }, + { + "Country": "Austria", + "Units": 201, + "Medals": "Total amount" + }, + { + "Country": "Austria", + "Units": 50, + "Medals": "Gold" + }, + { + "Country": "Austria", + "Units": 70, + "Medals": "Silver" + }, + { + "Country": "Austria", + "Units": 76, + "Medals": "Bronze" + }, + { + "Country": "Canada", + "Units": 145, + "Medals": "Total amount" + }, + { + "Country": "Canada", + "Units": 52, + "Medals": "Gold" + }, + { + "Country": "Canada", + "Units": 45, + "Medals": "Silver" + }, + { + "Country": "Canada", + "Units": 48, + "Medals": "Bronze" + }, + { + "Country": "Sweden", + "Units": 129, + "Medals": "Total amount" + }, + { + "Country": "Sweden", + "Units": 48, + "Medals": "Gold" + }, + { + "Country": "Sweden", + "Units": 33, + "Medals": "Silver" + }, + { + "Country": "Sweden", + "Units": 48, + "Medals": "Bronze" + }, + { + "Country": "Switzerland", + "Units": 127, + "Medals": "Total amount" + }, + { + "Country": "Switzerland", + "Units": 44, + "Medals": "Gold" + }, + { + "Country": "Switzerland", + "Units": 37, + "Medals": "Silver" + }, + { + "Country": "Switzerland", + "Units": 46, + "Medals": "Bronze" + }, + { + "Country": "Finland", + "Units": 156, + "Medals": "Total amount" + }, + { + "Country": "Finland", + "Units": 41, + "Medals": "Gold" + }, + { + "Country": "Finland", + "Units": 59, + "Medals": "Silver" + }, + { + "Country": "Finland", + "Units": 56, + "Medals": "Bronze" + }, + { + "Country": "Italy", + "Units": 106, + "Medals": "Total amount" + }, + { + "Country": "Italy", + "Units": 37, + "Medals": "Gold" + }, + { + "Country": "Italy", + "Units": 32, + "Medals": "Silver" + }, + { + "Country": "Italy", + "Units": 37, + "Medals": "Bronze" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Participating country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding medals", + "role": "y" + }, + { + "name": "Medals", + "importance": "primary", + "description": "The medals of the data point", + "role": "group" + } + ] + } + }, + "252": { + "metadata": { + "title": "Spirit is America's Most Complained-About Airline", + "description": "Total airline complaints per 100,000 enplanements in the U.S. in 2013", + "main_insight": "Spirit Airlines had significantly more complaints per 100,000 enplanements in the U.S. in 2013 than any other airline." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Airline": "Spirit Airlines", + "Complaints per 100,000": 9.4 + } + ], + "score": 0.98, + "annotation": "The has a maximum value at Spirit Airlines", + "reason": "The [{'Airline': 'Spirit Airlines', 'Complaints per 100,000': 9.4}] of has a maximum value of 9.4, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Airline": "Spirit Airlines", + "Complaints per 100,000": 9.4 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in Complaints per 100,000 from Frontier Airlines to Spirit Airlines.", + "reason": "The Complaints per 100,000 for increases from 3.1 at Frontier Airlines to 9.4 at Spirit Airlines, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Airline": "Jetblue Airways", + "Complaints per 100,000": 0.6 + } + ], + "score": 0.42, + "annotation": "The has a minimum value at Jetblue Airways", + "reason": "The [{'Airline': 'Jetblue Airways', 'Complaints per 100,000': 0.6}] of has a mainimum value of 0.6, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Airline": "Spirit Airlines", + "Complaints per 100,000": 9.4 + }, + { + "Airline": "Frontier Airlines", + "Complaints per 100,000": 3.1 + }, + { + "Airline": "United Airlines", + "Complaints per 100,000": 2.1 + }, + { + "Airline": "American Airlines", + "Complaints per 100,000": 2.0 + }, + { + "Airline": "American Eagle Airlines", + "Complaints per 100,000": 1.7 + }, + { + "Airline": "US Airways", + "Complaints per 100,000": 1.4 + }, + { + "Airline": "Pinnacle Airlines", + "Complaints per 100,000": 0.9 + }, + { + "Airline": "Skywest Airlines", + "Complaints per 100,000": 0.8 + }, + { + "Airline": "Airtran Airways", + "Complaints per 100,000": 0.7 + }, + { + "Airline": "Jetblue Airways", + "Complaints per 100,000": 0.6 + } + ], + "columns": [ + { + "name": "Airline", + "importance": "primary", + "description": "Name of the airline", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Complaints per 100,000", + "importance": "primary", + "description": "Total airline complaints per 100,000 enplanements", + "unit": "Complaints per 100,000", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2345": { + "metadata": { + "title": "Tattoos Remain Taboo in Korea", + "description": "Survey responses among South Koreans about tattoos (2019)", + "main_insight": "A significant majority of South Koreans in 2019 did not have tattoos, and a large percentage had a negative impression of body tattoos. Additionally, most prefered getting tattoos done in medical settings." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Survey Question": "Have no tattoos", + "Units": 63.0, + "Percentage": "Percentage" + } + ], + "score": 0.85, + "annotation": "The Percentage shows a sudden jump in Units from Have a cosmetic tattoo to Have no tattoos.", + "reason": "The Units for Percentage increases from 25.0 at Have a cosmetic tattoo to 63.0 at Have no tattoos, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Survey Question": "Have a body tattoo", + "Units": 12.0, + "Percentage": "Percentage" + } + ], + "score": 0.71, + "annotation": "The Percentage has a minimum value at Have a body tattoo", + "reason": "The [{'Survey Question': 'Have a body tattoo', 'Units': 12.0, 'Percentage': 'Percentage'}] of Percentage has a mainimum value of 12.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Survey Question": "Prefer to get tattoos at specialized medical institutions", + "Units": 84.0, + "Percentage": "Percentage" + } + ], + "score": 0.59, + "annotation": "The Percentage has a maximum value at Prefer to get tattoos at specialized medical institutions", + "reason": "The [{'Survey Question': 'Prefer to get tattoos at specialized medical institutions', 'Units': 84.0, 'Percentage': 'Percentage'}] of Percentage has a maximum value of 84.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Survey Question": "Have no tattoos", + "Units": 63.0, + "Percentage": "Percentage" + }, + { + "Survey Question": "Have a cosmetic tattoo", + "Units": 25.0, + "Percentage": "Percentage" + }, + { + "Survey Question": "Have a body tattoo", + "Units": 12.0, + "Percentage": "Percentage" + }, + { + "Survey Question": "Have negative impression of body tattoos", + "Units": 78.0, + "Percentage": "Percentage" + }, + { + "Survey Question": "Prefer to get tattoos at specialized medical institutions", + "Units": 84.0, + "Percentage": "Percentage" + } + ], + "columns": [ + { + "name": "Survey Question", + "importance": "primary", + "description": "The question asked in the survey", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding percentage", + "role": "y" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "The percentage of the data point", + "role": "group" + } + ] + } + }, + "713": { + "metadata": { + "title": "Chainification of the U.S. Accommodation Market", + "description": "U.S. hotel room share in 1990 and 2022, by accommodation type", + "main_insight": "Chain hotels have significantly increased their market share in the U.S. accommodation market between 1990 and 2022, while the share of independent hotels has decreased." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2022, + "Score": 72.0, + "Hotel Type": "Chain Hotel" + }, + { + "Year": 2022, + "Score": 28.0, + "Hotel Type": "Independent Hotel" + } + ], + "score": 1.0, + "annotation": "The Score of Chain Hotel and Independent Hotel show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Chain Hotel and Independent Hotel is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 1990, + "Score": 35.0, + "Hotel Type": "Chain Hotel" + }, + { + "Year": 1990, + "Score": 65.0, + "Hotel Type": "Independent Hotel" + }, + { + "Year": 2022, + "Score": 72.0, + "Hotel Type": "Chain Hotel" + }, + { + "Year": 2022, + "Score": 28.0, + "Hotel Type": "Independent Hotel" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding hotel type", + "role": "y" + }, + { + "name": "Hotel Type", + "importance": "primary", + "description": "The hotel type of the data point", + "role": "group" + } + ] + } + }, + "1234": { + "metadata": { + "title": "Global Optimism Ticks Up From Last Year", + "description": "Share of respondents who are optimistic that the next year will be better than the year they've just had", + "main_insight": "Global optimism has slightly increased in 2024, reaching 71%, after a dip in 2022. Indonesia (ID) shows the highest optimism at 90%, while Germany (DE) shows the lowest among the listed countries at 56%." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2012, + "Global Optimism": 80.0 + }, + { + "Year": 2013, + "Global Optimism": 78.0 + }, + { + "Year": 2014, + "Global Optimism": 78.0 + }, + { + "Year": 2015, + "Global Optimism": 78.0 + }, + { + "Year": 2016, + "Global Optimism": 78.0 + }, + { + "Year": 2017, + "Global Optimism": 78.0 + }, + { + "Year": 2018, + "Global Optimism": 78.0 + }, + { + "Year": 2019, + "Global Optimism": 78.0 + }, + { + "Year": 2020, + "Global Optimism": 78.0 + }, + { + "Year": 2021, + "Global Optimism": 78.0 + }, + { + "Year": 2022, + "Global Optimism": 65.0 + }, + { + "Year": 2023, + "Global Optimism": 70.0 + }, + { + "Year": 2024, + "Global Optimism": 71.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Global Optimism", + "importance": "primary", + "description": "Percentage of respondents globally who are optimistic about the next year", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1391": { + "metadata": { + "title": "Wind, Solar Dominate Energy Future", + "description": "Total global installed power capacity from 2019-2025, by fuel and technology (in gigawatts)", + "main_insight": "Wind/Solar power capacity is projected to increase significantly between 2019 and 2025, surpassing all other energy sources including Coal, Natural Gas, and Hydro." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2025, + "Units": 1999.0, + "Energy Source": "Natural gas" + }, + { + "Year": 2025, + "Units": 1427.0, + "Energy Source": "Hydro" + } + ], + "score": 1.0, + "annotation": "The Units of Natural gas and Hydro show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Natural gas and Hydro is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2025, + "Units": 1999.0, + "Energy Source": "Natural gas" + }, + { + "Year": 2025, + "Units": 2079.0, + "Energy Source": "Coal" + } + ], + "score": 1.0, + "annotation": "The Units of Natural gas and Coal show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Natural gas and Coal is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2025, + "Units": 2079.0, + "Energy Source": "Coal" + }, + { + "Year": 2025, + "Units": 1427.0, + "Energy Source": "Hydro" + } + ], + "score": 1.0, + "annotation": "The Units of Coal and Hydro show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Coal and Hydro is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2025, + "Units": 2349.0, + "Energy Source": "Wind/Solar" + }, + { + "Year": 2025, + "Units": 1999.0, + "Energy Source": "Natural gas" + } + ], + "score": 0.99, + "annotation": "The Units of Wind/Solar and Natural gas show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Wind/Solar and Natural gas is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2025, + "Units": 2349.0, + "Energy Source": "Wind/Solar" + }, + { + "Year": 2025, + "Units": 1427.0, + "Energy Source": "Hydro" + } + ], + "score": 0.99, + "annotation": "The Units of Wind/Solar and Hydro show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Wind/Solar and Hydro is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2019, + "Units": 1256.0, + "Energy Source": "Wind/Solar" + }, + { + "Year": 2019, + "Units": 1837.0, + "Energy Source": "Natural gas" + }, + { + "Year": 2019, + "Units": 2145.0, + "Energy Source": "Coal" + }, + { + "Year": 2019, + "Units": 1308.0, + "Energy Source": "Hydro" + }, + { + "Year": 2020, + "Units": 1454.0, + "Energy Source": "Wind/Solar" + }, + { + "Year": 2020, + "Units": 1873.0, + "Energy Source": "Natural gas" + }, + { + "Year": 2020, + "Units": 2129.0, + "Energy Source": "Coal" + }, + { + "Year": 2020, + "Units": 1336.0, + "Energy Source": "Hydro" + }, + { + "Year": 2021, + "Units": 1644.0, + "Energy Source": "Wind/Solar" + }, + { + "Year": 2021, + "Units": 1908.0, + "Energy Source": "Natural gas" + }, + { + "Year": 2021, + "Units": 2112.0, + "Energy Source": "Coal" + }, + { + "Year": 2021, + "Units": 1360.0, + "Energy Source": "Hydro" + }, + { + "Year": 2022, + "Units": 1847.0, + "Energy Source": "Wind/Solar" + }, + { + "Year": 2022, + "Units": 1944.0, + "Energy Source": "Natural gas" + }, + { + "Year": 2022, + "Units": 2101.0, + "Energy Source": "Coal" + }, + { + "Year": 2022, + "Units": 1387.0, + "Energy Source": "Hydro" + }, + { + "Year": 2023, + "Units": 2014.0, + "Energy Source": "Wind/Solar" + }, + { + "Year": 2023, + "Units": 1968.0, + "Energy Source": "Natural gas" + }, + { + "Year": 2023, + "Units": 2091.0, + "Energy Source": "Coal" + }, + { + "Year": 2023, + "Units": 1405.0, + "Energy Source": "Hydro" + }, + { + "Year": 2024, + "Units": 2183.0, + "Energy Source": "Wind/Solar" + }, + { + "Year": 2024, + "Units": 1984.0, + "Energy Source": "Natural gas" + }, + { + "Year": 2024, + "Units": 2085.0, + "Energy Source": "Coal" + }, + { + "Year": 2024, + "Units": 1416.0, + "Energy Source": "Hydro" + }, + { + "Year": 2025, + "Units": 2349.0, + "Energy Source": "Wind/Solar" + }, + { + "Year": 2025, + "Units": 1999.0, + "Energy Source": "Natural gas" + }, + { + "Year": 2025, + "Units": 2079.0, + "Energy Source": "Coal" + }, + { + "Year": 2025, + "Units": 1427.0, + "Energy Source": "Hydro" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of installed power capacity", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding energy source", + "role": "y" + }, + { + "name": "Energy Source", + "importance": "primary", + "description": "The energy source of the data point", + "role": "group" + } + ] + } + }, + "80": { + "metadata": { + "title": "The French Tradition of Protesting Pension Reforms", + "description": "Government figures for protesters at demonstrations against pension reforms in France (1995-2023)", + "main_insight": "The number of protesters against pension reforms in France varies significantly across different years and days of protest. The 2023 protests saw a high initial turnout, peaking at 1.28 million on the 6th day, and declining thereafter, but seeing a resurgence by the 13th day." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Day of protest": "6th", + "Ratio": 0.3, + "Year": "1995" + }, + { + "Day of protest": "6th", + "Ratio": 0.43, + "Year": "2003" + } + ], + "score": 1.0, + "annotation": "The Ratio of 1995 and 2003 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between 1995 and 2003 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Day of protest": "13th", + "Ratio": 0.78, + "Year": "2023" + }, + { + "Day of protest": "13th", + "Ratio": 0.38, + "Year": "2010" + } + ], + "score": 0.99, + "annotation": "The Ratio of 2023 and 2010 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between 2023 and 2010 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Day of protest": "1st", + "Ratio": 0.45, + "Year": "1995" + } + ], + "score": 0.97, + "annotation": "The 1995 shows a sudden jump in Ratio from 6th to 1st.", + "reason": "The Ratio for 1995 increases from 0.3 at 6th to 0.45 at 1st, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Day of protest": "1st", + "Ratio": 1.0, + "Year": "2003" + } + ], + "score": 0.97, + "annotation": "The 2003 shows a sudden jump in Ratio from 6th to 1st.", + "reason": "The Ratio for 2003 increases from 0.43 at 6th to 1.0 at 1st, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Day of protest": "1st", + "Ratio": 0.81, + "Year": "2019-2020" + } + ], + "score": 0.96, + "annotation": "The 2019-2020 shows a sudden jump in Ratio from 6th to 1st.", + "reason": "The Ratio for 2019-2020 increases from 0.15 at 6th to 0.81 at 1st, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Day of protest": "1st", + "Ratio": 1.09, + "Year": "2023" + }, + { + "Day of protest": "1st", + "Ratio": 0.8, + "Year": "2010" + }, + { + "Day of protest": "1st", + "Ratio": 0.81, + "Year": "2019-2020" + }, + { + "Day of protest": "1st", + "Ratio": 0.45, + "Year": "1995" + }, + { + "Day of protest": "1st", + "Ratio": 1.0, + "Year": "2003" + }, + { + "Day of protest": "6th", + "Ratio": 1.28, + "Year": "2023" + }, + { + "Day of protest": "6th", + "Ratio": 1.23, + "Year": "2010" + }, + { + "Day of protest": "6th", + "Ratio": 0.15, + "Year": "2019-2020" + }, + { + "Day of protest": "6th", + "Ratio": 0.3, + "Year": "1995" + }, + { + "Day of protest": "6th", + "Ratio": 0.43, + "Year": "2003" + }, + { + "Day of protest": "13th", + "Ratio": 0.78, + "Year": "2023" + }, + { + "Day of protest": "13th", + "Ratio": 0.38, + "Year": "2010" + }, + { + "Day of protest": "13th", + "Ratio": 0.07, + "Year": "2019-2020" + } + ], + "columns": [ + { + "name": "Day of protest", + "importance": "primary", + "description": "Day of protest", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "735": { + "metadata": { + "title": "Where Survival Becomes a Full-Time Job", + "description": "Working hours per family at minimum wage needed to exit poverty per week", + "main_insight": "The number of hours needed to work at minimum wage to exit poverty varies significantly by country and family structure. A jobless couple with two children needs to work significantly more hours than a single person with no children across all countries." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United States", + "Score": 61.0, + "Household": "Single, no children" + } + ], + "score": 0.91, + "annotation": "The Single, no children has a maximum value at United States", + "reason": "The [{'Country': 'United States', 'Score': 61.0, 'Household': 'Single, no children'}] of Single, no children has a maximum value of 61.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Spain", + "Score": 27.0, + "Household": "Single, no children" + }, + { + "Country": "Spain", + "Score": 55.0, + "Household": "Jobless couple, two children" + } + ], + "score": 0.87, + "annotation": "The Score of Single, no children and Jobless couple, two children show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Single, no children and Jobless couple, two children is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Score": 61.0, + "Household": "Single, no children" + } + ], + "score": 0.82, + "annotation": "The Single, no children shows a sudden jump in Score from Latvia to United States.", + "reason": "The Score for Single, no children increases from 44.0 at Latvia to 61.0 at United States, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Estonia", + "Score": 72.0, + "Household": "Jobless couple, two children" + } + ], + "score": 0.8, + "annotation": "The Jobless couple, two children shows a sudden jump in Score from Bulgaria to Estonia.", + "reason": "The Score for Jobless couple, two children increases from 61.0 at Bulgaria to 72.0 at Estonia, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "United States", + "Score": 80.0, + "Household": "Jobless couple, two children" + } + ], + "score": 0.78, + "annotation": "The Jobless couple, two children accounts for the majority in United States.", + "reason": "The Score of Jobless couple, two children in United States accounts for 0.5673758865248227 (more than 50%) of the total of Score of all Household." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Score": 61.0, + "Household": "Single, no children" + }, + { + "Country": "United States", + "Score": 80.0, + "Household": "Jobless couple, two children" + }, + { + "Country": "Malta", + "Score": 44.0, + "Household": "Single, no children" + }, + { + "Country": "Malta", + "Score": 80.0, + "Household": "Jobless couple, two children" + }, + { + "Country": "Latvia", + "Score": 44.0, + "Household": "Single, no children" + }, + { + "Country": "Latvia", + "Score": 80.0, + "Household": "Jobless couple, two children" + }, + { + "Country": "Estonia", + "Score": 40.0, + "Household": "Single, no children" + }, + { + "Country": "Estonia", + "Score": 72.0, + "Household": "Jobless couple, two children" + }, + { + "Country": "Croatia", + "Score": 33.0, + "Household": "Single, no children" + }, + { + "Country": "Croatia", + "Score": 61.0, + "Household": "Jobless couple, two children" + }, + { + "Country": "Bulgaria", + "Score": 34.0, + "Household": "Single, no children" + }, + { + "Country": "Bulgaria", + "Score": 61.0, + "Household": "Jobless couple, two children" + }, + { + "Country": "Netherlands", + "Score": 26.0, + "Household": "Single, no children" + }, + { + "Country": "Netherlands", + "Score": 59.0, + "Household": "Jobless couple, two children" + }, + { + "Country": "Spain", + "Score": 27.0, + "Household": "Single, no children" + }, + { + "Country": "Spain", + "Score": 55.0, + "Household": "Jobless couple, two children" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "OECD Countries with available data for 2020", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding household", + "role": "y" + }, + { + "name": "Household", + "importance": "primary", + "description": "The household of the data point", + "role": "group" + } + ] + } + }, + "308": { + "metadata": { + "title": "Where Equal Pay is Considered Most Important", + "description": "Respondents who think equal pay is a top issue facing women and girls, by country (in %)", + "main_insight": "There is a significant difference in the percentage of men, women, and the total population who consider equal pay a top issue. Women consistently rank equal pay as a more important issue than men across all surveyed countries." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Hungary", + "Points": 25.0, + "Gender": "Women" + }, + { + "Country": "Hungary", + "Points": 24.0, + "Gender": "Total" + } + ], + "score": 0.92, + "annotation": "The Points of Women and Total show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Points between Women and Total is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "France", + "Points": 33.0, + "Gender": "Women" + } + ], + "score": 0.9, + "annotation": "The Women has the largest proportion in France.", + "reason": "The Points of Women in France accounts for 0.34375, larger than all other Gender, of the total of Points of all Gender." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Hungary", + "Points": 25.0, + "Gender": "Women" + } + ], + "score": 0.9, + "annotation": "The Women has the largest proportion in Hungary.", + "reason": "The Points of Women in Hungary accounts for 0.3472222222222222, larger than all other Gender, of the total of Points of all Gender." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Great Britain", + "Points": 31.0, + "Gender": "Women" + } + ], + "score": 0.9, + "annotation": "The Women has the largest proportion in Great Britain.", + "reason": "The Points of Women in Great Britain accounts for 0.34831460674157305, larger than all other Gender, of the total of Points of all Gender." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Germany", + "Points": 37.0, + "Gender": "Women" + } + ], + "score": 0.89, + "annotation": "The Women has the largest proportion in Germany.", + "reason": "The Points of Women in Germany accounts for 0.3592233009708738, larger than all other Gender, of the total of Points of all Gender." + } + ], + "data": { + "data": [ + { + "Country": "Sweden", + "Points": 43.0, + "Gender": "Women" + }, + { + "Country": "Sweden", + "Points": 36.0, + "Gender": "Total" + }, + { + "Country": "Sweden", + "Points": 28.0, + "Gender": "Men" + }, + { + "Country": "Germany", + "Points": 37.0, + "Gender": "Women" + }, + { + "Country": "Germany", + "Points": 34.0, + "Gender": "Total" + }, + { + "Country": "Germany", + "Points": 32.0, + "Gender": "Men" + }, + { + "Country": "Belgium", + "Points": 38.0, + "Gender": "Women" + }, + { + "Country": "Belgium", + "Points": 32.0, + "Gender": "Total" + }, + { + "Country": "Belgium", + "Points": 27.0, + "Gender": "Men" + }, + { + "Country": "France", + "Points": 33.0, + "Gender": "Women" + }, + { + "Country": "France", + "Points": 32.0, + "Gender": "Total" + }, + { + "Country": "France", + "Points": 31.0, + "Gender": "Men" + }, + { + "Country": "Great Britain", + "Points": 31.0, + "Gender": "Women" + }, + { + "Country": "Great Britain", + "Points": 30.0, + "Gender": "Total" + }, + { + "Country": "Great Britain", + "Points": 28.0, + "Gender": "Men" + }, + { + "Country": "Chile", + "Points": 35.0, + "Gender": "Women" + }, + { + "Country": "Chile", + "Points": 29.0, + "Gender": "Total" + }, + { + "Country": "Chile", + "Points": 23.0, + "Gender": "Men" + }, + { + "Country": "United States", + "Points": 33.0, + "Gender": "Women" + }, + { + "Country": "United States", + "Points": 28.0, + "Gender": "Total" + }, + { + "Country": "United States", + "Points": 23.0, + "Gender": "Men" + }, + { + "Country": "Canada", + "Points": 30.0, + "Gender": "Women" + }, + { + "Country": "Canada", + "Points": 27.0, + "Gender": "Total" + }, + { + "Country": "Canada", + "Points": 24.0, + "Gender": "Men" + }, + { + "Country": "Australia", + "Points": 27.0, + "Gender": "Women" + }, + { + "Country": "Australia", + "Points": 25.0, + "Gender": "Total" + }, + { + "Country": "Australia", + "Points": 23.0, + "Gender": "Men" + }, + { + "Country": "Hungary", + "Points": 25.0, + "Gender": "Women" + }, + { + "Country": "Hungary", + "Points": 24.0, + "Gender": "Total" + }, + { + "Country": "Hungary", + "Points": 23.0, + "Gender": "Men" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding gender", + "role": "y" + }, + { + "name": "Gender", + "importance": "primary", + "description": "The gender of the data point", + "role": "group" + } + ] + } + }, + "1491": { + "metadata": { + "title": "1 in 3 European Smartphones is Sold by a Chinese Brand", + "description": "Market share of Chinese vendors in the European smartphone market (in % of shipments)", + "main_insight": "The market share of Chinese smartphone vendors in Europe has steadily increased from 2013 to 2018, reaching 32% in 2018." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2013, + "Market Share": 0.11 + }, + { + "Year": 2014, + "Market Share": 0.14 + }, + { + "Year": 2015, + "Market Share": 0.18 + }, + { + "Year": 2016, + "Market Share": 0.23 + }, + { + "Year": 2017, + "Market Share": 0.24 + }, + { + "Year": 2018, + "Market Share": 0.32 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of market share data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Market Share", + "importance": "primary", + "description": "Percentage of European smartphone shipments by Chinese vendors", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1153": { + "metadata": { + "title": "The Countries With the Most Active Volcanos", + "description": "Countries with the most volcanos that have been active since 1960", + "main_insight": "Indonesia has the highest number of active volcanoes since 1960, significantly more than any other country. Japan and the US follow, with a considerable drop in numbers." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country Code": "ID", + "Active Volcanoes": 55 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at ID", + "reason": "The [{'Country Code': 'ID', 'Active Volcanoes': 55}] of has a maximum value of 55, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country Code": "ID", + "Active Volcanoes": 55 + } + ], + "score": 0.71, + "annotation": "The shows a sudden jump in Active Volcanoes from JP to ID.", + "reason": "The Active Volcanoes for increases from 40 at JP to 55 at ID, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country Code": "TO", + "Active Volcanoes": 9 + }, + { + "Country Code": "IS", + "Active Volcanoes": 9 + }, + { + "Country Code": "FR*", + "Active Volcanoes": 9 + }, + { + "Country Code": "VU", + "Active Volcanoes": 9 + }, + { + "Country Code": "GB*", + "Active Volcanoes": 9 + } + ], + "score": 0.47, + "annotation": "The has minimum values at TO, IS, FR*, VU, GB*", + "reason": "The [{'Country Code': 'TO', 'Active Volcanoes': 9}, {'Country Code': 'IS', 'Active Volcanoes': 9}, {'Country Code': 'FR*', 'Active Volcanoes': 9}, {'Country Code': 'VU', 'Active Volcanoes': 9}, {'Country Code': 'GB*', 'Active Volcanoes': 9}] of have minimum values of 9, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country Code": "ID", + "Active Volcanoes": 55 + }, + { + "Country Code": "JP", + "Active Volcanoes": 40 + }, + { + "Country Code": "US", + "Active Volcanoes": 39 + }, + { + "Country Code": "RU", + "Active Volcanoes": 27 + }, + { + "Country Code": "CL", + "Active Volcanoes": 19 + }, + { + "Country Code": "PG", + "Active Volcanoes": 13 + }, + { + "Country Code": "EC", + "Active Volcanoes": 12 + }, + { + "Country Code": "TO", + "Active Volcanoes": 9 + }, + { + "Country Code": "IS", + "Active Volcanoes": 9 + }, + { + "Country Code": "FR*", + "Active Volcanoes": 9 + }, + { + "Country Code": "VU", + "Active Volcanoes": 9 + }, + { + "Country Code": "GB*", + "Active Volcanoes": 9 + } + ], + "columns": [ + { + "name": "Country Code", + "importance": "primary", + "description": "Two-letter country code, except for France, Vanuatu and United Kingdom, which also include overseas territories. ", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Active Volcanoes", + "importance": "primary", + "description": "Number of active volcanoes since 1960", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2031": { + "metadata": { + "title": "Podcasts Rising in Prominence", + "description": "Share of people that listen to podcasts on a weekly basis", + "main_insight": "The percentage of people who listen to podcasts weekly increased in all listed countries from 2019 to 2020." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Germany", + "Units": 15.0, + "Year": "2020" + }, + { + "Country": "Germany", + "Units": 11.0, + "Year": "2019" + } + ], + "score": 0.97, + "annotation": "The Units of 2020 and 2019 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2020 and 2019 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Germany", + "Units": 15.0, + "Year": "2020" + } + ], + "score": 0.87, + "annotation": "The 2020 has a minimum value at Germany", + "reason": "The [{'Country': 'Germany', 'Units': 15.0, 'Year': '2020'}] of 2020 has a mainimum value of 15.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Germany", + "Units": 11.0, + "Year": "2019" + } + ], + "score": 0.86, + "annotation": "The 2019 has a minimum value at Germany", + "reason": "The [{'Country': 'Germany', 'Units': 11.0, 'Year': '2019'}] of 2019 has a mainimum value of 11.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United Kingdom", + "Units": 23.0, + "Year": "2020" + } + ], + "score": 0.8, + "annotation": "The 2020 shows a sudden jump in Units from Germany to United Kingdom.", + "reason": "The Units for 2020 increases from 15.0 at Germany to 23.0 at United Kingdom, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Denmark", + "Units": 26.0, + "Year": "2020" + } + ], + "score": 0.79, + "annotation": "The 2020 accounts for the majority in Denmark.", + "reason": "The Units of 2020 in Denmark accounts for 0.52 (more than 50%) of the total of Units of all Year." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Units": 33.0, + "Year": "2020" + }, + { + "Country": "United States", + "Units": 27.0, + "Year": "2019" + }, + { + "Country": "Sweden", + "Units": 30.0, + "Year": "2020" + }, + { + "Country": "Sweden", + "Units": 26.0, + "Year": "2019" + }, + { + "Country": "Norway", + "Units": 30.0, + "Year": "2020" + }, + { + "Country": "Norway", + "Units": 24.0, + "Year": "2019" + }, + { + "Country": "Denmark", + "Units": 26.0, + "Year": "2020" + }, + { + "Country": "Denmark", + "Units": 24.0, + "Year": "2019" + }, + { + "Country": "United Kingdom", + "Units": 23.0, + "Year": "2020" + }, + { + "Country": "United Kingdom", + "Units": 18.0, + "Year": "2019" + }, + { + "Country": "Finland", + "Units": 23.0, + "Year": "2020" + }, + { + "Country": "Finland", + "Units": 17.0, + "Year": "2019" + }, + { + "Country": "Germany", + "Units": 15.0, + "Year": "2020" + }, + { + "Country": "Germany", + "Units": 11.0, + "Year": "2019" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "236": { + "metadata": { + "title": "Chrome's Rise to Browser Dominance", + "description": "Global market share of the most popular internet browsers (All platforms except for gaming consoles)", + "main_insight": "Chrome's market share has increased significantly since 2009, becoming the dominant browser, while Internet Explorer's share has drastically decreased." + }, + "chart_type": "Stacked Area Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2024, + "Units": 70.0, + "Browsers": "Chrome" + }, + { + "Year": 2024, + "Units": 0.5, + "Browsers": "Internet Explorer" + } + ], + "score": 1.0, + "annotation": "The Units of Chrome and Internet Explorer show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Chrome and Internet Explorer is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2024, + "Units": 70.0, + "Browsers": "Chrome" + }, + { + "Year": 2024, + "Units": 4.0, + "Browsers": "Firefox" + } + ], + "score": 0.97, + "annotation": "The Units of Chrome and Firefox show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Chrome and Firefox is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Units": 0.5, + "Browsers": "Internet Explorer" + }, + { + "Year": 2024, + "Units": 4.0, + "Browsers": "Firefox" + } + ], + "score": 0.95, + "annotation": "The Units of Internet Explorer and Firefox show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Internet Explorer and Firefox is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2024, + "Units": 4.0, + "Browsers": "Firefox" + }, + { + "Year": 2024, + "Units": 14.5, + "Browsers": "Safari" + } + ], + "score": 0.94, + "annotation": "The Units of Firefox and Safari show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Firefox and Safari is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Units": 70.0, + "Browsers": "Chrome" + }, + { + "Year": 2024, + "Units": 14.5, + "Browsers": "Safari" + } + ], + "score": 0.93, + "annotation": "The Units of Chrome and Safari show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Chrome and Safari is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2009, + "Units": 1.5, + "Browsers": "Chrome" + }, + { + "Year": 2009, + "Units": 67.0, + "Browsers": "Internet Explorer" + }, + { + "Year": 2009, + "Units": 23.0, + "Browsers": "Firefox" + }, + { + "Year": 2009, + "Units": 4.5, + "Browsers": "Safari" + }, + { + "Year": 2009, + "Units": 2.5, + "Browsers": "Opera" + }, + { + "Year": 2009, + "Units": 1.5, + "Browsers": "Others" + }, + { + "Year": 2010, + "Units": 7.0, + "Browsers": "Chrome" + }, + { + "Year": 2010, + "Units": 59.0, + "Browsers": "Internet Explorer" + }, + { + "Year": 2010, + "Units": 24.5, + "Browsers": "Firefox" + }, + { + "Year": 2010, + "Units": 5.0, + "Browsers": "Safari" + }, + { + "Year": 2010, + "Units": 2.5, + "Browsers": "Opera" + }, + { + "Year": 2010, + "Units": 2.0, + "Browsers": "Others" + }, + { + "Year": 2011, + "Units": 15.0, + "Browsers": "Chrome" + }, + { + "Year": 2011, + "Units": 50.0, + "Browsers": "Internet Explorer" + }, + { + "Year": 2011, + "Units": 24.0, + "Browsers": "Firefox" + }, + { + "Year": 2011, + "Units": 5.5, + "Browsers": "Safari" + }, + { + "Year": 2011, + "Units": 2.0, + "Browsers": "Opera" + }, + { + "Year": 2011, + "Units": 3.5, + "Browsers": "Others" + }, + { + "Year": 2012, + "Units": 24.0, + "Browsers": "Chrome" + }, + { + "Year": 2012, + "Units": 40.0, + "Browsers": "Internet Explorer" + }, + { + "Year": 2012, + "Units": 23.5, + "Browsers": "Firefox" + }, + { + "Year": 2012, + "Units": 6.5, + "Browsers": "Safari" + }, + { + "Year": 2012, + "Units": 1.8, + "Browsers": "Opera" + }, + { + "Year": 2012, + "Units": 4.2, + "Browsers": "Others" + }, + { + "Year": 2013, + "Units": 34.0, + "Browsers": "Chrome" + }, + { + "Year": 2013, + "Units": 32.0, + "Browsers": "Internet Explorer" + }, + { + "Year": 2013, + "Units": 19.5, + "Browsers": "Firefox" + }, + { + "Year": 2013, + "Units": 7.0, + "Browsers": "Safari" + }, + { + "Year": 2013, + "Units": 1.5, + "Browsers": "Opera" + }, + { + "Year": 2013, + "Units": 6.0, + "Browsers": "Others" + }, + { + "Year": 2014, + "Units": 41.0, + "Browsers": "Chrome" + }, + { + "Year": 2014, + "Units": 24.0, + "Browsers": "Internet Explorer" + }, + { + "Year": 2014, + "Units": 17.0, + "Browsers": "Firefox" + }, + { + "Year": 2014, + "Units": 8.5, + "Browsers": "Safari" + }, + { + "Year": 2014, + "Units": 1.4, + "Browsers": "Opera" + }, + { + "Year": 2014, + "Units": 8.1, + "Browsers": "Others" + }, + { + "Year": 2015, + "Units": 47.0, + "Browsers": "Chrome" + }, + { + "Year": 2015, + "Units": 18.5, + "Browsers": "Internet Explorer" + }, + { + "Year": 2015, + "Units": 15.5, + "Browsers": "Firefox" + }, + { + "Year": 2015, + "Units": 9.0, + "Browsers": "Safari" + }, + { + "Year": 2015, + "Units": 1.5, + "Browsers": "Opera" + }, + { + "Year": 2015, + "Units": 0.5, + "Browsers": "Edge" + }, + { + "Year": 2015, + "Units": 8.0, + "Browsers": "Others" + }, + { + "Year": 2016, + "Units": 54.0, + "Browsers": "Chrome" + }, + { + "Year": 2016, + "Units": 13.5, + "Browsers": "Internet Explorer" + }, + { + "Year": 2016, + "Units": 11.0, + "Browsers": "Firefox" + }, + { + "Year": 2016, + "Units": 9.0, + "Browsers": "Safari" + }, + { + "Year": 2016, + "Units": 1.5, + "Browsers": "Opera" + }, + { + "Year": 2016, + "Units": 2.0, + "Browsers": "Edge" + }, + { + "Year": 2016, + "Units": 9.0, + "Browsers": "Others" + }, + { + "Year": 2017, + "Units": 59.0, + "Browsers": "Chrome" + }, + { + "Year": 2017, + "Units": 10.0, + "Browsers": "Internet Explorer" + }, + { + "Year": 2017, + "Units": 9.0, + "Browsers": "Firefox" + }, + { + "Year": 2017, + "Units": 9.5, + "Browsers": "Safari" + }, + { + "Year": 2017, + "Units": 1.5, + "Browsers": "Opera" + }, + { + "Year": 2017, + "Units": 3.0, + "Browsers": "Edge" + }, + { + "Year": 2017, + "Units": 8.0, + "Browsers": "Others" + }, + { + "Year": 2018, + "Units": 62.0, + "Browsers": "Chrome" + }, + { + "Year": 2018, + "Units": 7.5, + "Browsers": "Internet Explorer" + }, + { + "Year": 2018, + "Units": 8.0, + "Browsers": "Firefox" + }, + { + "Year": 2018, + "Units": 10.0, + "Browsers": "Safari" + }, + { + "Year": 2018, + "Units": 2.0, + "Browsers": "Opera" + }, + { + "Year": 2018, + "Units": 3.5, + "Browsers": "Edge" + }, + { + "Year": 2018, + "Units": 7.0, + "Browsers": "Others" + }, + { + "Year": 2019, + "Units": 64.0, + "Browsers": "Chrome" + }, + { + "Year": 2019, + "Units": 6.0, + "Browsers": "Internet Explorer" + }, + { + "Year": 2019, + "Units": 7.5, + "Browsers": "Firefox" + }, + { + "Year": 2019, + "Units": 11.0, + "Browsers": "Safari" + }, + { + "Year": 2019, + "Units": 2.5, + "Browsers": "Opera" + }, + { + "Year": 2019, + "Units": 4.0, + "Browsers": "Edge" + }, + { + "Year": 2019, + "Units": 5.0, + "Browsers": "Others" + }, + { + "Year": 2020, + "Units": 67.0, + "Browsers": "Chrome" + }, + { + "Year": 2020, + "Units": 5.0, + "Browsers": "Internet Explorer" + }, + { + "Year": 2020, + "Units": 7.0, + "Browsers": "Firefox" + }, + { + "Year": 2020, + "Units": 12.0, + "Browsers": "Safari" + }, + { + "Year": 2020, + "Units": 2.5, + "Browsers": "Opera" + }, + { + "Year": 2020, + "Units": 4.5, + "Browsers": "Edge" + }, + { + "Year": 2020, + "Units": 2.0, + "Browsers": "Others" + }, + { + "Year": 2021, + "Units": 68.0, + "Browsers": "Chrome" + }, + { + "Year": 2021, + "Units": 3.0, + "Browsers": "Internet Explorer" + }, + { + "Year": 2021, + "Units": 6.5, + "Browsers": "Firefox" + }, + { + "Year": 2021, + "Units": 13.5, + "Browsers": "Safari" + }, + { + "Year": 2021, + "Units": 2.5, + "Browsers": "Opera" + }, + { + "Year": 2021, + "Units": 5.5, + "Browsers": "Edge" + }, + { + "Year": 2021, + "Units": 1.0, + "Browsers": "Others" + }, + { + "Year": 2022, + "Units": 69.0, + "Browsers": "Chrome" + }, + { + "Year": 2022, + "Units": 1.5, + "Browsers": "Internet Explorer" + }, + { + "Year": 2022, + "Units": 5.5, + "Browsers": "Firefox" + }, + { + "Year": 2022, + "Units": 14.5, + "Browsers": "Safari" + }, + { + "Year": 2022, + "Units": 2.5, + "Browsers": "Opera" + }, + { + "Year": 2022, + "Units": 6.0, + "Browsers": "Edge" + }, + { + "Year": 2022, + "Units": 1.0, + "Browsers": "Others" + }, + { + "Year": 2023, + "Units": 70.0, + "Browsers": "Chrome" + }, + { + "Year": 2023, + "Units": 0.8, + "Browsers": "Internet Explorer" + }, + { + "Year": 2023, + "Units": 4.7, + "Browsers": "Firefox" + }, + { + "Year": 2023, + "Units": 14.5, + "Browsers": "Safari" + }, + { + "Year": 2023, + "Units": 2.0, + "Browsers": "Opera" + }, + { + "Year": 2023, + "Units": 7.0, + "Browsers": "Edge" + }, + { + "Year": 2023, + "Units": 1.0, + "Browsers": "Others" + }, + { + "Year": 2024, + "Units": 70.0, + "Browsers": "Chrome" + }, + { + "Year": 2024, + "Units": 0.5, + "Browsers": "Internet Explorer" + }, + { + "Year": 2024, + "Units": 4.0, + "Browsers": "Firefox" + }, + { + "Year": 2024, + "Units": 14.5, + "Browsers": "Safari" + }, + { + "Year": 2024, + "Units": 2.0, + "Browsers": "Opera" + }, + { + "Year": 2024, + "Units": 7.5, + "Browsers": "Edge" + }, + { + "Year": 2024, + "Units": 1.5, + "Browsers": "Others" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 2009 to 2024", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding browsers", + "role": "y" + }, + { + "name": "Browsers", + "importance": "primary", + "description": "The browsers of the data point", + "role": "group" + } + ] + } + }, + "1332": { + "metadata": { + "title": "INTERNATIONAL TOURISM IS GROWING FASTER THAN DOMESTIC", + "description": "International vs. domestic tourist arrivals in Europe (in millions)", + "main_insight": "International tourist arrivals in Europe are growing at a faster rate (4.6% CAGR) than domestic arrivals (1.3% CAGR) between 2015 and 2019, although domestic arrivals are still significantly higher in absolute numbers." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Units": 943.0, + "Arrival Type": "Domestic Arrivals" + }, + { + "Year": 2019, + "Units": 430.0, + "Arrival Type": "International Arrivals" + } + ], + "score": 0.94, + "annotation": "The Units of Domestic Arrivals and International Arrivals show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Domestic Arrivals and International Arrivals is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2015, + "Units": 897.0, + "Arrival Type": "Domestic Arrivals" + }, + { + "Year": 2015, + "Units": 359.0, + "Arrival Type": "International Arrivals" + }, + { + "Year": 2016, + "Units": 912.0, + "Arrival Type": "Domestic Arrivals" + }, + { + "Year": 2016, + "Units": 373.0, + "Arrival Type": "International Arrivals" + }, + { + "Year": 2017, + "Units": 915.0, + "Arrival Type": "Domestic Arrivals" + }, + { + "Year": 2017, + "Units": 400.0, + "Arrival Type": "International Arrivals" + }, + { + "Year": 2018, + "Units": 926.0, + "Arrival Type": "Domestic Arrivals" + }, + { + "Year": 2018, + "Units": 419.0, + "Arrival Type": "International Arrivals" + }, + { + "Year": 2019, + "Units": 943.0, + "Arrival Type": "Domestic Arrivals" + }, + { + "Year": 2019, + "Units": 430.0, + "Arrival Type": "International Arrivals" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of recorded tourist arrivals", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding arrival type", + "role": "y" + }, + { + "name": "Arrival Type", + "importance": "primary", + "description": "The arrival type of the data point", + "role": "group" + } + ] + } + }, + "1230": { + "metadata": { + "title": "Has Twitter Reached Its Natural Growth Limit?", + "description": "Twitter's worldwide monthly active users", + "main_insight": "Twitter's monthly active user growth has slowed significantly since 2015, reaching 330 million by 2019. The company announced it will stop sharing its monthly active user count after Q1 2019." + }, + "chart_type": "Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2010, + "Monthly Active Users": 30.0 + }, + { + "Year": 2011, + "Monthly Active Users": 100.0 + }, + { + "Year": 2012, + "Monthly Active Users": 185.0 + }, + { + "Year": 2013, + "Monthly Active Users": 204.0 + }, + { + "Year": 2014, + "Monthly Active Users": 255.0 + }, + { + "Year": 2015, + "Monthly Active Users": 302.0 + }, + { + "Year": 2016, + "Monthly Active Users": 310.0 + }, + { + "Year": 2017, + "Monthly Active Users": 327.0 + }, + { + "Year": 2018, + "Monthly Active Users": 330.0 + }, + { + "Year": 2019, + "Monthly Active Users": 330.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Monthly Active Users", + "importance": "primary", + "description": "Number of Twitter's worldwide monthly active users (in millions)", + "unit": "millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "962": { + "metadata": { + "title": "U.S. Dollar Dominates Global Payment Network SWIFT", + "description": "Share of global payments processed on SWIFT in August 2024, by currency (based on transaction value)", + "main_insight": "The US Dollar is the dominant currency on the SWIFT network, accounting for almost half of all transactions by value, more than double the Euro." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Currency": "USD", + "Share": 49.1 + } + ], + "score": 0.96, + "annotation": "The has a maximum value at USD", + "reason": "The [{'Currency': 'USD', 'Share': 49.1}] of has a maximum value of 49.1, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Currency": "USD", + "Share": 49.1 + } + ], + "score": 0.87, + "annotation": "The shows a sudden jump in Share from EUR to USD.", + "reason": "The Share for increases from 21.6 at EUR to 49.1 at USD, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Currency": "HKD", + "Share": 1.6 + } + ], + "score": 0.41, + "annotation": "The has a minimum value at HKD", + "reason": "The [{'Currency': 'HKD', 'Share': 1.6}] of has a mainimum value of 1.6, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Currency": "USD", + "Share": 49.1 + }, + { + "Currency": "EUR", + "Share": 21.6 + }, + { + "Currency": "GBP", + "Share": 6.5 + }, + { + "Currency": "CNY", + "Share": 4.7 + }, + { + "Currency": "JPY", + "Share": 4.0 + }, + { + "Currency": "CAD", + "Share": 2.4 + }, + { + "Currency": "AUD", + "Share": 1.7 + }, + { + "Currency": "HKD", + "Share": 1.6 + }, + { + "Currency": "Others", + "Share": 8.4 + } + ], + "columns": [ + { + "name": "Currency", + "importance": "primary", + "description": "Currency used in SWIFT transactions", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Share", + "importance": "primary", + "description": "Percentage of global payments processed on SWIFT", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1583": { + "metadata": { + "title": "How Will AI Change Our Lives?", + "description": "Share of respondents expecting the following areas to be changed most by the use of AI in the next 3-5 years", + "main_insight": "Education, Safety, and Employment are the top three areas where respondents expect AI to have the most significant impact in the next 3-5 years. Food/nutrition and Personal relationships are predicted to see the least change." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Area of Life": "Food/nutrition", + "Units": 15.0, + "Response Rate": "Respondents (%)" + }, + { + "Area of Life": "Personal relationships", + "Units": 15.0, + "Response Rate": "Respondents (%)" + } + ], + "score": 0.85, + "annotation": "The Respondents (%) has minimum values at Food/nutrition, Personal relationships", + "reason": "The [{'Area of Life': 'Food/nutrition', 'Units': 15.0, 'Response Rate': 'Respondents (%)'}, {'Area of Life': 'Personal relationships', 'Units': 15.0, 'Response Rate': 'Respondents (%)'}] of Respondents (%) have minimum values of 15.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Area of Life": "Education", + "Units": 35.0, + "Response Rate": "Respondents (%)" + } + ], + "score": 0.75, + "annotation": "The Respondents (%) has a maximum value at Education", + "reason": "The [{'Area of Life': 'Education', 'Units': 35.0, 'Response Rate': 'Respondents (%)'}] of Respondents (%) has a maximum value of 35.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Area of Life": "Environment", + "Units": 22.0, + "Response Rate": "Respondents (%)" + } + ], + "score": 0.73, + "annotation": "The Respondents (%) shows a sudden jump in Units from Personal relationships to Environment.", + "reason": "The Units for Respondents (%) increases from 15.0 at Personal relationships to 22.0 at Environment, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Area of Life": "Education", + "Units": 35.0, + "Response Rate": "Respondents (%)" + }, + { + "Area of Life": "Safety", + "Units": 33.0, + "Response Rate": "Respondents (%)" + }, + { + "Area of Life": "Employment", + "Units": 32.0, + "Response Rate": "Respondents (%)" + }, + { + "Area of Life": "Shopping", + "Units": 31.0, + "Response Rate": "Respondents (%)" + }, + { + "Area of Life": "Transportation", + "Units": 30.0, + "Response Rate": "Respondents (%)" + }, + { + "Area of Life": "Entertainment", + "Units": 27.0, + "Response Rate": "Respondents (%)" + }, + { + "Area of Life": "Cost of living", + "Units": 26.0, + "Response Rate": "Respondents (%)" + }, + { + "Area of Life": "Home", + "Units": 23.0, + "Response Rate": "Respondents (%)" + }, + { + "Area of Life": "Income", + "Units": 23.0, + "Response Rate": "Respondents (%)" + }, + { + "Area of Life": "Environment", + "Units": 22.0, + "Response Rate": "Respondents (%)" + }, + { + "Area of Life": "Food/nutrition", + "Units": 15.0, + "Response Rate": "Respondents (%)" + }, + { + "Area of Life": "Personal relationships", + "Units": 15.0, + "Response Rate": "Respondents (%)" + } + ], + "columns": [ + { + "name": "Area of Life", + "importance": "primary", + "description": "The area of life expected to be changed by AI", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding response rate", + "role": "y" + }, + { + "name": "Response Rate", + "importance": "primary", + "description": "The response rate of the data point", + "role": "group" + } + ] + } + }, + "798": { + "metadata": { + "title": "Intention to Cancel Netflix High Amid Price Hikes", + "description": "Share of U.S. subscribers who said they would do the following if Netflix were to increase prices", + "main_insight": "A significant portion of Netflix subscribers, particularly those with ad-free subscriptions, indicate they would cancel their service if prices increased. A smaller, but still relevant group would change to a cheaper plan." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Subscriber Group": "Among all ad-free subscribers", + "Units": 48.0, + "Subscriber Action": "Stick with/change to ad-free subscription" + }, + { + "Subscriber Group": "Among all ad-free subscribers", + "Units": 17.0, + "Subscriber Action": "Stick with/change to cheaper ad-supported rate" + } + ], + "score": 1.0, + "annotation": "The Units of Stick with/change to ad-free subscription and Stick with/change to cheaper ad-supported rate show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Stick with/change to ad-free subscription and Stick with/change to cheaper ad-supported rate is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Subscriber Group": "Among all ad-free subscribers", + "Units": 48.0, + "Subscriber Action": "Stick with/change to ad-free subscription" + }, + { + "Subscriber Group": "Among all ad-free subscribers", + "Units": 35.0, + "Subscriber Action": "Cancel Netflix" + } + ], + "score": 1.0, + "annotation": "The Units of Stick with/change to ad-free subscription and Cancel Netflix show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Stick with/change to ad-free subscription and Cancel Netflix is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Subscriber Group": "Among all ad-free subscribers", + "Units": 17.0, + "Subscriber Action": "Stick with/change to cheaper ad-supported rate" + }, + { + "Subscriber Group": "Among all ad-free subscribers", + "Units": 35.0, + "Subscriber Action": "Cancel Netflix" + } + ], + "score": 1.0, + "annotation": "The Units of Stick with/change to cheaper ad-supported rate and Cancel Netflix show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Stick with/change to cheaper ad-supported rate and Cancel Netflix is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Subscriber Group": "Among all ad-free subscribers", + "Units": 48.0, + "Subscriber Action": "Stick with/change to ad-free subscription" + } + ], + "score": 0.97, + "annotation": "The Stick with/change to ad-free subscription shows a sudden jump in Units from Among all Netflix subscribers/those with intention to subscribe to Among all ad-free subscribers.", + "reason": "The Units for Stick with/change to ad-free subscription increases from 29.0 at Among all Netflix subscribers/those with intention to subscribe to 48.0 at Among all ad-free subscribers, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Subscriber Group": "Among all Netflix subscribers/those with intention to subscribe", + "Units": 31.0, + "Subscriber Action": "Stick with/change to cheaper ad-supported rate" + } + ], + "score": 0.97, + "annotation": "The Stick with/change to cheaper ad-supported rate shows a sudden jump in Units from Among all ad-free subscribers to Among all Netflix subscribers/those with intention to subscribe.", + "reason": "The Units for Stick with/change to cheaper ad-supported rate increases from 17.0 at Among all ad-free subscribers to 31.0 at Among all Netflix subscribers/those with intention to subscribe, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Subscriber Group": "Among all Netflix subscribers/those with intention to subscribe", + "Units": 29.0, + "Subscriber Action": "Stick with/change to ad-free subscription" + }, + { + "Subscriber Group": "Among all Netflix subscribers/those with intention to subscribe", + "Units": 31.0, + "Subscriber Action": "Stick with/change to cheaper ad-supported rate" + }, + { + "Subscriber Group": "Among all Netflix subscribers/those with intention to subscribe", + "Units": 39.0, + "Subscriber Action": "Cancel Netflix" + }, + { + "Subscriber Group": "Among all ad-free subscribers", + "Units": 48.0, + "Subscriber Action": "Stick with/change to ad-free subscription" + }, + { + "Subscriber Group": "Among all ad-free subscribers", + "Units": 17.0, + "Subscriber Action": "Stick with/change to cheaper ad-supported rate" + }, + { + "Subscriber Group": "Among all ad-free subscribers", + "Units": 35.0, + "Subscriber Action": "Cancel Netflix" + } + ], + "columns": [ + { + "name": "Subscriber Group", + "importance": "primary", + "description": "Category of Netflix subscribers", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding subscriber action", + "role": "y" + }, + { + "name": "Subscriber Action", + "importance": "primary", + "description": "The subscriber action of the data point", + "role": "group" + } + ] + } + }, + "405": { + "metadata": { + "title": "Where do people find news on their smartphones?", + "description": "Proportion of smartphone-first users that used each for first contact with news", + "main_insight": "Smartphone users in the UK and US primarily use news websites or apps for their first contact with news, while in Finland and Italy, social media/messaging apps are the most popular." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "United States", + "Units": 11.0, + "Information Source": "Alerts/notifications" + }, + { + "Country": "United States", + "Units": 6.0, + "Information Source": "Aggregator" + } + ], + "score": 0.99, + "annotation": "The Units of Alerts/notifications and Aggregator show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Alerts/notifications and Aggregator is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United Kingdom", + "Units": 43.0, + "Information Source": "News website or app" + } + ], + "score": 0.96, + "annotation": "The News website or app shows a sudden jump in Units from Italy to United Kingdom.", + "reason": "The Units for News website or app increases from 19.0 at Italy to 43.0 at United Kingdom, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Information Source": "News website or app", + "Country": "total", + "Units": 153.0 + } + ], + "score": 0.94, + "annotation": "The total value of News website or app has the largest proportion.", + "reason": "The total value of Units of News website or app accounts for 0.37965260545905705, larger than all other Information Source, of the total of Units of all Information Source." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "United States", + "Units": 46.0, + "Information Source": "News website or app" + }, + { + "Country": "United States", + "Units": 21.0, + "Information Source": "Social media/messaging apps" + } + ], + "score": 0.94, + "annotation": "The Units of News website or app and Social media/messaging apps show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between News website or app and Social media/messaging apps is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "United Kingdom", + "Units": 43.0, + "Information Source": "News website or app" + } + ], + "score": 0.93, + "annotation": "The News website or app has the largest proportion in United Kingdom.", + "reason": "The Units of News website or app in United Kingdom accounts for 0.42574257425742573, larger than all other Information Source, of the total of Units of all Information Source." + } + ], + "data": { + "data": [ + { + "Country": "United Kingdom", + "Units": 43.0, + "Information Source": "News website or app" + }, + { + "Country": "United Kingdom", + "Units": 35.0, + "Information Source": "Social media/messaging apps" + }, + { + "Country": "United Kingdom", + "Units": 12.0, + "Information Source": "Alerts/notifications" + }, + { + "Country": "United Kingdom", + "Units": 3.0, + "Information Source": "Email" + }, + { + "Country": "United Kingdom", + "Units": 3.0, + "Information Source": "Aggregator" + }, + { + "Country": "United Kingdom", + "Units": 5.0, + "Information Source": "Other/Don't Know" + }, + { + "Country": "Finland", + "Units": 45.0, + "Information Source": "News website or app" + }, + { + "Country": "Finland", + "Units": 26.0, + "Information Source": "Social media/messaging apps" + }, + { + "Country": "Finland", + "Units": 7.0, + "Information Source": "Alerts/notifications" + }, + { + "Country": "Finland", + "Units": 2.0, + "Information Source": "Email" + }, + { + "Country": "Finland", + "Units": 14.0, + "Information Source": "Aggregator" + }, + { + "Country": "Finland", + "Units": 6.0, + "Information Source": "Other/Don't Know" + }, + { + "Country": "Italy", + "Units": 19.0, + "Information Source": "News website or app" + }, + { + "Country": "Italy", + "Units": 51.0, + "Information Source": "Social media/messaging apps" + }, + { + "Country": "Italy", + "Units": 10.0, + "Information Source": "Alerts/notifications" + }, + { + "Country": "Italy", + "Units": 7.0, + "Information Source": "Email" + }, + { + "Country": "Italy", + "Units": 7.0, + "Information Source": "Aggregator" + }, + { + "Country": "Italy", + "Units": 7.0, + "Information Source": "Other/Don't Know" + }, + { + "Country": "United States", + "Units": 46.0, + "Information Source": "News website or app" + }, + { + "Country": "United States", + "Units": 21.0, + "Information Source": "Social media/messaging apps" + }, + { + "Country": "United States", + "Units": 11.0, + "Information Source": "Alerts/notifications" + }, + { + "Country": "United States", + "Units": 9.0, + "Information Source": "Email" + }, + { + "Country": "United States", + "Units": 6.0, + "Information Source": "Aggregator" + }, + { + "Country": "United States", + "Units": 8.0, + "Information Source": "Other/Don't Know" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where the survey was conducted", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding information source", + "role": "y" + }, + { + "name": "Information Source", + "importance": "primary", + "description": "The information source of the data point", + "role": "group" + } + ] + } + }, + "2148": { + "metadata": { + "title": "The World's Top Cities For Digital Security", + "description": "Top-10 digital security scorers in the 2019 Safe Cities Index (100=best)", + "main_insight": "Tokyo ranks highest in digital security among the world's top cities, followed closely by Singapore and Chicago, according to the 2019 Safe Cities Index." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Tokyo", + "Digital Security Score": 94.4 + } + ], + "score": 0.91, + "annotation": "The has a maximum value at Tokyo", + "reason": "The [{'City': 'Tokyo', 'Digital Security Score': 94.4}] of has a maximum value of 94.4, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "London", + "Digital Security Score": 90.2 + } + ], + "score": 0.73, + "annotation": "The has a minimum value at London", + "reason": "The [{'City': 'London', 'Digital Security Score': 90.2}] of has a mainimum value of 90.2, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Tokyo", + "Digital Security Score": 94.4 + } + ], + "score": 0.7, + "annotation": "The shows a sudden jump in Digital Security Score from Singapore to Tokyo.", + "reason": "The Digital Security Score for increases from 93.1 at Singapore to 94.4 at Tokyo, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "City": "Tokyo", + "Digital Security Score": 94.4 + }, + { + "City": "Singapore", + "Digital Security Score": 93.1 + }, + { + "City": "Chicago", + "Digital Security Score": 92.9 + }, + { + "City": "Washington D.C.", + "Digital Security Score": 92.2 + }, + { + "City": "Los Angeles", + "Digital Security Score": 91.4 + }, + { + "City": "San Francisco", + "Digital Security Score": 91.4 + }, + { + "City": "Dallas", + "Digital Security Score": 91.3 + }, + { + "City": "New York", + "Digital Security Score": 91.1 + }, + { + "City": "Toronto", + "Digital Security Score": 90.6 + }, + { + "City": "London", + "Digital Security Score": 90.2 + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "Name of the city", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Digital Security Score", + "importance": "primary", + "description": "Score in the 2019 Safe Cities Index (100=best)", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "273": { + "metadata": { + "title": "Live Nation Barely Skipped a Beat in the Past Decade", + "description": "Concert and ticketing revenue of Live Nation Entertainment", + "main_insight": "Live Nation's revenue, primarily from concerts, has grown significantly over the past decade, with a notable surge in 2022 and 2023, far surpassing pre-pandemic levels." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Ratio": 18.8, + "Event Industry": "Concerts" + }, + { + "Year": 2023, + "Ratio": 3.0, + "Event Industry": "Ticketing" + } + ], + "score": 0.9, + "annotation": "The Ratio of Concerts and Ticketing show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Concerts and Ticketing is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2009, + "Ratio": 3.3, + "Event Industry": "Concerts" + }, + { + "Year": 2009, + "Ratio": 0.8, + "Event Industry": "Ticketing" + }, + { + "Year": 2010, + "Ratio": 3.5, + "Event Industry": "Concerts" + }, + { + "Year": 2010, + "Ratio": 0.9, + "Event Industry": "Ticketing" + }, + { + "Year": 2011, + "Ratio": 4.0, + "Event Industry": "Concerts" + }, + { + "Year": 2011, + "Ratio": 1.0, + "Event Industry": "Ticketing" + }, + { + "Year": 2012, + "Ratio": 4.3, + "Event Industry": "Concerts" + }, + { + "Year": 2012, + "Ratio": 1.0, + "Event Industry": "Ticketing" + }, + { + "Year": 2013, + "Ratio": 5.1, + "Event Industry": "Concerts" + }, + { + "Year": 2013, + "Ratio": 1.1, + "Event Industry": "Ticketing" + }, + { + "Year": 2014, + "Ratio": 5.2, + "Event Industry": "Concerts" + }, + { + "Year": 2014, + "Ratio": 1.4, + "Event Industry": "Ticketing" + }, + { + "Year": 2015, + "Ratio": 5.6, + "Event Industry": "Concerts" + }, + { + "Year": 2015, + "Ratio": 1.5, + "Event Industry": "Ticketing" + }, + { + "Year": 2016, + "Ratio": 6.5, + "Event Industry": "Concerts" + }, + { + "Year": 2016, + "Ratio": 1.6, + "Event Industry": "Ticketing" + }, + { + "Year": 2017, + "Ratio": 8.4, + "Event Industry": "Concerts" + }, + { + "Year": 2017, + "Ratio": 1.9, + "Event Industry": "Ticketing" + }, + { + "Year": 2018, + "Ratio": 8.8, + "Event Industry": "Concerts" + }, + { + "Year": 2018, + "Ratio": 2.0, + "Event Industry": "Ticketing" + }, + { + "Year": 2019, + "Ratio": 9.4, + "Event Industry": "Concerts" + }, + { + "Year": 2019, + "Ratio": 2.1, + "Event Industry": "Ticketing" + }, + { + "Year": 2020, + "Ratio": 1.4, + "Event Industry": "Concerts" + }, + { + "Year": 2020, + "Ratio": 0.4, + "Event Industry": "Ticketing" + }, + { + "Year": 2021, + "Ratio": 5.6, + "Event Industry": "Concerts" + }, + { + "Year": 2021, + "Ratio": 0.9, + "Event Industry": "Ticketing" + }, + { + "Year": 2022, + "Ratio": 14.9, + "Event Industry": "Concerts" + }, + { + "Year": 2022, + "Ratio": 1.8, + "Event Industry": "Ticketing" + }, + { + "Year": 2023, + "Ratio": 18.8, + "Event Industry": "Concerts" + }, + { + "Year": 2023, + "Ratio": 3.0, + "Event Industry": "Ticketing" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of revenue report", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding event industry", + "role": "y" + }, + { + "name": "Event Industry", + "importance": "primary", + "description": "The event industry of the data point", + "role": "group" + } + ] + } + }, + "709": { + "metadata": { + "title": "Retail Store Closures Spiked in 2019", + "description": "Number of major retail store openings and closures in the U.S. announced in 2018 and 2019", + "main_insight": "Retail store closures significantly increased in 2019 compared to 2018, while store openings saw a modest increase." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Units": 9275, + "Event Type": "Closures" + }, + { + "Year": 2019, + "Units": 4454, + "Event Type": "Openings" + } + ], + "score": 1.0, + "annotation": "The Units of Closures and Openings show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Closures and Openings is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2018, + "Units": 6897, + "Event Type": "Closures" + }, + { + "Year": 2018, + "Units": 4311, + "Event Type": "Openings" + }, + { + "Year": 2019, + "Units": 9275, + "Event Type": "Closures" + }, + { + "Year": 2019, + "Units": 4454, + "Event Type": "Openings" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding event type", + "role": "y" + }, + { + "name": "Event Type", + "importance": "primary", + "description": "The event type of the data point", + "role": "group" + } + ] + } + }, + "1212": { + "metadata": { + "title": "How Safe Is U.S. Food?", + "description": "Total number of food recalls in the U.S. from 2013 to 2019", + "main_insight": "Food recalls peaked in 2016 and have generally declined since, although remaining higher than in 2013. The majority of recalls are for Produce, Processed Food, and Other categories, with Meat & Poultry making up a smaller portion." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Units": 641, + "Recall Category": "Total Recalls" + }, + { + "Year": 2019, + "Units": 517, + "Recall Category": "Produce, Processed Food, Other" + } + ], + "score": 0.97, + "annotation": "The Units of Total Recalls and Produce, Processed Food, Other show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Recalls and Produce, Processed Food, Other is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Units": 641, + "Recall Category": "Total Recalls" + }, + { + "Year": 2019, + "Units": 124, + "Recall Category": "Meat & Poultry" + } + ], + "score": 0.48, + "annotation": "The Units of Total Recalls and Meat & Poultry show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Recalls and Meat & Poultry is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Units": 517, + "Recall Category": "Produce, Processed Food, Other" + }, + { + "Year": 2019, + "Units": 124, + "Recall Category": "Meat & Poultry" + } + ], + "score": 0.25, + "annotation": "The Units of Produce, Processed Food, Other and Meat & Poultry show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Produce, Processed Food, Other and Meat & Poultry is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2013, + "Units": 639, + "Recall Category": "Total Recalls" + }, + { + "Year": 2013, + "Units": 564, + "Recall Category": "Produce, Processed Food, Other" + }, + { + "Year": 2013, + "Units": 75, + "Recall Category": "Meat & Poultry" + }, + { + "Year": 2014, + "Units": 647, + "Recall Category": "Total Recalls" + }, + { + "Year": 2014, + "Units": 553, + "Recall Category": "Produce, Processed Food, Other" + }, + { + "Year": 2014, + "Units": 94, + "Recall Category": "Meat & Poultry" + }, + { + "Year": 2015, + "Units": 737, + "Recall Category": "Total Recalls" + }, + { + "Year": 2015, + "Units": 587, + "Recall Category": "Produce, Processed Food, Other" + }, + { + "Year": 2015, + "Units": 150, + "Recall Category": "Meat & Poultry" + }, + { + "Year": 2016, + "Units": 905, + "Recall Category": "Total Recalls" + }, + { + "Year": 2016, + "Units": 783, + "Recall Category": "Produce, Processed Food, Other" + }, + { + "Year": 2016, + "Units": 122, + "Recall Category": "Meat & Poultry" + }, + { + "Year": 2017, + "Units": 817, + "Recall Category": "Total Recalls" + }, + { + "Year": 2017, + "Units": 686, + "Recall Category": "Produce, Processed Food, Other" + }, + { + "Year": 2017, + "Units": 131, + "Recall Category": "Meat & Poultry" + }, + { + "Year": 2018, + "Units": 703, + "Recall Category": "Total Recalls" + }, + { + "Year": 2018, + "Units": 578, + "Recall Category": "Produce, Processed Food, Other" + }, + { + "Year": 2018, + "Units": 125, + "Recall Category": "Meat & Poultry" + }, + { + "Year": 2019, + "Units": 641, + "Recall Category": "Total Recalls" + }, + { + "Year": 2019, + "Units": 517, + "Recall Category": "Produce, Processed Food, Other" + }, + { + "Year": 2019, + "Units": 124, + "Recall Category": "Meat & Poultry" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of food recall", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding recall category", + "role": "y" + }, + { + "name": "Recall Category", + "importance": "primary", + "description": "The recall category of the data point", + "role": "group" + } + ] + } + }, + "1339": { + "metadata": { + "title": "South Korea Reports No New Domestic COVID-19 Cases", + "description": "Newly confirmed COVID-19 infections in South Korea", + "main_insight": "South Korea experienced a dramatic spike in COVID-19 cases in late February and early March, but successfully reduced new infections to near zero by the end of April 2020." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "Feb 29", + "New Cases": 909 + } + ], + "score": 1.0, + "annotation": "The has a maximum value at Feb 29", + "reason": "The [{'Date': 'Feb 29', 'New Cases': 909}] of has a maximum value of 909, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "Mar 05", + "New Cases": 600 + } + ], + "score": 0.76, + "annotation": "The shows a sudden jump in New Cases from Mar 10 to Mar 05.", + "reason": "The New Cases for increases from 250 at Mar 10 to 600 at Mar 05, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "Jan 20", + "New Cases": 0 + }, + { + "Date": "Jan 25", + "New Cases": 0 + }, + { + "Date": "Jan 30", + "New Cases": 0 + }, + { + "Date": "Feb 04", + "New Cases": 0 + }, + { + "Date": "Feb 09", + "New Cases": 0 + }, + { + "Date": "Feb 14", + "New Cases": 0 + } + ], + "score": 0.38, + "annotation": "The has minimum values at Jan 20, Jan 25, Jan 30, Feb 04, Feb 09, Feb 14", + "reason": "The [{'Date': 'Jan 20', 'New Cases': 0}, {'Date': 'Jan 25', 'New Cases': 0}, {'Date': 'Jan 30', 'New Cases': 0}, {'Date': 'Feb 04', 'New Cases': 0}, {'Date': 'Feb 09', 'New Cases': 0}, {'Date': 'Feb 14', 'New Cases': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Date": "Jan 20", + "New Cases": 0 + }, + { + "Date": "Jan 25", + "New Cases": 0 + }, + { + "Date": "Jan 30", + "New Cases": 0 + }, + { + "Date": "Feb 04", + "New Cases": 0 + }, + { + "Date": "Feb 09", + "New Cases": 0 + }, + { + "Date": "Feb 14", + "New Cases": 0 + }, + { + "Date": "Feb 19", + "New Cases": 50 + }, + { + "Date": "Feb 24", + "New Cases": 200 + }, + { + "Date": "Feb 29", + "New Cases": 909 + }, + { + "Date": "Mar 05", + "New Cases": 600 + }, + { + "Date": "Mar 10", + "New Cases": 250 + }, + { + "Date": "Mar 15", + "New Cases": 100 + }, + { + "Date": "Mar 20", + "New Cases": 150 + }, + { + "Date": "Mar 25", + "New Cases": 100 + }, + { + "Date": "Mar 30", + "New Cases": 80 + }, + { + "Date": "Apr 04", + "New Cases": 100 + }, + { + "Date": "Apr 09", + "New Cases": 50 + }, + { + "Date": "Apr 14", + "New Cases": 30 + }, + { + "Date": "Apr 19", + "New Cases": 10 + }, + { + "Date": "Apr 24", + "New Cases": 10 + }, + { + "Date": "Apr 30", + "New Cases": 4 + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Date, ranging from January to April 2020", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "New Cases", + "importance": "primary", + "description": "Number of newly confirmed COVID-19 infections", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "707": { + "metadata": { + "title": "When it Comes to Average Team Value, NFL Reigns Supreme", + "description": "Highest and average team values across America's sporting leagues in 2013 (in million U.S. dollars)", + "main_insight": "The NFL had the highest average team value in 2013 at $1,170 million, significantly higher than other major sporting leagues. The Dallas Cowboys and New York Yankees were tied for highest individual team value." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "League": "NFL", + "Values": 1170.0, + "Valuation Metrics": "Average Team Value" + }, + { + "League": "NFL", + "Values": 2300.0, + "Valuation Metrics": "Highest Team Value" + }, + { + "League": "NFL", + "Values": "Dallas Cowboys", + "Valuation Metrics": "Highest Valued Team" + }, + { + "League": "MLB", + "Values": 744.0, + "Valuation Metrics": "Average Team Value" + }, + { + "League": "MLB", + "Values": 2300.0, + "Valuation Metrics": "Highest Team Value" + }, + { + "League": "MLB", + "Values": "New York Yankees", + "Valuation Metrics": "Highest Valued Team" + }, + { + "League": "NHL", + "Values": 413.0, + "Valuation Metrics": "Average Team Value" + }, + { + "League": "NHL", + "Values": 1150.0, + "Valuation Metrics": "Highest Team Value" + }, + { + "League": "NHL", + "Values": "Toronto Maple Leafs", + "Valuation Metrics": "Highest Valued Team" + }, + { + "League": "NBA", + "Values": 509.0, + "Valuation Metrics": "Average Team Value" + }, + { + "League": "NBA", + "Values": 1100.0, + "Valuation Metrics": "Highest Team Value" + }, + { + "League": "NBA", + "Values": "New York Knicks", + "Valuation Metrics": "Highest Valued Team" + }, + { + "League": "MLS", + "Values": 37.0, + "Valuation Metrics": "Average Team Value" + }, + { + "League": "MLS", + "Values": 175.0, + "Valuation Metrics": "Highest Team Value" + }, + { + "League": "MLS", + "Values": "Seattle Sounders", + "Valuation Metrics": "Highest Valued Team" + } + ], + "columns": [ + { + "name": "League", + "importance": "primary", + "description": "Sports league", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Values", + "importance": "primary", + "description": "Values for the corresponding valuation metrics", + "role": "y" + }, + { + "name": "Valuation Metrics", + "importance": "primary", + "description": "The valuation metrics of the data point", + "role": "group" + } + ] + } + }, + "2039": { + "metadata": { + "title": "Rishi Sunak: Best of a Bad Bunch?", + "description": "\"Do you think the following would do a good or a bad job as prime minister?\"", + "main_insight": "Among the three candidates, Rishi Sunak had the highest percentage of respondents thinking he would do a good job as prime minister, and the lowest percentage thinking he would do a bad job." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Candidate": "Boris Johnson", + "Units": 56.0, + "Sentiment": "Bad" + } + ], + "score": 0.93, + "annotation": "The Bad shows a sudden jump in Units from Rishi Sunak to Boris Johnson.", + "reason": "The Units for Bad increases from 40.0 at Rishi Sunak to 56.0 at Boris Johnson, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Candidate": "Rishi Sunak", + "Units": 43.0, + "Sentiment": "Good" + } + ], + "score": 0.85, + "annotation": "The Good shows a sudden jump in Units from Boris Johnson to Rishi Sunak.", + "reason": "The Units for Good increases from 34.0 at Boris Johnson to 43.0 at Rishi Sunak, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Candidate": "Rishi Sunak", + "Units": 43.0, + "Sentiment": "Good" + } + ], + "score": 0.79, + "annotation": "The Good accounts for the majority in Rishi Sunak.", + "reason": "The Units of Good in Rishi Sunak accounts for 0.5180722891566265 (more than 50%) of the total of Units of all Sentiment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Sentiment": "Bad", + "Candidate": "total", + "Units": 131.0 + } + ], + "score": 0.78, + "annotation": "The total value of Bad accounts for the majority in all Sentiment.", + "reason": "The total value of Units of Bad accounts for 0.5598290598290598 (more than 50%) of the total of Units of all Sentiment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Candidate": "Penny Mordaunt", + "Units": 35.0, + "Sentiment": "Bad" + } + ], + "score": 0.78, + "annotation": "The Bad accounts for the majority in Penny Mordaunt.", + "reason": "The Units of Bad in Penny Mordaunt accounts for 0.5737704918032787 (more than 50%) of the total of Units of all Sentiment." + } + ], + "data": { + "data": [ + { + "Candidate": "Rishi Sunak", + "Units": 43.0, + "Sentiment": "Good" + }, + { + "Candidate": "Rishi Sunak", + "Units": 40.0, + "Sentiment": "Bad" + }, + { + "Candidate": "Boris Johnson", + "Units": 34.0, + "Sentiment": "Good" + }, + { + "Candidate": "Boris Johnson", + "Units": 56.0, + "Sentiment": "Bad" + }, + { + "Candidate": "Penny Mordaunt", + "Units": 26.0, + "Sentiment": "Good" + }, + { + "Candidate": "Penny Mordaunt", + "Units": 35.0, + "Sentiment": "Bad" + } + ], + "columns": [ + { + "name": "Candidate", + "importance": "primary", + "description": "Name of the political candidate", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "41": { + "metadata": { + "title": "1 in 5 Americans Eat Fast Food Several Times a Week", + "description": "Frequency of dining at fast food restaurants among Americans", + "main_insight": "The frequency of Americans eating fast food has generally decreased from 2003 to 2013, with a notable drop in those eating it several times a week or about once a week." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Frequency": "Never", + "Score": 5.0, + "Timepoints": "July 2003" + }, + { + "Frequency": "Never", + "Score": 5.0, + "Timepoints": "July 2013" + } + ], + "score": 0.98, + "annotation": "The Score of July 2003 and July 2013 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between July 2003 and July 2013 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Frequency": "Never", + "Score": 5.0, + "Timepoints": "July 2003" + }, + { + "Frequency": "Never", + "Score": 4.0, + "Timepoints": "December 2006" + } + ], + "score": 0.96, + "annotation": "The Score of July 2003 and December 2006 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between July 2003 and December 2006 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Timepoints": "July 2003", + "Frequency": "total", + "Score": 100.0 + } + ], + "score": 0.9, + "annotation": "The total value of July 2003 has the largest proportion.", + "reason": "The total value of Score of July 2003 accounts for 0.33783783783783783, larger than all other Timepoints, of the total of Score of all Timepoints." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Frequency": "Never", + "Score": 4.0, + "Timepoints": "December 2006" + }, + { + "Frequency": "Never", + "Score": 5.0, + "Timepoints": "July 2013" + } + ], + "score": 0.9, + "annotation": "The Score of December 2006 and July 2013 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between December 2006 and July 2013 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Frequency": "A few Times a Year", + "Score": 15.0, + "Timepoints": "July 2013" + } + ], + "score": 0.89, + "annotation": "The July 2013 has the largest proportion in A few Times a Year.", + "reason": "The Score of July 2013 in A few Times a Year accounts for 0.35714285714285715, larger than all other Timepoints, of the total of Score of all Timepoints." + } + ], + "data": { + "data": [ + { + "Frequency": "Every Day", + "Score": 4.0, + "Timepoints": "July 2003" + }, + { + "Frequency": "Every Day", + "Score": 3.0, + "Timepoints": "December 2006" + }, + { + "Frequency": "Every Day", + "Score": 3.0, + "Timepoints": "July 2013" + }, + { + "Frequency": "Several Times a Week", + "Score": 17.0, + "Timepoints": "July 2003" + }, + { + "Frequency": "Several Times a Week", + "Score": 20.0, + "Timepoints": "December 2006" + }, + { + "Frequency": "Several Times a Week", + "Score": 15.0, + "Timepoints": "July 2013" + }, + { + "Frequency": "About Once a Week", + "Score": 31.0, + "Timepoints": "July 2003" + }, + { + "Frequency": "About Once a Week", + "Score": 33.0, + "Timepoints": "December 2006" + }, + { 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in Percentage from Very positive impact to Somewhat positive impact.", + "reason": "The Percentage for increases from 43 at Very positive impact to 44 at Somewhat positive impact, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Response": "Somewhat positive impact", + "Percentage": 44 + } + ], + "score": 0.45, + "annotation": "The has a maximum value at Somewhat positive impact", + "reason": "The [{'Response': 'Somewhat positive impact', 'Percentage': 44}] of has a maximum value of 44, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Response": "Very positive impact", + "Percentage": 43 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at Very positive impact", + "reason": "The [{'Response': 'Very positive impact', 'Percentage': 43}] of has a mainimum value of 43, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Response": "Very positive impact", + "Percentage": 43 + }, + { + "Response": "Somewhat positive impact", + "Percentage": 44 + } + ], + "columns": [ + { + "name": "Response", + "importance": "primary", + "description": "Survey response category", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents in each category", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1599": { + "metadata": { + "title": "Google Paid More in EU Fines Than in Taxes in 2018", + "description": "Alphabet's income tax compared to fines imposed by the European Commission", + "main_insight": "In 2018, Google's parent company, Alphabet, paid more in EU antitrust fines ($5.1 billion) than it did in income taxes ($4.2 billion)." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2018, + "Ratio": 5.1, + "Expense Type": "EU antitrust fines" + }, + { + "Year": 2018, + "Ratio": 4.2, + "Expense Type": "Provision for income taxes" + } + ], + "score": 1.0, + "annotation": "The Ratio of EU antitrust fines and Provision for income taxes show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between EU antitrust fines and Provision for income taxes is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2017, + "Ratio": 2.7, + "Expense Type": "EU antitrust fines" + }, + { + "Year": 2017, + "Ratio": 4.3, + "Expense Type": "Provision for income taxes" + }, + { + "Year": 2018, + "Ratio": 5.1, + "Expense Type": "EU antitrust fines" + }, + { + "Year": 2018, + "Ratio": 4.2, + "Expense Type": "Provision for income taxes" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "The year of the data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + 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"Timeframe": "First Half of 2014" + }, + { + "Category": "Downloads", + "Units": 1268.0, + "Timeframe": "First Half of 2015" + }, + { + "Category": "Streaming", + "Units": 834.0, + "Timeframe": "First Half of 2014" + }, + { + "Category": "Streaming", + "Units": 1028.0, + "Timeframe": "First Half of 2015" + }, + { + "Category": "Physical", + "Units": 905.0, + "Timeframe": "First Half of 2014" + }, + { + "Category": "Physical", + "Units": 748.0, + "Timeframe": "First Half of 2015" + }, + { + "Category": "Others*", + "Units": 127.0, + "Timeframe": "First Half of 2014" + }, + { + "Category": "Others*", + "Units": 123.0, + "Timeframe": "First Half of 2015" + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Music industry revenue category", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding timeframe", + "role": "y" + }, + { + "name": "Timeframe", + "importance": "primary", + "description": "The timeframe of the data point", + "role": "group" + } + ] + } + }, + "453": { + "metadata": { + "title": "Holiday Spending Grows Despite Inflation Woes", + "description": "Estimated online holiday season spending in the United States in 2022, by event (in billion U.S. dollars)", + "main_insight": "Online holiday spending in the US increased in 2022 compared to 2021 across Thanksgiving Day, Black Friday, and Cyber Monday, with Cyber Monday having the highest spending and growth." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Event": "Black Friday", + "Spending (billion USD)": 9.1 + } + ], + "score": 0.9, + "annotation": "The shows a sudden jump in Spending (billion USD) from Thanksgiving Day to Black Friday.", + "reason": "The Spending (billion USD) for increases from 5.3 at Thanksgiving Day to 9.1 at Black Friday, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Event": "Thanksgiving Day", + "Spending (billion USD)": 5.3 + } + ], + "score": 0.64, + "annotation": "The has a minimum value at Thanksgiving Day", + "reason": "The [{'Event': 'Thanksgiving Day', 'Spending (billion USD)': 5.3}] of has a mainimum value of 5.3, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Event": "Cyber Monday", + "Spending (billion USD)": 11.2 + } + ], + "score": 0.55, + "annotation": "The has a maximum value at Cyber Monday", + "reason": "The [{'Event': 'Cyber Monday', 'Spending (billion USD)': 11.2}] of has a maximum value of 11.2, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Event": "Thanksgiving Day", + "Spending (billion USD)": 5.3 + }, + { + "Event": "Black Friday", + "Spending (billion USD)": 9.1 + }, + { + "Event": "Cyber Monday", + "Spending (billion USD)": 11.2 + } + ], + "columns": [ + { + "name": "Event", + "importance": "primary", + "description": "Holiday shopping event", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Spending (billion USD)", + "importance": "primary", + "description": "Estimated online spending in billion US dollars", + "unit": "billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "986": { + "metadata": { + "title": "Marijuana Legalization Enjoys Surging Support in the U.S.", + "description": "Do you think the use of marijuana should be legal or not?", + "main_insight": "Support for marijuana legalization in the U.S. has significantly increased since 1969, surpassing the opposition and becoming the majority opinion around 2012." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2013, + "Points": 58.0, + "Legality": "Yes, legal" + }, + { + "Year": 2013, + "Points": 39.0, + "Legality": "No, not legal" + } + ], + "score": 1.0, + "annotation": "The Points of Yes, legal and No, not legal show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Points between Yes, legal and No, not legal is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 1969, + "Points": 12.0, + "Legality": "Yes, legal" + }, + { + "Year": 1969, + "Points": 84.0, + "Legality": "No, not legal" + }, + { + "Year": 1973, + "Points": 19.0, + "Legality": "Yes, legal" + }, + { + "Year": 1973, + "Points": 78.0, + "Legality": "No, not legal" + }, + { + "Year": 1977, + "Points": 28.0, + "Legality": "Yes, legal" + }, + { + "Year": 1977, + "Points": 69.0, + "Legality": "No, not legal" + }, + { + "Year": 1980, + "Points": 25.0, + "Legality": "Yes, legal" + }, + { + "Year": 1980, + "Points": 73.0, + "Legality": "No, not legal" + }, + { + "Year": 1985, + "Points": 23.0, + "Legality": "Yes, legal" + }, + { + "Year": 1985, + "Points": 73.0, + "Legality": "No, not legal" + }, + { + "Year": 1995, + "Points": 24.0, + "Legality": "Yes, legal" + }, + { + "Year": 1995, + "Points": 73.0, + "Legality": "No, not legal" + }, + { + "Year": 2000, + "Points": 31.0, + "Legality": "Yes, legal" + }, + { + "Year": 2000, + "Points": 64.0, + "Legality": "No, not legal" + }, + { + "Year": 2003, + "Points": 34.0, + "Legality": "Yes, legal" + }, + { + "Year": 2003, + "Points": 62.0, + "Legality": "No, not legal" + }, + { + "Year": 2005, + "Points": 36.0, + "Legality": "Yes, legal" + }, + { + "Year": 2005, + "Points": 60.0, + "Legality": "No, not legal" + }, + { + "Year": 2009, + "Points": 44.0, + "Legality": "Yes, legal" + }, + { + "Year": 2009, + "Points": 54.0, + "Legality": "No, not legal" + }, + { + "Year": 2012, + "Points": 50.0, + "Legality": "Yes, legal" + }, + { + "Year": 2012, + "Points": 48.0, + "Legality": "No, not legal" + }, + { + "Year": 2013, + "Points": 58.0, + "Legality": "Yes, legal" + }, + { + "Year": 2013, + 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UK Wage', 'Annual Salary/Wage': 26500.0}] of has a mainimum value of 26500.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Entity": "WPP (Sir Martin Sorrell)", + "Annual Salary/Wage": 43000000.0 + }, + { + "Entity": "Average UK Wage", + "Annual Salary/Wage": 26500.0 + } + ], + "columns": [ + { + "name": "Entity", + "importance": "primary", + "description": "The entity being described (either WPP's CEO or the average UK worker)", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Annual Salary/Wage", + "importance": "primary", + "description": "The annual salary or wage in British Pounds", + "unit": "GBP", + "data_type": "number", + "role": "y" + } + ] + } + }, + "602": { + "metadata": { + "title": "Political Attitudes of the Snapchat Generation", + "description": "What American first- and second-time voters think*", + "main_insight": "The Snapchat generation, aged 18-26, shows a strong preference for Bernie Sanders and the Democratic party, and a majority believes socialism is the most compassionate political system." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Candidate": "Bernie Sanders", + "Percentage_Vote": 45.0 + } + ], + "score": 0.91, + "annotation": "The shows a sudden jump in Percentage_Vote from Hillary Clinton to Bernie Sanders.", + "reason": "The Percentage_Vote for increases from 19.0 at Hillary Clinton to 45.0 at Bernie Sanders, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Candidate": "Bernie Sanders", + "Percentage_Vote": 45.0 + } + ], + "score": 0.91, + "annotation": "The has a maximum value at Bernie Sanders", + "reason": "The [{'Candidate': 'Bernie Sanders', 'Percentage_Vote': 45.0}] of has a maximum value of 45.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Candidate": "Marco Rubio", + "Percentage_Vote": 7.0 + } + ], + "score": 0.44, + "annotation": "The has a minimum value at Marco Rubio", + "reason": "The [{'Candidate': 'Marco Rubio', 'Percentage_Vote': 7.0}] of has a mainimum value of 7.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Candidate": "Bernie Sanders", + "Percentage_Vote": 45.0 + }, + { + "Candidate": "Hillary Clinton", + "Percentage_Vote": 19.0 + }, + { + "Candidate": "Donald Trump", + "Percentage_Vote": 10.0 + }, + { + "Candidate": "Ted Cruz", + "Percentage_Vote": 8.0 + }, + { + "Candidate": "Marco Rubio", + "Percentage_Vote": 7.0 + }, + { + "Candidate": "Others", + "Percentage_Vote": 12.0 + } + ], + "columns": [ + { + "name": "Candidate", + "importance": "primary", + "description": "Who they would vote for", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage_Vote", + "importance": "primary", + "description": "Percentage of voters choosing that candidate", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1631": { + "metadata": { + "title": "Nvidia Sees Near-Record Jump in Market Cap", + "description": "Largest single-day market cap gains in U.S. stock market history", + "main_insight": "Nvidia's single-day market cap gain on May 25, 2023, was among the largest in U.S. stock market history, reaching $184 billion, though slightly less than Apple and Amazon's gains in 2022." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "Apple (Jan. 28, 2022)", + "Market Cap Gain": 179.0 + } + ], + "score": 0.89, + "annotation": "The shows a sudden jump in Market Cap Gain from Microsoft (Mar. 13, 2020) to Apple (Jan. 28, 2022).", + "reason": "The Market Cap Gain for increases from 150.0 at Microsoft (Mar. 13, 2020) to 179.0 at Apple (Jan. 28, 2022), indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Company": "Tesla (Jan. 3, 2022)", + "Market Cap Gain": 144.0 + } + ], + "score": 0.77, + "annotation": "The has a minimum value at Tesla (Jan. 3, 2022)", + "reason": "The [{'Company': 'Tesla (Jan. 3, 2022)', 'Market Cap Gain': 144.0}] of has a mainimum value of 144.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Company": "Apple (Nov. 10, 2022)", + "Market Cap Gain": 191.0 + }, + { + "Company": "Amazon (Feb. 4, 2022)", + "Market Cap Gain": 191.0 + } + ], + "score": 0.53, + "annotation": "The has maximum values at Apple (Nov. 10, 2022), Amazon (Feb. 4, 2022)", + "reason": "The [{'Company': 'Apple (Nov. 10, 2022)', 'Market Cap Gain': 191.0}, {'Company': 'Amazon (Feb. 4, 2022)', 'Market Cap Gain': 191.0}] of have maximum values of 191.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Company": "Apple (Nov. 10, 2022)", + "Market Cap Gain": 191.0 + }, + { + "Company": "Amazon (Feb. 4, 2022)", + "Market Cap Gain": 191.0 + }, + { + "Company": "Nvidia (May 25, 2023)", + "Market Cap Gain": 184.0 + }, + { + "Company": "Apple (Jan. 28, 2022)", + "Market Cap Gain": 179.0 + }, + { + "Company": "Microsoft (Mar. 13, 2020)", + "Market Cap Gain": 150.0 + }, + { + "Company": "Tesla (Jan. 3, 2022)", + "Market Cap Gain": 144.0 + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Company Name and Date of Market Cap Gain", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Market Cap Gain", + "importance": "primary", + "description": "Single-day market capitalization gain in billions of US dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1955": { + "metadata": { + "title": "The Shocking Size of the Australian Wildfires", + "description": "Acres burned in selected recent major wildfire events", + "main_insight": "The 2019/20 Australian wildfires burned significantly more acreage than other major recent wildfires, like the 2018 California fires and 2019 Siberian fires." + }, + "chart_type": "Proportional Icon Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Wildfire Event": "2019/20 Australian fires", + "Acres Burned (Millions)": 12.4 + } + ], + "score": 0.86, + "annotation": "The shows a sudden jump in Acres Burned (Millions) from 2019 Siberian fires to 2019/20 Australian fires.", + "reason": "The Acres Burned (Millions) for increases from 6.7 at 2019 Siberian fires to 12.4 at 2019/20 Australian fires, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Wildfire Event": "2019/20 Australian fires", + "Acres Burned (Millions)": 12.4 + } + ], + "score": 0.61, + "annotation": "The has a maximum value at 2019/20 Australian fires", + "reason": "The [{'Wildfire Event': '2019/20 Australian fires', 'Acres Burned (Millions)': 12.4}] of has a maximum value of 12.4, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Wildfire Event": "2018 California fires", + "Acres Burned (Millions)": 2.0 + } + ], + "score": 0.58, + "annotation": "The has a minimum value at 2018 California fires", + "reason": "The [{'Wildfire Event': '2018 California fires', 'Acres Burned (Millions)': 2.0}] of has a mainimum value of 2.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Wildfire Event": "2018 California fires", + "Acres Burned (Millions)": 2.0 + }, + { + "Wildfire Event": "2019 Siberian fires", + "Acres Burned (Millions)": 6.7 + }, + { + "Wildfire Event": "2019/20 Australian fires", + "Acres Burned (Millions)": 12.4 + } + ], + "columns": [ + { + "name": "Wildfire Event", + "importance": "primary", + "description": "The year and location of the wildfire", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Acres Burned (Millions)", + "importance": "primary", + "description": "The number of acres burned, in millions", + "unit": "Millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "465": { + "metadata": { + "title": "B2B Marketers Choose LinkedIn Over Facebook", + "description": "% of respondents naming platform the single most important for their business", + "main_insight": "B2B marketers consider LinkedIn the most important platform for their business, while B2C marketers favor Facebook." + }, + "chart_type": "Multiple Semi Circle Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Platform": "Facebook", + "Score": 65.0, + "Audience": "B2C Marketers" + } + ], + "score": 0.96, + "annotation": "The B2C Marketers has a maximum value at Facebook", + "reason": "The [{'Platform': 'Facebook', 'Score': 65.0, 'Audience': 'B2C Marketers'}] of B2C Marketers has a maximum value of 65.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Platform": "Facebook", + "Score": 65.0, + "Audience": "B2C Marketers" + } + ], + "score": 0.96, + "annotation": "The B2C Marketers shows a sudden jump in Score from Twitter to Facebook.", + "reason": "The Score for B2C Marketers increases from 10.0 at Twitter to 65.0 at Facebook, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Platform": "LinkedIn", + "Score": 41.0, + "Audience": "B2B Marketers" + } + ], + "score": 0.85, + "annotation": "The B2B Marketers has a maximum value at LinkedIn", + "reason": "The [{'Platform': 'LinkedIn', 'Score': 41.0, 'Audience': 'B2B Marketers'}] of B2B Marketers has a maximum value of 41.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Platform": "YouTube", + "Score": 4.0, + "Audience": "B2B Marketers" + }, + { + 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"Score": 30.0, + "Audience": "B2B Marketers" + }, + { + "Platform": "Facebook", + "Score": 65.0, + "Audience": "B2C Marketers" + }, + { + "Platform": "Twitter", + "Score": 19.0, + "Audience": "B2B Marketers" + }, + { + "Platform": "Twitter", + "Score": 10.0, + "Audience": "B2C Marketers" + }, + { + "Platform": "YouTube", + "Score": 4.0, + "Audience": "B2B Marketers" + }, + { + "Platform": "YouTube", + "Score": 4.0, + "Audience": "B2C Marketers" + }, + { + "Platform": "Google+", + "Score": 3.0, + "Audience": "B2B Marketers" + }, + { + "Platform": "Google+", + "Score": 4.0, + "Audience": "B2C Marketers" + }, + { + "Platform": "Pinterest", + "Score": 2.0, + "Audience": "B2B Marketers" + }, + { + "Platform": "Pinterest", + "Score": 4.0, + "Audience": "B2C Marketers" + }, + { + "Platform": "Instagram", + "Score": 3.0, + "Audience": "B2C Marketers" + }, + { + "Platform": "Others", + "Score": 1.0, + "Audience": "B2B Marketers" + }, + { + "Platform": "Others", + "Score": 2.0, + "Audience": "B2C Marketers" + } + ], + "columns": [ + { + "name": "Platform", + "importance": "primary", + "description": "Social media platform", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding audience", + "role": "y" + }, + { + "name": "Audience", + "importance": "primary", + "description": "The audience of the data point", + "role": "group" + } + ] + } + }, + "333": { + "metadata": { + "title": "Where Most Investment Fund Assets Are Located", + "description": "Domiciles of global investment fund assets (as of Q1 2020)", + "main_insight": "Almost half of global investment fund assets are located in the U.S., significantly more than any other single country. The 'Others' category collectively accounts for the second largest share." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "U.S.", + "Percentage of Assets": 47.9 + } + ], + "score": 0.95, + "annotation": "The has a maximum value at U.S.", + "reason": "The [{'Country': 'U.S.', 'Percentage of Assets': 47.9}] of has a maximum value of 47.9, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "U.S.", + "Percentage of Assets": 47.9 + } + ], + "score": 0.88, + "annotation": "The shows a sudden jump in Percentage of Assets from Others to U.S..", + "reason": "The Percentage of Assets for increases from 21.4 at Others to 47.9 at U.S., indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Japan", + "Percentage of Assets": 3.7 + } + ], + "score": 0.39, + "annotation": "The has a minimum value at Japan", + "reason": "The [{'Country': 'Japan', 'Percentage of Assets': 3.7}] of has a mainimum value of 3.7, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "U.S.", + "Percentage of Assets": 47.9 + }, + { + "Country": "Luxembourg", + "Percentage of Assets": 8.8 + }, + { + "Country": "Ireland", + "Percentage of Assets": 5.8 + }, + { + "Country": "Germany", + "Percentage of Assets": 4.6 + }, + { + "Country": "China", + "Percentage of Assets": 4.1 + }, + { + "Country": "France", + "Percentage of Assets": 3.8 + }, + { + "Country": "Japan", + "Percentage of Assets": 3.7 + }, + { + "Country": "Others", + "Percentage of Assets": 21.4 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where the fund is legally organized", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Assets", + "importance": "primary", + "description": "Percentage of global investment fund assets located in that country", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "724": { + "metadata": { + "title": "The Best Countries To Grow Old In", + "description": "Best-ranked countries according to the Global Retirement Index 2022", + "main_insight": "Norway is the best-ranked country to grow old in, according to the 2022 Global Retirement Index, followed by Switzerland and Iceland. Most countries scores slightly decreased vs 2021" + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "NO", + "GRI Score": 81.0 + } + ], + "score": 0.87, + "annotation": "The has a maximum value at NO", + "reason": "The [{'Country': 'NO', 'GRI Score': 81.0}] of has a maximum value of 81.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "DE", + "GRI Score": 72.0 + } + ], + "score": 0.75, + "annotation": "The has a minimum value at DE", + "reason": "The [{'Country': 'DE', 'GRI Score': 72.0}] of has a mainimum value of 72.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "IS", + "GRI Score": 79.0 + } + ], + "score": 0.72, + "annotation": "The shows a sudden jump in GRI Score from IE to IS.", + "reason": "The GRI Score for increases from 76.0 at IE to 79.0 at IS, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "NO", + "GRI Score": 81.0 + }, + { + "Country": "CH", + "GRI Score": 80.0 + }, + { + "Country": "IS", + "GRI Score": 79.0 + }, + { + "Country": "IE", + "GRI Score": 76.0 + }, + { + "Country": "AU", + "GRI Score": 75.0 + }, + { + "Country": "NZ", + "GRI Score": 75.0 + }, + { + "Country": "LU", + "GRI Score": 75.0 + }, + { + "Country": "NL", + "GRI Score": 75.0 + }, + { + "Country": "DK", + "GRI Score": 74.0 + }, + { + "Country": "CZ", + "GRI Score": 73.0 + }, + { + "Country": "DE", + "GRI Score": 72.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country abbreviation", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "GRI Score", + "importance": "primary", + "description": "Global Retirement Index Score in 2022", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "350": { + "metadata": { + "title": "AI Sucks Up a Growing Chunk of VC Funding in the U.S.", + "description": "Share of equity investments into U.S. VC-backed companies that are AI-related", + "main_insight": "The share of equity investments in AI-related companies in the U.S. has significantly increased from 2020 to 2024, reaching 33% in the first three quarters of 2024." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": "Q1-Q3 2024", + "Score": 33.0, + "AI-Involvement": "AI-related companies" + }, + { + "Year": "Q1-Q3 2024", + "Score": 67.0, + "AI-Involvement": "Non-AI companies" + } + ], + "score": 1.0, + "annotation": "The Score of AI-related companies and Non-AI companies show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between AI-related companies and Non-AI companies is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Year": "2023", + "Score": 28.0, + "AI-Involvement": "AI-related companies" + } + ], + "score": 0.9, + "annotation": "The AI-related companies shows a sudden jump in Score from 2021 to 2023.", + "reason": "The Score for AI-related companies increases from 15.0 at 2021 to 28.0 at 2023, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Year": "2021", + "Score": 85.0, + "AI-Involvement": "Non-AI companies" + } + ], + "score": 0.9, + "annotation": "The Non-AI companies shows a sudden jump in Score from 2023 to 2021.", + "reason": "The Score for Non-AI companies increases from 72.0 at 2023 to 85.0 at 2021, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "Q1-Q3 2024", + "Score": 67.0, + "AI-Involvement": "Non-AI companies" + } + ], + "score": 0.75, + "annotation": "The Non-AI 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+ { + "Year": "2022", + "Score": 13.0, + "AI-Involvement": "AI-related companies" + }, + { + "Year": "2022", + "Score": 87.0, + "AI-Involvement": "Non-AI companies" + }, + { + "Year": "2023", + "Score": 28.0, + "AI-Involvement": "AI-related companies" + }, + { + "Year": "2023", + "Score": 72.0, + "AI-Involvement": "Non-AI companies" + }, + { + "Year": "Q1-Q3 2024", + "Score": 33.0, + "AI-Involvement": "AI-related companies" + }, + { + "Year": "Q1-Q3 2024", + "Score": 67.0, + "AI-Involvement": "Non-AI companies" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year or period of investment", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding ai-involvement", + "role": "y" + }, + { + "name": "AI-Involvement", + "importance": "primary", + "description": "The ai-involvement of the data point", + "role": "group" + } + ] + } + }, + "737": { + "metadata": { + "title": "Cupid's Arrow Largely Aims True", + "description": "Countries where most respondents said they were married or in a relationship (in %)", + "main_insight": "A significant majority of respondents in the surveyed countries reported being either married or in a relationship, with China having the highest combined percentage." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "U.S.", + "Score": 56.0, + "Relationship Status": "Married" + }, + { + "Country": "U.S.", + "Score": 33.0, + "Relationship Status": "Single" + } + ], + "score": 0.85, + "annotation": "The Score of Married and Single show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Married and Single is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "U.S.", + "Score": 56.0, + "Relationship Status": "Married" + } + ], + "score": 0.84, + "annotation": "The Married has a minimum value at U.S.", + "reason": "The [{'Country': 'U.S.', 'Score': 56.0, 'Relationship Status': 'Married'}] of Married has a mainimum value of 56.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "China", + "Score": 77.0, + "Relationship Status": "Married" + } + ], + "score": 0.8, + "annotation": "The Married has a maximum value at China", + "reason": "The [{'Country': 'China', 'Score': 77.0, 'Relationship Status': 'Married'}] of Married has a maximum value of 77.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "U.S.", + "Score": 33.0, + "Relationship Status": "Single" + } + ], + "score": 0.78, + "annotation": "The Single has a maximum value at U.S.", + "reason": "The [{'Country': 'U.S.', 'Score': 33.0, 'Relationship Status': 'Single'}] of Single has a maximum value of 33.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "UK", + "Score": 32.0, + "Relationship Status": "Single" + } + ], + "score": 0.77, + "annotation": "The Single shows a sudden jump in Score from Italy to UK.", + "reason": "The Score for Single increases from 26.0 at Italy to 32.0 at UK, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "China", + "Score": 77.0, + "Relationship Status": "Married" + }, + { + "Country": "China", + "Score": 21.0, + "Relationship Status": "Single" + }, + { + "Country": "Poland", + "Score": 70.0, + "Relationship Status": "Married" + }, + { + "Country": "Poland", + "Score": 20.0, + "Relationship Status": "Single" + }, + { + "Country": "Spain", + "Score": 69.0, + "Relationship Status": "Married" + }, + { + "Country": "Spain", + "Score": 22.0, + "Relationship Status": "Single" + }, + { + "Country": "Russia", + "Score": 69.0, + "Relationship Status": "Married" + }, + { + "Country": "Russia", + "Score": 18.0, + "Relationship Status": "Single" + }, + { + "Country": "Italy", + "Score": 67.0, + "Relationship Status": "Married" + }, + { + "Country": "Italy", + "Score": 26.0, + "Relationship Status": "Single" + }, + { + "Country": "UK", + "Score": 61.0, + "Relationship Status": "Married" + }, + { + "Country": "UK", + "Score": 32.0, + "Relationship Status": "Single" + }, + { + "Country": "U.S.", + "Score": 56.0, + "Relationship Status": "Married" + }, + { + "Country": "U.S.", + "Score": 33.0, + "Relationship Status": "Single" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding relationship status", + "role": "y" + }, + { + "name": "Relationship Status", + "importance": "primary", + "description": "The relationship status of the data point", + "role": "group" + } + ] + } + }, + "1543": { + "metadata": { + "title": "India's public cloud market to grow by 54% until 2020", + "description": "Estimated public cloud market size in India from 2017 to 2020", + "main_insight": "India's public cloud market is projected to experience significant growth, increasing by 54% from $2.60 billion in 2018 to $4.00 billion in 2020." + }, + "chart_type": "Proportional Icon Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2017, + "Market Size (USD)": 1.81 + }, + { + "Year": 2018, + "Market Size (USD)": 2.6 + }, + { + "Year": 2020, + "Market Size (USD)": 4.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of market size estimation", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Market Size (USD)", + "importance": "primary", + "description": "Estimated public cloud market size in billions of US dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2487": { + "metadata": { + "title": "Too Good To Be True?", + "description": "Paid subscribers of movie theater subscription service MoviePass in the United States", + "main_insight": "MoviePass subscribers grew rapidly from 20,000 in August 2017 to 3,000,000 by June 2018." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "June 13, 2018", + "Subscribers": 3000000.0 + } + ], + "score": 0.86, + "annotation": "The has a maximum value at June 13, 2018", + "reason": "The [{'Date': 'June 13, 2018', 'Subscribers': 3000000.0}] of has a maximum value of 3000000.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "June 13, 2018", + "Subscribers": 3000000.0 + } + ], + "score": 0.72, + "annotation": "The shows a sudden jump in Subscribers from February 8, 2018 to June 13, 2018.", + "reason": "The Subscribers for increases from 2000000.0 at February 8, 2018 to 3000000.0 at June 13, 2018, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "August 14, 2017", + "Subscribers": 20000.0 + } + ], + "score": 0.67, + "annotation": "The has a minimum value at August 14, 2017", + "reason": "The [{'Date': 'August 14, 2017', 'Subscribers': 20000.0}] of has a mainimum value of 20000.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Date": "August 14, 2017", + "Subscribers": 20000.0 + }, + { + "Date": "September 14, 2017", + "Subscribers": 400000.0 + }, + { + "Date": "October 18, 2017", + "Subscribers": 600000.0 + }, + { + "Date": "December 20, 2017", + "Subscribers": 1000000.0 + }, + { + "Date": "January 9, 2018", + "Subscribers": 1500000.0 + }, + { + "Date": "February 8, 2018", + "Subscribers": 2000000.0 + }, + { + "Date": "June 13, 2018", + "Subscribers": 3000000.0 + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Date of subscriber count", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Subscribers", + "importance": "primary", + "description": "Number of MoviePass subscribers", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2085": { + "metadata": { + "title": "The World's Love Affair With Plastic Surgery", + "description": "Total number of plastic surgery procedures and share of the global total in 2019", + "main_insight": "Brazil and the United States accounted for the highest number of plastic surgery procedures in 2019, representing 13.1% and 11.9% of the global total, respectively." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Number of Procedures": 1351917.0 + } + ], + "score": 0.9, + "annotation": "The shows a sudden jump in Number of Procedures from Mexico to United States.", + "reason": "The Number of Procedures for increases from 580659.0 at Mexico to 1351917.0 at United States, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Brazil", + "Number of Procedures": 1493673.0 + } + ], + "score": 0.87, + "annotation": "The has a maximum value at Brazil", + "reason": "The [{'Country': 'Brazil', 'Number of Procedures': 1493673.0}] of has a maximum value of 1493673.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Italy", + "Number of Procedures": 314432.0 + } + ], + "score": 0.46, + "annotation": "The has a minimum value at Italy", + "reason": "The [{'Country': 'Italy', 'Number of Procedures': 314432.0}] of has a mainimum value of 314432.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Brazil", + "Number of Procedures": 1493673.0 + }, + { + "Country": "United States", + "Number of Procedures": 1351917.0 + }, + { + "Country": "Mexico", + "Number of Procedures": 580659.0 + }, + { + "Country": "Russia", + "Number of Procedures": 483152.0 + }, + { + "Country": "India", + "Number of Procedures": 394728.0 + }, + { + "Country": "Turkey", + "Number of Procedures": 351930.0 + }, + { + "Country": "Germany", + "Number of Procedures": 336244.0 + }, + { + "Country": "Italy", + "Number of Procedures": 314432.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where plastic surgery procedures were performed", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Procedures", + "importance": "primary", + "description": "Total number of plastic surgery procedures", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1780": { + "metadata": { + "title": "Laundry's Contribution to the Microplastic Problem", + "description": "Estimated number of fibers released when laundering a washing load of 6kg, by fabric type", + "main_insight": "Acrylic fabric releases the most microplastic fibers when laundered, significantly more than polyester or polyester-cotton blends." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Fabric Type": "Polyester", + "Fibers Released": 496030.0 + } + ], + "score": 0.88, + "annotation": "The shows a sudden jump in Fibers Released from Polyester-cotton blend to Polyester.", + "reason": "The Fibers Released for increases from 137951.0 at Polyester-cotton blend to 496030.0 at Polyester, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Fabric Type": "Polyester-cotton blend", + "Fibers Released": 137951.0 + } + ], + "score": 0.63, + "annotation": "The has a minimum value at Polyester-cotton blend", + "reason": "The [{'Fabric Type': 'Polyester-cotton blend', 'Fibers Released': 137951.0}] of has a mainimum value of 137951.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Fabric Type": "Acrylic", + "Fibers Released": 728789.0 + } + ], + "score": 0.56, + "annotation": "The has a maximum value at Acrylic", + "reason": "The [{'Fabric Type': 'Acrylic', 'Fibers Released': 728789.0}] of has a maximum value of 728789.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Fabric Type": "Acrylic", + "Fibers Released": 728789.0 + }, + { + "Fabric Type": "Polyester", + "Fibers Released": 496030.0 + }, + { + "Fabric Type": "Polyester-cotton blend", + "Fibers Released": 137951.0 + } + ], + "columns": [ + { + "name": "Fabric Type", + "importance": "primary", + "description": "Type of fabric being laundered", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Fibers Released", + "importance": "primary", + "description": "Estimated number of fibers released per 6kg load", + "unit": "fibers", + "data_type": "number", + "role": "y" + } + ] + } + }, + "438": { + "metadata": { + "title": "Sharp Rise in U.S. Firearm Homicide Rate", + "description": "Homicides by firearm per 100,000 population in the United States", + "main_insight": "The U.S. firearm homicide rate has increased significantly between 2014 and 2016, reversing a previous downward trend." + }, + "chart_type": "Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2006, + "Firearm Homicide Rate": 3.42 + }, + { + "Year": 2007, + "Firearm Homicide Rate": 3.35 + }, + { + "Year": 2008, + "Firearm Homicide Rate": 3.18 + }, + { + "Year": 2009, + "Firearm Homicide Rate": 3.06 + }, + { + "Year": 2010, + "Firearm Homicide Rate": 2.95 + }, + { + "Year": 2011, + "Firearm Homicide Rate": 2.89 + }, + { + "Year": 2012, + "Firearm Homicide Rate": 2.93 + }, + { + "Year": 2013, + "Firearm Homicide Rate": 2.74 + }, + { + "Year": 2014, + "Firearm Homicide Rate": 2.61 + }, + { + "Year": 2015, + "Firearm Homicide Rate": 3.04 + }, + { + "Year": 2016, + "Firearm Homicide Rate": 3.8 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the recorded homicide rate", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Firearm Homicide Rate", + "importance": "primary", + "description": "Homicides by firearm per 100,000 population", + "unit": "per 100,000 population", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2311": { + "metadata": { + "title": "Putin Forever?", + "description": "Longest-serving Russian leaders since the start of the 20th century (in days in office)", + "main_insight": "Joseph Stalin has served the longest number of days in office, followed by Vladimir Putin. The chart compares their tenures and those of other key Russian leaders." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Leader": "Joseph Stalin", + "Days in Office": 10636 + } + ], + "score": 0.77, + "annotation": "The has a maximum value at Joseph Stalin", + "reason": "The [{'Leader': 'Joseph Stalin', 'Days in Office': 10636}] of has a maximum value of 10636, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Leader": "Leonid Brezhnev", + "Days in Office": 6601 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Days in Office from Nikita Khrushchev to Leonid Brezhnev.", + "reason": "The Days in Office for increases from 3536 at Nikita Khrushchev to 6601 at Leonid Brezhnev, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Leader": "Mikhail Gorbachev", + "Days in Office": 2480 + } + ], + "score": 0.6, + "annotation": "The has a minimum value at Mikhail Gorbachev", + "reason": "The [{'Leader': 'Mikhail Gorbachev', 'Days in Office': 2480}] of has a mainimum value of 2480, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Leader": "Joseph Stalin", + "Days in Office": 10636 + }, + { + "Leader": "Vladimir Putin", + "Days in Office": 8978 + }, + { + "Leader": "Leonid Brezhnev", + "Days in Office": 6601 + }, + { + "Leader": "Nikita Khrushchev", + "Days in Office": 3536 + }, + { + "Leader": "Boris Yeltsin", + "Days in Office": 3096 + }, + { + "Leader": "Mikhail Gorbachev", + "Days in Office": 2480 + } + ], + "columns": [ + { + "name": "Leader", + "importance": "primary", + "description": "Name of the Russian leader", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Days in Office", + "importance": "primary", + "description": "Number of days in office", + "unit": "days", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2229": { + "metadata": { + "title": "Why Everyone Wants a Piece of the Streaming Pie", + "description": "Worldwide digital music revenue broken down by format", + "main_insight": "The visualization shows a significant shift in digital music revenue from permanent downloads in 2008 to subscription streaming in 2014, with overall revenue increasing." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2014, + "Units": 52.0, + "Revenue Source": "Permanent Downloads" + }, + { + "Year": 2014, + "Units": 23.0, + "Revenue Source": "Subscription Streaming" + } + ], + "score": 1.0, + "annotation": "The Units of Permanent Downloads and Subscription Streaming show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Permanent Downloads and Subscription Streaming is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2014, + "Units": 52.0, + "Revenue Source": "Permanent Downloads" + }, + { + "Year": 2014, + "Units": 9.0, + "Revenue Source": "Ad-Supported Streaming" + } + ], + "score": 1.0, + "annotation": "The Units of Permanent Downloads and Ad-Supported Streaming show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Permanent Downloads and Ad-Supported Streaming is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2014, + "Units": 52.0, + "Revenue Source": "Permanent Downloads" + }, + { + "Year": 2014, + "Units": 3.0, + "Revenue Source": "Mobile Personalization" + } + ], + "score": 1.0, + "annotation": "The Units of Permanent Downloads and Mobile Personalization show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Permanent Downloads and Mobile Personalization is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2014, + "Units": 52.0, + "Revenue Source": "Permanent Downloads" + }, + { + "Year": 2014, + "Units": 12.0, + "Revenue Source": "Others" + } + ], + "score": 1.0, + "annotation": "The Units of Permanent Downloads and Others show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Permanent Downloads and Others is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2014, + "Units": 52.0, + "Revenue Source": "Permanent Downloads" + }, + { + "Year": 2014, + "Units": 6.9, + "Revenue Source": "Total Revenue (Billions)" + } + ], + "score": 1.0, + "annotation": "The Units of Permanent Downloads and Total Revenue (Billions) show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Permanent Downloads and Total Revenue (Billions) is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2008, + "Units": 64.0, + "Revenue Source": "Permanent Downloads" + }, + { + "Year": 2008, + "Units": 6.0, + "Revenue Source": "Subscription Streaming" + }, + { + "Year": 2008, + "Units": 3.0, + "Revenue Source": "Ad-Supported Streaming" + }, + { + "Year": 2008, + "Units": 26.0, + "Revenue Source": "Mobile Personalization" + }, + { + "Year": 2008, + "Units": 1.0, + "Revenue Source": "Others" + }, + { + "Year": 2008, + "Units": 4.0, + "Revenue Source": "Total Revenue (Billions)" + }, + { + "Year": 2014, + "Units": 52.0, + "Revenue Source": "Permanent Downloads" + }, + { + "Year": 2014, + "Units": 23.0, + "Revenue Source": "Subscription Streaming" + }, + { + "Year": 2014, + "Units": 9.0, + "Revenue Source": "Ad-Supported Streaming" + }, + { + "Year": 2014, + "Units": 3.0, + "Revenue Source": "Mobile Personalization" + }, + { + "Year": 2014, + "Units": 12.0, + "Revenue Source": "Others" + }, + { + "Year": 2014, + "Units": 6.9, + "Revenue Source": "Total Revenue (Billions)" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of revenue data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding revenue source", + "role": "y" + }, + { + "name": "Revenue Source", + "importance": "primary", + "description": "The revenue source of the data point", + "role": "group" + } + ] + } + }, + "423": { + "metadata": { + "title": "Campbell Soup Drops Fresh Division After Losing Money", + "description": "Global operating earnings of Campbell Soup Company, by division (in million USD)", + "main_insight": "Campbell Fresh division's operating earnings decreased significantly from 2014 to 2017, becoming a loss in 2017, while other divisions and total earnings remained positive." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2017, + "Units": 1565.0, + "Segments": "Total" + }, + { + "Year": 2017, + "Units": 1120.0, + "Segments": "Americas Simple Meals and Beverages" + } + ], + "score": 0.97, + "annotation": "The Units of Total and Americas Simple Meals and Beverages show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total and Americas Simple Meals and Beverages is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2017, + "Units": 454.0, + "Segments": "Global Biscuits and Snacks" + }, + { + "Year": 2017, + "Units": -9.0, + "Segments": "Campbell Fresh" + } + ], + "score": 0.85, + "annotation": "The Units of Global Biscuits and Snacks and Campbell Fresh show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Global Biscuits and Snacks and Campbell Fresh is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2017, + "Units": 1565.0, + "Segments": "Total" + }, + { + "Year": 2017, + "Units": 454.0, + "Segments": "Global Biscuits and Snacks" + } + ], + "score": 0.82, + "annotation": "The Units of Total and Global Biscuits and Snacks show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total and Global Biscuits and Snacks is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2017, + "Units": 1120.0, + "Segments": "Americas Simple Meals and Beverages" + }, + { + "Year": 2017, + "Units": 454.0, + "Segments": "Global Biscuits and Snacks" + } + ], + "score": 0.79, + "annotation": "The Units of Americas Simple Meals and Beverages and Global Biscuits and Snacks show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Americas Simple Meals and Beverages and Global Biscuits and Snacks is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2017, + "Units": 1120.0, + "Segments": "Americas Simple Meals and Beverages" + }, + { + "Year": 2017, + "Units": -9.0, + "Segments": "Campbell Fresh" + } + ], + "score": 0.71, + "annotation": "The Units of Americas Simple Meals and Beverages and Campbell Fresh show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Americas Simple Meals and Beverages and Campbell Fresh is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2014, + "Units": 1464.0, + "Segments": "Total" + }, + { + "Year": 2014, + "Units": 1030.0, + "Segments": "Americas Simple Meals and Beverages" + }, + { + "Year": 2014, + "Units": 366.0, + "Segments": "Global Biscuits and Snacks" + }, + { + "Year": 2014, + "Units": 68.0, + "Segments": "Campbell Fresh" + }, + { + "Year": 2015, + "Units": 1392.0, + "Segments": "Total" + }, + { + "Year": 2015, + "Units": 948.0, + "Segments": "Americas Simple Meals and Beverages" + }, + { + "Year": 2015, + "Units": 383.0, + "Segments": "Global Biscuits and Snacks" + }, + { + "Year": 2015, + "Units": 61.0, + "Segments": "Campbell Fresh" + }, + { + "Year": 2016, + "Units": 1551.0, + "Segments": "Total" + }, + { + "Year": 2016, + "Units": 1069.0, + "Segments": "Americas Simple Meals and Beverages" + }, + { + "Year": 2016, + "Units": 422.0, + "Segments": "Global Biscuits and Snacks" + }, + { + "Year": 2016, + "Units": 60.0, + "Segments": "Campbell Fresh" + }, + { + "Year": 2017, + "Units": 1565.0, + "Segments": "Total" + }, + { + "Year": 2017, + "Units": 1120.0, + "Segments": 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Only a small number manage to win the tournament." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Stage Reached": "Group stage", + "Number of Teams": 8 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at Group stage", + "reason": "The [{'Stage Reached': 'Group stage', 'Number of Teams': 8}] of has a maximum value of 8, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Stage Reached": "Group stage", + "Number of Teams": 8 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in Number of Teams from Semifinals* to Group stage.", + "reason": "The Number of Teams for increases from 3 at Semifinals* to 8 at Group stage, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Stage Reached": "No participation", + "Number of Teams": 1 + }, + { + "Stage 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"Weight (g)": 7.8 + }, + { + "Material": "Cobalt", + "Weight (g)": 6.6 + }, + { + "Material": "Hydrogen", + "Weight (g)": 5.5 + }, + { + "Material": "Chrome", + "Weight (g)": 4.9 + }, + { + "Material": "Others", + "Weight (g)": 4.9 + }, + { + "Material": "Nickel", + "Weight (g)": 2.7 + }, + { + "Material": "Total", + "Weight (g)": 129.0 + } + ], + "columns": [ + { + "name": "Material", + "importance": "primary", + "description": "The element or material that makes up the iPhone", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Weight (g)", + "importance": "primary", + "description": "Weight of each material in grams", + "unit": "g", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1819": { + "metadata": { + "title": "India's Monumental New Statue in Perspective", + "description": "Size comparison of some of the largest statues in the world*", + "main_insight": "The Statue of Unity in India is significantly taller than other large statues worldwide, such as the Spring Temple Buddha and the Statue of Liberty." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Statue Name": "Statue of Unity (2018)", + "Height (m)": 182.0 + } + ], + "score": 0.77, + "annotation": "The has a maximum value at Statue of Unity (2018)", + "reason": "The [{'Statue Name': 'Statue of Unity (2018)', 'Height (m)': 182.0}] of has a maximum value of 182.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Statue Name": "Statue of Unity (2018)", + "Height (m)": 182.0 + } + ], + "score": 0.74, + "annotation": "The shows a sudden jump in Height (m) from Spring Temple Buddha (2002) to Statue of Unity (2018).", + "reason": "The Height (m) for increases from 128.0 at Spring Temple Buddha (2002) to 182.0 at Statue of Unity (2018), indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Statue Name": "Christ the Redeemer (1931)", + "Height (m)": 30.0 + } + ], + "score": 0.62, + "annotation": "The has a minimum value at Christ the Redeemer (1931)", + "reason": "The [{'Statue Name': 'Christ the Redeemer (1931)', 'Height (m)': 30.0}] of has a mainimum value of 30.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Statue Name": "Statue of Unity (2018)", + "Height (m)": 182.0 + }, + { + "Statue Name": "Spring Temple Buddha (2002)", + "Height (m)": 128.0 + }, + { + "Statue Name": "The Motherland Calls (1967)", + "Height (m)": 85.0 + }, + { + "Statue Name": "Statue of Liberty (1886)", + "Height (m)": 46.0 + }, + { + "Statue Name": "Christ the Redeemer (1931)", + "Height (m)": 30.0 + } + ], + "columns": [ + { + "name": "Statue Name", + "importance": "primary", + "description": "Name and year of construction of the statue", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Height (m)", + "importance": "primary", + "description": "Height of the statue in meters", + "unit": "m", + "data_type": "number", + "role": "y" + } + ] + } + }, + "679": { + "metadata": { + "title": "Pop Culture Drives Travelers' Destination Decisions", + "description": "Factors influencing consumers' decision to book a trip to a certain destination, by generation", + "main_insight": "Pop culture significantly influences travel decisions, with TV shows/movies and Instagram being the top factors for Gen-Z and millennials, and also the highest factors for Gen-X." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Factor": "TikTok", + "Units": 29.0, + "Age Group": "Gen-Z and millennials" + }, + { + "Factor": "TikTok", + "Units": 7.0, + "Age Group": "Gen-X" + } + ], + "score": 0.88, + "annotation": "The Units of Gen-Z and millennials and Gen-X show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Gen-Z and millennials and Gen-X is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Factor": "TV shows or movies", + "Units": 43.0, + "Age Group": "Gen-X" + } + ], + "score": 0.87, + "annotation": "The Gen-X shows a sudden jump in Units from Facebook to TV shows or movies.", + "reason": "The Units for Gen-X increases from 22.0 at Facebook to 43.0 at TV shows or movies, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Factor": "Instagram", + "Units": 46.0, + "Age Group": "Gen-Z and millennials" + } + ], + "score": 0.84, + "annotation": "The Gen-Z and millennials shows a sudden jump in Units from Facebook to Instagram.", + "reason": "The Units for Gen-Z and millennials increases from 34.0 at Facebook to 46.0 at Instagram, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Factor": "TV shows or movies", + "Units": 52.0, + "Age Group": "Gen-Z and millennials" + } + ], + "score": 0.79, + "annotation": "The Gen-Z and millennials accounts for the majority in TV shows or movies.", + "reason": "The Units of Gen-Z and millennials in TV shows or movies accounts for 0.5473684210526316 (more than 50%) of the total of Units of all Age Group." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Factor": "Facebook", + "Units": 34.0, + "Age Group": "Gen-Z and millennials" + } + ], + "score": 0.77, + "annotation": "The Gen-Z and millennials accounts for the majority in Facebook.", + "reason": "The Units of Gen-Z and millennials in Facebook accounts for 0.6071428571428571 (more than 50%) of the total of Units of all Age Group." + } + ], + "data": { + "data": [ + { + "Factor": "TV shows or movies", + "Units": 52.0, + "Age Group": "Gen-Z and millennials" + }, + { + "Factor": "TV 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"605": { + "metadata": { + "title": "Where People Spend the Most Time Eating & Drinking", + "description": "Time spent eating and drinking each day in hours/minutes", + "main_insight": "People in France spend the most time eating and drinking, at 2 hours and 13 minutes per day, while people in the United States spend the least, at 1 hour and 2 minutes." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "France", + "Time Spent": 2.216666666666667 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at France", + "reason": "The [{'Country': 'France', 'Time Spent': 2.216666666666667}] of has a maximum value of 2.216666666666667, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "United States", + "Time Spent": 1.0333333333333332 + } + ], + "score": 0.74, + "annotation": "The has a minimum value at United 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"description": "Share of U.S. adults who say the following illnesses/issues are the biggest health problems in the country today", + "main_insight": "Mental health has become the top health concern among Americans, surpassing other issues like obesity, cancer, stress, and Coronavirus." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Nulls": 53.0, + "Health Topics": "Mental health" + }, + { + "Year": 2023, + "Nulls": 30.0, + "Health Topics": "Obesity" + } + ], + "score": 0.99, + "annotation": "The Nulls of Mental health and Obesity show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Nulls between Mental health and Obesity is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Nulls": 53.0, + "Health Topics": "Mental health" + }, + { + "Year": 2023, + "Nulls": 18.0, + "Health Topics": "Stress" + } + ], + "score": 0.94, + "annotation": "The Nulls of Mental health and Stress show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Nulls between Mental health and Stress is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Nulls": 30.0, + "Health Topics": "Obesity" + }, + { + "Year": 2023, + "Nulls": 18.0, + "Health Topics": "Stress" + } + ], + "score": 0.89, + "annotation": "The Nulls of Obesity and Stress show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Nulls between Obesity and Stress is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Nulls": 29.0, + "Health Topics": "Cancer" + }, + { + "Year": 2023, + "Nulls": 18.0, + "Health Topics": "Stress" + } + ], + "score": 0.88, + "annotation": "The Nulls of Cancer and Stress show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Nulls between Cancer and Stress is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Nulls": 53.0, + "Health Topics": "Mental health" + }, + { + "Year": 2023, + "Nulls": 29.0, + "Health Topics": "Cancer" + } + ], + "score": 0.67, + "annotation": "The Nulls of Mental health and Cancer show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Nulls between Mental health and Cancer is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2020, + "Nulls": 68.0, + "Health Topics": "Coronavirus" + }, + { + "Year": 2021, + "Nulls": 37.0, + "Health Topics": "Mental health" + }, + { + "Year": 2021, + "Nulls": 66.0, + "Health Topics": "Coronavirus" + }, + { + "Year": 2021, + "Nulls": 24.0, + "Health Topics": "Obesity" + }, + { + "Year": 2021, + "Nulls": 26.0, + "Health Topics": "Cancer" + }, + { + "Year": 2021, + "Nulls": 14.0, + "Health Topics": "Stress" + }, + { + "Year": 2022, + "Nulls": 44.0, + "Health Topics": "Mental health" + }, + { + "Year": 2022, + "Nulls": 41.0, + "Health Topics": "Coronavirus" + }, + { + "Year": 2022, + "Nulls": 26.0, + "Health Topics": "Obesity" + }, + { + "Year": 2022, + "Nulls": 30.0, + "Health Topics": "Cancer" + }, + { + "Year": 2022, + "Nulls": 17.0, + "Health Topics": "Stress" + }, + { + "Year": 2023, + "Nulls": 53.0, + "Health Topics": "Mental health" + }, + { + "Year": 2023, + "Nulls": 15.0, + "Health Topics": "Coronavirus" + }, + { + "Year": 2023, + "Nulls": 30.0, + "Health Topics": "Obesity" + }, + { + "Year": 2023, + "Nulls": 29.0, + "Health Topics": "Cancer" + }, + { + "Year": 2023, + "Nulls": 18.0, + "Health Topics": "Stress" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Nulls", + "importance": "primary", + "description": "Nulls for the corresponding health topics", + "role": "y" + }, + { + "name": "Health Topics", + "importance": "primary", + "description": "The health topics of the data point", + "role": "group" + } + ] + } + }, + "47": { + "metadata": { + "title": "Russell Brand's political commentary is widely disliked", + "description": "Who makes the most positive/negative contribution to political debate? (%)", + "main_insight": "Russell Brand has the highest negative contribution percentage among the listed celebrities, significantly higher than his positive contribution percentage. Most celebrities receive higher positive than negative contribution responses." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Celebrity": "Angelina Jolie", + "Positive": 28.0 + }, + { + "Celebrity": "Bob Geldof", + "Positive": 28.0 + } + ], + "score": 0.82, + "annotation": "The has maximum values at Angelina Jolie, Bob Geldof", + "reason": "The [{'Celebrity': 'Angelina Jolie', 'Positive': 28.0}, {'Celebrity': 'Bob Geldof', 'Positive': 28.0}] of have maximum values of 28.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Celebrity": "Angelina Jolie", + "Positive": 28.0 + } + ], + "score": 0.82, + "annotation": "The shows a sudden jump in Positive from Eddie Izzard to Angelina Jolie.", + "reason": "The Positive for increases from 18.0 at Eddie Izzard to 28.0 at Angelina Jolie, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Celebrity": "Leonardo DiCaprio", + "Positive": 7.0 + }, + { + "Celebrity": "Jeremy Clarkson", + "Positive": 7.0 + } + ], + "score": 0.6, + "annotation": "The has minimum values at Leonardo DiCaprio, Jeremy Clarkson", + "reason": "The [{'Celebrity': 'Leonardo DiCaprio', 'Positive': 7.0}, {'Celebrity': 'Jeremy Clarkson', 'Positive': 7.0}] of have minimum values of 7.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Celebrity": "Angelina Jolie", + "Positive": 28.0 + }, + { + "Celebrity": "Bob Geldof", + "Positive": 28.0 + }, + { + "Celebrity": "Emma Watson", + "Positive": 16.0 + }, + { + "Celebrity": "Eddie Izzard", + "Positive": 18.0 + }, + { + "Celebrity": "George Clooney", + "Positive": 11.0 + }, + { + "Celebrity": "Leonardo DiCaprio", + "Positive": 7.0 + }, + { + "Celebrity": "Bono", + "Positive": 9.0 + }, + { + "Celebrity": "Russell Brand", + "Positive": 13.0 + }, + { + "Celebrity": "Jeremy Clarkson", + "Positive": 7.0 + } + ], + "columns": [ + { + "name": "Celebrity", + "importance": "primary", + "description": "Name of the celebrity", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Positive", + "importance": "primary", + "description": "Percentage of respondents who believe the celebrity makes a positive contribution to political debate", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "603": { + "metadata": { + "title": "Air Conditioning Biggest Factor in Growing Electricity Demand", + "description": "Global electricity demand growth from 2018 to 2050, by energy use category", + "main_insight": "Space cooling (air conditioning) is projected to be the largest contributor to global electricity demand growth between 2018 and 2050, accounting for 37% of the increase." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Energy Use Category": "Space cooling", + "Percentage of Growth": 37.0 + } + ], + "score": 0.79, + "annotation": "The has a maximum value at Space cooling", + "reason": "The [{'Energy Use Category': 'Space cooling', 'Percentage of Growth': 37.0}] of has a maximum value of 37.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Energy Use Category": "Space cooling", + "Percentage of Growth": 37.0 + } + ], + "score": 0.76, + "annotation": "The shows a sudden jump in Percentage of Growth from Residential appliances to Space cooling.", + "reason": "The Percentage of Growth for increases from 25.5 at Residential appliances to 37.0 at Space cooling, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Energy Use Category": "Lighting", + "Percentage of Growth": 7.8 + } + ], + "score": 0.63, + "annotation": "The has a minimum value at Lighting", + "reason": "The [{'Energy Use Category': 'Lighting', 'Percentage of Growth': 7.8}] of has a mainimum value of 7.8, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Energy Use Category": "Space cooling", + "Percentage of Growth": 37.0 + }, + { + "Energy Use Category": "Residential appliances", + "Percentage of Growth": 25.5 + }, + { + "Energy Use Category": "Heating", + "Percentage of Growth": 12.4 + }, + { + "Energy Use Category": "Lighting", + "Percentage of Growth": 7.8 + }, + { + "Energy Use Category": "Other services", + "Percentage of Growth": 17.4 + } + ], + "columns": [ + { + "name": "Energy Use Category", + "importance": "primary", + "description": "Categories of energy usage", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Growth", + "importance": "primary", + "description": "Percentage of global electricity demand growth from 2018 to 2050", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "880": { + "metadata": { + "title": "Who wants to live forever?", + "description": "If you remained healthy, how long would you want to live for?", + "main_insight": "A significant portion of British adults (17%) would choose to live forever if they remained healthy, while the most popular lifespan range chosen is 91-100 years." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Lifespan": "91-100", + "Percentage of Respondents": 17.0 + }, + { + "Lifespan": "Forever", + "Percentage of Respondents": 17.0 + } + ], + "score": 0.82, + "annotation": "The has maximum values at 91-100, Forever", + "reason": "The [{'Lifespan': '91-100', 'Percentage of Respondents': 17.0}, {'Lifespan': 'Forever', 'Percentage of Respondents': 17.0}] of have maximum values of 17.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Lifespan": "91-100", + "Percentage of Respondents": 17.0 + } + ], + "score": 0.72, + "annotation": "The shows a sudden jump in Percentage of Respondents from 81-90 to 91-100.", + "reason": "The Percentage of Respondents for increases from 12.0 at 81-90 to 17.0 at 91-100, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Lifespan": "151-200", + "Percentage of Respondents": 2.0 + } + ], + "score": 0.65, + "annotation": "The has a minimum value at 151-200", + "reason": "The [{'Lifespan': '151-200', 'Percentage of Respondents': 2.0}] of has a mainimum value of 2.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Lifespan": "≤70", + "Percentage of Respondents": 3.0 + }, + { + "Lifespan": "71-80", + "Percentage of Respondents": 7.0 + }, + { + "Lifespan": "81-90", + "Percentage of Respondents": 12.0 + }, + { + "Lifespan": "91-100", + "Percentage of Respondents": 17.0 + }, + { + "Lifespan": "101-110", + "Percentage of Respondents": 8.0 + }, + { + "Lifespan": "111-125", + "Percentage of Respondents": 3.0 + }, + { + "Lifespan": "126-150", + "Percentage of Respondents": 4.0 + }, + { + "Lifespan": "151-200", + "Percentage of Respondents": 2.0 + }, + { + "Lifespan": "200+", + "Percentage of Respondents": 9.0 + }, + { + "Lifespan": "Forever", + "Percentage of Respondents": 17.0 + } + ], + "columns": [ + { + "name": "Lifespan", + "importance": "primary", + "description": "Desired lifespan ranges if health was guaranteed", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Respondents", + "importance": "primary", + "description": "Percentage of British adults who chose each lifespan range", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1873": { + "metadata": { + "title": "Which Countries Are Meeting Their Paris Agreement Goals?", + "description": "Selected national emission reduction goals and projected achievements by 2030, by associated limit on global warming", + "main_insight": "Most countries are not on track to meet their Paris Agreement goals, with many projected to exceed their target temperature increases. Only a few countries, like Nigeria, Kenya, Morocco are projected to meet their targets." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "South Korea", + "Target": 3.0 + }, + { + "Country": "Saudi Arabia", + "Target": 4.0 + } + ], + "score": 0.77, + "annotation": "The exhibits multiple sudden jumps in Target, transitioning from Morroco, European Union to South Korea, Saudi Arabia.", + "reason": "Across these points, the Target for increases from 2.0, 3.0 at Morroco, European Union to 3.0, 4.0 at South Korea, Saudi Arabia, suggesting significant shifts in value." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Saudi Arabia", + "Target": 4.0 + }, + { + "Country": "Indonesia", + "Target": 4.0 + }, + { + "Country": "Russia", + "Target": 4.0 + }, + { + "Country": "Mexico", + "Target": 4.0 + }, + { + "Country": "Egypt", + "Target": 4.0 + } + ], + "score": 0.72, + "annotation": "The has maximum values at Saudi Arabia, Indonesia, Russia, Mexico, Egypt", + "reason": "The [{'Country': 'Saudi Arabia', 'Target': 4.0}, {'Country': 'Indonesia', 'Target': 4.0}, {'Country': 'Russia', 'Target': 4.0}, {'Country': 'Mexico', 'Target': 4.0}, {'Country': 'Egypt', 'Target': 4.0}] of have maximum values of 4.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Norway", + "Target": 1.5 + }, + { + "Country": "Nigeria", + "Target": 1.5 + }, + { + "Country": "Kenya", + "Target": 1.5 + } + ], + "score": 0.71, + "annotation": "The has minimum values at Norway, Nigeria, Kenya", + "reason": "The [{'Country': 'Norway', 'Target': 1.5}, {'Country': 'Nigeria', 'Target': 1.5}, {'Country': 'Kenya', 'Target': 1.5}] of have minimum values of 1.5, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Saudi Arabia", + "Target": 4.0 + }, + { + "Country": "Indonesia", + "Target": 4.0 + }, + { + "Country": "United Arab Emirates", + "Target": 2.0 + }, + { + "Country": "Russia", + "Target": 4.0 + }, + { + "Country": "Mexico", + "Target": 4.0 + }, + { + "Country": "South Korea", + "Target": 3.0 + }, + { + "Country": "Egypt", + "Target": 4.0 + }, + { + "Country": "China", + "Target": 3.0 + }, + { + "Country": "India", + "Target": 3.0 + }, + { + "Country": "European Union", + "Target": 3.0 + }, + { + "Country": "Brazil", + "Target": 2.0 + }, + { + "Country": "United States", + "Target": 2.0 + }, + { + "Country": "United Kingdom", + "Target": 2.0 + }, + { + "Country": "Norway", + "Target": 1.5 + }, + { + "Country": "Morroco", + "Target": 2.0 + }, + { + "Country": "Nigeria", + "Target": 1.5 + }, + { + "Country": "Kenya", + "Target": 1.5 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Name of the country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Target", + "importance": "primary", + "description": "Target temperature increase", + "unit": "°C", + "data_type": "number", + "role": "y" + } + ] + } + }, + "835": { + "metadata": { + "title": "The UK's favourite supermarkets", + "description": "In-store supermarkets in the UK ranked by customer satisfaction in 2020", + "main_insight": "Aldi had the highest customer satisfaction rating among UK supermarkets in 2020, followed by Marks & Spencer. Co-op had the lowest rating." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Supermarket": "Co-op", + "Customer Satisfaction": 59.0 + } + ], + "score": 0.87, + "annotation": "The has a minimum value at Co-op", + "reason": "The [{'Supermarket': 'Co-op', 'Customer Satisfaction': 59.0}] of has a mainimum value of 59.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Supermarket": "Aldi", + "Customer Satisfaction": 73.0 + } + ], + "score": 0.86, + "annotation": "The has a maximum value at Aldi", + "reason": "The [{'Supermarket': 'Aldi', 'Customer Satisfaction': 73.0}] of has a maximum value of 73.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Supermarket": "Marks & Spencer", + "Customer Satisfaction": 71.0 + } + ], + "score": 0.68, + "annotation": "The shows a sudden jump in Customer Satisfaction from Waitrose to Marks & Spencer.", + "reason": "The Customer Satisfaction for increases from 67.0 at Waitrose to 71.0 at Marks & Spencer, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Supermarket": "Aldi", + "Customer Satisfaction": 73.0 + }, + { + "Supermarket": "Marks & Spencer", + "Customer Satisfaction": 71.0 + }, + { + "Supermarket": "Lidl", + "Customer Satisfaction": 67.0 + }, + { + "Supermarket": "Tesco", + "Customer Satisfaction": 67.0 + }, + { + "Supermarket": "Waitrose", + "Customer Satisfaction": 67.0 + }, + { + "Supermarket": "Sainsbury's", + "Customer Satisfaction": 66.0 + }, + { + "Supermarket": "Morrisons", + "Customer Satisfaction": 65.0 + }, + { + "Supermarket": "Iceland", + "Customer Satisfaction": 64.0 + }, + { + "Supermarket": "Asda", + "Customer Satisfaction": 62.0 + }, + { + "Supermarket": "Co-op", + "Customer Satisfaction": 59.0 + } + ], + "columns": [ + { + "name": "Supermarket", + "importance": "primary", + "description": "Name of the supermarket", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Customer Satisfaction", + "importance": "primary", + "description": "Customer satisfaction rating (percentage)", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1425": { + "metadata": { + "title": "Everybody Loves Birks", + "description": "Age and income breakdown of Birkenstock customers", + "main_insight": "Birkenstock's customer base is fairly evenly distributed across Baby Boomers, Gen X, and Millennials, with the largest income bracket being $50k-$100k." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [], + "data": { + "data": [ + { + "Age Group": "Baby Boomers", + "Mixed": 0.3, + "Customer Profile": "Percentage of Customers" + }, + { + "Age Group": "Baby Boomers", + "Mixed": "Under $50K", + "Customer Profile": "Income Bracket" + }, + { + "Age Group": "Baby Boomers", + "Mixed": 0.2, + "Customer Profile": "Percentage of Customers by Income" + }, + { + "Age Group": "Gen X", + "Mixed": 0.27, + "Customer Profile": "Percentage of Customers" + }, + { + "Age Group": "Gen X", + "Mixed": "$50K-$100K", + "Customer Profile": "Income Bracket" + }, + { + "Age Group": "Gen X", + "Mixed": 0.35, + "Customer Profile": "Percentage of Customers by Income" + }, + { + "Age Group": "Millennials", + "Mixed": 0.31, + "Customer Profile": "Percentage of Customers" + }, + { + "Age Group": "Millennials", + "Mixed": "$100K-$200K", + "Customer Profile": "Income Bracket" + }, + { + "Age Group": "Millennials", + "Mixed": 0.34, + "Customer Profile": "Percentage of Customers by Income" + }, + { + "Age Group": "Gen Z", + "Mixed": 0.12, + "Customer Profile": "Percentage of Customers" + }, + { + "Age Group": "Gen Z", + "Mixed": "Over $200K", + "Customer Profile": "Income Bracket" + }, + { + "Age Group": "Gen Z", + "Mixed": 0.11, + "Customer Profile": "Percentage of Customers by Income" + } + ], + "columns": [ + { + "name": "Age Group", + "importance": "primary", + "description": "Customer age demographics", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Mixed", + "importance": "primary", + "description": "Mixed for the corresponding customer profile", + "role": "y" + }, + { + "name": "Customer Profile", + "importance": "primary", + "description": "The customer profile of the data point", + "role": "group" + } + ] + } + }, + "2365": { + "metadata": { + "title": "Indian Hindus and Muslims See Neighbors Negatively", + "description": "Share of respondents from India, Bangladesh and Sri Lanka who have favorable views of the others' nation (in %)", + "main_insight": "The infographic shows the percentage of respondents from different religious groups in India, Bangladesh, and Sri Lanka who have favorable views of neighboring countries. Generally, favorable views are relatively low, particularly among Indian Hindus and Muslims regarding other nations." + }, + "chart_type": "Dot Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Respondent Group": "Bangladeshi Hindus", + "Favorable views of Pakistan": 0 + } + ], + "score": 0.78, + "annotation": "The has a minimum value at Bangladeshi Hindus", + "reason": "The [{'Respondent Group': 'Bangladeshi Hindus', 'Favorable views of Pakistan': 0}] of has a mainimum value of 0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Respondent Group": "Bangladeshi Muslims", + "Favorable views of Pakistan": 39.0 + } + ], + "score": 0.77, + "annotation": "The shows a sudden jump in Favorable views of Pakistan from Indian Muslims to Bangladeshi Muslims.", + "reason": "The Favorable views of Pakistan for increases from 22.0 at Indian Muslims to 39.0 at Bangladeshi Muslims, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Respondent Group": "Hindu Sri Lankans", + "Favorable views of Pakistan": 43.0 + }, + { + "Respondent Group": "Muslim Sri Lankans", + "Favorable views of Pakistan": 43.0 + } + ], + "score": 0.56, + "annotation": "The has maximum values at Hindu Sri Lankans, Muslim Sri Lankans", + "reason": "The [{'Respondent Group': 'Hindu Sri Lankans', 'Favorable views of Pakistan': 43.0}, {'Respondent Group': 'Muslim Sri Lankans', 'Favorable views of Pakistan': 43.0}] of have maximum values of 43.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Respondent Group": "Indian Hindus", + "Favorable views of Pakistan": 11.0 + }, + { + "Respondent Group": "Indian Muslims", + "Favorable views of Pakistan": 22.0 + }, + { + "Respondent Group": "Bangladeshi Hindus", + "Favorable views of Pakistan": 0 + }, + { + "Respondent Group": "Bangladeshi Muslims", + "Favorable views of Pakistan": 39.0 + }, + { + "Respondent Group": "Hindu Sri Lankans", + "Favorable views of Pakistan": 43.0 + }, + { + "Respondent Group": "Muslim Sri Lankans", + "Favorable views of Pakistan": 43.0 + } + ], + "columns": [ + { + "name": "Respondent Group", + "importance": "primary", + "description": "The religious group of respondents from each country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Favorable views of Pakistan", + "importance": "secondary", + "description": "Percentage of respondents with favorable views of Pakistan", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2336": { + "metadata": { + "title": "School Shootings in the United States", + "description": "Attacks on others using firearms on school grounds in the U.S. from 2013 to 2019*", + "main_insight": "School shootings in the United States peaked in 2018 and saw a significant decline in 2019." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2013, + "Number of Shootings": 28 + }, + { + "Year": 2014, + "Number of Shootings": 40 + }, + { + "Year": 2015, + "Number of Shootings": 38 + }, + { + "Year": 2016, + "Number of Shootings": 30 + }, + { + "Year": 2017, + "Number of Shootings": 36 + }, + { + "Year": 2018, + "Number of Shootings": 47 + }, + { + "Year": 2019, + "Number of Shootings": 9 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of school shooting incidents", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Number of Shootings", + "importance": "primary", + "description": "Number of attacks on others using firearms on school grounds", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1183": { + "metadata": { + "title": "Which New Apple Product Sparks The Most Interest On Wikipedia?", + "description": "Page views of articles on Apple's new products/services on English-language Wikipedia", + "main_insight": "The iPhone 6 and iPhone 6 Plus generated significantly more interest on Wikipedia than the Apple Watch or Apple Pay on the days following their announcement." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Product": "Pay", + "Units": 2719, + "Date": "September 10, 2014" + }, + { + "Product": "Pay", + "Units": 1538, + "Date": "September 9, 2014" + } + ], + "score": 0.96, + "annotation": "The Units of September 10, 2014 and September 9, 2014 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between September 10, 2014 and September 9, 2014 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Product": "iPhone 6", + "Units": 32149, + "Date": "September 9, 2014" + } + ], + "score": 0.83, + "annotation": "The September 9, 2014 shows a sudden jump in Units from iPhone 6 Plus to iPhone 6.", + "reason": "The Units for September 9, 2014 increases from 16784 at iPhone 6 Plus to 32149 at iPhone 6, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Product": "iPhone 6", + "Units": 43586, + "Date": "September 10, 2014" + } + ], + "score": 0.78, + "annotation": "The September 10, 2014 accounts for the majority in iPhone 6.", + "reason": "The Units of September 10, 2014 in iPhone 6 accounts for 0.575506700996897 (more than 50%) of the total of Units of all Date." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Date": "September 10, 2014", + "Product": "total", + "Units": 95972 + } + ], + "score": 0.76, + "annotation": "The total value of September 10, 2014 accounts for the majority in all Date.", + "reason": "The total value of Units of September 10, 2014 accounts for 0.6276659058357259 (more than 50%) of the total of Units of all Date." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Product": "iPhone 6 Plus", + "Units": 32730, + "Date": "September 10, 2014" + } + ], + "score": 0.76, + "annotation": "The September 10, 2014 shows a sudden jump in Units from WATCH to iPhone 6 Plus.", + "reason": "The Units for September 10, 2014 increases from 16937 at WATCH to 32730 at iPhone 6 Plus, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Product": "iPhone 6", + "Units": 43586, + "Date": "September 10, 2014" + }, + { + "Product": "iPhone 6", + "Units": 32149, + "Date": "September 9, 2014" + }, + { + "Product": "iPhone 6 Plus", + "Units": 32730, + "Date": "September 10, 2014" + }, + { + "Product": "iPhone 6 Plus", + "Units": 16784, + "Date": "September 9, 2014" + }, + { + "Product": "WATCH", + "Units": 16937, + "Date": "September 10, 2014" + }, + { + "Product": "WATCH", + "Units": 6460, + "Date": "September 9, 2014" + }, + { + "Product": "Pay", + "Units": 2719, + "Date": "September 10, 2014" + }, + { + "Product": "Pay", + "Units": 1538, + "Date": "September 9, 2014" + } + ], + "columns": [ + { + "name": "Product", + "importance": "primary", + "description": "Name of the Apple product or service", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding date", + "role": "y" + }, + { + "name": "Date", + "importance": "primary", + "description": "The date of the data point", + "role": "group" + } + ] + } + }, + "1732": { + "metadata": { + "title": "One-Man Team?", + "description": "Premier League teams with highest share of goals scored by one player (17/18 season)", + "main_insight": "In the 17/18 Premier League season, Kane of Tottenham and Salah of Liverpool had the highest share of their team's total goals, with 41% and 38% respectively." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Team and Player": "Murray, Brighton", + "Player Goals Share": 35.0 + } + ], + "score": 0.85, + "annotation": "The shows a sudden jump in Player Goals Share from Wood, Burnley to Murray, Brighton.", + "reason": "The Player Goals Share for increases from 28.0 at Wood, Burnley to 35.0 at Murray, Brighton, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Team and Player": "Wood, Burnley", + "Player Goals Share": 28.0 + } + ], + "score": 0.82, + "annotation": "The has a minimum value at Wood, Burnley", + "reason": "The [{'Team and Player': 'Wood, Burnley', 'Player Goals Share': 28.0}] of has a mainimum value of 28.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Team and Player": "Kane, Tottenham", + "Player Goals Share": 41.0 + } + ], + "score": 0.65, + "annotation": "The has a maximum value at Kane, Tottenham", + "reason": "The [{'Team and Player': 'Kane, Tottenham', 'Player Goals Share': 41.0}] of has a maximum value of 41.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Team and Player": "Kane, Tottenham", + "Player Goals Share": 41.0 + }, + { + "Team and Player": "Salah, Liverpool", + "Player Goals Share": 38.0 + }, + { + "Team and Player": "Vardy, Leicester", + "Player Goals Share": 36.0 + }, + { + "Team and Player": "Murray, Brighton", + "Player Goals Share": 35.0 + }, + { + "Team and Player": "Wood, Burnley", + "Player Goals Share": 28.0 + } + ], + "columns": [ + { + "name": "Team and Player", + "importance": "primary", + "description": "Premier League team and the player with the highest goal share", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Player Goals Share", + "importance": "primary", + "description": "Percentage of the team's total goals scored by the named player", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1485": { + "metadata": { + "title": "Women make up less than 1/3 of Australian ICT workforce", + "description": "Percentage of women per ICT occupation in Australia in 2017", + "main_insight": "Women represent less than a third of the Australian ICT workforce across most occupation categories in 2017, with the highest representation in ICT industry and logistics support (41%) and the lowest in ICT trades (15%)." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "ICT Occupation": "ICT trades", + "Percentage of Women": 15.0 + } + ], + "score": 0.82, + "annotation": "The has a minimum value at ICT trades", + "reason": "The [{'ICT Occupation': 'ICT trades', 'Percentage of Women': 15.0}] of has a mainimum value of 15.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "ICT Occupation": "ICT industry and logistics support", + "Percentage of Women": 41.0 + } + ], + "score": 0.79, + "annotation": "The has a maximum value at ICT industry and logistics support", + "reason": "The [{'ICT Occupation': 'ICT industry and logistics support', 'Percentage of Women': 41.0}] of has a maximum value of 41.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "ICT Occupation": "ICT industry and logistics support", + "Percentage of Women": 41.0 + } + ], + "score": 0.76, + "annotation": "The shows a sudden jump in Percentage of Women from ICT sales to ICT industry and logistics support.", + "reason": "The Percentage of Women for increases from 31.0 at ICT sales to 41.0 at ICT industry and logistics support, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "ICT Occupation": "All ICT Occupations", + "Percentage of Women": 28.0 + }, + { + "ICT Occupation": "ICT industry and logistics support", + "Percentage of Women": 41.0 + }, + { + "ICT Occupation": "ICT management and operations", + "Percentage of Women": 31.0 + }, + { + "ICT Occupation": "ICT sales", + "Percentage of Women": 31.0 + }, + { + "ICT Occupation": "ICT technical and professional", + "Percentage of Women": 24.0 + }, + { + "ICT Occupation": "ICT trades", + "Percentage of Women": 15.0 + } + ], + "columns": [ + { + "name": "ICT Occupation", + "importance": "primary", + "description": "Type of ICT occupation", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Women", + "importance": "primary", + "description": "Percentage of women in each ICT occupation", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1000": { + "metadata": { + "title": "Antisemitic incidents in the UK", + "description": "Antisemitic incidents recorded by Community Security Trust in the UK", + "main_insight": "Antisemitic incidents in the UK have generally increased from 2012 to 2017, with a significant rise after 2013." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2012, + "Incidents": 650 + }, + { + "Year": 2013, + "Incidents": 535 + }, + { + "Year": 2014, + "Incidents": 1182 + }, + { + "Year": 2015, + "Incidents": 960 + }, + { + "Year": 2016, + "Incidents": 1346 + }, + { + "Year": 2017, + "Incidents": 1382 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the incident", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Incidents", + "importance": "primary", + "description": "Number of antisemitic incidents", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1510": { + "metadata": { + "title": "Home Sharing Here to Stay, but Hotels Remain Top Choice", + "description": "Accommodation booking value, by type of stay (in USD)", + "main_insight": "Hotel booking value is significantly higher than home sharing and other accommodation types, and while it dipped in 2021, it's projected to grow substantially by 2027. Home sharing is also projected to grow but remains a distant second." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2027, + "Units": 240.0, + "Accommodation Type": "Home sharing" + }, + { + "Year": 2027, + "Units": 240.0, + "Accommodation Type": "Other" + } + ], + "score": 1.0, + "annotation": "The Units of Home sharing and Other show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Home sharing and Other is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2027, + "Units": 1190.0, + "Accommodation Type": "Hotels" + }, + { + "Year": 2027, + "Units": 240.0, + "Accommodation Type": "Home sharing" + } + ], + "score": 0.78, + "annotation": "The Units of Hotels and Home sharing show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Hotels and Home sharing is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2027, + "Units": 1190.0, + "Accommodation Type": "Hotels" + }, + { + "Year": 2027, + "Units": 240.0, + "Accommodation Type": "Other" + } + ], + "score": 0.78, + "annotation": "The Units of Hotels and Other show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Hotels and Other is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2017, + "Units": 850.0, + "Accommodation Type": "Hotels" + }, + { + "Year": 2017, + "Units": 170.0, + "Accommodation Type": "Home sharing" + }, + { + "Year": 2017, + "Units": 170.0, + "Accommodation Type": "Other" + }, + { + "Year": 2019, + "Units": 910.0, + "Accommodation Type": "Hotels" + }, + { + "Year": 2019, + "Units": 170.0, + "Accommodation Type": "Home sharing" + }, + { + "Year": 2019, + "Units": 170.0, + "Accommodation Type": "Other" + }, + { + "Year": 2021, + "Units": 510.0, + "Accommodation Type": "Hotels" + }, + { + "Year": 2021, + "Units": 170.0, + "Accommodation Type": "Home sharing" + }, + { + "Year": 2021, + "Units": 170.0, + "Accommodation Type": "Other" + }, + { + "Year": 2023, + "Units": 850.0, + "Accommodation Type": "Hotels" + }, + { + "Year": 2023, + "Units": 180.0, + "Accommodation Type": "Home sharing" + }, + { + "Year": 2023, + "Units": 180.0, + "Accommodation Type": "Other" + }, + { + "Year": 2025, + "Units": 1020.0, + "Accommodation Type": "Hotels" + }, + { + "Year": 2025, + "Units": 200.0, + "Accommodation Type": "Home sharing" + }, + { + "Year": 2025, + "Units": 200.0, + "Accommodation Type": "Other" + }, + { + "Year": 2027, + "Units": 1190.0, + "Accommodation Type": "Hotels" + }, + { + "Year": 2027, + "Units": 240.0, + "Accommodation Type": "Home sharing" + }, + { + "Year": 2027, + "Units": 240.0, + "Accommodation Type": "Other" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of booking value, including projections (p).", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding accommodation type", + "role": "y" + }, + { + "name": "Accommodation Type", + "importance": "primary", + "description": "The accommodation type of the data point", + "role": "group" + } + ] + } + }, + "1188": { + "metadata": { + "title": "The World's Refugee Crises", + "description": "Number of internationally displaced people by country of origin*", + "main_insight": "The number of internationally displaced people has increased significantly from 2013 to 2020, with Syria, Palestine, and Venezuela having among the highest number. By 2020, the overall number increased and became more distributed." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Value": 34.4, + "Origin": "Total" + }, + { + "Year": 2020, + "Value": 6.8, + "Origin": "Syria" + } + ], + "score": 1.0, + "annotation": "The Value of Total and Syria show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Value between Total and Syria is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Value": 34.4, + "Origin": "Total" + }, + { + "Year": 2020, + "Value": 5.7, + "Origin": "Palestine" + } + ], + "score": 1.0, + "annotation": "The Value of Total and Palestine show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Value between Total and Palestine is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Value": 34.4, + "Origin": "Total" + }, + { + "Year": 2020, + "Value": 4.8, + "Origin": "Venezuela" + } + ], + "score": 1.0, + "annotation": "The Value of Total and Venezuela show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Value between Total and Venezuela is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Value": 34.4, + "Origin": "Total" + }, + { + "Year": 2020, + "Value": 3.0, + "Origin": "Sudan & South Sudan" + } + ], + "score": 1.0, + "annotation": "The Value of Total and Sudan & South Sudan show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Value between Total and Sudan & South Sudan is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Value": 34.4, + "Origin": "Total" + }, + { + "Year": 2020, + "Value": 2.8, + "Origin": "Afghanistan" + } + ], + "score": 1.0, + "annotation": "The Value of Total and Afghanistan show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Value between Total and Afghanistan is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2013, + "Value": 17.9, + "Origin": "Total" + }, + { + "Year": 2013, + "Value": 2.5, + "Origin": "Syria" + }, + { + "Year": 2013, + "Value": 5.0, + "Origin": "Palestine" + }, + { + "Year": 2013, + "Value": 0.1, + "Origin": "Venezuela" + }, + { + "Year": 2013, + "Value": 1.5, + "Origin": "Sudan & South Sudan" + }, + { + "Year": 2013, + "Value": 2.5, + "Origin": "Afghanistan" + }, + { + "Year": 2013, + "Value": 0.8, + "Origin": "Myanmar" + }, + { + "Year": 2013, + "Value": 5.5, + "Origin": "Others" + }, + { + "Year": 2020, + "Value": 34.4, + "Origin": "Total" + }, + { + "Year": 2020, + "Value": 6.8, + "Origin": "Syria" + }, + { + "Year": 2020, + "Value": 5.7, + "Origin": "Palestine" + }, + { + "Year": 2020, + "Value": 4.8, + "Origin": "Venezuela" + }, + { + "Year": 2020, + "Value": 3.0, + "Origin": "Sudan & South Sudan" + }, + { + "Year": 2020, + "Value": 2.8, + "Origin": "Afghanistan" + }, + { + "Year": 2020, + "Value": 1.1, + "Origin": "Myanmar" + }, + { + "Year": 2020, + "Value": 10.2, + "Origin": "Others" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "The year of the data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding origin", + "role": "y" + }, + { + "name": "Origin", + "importance": "primary", + "description": "The origin of the data point", + "role": "group" + } + ] + } + }, + "2309": { + "metadata": { + "title": "Women Are More Successful and More Efficient", + "description": "Crowdfunding success by gender (Campaigns 2015/2016)", + "main_insight": "While men launch a significantly greater number of crowdfunding campaigns, women have higher success rates across various categories and a slightly higher average contribution." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Metric": "Average Contribution", + "Dollars": 83.0, + "Gender": "Male" + }, + { + "Metric": "Average Contribution", + "Dollars": 87.0, + "Gender": "Female" + } + ], + "score": 1.0, + "annotation": "The Dollars of Male and Female show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between Male and Female is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Metric": "Number of Campaigns", + "Dollars": 139000.0, + "Gender": "Male" + } + ], + "score": 0.97, + "annotation": "The Male shows a sudden jump in Dollars from Average Contribution to Number of Campaigns.", + "reason": "The Dollars for Male increases from 83.0 at Average Contribution to 139000.0 at Number of Campaigns, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Metric": "Number of Campaigns", + "Dollars": 55000.0, + "Gender": "Female" + } + ], + "score": 0.97, + "annotation": "The Female shows a sudden jump in Dollars from Average Contribution to Number of Campaigns.", + "reason": "The Dollars for Female increases from 87.0 at Average Contribution to 55000.0 at Number of Campaigns, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Metric": "Number of Campaigns", + "Dollars": 139000.0, + "Gender": "Male" + } + ], + "score": 0.95, + "annotation": "The Male has a maximum value at Number of Campaigns", + "reason": "The [{'Metric': 'Number of Campaigns', 'Dollars': 139000.0, 'Gender': 'Male'}] of Male has a maximum value of 139000.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Metric": "Number of Campaigns", + "Dollars": 55000.0, + "Gender": "Female" + } + ], + "score": 0.95, + "annotation": "The Female has a maximum value at Number of Campaigns", + "reason": "The [{'Metric': 'Number of Campaigns', 'Dollars': 55000.0, 'Gender': 'Female'}] of Female has a maximum value of 55000.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Metric": "Number of Campaigns", + "Dollars": 139000.0, + "Gender": "Male" + }, + { + "Metric": "Number of Campaigns", + "Dollars": 55000.0, + "Gender": "Female" + }, + { + "Metric": "Success Rate", + "Dollars": 0.17, + "Gender": "Male" + }, + { + "Metric": "Success Rate", + "Dollars": 0.22, + "Gender": "Female" + }, + { + "Metric": "Success Rate Technology", + "Dollars": 0.1, + "Gender": "Male" + }, + { + "Metric": "Success Rate Technology", + "Dollars": 0.13, + "Gender": "Female" + }, + { + "Metric": "Success Rate Entertainment and Media", + "Dollars": 0.23, + "Gender": "Male" + }, + { + "Metric": "Success Rate Entertainment and Media", + "Dollars": 0.3, + "Gender": "Female" + }, + { + "Metric": "Success Rate Digital Technology", + "Dollars": 0.09, + "Gender": "Male" + }, + { + "Metric": "Success Rate Digital Technology", + "Dollars": 0.16, + "Gender": "Female" + }, + { + "Metric": "Success Rate Retail and Consumer Goods", + "Dollars": 0.18, + "Gender": "Male" + }, + { + "Metric": "Success Rate Retail and Consumer Goods", + "Dollars": 0.22, + "Gender": "Female" + }, + { + "Metric": "Average Contribution", + "Dollars": 83.0, + "Gender": "Male" + }, + { + "Metric": "Average Contribution", + "Dollars": 87.0, + "Gender": "Female" + } + ], + "columns": [ + { + "name": "Metric", + "importance": "primary", + "description": "The crowdfunding metric being measured", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding gender", + "role": "y" + }, + { + "name": "Gender", + "importance": "primary", + "description": "The gender of the data point", + "role": "group" + } + ] + } + }, + "1409": { + "metadata": { + "title": "BRICS Nations Still Trail G7 in Per-Capita GDP", + "description": "G7 and BRICS countries' GDP per capita in 2023 in international dollars at purchasing power parity", + "main_insight": "G7 countries have significantly higher GDP per capita compared to BRICS countries in 2023." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "US", + "Count": 80034, + "Metric": "G7 GDP per capita" + } + ], + "score": 0.92, + "annotation": "The G7 GDP per capita has a maximum value at US", + "reason": "The [{'Country': 'US', 'Count': 80034, 'Metric': 'G7 GDP per capita'}] of G7 GDP per capita has a maximum value of 80034, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "US", + "Count": 80034, + "Metric": "G7 GDP per capita" + } + ], + "score": 0.86, + "annotation": "The G7 GDP per capita shows a sudden jump in Count from DE to US.", + "reason": "The Count for G7 GDP per capita increases from 63150 at DE to 80034 at US, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "JP", + "Count": 49546, + "Metric": "G7 GDP per capita" + } + ], + "score": 0.57, + "annotation": "The G7 GDP per capita has a minimum value at JP", + "reason": "The [{'Country': 'JP', 'Count': 49546, 'Metric': 'G7 GDP per capita'}] of G7 GDP per capita has a mainimum value of 49546, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "US", + "Count": 80034, + "Metric": "G7 GDP per capita" + }, + { + "Country": "DE", + "Count": 63150, + "Metric": "G7 GDP per capita" + }, + { + "Country": "CA", + "Count": 58400, + "Metric": "G7 GDP per capita" + }, + { + "Country": "FR", + "Count": 56036, + "Metric": "G7 GDP per capita" + }, + { + "Country": "GB", + "Count": 54603, + "Metric": "G7 GDP per capita" + }, + { + "Country": "IT", + "Count": 51856, + "Metric": "G7 GDP per capita" + }, + { + "Country": "JP", + "Count": 49546, + "Metric": "G7 GDP per capita" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country Name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Count", + "importance": "primary", + "description": "Count for the corresponding metric", + "role": "y" + }, + { + "name": "Metric", + "importance": "primary", + "description": "The metric of the data point", + "role": "group" + } + ] + } + }, + "668": { + "metadata": { + "title": "The US Industries Most At Risk In A Trade War With China", + "description": "Leading export categories of the United States to China in 2015 (in billion U.S. dollars)", + "main_insight": "Transportation equipment was the largest US export category to China in 2015, valued at 26 billion U.S. dollars, making it the industry most at risk in a trade war." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Industry": "Transportation equipment", + "Export Value (Billion USD)": 26.0 + } + ], + "score": 0.85, + "annotation": "The shows a sudden jump in Export Value (Billion USD) from Computers and electronics to Transportation equipment.", + "reason": "The Export Value (Billion USD) for increases from 17.0 at Computers and electronics to 26.0 at Transportation equipment, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Industry": "Transportation equipment", + "Export Value (Billion USD)": 26.0 + } + ], + "score": 0.83, + "annotation": "The has a maximum value at Transportation equipment", + "reason": "The [{'Industry': 'Transportation equipment', 'Export Value (Billion USD)': 26.0}] of has a maximum value of 26.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Industry": "Machinery (except electrical)", + "Export Value (Billion USD)": 9.0 + } + ], + "score": 0.65, + "annotation": "The has a minimum value at Machinery (except electrical)", + "reason": "The [{'Industry': 'Machinery (except electrical)', 'Export Value (Billion USD)': 9.0}] of has a mainimum value of 9.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Industry": "Transportation equipment", + "Export Value (Billion USD)": 26.0 + }, + { + "Industry": "Computers and electronics", + "Export Value (Billion USD)": 17.0 + }, + { + "Industry": "Agricultural Products", + "Export Value (Billion USD)": 15.0 + }, + { + "Industry": "Chemicals", + "Export Value (Billion USD)": 13.0 + }, + { + "Industry": "Machinery (except electrical)", + "Export Value (Billion USD)": 9.0 + } + ], + "columns": [ + { + "name": "Industry", + "importance": "primary", + "description": "US Industry Category", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Export Value (Billion USD)", + "importance": "primary", + "description": "Value of exports to China in 2015 in billions of US dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "601": { + "metadata": { + "title": "How Drug Habits Differ in the U.S. and the UK", + "description": "Share different drugs had of total seizures in the UK and U.S. in 2018/19 (in percent)", + "main_insight": "Cannabis accounts for the majority of drug seizures in both the US and UK, but the proportion is much higher in the US. Cocaine represents a significantly larger share of seizures in the UK compared to the US." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "U.S.", + "Score": 74.0, + "Drug Type": "Cannabis" + }, + { + "Country": "U.S.", + "Score": 13.0, + "Drug Type": "Cocaine" + } + ], + "score": 1.0, + "annotation": "The Score of Cannabis and Cocaine show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Cannabis and Cocaine is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "U.S.", + "Score": 74.0, + "Drug Type": "Cannabis" + }, + { + "Country": "U.S.", + "Score": 11.0, + "Drug Type": "Any amphetamines" + } + ], + "score": 1.0, + "annotation": "The Score of Cannabis and Any amphetamines show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Cannabis and Any amphetamines is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "U.S.", + "Score": 74.0, + "Drug Type": "Cannabis" + }, + { + "Country": "U.S.", + "Score": 1.0, + "Drug Type": "Heroin" + } + ], + "score": 1.0, + "annotation": "The Score of Cannabis and Heroin show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Cannabis and Heroin is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "U.S.", + "Score": 13.0, + "Drug Type": "Cocaine" + }, + { + "Country": "U.S.", + "Score": 11.0, + "Drug Type": "Any amphetamines" + } + ], + "score": 1.0, + "annotation": "The Score of Cocaine and Any amphetamines show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Cocaine and Any amphetamines is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "U.S.", + "Score": 13.0, + "Drug Type": "Cocaine" + }, + { + "Country": "U.S.", + "Score": 1.0, + "Drug Type": "Heroin" + } + ], + "score": 1.0, + "annotation": "The Score of Cocaine and Heroin show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Cocaine and Heroin is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Country": "UK", + "Score": 65.0, + "Drug Type": "Cannabis" + }, + { + "Country": "UK", + "Score": 30.0, + "Drug Type": "Cocaine" + }, + { + "Country": "UK", + "Score": 2.0, + "Drug Type": "Any amphetamines" + }, + { + "Country": "UK", + "Score": 2.0, + "Drug Type": "Heroin" + }, + { + "Country": "U.S.", + "Score": 74.0, + "Drug Type": "Cannabis" + }, + { + "Country": "U.S.", + "Score": 13.0, + "Drug Type": 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"Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Spain", + "Protest Likelihood": 31.0 + } + ], + "score": 0.89, + "annotation": "The has a maximum value at Spain", + "reason": "The [{'Country': 'Spain', 'Protest Likelihood': 31.0}] of has a maximum value of 31.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Spain", + "Protest Likelihood": 31.0 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Protest Likelihood from France to Spain.", + "reason": "The Protest Likelihood for increases from 21.0 at France to 31.0 at Spain, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "UK", + "Protest Likelihood": 11.0 + } + ], + "score": 0.74, + "annotation": "The has a minimum value at UK", + "reason": "The [{'Country': 'UK', 'Protest Likelihood': 11.0}] of has a mainimum value of 11.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Spain", + "Protest Likelihood": 31.0 + }, + { + "Country": "France", + "Protest Likelihood": 21.0 + }, + { + "Country": "Italy", + "Protest Likelihood": 20.0 + }, + { + "Country": "EU average", + "Protest Likelihood": 19.0 + }, + { + "Country": "Poland", + "Protest Likelihood": 19.0 + }, + { + "Country": "Germany", + "Protest Likelihood": 14.0 + }, + { + "Country": "UK", + "Protest Likelihood": 11.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "European country or EU average", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Protest Likelihood", + "importance": "primary", + "description": "Percentage of citizens likely to join a protest", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1598": { + "metadata": { + "title": "New 'Pornocrime' Report Reveals Damning Figures", + "description": "Acts of violence in pornographic films hosted by Pornhub, XVideos, Xnxx and Xhamsters", + "main_insight": "A vast majority of the most viewed pornographic videos on popular websites display physical or verbal aggression, with a high percentage containing content punishable by law." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "Display physical aggression", + "Ratio": 0.88, + "Proportion": "Percentage" + } + ], + "score": 0.97, + "annotation": "The Percentage shows a sudden jump in Ratio from Display verbal aggression to Display physical aggression.", + "reason": "The Ratio for Percentage increases from 0.48 at Display verbal aggression to 0.88 at Display physical aggression, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Category": "Display verbal aggression", + "Ratio": 0.48, + "Proportion": "Percentage" + } + ], + "score": 0.67, + "annotation": "The Percentage has a minimum value at Display verbal aggression", + "reason": "The [{'Category': 'Display verbal aggression', 'Ratio': 0.48, 'Proportion': 'Percentage'}] of Percentage has a mainimum value of 0.48, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Contain physical or verbal violence and are therefore punishable by law", + "Ratio": 0.9, + "Proportion": "Percentage" + } + ], + "score": 0.41, + "annotation": "The Percentage has a maximum value at Contain physical or verbal violence and are therefore punishable by law", + "reason": "The [{'Category': 'Contain physical or verbal violence and are therefore punishable by law', 'Ratio': 0.9, 'Proportion': 'Percentage'}] of Percentage has a maximum value of 0.9, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Category": "Display physical aggression", + "Ratio": 0.88, + "Proportion": "Percentage" + }, + { + "Category": "Display verbal aggression", + "Ratio": 0.48, + "Proportion": "Percentage" + }, + { + "Category": "Contain physical or verbal violence and are therefore punishable by law", + "Ratio": 0.9, + "Proportion": "Percentage" + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Type of content", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding proportion", + "role": "y" + }, + { + "name": "Proportion", + "importance": "primary", + "description": "The proportion of the data point", + "role": "group" + } + ] + } + }, + "344": { + "metadata": { + "title": "Pfizer Revenue Boosted by Covid-19 Drugs", + "description": "Pfizer's annual revenue since 2016", + "main_insight": "Pfizer's revenue significantly increased in 2021 and 2022, largely due to the contribution of its Covid-19 vaccine (Comirnaty) and antiviral drug (Paxlovid)." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Units": 81.3, + "Revenue Breakdown": "Total Revenue" + }, + { + "Year": 2021, + "Units": 44.5, + "Revenue Breakdown": "Revenue Excluding Covid-19 Products" + } + ], + "score": 0.84, + "annotation": "The Units of Total Revenue and Revenue Excluding Covid-19 Products show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Revenue and Revenue Excluding Covid-19 Products is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2016, + "Units": 38.7, + "Revenue Breakdown": "Total Revenue" + }, + { + "Year": 2016, + "Units": 38.7, + "Revenue Breakdown": "Revenue Excluding Covid-19 Products" + }, + { + "Year": 2017, + "Units": 38.8, + "Revenue Breakdown": "Total Revenue" + }, + { + "Year": 2017, + "Units": 38.8, + "Revenue Breakdown": "Revenue Excluding Covid-19 Products" + }, + { + "Year": 2018, + "Units": 40.8, + "Revenue Breakdown": "Total Revenue" + }, + { + "Year": 2018, + "Units": 40.8, + "Revenue Breakdown": "Revenue Excluding Covid-19 Products" + }, + { + "Year": 2019, + "Units": 41.2, + "Revenue Breakdown": "Total Revenue" + }, + { + "Year": 2019, + "Units": 41.2, + "Revenue Breakdown": "Revenue Excluding Covid-19 Products" + }, + { + "Year": 2020, + "Units": 41.9, + "Revenue Breakdown": "Total Revenue" + }, + { + "Year": 2020, + "Units": 41.9, + "Revenue Breakdown": "Revenue Excluding Covid-19 Products" + }, + { + "Year": 2021, + "Units": 81.3, + "Revenue Breakdown": "Total Revenue" + }, + { + "Year": 2021, + "Units": 36.8, + "Revenue Breakdown": "Comirnaty Revenue" + }, + { + "Year": 2021, + "Units": 44.5, + "Revenue Breakdown": "Revenue Excluding Covid-19 Products" + }, + { + "Year": 0, + "Units": 32.0, + "Revenue Breakdown": "Comirnaty Revenue" + }, 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"datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Platform": "Instagram", + "Interactions (millions)": 187.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Interactions (millions) from Facebook to Instagram.", + "reason": "The Interactions (millions) for increases from 77.0 at Facebook to 187.0 at Instagram, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Platform": "Instagram", + "Interactions (millions)": 187.0 + } + ], + "score": 0.67, + "annotation": "The has a maximum value at Instagram", + "reason": "The [{'Platform': 'Instagram', 'Interactions (millions)': 187.0}] of has a maximum value of 187.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Platform": "Twitter", + "Interactions (millions)": 73.0 + } + ], + "score": 0.41, + "annotation": "The has a minimum value at Twitter", + "reason": "The [{'Platform': 'Twitter', 'Interactions (millions)': 73.0}] of has a mainimum value of 73.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Platform": "Instagram", + "Interactions (millions)": 187.0 + }, + { + "Platform": "Facebook", + "Interactions (millions)": 77.0 + }, + { + "Platform": "Twitter", + "Interactions (millions)": 73.0 + } + ], + "columns": [ + { + "name": "Platform", + "importance": "primary", + "description": "Social media platform", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Interactions (millions)", + "importance": "primary", + "description": "Estimated number of interactions with Russian troll farm content", + "unit": "millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2544": { + "metadata": { + "title": "Canada to Welcome Over 1 Million Immigrants over 3 Years", + "description": "Planned admissions of immigrants to Canada", + "main_insight": "Canada plans to admit an increasing number of immigrants each year from 2019 to 2021, totaling over 1 million. The number of immigrants admitted under 'family reunification' and as refugees are also planned to increase." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Dollars": 370000, + "Immigrant Category": "Total Immigrants" + }, + { + "Year": 2021, + "Dollars": 64500, + "Immigrant Category": "Refugees" + } + ], + "score": 1.0, + "annotation": "The Dollars of Total Immigrants and Refugees show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between Total Immigrants and Refugees is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Dollars": 370000, + "Immigrant Category": "Total Immigrants" + }, + { + "Year": 2021, + "Dollars": 102000, + "Immigrant Category": "Family Reunification" + } + ], + "score": 0.87, + "annotation": "The Dollars of Total Immigrants and Family Reunification show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between Total Immigrants and Family Reunification is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Dollars": 102000, + "Immigrant Category": "Family Reunification" + }, + { + "Year": 2021, + "Dollars": 64500, + "Immigrant Category": "Refugees" + } + ], + "score": 0.87, + "annotation": "The Dollars of Family Reunification and Refugees show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between Family Reunification and Refugees is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2019, + "Dollars": 350000, + "Immigrant Category": "Total Immigrants" + }, + { + "Year": 2019, + "Dollars": 98000, + "Immigrant Category": "Family Reunification" + }, + { + "Year": 2019, + "Dollars": 58500, + "Immigrant Category": "Refugees" + }, + { + "Year": 2020, + "Dollars": 360000, + "Immigrant Category": "Total Immigrants" + }, + { + "Year": 2020, + "Dollars": 102000, + "Immigrant Category": "Family Reunification" + }, + { + "Year": 2020, + "Dollars": 61500, + "Immigrant Category": "Refugees" + }, + { + "Year": 2021, + "Dollars": 370000, + "Immigrant Category": "Total Immigrants" + }, + { + "Year": 2021, + "Dollars": 102000, + "Immigrant Category": "Family Reunification" + }, + { + "Year": 2021, + "Dollars": 64500, + "Immigrant Category": "Refugees" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of planned immigrant admission", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding immigrant category", + "role": "y" + }, + { + "name": "Immigrant Category", + "importance": "primary", + "description": "The immigrant category of the data point", + "role": "group" + } + ] + } + }, + "1648": { + "metadata": { + "title": "State of the Union Addresses on TV", + "description": "Number of viewers of the State of the Union addresses from (in millions)", + "main_insight": "The number of viewers for State of the Union addresses has generally decreased from 1993 to 2017, with President Clinton's 1993 address having the highest viewership and a notable decrease over the years." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1993, + "Viewers": 66.9 + }, + { + "Year": 1994, + "Viewers": 45.7 + }, + { + "Year": 1995, + "Viewers": 41.8 + }, + { + "Year": 1996, + "Viewers": 41.9 + }, + { + "Year": 1997, + "Viewers": 41.9 + }, + { + "Year": 1998, + "Viewers": 51.8 + }, + { + "Year": 1999, + "Viewers": 43.5 + }, + { + "Year": 2000, + "Viewers": 31.5 + }, + { + "Year": 2001, + "Viewers": 40.1 + }, + { + "Year": 2002, + "Viewers": 51.8 + }, + { + "Year": 2003, + "Viewers": 62.06 + }, + { + "Year": 2004, + "Viewers": 43.48 + }, + { + "Year": 2005, + "Viewers": 40.58 + }, + { + "Year": 2006, + "Viewers": 41.68 + }, + { + "Year": 2007, + "Viewers": 45.47 + }, + { + "Year": 2008, + "Viewers": 37.77 + }, + { + "Year": 2009, + "Viewers": 52.37 + }, + { + "Year": 2010, + "Viewers": 48.0 + }, + { + "Year": 2011, + "Viewers": 42.81 + }, + { + "Year": 2012, + "Viewers": 37.81 + }, + { + "Year": 2013, + "Viewers": 33.48 + }, + { + "Year": 2014, + "Viewers": 33.3 + }, + { + "Year": 2015, + "Viewers": 31.71 + }, + { + "Year": 2016, + "Viewers": 31.33 + }, + { + "Year": 2017, + "Viewers": 47.74 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the State of the Union Address", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Viewers", + "importance": "primary", + "description": "Number of viewers in millions", + "unit": "millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "853": { + "metadata": { + "title": "Parents More Pessimistic Than Teens About Fortnite Skills", + "description": "Share of parents/teens who have witnessed/experienced the following on Fortnite*", + "main_insight": "Teens are significantly more likely than parents to report positive experiences and skill development from playing Fortnite, across all categories listed." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Experiences": "Learned teamwork", + "Ratio": 0.26, + "Demographic": "Parents" + } + ], + "score": 0.95, + "annotation": "The Parents shows a sudden jump in Ratio from Bonded with sibling(s) to Learned teamwork.", + "reason": "The Ratio for Parents increases from 0.16 at Bonded with sibling(s) to 0.26 at Learned teamwork, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Experiences": "Bonded with sibling(s)", + "Ratio": 0.39, + "Demographic": "Teens" + } + ], + "score": 0.84, + "annotation": "The Teens shows a sudden jump in Ratio from Learned to handle online conflicts to Bonded with sibling(s).", + "reason": "The Ratio for Teens increases from 0.27 at Learned to handle online conflicts to 0.39 at Bonded with sibling(s), indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Experiences": "Learned to handle online conflicts", + "Ratio": 0.27, + "Demographic": "Teens" + } + ], + "score": 0.81, + "annotation": "The Teens has a minimum value at Learned to handle online conflicts", + "reason": "The [{'Experiences': 'Learned to handle online conflicts', 'Ratio': 0.27, 'Demographic': 'Teens'}] of Teens has a mainimum value of 0.27, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Experiences": "Learned to handle online conflicts", + 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"Demographic": "Parents" + }, + { + "Experiences": "Made a friend online", + "Ratio": 0.44, + "Demographic": "Teens" + }, + { + "Experiences": "Made a friend online", + "Ratio": 0.26, + "Demographic": "Parents" + }, + { + "Experiences": "Improved my communication skills", + "Ratio": 0.4, + "Demographic": "Teens" + }, + { + "Experiences": "Improved my communication skills", + "Ratio": 0.14, + "Demographic": "Parents" + }, + { + "Experiences": "Bonded with sibling(s)", + "Ratio": 0.39, + "Demographic": "Teens" + }, + { + "Experiences": "Bonded with sibling(s)", + "Ratio": 0.16, + "Demographic": "Parents" + }, + { + "Experiences": "Learned to handle online conflicts", + "Ratio": 0.27, + "Demographic": "Teens" + }, + { + "Experiences": "Learned to handle online conflicts", + "Ratio": 0.14, + "Demographic": "Parents" + } + ], + "columns": [ + { + "name": "Experiences", + "importance": "primary", + "description": "Experiences on Fortnite", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding demographic", + "role": "y" + }, + { + "name": "Demographic", + "importance": "primary", + "description": "The demographic of the data point", + "role": "group" + } + ] + } + }, + "1534": { + "metadata": { + "title": "Who Did The Most To Defeat The Nazis?", + "description": "\"Who played the most important role defeating the nazis in WWII?\"*", + "main_insight": "Perceptions of who played the most important role in defeating the Nazis in WWII vary significantly across countries, with respondents often favoring their own nation's contribution." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country Surveyed": "United Kingdom", + "Units": 50.0, + "Nationality": "The British" + } + ], + "score": 0.96, + "annotation": "The The British shows a sudden jump in Units from France to United Kingdom.", + "reason": "The Units for The British increases from 11.0 at France to 50.0 at United Kingdom, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country Surveyed": "Germany", + "Units": 22.0, + "Nationality": "The Russians" + } + ], + "score": 0.92, + "annotation": "The The Russians shows a sudden jump in Units from France to Germany.", + "reason": "The Units for The Russians increases from 15.0 at France to 22.0 at Germany, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country Surveyed": "France", + "Units": 11.0, + "Nationality": "The British" + }, + { + "Country Surveyed": "France", + "Units": 56.0, + "Nationality": "The Americans" + } + ], + "score": 0.86, + "annotation": "The Units of The British and The Americans show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between The British and The Americans is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Nationality": "The Americans", + "Country Surveyed": "total", + "Units": 146.0 + } + ], + "score": 0.85, + "annotation": "The total value of The Americans accounts for the majority in all Nationality.", + "reason": "The total value of Units of The Americans accounts for 0.512280701754386 (more than 50%) of the total of Units of all Nationality." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country Surveyed": "Germany", + "Units": 34.0, + "Nationality": "The Americans" + } + ], + "score": 0.85, + "annotation": "The The Americans shows a sudden jump in Units from United Kingdom to Germany.", + "reason": "The Units for The Americans increases from 9.0 at United Kingdom to 34.0 at Germany, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country Surveyed": "United Kingdom", + "Units": 50.0, + "Nationality": "The British" + }, + { + "Country Surveyed": "United Kingdom", + "Units": 9.0, + "Nationality": "The Americans" + }, + { + "Country Surveyed": "United Kingdom", + "Units": 13.0, + "Nationality": "The Russians" + }, + { + "Country Surveyed": "United States", + "Units": 9.0, + "Nationality": "The British" + }, + { + "Country Surveyed": "United States", + "Units": 47.0, + "Nationality": "The Americans" + }, + { + "Country Surveyed": "United States", + "Units": 12.0, + "Nationality": "The Russians" + }, + { + "Country Surveyed": "Germany", + "Units": 7.0, + "Nationality": "The British" + }, + { + "Country Surveyed": "Germany", + "Units": 34.0, + "Nationality": "The Americans" + }, + { + "Country Surveyed": "Germany", + "Units": 22.0, + "Nationality": "The Russians" + }, + { + "Country Surveyed": "France", + "Units": 11.0, + "Nationality": "The British" + }, + { + "Country Surveyed": "France", + "Units": 56.0, + "Nationality": "The Americans" + }, + { + "Country Surveyed": "France", + "Units": 15.0, + "Nationality": "The Russians" + } + ], + "columns": [ + { + "name": "Country Surveyed", + "importance": "primary", + "description": "The country where the survey was conducted", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding nationality", + "role": "y" + }, + { + "name": "Nationality", + "importance": "primary", + "description": "The nationality of the data point", + "role": "group" + } + ] + } + }, + "662": { + "metadata": { + "title": "Trump's Favorite Twitter Insults", + "description": "Number of times @realdonaldtrump has posted a tweet with the following words", + "main_insight": "The infographic shows the frequency of various insults used by Donald Trump on Twitter. \"Loser\" is his most frequently used insult, followed by \"terrible\" and \"dumb/dummy\"." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Insult": "loser", + "Frequency": 246 + } + ], + "score": 0.71, + "annotation": "The has a maximum value at loser", + "reason": "The [{'Insult': 'loser', 'Frequency': 246}] of has a maximum value of 246, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Insult": "incompetent/incompetence", + "Frequency": 98 + } + ], + "score": 0.67, + "annotation": "The has a minimum value at incompetent/incompetence", + "reason": "The [{'Insult': 'incompetent/incompetence', 'Frequency': 98}] of has a mainimum value of 98, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Insult": "stupid", + "Frequency": 192 + } + ], + "score": 0.66, + "annotation": "The shows a sudden jump in Frequency from dishonest to stupid.", + "reason": "The Frequency for increases from 152 at dishonest to 192 at stupid, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Insult": "loser", + "Frequency": 246 + }, + { + "Insult": "terrible", + "Frequency": 244 + }, + { + "Insult": "dumb/dummy", + "Frequency": 232 + }, + { + "Insult": "weak", + "Frequency": 203 + }, + { + "Insult": "stupid", + "Frequency": 192 + }, + { + "Insult": "dishonest", + "Frequency": 152 + }, + { + "Insult": "dope/dopey", + "Frequency": 117 + }, + { + "Insult": "lightweight", + "Frequency": 103 + }, + { + "Insult": "fool", + "Frequency": 100 + }, + { + "Insult": "incompetent/incompetence", + "Frequency": 98 + } + ], + "columns": [ + { + "name": "Insult", + "importance": "primary", + "description": "The specific insult used in a tweet", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Frequency", + "importance": "primary", + "description": "Number of times the insult was used", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1361": { + "metadata": { + "title": "Sending Weapons To Ukraine: Americans Are Divided", + "description": "Percentage supporting the following measures in the US in February 2015*", + "main_insight": "Americans were most supportive of holding diplomatic relations with Russia and imposing economic sanctions, while they were least supportive of sending US troops to Ukraine. There was considerable division regarding providing military weapons." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Measure": "Sending US troops to Ukraine", + "Points": 11.0, + "Sentiment": "Support" + }, + { + "Measure": "Sending US troops to Ukraine", + "Points": 61.0, + "Sentiment": "Oppose" + } + ], + "score": 0.99, + "annotation": "The Points of Support and Oppose show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Points between Support and Oppose is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Measure": "Providing military weapons to Ukraine", + "Points": 37.0, + "Sentiment": "Oppose" + } + ], + "score": 0.89, + "annotation": "The Oppose has the largest proportion in Providing military weapons to Ukraine.", + "reason": "The Points of Oppose in Providing military weapons to Ukraine accounts for 0.37373737373737376, larger than all other Sentiment, of the total of Points of all Sentiment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Sentiment": "Support", + "Measure": "total", + "Points": 195.0 + } + ], + "score": 0.88, + "annotation": "The total value of Support has the largest proportion.", + "reason": "The total value of Points of Support accounts for 0.39156626506024095, larger than all other Sentiment, of the total of Points of all Sentiment." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Measure": "Providing military weapons to Ukraine", + "Points": 30.0, + "Sentiment": "Not sure" + } + ], + "score": 0.88, + "annotation": "The Not sure shows a sudden jump in Points from Sending US troops to Ukraine to Providing military weapons to Ukraine.", + "reason": "The Points for Not sure increases from 27.0 at Sending US troops to Ukraine to 30.0 at Providing military weapons to Ukraine, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Measure": "Providing economic aid to Ukraine", + "Points": 46.0, + "Sentiment": "Support" + } + ], + "score": 0.87, + "annotation": "The Support has the largest proportion in Providing economic aid to Ukraine.", + "reason": "The Points of Support in Providing economic aid to Ukraine accounts for 0.46, larger than all other Sentiment, of the total of Points of all Sentiment." + } + ], + "data": { + "data": [ + { + "Measure": "Holding diplomatic relations with Russia", + "Points": 55.0, + "Sentiment": "Support" + }, + { + "Measure": "Holding diplomatic relations with Russia", + "Points": 18.0, + "Sentiment": "Oppose" + }, + { + "Measure": "Holding diplomatic relations with Russia", + "Points": 27.0, + "Sentiment": "Not sure" + }, + { + "Measure": "Imposing greater economic sanctions on Russia", + "Points": 51.0, + "Sentiment": "Support" + }, + { + "Measure": "Imposing greater economic sanctions on Russia", + "Points": 18.0, + "Sentiment": "Oppose" + }, + { + "Measure": "Imposing greater economic sanctions on Russia", + "Points": 31.0, + "Sentiment": "Not sure" + }, + { + "Measure": "Providing economic aid to Ukraine", + "Points": 46.0, + "Sentiment": "Support" + }, + { + "Measure": "Providing economic aid to Ukraine", + "Points": 28.0, + "Sentiment": "Oppose" + }, + { + "Measure": "Providing economic aid to Ukraine", + "Points": 26.0, + "Sentiment": "Not sure" + }, + { + "Measure": "Providing military weapons to Ukraine", + "Points": 32.0, + "Sentiment": "Support" + }, + { + "Measure": "Providing military weapons to Ukraine", + "Points": 37.0, + "Sentiment": "Oppose" + }, + { + "Measure": "Providing military weapons to Ukraine", + "Points": 30.0, + "Sentiment": "Not sure" + }, + { + "Measure": "Sending US troops to Ukraine", + "Points": 11.0, + "Sentiment": "Support" + }, + { + "Measure": "Sending US troops to Ukraine", + "Points": 61.0, + "Sentiment": "Oppose" + }, + { + "Measure": "Sending US troops to Ukraine", + "Points": 27.0, + "Sentiment": "Not sure" + } + ], + "columns": [ + { + "name": "Measure", + "importance": "primary", + "description": "Different measures related to the Ukraine situation", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "983": { + "metadata": { + "title": "Facebook Finally Returned to its IPO Price", + "description": "Facebook's stock performance since the company's IPO on May 18, 2012", + "main_insight": "Facebook's stock price fluctuated significantly after its IPO, finally returning to its initial price around July 31, 2013." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "May 18, 2012", + "Stock Price": 38.0 + } + ], + "score": 0.89, + "annotation": "The shows a sudden jump in Stock Price from Jan 15, 2013 to May 18, 2012.", + "reason": "The Stock Price for increases from 30.0 at Jan 15, 2013 to 38.0 at May 18, 2012, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "May 18, 2012", + "Stock Price": 38.0 + }, + { + "Date": "July 31, 2013", + "Stock Price": 38.0 + } + ], + "score": 0.85, + "annotation": "The has maximum values at May 18, 2012, July 31, 2013", + "reason": "The [{'Date': 'May 18, 2012', 'Stock Price': 38.0}, {'Date': 'July 31, 2013', 'Stock Price': 38.0}] of have maximum values of 38.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "Jan 30, 2013", + "Stock Price": 25.0 + } + ], + "score": 0.62, + "annotation": "The has a minimum value at Jan 30, 2013", + "reason": "The [{'Date': 'Jan 30, 2013', 'Stock Price': 25.0}] of has a mainimum value of 25.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Date": "May 18, 2012", + "Stock Price": 38.0 + }, + { + "Date": "Jul 26, 2012", + "Stock Price": 28.0 + }, + { + "Date": "Oct 23, 2012", + "Stock Price": 29.0 + }, + { + "Date": "Jan 15, 2013", + "Stock Price": 30.0 + }, + { + "Date": "Jan 30, 2013", + "Stock Price": 25.0 + }, + { + "Date": "Apr 4, 2013", + "Stock Price": 27.0 + }, + { + "Date": "May 1, 2013", + "Stock Price": 29.0 + }, + { + "Date": "Jul 24, 2013", + "Stock Price": 26.0 + }, + { + "Date": "July 31, 2013", + "Stock Price": 38.0 + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Timeline from May 18, 2012, to July 31, 2013", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Stock Price", + "importance": "primary", + "description": "Facebook's stock price in USD", + "unit": "USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1142": { + "metadata": { + "title": "Trust in Public News Remains High in Western Europe", + "description": "Share of respondents who generally trust or distrust each public news outlet per country", + "main_insight": "Public news outlets in Western Europe generally enjoy high levels of trust, with the lowest trust level being 57% in Spain. Northern European countries exhibit higher trust levels compared to Southern European countries." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Spain (TVE)", + "Score": 57.0, + "Sentiment": "Trust" + }, + { + "Country": "Spain (TVE)", + "Score": 38.0, + "Sentiment": "Distrust" + } + ], + "score": 0.94, + "annotation": "The Score of Trust and Distrust show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Trust and Distrust is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Spain (TVE)", + "Score": 38.0, + "Sentiment": "Distrust" + } + ], + "score": 0.93, + "annotation": "The Distrust has a maximum value at Spain (TVE)", + "reason": "The [{'Country': 'Spain (TVE)', 'Score': 38.0, 'Sentiment': 'Distrust'}] of Distrust has a maximum value of 38.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Spain (TVE)", + "Score": 57.0, + "Sentiment": "Trust" + } + ], + "score": 0.87, + "annotation": "The Trust has a minimum value at Spain (TVE)", + "reason": "The [{'Country': 'Spain (TVE)', 'Score': 57.0, 'Sentiment': 'Trust'}] of Trust has a mainimum value of 57.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Spain (TVE)", + "Score": 38.0, + "Sentiment": "Distrust" + } + ], + "score": 0.85, + "annotation": "The Distrust shows a sudden jump in Score from Italy (Rai News) to Spain (TVE).", + "reason": "The Score for Distrust increases from 22.0 at Italy (Rai News) to 38.0 at Spain (TVE), indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Spain (TVE)", + "Score": 57.0, + "Sentiment": "Trust" + } + ], + "score": 0.77, + "annotation": "The Trust accounts for the majority in Spain (TVE).", + "reason": "The Score of Trust in Spain (TVE) accounts for 0.6 (more than 50%) of the total of Score of all Sentiment." + } + ], + "data": { + "data": [ + { + "Country": "Sweden (SVT)", + "Score": 90.0, + "Sentiment": "Trust" + }, + { + "Country": "Sweden (SVT)", + "Score": 8.0, + "Sentiment": "Distrust" + }, + { + "Country": "Netherlands (NOS)", + "Score": 89.0, + "Sentiment": "Trust" + }, + { + "Country": "Netherlands (NOS)", + "Score": 8.0, + "Sentiment": "Distrust" + }, + { + "Country": "Germany (ARD)", + "Score": 80.0, + "Sentiment": "Trust" + }, + { + "Country": "Germany (ARD)", + "Score": 15.0, + "Sentiment": "Distrust" + }, + { + "Country": "Denmark (TV 2 News)", + "Score": 80.0, + "Sentiment": "Trust" + }, + { + "Country": "Denmark (TV 2 News)", + "Score": 12.0, + "Sentiment": "Distrust" + }, + { + "Country": "United Kingdom (BBC)", + "Score": 79.0, + "Sentiment": "Trust" + }, + { + "Country": "United Kingdom (BBC)", + "Score": 19.0, + "Sentiment": "Distrust" + }, + { + "Country": "France (France 2)", + "Score": 73.0, + "Sentiment": "Trust" + }, + { + "Country": "France (France 2)", + "Score": 18.0, + "Sentiment": "Distrust" + }, + { + "Country": "Italy (Rai News)", + "Score": 65.0, + "Sentiment": "Trust" + }, + { + "Country": "Italy (Rai News)", + "Score": 22.0, + "Sentiment": "Distrust" + }, + { + "Country": "Spain (TVE)", + "Score": 57.0, + "Sentiment": "Trust" + }, + { + "Country": "Spain (TVE)", + "Score": 38.0, + "Sentiment": "Distrust" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country and associated news outlet", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "1841": { + "metadata": { + "title": "Online Travel Platforms Boost Occupancy for Hotels in the EU", + "description": "Share of responses to the question \"Online travel platforms enable my accommodation business to increase my occupancy rate\"", + "main_insight": "A large majority (82%) of EU accommodations that use at least one online travel platform fully agree that these platforms help increase their occupancy rates." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Response": "Survey Responses", + "Score": 82.0, + "Agreement": "(Fully) agree" + }, + { + "Response": "Survey Responses", + "Score": 13.0, + "Agreement": "Neutral" + }, + { + "Response": "Survey Responses", + "Score": 5.0, + "Agreement": "Do not agree (at all)" + } + ], + "columns": [ + { + "name": "Response", + "importance": "primary", + "description": "The response categories to the survey question", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding agreement", + "role": "y" + }, + { + "name": "Agreement", + "importance": "primary", + "description": "The agreement of the data point", + "role": "group" + } + ] + } + }, + "1844": { + "metadata": { + "title": "Majority Considers Internet Access a Human Right", + "description": "% of respondents agreeing that Internet access should be a basic human right", + "main_insight": "A large majority of respondents worldwide believe internet access is a basic human right, with significant regional variations in the strength of this belief." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Region": "Latin America", + "Units": 60.0, + "Agreement": "Strongly Agree" + }, + { + "Region": "Latin America", + "Units": 26.0, + "Agreement": "Somewhat Agree" + } + ], + "score": 0.99, + "annotation": "The Units of Strongly Agree and Somewhat Agree show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Strongly Agree and Somewhat Agree is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "Middle East & Africa", + "Units": 72.0, + "Agreement": "Strongly Agree" + } + ], + "score": 0.83, + "annotation": "The Strongly Agree has a maximum value at Middle East & Africa", + "reason": "The [{'Region': 'Middle East & Africa', 'Units': 72.0, 'Agreement': 'Strongly Agree'}] of Strongly Agree has a maximum value of 72.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Region": "Middle East & Africa", + "Units": 18.0, + "Agreement": "Somewhat Agree" + } + ], + "score": 0.83, + "annotation": "The Somewhat Agree has a minimum value at Middle East & Africa", + "reason": "The [{'Region': 'Middle East & Africa', 'Units': 18.0, 'Agreement': 'Somewhat Agree'}] of Somewhat Agree has a mainimum value of 18.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Europe", + "Units": 40.0, + "Agreement": "Strongly Agree" + }, + { + "Region": "Europe", + "Units": 40.0, + "Agreement": "Somewhat Agree" + } + ], + "score": 0.8, + "annotation": "The Strongly Agree, Somewhat Agree all have the largest proportion in Europe.", + "reason": "The Units of Strongly Agree, Somewhat Agree in Europe all account for 0.5, larger than all other Agreement, of the total of Units of all Agreement." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "North America", + "Units": 40.0, + "Agreement": "Somewhat Agree" + } + ], + "score": 0.79, + "annotation": "The Somewhat Agree accounts for the majority in North America.", + "reason": "The Units of Somewhat Agree in North America accounts for 0.5263157894736842 (more than 50%) of the total of Units of all Agreement." + } + ], + "data": { + "data": [ + { + "Region": "Worldwide", + "Units": 49.0, + "Agreement": "Strongly Agree" + }, + { + "Region": "Worldwide", + "Units": 34.0, + "Agreement": "Somewhat Agree" + }, + { + "Region": "North America", + "Units": 36.0, + "Agreement": "Strongly Agree" + }, + { + "Region": "North America", + "Units": 40.0, + "Agreement": "Somewhat Agree" + }, + { + "Region": "Europe", + "Units": 40.0, + "Agreement": "Strongly Agree" + }, + { + "Region": "Europe", + "Units": 40.0, + "Agreement": "Somewhat Agree" + }, + { + "Region": "Middle East & Africa", + "Units": 72.0, + "Agreement": "Strongly Agree" + }, + { + "Region": "Middle East & Africa", + "Units": 18.0, + "Agreement": "Somewhat Agree" + }, + { + "Region": "Asia-Pacific", + "Units": 38.0, + "Agreement": "Strongly Agree" + }, + { + "Region": "Asia-Pacific", + "Units": 44.0, + "Agreement": "Somewhat Agree" + }, + { + "Region": "Latin America", + "Units": 60.0, + "Agreement": "Strongly Agree" + }, + { + "Region": "Latin America", + "Units": 26.0, + "Agreement": "Somewhat Agree" + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Geographic region of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding agreement", + "role": "y" + }, + { + "name": "Agreement", + "importance": "primary", + "description": "The agreement of the data point", + "role": "group" + } + ] + } + }, + "1866": { + "metadata": { + "title": "Investors Don't Believe the Smartphone Hype", + "description": "Stock price change of the world's leading smartphone vendors in 2013", + "main_insight": "The stock prices of all major smartphone vendors declined in 2013, indicating a lack of investor confidence in the industry. HTC and Blackberry experienced the most significant drops." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "Apple", + "Samsung": 0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Samsung from Samsung to Apple.", + "reason": "The Samsung for increases from -16.8 at Samsung to 0 at Apple, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Company": "Samsung", + "Samsung": -16.8 + } + ], + "score": 0.97, + "annotation": "The has a minimum value at Samsung", + "reason": "The [{'Company': 'Samsung', 'Samsung': -16.8}] of has a mainimum value of -16.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Company": "Apple", + "Samsung": 0 + }, + { + "Company": "ZTE", + "Samsung": 0 + }, + { + "Company": "Huawei", + "Samsung": 0 + }, + { + "Company": "Nokia", + "Samsung": 0 + }, + { + "Company": "HTC", + "Samsung": 0 + }, + { + "Company": "BlackBerry", + "Samsung": 0 + }, + { + "Company": "LG", + "Samsung": 0 + } + ], + "score": 0.29, + "annotation": "The has maximum values at Apple, ZTE, Huawei, Nokia, HTC, BlackBerry, LG", + "reason": "The [{'Company': 'Apple', 'Samsung': 0}, {'Company': 'ZTE', 'Samsung': 0}, {'Company': 'Huawei', 'Samsung': 0}, {'Company': 'Nokia', 'Samsung': 0}, {'Company': 'HTC', 'Samsung': 0}, {'Company': 'BlackBerry', 'Samsung': 0}, {'Company': 'LG', 'Samsung': 0}] of have maximum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Company": "Samsung", + "Samsung": -16.8 + }, + { + "Company": "Apple", + "Samsung": 0 + }, + { + "Company": "ZTE", + "Samsung": 0 + }, + { + "Company": "Huawei", + "Samsung": 0 + }, + { + "Company": "Nokia", + "Samsung": 0 + }, + { + "Company": "HTC", + "Samsung": 0 + }, + { + "Company": "BlackBerry", + "Samsung": 0 + }, + { + "Company": "LG", + "Samsung": 0 + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Name of the smartphone vendor", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Samsung", + "importance": "primary", + "description": "Stock price change in 2013", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2260": { + "metadata": { + "title": "Team GB's most successful Olympic sports", + "description": "Gold medals won by Team GB at the Summer Olympics (1996-2016)", + "main_insight": "Cycling has become Team GB's most successful Olympic sport in terms of gold medals since 2008, surpassing other sports like Rowing, Athletics, Sailing, and Boxing." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Year": "2012", + "Binary": 3, + "Participating": "Boxing" + } + ], + "score": 0.97, + "annotation": "The Boxing shows a sudden jump in Binary from 2016 to 2012.", + "reason": "The Binary for Boxing increases from 1 at 2016 to 3 at 2012, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "2012", + "Binary": 3, + "Participating": "Boxing" + } + ], + "score": 0.94, + "annotation": "The Boxing has the largest proportion in 2000.", + "reason": "The Binary of Boxing in 2000 accounts for 0.3, larger than all other Participating, of the total of Binary of all Participating." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Participating": "Cycling", + "Year": "total", + "Binary": 25 + } + ], + "score": 0.93, + "annotation": "The total value of Cycling has the largest proportion.", + "reason": "The total value of Binary of Cycling accounts for 0.373134328358209, larger than all other Participating, of the total of Binary of all Participating." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "2000", + "Binary": 3, + "Participating": "Sailing" + } + ], + "score": 0.92, + "annotation": "The Sailing has the largest proportion in 1996.", + "reason": "The Binary of Sailing in 1996 accounts for 0.42857142857142855, larger than all other Participating, of the total of Binary of all Participating." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "2008", + "Binary": 8, + "Participating": "Cycling" + } + ], + "score": 0.91, + "annotation": "The Cycling has the largest proportion in 2004.", + "reason": "The Binary of Cycling in 2004 accounts for 0.47058823529411764, larger than all other Participating, of the total of Binary of all Participating." + } + ], + "data": { + "data": [ + { + "Year": "1996", + "Binary": 1, + "Participating": "Rowing" + }, + { + "Year": "1996", + "Binary": 1, + "Participating": "Athletics" + }, + { + "Year": "2000", + "Binary": 1, + "Participating": "Cycling" + }, + { + "Year": "2000", + "Binary": 1, + "Participating": "Rowing" + }, + { + "Year": "2000", + "Binary": 2, + "Participating": "Athletics" + }, + { + "Year": "2000", + "Binary": 3, + "Participating": "Sailing" + }, + { + "Year": "2004", + "Binary": 2, + "Participating": "Cycling" + }, + { + "Year": "2004", + "Binary": 1, + "Participating": "Rowing" + }, + { + "Year": "2004", + "Binary": 3, + "Participating": "Athletics" + }, + { + "Year": "2004", + "Binary": 2, + "Participating": "Sailing" + }, + { + "Year": "2008", + "Binary": 8, + "Participating": "Cycling" + }, + { + "Year": "2008", + "Binary": 2, + "Participating": "Rowing" + }, + { + "Year": "2008", + "Binary": 1, + "Participating": "Athletics" + }, + { + "Year": "2008", + "Binary": 4, + "Participating": "Sailing" + }, + { + "Year": "2008", + "Binary": 1, + "Participating": "Boxing" + }, + { + "Year": "2012", + "Binary": 8, + "Participating": "Cycling" + }, + { + "Year": "2012", + "Binary": 4, + "Participating": "Rowing" + }, + { + "Year": "2012", + "Binary": 4, + "Participating": "Athletics" + }, + { + "Year": "2012", + "Binary": 1, + "Participating": "Sailing" + }, + { + "Year": "2012", + "Binary": 3, + "Participating": "Boxing" + }, + { + "Year": "2016", + "Binary": 6, + "Participating": "Cycling" + }, + { + "Year": "2016", + "Binary": 3, + "Participating": "Rowing" + }, + { + "Year": "2016", + "Binary": 2, + "Participating": "Athletics" + }, + { + "Year": "2016", + "Binary": 2, + "Participating": "Sailing" + }, + { + "Year": "2016", + "Binary": 1, + "Participating": "Boxing" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Summer Olympic Games Year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Binary", + "importance": "primary", + "description": "Binary for the corresponding participating", + "role": "y" + }, + { + "name": "Participating", + "importance": "primary", + "description": "The participating of the data point", + "role": "group" + } + ] + } + }, + "2321": { + "metadata": { + "title": "European Election Turnout Reaches 20-Year High", + "description": "Voter turnout in the European Parliament Elections from 1979 to 2019*", + "main_insight": "European Parliament election turnout in 2019 reached 50.95%, the highest level in 20 years, after a continuous decline from 1979 to 2014." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1979, + "Turnout": 61.99 + }, + { + "Year": 1984, + "Turnout": 59.0 + }, + { + "Year": 1989, + "Turnout": 58.5 + }, + { + "Year": 1994, + "Turnout": 56.8 + }, + { + "Year": 1999, + "Turnout": 49.8 + }, + { + "Year": 2004, + "Turnout": 45.6 + }, + { + "Year": 2009, + "Turnout": 43.1 + }, + { + "Year": 2014, + "Turnout": 42.61 + }, + { + "Year": 2019, + "Turnout": 50.95 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of European Parliament Election", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Turnout", + "importance": "primary", + "description": "Percentage of voter turnout", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1175": { + "metadata": { + "title": "UK: who wears the purchasing trousers, and when?", + "description": "% saying they personally make purchasing decisions in the following categories, by gender", + "main_insight": "Females are more likely to make purchasing decisions for clothing, daily consumer goods, and toys & baby products, while males are more likely to make decisions for cars, consumer electronics, and other traditionally male-dominated categories." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Category": "Toys & baby products", + "Score": 23.0, + "Percentage": "Male" + } + ], + "score": 0.95, + "annotation": "The Male has a minimum value at Toys & baby products", + "reason": "The [{'Category': 'Toys & baby products', 'Score': 23.0, 'Percentage': 'Male'}] of Male has a mainimum value of 23.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Category": "Household appliances", + "Score": 4.0, + "Percentage": "P.P. difference" + } + ], + "score": 0.87, + "annotation": "The P.P. difference has the smallest proportion in Household appliances.", + "reason": "The Score of P.P. difference in Household appliances accounts for 0.028985507246376812, smaller than all other Percentage, of the total of Score of all Percentage." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Percentage": "Female", + "Category": "total", + "Score": 609.0 + } + ], + "score": 0.87, + "annotation": "The total value of Female has the largest proportion.", + "reason": "The total value of Score of Female accounts for 0.46559633027522934, larger than all other Percentage, of the total of Score of all Percentage." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Category": "Clothing, shoes, accessories", + "Score": 91.0, + "Percentage": "Female" + } + ], + "score": 0.86, + "annotation": "The Female has the largest proportion in Clothing, shoes, accessories.", + "reason": "The Score of Female in Clothing, shoes, accessories accounts for 0.5, larger than all other Percentage, of the total of Score of all Percentage." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Category": "Toys & baby products", + "Score": 34.0, + "Percentage": "Female" + } + ], + "score": 0.86, + "annotation": "The Female has the largest proportion in Toys & baby products.", + "reason": "The Score of Female in Toys & baby products accounts for 0.5, larger than all other Percentage, of the total of Score of all Percentage." + } + ], + "data": { + "data": [ + { + "Category": "Clothing, shoes, accessories", + "Score": 91.0, + "Percentage": "Female" + }, + { + "Category": "Clothing, shoes, accessories", + "Score": 73.0, + "Percentage": "Male" + }, + { + "Category": "Clothing, shoes, accessories", + "Score": 18.0, + "Percentage": "P.P. difference" + }, + { + "Category": "Daily consumer goods", + "Score": 82.0, + "Percentage": "Female" + }, + { + "Category": "Daily consumer goods", + "Score": 65.0, + "Percentage": "Male" + }, + { + "Category": "Daily consumer goods", + "Score": 16.0, + "Percentage": "P.P. difference" + }, + { + "Category": "Toys & baby products", + "Score": 34.0, + "Percentage": "Female" + }, + { + "Category": "Toys & baby products", + "Score": 23.0, + "Percentage": "Male" + }, + { + "Category": "Toys & baby products", + "Score": 11.0, + "Percentage": "P.P. difference" + }, + { + "Category": "Furniture & household goods", + "Score": 67.0, + "Percentage": "Female" + }, + { + "Category": "Furniture & household goods", + "Score": 56.0, + "Percentage": "Male" + }, + { + "Category": "Furniture & household goods", + "Score": 10.0, + "Percentage": "P.P. difference" + }, + { + "Category": "Household appliances", + "Score": 69.0, + "Percentage": "Female" + }, + { + "Category": "Household appliances", + "Score": 65.0, + "Percentage": "Male" + }, + { + "Category": "Household appliances", + "Score": 4.0, + "Percentage": "P.P. difference" + }, + { + "Category": "Real estate", + "Score": 39.0, + "Percentage": "Female" + }, + { + "Category": "Real estate", + "Score": 44.0, + "Percentage": "Male" + }, + { + "Category": "Real estate", + "Score": 5.0, + "Percentage": "P.P. difference" + }, + { + "Category": "Media (video, music, games)", + "Score": 58.0, + "Percentage": "Female" + }, + { + "Category": "Media (video, music, games)", + "Score": 65.0, + "Percentage": "Male" + }, + { + "Category": "Media (video, music, games)", + "Score": 7.0, + "Percentage": "P.P. difference" + }, + { + "Category": "Finance & insurance, telecommunications & electricity provider", + "Score": 60.0, + "Percentage": "Female" + }, + { + "Category": "Finance & insurance, telecommunications & electricity provider", + "Score": 67.0, + "Percentage": "Male" + }, + { + "Category": "Finance & insurance, telecommunications & electricity provider", + "Score": 7.0, + "Percentage": "P.P. difference" + }, + { + "Category": "Consumer electronics", + "Score": 67.0, + "Percentage": "Female" + }, + { + "Category": "Consumer electronics", + "Score": 78.0, + "Percentage": "Male" + }, + { + "Category": "Consumer electronics", + "Score": 11.0, + "Percentage": "P.P. difference" + }, + { + "Category": "Cars, motorcycles, bicycles", + "Score": 42.0, + "Percentage": "Female" + }, + { + "Category": "Cars, motorcycles, bicycles", + "Score": 58.0, + "Percentage": "Male" + }, + { + "Category": "Cars, motorcycles, bicycles", + "Score": 16.0, + "Percentage": "P.P. difference" + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Purchasing category", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding percentage", + "role": "y" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "The percentage of the data point", + "role": "group" + } + ] + } + }, + "2375": { + "metadata": { + "title": "Websites See Drastic Decline in Facebook Traffic", + "description": "Social media referrals as a percentage of overall traffic to sampled websites (in %)*", + "main_insight": "Facebook referrals as a percentage of overall website traffic have significantly decreased from the first half of 2016 to the second half of 2017, while other platforms like Pinterest, Instagram and Twitter remained relatively stable." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Time Period": "1H '16", + "Ratio": 0.01, + "Social Platforms": "Instagram" + } + ], + "score": 1.0, + "annotation": "The Instagram has the smallest proportion in 1H '16.", + "reason": "The Ratio of Instagram in 1H '16 accounts for 0.0002754062241806665, smaller than all other Social Platforms, of the total of Ratio of all Social Platforms." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Time Period": "2H '17", + "Ratio": 18.2, + "Social Platforms": "Facebook" + }, + { + "Time Period": "2H '17", + "Ratio": 0.7, + "Social Platforms": "Instagram" + } + ], + "score": 0.99, + "annotation": "The Ratio of Facebook and Instagram show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Facebook and Instagram is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Time Period": "2H '16", + "Ratio": 0.1, + "Social Platforms": "Instagram" + } + ], + "score": 0.99, + "annotation": "The Instagram has the smallest proportion in 2H '16.", + "reason": "The Ratio of Instagram in 2H '16 accounts for 0.0026455026455026454, smaller than all other Social Platforms, of the total of Ratio of all Social Platforms." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time Period": "1H '16", + "Ratio": 0.9, + "Social Platforms": "Twitter" + } + ], + "score": 0.97, + "annotation": "The Twitter shows a sudden jump in Ratio from 2H '17 to 1H '16.", + "reason": "The Ratio for Twitter increases from 0.7 at 2H '17 to 0.9 at 1H '16, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Social Platforms": "Facebook", + "Time Period": "total", + "Ratio": 107.10000000000001 + } + ], + "score": 0.96, + "annotation": "The total value of Facebook has the smallest proportion.", + "reason": "The total value of Ratio of Facebook accounts for 0.00819732663761908, smaller than all other Social Platforms, of the total of Ratio of all Social Platforms." + } + ], + "data": { + "data": [ + { + "Time Period": "1H '16", + "Ratio": 30.9, + "Social Platforms": "Facebook" + }, + { + "Time Period": "1H '16", + "Ratio": 4.5, + "Social Platforms": "Pinterest" + }, + { + "Time Period": "1H '16", + "Ratio": 0.01, + "Social Platforms": "Instagram" + }, + { + "Time Period": "1H '16", + "Ratio": 0.9, + "Social Platforms": "Twitter" + }, + { + "Time Period": "2H '16", + "Ratio": 30.9, + "Social Platforms": "Facebook" + }, + { + "Time Period": "2H '16", + "Ratio": 6.1, + "Social Platforms": "Pinterest" + }, + { + "Time Period": "2H '16", + "Ratio": 0.1, + "Social Platforms": "Instagram" + }, + { + "Time Period": "2H '16", + "Ratio": 0.7, + "Social Platforms": "Twitter" + }, + { + "Time Period": "1H '17", + "Ratio": 27.1, + "Social Platforms": "Facebook" + }, + { + "Time Period": "1H '17", + "Ratio": 6.1, + "Social Platforms": "Pinterest" + }, + { + "Time Period": "1H '17", + "Ratio": 0.3, + "Social Platforms": "Instagram" + }, + { + "Time Period": "1H '17", + "Ratio": 0.7, + "Social Platforms": "Twitter" + }, + { + "Time Period": "2H '17", + "Ratio": 18.2, + "Social Platforms": "Facebook" + }, + { + "Time Period": "2H '17", + "Ratio": 7.5, + "Social Platforms": "Pinterest" + }, + { + "Time Period": "2H '17", + "Ratio": 0.7, + "Social Platforms": "Instagram" + }, + { + "Time Period": "2H '17", + "Ratio": 0.7, + "Social Platforms": "Twitter" + } + ], + "columns": [ + { + "name": "Time Period", + "importance": "primary", + "description": "Time periods represented by 1H '16, 2H '16, 1H '17, and 2H '17", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding social platforms", + "role": "y" + }, + { + "name": "Social Platforms", + "importance": "primary", + "description": "The social platforms of the data point", + "role": "group" + } + ] + } + }, + "249": { + "metadata": { + "title": "Trump Reigns Supreme In 2016 Book Sales", + "description": "Books sold by 2016 presidential candidates since 2015", + "main_insight": "Donald Trump had significantly higher book sales in 2016 compared to other presidential candidates, with sales exceeding all other listed candidates combined." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Candidate": "Donald Trump", + "Dollars": 239900, + "Sales Metric": "Total Sales" + } + ], + "score": 0.93, + "annotation": "The Total Sales shows a sudden jump in Dollars from Ted Cruz to Donald Trump.", + "reason": "The Dollars for Total Sales increases from 65100 at Ted Cruz to 239900 at Donald Trump, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Candidate": "Donald Trump", + "Dollars": 239900, + "Sales Metric": "Total Sales" + } + ], + "score": 0.87, + "annotation": "The Total Sales has a maximum value at Donald Trump", + "reason": "The [{'Candidate': 'Donald Trump', 'Dollars': 239900, 'Sales Metric': 'Total Sales'}] of Total Sales has a maximum value of 239900, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Candidate": "Bernie Sanders", + "Dollars": 8600, + "Sales Metric": "Total Sales" + } + ], + "score": 0.42, + "annotation": "The Total Sales has a minimum value at Bernie Sanders", + "reason": "The [{'Candidate': 'Bernie Sanders', 'Dollars': 8600, 'Sales Metric': 'Total Sales'}] of Total Sales has a mainimum value of 8600, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Candidate": "Donald Trump", + "Dollars": 239900, + "Sales Metric": "Total Sales" + }, + { + "Candidate": "Ted Cruz", + "Dollars": 65100, + "Sales Metric": "Total Sales" + }, + { + "Candidate": "Hillary Clinton", + "Dollars": 23400, + "Sales Metric": "Total Sales" + }, + { + "Candidate": "Marco Rubio", + "Dollars": 13200, + "Sales Metric": "Total Sales" + }, + { + "Candidate": "Bernie Sanders", + "Dollars": 8600, + "Sales Metric": "Total Sales" + } + ], + "columns": [ + { + "name": "Candidate", + "importance": "primary", + "description": "Name of the 2016 presidential candidate", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding sales metric", + "role": "y" + }, + { + "name": "Sales Metric", + "importance": "primary", + "description": "The sales metric of the data point", + "role": "group" + } + ] + } + }, + "1176": { + "metadata": { + "title": "No Growth in Sight for Global PC Market", + "description": "Global PC shipment forecast (in million units)", + "main_insight": "Global PC shipments peaked in 2011 and have been declining since, with forecasts predicting further decline." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2009, + "Units": 296.1, + "Shipment Type": "Actual Shipments" + }, + { + "Year": 2010, + "Units": 358.0, + "Shipment Type": "Actual Shipments" + }, + { + "Year": 2011, + "Units": 364.0, + "Shipment Type": "Actual Shipments" + }, + { + "Year": 2012, + "Units": 349.3, + "Shipment Type": "Actual Shipments" + }, + { + "Year": 2013, + "Units": 315.1, + "Shipment Type": "Actual Shipments" + }, + { + "Year": 2014, + "Units": 308.3, + "Shipment Type": "Actual Shipments" + }, + { + "Year": 2015, + "Units": 275.8, + "Shipment Type": "Actual Shipments" + }, + { + "Year": 2016, + "Units": 260.8, + "Shipment Type": "Forecast Shipments" + }, + { + "Year": 2017, + "Units": 257.9, + "Shipment Type": "Forecast Shipments" + }, + { + "Year": 2020, + "Units": 254.3, + "Shipment Type": "Forecast Shipments" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of PC shipment", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding shipment type", + "role": "y" + }, + { + "name": "Shipment Type", + "importance": "primary", + "description": "The shipment type of the data point", + "role": "group" + } + ] + } + }, + "1847": { + "metadata": { + "title": "Illegal EU Border Crossings Have Plummeted", + "description": "Total number of detected illegal EU border crossings", + "main_insight": "The total number of detected illegal EU border crossings has significantly decreased from 2015 to 2018, and the number of crossings involving Syrian nationals has also decreased." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2015, + "Total Crossings": 1822177.0 + }, + { + "Year": 2016, + "Total Crossings": 511146.0 + }, + { + "Year": 2017, + "Total Crossings": 204750.0 + }, + { + "Year": 2018, + "Total Crossings": 150114.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of detected illegal border crossings", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Total Crossings", + "importance": "primary", + "description": "Total number of detected illegal EU border crossings", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "788": { + "metadata": { + "title": "Tens of Millions Still Relying on Unemployment Insurance", + "description": "Percentage of U.S. workers who are on or are waiting for unemployment benefits", + "main_insight": "As of September 26, 28 million U.S. workers were either on or waiting for unemployment benefits, with the majority split between continued state UI and continued pandemic programs." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "Continued state UI", + "Number of Workers (Millions)": 11.8 + } + ], + "score": 0.95, + "annotation": "The shows a sudden jump in Number of Workers (Millions) from Other programs to Continued state UI.", + "reason": "The Number of Workers (Millions) for increases from 2.3 at Other programs to 11.8 at Continued state UI, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Continued state UI", + "Number of Workers (Millions)": 11.8 + }, + { + "Category": "Continued pandemic", + "Number of Workers (Millions)": 11.8 + } + ], + "score": 0.64, + "annotation": "The has maximum values at Continued state UI, Continued pandemic", + "reason": "The [{'Category': 'Continued state UI', 'Number of Workers (Millions)': 11.8}, {'Category': 'Continued pandemic', 'Number of Workers (Millions)': 11.8}] of have maximum values of 11.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Category": "Initial state UI", + "Number of Workers (Millions)": 0.8 + } + ], + "score": 0.52, + "annotation": "The has a minimum value at Initial state UI", + "reason": "The [{'Category': 'Initial state UI', 'Number of Workers (Millions)': 0.8}] of has a mainimum value of 0.8, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Category": "Continued state UI", + "Number of Workers (Millions)": 11.8 + }, + { + "Category": "Continued pandemic", + "Number of Workers (Millions)": 11.8 + }, + { + "Category": "Other programs", + "Number of Workers (Millions)": 2.3 + }, + { + "Category": "Initial pandemic", + "Number of Workers (Millions)": 1.3 + }, + { + "Category": "Initial state UI", + "Number of Workers (Millions)": 0.8 + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Type of unemployment insurance program", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Workers (Millions)", + "importance": "primary", + "description": "Number of workers relying on each type of unemployment insurance", + "unit": "Millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2368": { + "metadata": { + "title": "American & Chinese Beliefs", + "description": "Americans and Chinese who are religious, not religious or atheist (in percent)", + "main_insight": "There is a significant difference in religious beliefs between Americans and Chinese, with a much larger percentage of Americans identifying as religious compared to Chinese, and a much larger percentage of Chinese identifying as atheist." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "CHN", + "Score": 9.0, + "Religious Affiliation": "Religious" + }, + { + "Country": "CHN", + "Score": 23.0, + "Religious Affiliation": "Not Religious" + } + ], + "score": 1.0, + "annotation": "The Score of Religious and Not Religious show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Religious and Not Religious is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "CHN", + "Score": 9.0, + "Religious Affiliation": "Religious" + }, + { + "Country": "CHN", + "Score": 67.0, + "Religious Affiliation": "Atheist" + } + ], + "score": 1.0, + "annotation": "The Score of Religious and Atheist show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Religious and Atheist is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "CHN", + "Score": 23.0, + "Religious Affiliation": "Not Religious" + }, + { + "Country": "CHN", + "Score": 67.0, + "Religious Affiliation": "Atheist" + } + ], + "score": 1.0, + "annotation": "The Score of Not Religious and Atheist show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Not Religious and Atheist is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "USA", + "Score": 56.0, + "Religious Affiliation": "Religious" + } + ], + "score": 0.97, + "annotation": "The Religious shows a sudden jump in Score from CHN to USA.", + "reason": "The Score for Religious increases from 9.0 at CHN to 56.0 at USA, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "USA", + "Score": 32.0, + "Religious Affiliation": "Not Religious" + } + ], + "score": 0.97, + "annotation": "The Not Religious shows a sudden jump in Score from CHN to USA.", + "reason": "The Score for Not Religious increases from 23.0 at CHN to 32.0 at USA, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "USA", + "Score": 56.0, + "Religious Affiliation": "Religious" + }, + { + "Country": "USA", + "Score": 32.0, + "Religious Affiliation": "Not Religious" + }, + { + "Country": "USA", + "Score": 7.0, + "Religious Affiliation": "Atheist" + }, + { + "Country": "CHN", + "Score": 9.0, + "Religious Affiliation": "Religious" + }, + { + "Country": "CHN", + "Score": 23.0, + "Religious Affiliation": "Not Religious" + }, + { + "Country": "CHN", + "Score": 67.0, + "Religious Affiliation": "Atheist" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country: USA or China", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding religious affiliation", + "role": "y" + }, + { + "name": "Religious Affiliation", + "importance": "primary", + "description": "The religious affiliation of the data point", + "role": "group" + } + ] + } + }, + "269": { + "metadata": { + "title": "Asian Influencers Reach for the Stars", + "description": "Estimated reach of influencer marketing in selected countries (in million users)", + "main_insight": "The reach of influencer marketing is projected to grow significantly in China, India, and the United States between 2018 and 2026, with China consistently having the largest reach." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2026, + "Units": 1287.0, + "Country": "China" + }, + { + "Year": 2026, + "Units": 1115.0, + "Country": "India" + } + ], + "score": 1.0, + "annotation": "The Units of China and India show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between China and India is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2026, + "Units": 1287.0, + "Country": "China" + }, + { + "Year": 2026, + "Units": 323.0, + "Country": "United States" + } + ], + "score": 1.0, + "annotation": "The Units of China and United States show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between China and United States is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2026, + "Units": 1115.0, + "Country": "India" + }, + { + "Year": 2026, + "Units": 323.0, + "Country": "United States" + } + ], + "score": 1.0, + "annotation": "The Units of India and United States show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between India and United States is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2018, + "Units": 765.0, + "Country": "China" + }, + { + "Year": 2018, + "Units": 270.0, + "Country": "India" + }, + { + "Year": 2018, + "Units": 269.0, + "Country": "United States" + }, + { + "Year": 2020, + "Units": 933.0, + "Country": "China" + }, + { + "Year": 2020, + "Units": 519.0, + "Country": "India" + }, + { + "Year": 2020, + "Units": 288.0, + "Country": "United States" + }, + { + "Year": 2022, + "Units": 1076.0, + "Country": "China" + }, + { + "Year": 2022, + "Units": 756.0, + "Country": "India" + }, + { + "Year": 2022, + "Units": 302.0, + "Country": "United States" + }, + { + "Year": 2024, + "Units": 1194.0, + "Country": "China" + }, + { + "Year": 2024, + "Units": 958.0, + "Country": "India" + }, + { + "Year": 2024, + "Units": 314.0, + "Country": "United States" + }, + { + "Year": 2026, + "Units": 1287.0, + "Country": "China" + }, + { + "Year": 2026, + "Units": 1115.0, + "Country": "India" + }, + { + "Year": 2026, + "Units": 323.0, + "Country": "United States" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of estimated reach", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding country", + "role": "y" + }, + { + "name": "Country", + "importance": "primary", + "description": "The country of the data point", + "role": "group" + } + ] + } + }, + "2371": { + "metadata": { + "title": "Kate's Luxury Birth is Still Cheaper Than a Birth in the U.S.", + "description": "Average price of a normal birth in the private sector in 2015* (in U.S. Dollars)", + "main_insight": "The average cost of a normal birth in the private sector in the United States is higher than the cost of a luxury birth in the Lindo Wing and significantly higher than in other developed countries." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country/Location": "United States", + "United States": 10800.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in United States from South Africa to United States.", + "reason": "The United States for increases from 0 at South Africa to 10800.0 at United States, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country/Location": "United States", + "United States": 10800.0 + } + ], + "score": 0.92, + "annotation": "The has a maximum value at United States", + "reason": "The [{'Country/Location': 'United States', 'United States': 10800.0}] of has a maximum value of 10800.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country/Location": "Lindo Wing", + "United States": 0 + }, + { + "Country/Location": "Switzerland", + "United States": 0 + }, + { + "Country/Location": "Australia", + "United States": 0 + }, + { + "Country/Location": "Spain", + "United States": 0 + }, + { + "Country/Location": "South Africa", + "United States": 0 + } + ], + "score": 0.31, + "annotation": "The has minimum values at Lindo Wing, Switzerland, Australia, Spain, South Africa", + "reason": "The [{'Country/Location': 'Lindo Wing', 'United States': 0}, {'Country/Location': 'Switzerland', 'United States': 0}, {'Country/Location': 'Australia', 'United States': 0}, {'Country/Location': 'Spain', 'United States': 0}, {'Country/Location': 'South Africa', 'United States': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country/Location": "United States", + "United States": 10800.0 + }, + { + "Country/Location": "Lindo Wing", + "United States": 0 + }, + { + "Country/Location": "Switzerland", + "United States": 0 + }, + { + "Country/Location": "Australia", + "United States": 0 + }, + { + "Country/Location": "Spain", + "United States": 0 + }, + { + "Country/Location": "South Africa", + "United States": 0 + } + ], + "columns": [ + { + "name": "Country/Location", + "importance": "primary", + "description": "Country or location of birth", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "United States", + "importance": "primary", + "description": "Average cost of a normal birth", + "unit": "USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1080": { + "metadata": { + "title": "The Economies Most Reliant on Trade", + "description": "Trade as a share of GDP in selected countries/regional economies in 2019 (in percent)", + "main_insight": "Luxembourg, Hong Kong, and Singapore are the economies most reliant on trade, with trade representing a very high percentage of their GDP in 2019." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country/Region": "Luxembourg", + "Trade as % of GDP": 382.0 + } + ], + "score": 0.76, + "annotation": "The has a maximum value at Luxembourg", + "reason": "The [{'Country/Region': 'Luxembourg', 'Trade as % of GDP': 382.0}] of has a maximum value of 382.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country/Region": "Singapore", + "Trade as % of GDP": 319.0 + } + ], + "score": 0.74, + "annotation": "The shows a sudden jump in Trade as % of GDP from Vietnam to Singapore.", + "reason": "The Trade as % of GDP for increases from 210.0 at Vietnam to 319.0 at Singapore, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country/Region": "South Korea", + "Trade as % of GDP": 77.0 + } + ], + "score": 0.68, + "annotation": "The has a minimum value at South Korea", + "reason": "The [{'Country/Region': 'South Korea', 'Trade as % of GDP': 77.0}] of has a mainimum value of 77.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country/Region": "Luxembourg", + "Trade as % of GDP": 382.0 + }, + { + "Country/Region": "Hong Kong", + "Trade as % of GDP": 353.0 + }, + { + "Country/Region": "Singapore", + "Trade as % of GDP": 319.0 + }, + { + "Country/Region": "Vietnam", + "Trade as % of GDP": 210.0 + }, + { + "Country/Region": "UAE", + "Trade as % of GDP": 161.0 + }, + { + "Country/Region": "Netherlands", + "Trade as % of GDP": 156.0 + }, + { + "Country/Region": "Germany", + "Trade as % of GDP": 88.0 + }, + { + "Country/Region": "South Korea", + "Trade as % of GDP": 77.0 + } + ], + "columns": [ + { + "name": "Country/Region", + "importance": "primary", + "description": "Name of the country or region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Trade as % of GDP", + "importance": "primary", + "description": "Trade as a share of GDP in 2019", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1217": { + "metadata": { + "title": "The Rising Cost of Living Is Hitting All Groups", + "description": "Amount needed each week to reach minimum standard of living in the UK in 2022", + "main_insight": "The minimum cost of living in the UK has significantly increased for all household types between 2021 and 2022, with percentage increases ranging from 17.2% to 26.8%." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Household Type": "Two parents two children", + "Units": 168.95, + "Expenditure Category": "Food and drink" + }, + { + "Household Type": "Two parents two children", + "Units": 39.2, + "Expenditure Category": "Personal goods and services" + } + ], + "score": 1.0, + "annotation": "The Units of Food and drink and Personal goods and services show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Food and drink and Personal goods and services is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Household Type": "Two parents two children", + "Units": 598.57, + "Expenditure Category": "Total for 2021" + }, + { + "Household Type": "Two parents two children", + "Units": 35.43, + "Expenditure Category": "Other housing costs" + } + ], + "score": 1.0, + "annotation": "The Units of Total for 2021 and Other housing costs show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total for 2021 and Other housing costs is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Household Type": "Two parents two children", + "Units": 35.43, + "Expenditure Category": "Other housing costs" + }, + { + "Household Type": "Two parents two children", + "Units": 79.12, + "Expenditure Category": "Household goods and services" + } + ], + "score": 1.0, + "annotation": "The Units of Other housing costs and Household goods and services show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Other housing costs and Household goods and services is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Household Type": "Two parents two children", + "Units": 598.57, + "Expenditure Category": "Total for 2021" + }, + { + "Household Type": "Two parents two children", + "Units": 79.12, + "Expenditure Category": "Household goods and services" + } + ], + "score": 1.0, + "annotation": "The Units of Total for 2021 and Household goods and services show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total for 2021 and Household goods and services is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Household Type": "Two parents two children", + "Units": 79.12, + "Expenditure Category": "Household goods and services" + }, + { + "Household Type": "Two parents two children", + "Units": 39.2, + "Expenditure Category": "Personal goods and services" + } + ], + "score": 0.99, + "annotation": "The Units of Household goods and services and Personal goods and services show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Household goods and services and Personal goods and services is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Household Type": "Single working adult", + "Units": 293.28, + "Expenditure Category": "Total for 2021" + }, + { + "Household Type": "Single working adult", + "Units": 57.75, + "Expenditure Category": "Food and drink" + }, + { + "Household Type": "Single working adult", + "Units": 13.01, + "Expenditure Category": "Clothing" + }, + { + "Household Type": "Single working adult", + "Units": 28.78, + "Expenditure Category": "Domestic fuel" + }, + { + "Household Type": "Single working adult", + "Units": 17.76, + "Expenditure Category": "Other housing costs" + }, + { + "Household Type": "Single working adult", + "Units": 32.62, + "Expenditure Category": "Household goods and services" + }, + { + "Household Type": "Single working adult", + "Units": 14.68, + "Expenditure Category": "Personal goods and services" + }, + { + "Household Type": "Single working adult", + "Units": 47.15, + "Expenditure Category": "Travel" + }, + { + "Household Type": "Single working adult", + "Units": 81.14, + "Expenditure Category": "Social and cultural participation" + }, + { + "Household Type": "Couple pensioners", + "Units": 322.87, + "Expenditure Category": "Total for 2021" + }, + { + "Household Type": "Couple pensioners", + "Units": 84.48, + "Expenditure Category": "Food and drink" + }, + { + "Household Type": "Couple pensioners", + "Units": 21.81, + "Expenditure Category": "Clothing" + }, + { + "Household Type": "Couple pensioners", + "Units": 38.37, + "Expenditure Category": "Domestic fuel" + }, + { + "Household Type": "Couple pensioners", + "Units": 19.84, + "Expenditure Category": "Other housing costs" + }, + { + "Household Type": "Couple pensioners", + "Units": 37.71, + "Expenditure Category": "Household goods and services" + }, + { + "Household Type": "Couple pensioners", + "Units": 19.9, + "Expenditure Category": "Personal goods and services" + }, + { + "Household Type": "Couple pensioners", + "Units": 28.18, + "Expenditure Category": "Travel" + }, + { + "Household Type": "Couple pensioners", + "Units": 72.59, + "Expenditure Category": "Social and cultural participation" + }, + { + "Household Type": "One parent with two children", + "Units": 410.74, + "Expenditure Category": "Total for 2021" + }, + { + "Household Type": "One parent with two children", + "Units": 115.49, + "Expenditure Category": "Food and drink" + }, + { + "Household Type": "One parent with two children", + "Units": 39.03, + "Expenditure Category": "Clothing" + }, + { + "Household Type": "One parent with two children", + "Units": 38.37, + "Expenditure Category": "Domestic fuel" + }, + { + "Household Type": "One parent with two children", + "Units": 25.05, + "Expenditure Category": "Other housing costs" + }, + { + "Household Type": "One parent with two children", + "Units": 54.66, + "Expenditure Category": "Household goods and services" + }, + { + "Household Type": "One parent with two children", + "Units": 27.0, + "Expenditure Category": "Personal goods and services" + }, + { + "Household Type": "One parent with two children", + "Units": 47.15, + "Expenditure Category": "Travel" + }, + { + "Household Type": "One parent with two children", + "Units": 63.99, + "Expenditure Category": "Social and cultural participation" + }, + { + "Household Type": "Two parents two children", + "Units": 598.57, + "Expenditure Category": "Total for 2021" + }, + { + "Household Type": "Two parents two children", + "Units": 168.95, + "Expenditure Category": "Food and drink" + }, + { + "Household Type": "Two parents two children", + "Units": 52.04, + "Expenditure Category": "Clothing" + }, + { + "Household Type": "Two parents two children", + "Units": 53.72, + "Expenditure Category": "Domestic fuel" + }, + { + "Household Type": "Two parents two children", + "Units": 35.43, + "Expenditure Category": "Other housing costs" + }, + { + "Household Type": "Two parents two children", + "Units": 79.12, + "Expenditure Category": "Household goods and services" + }, + { + "Household Type": "Two parents two children", + "Units": 39.2, + "Expenditure Category": "Personal goods and services" + }, + { + "Household Type": "Two parents two children", + "Units": 94.31, + "Expenditure Category": "Travel" + }, + { + "Household Type": "Two parents two children", + "Units": 75.81, + "Expenditure Category": "Social and cultural participation" + } + ], + "columns": [ + { + "name": "Household Type", + "importance": "primary", + "description": "Different types of households in the UK", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding expenditure category", + "role": "y" + }, + { + "name": "Expenditure Category", + "importance": "primary", + "description": "The expenditure category of the data point", + "role": "group" + } + ] + } + }, + "2320": { + "metadata": { + "title": "Premier League Tops the \"Big Five\"", + "description": "Revenue of the biggest European football leagues (in billion euros)", + "main_insight": "The Premier League's revenue has consistently outpaced other major European football leagues, experiencing significant growth from 1996/97 to 2015/16." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Season": "15/16", + "Ratio": 2.71, + "League": "Bundesliga" + }, + { + "Season": "15/16", + "Ratio": 2.44, + "League": "Primera Division" + } + ], + "score": 0.99, + "annotation": "The Ratio of Bundesliga and Primera Division show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Bundesliga and Primera Division is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Season": "15/16", + "Ratio": 2.44, + "League": "Primera Division" + }, + { + "Season": "15/16", + "Ratio": 1.49, + "League": "Ligue 1" + } + ], + "score": 0.99, + "annotation": "The Ratio of Primera Division and Ligue 1 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Primera Division and Ligue 1 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Season": "15/16", + "Ratio": 2.44, + "League": "Primera Division" + }, + { + "Season": "15/16", + "Ratio": 1.92, + "League": "Serie A" + } + ], + "score": 0.99, + "annotation": "The Ratio of Primera Division and Serie A show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Primera Division and Serie A is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Season": "15/16", + "Ratio": 2.71, + "League": "Bundesliga" + }, + { + "Season": "15/16", + "Ratio": 1.49, + "League": "Ligue 1" + } + ], + "score": 0.98, + "annotation": "The Ratio of Bundesliga and Ligue 1 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Bundesliga and Ligue 1 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Season": "15/16", + "Ratio": 1.92, + "League": "Serie A" + }, + { + "Season": "15/16", + "Ratio": 1.49, + "League": "Ligue 1" + } + ], + "score": 0.98, + "annotation": "The Ratio of Serie A and Ligue 1 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Serie A and Ligue 1 is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Season": "96/97", + "Ratio": 0.64, + "League": "Premier League" + }, + { + "Season": "96/97", + "Ratio": 0.46, + "League": "Bundesliga" + }, + { + "Season": "96/97", + "Ratio": 0.33, + "League": "Primera Division" + }, + { + "Season": "96/97", + "Ratio": 0.44, + "League": "Serie A" + }, + { + "Season": "96/97", + "Ratio": 0.22, + "League": "Ligue 1" + }, + { + "Season": "98/99", + "Ratio": 0.99, + "League": "Premier League" + }, + { + "Season": "98/99", + "Ratio": 0.6, + "League": "Bundesliga" + }, + { + "Season": "98/99", + "Ratio": 0.51, + "League": "Primera Division" + }, + { + "Season": "98/99", + "Ratio": 0.63, + "League": "Serie A" + }, + { + "Season": "98/99", + "Ratio": 0.31, + "League": "Ligue 1" + }, + { + "Season": "00/01", + "Ratio": 1.37, + "League": "Premier League" + }, + { + "Season": "00/01", + "Ratio": 0.79, + "League": "Bundesliga" + }, + { + "Season": "00/01", + "Ratio": 0.64, + "League": "Primera Division" + }, + { + "Season": "00/01", + "Ratio": 0.88, + "League": "Serie A" + }, + { + "Season": "00/01", + "Ratio": 0.37, + "League": "Ligue 1" + }, + { + "Season": "02/03", + "Ratio": 1.71, + "League": "Premier League" + }, + { + "Season": "02/03", + "Ratio": 0.91, + "League": "Bundesliga" + }, + { + "Season": "02/03", + "Ratio": 0.89, + "League": "Primera Division" + }, + { + "Season": "02/03", + "Ratio": 0.95, + "League": "Serie A" + }, + { + "Season": "02/03", + "Ratio": 0.5, + "League": "Ligue 1" + }, + { + "Season": "04/05", + "Ratio": 1.97, + "League": "Premier League" + }, + { + "Season": "04/05", + "Ratio": 1.03, + "League": "Bundesliga" + }, + { + "Season": "04/05", + "Ratio": 0.94, + "League": "Primera Division" + }, + { + "Season": "04/05", + "Ratio": 1.04, + "League": "Serie A" + }, + { + "Season": "04/05", + "Ratio": 0.7, + "League": "Ligue 1" + }, + { + "Season": "06/07", + "Ratio": 2.36, + "League": "Premier League" + }, + { + "Season": "06/07", + "Ratio": 1.18, + "League": "Bundesliga" + }, + { + "Season": "06/07", + "Ratio": 1.04, + "League": "Primera Division" + }, + { + "Season": "06/07", + "Ratio": 1.13, + "League": "Serie A" + }, + { + "Season": "06/07", + "Ratio": 0.77, + "League": "Ligue 1" + }, + { + "Season": "08/09", + "Ratio": 2.47, + "League": "Premier League" + }, + { + "Season": "08/09", + "Ratio": 1.38, + "League": "Bundesliga" + }, + { + "Season": "08/09", + "Ratio": 1.41, + "League": "Primera Division" + }, + { + "Season": "08/09", + "Ratio": 1.38, + "League": "Serie A" + }, + { + "Season": "08/09", + "Ratio": 0.9, + "League": "Ligue 1" + }, + { + "Season": "10/11", + "Ratio": 2.5, + "League": "Premier League" + }, + { + "Season": "10/11", + "Ratio": 1.57, + "League": "Bundesliga" + }, + { + "Season": "10/11", + "Ratio": 1.62, + "League": "Primera Division" + }, + { + "Season": "10/11", + "Ratio": 1.55, + "League": "Serie A" + }, + { + "Season": "10/11", + "Ratio": 1.04, + "League": "Ligue 1" + }, + { + "Season": "12/13", + "Ratio": 2.94, + "League": "Premier League" + }, + { + "Season": "12/13", + "Ratio": 1.93, + "League": "Bundesliga" + }, + { + "Season": "12/13", + "Ratio": 1.78, + "League": "Primera Division" + }, + { + "Season": "12/13", + "Ratio": 1.6, + "League": "Serie A" + }, + { + "Season": "12/13", + "Ratio": 1.11, + "League": "Ligue 1" + }, + { + "Season": "14/15", + "Ratio": 4.4, + "League": "Premier League" + }, + { + "Season": "14/15", + "Ratio": 2.16, + "League": "Bundesliga" + }, + { + "Season": "14/15", + "Ratio": 1.99, + "League": "Primera Division" + }, + { + "Season": "14/15", + "Ratio": 1.7, + "League": "Serie A" + }, + { + "Season": "14/15", + "Ratio": 1.4, + "League": "Ligue 1" + }, + { + "Season": "15/16", + "Ratio": 4.87, + "League": "Premier League" + }, + { + "Season": "15/16", + "Ratio": 2.71, + "League": "Bundesliga" + }, + { + "Season": "15/16", + "Ratio": 2.44, + "League": "Primera Division" + }, + { + "Season": "15/16", + "Ratio": 1.92, + "League": "Serie A" + }, + { + "Season": "15/16", + "Ratio": 1.49, + "League": "Ligue 1" + } + ], + "columns": [ + { + "name": "Season", + "importance": "primary", + "description": "Football season spanning two calendar years", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding league", + "role": "y" + }, + { + "name": "League", + "importance": "primary", + "description": "The league of the data point", + "role": "group" + } + ] + } + }, + "512": { + "metadata": { + "title": "Where Container Shipping Is Flourishing", + "description": "Selected countries on the Liner Shipping Connectivity Index 2021 (2004 max. result=100)", + "main_insight": "China (CN) has the highest Liner Shipping Connectivity Index in 2021, significantly exceeding other countries. The index is based on various factors like the number of ships, container capacity, and vessel size." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country Code": "CN", + "LSCI": 171.2 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at CN", + "reason": "The [{'Country Code': 'CN', 'LSCI': 171.2}] of has a maximum value of 171.2, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country Code": "CN", + "LSCI": 171.2 + } + ], + "score": 0.81, + "annotation": "The shows a sudden jump in LSCI from KR to CN.", + "reason": "The LSCI for increases from 111.3 at KR to 171.2 at CN, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country Code": "BR", + "LSCI": 39.7 + } + ], + "score": 0.74, + "annotation": "The has a minimum value at BR", + "reason": "The [{'Country Code': 'BR', 'LSCI': 39.7}] of has a mainimum value of 39.7, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country Code": "CN", + "LSCI": 171.2 + }, + { + "Country Code": "KR", + "LSCI": 111.3 + }, + { + "Country Code": "SG", + "LSCI": 110.7 + }, + { + "Country Code": "US", + "LSCI": 102.6 + }, + { + "Country Code": "NL", + "LSCI": 90.7 + }, + { + "Country Code": "AE", + "LSCI": 73.9 + }, + { + "Country Code": "LK", + "LSCI": 70.7 + }, + { + "Country Code": "TR", + "LSCI": 61.5 + }, + { + "Country Code": "IN", + "LSCI": 58.9 + }, + { + "Country Code": "BR", + "LSCI": 39.7 + } + ], + "columns": [ + { + "name": "Country Code", + "importance": "primary", + "description": "Two-letter country code", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "LSCI", + "importance": "primary", + "description": "Liner Shipping Connectivity Index 2021 (2004 max. result = 100)", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "529": { + "metadata": { + "title": "The Cost of a Royal Coronation", + "description": "The costs of the coronation for the following UK monarchs (adjusted for inflation)", + "main_insight": "The estimated cost of King Charles III's coronation is significantly higher than those of previous monarchs in the past two centuries, even when adjusted for inflation." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Monarch": "George V", + "Coronation Cost": 17.4 + } + ], + "score": 0.85, + "annotation": "The shows a sudden jump in Coronation Cost from Victoria to George V.", + "reason": "The Coronation Cost for increases from 6.2 at Victoria to 17.4 at George V, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Monarch": "William IV", + "Coronation Cost": 3.6 + } + ], + "score": 0.76, + "annotation": "The has a minimum value at William IV", + "reason": "The [{'Monarch': 'William IV', 'Coronation Cost': 3.6}] of has a mainimum value of 3.6, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Monarch": "George VI", + "Coronation Cost": 24.8 + } + ], + "score": 0.63, + "annotation": "The has a maximum value at George VI", + "reason": "The [{'Monarch': 'George VI', 'Coronation Cost': 24.8}] of has a maximum value of 24.8, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Monarch": "Queen Elizabeth II", + "Coronation Cost": 20.5 + }, + { + "Monarch": "George VI", + "Coronation Cost": 24.8 + }, + { + "Monarch": "George V", + "Coronation Cost": 17.4 + }, + { + "Monarch": "Victoria", + "Coronation Cost": 6.2 + }, + { + "Monarch": "William IV", + "Coronation Cost": 3.6 + }, + { + "Monarch": "George IV", + "Coronation Cost": 20.9 + } + ], + "columns": [ + { + "name": "Monarch", + "importance": "primary", + "description": "Name of the UK Monarch", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Coronation Cost", + "importance": "primary", + "description": "Cost of the coronation in millions of pounds, adjusted for inflation", + "unit": "Million GBP", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2409": { + "metadata": { + "title": "Let's (Not) Get Physical!", + "description": "Share of respondents saying they never listen to music from physical media", + "main_insight": "A significant majority of respondents in several major countries report never listening to music from physical media, with China having the highest percentage." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "China", + "Percentage": 0.86 + } + ], + "score": 0.92, + "annotation": "The has a maximum value at China", + "reason": "The [{'Country': 'China', 'Percentage': 0.86}] of has a maximum value of 0.86, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "China", + "Percentage": 0.86 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Percentage from India to China.", + "reason": "The Percentage for increases from 0.78 at India to 0.86 at China, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Germany", + "Percentage": 0.7 + } + ], + "score": 0.77, + "annotation": "The has a minimum value at Germany", + "reason": "The [{'Country': 'Germany', 'Percentage': 0.7}] of has a mainimum value of 0.7, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "China", + "Percentage": 0.86 + }, + { + "Country": "France", + "Percentage": 0.78 + }, + { + "Country": "India", + "Percentage": 0.78 + }, + { + "Country": "United States", + "Percentage": 0.76 + }, + { + "Country": "Sweden", + "Percentage": 0.76 + }, + { + "Country": "Spain", + "Percentage": 0.75 + }, + { + "Country": "UK", + "Percentage": 0.73 + }, + { + "Country": "Germany", + "Percentage": 0.7 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents who never listen to music from physical media", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "55": { + "metadata": { + "title": "Inflation in India, Pre- and Post-Covid", + "description": "Year-over-year change of selected consumer price indices in urban India", + "main_insight": "Inflation in India, as measured by various consumer price indices, saw significant shifts between 2015 and 2024. General inflation initially rose, then decreased after 2020, and finally rose again, while housing inflation peaked in 2018-2019 and then declined." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Inflation Metric": "General", + "Fiscal Year": "total", + "Units": 44.7 + } + ], + "score": 0.9, + "annotation": "The total value of General has the largest proportion.", + "reason": "The total value of Units of General accounts for 0.34252873563218394, larger than all other Inflation Metric, of the total of Units of all Inflation Metric." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Fiscal Year": "2017-2018", + "Units": 6.7, + "Inflation Metric": "Housing" + } + ], + "score": 0.9, + "annotation": "The Housing has a maximum value at 2017-2018", + "reason": "The [{'Fiscal Year': '2017-2018', 'Units': 6.7, 'Inflation Metric': 'Housing'}] of Housing has a maximum value of 6.7, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Fiscal Year": "2019-2020", + "Units": 4.9, + "Inflation Metric": "Housing" + } + ], + "score": 0.89, + "annotation": "The Housing has the largest proportion in 2019-2020.", + "reason": "The Units of Housing in 2019-2020 accounts for 0.35766423357664234, larger than all other Inflation Metric, of the total of Units of all Inflation Metric." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Fiscal Year": "2016-2017", + "Units": 5.0, + "Inflation Metric": "Housing" + } + ], + "score": 0.89, + "annotation": "The Housing has the largest proportion in 2016-2017.", + "reason": "The Units of Housing in 2016-2017 accounts for 0.36496350364963503, larger than all other Inflation Metric, of the total of Units of all Inflation Metric." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Fiscal Year": "2015-2016", + "Units": 4.9, + "Inflation Metric": "General" + } + ], + "score": 0.89, + "annotation": "The General has the largest proportion in 2015-2016.", + "reason": "The Units of General in 2015-2016 accounts for 0.3798449612403101, larger than all other Inflation Metric, of the total of Units of all Inflation Metric." + } + ], + "data": { + "data": [ + { + "Fiscal Year": "2015-2016", + "Units": 4.9, + "Inflation Metric": "General" + }, + { + "Fiscal Year": "2015-2016", + "Units": 4.1, + "Inflation Metric": "General excl. food & fuel" + }, + { + "Fiscal Year": "2015-2016", + "Units": 3.9, + "Inflation Metric": "Housing" + }, + { + "Fiscal Year": "2016-2017", + "Units": 4.5, + "Inflation Metric": "General" + }, + { + "Fiscal Year": "2016-2017", + "Units": 4.2, + "Inflation Metric": "General excl. food & fuel" + }, + { + "Fiscal Year": "2016-2017", + "Units": 5.0, + "Inflation Metric": "Housing" + }, + { + "Fiscal Year": "2017-2018", + "Units": 3.6, + "Inflation Metric": "General" + }, + { + "Fiscal Year": "2017-2018", + "Units": 4.7, + "Inflation Metric": "General excl. food & fuel" + }, + { + "Fiscal Year": "2017-2018", + "Units": 6.7, + "Inflation Metric": "Housing" + }, + { + "Fiscal Year": "2018-2019", + "Units": 3.4, + "Inflation Metric": "General" + }, + { + "Fiscal Year": "2018-2019", + "Units": 5.9, + "Inflation Metric": "General excl. food & fuel" + }, + { + "Fiscal Year": "2018-2019", + "Units": 5.9, + "Inflation Metric": "Housing" + }, + { + "Fiscal Year": "2019-2020", + "Units": 4.8, + "Inflation Metric": "General" + }, + { + "Fiscal Year": "2019-2020", + "Units": 4.0, + "Inflation Metric": "General excl. food & fuel" + }, + { + "Fiscal Year": "2019-2020", + "Units": 4.9, + "Inflation Metric": "Housing" + }, + { + "Fiscal Year": "2020-2021", + "Units": 6.2, + "Inflation Metric": "General" + }, + { + "Fiscal Year": "2020-2021", + "Units": 5.3, + "Inflation Metric": "General excl. food & fuel" + }, + { + "Fiscal Year": "2020-2021", + "Units": 3.5, + "Inflation Metric": "Housing" + }, + { + "Fiscal Year": "2021-2022", + "Units": 5.5, + "Inflation Metric": "General" + }, + { + "Fiscal Year": "2021-2022", + "Units": 5.9, + "Inflation Metric": "General excl. food & fuel" + }, + { + "Fiscal Year": "2021-2022", + "Units": 3.5, + "Inflation Metric": "Housing" + }, + { + "Fiscal Year": "2022-2023", + "Units": 6.7, + "Inflation Metric": "General" + }, + { + "Fiscal Year": "2022-2023", + "Units": 6.0, + "Inflation Metric": "General excl. food & fuel" + }, + { + "Fiscal Year": "2022-2023", + "Units": 4.2, + "Inflation Metric": "Housing" + }, + { + "Fiscal Year": "2023-2024", + "Units": 5.1, + "Inflation Metric": "General" + }, + { + "Fiscal Year": "2023-2024", + "Units": 4.2, + "Inflation Metric": "General excl. food & fuel" + }, + { + "Fiscal Year": "2023-2024", + "Units": 3.9, + "Inflation Metric": "Housing" + } + ], + "columns": [ + { + "name": "Fiscal Year", + "importance": "primary", + "description": "Fiscal year from April of the former to March of the latter", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding inflation metric", + "role": "y" + }, + { + "name": "Inflation Metric", + "importance": "primary", + "description": "The inflation metric of the data point", + "role": "group" + } + ] + } + }, + "581": { + "metadata": { + "title": "60% of Firearm Deaths Are Suicides", + "description": "Firearm deaths by category in the U.S. in 2017", + "main_insight": "In 2017, suicides accounted for 60% of all firearm deaths in the U.S., significantly exceeding homicides and other causes." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "Homicide", + "Percentage": 37.0 + } + ], + "score": 0.89, + "annotation": "The shows a sudden jump in Percentage from Firearm Total to Homicide.", + "reason": "The Percentage for increases from 1.0 at Firearm Total to 37.0 at Homicide, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Suicide", + "Percentage": 60.0 + } + ], + "score": 0.79, + "annotation": "The has a maximum value at Suicide", + "reason": "The [{'Category': 'Suicide', 'Percentage': 60.0}] of has a maximum value of 60.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Category": "Legal intervention/war", + "Percentage": 1.0 + }, + { + "Category": "Unintentional", + "Percentage": 1.0 + }, + { + "Category": "Firearm Total", + "Percentage": 1.0 + } + ], + "score": 0.45, + "annotation": "The has minimum values at Legal intervention/war, Unintentional, Firearm Total", + "reason": "The [{'Category': 'Legal intervention/war', 'Percentage': 1.0}, {'Category': 'Unintentional', 'Percentage': 1.0}, {'Category': 'Firearm Total', 'Percentage': 1.0}] of have minimum values of 1.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Category": "Suicide", + "Percentage": 60.0 + }, + { + "Category": "Homicide", + "Percentage": 37.0 + }, + { + "Category": "Legal intervention/war", + "Percentage": 1.0 + }, + { + "Category": "Unintentional", + "Percentage": 1.0 + }, + { + "Category": "Firearm Total", + "Percentage": 1.0 + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Category of firearm death", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of total firearm deaths", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2066": { + "metadata": { + "title": "Print is Losing Ground in North America & Europe", + "description": "Advertising expenditure Q1 2012, year on year change in %", + "main_insight": "Advertising expenditure on newspapers and magazines decreased in North America and Europe in Q1 2012 compared to the previous year, while other media types generally saw growth, especially Internet and Outdoor advertising." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Central & Eastern Europe", + "TV": 33.8 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in TV from Asia Pacific to Central & Eastern Europe.", + "reason": "The TV for increases from 7.5 at Asia Pacific to 33.8 at Central & Eastern Europe, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "Central & Eastern Europe", + "TV": 33.8 + } + ], + "score": 0.87, + "annotation": "The has a maximum value at Central & Eastern Europe", + "reason": "The [{'Region': 'Central & Eastern Europe', 'TV': 33.8}] of has a maximum value of 33.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Region": "Western Europe", + "TV": -1.2 + } + ], + "score": 0.44, + "annotation": "The has a minimum value at Western Europe", + "reason": "The [{'Region': 'Western Europe', 'TV': -1.2}] of has a mainimum value of -1.2, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Region": "North America", + "TV": 4.0 + }, + { + "Region": "Western Europe", + "TV": -1.2 + }, + { + "Region": "Central & Eastern Europe", + "TV": 33.8 + }, + { + "Region": "Asia Pacific", + "TV": 7.5 + }, + { + "Region": "Latin America", + "TV": -0.6 + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Geographic region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "TV", + "importance": "secondary", + "description": "Year-on-year change in advertising expenditure for TV", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "183": { + "metadata": { + "title": "Twitter's Stock Price Crashes", + "description": "Stock price in $ (adjusted close)", + "main_insight": "Twitter's stock price experienced significant volatility between 2013 and 2015, reaching an all-time high in early 2013 and declining to an all-time low by the end of 2015." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2013, + "Stock Price": 42.0 + }, + { + "Year": 2013, + "Stock Price": 72.0 + }, + { + "Year": 2013, + "Stock Price": 62.0 + }, + { + "Year": 2013, + "Stock Price": 55.0 + }, + { + "Year": 2013, + "Stock Price": 60.0 + }, + { + "Year": 2014, + "Stock Price": 50.0 + }, + { + "Year": 2014, + "Stock Price": 40.0 + }, + { + "Year": 2014, + "Stock Price": 32.0 + }, + { + "Year": 2014, + "Stock Price": 42.0 + }, + { + "Year": 2014, + "Stock Price": 53.0 + }, + { + "Year": 2014, + "Stock Price": 48.0 + }, + { + "Year": 2015, + "Stock Price": 52.0 + }, + { + "Year": 2015, + "Stock Price": 48.0 + }, + { + "Year": 2015, + "Stock Price": 36.0 + }, + { + "Year": 2015, + "Stock Price": 37.0 + }, + { + "Year": 2015, + "Stock Price": 38.0 + }, + { + "Year": 2015, + "Stock Price": 28.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of stock price", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Stock Price", + "importance": "primary", + "description": "Adjusted closing stock price in US dollars", + "unit": "US dollars", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1748": { + "metadata": { + "title": "Biden's Ambitious Aim To Make Federal Vehicle Fleet Electric", + "description": "Number of vehicles in U.S. federal vehicle fleets in 2019 and their total value", + "main_insight": "The visualization shows the number and value of vehicles across different U.S. federal agencies in 2019. Civilian Agencies have the highest number of vehicles, while Postal Service vehicles have the highest total value." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Agency": "Postal Service", + "Number of Vehicles": 225668.0 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in Number of Vehicles from Military Agencies to Postal Service.", + "reason": "The Number of Vehicles for increases from 173429.0 at Military Agencies to 225668.0 at Postal Service, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Agency": "Military Agencies", + "Number of Vehicles": 173429.0 + } + ], + "score": 0.65, + "annotation": "The has a minimum value at Military Agencies", + "reason": "The [{'Agency': 'Military Agencies', 'Number of Vehicles': 173429.0}] of has a mainimum value of 173429.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Agency": "Civilian Agencies", + "Number of Vehicles": 245920.0 + } + ], + "score": 0.51, + "annotation": "The has a maximum value at Civilian Agencies", + "reason": "The [{'Agency': 'Civilian Agencies', 'Number of Vehicles': 245920.0}] of has a maximum value of 245920.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Agency": "Civilian Agencies", + "Number of Vehicles": 245920.0 + }, + { + "Agency": "Postal Service", + "Number of Vehicles": 225668.0 + }, + { + "Agency": "Military Agencies", + "Number of Vehicles": 173429.0 + } + ], + "columns": [ + { + "name": "Agency", + "importance": "primary", + "description": "Type of federal agency", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Vehicles", + "importance": "primary", + "description": "Number of vehicles in the agency's fleet in 2019", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1961": { + "metadata": { + "title": "Super Bowl LII: Most Rooting for the Eagles", + "description": "\"Who would you personally like to see win the Super Bowl?\" (by age)", + "main_insight": "The Philadelphia Eagles had more overall support than the New England Patriots across all age groups, and support for the Eagles generally increased with age." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Age Group": "All", + "Units": 45.0, + "Super Bowl": "Philadelphia Eagles" + }, + { + "Age Group": "All", + "Units": 26.0, + "Super Bowl": "New England Patriots" + } + ], + "score": 0.94, + "annotation": "The Units of Philadelphia Eagles and New England Patriots show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Philadelphia Eagles and New England Patriots is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Age Group": "18-29", + "Units": 38.0, + "Super Bowl": "Philadelphia Eagles" + } + ], + "score": 0.9, + "annotation": "The Philadelphia Eagles has a minimum value at 18-29", + "reason": "The [{'Age Group': '18-29', 'Units': 38.0, 'Super Bowl': 'Philadelphia Eagles'}] of Philadelphia Eagles has a mainimum value of 38.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Age Group": "All", + "Units": 45.0, + "Super Bowl": "Philadelphia Eagles" + } + ], + "score": 0.9, + "annotation": "The Philadelphia Eagles shows a sudden jump in Units from 18-29 to All.", + "reason": "The Units for Philadelphia Eagles increases from 38.0 at 18-29 to 45.0 at All, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Age Group": "18-29", + "Units": 38.0, + "Super Bowl": "New England Patriots" + } + ], + "score": 0.89, + "annotation": "The New England Patriots has a maximum value at 18-29", + "reason": "The [{'Age Group': '18-29', 'Units': 38.0, 'Super Bowl': 'New England Patriots'}] of New England Patriots has a maximum value of 38.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Age Group": "18-29", + "Units": 38.0, + "Super Bowl": "New England Patriots" + } + ], + "score": 0.89, + "annotation": "The New England Patriots shows a sudden jump in Units from 30-44 to 18-29.", + "reason": "The Units for New England Patriots increases from 27.0 at 30-44 to 38.0 at 18-29, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Age Group": "18-29", + "Units": 38.0, + "Super Bowl": "Philadelphia Eagles" + }, + { + "Age Group": "18-29", + "Units": 38.0, + "Super Bowl": "New England Patriots" + }, + { + "Age Group": "30-44", + "Units": 46.0, + "Super Bowl": "Philadelphia Eagles" + }, + { + "Age Group": "30-44", + "Units": 27.0, + "Super Bowl": "New England Patriots" + }, + { + "Age Group": "45-54", + "Units": 49.0, + "Super Bowl": "Philadelphia Eagles" + }, + { + "Age Group": "45-54", + "Units": 23.0, + "Super Bowl": "New England Patriots" + }, + { + "Age Group": "55-64", + "Units": 48.0, + "Super Bowl": "Philadelphia Eagles" + }, + { + "Age Group": "55-64", + "Units": 21.0, + "Super Bowl": "New England Patriots" + }, + { + "Age Group": "65+", + "Units": 47.0, + "Super Bowl": "Philadelphia Eagles" + }, + { + "Age Group": "65+", + "Units": 20.0, + "Super Bowl": "New England Patriots" + }, + { + "Age Group": "All", + "Units": 45.0, + "Super Bowl": "Philadelphia Eagles" + }, + { + "Age Group": "All", + "Units": 26.0, + "Super Bowl": "New England Patriots" + } + ], + "columns": [ + { + "name": "Age Group", + "importance": "primary", + "description": "Age group of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding super bowl", + "role": "y" + }, + { + "name": "Super Bowl", + "importance": "primary", + "description": "The super bowl of the data point", + "role": "group" + } + ] + } + }, + "900": { + "metadata": { + "title": "Beast From The East: Europe's Feeling The Chill", + "description": "Temperature low on February 28th, 2018 compared to average February low", + "main_insight": "European cities experienced significantly lower temperatures on February 28, 2018, compared to their average February lows, with Munich experiencing the largest difference." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "Munich", + "Low on February 28, 2018": -17.0 + } + ], + "score": 0.9, + "annotation": "The has a minimum value at Munich", + "reason": "The [{'City': 'Munich', 'Low on February 28, 2018': -17.0}] of has a mainimum value of -17.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Barcelona", + "Low on February 28, 2018": 1.0 + } + ], + "score": 0.79, + "annotation": "The has a maximum value at Barcelona", + "reason": "The [{'City': 'Barcelona', 'Low on February 28, 2018': 1.0}] of has a maximum value of 1.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Berlin", + "Low on February 28, 2018": -12.0 + } + ], + "score": 0.67, + "annotation": "The shows a sudden jump in Low on February 28, 2018 from Munich to Berlin.", + "reason": "The Low on February 28, 2018 for increases from -17.0 at Munich to -12.0 at Berlin, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "City": "Munich", + "Low on February 28, 2018": -17.0 + }, + { + "City": "Berlin", + "Low on February 28, 2018": -12.0 + }, + { + "City": "Brussels", + "Low on February 28, 2018": -9.0 + }, + { + "City": "Paris", + "Low on February 28, 2018": -8.0 + }, + { + "City": "Amsterdam", + "Low on February 28, 2018": -7.0 + }, + { + "City": "London", + "Low on February 28, 2018": -7.0 + }, + { + "City": "Rome", + "Low on February 28, 2018": -6.0 + }, + { + "City": "Naples", + "Low on February 28, 2018": -2.0 + }, + { + "City": "Nice", + "Low on February 28, 2018": -1.0 + }, + { + "City": "Barcelona", + "Low on February 28, 2018": 1.0 + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "European city", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Low on February 28, 2018", + "importance": "primary", + "description": "Temperature low in Celsius", + "unit": "Celsius", + "data_type": "time", + "role": "y" + } + ] + } + }, + "14": { + "metadata": { + "title": "UK: where people have never used the internet", + "description": "Share of UK population saying they have never used the internet in 2018, by country/region", + "main_insight": "In 2018, there was significant variation in the percentage of people who had never used the internet across different regions of the UK, with Northern Ireland having the highest percentage and London the lowest." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "Northern Ireland", + "Non-Internet Users": 12.8 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at Northern Ireland", + "reason": "The [{'Region': 'Northern Ireland', 'Non-Internet Users': 12.8}] of has a maximum value of 12.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Region": "London", + "Non-Internet Users": 5.7 + } + ], + "score": 0.89, + "annotation": "The has a minimum value at London", + "reason": "The [{'Region': 'London', 'Non-Internet Users': 5.7}] of has a mainimum value of 5.7, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Northern Ireland", + "Non-Internet Users": 12.8 + } + ], + "score": 0.74, + "annotation": "The shows a sudden jump in Non-Internet Users from Yorkshire/Humber to Northern Ireland.", + "reason": "The Non-Internet Users for increases from 10.2 at Yorkshire/Humber to 12.8 at Northern Ireland, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Region": "Northern Ireland", + "Non-Internet Users": 12.8 + }, + { + "Region": "Yorkshire/Humber", + "Non-Internet Users": 10.2 + }, + { + "Region": "North East", + "Non-Internet Users": 10.0 + }, + { + "Region": "West Midlands", + "Non-Internet Users": 9.4 + }, + { + "Region": "East Midlands", + "Non-Internet Users": 9.2 + }, + { + "Region": "South West", + "Non-Internet Users": 9.1 + }, + { + "Region": "Scotland", + "Non-Internet Users": 9.1 + }, + { + "Region": "Wales", + "Non-Internet Users": 8.9 + }, + { + "Region": "North West", + "Non-Internet Users": 8.8 + }, + { + "Region": "East of England", + "Non-Internet Users": 7.7 + }, + { + "Region": "South East", + "Non-Internet Users": 6.8 + }, + { + "Region": "London", + "Non-Internet Users": 5.7 + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "UK country/region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Non-Internet Users", + "importance": "primary", + "description": "Percentage of population who have never used the internet", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1020": { + "metadata": { + "title": "6,500 Migrants Died Since Qatar Was Named World Cup Host", + "description": "Deaths of South Asian migrants in Qatar from 2011 to late 2020 by nationality", + "main_insight": "Thousands of South Asian migrants have died in Qatar since it was named World Cup host in 2010, with the largest number of deaths coming from India." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "India", + "Deaths": 2711 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Deaths from Nepal to India.", + "reason": "The Deaths for increases from 1641 at Nepal to 2711 at India, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "India", + "Deaths": 2711 + } + ], + "score": 0.83, + "annotation": "The has a maximum value at India", + "reason": "The [{'Country': 'India', 'Deaths': 2711}] of has a maximum value of 2711, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Sri Lanka", + "Deaths": 557 + } + ], + "score": 0.56, + "annotation": "The has a minimum value at Sri Lanka", + "reason": "The [{'Country': 'Sri Lanka', 'Deaths': 557}] of has a mainimum value of 557, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "India", + "Deaths": 2711 + }, + { + "Country": "Nepal", + "Deaths": 1641 + }, + { + "Country": "Bangladesh", + "Deaths": 1018 + }, + { + "Country": "Pakistan", + "Deaths": 824 + }, + { + "Country": "Sri Lanka", + "Deaths": 557 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Nationality of the migrants", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Deaths", + "importance": "primary", + "description": "Number of migrant deaths", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "342": { + "metadata": { + "title": "Facebook is up against a tide of hate speech", + "description": "Number of hate speech posts deleted by Facebook, per quarter (in millions)", + "main_insight": "The number of hate speech posts deleted by Facebook has generally increased from Q4 2017 to Q1 2019, indicating a growing challenge in content moderation." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Quarter": "Q1 2019", + "Deleted Posts": 4.0 + } + ], + "score": 0.79, + "annotation": "The has a maximum value at Q1 2019", + "reason": "The [{'Quarter': 'Q1 2019', 'Deleted Posts': 4.0}] of has a maximum value of 4.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Quarter": "Q4 2017", + "Deleted Posts": 1.6 + } + ], + "score": 0.79, + "annotation": "The has a minimum value at Q4 2017", + "reason": "The [{'Quarter': 'Q4 2017', 'Deleted Posts': 1.6}] of has a mainimum value of 1.6, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Quarter": "Q1 2018", + "Deleted Posts": 2.5 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Deleted Posts from Q4 2017 to Q1 2018.", + "reason": "The Deleted Posts for increases from 1.6 at Q4 2017 to 2.5 at Q1 2018, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Quarter": "Q4 2017", + "Deleted Posts": 1.6 + }, + { + "Quarter": "Q1 2018", + "Deleted Posts": 2.5 + }, + { + "Quarter": "Q2 2018", + "Deleted Posts": 2.5 + }, + { + "Quarter": "Q3 2018", + "Deleted Posts": 2.9 + }, + { + "Quarter": "Q4 2018", + "Deleted Posts": 3.3 + }, + { + "Quarter": "Q1 2019", + "Deleted Posts": 4.0 + } + ], + "columns": [ + { + "name": "Quarter", + "importance": "primary", + "description": "Reporting quarter", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Deleted Posts", + "importance": "primary", + "description": "Number of hate speech posts deleted by Facebook (in millions)", + "unit": "millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1683": { + "metadata": { + "title": "COVID Accelerates Declining Retail Industry", + "description": "Percentage of store closures in 2019 and estimated stores remaining by 2025", + "main_insight": "The retail industry is experiencing significant store closures, with some sectors like Office Supplies and Consumer Electronics being more affected than others. A few sectors, notably Home Improvement and Auto Parts, see positive growth." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Retail Sector": "Auto Parts", + "Proportion": -0.01, + "Projection Stage": "Store Closures in 2019" + }, + { + "Retail Sector": "Auto Parts", + "Proportion": 0.99, + "Projection Stage": "Estimated Stores Remaining in 2025" + } + ], + "score": 0.99, + "annotation": "The Proportion of Store Closures in 2019 and Estimated Stores Remaining in 2025 show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Proportion between Store Closures in 2019 and Estimated Stores Remaining in 2025 is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Retail Sector": "Office Supplies", + "Proportion": 0.5, + "Projection Stage": "Estimated Stores Remaining in 2025" + } + ], + "score": 0.86, + "annotation": "The Estimated Stores Remaining in 2025 has a minimum value at Office Supplies", + "reason": "The [{'Retail Sector': 'Office Supplies', 'Proportion': 0.5, 'Projection Stage': 'Estimated Stores Remaining in 2025'}] of Estimated Stores Remaining in 2025 has a mainimum value of 0.5, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Retail Sector": "Office Supplies", + "Proportion": 0.5, + "Projection Stage": "Store Closures in 2019" + } + ], + "score": 0.83, + "annotation": "The Store Closures in 2019 has a maximum value at Office Supplies", + "reason": "The [{'Retail Sector': 'Office Supplies', 'Proportion': 0.5, 'Projection Stage': 'Store Closures in 2019'}] of Store Closures in 2019 has a maximum value of 0.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Retail Sector": "Consumer Electronics", + "Proportion": 0.72, + "Projection Stage": "Estimated Stores Remaining in 2025" + } + ], + "score": 0.8, + "annotation": "The Estimated Stores Remaining in 2025 shows a sudden jump in Proportion from Office Supplies to Consumer Electronics.", + "reason": "The Proportion for Estimated Stores Remaining in 2025 increases from 0.5 at Office Supplies to 0.72 at Consumer Electronics, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Retail Sector": "Office Supplies", + "Proportion": 0.5, + "Projection Stage": "Store Closures in 2019" + } + ], + "score": 0.77, + "annotation": "The Store Closures in 2019 shows a sudden jump in Proportion from Consumer Electronics to Office Supplies.", + "reason": "The Proportion for Store Closures in 2019 increases from 0.28 at Consumer Electronics to 0.5 at Office Supplies, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Retail Sector": "Office Supplies", + "Proportion": 0.5, + "Projection Stage": "Store Closures in 2019" + }, + { + "Retail Sector": "Office Supplies", + "Proportion": 0.5, + "Projection Stage": "Estimated Stores Remaining in 2025" + }, + { + "Retail Sector": "Consumer Electronics", + "Proportion": 0.28, + "Projection Stage": "Store Closures in 2019" + }, + { + "Retail Sector": "Consumer Electronics", + "Proportion": 0.72, + "Projection Stage": "Estimated Stores Remaining in 2025" + }, + { + "Retail Sector": "Sporting Goods", + "Proportion": 0.27, + "Projection Stage": "Store Closures in 2019" + }, + { + "Retail Sector": "Sporting Goods", + "Proportion": 0.73, + "Projection Stage": "Estimated Stores Remaining in 2025" + }, + { + "Retail Sector": "Home Furnishings", + "Proportion": 0.24, + "Projection Stage": "Store Closures in 2019" + }, + { + "Retail Sector": "Home Furnishings", + "Proportion": 0.76, + "Projection Stage": "Estimated Stores Remaining in 2025" + }, + { + "Retail Sector": "Clothing", + "Proportion": 0.2, + "Projection Stage": "Store Closures in 2019" + }, + { + "Retail Sector": "Clothing", + "Proportion": 0.8, + "Projection Stage": "Estimated Stores Remaining in 2025" + }, + { + "Retail Sector": "Home Improvement", + "Proportion": -0.05, + "Projection Stage": "Store Closures in 2019" + }, + { + "Retail Sector": "Home Improvement", + "Proportion": 0.95, + "Projection Stage": "Estimated Stores Remaining in 2025" + }, + { + "Retail Sector": "Auto Parts", + "Proportion": -0.01, + "Projection Stage": "Store Closures in 2019" + }, + { + "Retail Sector": "Auto Parts", + "Proportion": 0.99, + "Projection Stage": "Estimated Stores Remaining in 2025" + } + ], + "columns": [ + { + "name": "Retail Sector", + "importance": "primary", + "description": "The different categories of retail stores", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Proportion", + "importance": "primary", + "description": "Proportion for the corresponding projection stage", + "role": "y" + }, + { + "name": "Projection Stage", + "importance": "primary", + "description": "The projection stage of the data point", + "role": "group" + } + ] + } + }, + "566": { + "metadata": { + "title": "The Changing Face of World Trade", + "description": "International merchandise trade by type of trading partner (in percent)", + "main_insight": "Trade between advanced economies has significantly decreased from 1990-94 to 2010-15, while trade between emerging markets and developing economies, and between advanced economies and emerging markets and developing economies has increased." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Time Period": "2010-15", + "Score": 38.0, + "Region": "Between AE" + }, + { + "Time Period": "2010-15", + "Score": 45.0, + "Region": "Between AE & EMDE" + } + ], + "score": 1.0, + "annotation": "The Score of Between AE and Between AE & EMDE show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Between AE and Between AE & EMDE is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Time Period": "2010-15", + "Score": 38.0, + "Region": "Between AE" + }, + { + "Time Period": "2010-15", + "Score": 17.0, + "Region": "Between EMDE" + } + ], + "score": 1.0, + "annotation": "The Score of Between AE and Between EMDE show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Between AE and Between EMDE is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Time Period": "2010-15", + "Score": 45.0, + "Region": "Between AE & EMDE" + }, + { + "Time Period": "2010-15", + "Score": 17.0, + "Region": "Between EMDE" + } + ], + "score": 1.0, + "annotation": "The Score of Between AE & EMDE and Between EMDE show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Between AE & EMDE and Between EMDE is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time Period": "1990-94", + "Score": 63.0, + "Region": "Between AE" + } + ], + "score": 0.97, + "annotation": "The Between AE shows a sudden jump in Score from 2010-15 to 1990-94.", + "reason": "The Score for Between AE increases from 38.0 at 2010-15 to 63.0 at 1990-94, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time Period": "2010-15", + "Score": 45.0, + "Region": "Between AE & EMDE" + } + ], + "score": 0.97, + "annotation": "The Between AE & EMDE shows a sudden jump in Score from 1990-94 to 2010-15.", + "reason": "The Score for Between AE & EMDE increases from 32.0 at 1990-94 to 45.0 at 2010-15, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Time Period": "1990-94", + "Score": 63.0, + "Region": "Between AE" + }, + { + "Time Period": "1990-94", + "Score": 32.0, + "Region": "Between AE & EMDE" + }, + { + "Time Period": "1990-94", + "Score": 5.0, + "Region": "Between EMDE" + }, + { + "Time Period": "2010-15", + "Score": 38.0, + "Region": "Between AE" + }, + { + "Time Period": "2010-15", + "Score": 45.0, + "Region": "Between AE & EMDE" + }, + { + "Time Period": "2010-15", + "Score": 17.0, + "Region": "Between EMDE" + } + ], + "columns": [ + { + "name": "Time Period", + "importance": "primary", + "description": "Time periods being compared", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding region", + "role": "y" + }, + { + "name": "Region", + "importance": "primary", + "description": "The region of the data point", + "role": "group" + } + ] + } + }, + "1340": { + "metadata": { + "title": "Zombie Apocalypse: Fight or Flight?", + "description": "Most commonly cited 'zombie plans'", + "main_insight": "Among US adults who have a zombie apocalypse plan, the most popular strategy is to take shelter at home, followed by gathering weapons, and finding shelter elsewhere." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Zombie Plan": "Take shelter at home or a family/friend's home", + "Percentage": 27.0 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Percentage from Gather weapons and go hunting for zombies to Take shelter at home or a family/friend's home.", + "reason": "The Percentage for increases from 18.0 at Gather weapons and go hunting for zombies to 27.0 at Take shelter at home or a family/friend's home, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Zombie Plan": "Take shelter at home or a family/friend's home", + "Percentage": 27.0 + } + ], + "score": 0.8, + "annotation": "The has a maximum value at Take shelter at home or a family/friend's home", + "reason": "The [{'Zombie Plan': \"Take shelter at home or a family/friend's home\", 'Percentage': 27.0}] of has a maximum value of 27.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Zombie Plan": "Relocate to a remote island", + "Percentage": 9.0 + } + ], + "score": 0.73, + "annotation": "The has a minimum value at Relocate to a remote island", + "reason": "The [{'Zombie Plan': 'Relocate to a remote island', 'Percentage': 9.0}] of has a mainimum value of 9.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Zombie Plan": "Take shelter at home or a family/friend's home", + "Percentage": 27.0 + }, + { + "Zombie Plan": "Gather weapons and go hunting for zombies", + "Percentage": 18.0 + }, + { + "Zombie Plan": "Find shelter somewhere else (e.g. supermarket)", + "Percentage": 17.0 + }, + { + "Zombie Plan": "Relocate to the wilderness", + "Percentage": 16.0 + }, + { + "Zombie Plan": "Relocate to a remote island", + "Percentage": 9.0 + } + ], + "columns": [ + { + "name": "Zombie Plan", + "importance": "primary", + "description": "Different strategies for dealing with a zombie apocalypse", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents who chose each strategy", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "546": { + "metadata": { + "title": "Is Your Government Ready for Another Pandemic?", + "description": "Share of adults in selected OECD countries rating how likely it is their government is ready for a new pandemic", + "main_insight": "Across the selected OECD countries, there's a significant variation in public confidence regarding their governments' preparedness for another pandemic, with most countries showing a larger percentage of people believing their government is likely ready than unlikely." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "JP", + "Score": 28.0, + "Likelihood": "Don't know" + } + ], + "score": 0.98, + "annotation": "The Don't know has a maximum value at JP", + "reason": "The [{'Country': 'JP', 'Score': 28.0, 'Likelihood': \"Don't know\"}] of Don't know has a maximum value of 28.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "JP", + "Score": 28.0, + "Likelihood": "Don't know" + } + ], + "score": 0.96, + "annotation": "The Don't know shows a sudden jump in Score from NL to JP.", + "reason": "The Score for Don't know increases from 4.0 at NL to 28.0 at JP, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "AT", + "Score": 59.0, + "Likelihood": "Unlikely" + } + ], + "score": 0.96, + "annotation": "The Unlikely has a maximum value at AT", + "reason": "The [{'Country': 'AT', 'Score': 59.0, 'Likelihood': 'Unlikely'}] of Unlikely has a maximum value of 59.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "GB", + "Score": 1.0, + "Likelihood": "Don't know" + } + ], + "score": 0.95, + "annotation": "The Don't know has the smallest proportion in KR.", + "reason": "The Score of Don't know in KR accounts for 0.009900990099009901, smaller than all other Likelihood, of the total of Score of all Likelihood." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "IE", + "Score": 1.0, + "Likelihood": "Don't know" + } + ], + "score": 0.95, + "annotation": "The Don't know has the smallest proportion in IE.", + "reason": "The Score of Don't know in IE accounts for 0.01, smaller than all other Likelihood, of the total of Score of all Likelihood." + } + ], + "data": { + "data": [ + { + "Country": "LU", + "Score": 70.0, + "Likelihood": "Likely" + }, + { + "Country": "LU", + "Score": 10.0, + "Likelihood": "Neutral" + }, + { + "Country": "LU", + "Score": 18.0, + "Likelihood": "Unlikely" + }, + { + "Country": "LU", + "Score": 2.0, + "Likelihood": "Don't know" + }, + { + "Country": "IE", + "Score": 60.0, + "Likelihood": "Likely" + }, + { + "Country": "IE", + "Score": 14.0, + "Likelihood": "Neutral" + }, + { + "Country": "IE", + "Score": 25.0, + "Likelihood": "Unlikely" + }, + { + "Country": "IE", + "Score": 1.0, + "Likelihood": "Don't know" + }, + { + "Country": "KR", + "Score": 58.0, + "Likelihood": "Likely" + }, + { + "Country": "KR", + "Score": 13.0, + "Likelihood": "Neutral" + }, + { + "Country": "KR", + "Score": 29.0, + "Likelihood": "Unlikely" + }, + { + "Country": "GB", + "Score": 57.0, + "Likelihood": "Likely" + }, + { + "Country": "GB", + "Score": 12.0, + "Likelihood": "Neutral" + }, + { + "Country": "GB", + "Score": 30.0, + "Likelihood": "Unlikely" + }, + { + "Country": "GB", + "Score": 1.0, + "Likelihood": "Don't know" + }, + { + "Country": "PT", + "Score": 56.0, + "Likelihood": "Likely" + }, + { + "Country": "PT", + "Score": 10.0, + "Likelihood": "Neutral" + }, + { + "Country": "PT", + "Score": 33.0, + "Likelihood": "Unlikely" + }, + { + "Country": "PT", + "Score": 1.0, + "Likelihood": "Don't know" + }, + { + "Country": "OECD average", + "Score": 51.0, + "Likelihood": "Likely" + }, + { + "Country": "OECD average", + "Score": 13.0, + "Likelihood": "Neutral" + }, + { + "Country": "OECD average", + "Score": 33.0, + "Likelihood": "Unlikely" + }, + { + "Country": "OECD average", + "Score": 3.0, + "Likelihood": "Don't know" + }, + { + "Country": "MX", + "Score": 47.0, + "Likelihood": "Likely" + }, + { + "Country": "MX", + "Score": 16.0, + "Likelihood": "Neutral" + }, + { + "Country": "MX", + "Score": 35.0, + "Likelihood": "Unlikely" + }, + { + "Country": "MX", + "Score": 2.0, + "Likelihood": "Don't know" + }, + { + "Country": "FR", + "Score": 46.0, + "Likelihood": "Likely" + }, + { + "Country": "FR", + "Score": 13.0, + "Likelihood": "Neutral" + }, + { + "Country": "FR", + "Score": 40.0, + "Likelihood": "Unlikely" + }, + { + "Country": "FR", + "Score": 1.0, + "Likelihood": "Don't know" + }, + { + "Country": "NL", + "Score": 42.0, + "Likelihood": "Likely" + }, + { + "Country": "NL", + "Score": 16.0, + "Likelihood": "Neutral" + }, + { + "Country": "NL", + "Score": 38.0, + "Likelihood": "Unlikely" + }, + { + "Country": "NL", + "Score": 4.0, + "Likelihood": "Don't know" + }, + { + "Country": "JP", + "Score": 34.0, + "Likelihood": "Likely" + }, + { + "Country": "JP", + "Score": 19.0, + "Likelihood": "Neutral" + }, + { + "Country": "JP", + "Score": 19.0, + "Likelihood": "Unlikely" + }, + { + "Country": "JP", + "Score": 28.0, + "Likelihood": "Don't know" + }, + { + "Country": "AT", + "Score": 31.0, + "Likelihood": "Likely" + }, + { + "Country": "AT", + "Score": 9.0, + "Likelihood": "Neutral" + }, + { + "Country": "AT", + "Score": 59.0, + "Likelihood": "Unlikely" + }, + { + "Country": "AT", + "Score": 1.0, + "Likelihood": "Don't know" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Selected OECD countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding likelihood", + "role": "y" + }, + { + "name": "Likelihood", + "importance": "primary", + "description": "The likelihood of the data point", + "role": "group" + } + ] + } + }, + "1577": { + "metadata": { + "title": "COVID-19 Hits Already Ailing Chinese Smartphone Sales", + "description": "Quarterly smartphone shipments in China, by vendor (in million units)", + "main_insight": "Chinese smartphone sales have been generally declining since Q4 2016, with a noticeable drop in Q1 2020, likely exacerbated by the COVID-19 pandemic. The 'Other' category represents a significant, and decreasing, portion of the market." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Quarter": "Q4 2017", + "Units": 22.0, + "Smartphone Brands": "Apple" + } + ], + "score": 0.98, + "annotation": "The Apple has a maximum value at Q4 2017", + "reason": "The [{'Quarter': 'Q4 2017', 'Units': 22.0, 'Smartphone Brands': 'Apple'}] of Apple has a maximum value of 22.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Quarter": "Q1 '20", + "Units": 12.0, + "Smartphone Brands": "OPPO" + } + ], + "score": 0.96, + "annotation": "The OPPO has a minimum value at Q1 '20", + "reason": "The [{'Quarter': \"Q1 '20\", 'Units': 12.0, 'Smartphone Brands': 'OPPO'}] of OPPO has a mainimum value of 12.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q3 2018", + "Units": 36.0, + "Smartphone Brands": "Other" + } + ], + "score": 0.96, + "annotation": "The Other has the largest proportion in Q3 2018.", + "reason": "The Units of Other in Q3 2018 accounts for 0.27692307692307694, larger than all other Smartphone Brands, of the total of Units of all Smartphone Brands." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q2 2018", + "Units": 37.0, + "Smartphone Brands": "Other" + } + ], + "score": 0.96, + "annotation": "The Other has the largest proportion in Q2 2018.", + "reason": "The Units of Other in Q2 2018 accounts for 0.2803030303030303, larger than all other Smartphone Brands, of the total of Units of all Smartphone Brands." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q4 2018", + "Units": 38.0, + "Smartphone Brands": "Other" + } + ], + "score": 0.95, + "annotation": "The Other has the largest proportion in Q4 2018.", + "reason": "The Units of Other in Q4 2018 accounts for 0.2878787878787879, larger than all other Smartphone Brands, of the total of Units of all Smartphone Brands." + } + ], + "data": { + "data": [ + { + "Quarter": "Q1 2016", + "Units": 45.0, + "Smartphone Brands": "Other" + }, + { + "Quarter": "Q1 2016", + "Units": 15.0, + "Smartphone Brands": "vivo" + }, + { + "Quarter": "Q1 2016", + "Units": 17.0, + "Smartphone Brands": "OPPO" + }, + { + "Quarter": "Q1 2016", + "Units": 17.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q1 2016", + "Units": 12.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q1 2016", + "Units": 13.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q2 2016", + "Units": 47.0, + "Smartphone Brands": "Other" + }, + { + "Quarter": "Q2 2016", + "Units": 18.0, + "Smartphone Brands": "vivo" + }, + { + "Quarter": "Q2 2016", + "Units": 21.0, + "Smartphone Brands": "OPPO" + }, + { + "Quarter": "Q2 2016", + "Units": 18.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q2 2016", + "Units": 10.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q2 2016", + "Units": 9.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q3 2016", + "Units": 52.0, + "Smartphone Brands": "Other" + }, + { + "Quarter": "Q3 2016", + "Units": 21.0, + "Smartphone Brands": "vivo" + }, + { + "Quarter": "Q3 2016", + "Units": 22.0, + "Smartphone Brands": "OPPO" + }, + { + "Quarter": "Q3 2016", + "Units": 17.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q3 2016", + "Units": 11.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q3 2016", + "Units": 8.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q4 2016", + "Units": 62.0, + "Smartphone Brands": "Other" + }, + { + "Quarter": "Q4 2016", + "Units": 25.0, + "Smartphone Brands": "vivo" + }, + { + "Quarter": "Q4 2016", + "Units": 26.0, + "Smartphone Brands": "OPPO" + }, + { + "Quarter": "Q4 2016", + "Units": 23.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q4 2016", + "Units": 10.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q4 2016", + "Units": 16.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q1 2017", + "Units": 42.0, + "Smartphone Brands": "Other" + }, + { + "Quarter": "Q1 2017", + "Units": 19.0, + "Smartphone Brands": "vivo" + }, + { + "Quarter": "Q1 2017", + "Units": 21.0, + "Smartphone Brands": "OPPO" + }, + { + "Quarter": "Q1 2017", + "Units": 20.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q1 2017", + "Units": 9.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q1 2017", + "Units": 10.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q2 2017", + "Units": 44.0, + "Smartphone Brands": "Other" + }, + { + "Quarter": "Q2 2017", + "Units": 17.0, + "Smartphone Brands": "vivo" + }, + { + "Quarter": "Q2 2017", + "Units": 21.0, + "Smartphone Brands": "OPPO" + }, + { + "Quarter": "Q2 2017", + "Units": 23.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q2 2017", + "Units": 14.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q2 2017", + "Units": 8.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q3 2017", + "Units": 47.0, + "Smartphone Brands": "Other" + }, + { + "Quarter": "Q3 2017", + "Units": 21.0, + "Smartphone Brands": "vivo" + }, + { + "Quarter": "Q3 2017", + "Units": 21.0, + "Smartphone Brands": "OPPO" + }, + { + "Quarter": "Q3 2017", + "Units": 22.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q3 2017", + "Units": 16.0, + "Smartphone Brands": "Xiaomi" + }, + { + "Quarter": "Q3 2017", + "Units": 11.0, + "Smartphone Brands": "Apple" + }, + { + "Quarter": "Q4 2017", + "Units": 50.0, + "Smartphone Brands": "Other" + }, + { + "Quarter": "Q4 2017", + "Units": 18.0, + "Smartphone Brands": "vivo" + }, + { + "Quarter": "Q4 2017", + "Units": 19.0, + "Smartphone Brands": "OPPO" + }, + { + "Quarter": "Q4 2017", + "Units": 24.0, + "Smartphone Brands": "Huawei" + }, + { + "Quarter": "Q4 2017", + 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"title": "USA in Connected Car Pole Position", + "description": "Forecasted numbers of connected cars in key markets (2017-2021)", + "main_insight": "The United States and China are projected to have the highest number of connected cars by 2021, with the US holding a slight lead. Both countries show significant growth in connected car adoption from 2017 to 2021." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Units": 35.6, + "Countries": "United States" + }, + { + "Year": 2021, + "Units": 10.82, + "Countries": "France" + } + ], + "score": 1.0, + "annotation": "The Units of United States and France show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between United States and France is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Units": 17.01, + "Countries": "Germany" + }, + { + "Year": 2021, + "Units": 11.05, + "Countries": "Russia" + } + ], + "score": 1.0, + "annotation": "The Units of Germany and Russia show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Germany and Russia is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Units": 11.05, + "Countries": "Russia" + }, + { + "Year": 2021, + "Units": 10.82, + "Countries": "France" + } + ], + "score": 1.0, + "annotation": "The Units of Russia and France show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Russia and France is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Units": 35.6, + "Countries": "United States" + }, + { + "Year": 2021, + "Units": 17.01, + "Countries": "Germany" + } + ], + "score": 1.0, + "annotation": "The Units of United States and Germany show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between United States and Germany is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Units": 17.01, + "Countries": "Germany" + }, + { + "Year": 2021, + "Units": 10.82, + "Countries": "France" + } + ], + "score": 1.0, + "annotation": "The Units of Germany and France show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Germany and France is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2017, + "Units": 12.86, + "Countries": "United States" + }, + { + "Year": 2017, + "Units": 3.82, + "Countries": "China" + }, + { + "Year": 2017, + "Units": 3.79, + "Countries": "Germany" + }, + { + "Year": 2017, + "Units": 2.62, + "Countries": "Russia" + }, + { + "Year": 2017, + "Units": 2.86, + "Countries": "France" + }, + { + "Year": 2017, + "Units": 2.75, + "Countries": "United Kingdom" + }, + { + "Year": 2018, + "Units": 18.32, + "Countries": "United States" + }, + { + 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} + ] + } + }, + "1572": { + "metadata": { + "title": "China's Covid Lockdowns Hit Industrial Production", + "description": "Manufacturing purchasing managers indices for China by Caixin and the National Bureau of Statistics", + "main_insight": "China's industrial production, as measured by both Caixin and NBS PMIs, experienced a significant drop in early 2020, followed by a recovery and a second, smaller dip towards the end of the observation period in early 2022." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 46.0, + "PMI Source": "Caixin PMI" + }, + { + "Year": 2022, + "Units": 47.4, + "PMI Source": "NBS PMI" + } + ], + "score": 0.99, + "annotation": "The Units of Caixin PMI and NBS PMI show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Caixin PMI and NBS PMI is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2019, + "Units": 50.2, + "PMI Source": "Caixin PMI" + }, + { + "Year": 2019, + "Units": 49.4, + "PMI Source": "NBS PMI" + }, + { + "Year": 2020, + "Units": 51.9, + "PMI Source": "Caixin PMI" + }, + { + "Year": 2020, + "Units": 50.6, + "PMI Source": "NBS PMI" + }, + { + "Year": 2021, + "Units": 50.9, + "PMI Source": "Caixin PMI" + }, + { + "Year": 2021, + "Units": 50.1, + "PMI Source": "NBS PMI" + }, + { + "Year": 2022, + "Units": 46.0, + "PMI Source": "Caixin PMI" + }, + { + "Year": 2022, + "Units": 47.4, + "PMI Source": "NBS PMI" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the PMI measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding pmi source", + "role": "y" + }, + { + "name": "PMI Source", + "importance": "primary", + "description": "The pmi source of the data point", + "role": "group" + } + ] + } + }, + "1500": { + "metadata": { + "title": "Korea and Japan: Unequal Trade Partners?", + "description": "Total exports from Korea to Japan/from Japan to Korea between 1990 and 2018", + "main_insight": "Korea's exports to Japan have significantly decreased as a percentage of total exports since 1990, while Japan's exports to Korea have remained relatively stable." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2018, + "Units": 4.8, + "Direction": "Korea to Japan" + }, + { + "Year": 2018, + "Units": 6.8, + "Direction": "Japan to Korea" + } + ], + "score": 0.45, + "annotation": "The Units of Korea to Japan and Japan to Korea show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Korea to Japan and Japan to Korea is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 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+ "Direction": "Japan to Korea" + }, + { + "Year": 2018, + "Units": 4.8, + "Direction": "Korea to Japan" + }, + { + "Year": 2018, + "Units": 6.8, + "Direction": "Japan to Korea" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year between 1990 and 2018", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding direction", + "role": "y" + }, + { + "name": "Direction", + "importance": "primary", + "description": "The direction of the data point", + "role": "group" + } + ] + } + }, + "1102": { + "metadata": { + "title": "In Times of Crisis, Americans Binged \"Comfort Food TV\"", + "description": "Most streamed TV series in the United States in 2020 (in total minutes streamed)", + "main_insight": "In 2020, Americans streamed a large amount of content, with 'The Office' being the most streamed show at 57.1 billion minutes, largely comprised of acquired content." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "TV Series": "Shameless", + "Value": 18.2, + "Streaming Metrics": "Total Minutes Streamed (billions)" + }, + { + "TV Series": "Shameless", + "Value": 122, + "Streaming Metrics": "Number of Episodes" + } + ], + "score": 0.39, + "annotation": "The Value of Total Minutes Streamed (billions) and Number of Episodes show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Value between Total Minutes Streamed (billions) and Number of Episodes is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "TV Series": "The Office", + "Value": 57.1, + "Streaming Metrics": "Total Minutes Streamed (billions)" + }, + { + "TV Series": "The Office", + "Value": 57.1, + "Streaming Metrics": "Acquired Content (billions)" + }, + { + "TV Series": "The Office", + "Value": "Netflix", + "Streaming Metrics": "Streaming Provider" + }, + { + "TV Series": "The Office", + "Value": 192, + "Streaming Metrics": "Number of Episodes" + }, + { + "TV Series": "Grey's Anatomy", + "Value": 39.4, + "Streaming Metrics": "Total Minutes Streamed (billions)" + }, + { + "TV Series": "Grey's Anatomy", + "Value": 39.4, + "Streaming Metrics": "Acquired Content (billions)" + }, + { + "TV Series": "Grey's Anatomy", + "Value": "Netflix", + "Streaming Metrics": "Streaming Provider" + }, + { + "TV Series": "Grey's Anatomy", + "Value": 366, + "Streaming Metrics": "Number of Episodes" + }, + { + "TV Series": "Criminal Minds", + "Value": 35.4, + "Streaming Metrics": "Total Minutes Streamed (billions)" + }, + { + "TV Series": "Criminal Minds", + "Value": 35.4, + "Streaming Metrics": "Acquired Content (billions)" + }, + { + "TV Series": "Criminal Minds", + "Value": "Netflix", + "Streaming Metrics": "Streaming Provider" + }, + { + "TV Series": "Criminal Minds", + "Value": 277, + "Streaming Metrics": "Number of Episodes" + }, + { + "TV Series": "Ozark", + "Value": 30.5, + "Streaming Metrics": "Total Minutes Streamed (billions)" + }, + { + "TV Series": "Ozark", + "Value": 30.5, + "Streaming Metrics": "Original Content (billions)" + }, + { + "TV Series": "Ozark", + "Value": "Netflix", + "Streaming Metrics": "Streaming Provider" + }, + { + "TV Series": "Ozark", + "Value": 30, + "Streaming Metrics": "Number of Episodes" + }, + { + "TV Series": "NCIS", + "Value": 28.1, + "Streaming Metrics": "Total Minutes Streamed (billions)" + }, + { + "TV Series": "NCIS", + "Value": 28.1, + "Streaming Metrics": "Acquired Content (billions)" + }, + { + "TV Series": "NCIS", + "Value": "Netflix", + "Streaming Metrics": "Streaming Provider" + }, + { + "TV Series": "NCIS", + "Value": 353, + "Streaming Metrics": "Number of Episodes" + }, + { + "TV Series": "Schitt's Creek", + "Value": 23.8, + "Streaming Metrics": "Total Minutes Streamed (billions)" + }, + { + "TV Series": "Schitt's Creek", + "Value": 23.8, + "Streaming Metrics": "Acquired Content (billions)" + }, + { + "TV Series": "Schitt's Creek", + "Value": "Netflix", + "Streaming Metrics": "Streaming Provider" + }, + { + "TV Series": "Schitt's Creek", + "Value": 70, + "Streaming Metrics": "Number of Episodes" + }, + { + "TV Series": "Lucifer", + "Value": 19.0, + "Streaming Metrics": "Total Minutes Streamed (billions)" + }, + { + "TV Series": "Lucifer", + "Value": 19.0, + "Streaming Metrics": "Original Content (billions)" + }, + { + "TV Series": "Lucifer", + "Value": "Netflix", + "Streaming Metrics": "Streaming Provider" + }, + { + "TV Series": "Lucifer", + "Value": 75, + "Streaming Metrics": "Number of Episodes" + }, + { + "TV Series": "Shameless", + "Value": 18.2, + "Streaming Metrics": "Total Minutes Streamed (billions)" + }, + { + "TV Series": "Shameless", + "Value": 18.2, + "Streaming Metrics": "Acquired Content (billions)" + }, + { + "TV Series": "Shameless", + "Value": "Netflix", + "Streaming Metrics": "Streaming Provider" + }, + { + "TV Series": "Shameless", + "Value": 122, + "Streaming Metrics": "Number of Episodes" + } + ], + "columns": [ + { + "name": "TV Series", + "importance": "primary", + "description": "Name of the TV series", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding streaming metrics", + "role": "y" + }, + { + "name": "Streaming Metrics", + "importance": "primary", + "description": "The streaming metrics of the data point", + "role": "group" + } + ] + } + }, + "1987": { + "metadata": { + "title": "Google Still Global M&A Leader", + "description": "Number of tech M&As between 2012-2016 by company", + "main_insight": "Google was the global leader in tech M&As between 2012 and 2016, peaking in 2014. Yahoo was a close second in 2013 and 2014, but decreased significantly afterwards." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2016, + "Score": 19, + "Tech Companies": "Google" + }, + { + "Year": 2016, + "Score": 11, + "Tech Companies": "Salesforce" + } + ], + "score": 0.61, + "annotation": "The Score of Google and Salesforce show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Google and Salesforce is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2016, + "Score": 7, + "Tech Companies": "Facebook" + }, + { + "Year": 2016, + "Score": 11, + "Tech Companies": "Salesforce" + } + ], + "score": 0.45, + "annotation": "The Score of Facebook and Salesforce show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Facebook and Salesforce is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2016, + "Score": 19, + "Tech Companies": "Google" + }, + { + "Year": 2016, + "Score": 12, + "Tech Companies": "Apple" + } + ], + "score": 0.28, + "annotation": "The Score of Google and Apple show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Google and Apple is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2016, + "Score": 12, + "Tech Companies": "Apple" + }, + { + "Year": 2016, + "Score": 11, + "Tech Companies": "Salesforce" + } + ], + "score": 0.16, + "annotation": "The Score of Apple and Salesforce show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Apple and Salesforce is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2016, + "Score": 19, + "Tech Companies": "Google" + }, + { + "Year": 2016, + "Score": 7, + "Tech Companies": "Facebook" + } + ], + "score": 0.15, + "annotation": "The Score of Google and Facebook show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Google and Facebook is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2012, + "Score": 16, + "Tech Companies": "Google" + }, + { + "Year": 2012, + "Score": 3, + "Tech Companies": "Yahoo" + }, + { + "Year": 2012, + "Score": 13, + "Tech Companies": "Facebook" + }, + { + "Year": 2012, + "Score": 6, + "Tech Companies": "Apple" + }, + { + "Year": 2012, + "Score": 6, + "Tech Companies": "Salesforce" + }, + { + "Year": 2013, + "Score": 19, + "Tech Companies": "Google" + }, + { + "Year": 2013, + "Score": 30, + "Tech Companies": "Yahoo" + }, + { + "Year": 2013, + "Score": 14, + "Tech Companies": "Facebook" + }, + { + "Year": 2013, + "Score": 15, + "Tech Companies": "Apple" + }, + { + "Year": 2013, + "Score": 4, + "Tech Companies": "Salesforce" + }, + { + "Year": 2014, + "Score": 35, + "Tech Companies": "Google" + }, + { + "Year": 2014, + "Score": 19, + "Tech Companies": "Yahoo" + }, + { + "Year": 2014, + "Score": 11, + "Tech Companies": "Facebook" + }, + { + "Year": 2014, + "Score": 12, + "Tech Companies": "Apple" + }, + { + "Year": 2014, + "Score": 1, + "Tech Companies": "Salesforce" + }, + { + "Year": 2015, + "Score": 16, + "Tech Companies": "Google" + }, + { + "Year": 2015, + "Score": 4, + "Tech Companies": "Yahoo" + }, + { + "Year": 2015, + "Score": 6, + "Tech Companies": "Facebook" + }, + { + "Year": 2015, + "Score": 11, + "Tech Companies": "Apple" + }, + { + "Year": 2015, + "Score": 5, + "Tech Companies": "Salesforce" + }, + { + "Year": 2016, + "Score": 19, + "Tech Companies": "Google" + }, + { + "Year": 2016, + "Score": 7, + "Tech Companies": "Facebook" + }, + { + "Year": 2016, + "Score": 12, + "Tech Companies": "Apple" + }, + { + "Year": 2016, + "Score": 11, + "Tech Companies": "Salesforce" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of M&A Activity", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding tech companies", + "role": "y" + }, + { + "name": "Tech Companies", + "importance": "primary", + "description": "The tech companies of the data point", + "role": "group" + } + ] + } + }, + "994": { + "metadata": { + "title": "Japanese Enthusiastic Since Abe's Election Victory", + "description": "Public sentiment on economic affairs in Japan", + "main_insight": "Public sentiment regarding Japan's economy significantly improved in 2013, particularly regarding expectations for economic improvement in the next 12 months, coinciding with Abe's election victory." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2013, + "Units": 30.0, + "Sentiment": "Satisfied with Japan's direction" + }, + { + "Year": 2013, + "Units": 40.0, + "Sentiment": "Economic Improvement will occur over the next 12 months" + } + ], + "score": 0.81, + "annotation": "The Units of Satisfied with Japan's direction and Economic Improvement will occur over the next 12 months show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Satisfied with Japan's direction and Economic Improvement will occur over the next 12 months is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2013, + "Units": 27.0, + "Sentiment": "Economy is in good shape" + }, + { + "Year": 2013, + "Units": 40.0, + "Sentiment": "Economic Improvement will occur over the next 12 months" + } + ], + "score": 0.79, + "annotation": "The Units of Economy is in good shape and Economic Improvement will occur over the next 12 months show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Economy is in good shape and Economic Improvement will occur over the next 12 months is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2013, + "Units": 30.0, + "Sentiment": "Satisfied with Japan's direction" + }, + { + "Year": 2013, + "Units": 27.0, + "Sentiment": "Economy is in good shape" + } + ], + "score": 0.76, + "annotation": "The Units of Satisfied with Japan's direction and Economy is in good shape show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Satisfied with Japan's direction and Economy is in good shape is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2008, + "Units": 23.0, + "Sentiment": "Satisfied with Japan's direction" + }, + { + "Year": 2008, + "Units": 13.0, + "Sentiment": "Economy is in good shape" + }, + { + "Year": 2008, + "Units": 5.0, + "Sentiment": "Economic Improvement will occur over the next 12 months" + }, + { + "Year": 2009, + "Units": 25.0, + "Sentiment": "Satisfied with Japan's direction" + }, + { + "Year": 2009, + "Units": 10.0, + "Sentiment": "Economy is in good shape" + }, + { + "Year": 2009, + "Units": 18.0, + "Sentiment": "Economic Improvement will occur over the next 12 months" + }, + { + "Year": 2010, + "Units": 20.0, + "Sentiment": "Satisfied with Japan's direction" + }, + { + "Year": 2010, + "Units": 13.0, + "Sentiment": "Economy is in good shape" + }, + { + "Year": 2010, + "Units": 13.0, + "Sentiment": "Economic Improvement will occur over the next 12 months" + }, + { + "Year": 2011, + "Units": 25.0, + "Sentiment": "Satisfied with Japan's direction" + }, + { + "Year": 2011, + "Units": 10.0, + "Sentiment": "Economy is in good shape" + }, + { + "Year": 2011, + "Units": 19.0, + "Sentiment": "Economic Improvement will occur over the next 12 months" + }, + { + "Year": 2012, + "Units": 20.0, + "Sentiment": "Satisfied with Japan's direction" + }, + { + "Year": 2012, + "Units": 8.0, + "Sentiment": "Economy is in good shape" + }, + { + "Year": 2012, + "Units": 15.0, + "Sentiment": "Economic Improvement will occur over the next 12 months" + }, + { + "Year": 2013, + "Units": 30.0, + "Sentiment": "Satisfied with Japan's direction" + }, + { + "Year": 2013, + "Units": 27.0, + "Sentiment": "Economy is in good shape" + }, + { + "Year": 2013, + "Units": 40.0, + "Sentiment": "Economic Improvement will occur over the next 12 months" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "65": { + "metadata": { + "title": "Was the Latest Jobs Report Too Good to Be True?", + "description": "Seasonally adjusted unemployment rate in the United States", + "main_insight": "The official US unemployment data starting March 2020 was subject to a misclassification error, leading to an underestimation of the true unemployment rate. The adjusted rate shows a much steeper increase in April and May." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "May", + "Score": 13.3, + "Adjustment Type": "As reported" + }, + { + "Month": "May", + "Score": 16.3, + "Adjustment Type": "Adjusted for misclassification error" + } + ], + "score": 1.0, + "annotation": "The Score of As reported and Adjusted for misclassification error show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between As reported and Adjusted for misclassification error is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "May", + "Score": 13.3, + "Adjustment Type": "As reported" + } + ], + "score": 0.94, + "annotation": "The As reported shows a sudden jump in Score from March to May.", + "reason": "The Score for As reported increases from 4.4 at March to 13.3 at May, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "May", + "Score": 16.3, + "Adjustment Type": "Adjusted for misclassification error" + } + ], + "score": 0.91, + "annotation": "The Adjusted for misclassification error shows a sudden jump in Score from March to May.", + "reason": "The Score for Adjusted for misclassification error increases from 5.4 at March to 16.3 at May, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "January", + "Score": 3.6, + "Adjustment Type": "As reported" + }, + { + "Month": "January", + "Score": 3.6, + "Adjustment Type": "Adjusted for misclassification error" + } + ], + "score": 0.8, + "annotation": "The As reported, Adjusted for misclassification error all have the largest proportion in January.", + "reason": "The Score of As reported, Adjusted for misclassification error in January all account for 0.5, larger than all other Adjustment Type, of the total of Score of all Adjustment Type." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "February", + "Score": 3.5, + "Adjustment Type": "As reported" + }, + { + "Month": "February", + "Score": 3.5, + "Adjustment Type": "Adjusted for misclassification error" + } + ], + "score": 0.8, + "annotation": "The As reported, Adjusted for misclassification error all have the largest proportion in February.", + "reason": "The Score of As reported, Adjusted for misclassification error in February all account for 0.5, larger than all other Adjustment Type, of the total of Score of all Adjustment Type." + } + ], + "data": { + "data": [ + { + "Month": "January", + "Score": 3.6, + "Adjustment Type": "As reported" + }, + { + "Month": "January", + "Score": 3.6, + "Adjustment Type": "Adjusted for misclassification error" + }, + { + "Month": "February", + "Score": 3.5, + "Adjustment Type": "As reported" + }, + { + "Month": "February", + "Score": 3.5, + "Adjustment Type": "Adjusted for misclassification error" + }, + { + "Month": "March", + "Score": 4.4, + "Adjustment Type": "As reported" + }, + { + "Month": "March", + "Score": 5.4, + "Adjustment Type": "Adjusted for misclassification error" + }, + { + "Month": "April", + "Score": 14.7, + "Adjustment Type": "As reported" + }, + { + "Month": "April", + "Score": 19.5, + "Adjustment Type": "Adjusted for misclassification error" + }, + { + "Month": "May", + "Score": 13.3, + "Adjustment Type": "As reported" + }, + { + "Month": "May", + "Score": 16.3, + "Adjustment Type": "Adjusted for misclassification error" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month in 2020", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding adjustment type", + "role": "y" + }, + { + "name": "Adjustment Type", + "importance": "primary", + "description": "The adjustment type of the data point", + "role": "group" + } + ] + } + }, + "1846": { + "metadata": { + "title": "Google Chrome Leaves Competition in the Dust", + "description": "Global market share of web browsers in June 2013", + "main_insight": "Chrome has the largest market share across all regions, significantly outperforming other browsers like Firefox, IE, and Safari." + }, + "chart_type": "Multiple Pie Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Region": "South America", + "Units": 1.0, + "Browsers": "Other" + } + ], + "score": 0.95, + "annotation": "The Other has the smallest proportion in South America.", + "reason": "The Units of Other in South America accounts for 0.01, smaller than all other Browsers, of the total of Units of all Browsers." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "North America", + "Units": 35.0, + "Browsers": "Chrome" + } + ], + "score": 0.93, + "annotation": "The Chrome has the largest proportion in North America.", + "reason": "The Units of Chrome in North America accounts for 0.35, larger than all other Browsers, of the total of Units of all Browsers." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Oceania", + "Units": 35.0, + "Browsers": "Chrome" + } + ], + "score": 0.93, + "annotation": "The Chrome has the largest proportion in Oceania.", + "reason": "The Units of Chrome in Oceania accounts for 0.35, larger than all other Browsers, of the total of Units of all Browsers." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Region": "South America", + "Units": 1.0, + "Browsers": "Other" + } + ], + "score": 0.93, + "annotation": "The Other has a minimum value at South America", + "reason": "The [{'Region': 'South America', 'Units': 1.0, 'Browsers': 'Other'}] of Other has a mainimum value of 1.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Asia", + "Units": 4.0, + "Browsers": "Other" + } + ], + "score": 0.93, + "annotation": "The Other shows a sudden jump in Units from South America to Asia.", + "reason": "The Units for Other increases from 1.0 at South America to 4.0 at Asia, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Region": "North America", + "Units": 35.0, + "Browsers": "Chrome" + }, + { + "Region": "North America", + "Units": 17.0, + "Browsers": "Firefox" + }, + { + "Region": "North America", + "Units": 32.0, + "Browsers": "IE" + }, + { + "Region": "North America", + "Units": 11.0, + "Browsers": "Safari" + }, + { + "Region": "North America", + "Units": 5.0, + "Browsers": "Other" + }, + { + "Region": "Europe", + "Units": 38.0, + "Browsers": "Chrome" + }, + { + "Region": "Europe", + "Units": 27.0, + "Browsers": "Firefox" + }, + { + "Region": "Europe", + "Units": 21.0, + "Browsers": "IE" + }, + { + "Region": "Europe", + "Units": 9.0, + "Browsers": "Safari" + }, + { + "Region": "Europe", + "Units": 5.0, + "Browsers": "Other" + }, + { + "Region": "Asia", + "Units": 49.0, + "Browsers": "Chrome" + }, + { + "Region": "Asia", + "Units": 17.0, + "Browsers": "Firefox" + }, + { + "Region": "Asia", + "Units": 26.0, + "Browsers": "IE" + }, + { + "Region": "Asia", + "Units": 4.0, + "Browsers": "Safari" + }, + { + "Region": "Asia", + "Units": 4.0, + "Browsers": "Other" + }, + { + "Region": "Worldwide", + "Units": 43.0, + "Browsers": "Chrome" + }, + { + "Region": "Worldwide", + "Units": 20.0, + "Browsers": "Firefox" + }, + { + "Region": "Worldwide", + "Units": 25.0, + "Browsers": "IE" + }, + { + "Region": "Worldwide", + "Units": 8.0, + "Browsers": "Safari" + }, + { + "Region": "Worldwide", + "Units": 4.0, + "Browsers": "Other" + }, + { + "Region": "South America", + "Units": 62.0, + "Browsers": "Chrome" + }, + { + "Region": "South America", + "Units": 16.0, + "Browsers": "Firefox" + }, + { + "Region": "South America", + "Units": 19.0, + "Browsers": "IE" + }, + { + "Region": "South America", + "Units": 2.0, + "Browsers": "Safari" + }, + { + "Region": "South America", + "Units": 1.0, + "Browsers": "Other" + }, + { + "Region": "Africa", + "Units": 43.0, + "Browsers": "Chrome" + }, + { + "Region": "Africa", + "Units": 35.0, + "Browsers": "Firefox" + }, + { + "Region": "Africa", + "Units": 16.0, + "Browsers": "IE" + }, + { + "Region": "Africa", + "Units": 2.0, + "Browsers": "Safari" + }, + { + "Region": "Africa", + "Units": 4.0, + "Browsers": "Other" + }, + { + "Region": "Oceania", + "Units": 35.0, + "Browsers": "Chrome" + }, + { + "Region": "Oceania", + "Units": 17.0, + "Browsers": "Firefox" + }, + { + "Region": "Oceania", + "Units": 26.0, + "Browsers": "IE" + }, + { + "Region": "Oceania", + "Units": 18.0, + "Browsers": "Safari" + }, + { + "Region": "Oceania", + "Units": 4.0, + "Browsers": "Other" + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Geographic region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding browsers", + "role": "y" + }, + { + "name": "Browsers", + "importance": "primary", + "description": "The browsers of the data point", + "role": "group" + } + ] + } + }, + "1373": { + "metadata": { + "title": "The Natural Disasters That Inflict The Most Economic Damage", + "description": "Economic damage by disaster type and region from 1995 to 2015", + "main_insight": "Storms cause the most economic damage among disaster types, and the Americas and Asia experience the highest absolute losses by continent." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [], + "data": { + "data": [ + { + "Disaster Type": "Storm", + "Score": 1011.0, + "Economic Data": "Economic Damage (billion USD)" + }, + { + "Disaster Type": "Storm", + "Score": "Americas", + "Economic Data": "Continent" + }, + { + "Disaster Type": "Storm", + "Score": 870.0, + "Economic Data": "Economic Damage by Continent (billion USD)" + }, + { + "Disaster Type": "Geophysical", + "Score": 763.0, + "Economic Data": "Economic Damage (billion USD)" + }, + { + "Disaster Type": "Geophysical", + "Score": "Asia", + "Economic Data": "Continent" + }, + { + "Disaster Type": "Geophysical", + "Score": 709.0, + "Economic Data": "Economic Damage by Continent (billion USD)" + }, + { + "Disaster Type": "Flood", + "Score": 662.0, + "Economic Data": "Economic Damage (billion USD)" + }, + { + "Disaster Type": "Flood", + "Score": "Europe", + "Economic Data": "Continent" + }, + { + "Disaster Type": "Flood", + "Score": 262.0, + "Economic Data": "Economic Damage by Continent (billion USD)" + }, + { + "Disaster Type": "Weather related-other", + "Score": 119.0, + "Economic Data": "Economic Damage (billion USD)" + }, + { + "Disaster Type": "Weather related-other", + "Score": "Oceania", + "Economic Data": "Continent" + }, + { + "Disaster Type": "Weather related-other", + "Score": 40.0, + "Economic Data": "Economic Damage by Continent (billion USD)" + }, + { + "Disaster Type": "Drought", + "Score": 100.0, + "Economic Data": "Economic Damage (billion USD)" + }, + { + "Disaster Type": "Drought", + "Score": "Africa", + "Economic Data": "Continent" + }, + { + "Disaster Type": "Drought", + "Score": 10.0, + "Economic Data": "Economic Damage by Continent (billion USD)" + } + ], + "columns": [ + { + "name": "Disaster Type", + "importance": "primary", + "description": "Type of natural disaster", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding economic data", + "role": "y" + }, + { + "name": "Economic Data", + "importance": "primary", + "description": "The economic data of the data point", + "role": "group" + } + ] + } + }, + "1313": { + "metadata": { + "title": "How Does Tech Help Travelers?", + "description": "Share of respondents who feel the following benefit them when booking online/via app", + "main_insight": "The most significant benefit of using technology for travel booking is that it saves time, with 67% of respondents agreeing. Giving more options and saving money are tied for the second most significant benefits." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Benefit": "Provides better customer experience", + "Percentage": 32.0 + } + ], + "score": 0.81, + "annotation": "The shows a sudden jump in Percentage from Other to Provides better customer experience.", + "reason": "The Percentage for increases from 2.0 at Other to 32.0 at Provides better customer experience, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Benefit": "I do not like booking travel online or via apps", + "Percentage": 1.0 + } + ], + "score": 0.74, + "annotation": "The has a minimum value at I do not like booking travel online or via apps", + "reason": "The [{'Benefit': 'I do not like booking travel online or via apps', 'Percentage': 1.0}] of has a mainimum value of 1.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Benefit": "Saves time", + "Percentage": 67.0 + } + ], + "score": 0.7, + "annotation": "The has a maximum value at Saves time", + "reason": "The [{'Benefit': 'Saves time', 'Percentage': 67.0}] of has a maximum value of 67.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Benefit": "Saves time", + "Percentage": 67.0 + }, + { + "Benefit": "Tech gives me more options", + "Percentage": 54.0 + }, + { + "Benefit": "Saves money", + "Percentage": 54.0 + }, + { + "Benefit": "Prevents me from having to talk to a person on the phone", + "Percentage": 36.0 + }, + { + "Benefit": "Provides better customer experience", + "Percentage": 32.0 + }, + { + "Benefit": "Other", + "Percentage": 2.0 + }, + { + "Benefit": "I do not like booking travel online or via apps", + "Percentage": 1.0 + } + ], + "columns": [ + { + "name": "Benefit", + "importance": "primary", + "description": "The perceived benefit of online/app travel booking", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents who feel the benefit", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2119": { + "metadata": { + "title": "The EU Has Come a Long Way with Internet Access", + "description": "Share of people who had used the internet in the past 3 months, by country", + "main_insight": "Internet usage in the EU has significantly increased from 2002 to 2018, with most countries showing substantial growth. The UK had the highest internet usage in 2018." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Germany", + "Score": 49.0, + "Year": "2002" + } + ], + "score": 0.94, + "annotation": "The 2002 shows a sudden jump in Score from Spain to Germany.", + "reason": "The Score for 2002 increases from 20.0 at Spain to 49.0 at Germany, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "EU 28", + "Score": 85.0, + "Year": "2018" + }, + { + "Country": "EU 28", + "Score": 61.0, + "Year": "2008" + } + ], + "score": 0.91, + "annotation": "The Score of 2018 and 2008 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between 2018 and 2008 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "United Kingdom", + "Score": 95.0, + "Year": "2018" + } + ], + "score": 0.88, + "annotation": "The 2018 has the largest proportion in United Kingdom.", + "reason": "The Score of 2018 in United Kingdom accounts for 0.4185022026431718, larger than all other Year, of the total of Score of all Year." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Germany", + "Score": 92.0, + "Year": "2018" + } + ], + "score": 0.88, + "annotation": "The 2018 has the largest proportion in Germany.", + "reason": "The Score of 2018 in Germany accounts for 0.42592592592592593, larger than all other Year, of the total of Score of all Year." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "2018", + "Country": "total", + "Score": 446.0 + } + ], + "score": 0.86, + "annotation": "The total value of 2018 has the largest proportion.", + "reason": "The total value of Score of 2018 accounts for 0.49173098125689085, larger than all other Year, of the total of Score of all Year." + } + ], + "data": { + "data": [ + { + "Country": "Spain", + "Score": 86.0, + "Year": "2018" + }, + { + "Country": "Spain", + "Score": 56.0, + "Year": "2008" + }, + { + "Country": "Spain", + "Score": 20.0, + "Year": "2002" + }, + { + "Country": "Germany", + "Score": 92.0, + "Year": "2018" + }, + { + "Country": "Germany", + "Score": 75.0, + "Year": "2008" + }, + { + "Country": "Germany", + "Score": 49.0, + "Year": "2002" + }, + { + "Country": "United Kingdom", + "Score": 95.0, + "Year": "2018" + }, + { + "Country": "United Kingdom", + "Score": 76.0, + "Year": "2008" + }, + { + "Country": "United Kingdom", + "Score": 56.0, + "Year": "2002" + }, + { + "Country": "France", + "Score": 88.0, + "Year": "2018" + }, + { + "Country": "France", + "Score": 68.0, + "Year": "2008" + }, + { + "Country": "EU 28", + "Score": 85.0, + "Year": "2018" + }, + { + "Country": "EU 28", + "Score": 61.0, + "Year": "2008" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country or region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "1431": { + "metadata": { + "title": "Clinton & Trump Supporters See The World Very Differently", + "description": "Opinions of Trump & Clinton supporters on various issues (%)", + "main_insight": "The chart highlights the significant differences in opinions between Clinton and Trump supporters across a range of social and political issues. Trump supporters tend to hold more conservative views, while Clinton supporters hold more liberal views." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Issue": "Free trade agreements have been a ... for the U.S. Bad thing", + "Score": 59.0, + "Political Groups": "Clinton Supporters - Response 2" + } + ], + "score": 0.92, + "annotation": "The Clinton Supporters - Response 2 has the largest proportion in Free trade agreements have been a ... for the U.S. Bad thing.", + "reason": "The Score of Clinton Supporters - Response 2 in Free trade agreements have been a ... for the U.S. Bad thing accounts for 0.35542168674698793, larger than all other Political Groups, of the total of Score of all Political Groups." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Issue": "Free trade agreements have been a ... for the U.S. Good thing", + "Score": 59.0, + "Political Groups": "Clinton Supporters - Response 2" + } + ], + "score": 0.92, + "annotation": "The Clinton Supporters - Response 2 has the largest proportion in Free trade agreements have been a ... for the U.S. Good thing.", + "reason": "The Score of Clinton Supporters - Response 2 in Free trade agreements have been a ... for the U.S. Good thing accounts for 0.35542168674698793, larger than all other Political Groups, of the total of Score of all Political Groups." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Issue": "Can the government afford to do more to assist the needy? Can't afford to do more", + "Score": 72.0, + "Political Groups": "Clinton Supporters - Response 2" + } + ], + "score": 0.91, + "annotation": "The Clinton Supporters - Response 2 has the largest proportion in Can the government afford to do more to assist the needy? Can't afford to do more.", + "reason": "The Score of Clinton Supporters - Response 2 in Can the government afford to do more to assist the needy? Can't afford to do more accounts for 0.38095238095238093, larger than all other Political Groups, of the total of Score of all Political Groups." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Issue": "Can the government afford to do more to assist the needy? Should do more", + "Score": 72.0, + "Political Groups": "Clinton Supporters - Response 2" + } + ], + "score": 0.91, + "annotation": "The Clinton Supporters - Response 2 has the largest proportion in Can the government afford to do more to assist the needy? Should do more.", + "reason": "The Score of Clinton Supporters - Response 2 in Can the government afford to do more to assist the needy? Should do more accounts for 0.38095238095238093, larger than all other Political Groups, of the total of Score of all Political Groups." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Political Groups": "Trump Supporters - Response 1", + "Issue": "total", + "Score": 822.0 + } + ], + "score": 0.91, + "annotation": "The total value of Trump Supporters - Response 1 has the largest proportion.", + "reason": "The total value of Score of Trump Supporters - Response 1 accounts for 0.388468809073724, larger than all other Political Groups, of the total of Score of all Political Groups." + } + ], + "data": { + "data": [ + { + "Issue": "Compared with 50 years ago, life in America today is... Worse", + "Score": 19.0, + "Political Groups": "Clinton Supporters - Response 1" + }, + { + "Issue": "Compared with 50 years ago, life in America today is... Worse", + "Score": 59.0, + "Political Groups": "Clinton Supporters - Response 2" + }, + { + "Issue": "Compared with 50 years ago, life in America today is... Worse", + "Score": 81.0, + "Political Groups": "Trump Supporters - Response 1" + }, + { + "Issue": "Compared with 50 years ago, life in America today is... Worse", + "Score": 11.0, + "Political Groups": "Trump Supporters - Response 2" + }, + { + "Issue": "Compared with 50 years ago, life in America today is... Better", + "Score": 19.0, + "Political Groups": "Clinton Supporters - Response 1" + }, + { + "Issue": "Compared with 50 years ago, life in America today is... Better", + "Score": 59.0, + "Political Groups": "Clinton Supporters - Response 2" + }, + { + "Issue": "Compared with 50 years ago, life in America today is... Better", + "Score": 81.0, + "Political Groups": "Trump Supporters - Response 1" + }, + { + "Issue": "Compared with 50 years ago, life in America today is... Better", + "Score": 11.0, + "Political Groups": "Trump Supporters - Response 2" + }, + { + "Issue": "The future of the next generation of Americans will be ... compared with life today Worse", + "Score": 30.0, + "Political Groups": "Clinton Supporters - Response 1" + }, + { + "Issue": "The future of the next generation of Americans will be ... compared with life today Worse", + "Score": 38.0, + "Political Groups": "Clinton Supporters - Response 2" + }, + { + "Issue": "The future of the next generation of Americans will be ... compared with life today Worse", + "Score": 68.0, + "Political Groups": "Trump Supporters - Response 1" + }, + { + "Issue": "The future of the next generation of Americans will be ... compared with life today Worse", + "Score": 11.0, + "Political Groups": "Trump Supporters - Response 2" + }, + { + "Issue": "The future of the next generation of Americans will be ... compared with life today Better", + "Score": 30.0, + "Political Groups": "Clinton Supporters - Response 1" + }, + { + "Issue": "The future of the next generation of Americans will be ... compared with life today Better", + "Score": 38.0, + "Political Groups": "Clinton Supporters - Response 2" + }, + { + "Issue": "The future of the next generation of Americans will be ... compared with life today Better", + "Score": 68.0, + "Political Groups": "Trump Supporters - Response 1" + }, + { + "Issue": "The future of the next generation of Americans will be ... compared with life today Better", + "Score": 11.0, + "Political Groups": "Trump Supporters - Response 2" + }, + { + "Issue": "As part of Government's anti-terrorism efforts, Muslims living in the U.S. should... Be subject to more scrutiny", + "Score": 14.0, + "Political Groups": "Clinton Supporters - Response 1" + }, + { + "Issue": "As part of Government's anti-terrorism efforts, Muslims living in the U.S. should... Be subject to more scrutiny", + "Score": 82.0, + "Political Groups": "Clinton Supporters - Response 2" + }, + { + "Issue": "As part of Government's anti-terrorism efforts, Muslims living in the U.S. should... Be subject to more scrutiny", + "Score": 57.0, + "Political Groups": "Trump Supporters - Response 1" + }, + { + "Issue": "As part of Government's anti-terrorism efforts, Muslims living in the U.S. should... Be subject to more scrutiny", + "Score": 37.0, + "Political Groups": "Trump Supporters - Response 2" + }, + { + "Issue": "As part of Government's anti-terrorism efforts, Muslims living in the U.S. should... Not be subject to more scrutiny", + "Score": 14.0, + "Political Groups": "Clinton Supporters - Response 1" + }, + { + "Issue": "As part of Government's anti-terrorism efforts, Muslims living in the U.S. should... Not be subject to more scrutiny", + "Score": 82.0, + "Political Groups": "Clinton Supporters - Response 2" + }, + { + "Issue": "As part of Government's anti-terrorism efforts, Muslims living in the U.S. should... Not be subject to more scrutiny", + "Score": 57.0, + "Political Groups": "Trump Supporters - Response 1" + }, + { + "Issue": "As part of Government's anti-terrorism efforts, Muslims living in the U.S. should... Not be subject to more scrutiny", + "Score": 37.0, + "Political Groups": "Trump Supporters - Response 2" + }, + { + "Issue": "Free trade agreements have been a ... for the U.S. Bad thing", + "Score": 32.0, + "Political Groups": "Clinton Supporters - Response 1" + }, + { + "Issue": "Free trade agreements have been a ... for the U.S. Bad thing", + "Score": 59.0, + "Political Groups": "Clinton Supporters - Response 2" + }, + { + "Issue": "Free trade agreements have been a ... for the U.S. Bad thing", + "Score": 58.0, + "Political Groups": "Trump Supporters - Response 1" + }, + { + "Issue": "Free trade agreements have been a ... for the U.S. Bad thing", + "Score": 17.0, + "Political Groups": "Trump Supporters - Response 2" + }, + { + "Issue": "Free trade agreements have been a ... for the U.S. Good thing", + "Score": 32.0, + "Political Groups": "Clinton Supporters - Response 1" + }, + { + "Issue": "Free trade agreements have been a ... for the U.S. Good thing", + "Score": 59.0, + "Political Groups": "Clinton Supporters - Response 2" + }, + { + "Issue": "Free trade agreements have been a ... for the U.S. Good thing", + "Score": 58.0, + "Political Groups": "Trump Supporters - Response 1" + }, + { + "Issue": "Free trade agreements have been a ... for the U.S. Good thing", + "Score": 17.0, + "Political Groups": "Trump Supporters - Response 2" + }, + { + "Issue": "Increasing the minimum wage from $7.25 per hour to $15.00... Oppose", + "Score": 17.0, + "Political Groups": "Clinton Supporters - Response 1" + }, + { + "Issue": "Increasing the minimum wage from $7.25 per hour to $15.00... Oppose", + "Score": 82.0, + "Political Groups": "Clinton Supporters - Response 2" + }, + { + "Issue": "Increasing the minimum wage from $7.25 per hour to $15.00... Oppose", + "Score": 76.0, + "Political Groups": "Trump Supporters - Response 1" + }, + { + "Issue": "Increasing the minimum wage from $7.25 per hour to $15.00... Oppose", + "Score": 21.0, + "Political Groups": "Trump Supporters - Response 2" + }, + { + "Issue": "Increasing the minimum wage from $7.25 per hour to $15.00... Favour", + "Score": 17.0, + "Political Groups": "Clinton Supporters - Response 1" + }, + { + "Issue": "Increasing the minimum wage from $7.25 per hour to $15.00... Favour", + "Score": 82.0, + "Political Groups": "Clinton Supporters - Response 2" + }, + { + "Issue": "Increasing the minimum wage from $7.25 per hour to $15.00... Favour", + "Score": 76.0, + "Political Groups": "Trump Supporters - Response 1" + }, + { + "Issue": "Increasing the minimum wage from $7.25 per hour to $15.00... Favour", + "Score": 21.0, + "Political Groups": "Trump Supporters - Response 2" + }, + { + "Issue": "Can the government afford to do more to assist the needy? Can't afford to do more", + "Score": 25.0, + "Political Groups": "Clinton Supporters - Response 1" + }, + { + "Issue": "Can the government afford to do more to assist the needy? Can't afford to do more", + "Score": 72.0, + "Political Groups": "Clinton Supporters - Response 2" + }, + { + "Issue": "Can the government afford to do more to assist the needy? Can't afford to do more", + "Score": 71.0, + "Political Groups": "Trump Supporters - Response 1" + }, + { + "Issue": "Can the government afford to do more to assist the needy? Can't afford to do more", + "Score": 21.0, + "Political Groups": "Trump Supporters - Response 2" + }, + { + "Issue": "Can the government afford to do more to assist the needy? Should do more", + "Score": 25.0, + "Political Groups": "Clinton Supporters - Response 1" + }, + { + "Issue": "Can the government afford to do more to assist the needy? Should do more", + "Score": 72.0, + "Political Groups": "Clinton Supporters - Response 2" + }, + { + "Issue": "Can the government afford to do more to assist the needy? Should do more", + "Score": 71.0, + "Political Groups": "Trump Supporters - Response 1" + }, + { + "Issue": "Can the government afford to do more to assist the needy? Should do more", + "Score": 21.0, + "Political Groups": "Trump Supporters - Response 2" + } + ], + "columns": [ + { + "name": "Issue", + "importance": "primary", + "description": "The specific issue on which opinions were surveyed", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding political groups", + "role": "y" + }, + { + "name": "Political Groups", + "importance": "primary", + "description": "The political groups of the data point", + "role": "group" + } + ] + } + }, + "858": { + "metadata": { + "title": "Public support for May's negotiations", + "description": "% saying that each Brexit negotiating target is \"the right thing to do\"", + "main_insight": "There are significant differences in support between Leave and Remain voters across various Brexit negotiation targets. While some targets like intelligence sharing have high support from both groups, others, especially leaving the single market, see a large disparity." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Negotiating Target": "Maintaining an open border with the Irish Republic", + "Score": 80.0, + "Vote": "Remain voters" + } + ], + "score": 0.81, + "annotation": "The Remain voters shows a sudden jump in Score from The UK controlling immigration from the EU to Maintaining an open border with the Irish Republic.", + "reason": "The Score for Remain voters increases from 54.0 at The UK controlling immigration from the EU to 80.0 at Maintaining an open border with the Irish Republic, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Negotiating Target": "Continued intelligence sharing with the EU", + "Score": 90.0, + "Vote": "Remain voters" + } + ], + "score": 0.8, + "annotation": "The Remain voters accounts for the majority in Continued intelligence sharing with the EU.", + "reason": "The Score of Remain voters in Continued intelligence sharing with the EU accounts for 0.5084745762711864 (more than 50%) of the total of Score of all Vote." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Negotiating Target": "Guaranteeing EU citizens' rights in the UK", + "Score": 85.0, + "Vote": "Remain voters" + } + ], + "score": 0.79, + "annotation": "The Remain voters accounts for the majority in Guaranteeing EU citizens' rights in the UK.", + "reason": "The Score of Remain voters in Guaranteeing EU citizens' rights in the UK accounts for 0.5279503105590062 (more than 50%) of the total of Score of all Vote." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Negotiating Target": "Maintaining an open border with the Irish Republic", + "Score": 80.0, + "Vote": "Remain voters" + } + ], + "score": 0.79, + "annotation": "The Remain voters accounts for the majority in Maintaining an open border with the Irish Republic.", + "reason": "The Score of Remain voters in Maintaining an open border with the Irish Republic accounts for 0.54421768707483 (more than 50%) of the total of Score of all Vote." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Negotiating Target": "The UK controlling immigration from the EU", + "Score": 94.0, + "Vote": "Leave voters" + } + ], + "score": 0.78, + "annotation": "The Leave voters has a maximum value at The UK controlling immigration from the EU", + "reason": "The [{'Negotiating Target': 'The UK controlling immigration from the EU', 'Score': 94.0, 'Vote': 'Leave voters'}] of Leave voters has a maximum value of 94.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Negotiating Target": "Continued intelligence sharing with the EU", + "Score": 87.0, + "Vote": "Leave voters" + }, + { + "Negotiating Target": "Continued intelligence sharing with the EU", + "Score": 90.0, + "Vote": "Remain voters" + }, + { + "Negotiating Target": "Guaranteeing EU citizens' rights in the UK", + "Score": 76.0, + "Vote": "Leave voters" + }, + { + "Negotiating Target": "Guaranteeing EU citizens' rights in the UK", + "Score": 85.0, + "Vote": "Remain voters" + }, + { + "Negotiating Target": "Maintaining an open border with the Irish Republic", + "Score": 67.0, + "Vote": "Leave voters" + }, + { + "Negotiating Target": "Maintaining an open border with the Irish Republic", + "Score": 80.0, + "Vote": "Remain voters" + }, + { + "Negotiating Target": "Leaving the European Customs Union", + "Score": 76.0, + "Vote": "Leave voters" + }, + { + "Negotiating Target": "Leaving the European Customs Union", + "Score": 39.0, + "Vote": "Remain voters" + }, + { + "Negotiating Target": "The UK controlling immigration from the EU", + "Score": 94.0, + "Vote": "Leave voters" + }, + { + "Negotiating Target": "The UK controlling immigration from the EU", + "Score": 54.0, + "Vote": "Remain voters" + }, + { + "Negotiating Target": "Leaving the European single market", + "Score": 82.0, + "Vote": "Leave voters" + }, + { + "Negotiating Target": "Leaving the European single market", + "Score": 34.0, + "Vote": "Remain voters" + } + ], + "columns": [ + { + "name": "Negotiating Target", + "importance": "primary", + "description": "Brexit negotiating targets", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding vote", + "role": "y" + }, + { + "name": "Vote", + "importance": "primary", + "description": "The vote of the data point", + "role": "group" + } + ] + } + }, + "1177": { + "metadata": { + "title": "After Columbine: Victims of School Shootings in the U.S.", + "description": "Deaths and injuries due to school shootings in the United States since April 21, 1999*", + "main_insight": "School shooting deaths and injuries have fluctuated significantly since 1999, with peaks in 2007, 2012, and 2018. 2018 saw the highest number of combined deaths and injuries." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1999, + "Units": 2.0, + "Casualties": "Deaths" + }, + { + "Year": 1999, + "Units": 5.0, + "Casualties": "Injuries" + }, + { + "Year": 2000, + "Units": 2.0, + "Casualties": "Deaths" + }, + { + "Year": 2000, + "Units": 1.0, + "Casualties": "Injuries" + }, + { + "Year": 2001, + "Units": 6.0, + "Casualties": "Deaths" + }, + { + "Year": 2001, + "Units": 14.0, + "Casualties": "Injuries" + }, + { + "Year": 2002, + "Units": 6.0, + "Casualties": "Deaths" + }, + { + "Year": 2002, + "Units": 3.0, + "Casualties": "Injuries" + }, + { + "Year": 2005, + "Units": 9.0, + "Casualties": "Deaths" + }, + { + "Year": 2005, + "Units": 5.0, + "Casualties": "Injuries" + }, + { + "Year": 2006, + "Units": 5.0, + "Casualties": "Deaths" + }, + { + "Year": 2006, + "Units": 5.0, + "Casualties": "Injuries" + }, + { + "Year": 2007, + "Units": 36.0, + "Casualties": "Deaths" + }, + { + "Year": 2007, + "Units": 22.0, + "Casualties": "Injuries" + }, + { + "Year": 2008, + "Units": 7.0, + "Casualties": "Deaths" + }, + { + "Year": 2008, + "Units": 17.0, + "Casualties": "Injuries" + }, + { + "Year": 2012, + "Units": 37.0, + "Casualties": "Deaths" + }, + { + "Year": 2012, + "Units": 8.0, + "Casualties": "Injuries" + }, + { + "Year": 2013, + "Units": 8.0, + "Casualties": "Deaths" + }, + { + "Year": 2013, + "Units": 2.0, + "Casualties": "Injuries" + }, + { + "Year": 2014, + "Units": 10.0, + "Casualties": "Deaths" + }, + { + "Year": 2014, + "Units": 17.0, + "Casualties": "Injuries" + }, + { + "Year": 2015, + "Units": 9.0, + "Casualties": "Deaths" + }, + { + "Year": 2015, + "Units": 8.0, + "Casualties": "Injuries" + }, + { + "Year": 2017, + "Units": 2.0, + "Casualties": "Deaths" + }, + { + "Year": 2018, + "Units": 29.0, + "Casualties": "Deaths" + }, + { + "Year": 2018, + "Units": 44.0, + "Casualties": "Injuries" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of school shooting", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding casualties", + "role": "y" + }, + { + "name": "Casualties", + "importance": "primary", + "description": "The casualties of the data point", + "role": "group" + } + ] + } + }, + "1965": { + "metadata": { + "title": "Is Patience With the Cookie Crumbling?", + "description": "Share of U.S. respondents agreeing with the following statements about cookie tracking", + "main_insight": "The most common behavior among U.S. respondents regarding cookie tracking is regularly deleting cookies, while a significant portion is also aware of the consequences of cookie settings or find the options confusing." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Statement": "I regularly delete cookies", + "Units": 36.0, + "Agreement": "Agreement Percentage" + } + ], + "score": 0.92, + "annotation": "The Agreement Percentage shows a sudden jump in Units from I am aware of the consequences of cookie settings to I regularly delete cookies.", + "reason": "The Units for Agreement Percentage increases from 24.0 at I am aware of the consequences of cookie settings to 36.0 at I regularly delete cookies, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Statement": "I regularly delete cookies", + "Units": 36.0, + "Agreement": "Agreement Percentage" + } + ], + "score": 0.86, + "annotation": "The Agreement Percentage has a maximum value at I regularly delete cookies", + "reason": "The [{'Statement': 'I regularly delete cookies', 'Units': 36.0, 'Agreement': 'Agreement Percentage'}] of Agreement Percentage has a maximum value of 36.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Statement": "Cookie settings are often not solved appropriately", + "Units": 19.0, + "Agreement": "Agreement Percentage" + } + ], + "score": 0.57, + "annotation": "The Agreement Percentage has a minimum value at Cookie settings are often not solved appropriately", + "reason": "The [{'Statement': 'Cookie settings are often not solved appropriately', 'Units': 19.0, 'Agreement': 'Agreement Percentage'}] of Agreement Percentage has a mainimum value of 19.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Statement": "I regularly delete cookies", + "Units": 36.0, + "Agreement": "Agreement Percentage" + }, + { + "Statement": "I am aware of the consequences of cookie settings", + "Units": 24.0, + "Agreement": "Agreement Percentage" + }, + { + "Statement": "The many options for cookies confuse me", + "Units": 23.0, + "Agreement": "Agreement Percentage" + }, + { + "Statement": "I don't want to deal with cookie settings", + "Units": 23.0, + "Agreement": "Agreement Percentage" + }, + { + "Statement": "Cookie settings are often not solved appropriately", + "Units": 19.0, + "Agreement": "Agreement Percentage" + } + ], + "columns": [ + { + "name": "Statement", + "importance": "primary", + "description": "Different statements about cookie tracking", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding agreement", + "role": "y" + }, + { + "name": "Agreement", + "importance": "primary", + "description": "The agreement of the data point", + "role": "group" + } + ] + } + }, + "953": { + "metadata": { + "title": "Single Mothers Struggling in the U.S.", + "description": "Percentage of people who didn't have enough money to buy food needed for family in the past 12 months", + "main_insight": "Single mothers worldwide, and especially in Sub-Saharan Africa, are significantly more likely to struggle with food insecurity than other residents. The US/Canada region shows the largest gap between single mothers and other residents in terms of food insecurity." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "Sub-Saharan Africa", + "Units": 65.0, + "Population Segments": "Single women w/children" + } + ], + "score": 0.93, + "annotation": "The Single women w/children has a maximum value at Sub-Saharan Africa", + "reason": "The [{'Region': 'Sub-Saharan Africa', 'Units': 65.0, 'Population Segments': 'Single women w/children'}] of Single women w/children has a maximum value of 65.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Sub-Saharan Africa", + "Units": 65.0, + "Population Segments": "Single women w/children" + } + ], + "score": 0.91, + "annotation": "The Single women w/children shows a sudden jump in Units from Latin America to Sub-Saharan Africa.", + "reason": "The Units for Single women w/children increases from 45.0 at Latin America to 65.0 at Sub-Saharan Africa, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "Sub-Saharan Africa", + "Units": 61.0, + "Population Segments": "Other residents" + } + ], + "score": 0.9, + "annotation": "The Other residents has a maximum value at Sub-Saharan Africa", + "reason": "The [{'Region': 'Sub-Saharan Africa', 'Units': 61.0, 'Population Segments': 'Other residents'}] of Other residents has a maximum value of 61.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "U.S./Canada", + "Units": 26.0, + "Population Segments": "Gap" + } + ], + "score": 0.88, + "annotation": "The Gap has a maximum value at U.S./Canada", + "reason": "The [{'Region': 'U.S./Canada', 'Units': 26.0, 'Population Segments': 'Gap'}] of Gap has a maximum value of 26.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Sub-Saharan Africa", + "Units": 61.0, + "Population Segments": "Other residents" + } + ], + "score": 0.83, + "annotation": "The Other residents shows a sudden jump in Units from Southeast Asia to Sub-Saharan Africa.", + "reason": "The Units for Other residents increases from 39.0 at Southeast Asia to 61.0 at Sub-Saharan Africa, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Region": "World", + "Units": 44.0, + "Population Segments": "Single women w/children" + }, + { + "Region": "World", + "Units": 28.0, + "Population Segments": "Other residents" + }, + { + "Region": "World", + "Units": 16.0, + "Population Segments": "Gap" + }, + { + "Region": "Sub-Saharan Africa", + "Units": 65.0, + "Population Segments": "Single women w/children" + }, + { + "Region": "Sub-Saharan Africa", + "Units": 61.0, + "Population Segments": "Other residents" + }, + { + "Region": "Sub-Saharan Africa", + "Units": 4.0, + "Population Segments": "Gap" + }, + { + "Region": "Latin America", + "Units": 45.0, + "Population Segments": "Single women w/children" + }, + { + "Region": "Latin America", + "Units": 34.0, + "Population Segments": "Other residents" + }, + { + "Region": "Latin America", + "Units": 11.0, + "Population Segments": "Gap" + }, + { + "Region": "Southeast Asia", + "Units": 43.0, + "Population Segments": "Single women w/children" + }, + { + "Region": "Southeast Asia", + "Units": 39.0, + "Population Segments": "Other residents" + }, + { + "Region": "Southeast Asia", + "Units": 4.0, + "Population Segments": "Gap" + }, + { + "Region": "U.S./Canada", + "Units": 43.0, + "Population Segments": "Single women w/children" + }, + { + "Region": "U.S./Canada", + "Units": 17.0, + "Population Segments": "Other residents" + }, + { + "Region": "U.S./Canada", + "Units": 26.0, + "Population Segments": "Gap" + }, + { + "Region": "Middle East/North Africa", + "Units": 37.0, + "Population Segments": "Single women w/children" + }, + { + "Region": "Middle East/North Africa", + "Units": 38.0, + "Population Segments": "Other residents" + }, + { + "Region": "Middle East/North Africa", + "Units": -1.0, + "Population Segments": "Gap" + }, + { + "Region": "South Asia", + "Units": 36.0, + "Population Segments": "Single women w/children" + }, + { + "Region": "South Asia", + "Units": 34.0, + "Population Segments": "Other residents" + }, + { + "Region": "South Asia", + "Units": 2.0, + "Population Segments": "Gap" + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Geographic region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding population segments", + "role": "y" + }, + { + "name": "Population Segments", + "importance": "primary", + "description": "The population segments of the data point", + "role": "group" + } + ] + } + }, + "743": { + "metadata": { + "title": "Women File Majority of U.S. Jobless Claims After Shutdown", + "description": "Share of initial jobless claims by women in selected U.S. states", + "main_insight": "In most of the selected states, the share of jobless claims filed by women increased between March 14 and April 4, 2020, with a majority of states showing women filing a greater percentage of claims than men by April 4." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "State": "Alabama", + "Ratio": 0.6, + "Week": "Week ending April 4, 2020" + } + ], + "score": 0.86, + "annotation": "The Week ending April 4, 2020 has a maximum value at Alabama", + "reason": "The [{'State': 'Alabama', 'Ratio': 0.6, 'Week': 'Week ending April 4, 2020'}] of Week ending April 4, 2020 has a maximum value of 0.6, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "State": "North Dakota", + "Ratio": 0.18, + "Week": "Week ending March 14, 2020" + } + ], + "score": 0.85, + "annotation": "The Week ending March 14, 2020 has a minimum value at North Dakota", + "reason": "The [{'State': 'North Dakota', 'Ratio': 0.18, 'Week': 'Week ending March 14, 2020'}] of Week ending March 14, 2020 has a mainimum value of 0.18, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "State": "Oregon", + "Ratio": 0.48, + "Week": "Week ending April 4, 2020" + } + ], + "score": 0.81, + "annotation": "The Week ending April 4, 2020 has a minimum value at Oregon", + "reason": "The [{'State': 'Oregon', 'Ratio': 0.48, 'Week': 'Week ending April 4, 2020'}] of Week ending April 4, 2020 has a mainimum value of 0.48, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "State": "Rhode Island", + "Ratio": 0.56, + "Week": "Week ending April 4, 2020" + } + ], + "score": 0.79, + "annotation": "The Week ending April 4, 2020 accounts for the majority in Rhode Island.", + "reason": "The Ratio of Week ending April 4, 2020 in Rhode Island accounts for 0.5283018867924528 (more than 50%) of the total of Ratio of all Week." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "State": "Alabama", + "Ratio": 0.6, + "Week": "Week ending April 4, 2020" + } + ], + "score": 0.79, + "annotation": "The Week ending April 4, 2020 accounts for the majority in Alabama.", + "reason": "The Ratio of Week ending April 4, 2020 in Alabama accounts for 0.5357142857142857 (more than 50%) of the total of Ratio of all Week." + } + ], + "data": { + "data": [ + { + "State": "Alabama", + "Ratio": 0.6, + "Week": "Week ending April 4, 2020" + }, + { + "State": "Alabama", + "Ratio": 0.52, + "Week": "Week ending March 14, 2020" + }, + { + "State": "Utah", + "Ratio": 0.57, + "Week": "Week ending April 4, 2020" + }, + { + "State": "Utah", + "Ratio": 0.42, + "Week": "Week ending March 14, 2020" + }, + { + "State": "Rhode Island", + "Ratio": 0.56, + "Week": "Week ending April 4, 2020" + }, + { + "State": "Rhode Island", + "Ratio": 0.5, + "Week": "Week ending March 14, 2020" + }, + { + "State": "New Mexico", + "Ratio": 0.54, + "Week": "Week ending April 4, 2020" + }, + { + "State": "New Mexico", + "Ratio": 0.41, + "Week": "Week ending March 14, 2020" + }, + { + "State": "Montana", + "Ratio": 0.54, + "Week": "Week ending April 4, 2020" + }, + { + "State": "Montana", + "Ratio": 0.32, + "Week": "Week ending March 14, 2020" + }, + { + "State": "Idaho", + "Ratio": 0.53, + "Week": "Week ending April 4, 2020" + }, + { + "State": "Idaho", + "Ratio": 0.33, + "Week": "Week ending March 14, 2020" + }, + { + "State": "North Dakota", + "Ratio": 0.51, + "Week": "Week ending April 4, 2020" + }, + { + "State": "North Dakota", + "Ratio": 0.18, + "Week": "Week ending March 14, 2020" + }, + { + "State": "Wyoming", + "Ratio": 0.5, + "Week": "Week ending April 4, 2020" + }, + { + "State": "Wyoming", + "Ratio": 0.25, + "Week": "Week ending March 14, 2020" + }, + { + "State": "Oregon", + "Ratio": 0.48, + "Week": "Week ending April 4, 2020" + }, + { + "State": "Oregon", + "Ratio": 0.37, + "Week": "Week ending March 14, 2020" + } + ], + "columns": [ + { + "name": "State", + "importance": "primary", + "description": "U.S. State", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding week", + "role": "y" + }, + { + "name": "Week", + "importance": "primary", + "description": "The week of the data point", + "role": "group" + } + ] + } + }, + "653": { + "metadata": { + "title": "Škoda and Seat Growth Softens VW Group Delivery Decrease", + "description": "Annual deliveries by Volkswagen Group, by make/brand (in 1,000s)", + "main_insight": "Volkswagen Group's total deliveries decreased from 2021 to 2024, with a small increase in 2023. Volkswagen brand consistently makes up the largest portion of deliveries, followed by Audi." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Units": 4797.0, + "Brand": "Volkswagen" + }, + { + "Year": 2024, + "Units": 1671.0, + "Brand": "Audi" + } + ], + "score": 1.0, + "annotation": "The Units of Volkswagen and Audi show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Volkswagen and Audi is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2024, + "Units": 4797.0, + "Brand": "Volkswagen" + }, + { + "Year": 2024, + "Units": 927.0, + "Brand": "Škoda" + } + ], + "score": 1.0, + "annotation": "The Units of Volkswagen and Škoda show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Volkswagen and Škoda is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2024, + "Units": 4797.0, + "Brand": "Volkswagen" + }, + { + "Year": 2024, + "Units": 558.0, + "Brand": "Seat/Cupra" + } + ], + "score": 1.0, + "annotation": "The Units of Volkswagen and Seat/Cupra show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Volkswagen and Seat/Cupra is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2024, + "Units": 4797.0, + "Brand": "Volkswagen" + }, + { + "Year": 2024, + "Units": 408.0, + "Brand": "VW commercial vehicles" + } + ], + "score": 1.0, + "annotation": "The Units of Volkswagen and VW commercial vehicles show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Volkswagen and VW commercial vehicles is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2024, + "Units": 4797.0, + "Brand": "Volkswagen" + }, + { + "Year": 2024, + "Units": 356.0, + "Brand": "Porsche" + } + ], + "score": 1.0, + "annotation": "The Units of Volkswagen and Porsche show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Volkswagen and Porsche is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2021, + "Units": 4897.0, + "Brand": "Volkswagen" + }, + { + "Year": 2021, + "Units": 1681.0, + "Brand": "Audi" + }, + { + "Year": 2021, + "Units": 878.0, + "Brand": "Škoda" + }, + { + "Year": 2021, + "Units": 471.0, + "Brand": "Seat/Cupra" + }, + { + "Year": 2021, + "Units": 360.0, + "Brand": "VW commercial vehicles" + }, + { + "Year": 2021, + "Units": 293.0, + "Brand": "Porsche" + }, + { + "Year": 2021, + "Units": 302.0, + "Brand": "Other" + }, + { + "Year": 2024, + "Units": 4797.0, + "Brand": "Volkswagen" + }, + { + "Year": 2024, + "Units": 1671.0, + "Brand": "Audi" + }, + { + "Year": 2024, + "Units": 927.0, + "Brand": "Škoda" + }, + { + "Year": 2024, + "Units": 558.0, + "Brand": "Seat/Cupra" + }, + { + "Year": 2024, + "Units": 408.0, + "Brand": "VW commercial vehicles" + }, + { + "Year": 2024, + "Units": 356.0, + "Brand": "Porsche" + }, + { + "Year": 2024, + "Units": 311.0, + "Brand": "Other" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of delivery", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding brand", + "role": "y" + }, + { + "name": "Brand", + "importance": "primary", + "description": "The brand of the data point", + "role": "group" + } + ] + } + }, + "1430": { + "metadata": { + "title": "Russia Has the Fastest Hackers", + "description": "Average breakout speed of hackers from selected countries, 2018*", + "main_insight": "Russian hackers have a significantly faster average breakout speed compared to North Korean and Chinese hackers, according to a 2018 CrowdStrike analysis." + }, + "chart_type": "Proportional Icon Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Country": "Russia", + "Breakout Speed": "00:18:49" + }, + { + "Country": "North Korea", + "Breakout Speed": "02:20:14" + }, + { + "Country": "China", + "Breakout Speed": "04:00:26" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of origin for the hackers", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Breakout Speed", + "importance": "primary", + "description": "Average breakout speed in HH:MM:SS format", + "unit": "HH:MM:SS", + "data_type": "time", + "role": "y" + } + ] + } + }, + "1739": { + "metadata": { + "title": "Cigarette seizures in Ireland", + "description": "Quantity of cigarettes seized and total number of seizures (2012-2016)", + "main_insight": "The number of cigarettes seized peaked in 2012 and has generally declined since, while the total number of seizures has also decreased." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Year": "2012", + "Cigarettes Seized": 95604730.0 + } + ], + "score": 0.84, + "annotation": "The shows a sudden jump in Cigarettes Seized from 2015 to 2012.", + "reason": "The Cigarettes Seized for increases from 67917500.0 at 2015 to 95604730.0 at 2012, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Year": "2012", + "Cigarettes Seized": 95604730.0 + } + ], + "score": 0.83, + "annotation": "The has a maximum value at 2012", + "reason": "The [{'Year': '2012', 'Cigarettes Seized': 95604730.0}] of has a maximum value of 95604730.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Year": "2013", + "Cigarettes Seized": 40844800.0 + } + ], + "score": 0.54, + "annotation": "The has a minimum value at 2013", + "reason": "The [{'Year': '2013', 'Cigarettes Seized': 40844800.0}] of has a mainimum value of 40844800.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Year": "2012", + "Cigarettes Seized": 95604730.0 + }, + { + "Year": "2013", + "Cigarettes Seized": 40844800.0 + }, + { + "Year": "2014", + "Cigarettes Seized": 53445350.0 + }, + { + "Year": "2015", + "Cigarettes Seized": 67917500.0 + }, + { + "Year": "2016", + "Cigarettes Seized": 44563810.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of cigarette seizure", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Cigarettes Seized", + "importance": "primary", + "description": "Quantity of cigarettes seized", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1322": { + "metadata": { + "title": "Eurovision Winners Inspire Others to Ditch English", + "description": "Eurovision entries featuring a language other than English (2007-2021)", + "main_insight": "The percentage of Eurovision entries featuring a language other than English has fluctuated significantly between 2007 and 2021, with peaks after non-English songs won, but generally showing a decrease after 2008, then another increase after 2017." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2007, + "Non-English Entries": 0.38 + }, + { + "Year": 2008, + "Non-English Entries": 0.51 + }, + { + "Year": 2009, + "Non-English Entries": 0.44 + }, + { + "Year": 2010, + "Non-English Entries": 0.41 + }, + { + "Year": 2011, + "Non-English Entries": 0.41 + }, + { + "Year": 2012, + "Non-English Entries": 0.43 + }, + { + "Year": 2013, + "Non-English Entries": 0.4 + }, + { + "Year": 2014, + "Non-English Entries": 0.28 + }, + { + "Year": 2015, + "Non-English Entries": 0.21 + }, + { + "Year": 2016, + "Non-English Entries": 0.2 + }, + { + "Year": 2017, + "Non-English Entries": 0.2 + }, + { + "Year": 2018, + "Non-English Entries": 0.33 + }, + { + "Year": 2019, + "Non-English Entries": 0.35 + }, + { + "Year": 2020, + "Non-English Entries": 0.31 + }, + { + "Year": 2021, + "Non-English Entries": 0.33 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of Eurovision contest", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Non-English Entries", + "importance": "primary", + "description": "Percentage of Eurovision entries featuring a language other than English", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1105": { + "metadata": { + "title": "Euro 2020 Hopes and Fears", + "description": "% predicting own country will win Euro 2020 and which country they'd least like to see win", + "main_insight": "The chart shows a significant difference between the percentage of people who believe their own country will win and the percentage of people who least want a specific opposing country to win. France has by far the largest percentage of respondents believing their team will win." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Italy", + "Score": 21.0, + "Sentiment": "Least like to see win" + } + ], + "score": 0.89, + "annotation": "The Least like to see win shows a sudden jump in Score from France to Italy.", + "reason": "The Score for Least like to see win increases from 16.0 at France to 21.0 at Italy, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "France", + "Score": 63.0, + "Sentiment": "Think own country will win" + } + ], + "score": 0.87, + "annotation": "The Think own country will win shows a sudden jump in Score from Spain to France.", + "reason": "The Score for Think own country will win increases from 43.0 at Spain to 63.0 at France, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Italy", + "Score": 21.0, + "Sentiment": "Least like to see win" + } + ], + "score": 0.85, + "annotation": "The Least like to see win has a maximum value at Italy", + "reason": "The [{'Country': 'Italy', 'Score': 21.0, 'Sentiment': 'Least like to see win'}] of Least like to see win has a maximum value of 21.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "France", + "Score": 63.0, + "Sentiment": "Think own country will win" + } + ], + "score": 0.82, + "annotation": "The Think own country will win has a maximum value at France", + "reason": "The [{'Country': 'France', 'Score': 63.0, 'Sentiment': 'Think own country will win'}] of Think own country will win has a maximum value of 63.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "England", + "Score": 28.0, + "Sentiment": "Think own country will win" + } + ], + "score": 0.75, + "annotation": "The Think own country will win accounts for the majority in England.", + "reason": "The Score of Think own country will win in England accounts for 0.6511627906976745 (more than 50%) of the total of Score of all Sentiment." + } + ], + "data": { + "data": [ + { + "Country": "France", + "Score": 63.0, + "Sentiment": "Think own country will win" + }, + { + "Country": "France", + "Score": 16.0, + "Sentiment": "Least like to see win" + }, + { + "Country": "Spain", + "Score": 43.0, + "Sentiment": "Think own country will win" + }, + { + "Country": "Spain", + "Score": 14.0, + "Sentiment": "Least like to see win" + }, + { + "Country": "Italy", + "Score": 42.0, + "Sentiment": "Think own country will win" + }, + { + "Country": "Italy", + "Score": 21.0, + "Sentiment": "Least like to see win" + }, + { + "Country": "Germany", + "Score": 30.0, + "Sentiment": "Think own country will win" + }, + { + "Country": "Germany", + "Score": 13.0, + "Sentiment": "Least like to see win" + }, + { + "Country": "England", + "Score": 28.0, + "Sentiment": "Think own country will win" + }, + { + "Country": "England", + "Score": 15.0, + "Sentiment": "Least like to see win" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "1556": { + "metadata": { + "title": "Tiger Woods Ends Five-Year Drought to Reach 80 Career Titles", + "description": "Number of PGA Tour victories by Tiger Woods since 1996", + "main_insight": "Tiger Woods won his 80th PGA Tour title in 2018, ending a five-year period without any victories." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1996, + "PGA Tour Victories": 2 + }, + { + "Year": 1997, + "PGA Tour Victories": 4 + }, + { + "Year": 1998, + "PGA Tour Victories": 1 + }, + { + "Year": 1999, + "PGA Tour Victories": 8 + }, + { + "Year": 2000, + "PGA Tour Victories": 9 + }, + { + "Year": 2001, + "PGA Tour Victories": 5 + }, + { + "Year": 2002, + "PGA Tour Victories": 5 + }, + { + "Year": 2003, + "PGA Tour Victories": 5 + }, + { + "Year": 2004, + "PGA Tour Victories": 1 + }, + { + "Year": 2005, + "PGA Tour Victories": 6 + }, + { + "Year": 2006, + "PGA Tour Victories": 8 + }, + { + "Year": 2007, + "PGA Tour Victories": 7 + }, + { + "Year": 2008, + "PGA Tour Victories": 4 + }, + { + "Year": 2009, + "PGA Tour Victories": 6 + }, + { + "Year": 2010, + "PGA Tour Victories": 0 + }, + { + "Year": 2011, + "PGA Tour Victories": 0 + }, + { + "Year": 2012, + "PGA Tour Victories": 3 + }, + { + "Year": 2013, + "PGA Tour Victories": 5 + }, + { + "Year": 2014, + "PGA Tour Victories": 0 + }, + { + "Year": 2015, + "PGA Tour Victories": 0 + }, + { + "Year": 2016, + "PGA Tour Victories": 0 + }, + { + "Year": 2017, + "PGA Tour Victories": 0 + }, + { + "Year": 2018, + "PGA Tour Victories": 1 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of PGA Tour", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "PGA Tour Victories", + "importance": "primary", + "description": "Number of PGA Tour victories by Tiger Woods in that year", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "874": { + "metadata": { + "title": "Where Non-Returning Indian Travelers Go in Southeast Asia", + "description": "Southeast Asian countries with the highest number of visitors who have not returned to India", + "main_insight": "The vast majority of non-returning Indian travelers in Southeast Asia between January 2022 and May 2024 went to Thailand, followed by Vietnam, Cambodia, and Myanmar." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Thailand", + "Non-Returning Travelers": 20450 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in Non-Returning Travelers from Vietnam to Thailand.", + "reason": "The Non-Returning Travelers for increases from 6242 at Vietnam to 20450 at Thailand, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Thailand", + "Non-Returning Travelers": 20450 + } + ], + "score": 0.78, + "annotation": "The has a maximum value at Thailand", + "reason": "The [{'Country': 'Thailand', 'Non-Returning Travelers': 20450}] of has a maximum value of 20450, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Myanmar", + "Non-Returning Travelers": 503 + } + ], + "score": 0.48, + "annotation": "The has a minimum value at Myanmar", + "reason": "The [{'Country': 'Myanmar', 'Non-Returning Travelers': 503}] of has a mainimum value of 503, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Thailand", + "Non-Returning Travelers": 20450 + }, + { + "Country": "Vietnam", + "Non-Returning Travelers": 6242 + }, + { + "Country": "Cambodia", + "Non-Returning Travelers": 2271 + }, + { + "Country": "Myanmar", + "Non-Returning Travelers": 503 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Southeast Asian country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Non-Returning Travelers", + "importance": "primary", + "description": "Number of Indian travelers who have not returned to India (Jan 2022 - May 2024)", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1818": { + "metadata": { + "title": "Where Rapid Urbanization Pushes Homes Into Floodways", + "description": "Lower- and middle-income countries with the highest share of their settlement area at risk of severe flooding (in %)*\n* Inundation depths of more than 1.5 meters in case of a 100-year flood event. As of 2015", + "main_insight": "Vietnam and Laos have the highest share of settlement area at risk of severe flooding among the listed lower- and middle-income countries, significantly exceeding other countries on the list." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "VN", + "Flood Risk (%)": 31.2 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at VN", + "reason": "The [{'Country': 'VN', 'Flood Risk (%)': 31.2}] of has a maximum value of 31.2, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "BD", + "Flood Risk (%)": 23.4 + } + ], + "score": 0.81, + "annotation": "The shows a sudden jump in Flood Risk (%) from FJ to BD.", + "reason": "The Flood Risk (%) for increases from 12.7 at FJ to 23.4 at BD, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "ME", + "Flood Risk (%)": 8.0 + } + ], + "score": 0.53, + "annotation": "The has a minimum value at ME", + "reason": "The [{'Country': 'ME', 'Flood Risk (%)': 8.0}] of has a mainimum value of 8.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "VN", + "Flood Risk (%)": 31.2 + }, + { + "Country": "LA", + "Flood Risk (%)": 28.7 + }, + { + "Country": "BD", + "Flood Risk (%)": 23.4 + }, + { + "Country": "FJ", + "Flood Risk (%)": 12.7 + }, + { + "Country": "BT", + "Flood Risk (%)": 12.5 + }, + { + "Country": "EG", + "Flood Risk (%)": 10.7 + }, + { + "Country": "CN", + "Flood Risk (%)": 9.8 + }, + { + "Country": "KP", + "Flood Risk (%)": 9.5 + }, + { + "Country": "BA", + "Flood Risk (%)": 8.8 + }, + { + "Country": "ME", + "Flood Risk (%)": 8.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Two-letter country code", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Flood Risk (%)", + "importance": "primary", + "description": "Percentage of settlement area at risk of severe flooding", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "877": { + "metadata": { + "title": "How Smartphone Prices Differ Across Platforms", + "description": "% of worldwide smartphone shipments in Q2 2014, by operating system and price tier*", + "main_insight": "iOS smartphone shipments in Q2 2014 were overwhelmingly in the mid-range price tier, while Android and Windows shipments were predominantly low-end." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Operating System": "Windows", + "Units": 61.4, + "Price Range": "Low-End: $0-$200" + }, + { + "Operating System": "Windows", + "Units": 24.9, + "Price Range": "Mid-Range: $200<$400" + } + ], + "score": 0.99, + "annotation": "The Units of Low-End: $0-$200 and Mid-Range: $200<$400 show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Low-End: $0-$200 and Mid-Range: $200<$400 is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Operating System": "Android", + "Units": 19.82, + "Price Range": "High-End: $400+" + } + ], + "score": 0.97, + "annotation": "The High-End: $400+ shows a sudden jump in Units from Windows to Android.", + "reason": "The Units for High-End: $400+ increases from 13.7 at Windows to 19.82 at Android, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Operating System": "iOS", + "Units": 84.6, + "Price Range": "Mid-Range: $200<$400" + } + ], + "score": 0.97, + "annotation": "The Mid-Range: $200<$400 shows a sudden jump in Units from Windows to iOS.", + "reason": "The Units for Mid-Range: $200<$400 increases from 24.9 at Windows to 84.6 at iOS, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Operating System": "Android", + "Units": 58.66, + "Price Range": "Low-End: $0-$200" + } + ], + "score": 0.97, + "annotation": "The Low-End: $0-$200 shows a sudden jump in Units from iOS to Android.", + "reason": "The Units for Low-End: $0-$200 increases from 15.4 at iOS to 58.66 at Android, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Price Range": "Low-End: $0-$200", + "Operating System": "total", + "Units": 135.46 + } + ], + "score": 0.87, + "annotation": "The total value of Low-End: $0-$200 has the largest proportion.", + "reason": "The total value of Units of Low-End: $0-$200 accounts for 0.45153333333333334, larger than all other Price Range, of the total of Units of all Price Range." + } + ], + "data": { + "data": [ + { + "Operating System": "Android", + "Units": 58.66, + "Price Range": "Low-End: $0-$200" + }, + { + "Operating System": "Android", + "Units": 21.52, + "Price Range": "Mid-Range: $200<$400" + }, + { + "Operating System": "Android", + "Units": 19.82, + "Price Range": "High-End: $400+" + }, + { + "Operating System": "iOS", + "Units": 15.4, + "Price Range": "Low-End: $0-$200" + }, + { + "Operating System": "iOS", + "Units": 84.6, + "Price Range": "Mid-Range: $200<$400" + }, + { + "Operating System": "Windows", + "Units": 61.4, + "Price Range": "Low-End: $0-$200" + }, + { + "Operating System": "Windows", + "Units": 24.9, + "Price Range": "Mid-Range: $200<$400" + }, + { + "Operating System": "Windows", + "Units": 13.7, + "Price Range": "High-End: $400+" + } + ], + "columns": [ + { + "name": "Operating System", + "importance": "primary", + "description": "The mobile operating system", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding price range", + "role": "y" + }, + { + "name": "Price Range", + "importance": "primary", + "description": "The price range of the data point", + "role": "group" + } + ] + } + }, + "620": { + "metadata": { + "title": "Huge Decline in Local Commerce Due to Coronavirus", + "description": "Percentage growth rate for local commerce in selected U.S. cities", + "main_insight": "Local commerce growth rates in major U.S. cities plummeted from positive growth in February 2020 to significant negative growth in March 2020, demonstrating the impact of the Coronavirus." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Seattle", + "Value": 27.9, + "Month": "February 2020" + } + ], + "score": 0.96, + "annotation": "The February 2020 has a maximum value at Seattle", + "reason": "The [{'City': 'Seattle', 'Value': 27.9, 'Month': 'February 2020'}] of February 2020 has a maximum value of 27.9, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Seattle", + "Value": 27.9, + "Month": "February 2020" + } + ], + "score": 0.9, + "annotation": "The February 2020 shows a sudden jump in Value from Los Angeles to Seattle.", + "reason": "The Value for February 2020 increases from 18.1 at Los Angeles to 27.9 at Seattle, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "San Francisco", + "Value": -16.6, + "Month": "March 2020" + } + ], + "score": 0.84, + "annotation": "The March 2020 has a minimum value at San Francisco", + "reason": "The [{'City': 'San Francisco', 'Value': -16.6, 'Month': 'March 2020'}] of March 2020 has a mainimum value of -16.6, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Detroit", + "Value": -13.5, + "Month": "March 2020" + } + ], + "score": 0.7, + "annotation": "The March 2020 shows a sudden jump in Value from New York to Detroit.", + "reason": "The Value for March 2020 increases from -15.1 at New York to -13.5 at Detroit, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Atlanta", + "Value": -11.4, + "Month": "March 2020" + } + ], + "score": 0.69, + "annotation": "The March 2020 has a maximum value at Atlanta", + "reason": "The [{'City': 'Atlanta', 'Value': -11.4, 'Month': 'March 2020'}] of March 2020 has a maximum value of -11.4, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "City": "San Francisco", + "Value": 13.9, + "Month": "February 2020" + }, + { + "City": "San Francisco", + "Value": -16.6, + "Month": "March 2020" + }, + { + "City": "Chicago", + "Value": 13.1, + "Month": "February 2020" + }, + { + "City": "Chicago", + "Value": -15.3, + "Month": "March 2020" + }, + { + "City": "New York", + "Value": 14.8, + "Month": "February 2020" + }, + { + "City": "New York", + "Value": -15.1, + "Month": "March 2020" + }, + { + "City": "Detroit", + "Value": 13.6, + "Month": "February 2020" + }, + { + "City": "Detroit", + "Value": -13.5, + "Month": "March 2020" + }, + { + "City": "Houston", + "Value": 13.1, + "Month": "February 2020" + }, + { + "City": "Houston", + "Value": -12.6, + "Month": "March 2020" + }, + { + "City": "Los Angeles", + "Value": 18.1, + "Month": "February 2020" + }, + { + "City": "Los Angeles", + "Value": -12.5, + "Month": "March 2020" + }, + { + "City": "Seattle", + "Value": 27.9, + "Month": "February 2020" + }, + { + "City": "Seattle", + "Value": -12.0, + "Month": "March 2020" + }, + { + "City": "Atlanta", + "Value": 13.7, + "Month": "February 2020" + }, + { + "City": "Atlanta", + "Value": -11.4, + "Month": "March 2020" + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "Selected U.S. cities", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding month", + "role": "y" + }, + { + "name": "Month", + "importance": "primary", + "description": "The month of the data point", + "role": "group" + } + ] + } + }, + "1744": { + "metadata": { + "title": "Online retailers priming for Deepavali sales", + "description": "Number of shoppers during festive season across India in 2017 and 2018. Festive advertising expenditure of online retailers across India in 2017 and 2018", + "main_insight": "Both the number of shoppers and advertising expenditure increased from 2017 to 2018 during the Deepavali festive season in India." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2017, + "Ad Expenditure": 3600.0 + }, + { + "Year": 2018, + "Ad Expenditure": 4000.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of observation", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ad Expenditure", + "importance": "primary", + "description": "Festive advertising expenditure in millions of Indian rupees", + "unit": "Millions INR", + "data_type": "number", + "role": "y" + } + ] + } + }, + "206": { + "metadata": { + "title": "Where Indian hoteliers are investing in sustainability", + "description": "Respondents are executives/managers in the Indian accommodation industry; Question: Do you plan to invest in one of the following areas over the next 6 months?", + "main_insight": "Indian hoteliers are primarily investing in energy efficiency and waste reduction, with 46% and 45% planning to invest in these areas, respectively." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Sustainability Area": "Waste reduction", + "Investment Percentage": 45.0 + } + ], + "score": 0.89, + "annotation": "The shows a sudden jump in Investment Percentage from Water preservation to Waste reduction.", + "reason": "The Investment Percentage for increases from 26.0 at Water preservation to 45.0 at Waste reduction, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Sustainability Area": "Energy efficiency", + "Investment Percentage": 46.0 + } + ], + "score": 0.79, + "annotation": "The has a maximum value at Energy efficiency", + "reason": "The [{'Sustainability Area': 'Energy efficiency', 'Investment Percentage': 46.0}] of has a maximum value of 46.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Sustainability Area": "Preservation of natural resources", + "Investment Percentage": 16.0 + } + ], + "score": 0.61, + "annotation": "The has a minimum value at Preservation of natural resources", + "reason": "The [{'Sustainability Area': 'Preservation of natural resources', 'Investment Percentage': 16.0}] of has a mainimum value of 16.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Sustainability Area": "Energy efficiency", + "Investment Percentage": 46.0 + }, + { + "Sustainability Area": "Waste reduction", + "Investment Percentage": 45.0 + }, + { + "Sustainability Area": "Water preservation", + "Investment Percentage": 26.0 + }, + { + "Sustainability Area": "Local sourcing of products and services", + "Investment Percentage": 24.0 + }, + { + "Sustainability Area": "Inclusion and accessibility", + "Investment Percentage": 22.0 + }, + { + "Sustainability Area": "Greenhouse gas emissions reduction", + "Investment Percentage": 20.0 + }, + { + "Sustainability Area": "Preservation of natural resources", + "Investment Percentage": 16.0 + } + ], + "columns": [ + { + "name": "Sustainability Area", + "importance": "primary", + "description": "Areas where Indian hoteliers plan to invest", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Investment Percentage", + "importance": "primary", + "description": "Percentage of respondents planning to invest in each area", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1141": { + "metadata": { + "title": "Little Interest in Art Exhibitions & Museums?", + "description": "Share of respondents who bought tickets for museums/art exhibitions in the last 12 months (in %)", + "main_insight": "The chart shows the percentage of respondents in various countries who bought tickets for museums or art exhibitions in the past year. China, India, and Italy had the highest percentages, while Brazil had the lowest." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "CN", + "Respondents (%)": 16.0 + } + ], + "score": 0.84, + "annotation": "The has a maximum value at CN", + "reason": "The [{'Country': 'CN', 'Respondents (%)': 16.0}] of has a maximum value of 16.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "ES", + "Respondents (%)": 11.0 + }, + { + "Country": "IT", + "Respondents (%)": 14.0 + } + ], + "score": 0.69, + "annotation": "The exhibits multiple sudden jumps in Respondents (%), transitioning from MX, ES to ES, IT.", + "reason": "Across these points, the Respondents (%) for increases from 8.0, 11.0 at MX, ES to 11.0, 14.0 at ES, IT, suggesting significant shifts in value." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "BR", + "Respondents (%)": 6.0 + } + ], + "score": 0.61, + "annotation": "The has a minimum value at BR", + "reason": "The [{'Country': 'BR', 'Respondents (%)': 6.0}] of has a mainimum value of 6.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "CN", + "Respondents (%)": 16.0 + }, + { + "Country": "IN", + "Respondents (%)": 15.0 + }, + { + "Country": "IT", + "Respondents (%)": 14.0 + }, + { + "Country": "ES", + "Respondents (%)": 11.0 + }, + { + "Country": "FR", + "Respondents (%)": 8.0 + }, + { + "Country": "MX", + "Respondents (%)": 8.0 + }, + { + "Country": "US", + "Respondents (%)": 7.0 + }, + { + "Country": "ZA", + "Respondents (%)": 7.0 + }, + { + "Country": "DE", + "Respondents (%)": 7.0 + }, + { + "Country": "BR", + "Respondents (%)": 6.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of the respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Respondents (%)", + "importance": "primary", + "description": "Percentage of respondents who bought tickets for museums/art exhibitions", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "400": { + "metadata": { + "title": "Record Level of Russians Want to Leave the Country", + "description": "If you had the opportunity, would you like to move permanently to another country?", + "main_insight": "The percentage of Russians who want to leave the country reached a record high of 20% in 2018, significantly higher than in most of the preceding decade." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2007, + "Percentage": 17.0 + }, + { + "Year": 2008, + "Percentage": 12.0 + }, + { + "Year": 2009, + "Percentage": 11.0 + }, + { + "Year": 2010, + "Percentage": 14.0 + }, + { + "Year": 2011, + "Percentage": 14.0 + }, + { + "Year": 2012, + "Percentage": 14.0 + }, + { + "Year": 2013, + "Percentage": 16.0 + }, + { + "Year": 2014, + "Percentage": 7.0 + }, + { + "Year": 2015, + "Percentage": 10.0 + }, + { + "Year": 2016, + "Percentage": 9.0 + }, + { + "Year": 2017, + "Percentage": 17.0 + }, + { + "Year": 2018, + "Percentage": 20.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of Russians who would like to move permanently to another country", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1393": { + "metadata": { + "title": "UK kids' friendships: always online?", + "description": "% UK children spending >3 hours using social networking websites on a normal school day", + "main_insight": "The percentage of UK children spending more than 3 hours on social networking sites has increased significantly since 2009/2010, with girls consistently reporting higher usage than boys." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "School Year": "2015/2016", + "Units": 17.4, + "Gender": "Girls" + }, + { + "School Year": "2015/2016", + "Units": 8.3, + "Gender": "Boys" + } + ], + "score": 0.96, + "annotation": "The Units of Girls and Boys show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Girls and Boys is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "School Year": "2015/2016", + "Units": 8.3, + "Gender": "Boys" + } + ], + "score": 0.9, + "annotation": "The Boys has a maximum value at 2015/2016", + "reason": "The [{'School Year': '2015/2016', 'Units': 8.3, 'Gender': 'Boys'}] of Boys has a maximum value of 8.3, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "School Year": "2015/2016", + "Units": 17.4, + "Gender": "Girls" + } + ], + "score": 0.87, + "annotation": "The Girls has a maximum value at 2015/2016", + "reason": "The [{'School Year': '2015/2016', 'Units': 17.4, 'Gender': 'Girls'}] of Girls has a maximum value of 17.4, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "School Year": "2015/2016", + "Units": 17.4, + "Gender": "Girls" + } + ], + "score": 0.8, + "annotation": "The Girls shows a sudden jump in Units from 2014/2015 to 2015/2016.", + "reason": "The Units for Girls increases from 13.3 at 2014/2015 to 17.4 at 2015/2016, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "School Year": "2015/2016", + "Units": 8.3, + "Gender": "Boys" + } + ], + "score": 0.79, + "annotation": "The Boys shows a sudden jump in Units from 2014/2015 to 2015/2016.", + "reason": "The Units for Boys increases from 6.5 at 2014/2015 to 8.3 at 2015/2016, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "School Year": "2009/2010", + "Units": 8.1, + "Gender": "Girls" + }, + { + "School Year": "2009/2010", + "Units": 4.1, + "Gender": "Boys" + }, + { + "School Year": "2010/2011", + "Units": 12.1, + "Gender": "Girls" + }, + { + "School Year": "2010/2011", + "Units": 5.1, + "Gender": "Boys" + }, + { + "School Year": "2011/2012", + "Units": 8.7, + "Gender": "Girls" + }, + { + "School Year": "2011/2012", + "Units": 4.9, + "Gender": "Boys" + }, + { + "School Year": "2012/2013", + "Units": 10.9, + "Gender": "Girls" + }, + { + "School Year": "2012/2013", + "Units": 5.1, + "Gender": "Boys" + }, + { + "School Year": "2013/2014", + "Units": 12.6, + "Gender": "Girls" + }, + { + "School Year": "2013/2014", + "Units": 6.0, + "Gender": "Boys" + }, + { + "School Year": "2014/2015", + "Units": 13.3, + "Gender": "Girls" + }, + { + "School Year": "2014/2015", + "Units": 6.5, + "Gender": "Boys" + }, + { + "School Year": "2015/2016", + "Units": 17.4, + "Gender": "Girls" + }, + { + "School Year": "2015/2016", + "Units": 8.3, + "Gender": "Boys" + } + ], + "columns": [ + { + "name": "School Year", + "importance": "primary", + "description": "Academic year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding gender", + "role": "y" + }, + { + "name": "Gender", + "importance": "primary", + "description": "The gender of the data point", + "role": "group" + } + ] + } + }, + "1154": { + "metadata": { + "title": "Who Are America's Video Gamers?", + "description": "% of U.S. adults who often/sometimes play video games in 2017*", + "main_insight": "Younger adults (18-29) are the most likely to play video games, with 60% playing often or sometimes. Men are slightly more prone to playing video games often when compared to women." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Demographic": "Bachelor's degree+", + "Units": 36.0, + "Frequency": "Net" + }, + { + "Demographic": "Bachelor's degree+", + "Units": 17.0, + "Frequency": "Often" + } + ], + "score": 0.99, + "annotation": "The Units of Net and Often show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Net and Often is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Demographic": "Bachelor's degree+", + "Units": 36.0, + "Frequency": "Net" + }, + { + "Demographic": "Bachelor's degree+", + "Units": 19.0, + "Frequency": "Sometimes" + } + ], + "score": 0.99, + "annotation": "The Units of Net and Sometimes show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Net and Sometimes is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Demographic": "Bachelor's degree+", + "Units": 17.0, + "Frequency": "Often" + }, + { + "Demographic": "Bachelor's degree+", + "Units": 19.0, + "Frequency": "Sometimes" + } + ], + "score": 0.96, + "annotation": "The Units of Often and Sometimes show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Often and Sometimes is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Demographic": "Ages 18-29", + "Units": 31.0, + "Frequency": "Sometimes" + } + ], + "score": 0.88, + "annotation": "The Sometimes has a maximum value at Ages 18-29", + "reason": "The [{'Demographic': 'Ages 18-29', 'Units': 31.0, 'Frequency': 'Sometimes'}] of Sometimes has a maximum value of 31.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Demographic": "50-64", + "Units": 31.0, + "Frequency": "Net" + } + ], + "score": 0.86, + "annotation": "The Net has the largest proportion in 50-64.", + "reason": "The Units of Net in 50-64 accounts for 0.49206349206349204, larger than all other Frequency, of the total of Units of all Frequency." + } + ], + "data": { + "data": [ + { + "Demographic": "Men", + "Units": 47.0, + "Frequency": "Net" + }, + { + "Demographic": "Men", + "Units": 24.0, + "Frequency": "Often" + }, + { + "Demographic": "Men", + "Units": 23.0, + "Frequency": "Sometimes" + }, + { + "Demographic": "Women", + "Units": 39.0, + "Frequency": "Net" + }, + { + "Demographic": "Women", + "Units": 19.0, + "Frequency": "Often" + }, + { + "Demographic": "Women", + "Units": 21.0, + "Frequency": "Sometimes" + }, + { + "Demographic": "Ages 18-29", + "Units": 60.0, + "Frequency": "Net" + }, + { + "Demographic": "Ages 18-29", + "Units": 29.0, + "Frequency": "Often" + }, + { + "Demographic": "Ages 18-29", + "Units": 31.0, + "Frequency": "Sometimes" + }, + { + "Demographic": "30-49", + "Units": 53.0, + "Frequency": "Net" + }, + { + "Demographic": "30-49", + "Units": 28.0, + "Frequency": "Often" + }, + { + "Demographic": "30-49", + "Units": 25.0, + "Frequency": "Sometimes" + }, + { + "Demographic": "50-64", + "Units": 31.0, + "Frequency": "Net" + }, + { + "Demographic": "50-64", + "Units": 15.0, + "Frequency": "Often" + }, + { + "Demographic": "50-64", + "Units": 17.0, + "Frequency": "Sometimes" + }, + { + "Demographic": "65+", + "Units": 24.0, + "Frequency": "Net" + }, + { + "Demographic": "65+", + "Units": 11.0, + "Frequency": "Often" + }, + { + "Demographic": "65+", + "Units": 13.0, + "Frequency": "Sometimes" + }, + { + "Demographic": "High school or less", + "Units": 42.0, + "Frequency": "Net" + }, + { + "Demographic": "High school or less", + "Units": 21.0, + "Frequency": "Often" + }, + { + "Demographic": "High school or less", + "Units": 21.0, + "Frequency": "Sometimes" + }, + { + "Demographic": "Some college", + "Units": 50.0, + "Frequency": "Net" + }, + { + "Demographic": "Some college", + "Units": 25.0, + "Frequency": "Often" + }, + { + "Demographic": "Some college", + "Units": 25.0, + "Frequency": "Sometimes" + }, + { + "Demographic": "Bachelor's degree+", + "Units": 36.0, + "Frequency": "Net" + }, + { + "Demographic": "Bachelor's degree+", + "Units": 17.0, + "Frequency": "Often" + }, + { + "Demographic": "Bachelor's degree+", + "Units": 19.0, + "Frequency": "Sometimes" + } + ], + "columns": [ + { + "name": "Demographic", + "importance": "primary", + "description": "Demographic groups", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding frequency", + "role": "y" + }, + { + "name": "Frequency", + "importance": "primary", + "description": "The frequency of the data point", + "role": "group" + } + ] + } + }, + "99": { + "metadata": { + "title": "Spotify Reaches 83 Million Premium Subscribers", + "description": "Spotify's worldwide monthly active users and premium subscribers (in millions)", + "main_insight": "Spotify's premium subscribers and MAUs have steadily increased from Q1 2015 to Q2 2018, with premium subscribers reaching 83 million by Q2 2018." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Quarter": "Q2 2018", + "Units": 83.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q2 2018", + "Units": 180.0, + "Metric": "MAUs" + } + ], + "score": 0.99, + "annotation": "The Units of Premium Subscribers and MAUs show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Premium Subscribers and MAUs is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Quarter": "Q2 2018", + "Units": 83.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q2 2018", + "Units": 46.0, + "Metric": "Subscribers/MAUs" + } + ], + "score": 0.98, + "annotation": "The Units of Premium Subscribers and Subscribers/MAUs show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Premium Subscribers and Subscribers/MAUs is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Quarter": "Q2 2018", + "Units": 180.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q2 2018", + "Units": 46.0, + "Metric": "Subscribers/MAUs" + } + ], + "score": 0.97, + "annotation": "The Units of MAUs and Subscribers/MAUs show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between MAUs and Subscribers/MAUs is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Quarter": "Q2 2018", + "Units": 83.0, + "Metric": "Premium Subscribers" + } + ], + "score": 0.87, + "annotation": "The Premium Subscribers has a maximum value at Q2 2018", + "reason": "The [{'Quarter': 'Q2 2018', 'Units': 83.0, 'Metric': 'Premium Subscribers'}] of Premium Subscribers has a maximum value of 83.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q3 2017", + "Units": 148.0, + "Metric": "MAUs" + } + ], + "score": 0.84, + "annotation": "The MAUs accounts for the majority in Q3 2017.", + "reason": "The Units of MAUs in Q3 2017 accounts for 0.5803921568627451 (more than 50%) of the total of Units of all Metric." + } + ], + "data": { + "data": [ + { + "Quarter": "Q1 2015", + "Units": 18.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q1 2015", + "Units": 68.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q1 2015", + "Units": 26.0, + "Metric": "Subscribers/MAUs" + }, + { + "Quarter": "Q2 2015", + "Units": 22.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q2 2015", + "Units": 75.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q2 2015", + "Units": 29.0, + "Metric": "Subscribers/MAUs" + }, + { + "Quarter": "Q3 2015", + "Units": 24.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q3 2015", + "Units": 83.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q3 2015", + "Units": 29.0, + "Metric": "Subscribers/MAUs" + }, + { + "Quarter": "Q4 2015", + "Units": 28.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q4 2015", + "Units": 91.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q4 2015", + "Units": 31.0, + "Metric": "Subscribers/MAUs" + }, + { + "Quarter": "Q1 2016", + "Units": 31.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q1 2016", + "Units": 99.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q1 2016", + "Units": 31.0, + "Metric": "Subscribers/MAUs" + }, + { + "Quarter": "Q2 2016", + "Units": 35.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q2 2016", + "Units": 100.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q2 2016", + "Units": 35.0, + "Metric": "Subscribers/MAUs" + }, + { + "Quarter": "Q3 2016", + "Units": 39.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q3 2016", + "Units": 111.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q3 2016", + "Units": 35.0, + "Metric": "Subscribers/MAUs" + }, + { + "Quarter": "Q4 2016", + "Units": 43.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q4 2016", + "Units": 124.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q4 2016", + "Units": 35.0, + "Metric": "Subscribers/MAUs" + }, + { + "Quarter": "Q1 2017", + "Units": 49.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q1 2017", + "Units": 127.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q1 2017", + "Units": 39.0, + "Metric": "Subscribers/MAUs" + }, + { + "Quarter": "Q2 2017", + "Units": 58.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q2 2017", + "Units": 148.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q2 2017", + "Units": 39.0, + "Metric": "Subscribers/MAUs" + }, + { + "Quarter": "Q3 2017", + "Units": 64.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q3 2017", + "Units": 148.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q3 2017", + "Units": 43.0, + "Metric": "Subscribers/MAUs" + }, + { + "Quarter": "Q4 2017", + "Units": 71.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q4 2017", + "Units": 168.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q4 2017", + "Units": 42.0, + "Metric": "Subscribers/MAUs" + }, + { + "Quarter": "Q1 2018", + "Units": 75.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q1 2018", + "Units": 170.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q1 2018", + "Units": 45.0, + "Metric": "Subscribers/MAUs" + }, + { + "Quarter": "Q2 2018", + "Units": 83.0, + "Metric": "Premium Subscribers" + }, + { + "Quarter": "Q2 2018", + "Units": 180.0, + "Metric": "MAUs" + }, + { + "Quarter": "Q2 2018", + "Units": 46.0, + "Metric": "Subscribers/MAUs" + } + ], + "columns": [ + { + "name": "Quarter", + "importance": "primary", + "description": "Represents the quarter and year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding metric", + "role": "y" + }, + { + "name": "Metric", + "importance": "primary", + "description": "The metric of the data point", + "role": "group" + } + ] + } + }, + "2248": { + "metadata": { + "title": "Despite Covid, Vaccines Account for Minor Share of Pharma Sales", + "description": "Estimated global vaccine revenue as a share of total pharmaceuticals revenue", + "main_insight": "While vaccine revenue increased significantly in 2021, it still represented a relatively small portion of overall pharmaceutical sales compared to other drugs." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2019, + "Vaccines": 32.8 + }, + { + "Year": 2020, + "Vaccines": 34.1 + }, + { + "Year": 2021, + "Vaccines": 88.6 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of revenue", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Vaccines", + "importance": "primary", + "description": "Global vaccine revenue in billions of dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2033": { + "metadata": { + "title": "The U.S. and China's Battle for Global Reputation", + "description": "Countries with most respondents with favorable views towards the U.S. or China (in %)", + "main_insight": "The chart compares favorable views towards the U.S. and China across several countries, revealing that China generally holds a more favorable reputation in Russia, Singapore, and Greece, while the U.S. is favored in the presented European and Asian Countries." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Russia", + "Units": 39.0, + "Favorability": "U.S. Favorability" + } + ], + "score": 0.9, + "annotation": "The U.S. Favorability has a minimum value at Russia", + "reason": "The [{'Country': 'Russia', 'Units': 39.0, 'Favorability': 'U.S. Favorability'}] of U.S. Favorability has a mainimum value of 39.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Japan", + "Units": 71.0, + "Favorability": "U.S. Favorability" + }, + { + "Country": "Japan", + "Units": 10.0, + "Favorability": "China Favorability" + } + ], + "score": 0.89, + "annotation": "The Units of U.S. Favorability and China Favorability show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between U.S. Favorability and China Favorability is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Russia", + "Units": 74.0, + "Favorability": "China Favorability" + } + ], + "score": 0.82, + "annotation": "The China Favorability has a maximum value at Russia", + "reason": "The [{'Country': 'Russia', 'Units': 74.0, 'Favorability': 'China Favorability'}] of China Favorability has a maximum value of 74.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Singapore", + "Units": 61.0, + "Favorability": "China Favorability" + } + ], + "score": 0.79, + "annotation": "The China Favorability accounts for the majority in Singapore.", + "reason": "The Units of China Favorability in Singapore accounts for 0.5446428571428571 (more than 50%) of the total of Units of all Favorability." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Greece", + "Units": 63.0, + "Favorability": "U.S. Favorability" + } + ], + "score": 0.78, + "annotation": "The U.S. Favorability accounts for the majority in Greece.", + "reason": "The Units of U.S. Favorability in Greece accounts for 0.5478260869565217 (more than 50%) of the total of Units of all Favorability." + } + ], + "data": { + "data": [ + { + "Country": "Russia", + "Units": 39.0, + "Favorability": "U.S. Favorability" + }, + { + "Country": "Russia", + "Units": 74.0, + "Favorability": "China Favorability" + }, + { + "Country": "Singapore", + "Units": 51.0, + "Favorability": "U.S. Favorability" + }, + { + "Country": "Singapore", + "Units": 61.0, + "Favorability": "China Favorability" + }, + { + "Country": "Greece", + "Units": 63.0, + "Favorability": "U.S. Favorability" + }, + { + "Country": "Greece", + "Units": 52.0, + "Favorability": "China Favorability" + }, + { + "Country": "Spain", + "Units": 62.0, + "Favorability": "U.S. Favorability" + }, + { + "Country": "Spain", + "Units": 39.0, + "Favorability": "China Favorability" + }, + { + "Country": "Italy", + "Units": 74.0, + "Favorability": "U.S. Favorability" + }, + { + "Country": "Italy", + "Units": 38.0, + "Favorability": "China Favorability" + }, + { + "Country": "South Korea", + "Units": 77.0, + "Favorability": "U.S. Favorability" + }, + { + "Country": "South Korea", + "Units": 22.0, + "Favorability": "China Favorability" + }, + { + "Country": "U.S.", + "Units": 76.0, + "Favorability": "U.S. Favorability" + }, + { + "Country": "U.S.", + "Units": 20.0, + "Favorability": "China Favorability" + }, + { + "Country": "Japan", + "Units": 71.0, + "Favorability": "U.S. Favorability" + }, + { + "Country": "Japan", + "Units": 10.0, + "Favorability": "China Favorability" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where the survey was conducted", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding favorability", + "role": "y" + }, + { + "name": "Favorability", + "importance": "primary", + "description": "The favorability of the data point", + "role": "group" + } + ] + } + }, + "1764": { + "metadata": { + "title": "Youth Unemployment Hits High in China", + "description": "Monthly urban unemployment rate of people aged 16 to 24 in China", + "main_insight": "Youth unemployment in China has risen significantly since 2021, reaching a high of 21.3% in the latest data." + }, + "chart_type": "Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2021, + "Unemployment Rate": 14.3 + }, + { + "Year": 2022, + "Unemployment Rate": 16.7 + }, + { + "Year": 2023, + "Unemployment Rate": 21.3 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Unemployment Rate", + "importance": "primary", + "description": "Monthly urban unemployment rate (%) of people aged 16 to 24", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1524": { + "metadata": { + "title": "Where The World's Fake Goods Originate", + "description": "Top origin economies of fakes, as % of total seizures (2013)", + "main_insight": "China is the primary origin of fake goods, accounting for 63.2% of total seizures in 2013, significantly more than any other country." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "China", + "Seizure Percentage": 63.2 + } + ], + "score": 0.98, + "annotation": "The has a maximum value at China", + "reason": "The [{'Country': 'China', 'Seizure Percentage': 63.2}] of has a maximum value of 63.2, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "China", + "Seizure Percentage": 63.2 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Seizure Percentage from Turkey to China.", + "reason": "The Seizure Percentage for increases from 3.3 at Turkey to 63.2 at China, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Pakistan", + "Seizure Percentage": 0.4 + }, + { + "Country": "Egypt", + "Seizure Percentage": 0.4 + } + ], + "score": 0.3, + "annotation": "The has minimum values at Pakistan, Egypt", + "reason": "The [{'Country': 'Pakistan', 'Seizure Percentage': 0.4}, {'Country': 'Egypt', 'Seizure Percentage': 0.4}] of have minimum values of 0.4, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "China", + "Seizure Percentage": 63.2 + }, + { + "Country": "Turkey", + "Seizure Percentage": 3.3 + }, + { + "Country": "Singapore", + "Seizure Percentage": 1.9 + }, + { + "Country": "Thailand", + "Seizure Percentage": 1.6 + }, + { + "Country": "India", + "Seizure Percentage": 1.2 + }, + { + "Country": "Morocco", + "Seizure Percentage": 0.6 + }, + { + "Country": "United Arab Emirates", + "Seizure Percentage": 0.5 + }, + { + "Country": "Pakistan", + "Seizure Percentage": 0.4 + }, + { + "Country": "Egypt", + "Seizure Percentage": 0.4 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Origin country of fake goods", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Seizure Percentage", + "importance": "primary", + "description": "Percentage of total seizures", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1750": { + "metadata": { + "title": "Pope Francis is Highly Popular One Year On", + "description": "Trends in papal favorability among U.S. Catholics", + "main_insight": "Pope Francis's favorability ratings among US Catholics in his first year are comparable to or exceed those of his predecessors, John Paul II and Benedict XVI." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 1996, + "Score": 44.0, + "Favorability": "John Paul II Mostly" + }, + { + "Year": 2014, + "Score": 85.0, + "Favorability": "Francis Net favorable" + } + ], + "score": 1.0, + "annotation": "The Score of John Paul II Mostly and Francis Net favorable show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between John Paul II Mostly and Francis Net favorable is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": "Feb 2013", + "Score": 32.0, + "Favorability": "Benedict XVI Very" + }, + { + "Year": "Feb 2013", + "Score": 74.0, + "Favorability": "Benedict XVI Net favorable" + } + ], + "score": 0.98, + "annotation": "The Score of Benedict XVI Very and Benedict XVI Net favorable show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Benedict XVI Very and Benedict XVI Net favorable is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": "Feb 2013", + "Score": 32.0, + "Favorability": "Benedict XVI Very" + }, + { + "Year": "Feb 2013", + "Score": 41.0, + "Favorability": "Benedict XVI Mostly" + } + ], + "score": 0.98, + "annotation": "The Score of Benedict XVI Very and Benedict XVI Mostly show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Benedict XVI Very and Benedict XVI Mostly is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2014, + "Score": 51.0, + "Favorability": "Francis Very" + }, + { + "Year": 2014, + "Score": 34.0, + "Favorability": "Francis Mostly" + } + ], + "score": 0.95, + "annotation": "The Score of Francis Very and Francis Mostly show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Francis Very and Francis Mostly is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 1996, + "Score": 49.0, + "Favorability": "John Paul II Very" + }, + { + "Year": 2014, + "Score": 85.0, + "Favorability": "Francis Net favorable" + } + ], + "score": 0.94, + "annotation": "The Score of John Paul II Very and Francis Net favorable show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between John Paul II Very and Francis Net favorable is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 1987, + "Score": 48.0, + "Favorability": "John Paul II Very" + }, + { + "Year": 1987, + "Score": 43.0, + "Favorability": "John Paul II Mostly" + }, + { + "Year": 1987, + "Score": 91.0, + "Favorability": "John Paul II Net favorable" + }, + { + "Year": 1990, + "Score": 53.0, + "Favorability": "John Paul II Very" + }, + { + "Year": 1990, + "Score": 40.0, + "Favorability": "John Paul II Mostly" + }, + { + "Year": 1990, + "Score": 93.0, + "Favorability": "John Paul II Net favorable" + }, + { + "Year": 1996, + "Score": 49.0, + "Favorability": "John Paul II Very" + }, + { + "Year": 1996, + "Score": 44.0, + "Favorability": "John Paul II Mostly" + }, + { + "Year": 1996, + "Score": 93.0, + "Favorability": "John Paul II Net favorable" + }, + { + "Year": 2005, + "Score": 17.0, + "Favorability": "Benedict XVI Very" + }, + { + "Year": 2005, + "Score": 50.0, + "Favorability": "Benedict XVI Mostly" + }, + { + "Year": 2005, + "Score": 67.0, + "Favorability": "Benedict XVI Net favorable" + }, + { + "Year": 2007, + "Score": 31.0, + "Favorability": "Benedict XVI Very" + }, + { + "Year": 2007, + "Score": 43.0, + "Favorability": "Benedict XVI Mostly" + }, + { + "Year": 2007, + "Score": 74.0, + "Favorability": "Benedict XVI Net favorable" + }, + { + "Year": "Mar 2008", + "Score": 36.0, + "Favorability": "Benedict XVI Very" + }, + { + "Year": "Mar 2008", + "Score": 38.0, + "Favorability": "Benedict XVI Mostly" + }, + { + "Year": "Mar 2008", + "Score": 74.0, + "Favorability": "Benedict XVI Net favorable" + }, + { + "Year": "Apr 2008", + "Score": 49.0, + "Favorability": "Benedict XVI Very" + }, + { + "Year": "Apr 2008", + "Score": 34.0, + "Favorability": "Benedict XVI Mostly" + }, + { + "Year": "Apr 2008", + "Score": 83.0, + "Favorability": "Benedict XVI Net favorable" + }, + { + "Year": "Feb 2013", + "Score": 32.0, + "Favorability": "Benedict XVI Very" + }, + { + "Year": "Feb 2013", + "Score": 41.0, + "Favorability": "Benedict XVI Mostly" + }, + { + "Year": "Feb 2013", + "Score": 74.0, + "Favorability": "Benedict XVI Net favorable" + }, + { + "Year": "Mar 2013", + "Score": 43.0, + "Favorability": "Francis Very" + }, + { + "Year": "Mar 2013", + "Score": 41.0, + "Favorability": "Francis Mostly" + }, + { + "Year": "Mar 2013", + "Score": 84.0, + "Favorability": "Francis Net favorable" + }, + { + "Year": "Sep 2013", + "Score": 37.0, + "Favorability": "Francis Very" + }, + { + "Year": "Sep 2013", + "Score": 42.0, + "Favorability": "Francis Mostly" + }, + { + "Year": "Sep 2013", + "Score": 79.0, + "Favorability": "Francis Net favorable" + }, + { + "Year": 2014, + "Score": 51.0, + "Favorability": "Francis Very" + }, + { + "Year": 2014, + "Score": 34.0, + "Favorability": "Francis Mostly" + }, + { + "Year": 2014, + "Score": 85.0, + "Favorability": "Francis Net favorable" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of favorability rating", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding favorability", + "role": "y" + }, + { + "name": "Favorability", + "importance": "primary", + "description": "The favorability of the data point", + "role": "group" + } + ] + } + }, + "2391": { + "metadata": { + "title": "Trump's Taller Wall Is Killing Migrants", + "description": "Deaths/injuries from jumping/falling off the border wall in the San Diego area before and after it was heightened", + "main_insight": "Increasing the height of the border wall in the San Diego area from 18-ft to 30-ft has led to a significant increase in deaths, hospital admissions, and hospital costs." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Metric": "Median UCSD hospital costs*", + "18-ft wall (2016-2018)": 30714.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in 18-ft wall (2016-2018) from Hospital admissions for border wall falls (UCSD hospital) to Median UCSD hospital costs*.", + "reason": "The 18-ft wall (2016-2018) for increases from 67.0 at Hospital admissions for border wall falls (UCSD hospital) to 30714.0 at Median UCSD hospital costs*, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Metric": "Median UCSD hospital costs*", + "18-ft wall (2016-2018)": 30714.0 + } + ], + "score": 0.67, + "annotation": "The has a maximum value at Median UCSD hospital costs*", + "reason": "The [{'Metric': 'Median UCSD hospital costs*', '18-ft wall (2016-2018)': 30714.0}] of has a maximum value of 30714.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Metric": "Deaths (San Diego-Imperial county/UCSD hospital)", + "18-ft wall (2016-2018)": 0.0 + } + ], + "score": 0.39, + "annotation": "The has a minimum value at Deaths (San Diego-Imperial county/UCSD hospital)", + "reason": "The [{'Metric': 'Deaths (San Diego-Imperial county/UCSD hospital)', '18-ft wall (2016-2018)': 0.0}] of has a mainimum value of 0.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Metric": "Deaths (San Diego-Imperial county/UCSD hospital)", + "18-ft wall (2016-2018)": 0.0 + }, + { + "Metric": "Hospital admissions for border wall falls (UCSD hospital)", + "18-ft wall (2016-2018)": 67.0 + }, + { + "Metric": "Median UCSD hospital costs*", + "18-ft wall (2016-2018)": 30714.0 + } + ], + "columns": [ + { + "name": "Metric", + "importance": "primary", + "description": "Type of statistic measured", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "18-ft wall (2016-2018)", + "importance": "primary", + "description": "Values for the period when the wall was 18-ft tall", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "636": { + "metadata": { + "title": "Stocks Emerge From Covid Crash With Historic 12-Month Run", + "description": "Performance of major U.S. stock market indices since January 2020 (indexed to closing prices on March 23, 2021)", + "main_insight": "Major U.S. stock market indices experienced a significant crash and subsequent recovery between January 2020 and March 2021, with the Nasdaq showing the highest growth." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "Mar '21", + "Units": 94.99, + "Index": "Nasdaq" + }, + { + "Month": "Mar '21", + "Units": 76.12, + "Index": "S&P 500" + } + ], + "score": 0.99, + "annotation": "The Units of Nasdaq and S&P 500 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Nasdaq and S&P 500 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "Mar '21", + "Units": 76.12, + "Index": "S&P 500" + }, + { + "Month": "Mar '21", + "Units": 76.05, + "Index": "Dow Jones" + } + ], + "score": 0.98, + "annotation": "The Units of S&P 500 and Dow Jones show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between S&P 500 and Dow Jones is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "Mar '21", + "Units": 94.99, + "Index": "Nasdaq" + }, + { + "Month": "Mar '21", + "Units": 76.05, + "Index": "Dow Jones" + } + ], + "score": 0.96, + "annotation": "The Units of Nasdaq and Dow Jones show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Nasdaq and Dow Jones is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "May '20", + "Units": 40.0, + "Index": "Nasdaq" + } + ], + "score": 0.89, + "annotation": "The Nasdaq has the largest proportion in May '20.", + "reason": "The Units of Nasdaq in May '20 accounts for 0.36363636363636365, larger than all other Index, of the total of Units of all Index." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Sep '20", + "Units": 65.0, + "Index": "Nasdaq" + } + ], + "score": 0.89, + "annotation": "The Nasdaq has the largest proportion in Sep '20.", + "reason": "The Units of Nasdaq in Sep '20 accounts for 0.37142857142857144, larger than all other Index, of the total of Units of all Index." + } + ], + "data": { + "data": [ + { + "Month": "Jan '20", + "Units": 35.0, + "Index": "Nasdaq" + }, + { + "Month": "Jan '20", + "Units": 35.0, + "Index": "S&P 500" + }, + { + "Month": "Jan '20", + "Units": 45.0, + "Index": "Dow Jones" + }, + { + "Month": "Mar '20", + "Units": 5.0, + "Index": "Nasdaq" + }, + { + "Month": "Mar '20", + "Units": 10.0, + "Index": "S&P 500" + }, + { + "Month": "Mar '20", + "Units": 20.0, + "Index": "Dow Jones" + }, + { + "Month": "May '20", + "Units": 40.0, + "Index": "Nasdaq" + }, + { + "Month": "May '20", + "Units": 35.0, + "Index": "S&P 500" + }, + { + "Month": "May '20", + "Units": 35.0, + "Index": "Dow Jones" + }, + { + "Month": "Jul '20", + "Units": 60.0, + "Index": "Nasdaq" + }, + { + "Month": "Jul '20", + "Units": 50.0, + "Index": "S&P 500" + }, + { + "Month": "Jul '20", + "Units": 50.0, + "Index": "Dow Jones" + }, + { + "Month": "Sep '20", + "Units": 65.0, + "Index": "Nasdaq" + }, + { + "Month": "Sep '20", + "Units": 55.0, + "Index": "S&P 500" + }, + { + "Month": "Sep '20", + "Units": 55.0, + "Index": "Dow Jones" + }, + { + "Month": "Nov '20", + "Units": 85.0, + "Index": "Nasdaq" + }, + { + "Month": "Nov '20", + "Units": 65.0, + "Index": "S&P 500" + }, + { + "Month": "Nov '20", + "Units": 60.0, + "Index": "Dow Jones" + }, + { + "Month": "Jan '21", + "Units": 95.0, + "Index": "Nasdaq" + }, + { + "Month": "Jan '21", + "Units": 65.0, + "Index": "S&P 500" + }, + { + "Month": "Jan '21", + "Units": 65.0, + "Index": "Dow Jones" + }, + { + "Month": "Mar '21", + "Units": 94.99, + "Index": "Nasdaq" + }, + { + "Month": "Mar '21", + "Units": 76.12, + "Index": "S&P 500" + }, + { + "Month": "Mar '21", + "Units": 76.05, + "Index": "Dow Jones" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Timeline from January 2020 to March 2021", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding index", + "role": "y" + }, + { + "name": "Index", + "importance": "primary", + "description": "The index of the data point", + "role": "group" + } + ] + } + }, + "412": { + "metadata": { + "title": "Fitbit's Uphill Task To Conquer Indian Wearables Market", + "description": "Share of wearables market across India as of Q2 2018, by company", + "main_insight": "Xiaomi dominated the Indian wearables market in Q2 2018 with a 45.8% share, while Fitbit held a significantly smaller share of 3.8%." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Company": "Xiaomi", + "Market Share": 45.8 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at Xiaomi", + "reason": "The [{'Company': 'Xiaomi', 'Market Share': 45.8}] of has a maximum value of 45.8, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "Xiaomi", + "Market Share": 45.8 + } + ], + "score": 0.87, + "annotation": "The shows a sudden jump in Market Share from Others to Xiaomi.", + "reason": "The Market Share for increases from 21.3 at Others to 45.8 at Xiaomi, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Company": "Lenovo", + "Market Share": 3.0 + } + ], + "score": 0.52, + "annotation": "The has a minimum value at Lenovo", + "reason": "The [{'Company': 'Lenovo', 'Market Share': 3.0}] of has a mainimum value of 3.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Company": "Xiaomi", + "Market Share": 45.8 + }, + { + "Company": "GOQii", + "Market Share": 18.8 + }, + { + "Company": "Titan", + "Market Share": 7.4 + }, + { + "Company": "Fitbit", + "Market Share": 3.8 + }, + { + "Company": "Lenovo", + "Market Share": 3.0 + }, + { + "Company": "Others", + "Market Share": 21.3 + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Name of the wearable company", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Market Share", + "importance": "primary", + "description": "Percentage of the wearables market share in India as of Q2 2018", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1432": { + "metadata": { + "title": "Where People Are Most Optimistic About The Vaccine Race", + "description": "% who agree that a Covid-19 vaccine will be available before the end of 2020", + "main_insight": "There is a wide range of optimism across countries regarding the availability of a COVID-19 vaccine by the end of 2020, with China and India being the most optimistic, while Japan and Germany are the least optimistic." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Japan", + "Score": 22.0, + "Agreement": "Agree" + }, + { + "Country": "Japan", + "Score": 78.0, + "Agreement": "Disagree" + } + ], + "score": 1.0, + "annotation": "The Score of Agree and Disagree show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Agree and Disagree is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "China", + "Score": 87.0, + "Agreement": "Agree" + } + ], + "score": 0.95, + "annotation": "The Agree has a maximum value at China", + "reason": "The [{'Country': 'China', 'Score': 87.0, 'Agreement': 'Agree'}] of Agree has a maximum value of 87.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "China", + "Score": 13.0, + "Agreement": "Disagree" + } + ], + "score": 0.95, + "annotation": "The Disagree has a minimum value at China", + "reason": "The [{'Country': 'China', 'Score': 13.0, 'Agreement': 'Disagree'}] of Disagree has a mainimum value of 13.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Brazil", + "Score": 51.0, + "Agreement": "Agree" + } + ], + "score": 0.8, + "annotation": "The Agree accounts for the majority in Brazil.", + "reason": "The Score of Agree in Brazil accounts for 0.51 (more than 50%) of the total of Score of all Agreement." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Russia", + "Score": 53.0, + "Agreement": "Disagree" + } + ], + "score": 0.79, + "annotation": "The Disagree accounts for the majority in Russia.", + "reason": "The Score of Disagree in Russia accounts for 0.53 (more than 50%) of the total of Score of all Agreement." + } + ], + "data": { + "data": [ + { + "Country": "China", + "Score": 87.0, + "Agreement": "Agree" + }, + { + "Country": "China", + "Score": 13.0, + "Agreement": "Disagree" + }, + { + "Country": "India", + "Score": 74.0, + "Agreement": "Agree" + }, + { + "Country": "India", + "Score": 26.0, + "Agreement": "Disagree" + }, + { + "Country": "Brazil", + "Score": 51.0, + "Agreement": "Agree" + }, + { + "Country": "Brazil", + "Score": 49.0, + "Agreement": "Disagree" + }, + { + "Country": "Russia", + "Score": 47.0, + "Agreement": "Agree" + }, + { + "Country": "Russia", + "Score": 53.0, + "Agreement": "Disagree" + }, + { + "Country": "France", + "Score": 40.0, + "Agreement": "Agree" + }, + { + "Country": "France", + "Score": 61.0, + "Agreement": "Disagree" + }, + { + "Country": "Spain", + "Score": 36.0, + "Agreement": "Agree" + }, + { + "Country": "Spain", + "Score": 64.0, + "Agreement": "Disagree" + }, + { + "Country": "United States", + "Score": 34.0, + "Agreement": "Agree" + }, + { + "Country": "United States", + "Score": 66.0, + "Agreement": "Disagree" + }, + { + "Country": "South Korea", + "Score": 34.0, + "Agreement": "Agree" + }, + { + "Country": "South Korea", + "Score": 66.0, + "Agreement": "Disagree" + }, + { + "Country": "United Kingdom", + "Score": 31.0, + "Agreement": "Agree" + }, + { + "Country": "United Kingdom", + "Score": 69.0, + "Agreement": "Disagree" + }, + { + "Country": "Canada", + "Score": 26.0, + "Agreement": "Agree" + }, + { + "Country": "Canada", + "Score": 74.0, + "Agreement": "Disagree" + }, + { + "Country": "Germany", + "Score": 24.0, + "Agreement": "Agree" + }, + { + "Country": "Germany", + "Score": 77.0, + "Agreement": "Disagree" + }, + { + "Country": "Japan", + "Score": 22.0, + "Agreement": "Agree" + }, + { + "Country": "Japan", + "Score": 78.0, + "Agreement": "Disagree" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding agreement", + "role": "y" + }, + { + "name": "Agreement", + "importance": "primary", + "description": "The agreement of the data point", + "role": "group" + } + ] + } + }, + "2069": { + "metadata": { + "title": "Online Travel Platforms Bring International Guests to American Hotels", + "description": "Share of nights booked by international travelers on online travel platforms in the US compared to whole market", + "main_insight": "Online travel platforms account for a significantly larger share of hotel nights booked by international travelers in the US compared to the overall market, though the share decreased after 2019." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Units": 10.0, + "Source": "Online travel platforms" + }, + { + "Year": 2021, + "Units": 4.0, + "Source": "Whole market" + } + ], + "score": 0.99, + "annotation": "The Units of Online travel platforms and Whole market show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Online travel platforms and Whole market is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2019, + "Units": 46.0, + "Source": "Online travel platforms" + }, + { + "Year": 2019, + "Units": 15.0, + "Source": "Whole market" + }, + { + "Year": 2020, + "Units": 17.0, + "Source": "Online travel platforms" + }, + { + "Year": 2020, + "Units": 5.0, + "Source": "Whole market" + }, + { + "Year": 2021, + "Units": 10.0, + "Source": "Online travel platforms" + }, + { + "Year": 2021, + "Units": 4.0, + "Source": "Whole market" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of booking", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding source", + "role": "y" + }, + { + "name": "Source", + "importance": "primary", + "description": "The source of the data point", + "role": "group" + } + ] + } + }, + "2291": { + "metadata": { + "title": "Record Number Of Women Win House Seats", + "description": "Total number of women elected to the U.S. House of Representatives", + "main_insight": "The number of women elected to the U.S. House of Representatives has significantly increased over time, reaching a record high in recent years." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Congress": 65, + "Number of Women": 1 + }, + { + "Congress": 66, + "Number of Women": 1 + }, + { + "Congress": 67, + "Number of Women": 2 + }, + { + "Congress": 68, + "Number of Women": 3 + }, + { + "Congress": 69, + "Number of Women": 7 + }, + { + "Congress": 70, + "Number of Women": 5 + }, + { + "Congress": 71, + "Number of Women": 9 + }, + { + "Congress": 72, + "Number of Women": 8 + }, + { + "Congress": 73, + "Number of Women": 8 + }, + { + "Congress": 74, + "Number of Women": 7 + }, + { + "Congress": 75, + "Number of Women": 9 + }, + { + "Congress": 76, + "Number of Women": 8 + }, + { + "Congress": 77, + "Number of Women": 11 + }, + { + "Congress": 78, + "Number of Women": 10 + }, + { + "Congress": 79, + "Number of Women": 11 + }, + { + "Congress": 80, + "Number of Women": 10 + }, + { + "Congress": 81, + "Number of Women": 16 + }, + { + "Congress": 82, + "Number of Women": 13 + }, + { + "Congress": 83, + "Number of Women": 12 + }, + { + "Congress": 84, + "Number of Women": 17 + }, + { + "Congress": 85, + "Number of Women": 16 + }, + { + "Congress": 86, + "Number of Women": 17 + }, + { + "Congress": 87, + "Number of Women": 20 + }, + { + "Congress": 88, + "Number of Women": 18 + }, + { + "Congress": 89, + "Number of Women": 12 + }, + { + "Congress": 90, + "Number of Women": 11 + }, + { + "Congress": 91, + "Number of Women": 14 + }, + { + "Congress": 92, + "Number of Women": 15 + }, + { + "Congress": 93, + "Number of Women": 14 + }, + { + "Congress": 94, + "Number of Women": 16 + }, + { + "Congress": 95, + "Number of Women": 15 + }, + { + "Congress": 96, + "Number of Women": 19 + }, + { + "Congress": 97, + "Number of Women": 20 + }, + { + "Congress": 98, + "Number of Women": 20 + }, + { + "Congress": 99, + "Number of Women": 24 + }, + { + "Congress": 100, + "Number of Women": 25 + }, + { + "Congress": 101, + "Number of Women": 25 + }, + { + "Congress": 102, + "Number of Women": 29 + }, + { + "Congress": 103, + "Number of Women": 36 + }, + { + "Congress": 104, + "Number of Women": 48 + }, + { + "Congress": 105, + "Number of Women": 53 + }, + { + "Congress": 106, + "Number of Women": 59 + }, + { + "Congress": 107, + "Number of Women": 59 + }, + { + "Congress": 108, + "Number of Women": 64 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lifeguards with the highest compensation in 2021 (in U.S. dollars)", + "main_insight": "Los Angeles County lifeguards earned significant amounts in 2021, with some individuals exceeding $500,000 in total compensation, composed of base pay, other pay/benefits, and overtime." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Lifeguard": "Patrick O'Neill, Lifeguard Captain", + "Dollars": 112396.0, + "Compensation Components": "Overtime" + } + ], + "score": 0.94, + "annotation": "The Overtime shows a sudden jump in Dollars from Luke Williams, Ocean Lifeguard Specialist to Patrick O'Neill, Lifeguard Captain.", + "reason": "The Dollars for Overtime increases from 76003.0 at Luke Williams, Ocean Lifeguard Specialist to 112396.0 at Patrick O'Neill, Lifeguard Captain, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Lifeguard": "Fernando Boiteux, Chief", + "Dollars": 215349.0, + "Compensation Components": "Base Pay" + } + ], + "score": 0.94, + "annotation": "The Base Pay has a maximum value at Fernando Boiteux, Chief", + "reason": "The [{'Lifeguard': 'Fernando Boiteux, Chief', 'Dollars': 215349.0, 'Compensation Components': 'Base Pay'}] of Base Pay has a maximum value of 215349.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Lifeguard": "Kenichi Ballew-Haskett, Section Chief", + "Dollars": 409414.0, + "Compensation Components": "Total Compensation" + } + ], + "score": 0.9, + "annotation": "The Total Compensation has the largest proportion in Kenichi Ballew-Haskett, Section Chief.", + "reason": "The Dollars of Total Compensation in Kenichi Ballew-Haskett, Section Chief accounts for 0.4377078747304511, larger than all other Compensation Components, of the total of Dollars of all Compensation Components." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Lifeguard": "Fernando Boiteux, Chief", + "Dollars": 463917.0, + "Compensation Components": "Total Compensation" + } + ], + "score": 0.9, + "annotation": "The Total Compensation has the largest proportion in Fernando Boiteux, Chief.", + "reason": "The Dollars of Total Compensation in Fernando Boiteux, Chief accounts for 0.44597540928448515, larger than all other Compensation Components, of the total of Dollars of all Compensation Components." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Lifeguard": "Adam Uehara, Section Chief", + "Dollars": 371585.0, + "Compensation Components": "Total Compensation" + } + ], + "score": 0.9, + "annotation": "The Total Compensation has the largest proportion in Adam Uehara, Section Chief.", + "reason": "The Dollars of Total Compensation in Adam Uehara, Section Chief accounts for 0.45603827145190723, larger than all other Compensation Components, of the total of Dollars of all Compensation Components." + } + ], + "data": { + "data": [ + { + "Lifeguard": "Daniel Douglas, Captain", + "Dollars": 510283.0, + "Compensation Components": "Total Compensation" + }, + { + "Lifeguard": "Daniel Douglas, Captain", + "Dollars": 150034.0, + "Compensation Components": "Base Pay" + }, + { + "Lifeguard": "Daniel Douglas, Captain", + "Dollars": 289345.0, + "Compensation Components": "Other pay/benefits" + }, + { + "Lifeguard": "Daniel Douglas, Captain", + "Dollars": 70904.0, + "Compensation Components": "Overtime" + }, + { + "Lifeguard": "Fernando Boiteux, Chief", + "Dollars": 463917.0, + "Compensation Components": "Total Compensation" + }, + { + "Lifeguard": "Fernando Boiteux, Chief", + "Dollars": 215349.0, + "Compensation Components": "Base Pay" + }, + { + "Lifeguard": "Fernando Boiteux, Chief", + "Dollars": 248568.0, + "Compensation Components": "Other pay/benefits" + }, + { + "Lifeguard": "Kenichi Ballew-Haskett, Section Chief", + "Dollars": 409414.0, + "Compensation Components": "Total Compensation" + }, + { + "Lifeguard": "Kenichi Ballew-Haskett, Section Chief", + "Dollars": 167399.0, + "Compensation Components": "Base Pay" + }, + { + "Lifeguard": "Kenichi Ballew-Haskett, Section Chief", + "Dollars": 242015.0, + "Compensation Components": "Other pay/benefits" + }, + { + "Lifeguard": "Patrick O'Neill, Lifeguard Captain", + "Dollars": 383032.0, + "Compensation Components": "Total Compensation" + }, + { + "Lifeguard": "Patrick O'Neill, Lifeguard Captain", + "Dollars": 131674.0, + "Compensation Components": "Base Pay" + }, + { + "Lifeguard": "Patrick O'Neill, Lifeguard Captain", + "Dollars": 138962.0, + "Compensation Components": "Other pay/benefits" + }, + { + "Lifeguard": "Patrick O'Neill, Lifeguard Captain", + "Dollars": 112396.0, + "Compensation Components": "Overtime" + }, + { + "Lifeguard": "Adam Uehara, Section Chief", + "Dollars": 371585.0, + "Compensation Components": "Total Compensation" + }, + { + "Lifeguard": "Adam Uehara, Section Chief", + "Dollars": 145655.0, + "Compensation Components": "Base Pay" + }, + { + "Lifeguard": "Adam Uehara, Section Chief", + "Dollars": 225930.0, + "Compensation Components": "Other pay/benefits" + }, + { + "Lifeguard": "Remy Smith, Lifeguard Captain", + "Dollars": 358166.0, + "Compensation Components": "Total Compensation" + }, + { + "Lifeguard": "Remy Smith, Lifeguard Captain", + "Dollars": 125637.0, + "Compensation Components": "Base Pay" + }, + { + "Lifeguard": "Remy Smith, Lifeguard Captain", + "Dollars": 115998.0, + "Compensation Components": "Other pay/benefits" + }, + { + "Lifeguard": "Remy Smith, Lifeguard Captain", + "Dollars": 116531.0, + "Compensation Components": "Overtime" + }, + { + "Lifeguard": "Luke Williams, Ocean Lifeguard Specialist", + "Dollars": 350204.0, + "Compensation Components": "Total Compensation" + }, + { + "Lifeguard": "Luke Williams, Ocean Lifeguard Specialist", + "Dollars": 137534.0, + "Compensation Components": "Base Pay" + }, + { + "Lifeguard": "Luke Williams, Ocean Lifeguard Specialist", + "Dollars": 136667.0, + "Compensation Components": "Other pay/benefits" + }, + { + "Lifeguard": "Luke Williams, Ocean Lifeguard Specialist", + "Dollars": 76003.0, + "Compensation Components": "Overtime" + }, + { + "Lifeguard": "James Orr, Ocean Lifeguard Specialist", + "Dollars": 344346.0, + "Compensation Components": "Total Compensation" + }, + { + "Lifeguard": "James Orr, Ocean Lifeguard Specialist", + "Dollars": 120547.0, + "Compensation Components": "Base Pay" + }, + { + "Lifeguard": "James Orr, Ocean Lifeguard Specialist", + "Dollars": 152158.0, + "Compensation Components": "Other pay/benefits" + }, + { + "Lifeguard": "James Orr, Ocean Lifeguard Specialist", + "Dollars": 71641.0, + "Compensation Components": "Overtime" + } + ], + "columns": [ + { + "name": "Lifeguard", + "importance": "primary", + "description": "Name and title of the lifeguard", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding compensation components", + "role": "y" + }, + { + "name": "Compensation Components", + "importance": "primary", + "description": "The compensation components of the data point", + "role": "group" + } + ] + } + }, + "361": { + "metadata": { + "title": "Who's Getting The Most Sleep?", + "description": "Minutes above and below eight hours of sleep in selected countries", + "main_insight": "People in the Netherlands get the most sleep relative to an eight-hour benchmark, while people in Singapore get the least. There is a large gap between those sleeping more and those sleeping less than 8 hours." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Singapore", + "MinutesDifference": -36.23 + } + ], + "score": 0.94, + "annotation": "The has a minimum value at Singapore", + "reason": "The [{'Country': 'Singapore', 'MinutesDifference': -36.23}] of has a mainimum value of -36.23, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Netherlands", + "MinutesDifference": 5.22 + } + ], + "score": 0.69, + "annotation": "The has a maximum value at Netherlands", + "reason": "The [{'Country': 'Netherlands', 'MinutesDifference': 5.22}] of has a maximum value of 5.22, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Germany", + "MinutesDifference": -18.26 + } + ], + "score": 0.58, + "annotation": "The shows a sudden jump in MinutesDifference from Brazil to Germany.", + "reason": "The MinutesDifference for increases from -25.88 at Brazil to -18.26 at Germany, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Netherlands", + "MinutesDifference": 5.22 + }, + { + "Country": "New Zealand", + "MinutesDifference": 4.0 + }, + { + "Country": "France", + "MinutesDifference": 3.19 + }, + { + "Country": "Australia", + "MinutesDifference": 1.3 + }, + { + "Country": "Canada", + "MinutesDifference": -1.73 + }, + { + "Country": "United Arab Emirates", + "MinutesDifference": -3.16 + }, + { + "Country": "United Kingdom", + "MinutesDifference": -6.12 + }, + { + "Country": "Italy", + "MinutesDifference": -7.32 + }, + { + "Country": "United States", + "MinutesDifference": -7.88 + }, + { + "Country": "China", + "MinutesDifference": -8.51 + }, + { + "Country": "Switzerland", + "MinutesDifference": -9.85 + }, + { + "Country": "Mexico", + "MinutesDifference": -12.5 + }, + { + "Country": "Germany", + "MinutesDifference": -18.26 + }, + { + "Country": "Brazil", + "MinutesDifference": -25.88 + }, + { + "Country": "Japan", + "MinutesDifference": -29.49 + }, + { + "Country": "Singapore", + "MinutesDifference": -36.23 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country Name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "MinutesDifference", + "importance": "primary", + "description": "Minutes above or below eight hours of sleep", + "unit": "minutes", + "data_type": "number", + "role": "y" + } + ] + } + }, + "45": { + "metadata": { + "title": "Yahoo - Before and After 4 Years Under Marissa Mayer", + "description": "Revenue figures excluding traffic acquisition costs. Operating income excluding stock-based compensation expenses and other gains and expenses that aren't indicative of ongoing operating results.", + "main_insight": "Yahoo's financial performance, measured by various revenue streams and operating income, generally declined under Marissa Mayer's leadership between Q2 2012 and Q2 2016, with the exception of the Stock Price, although the number of Employees also shrunk." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Category": "Employees", + "Units": 12000.0, + "Metric": "Q2 2012" + }, + { + "Category": "Employees", + "Units": 8800.0, + "Metric": "Q2 2016" + } + ], + "score": 1.0, + "annotation": "The Units of Q2 2012 and Q2 2016 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Q2 2012 and Q2 2016 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "Employees", + "Units": 12000.0, + "Metric": "Q2 2012" + } + ], + "score": 0.97, + "annotation": "The Q2 2012 shows a sudden jump in Units from Display revenue to Employees.", + "reason": "The Units for Q2 2012 increases from 473.0 at Display revenue to 12000.0 at Employees, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "Employees", + "Units": 8800.0, + "Metric": "Q2 2016" + } + ], + "score": 0.97, + "annotation": "The Q2 2016 shows a sudden jump in Units from Display revenue to Employees.", + "reason": "The Units for Q2 2016 increases from 396.0 at Display revenue to 8800.0 at Employees, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Employees", + "Units": 12000.0, + "Metric": "Q2 2012" + } + ], + "score": 0.92, + "annotation": "The Q2 2012 has a maximum value at Employees", + "reason": "The [{'Category': 'Employees', 'Units': 12000.0, 'Metric': 'Q2 2012'}] of Q2 2012 has a maximum value of 12000.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Employees", + "Units": 8800.0, + "Metric": "Q2 2016" + } + ], + "score": 0.92, + "annotation": "The Q2 2016 has a maximum value at Employees", + "reason": "The [{'Category': 'Employees', 'Units': 8800.0, 'Metric': 'Q2 2016'}] of Q2 2016 has a maximum value of 8800.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Category": "Search revenue", + "Units": 386.0, + "Metric": "Q2 2012" + }, + { + "Category": "Search revenue", + "Units": 319.0, + "Metric": "Q2 2016" + }, + { + "Category": "Search revenue", + "Units": -0.17, + "Metric": "% Change" + }, + { + "Category": "Display revenue", + "Units": 473.0, + "Metric": "Q2 2012" + }, + { + "Category": "Display revenue", + "Units": 396.0, + "Metric": "Q2 2016" + }, + { + "Category": "Display revenue", + "Units": -0.16, + "Metric": "% Change" + }, + { + "Category": "Other revenue", + "Units": 222.0, + "Metric": "Q2 2012" + }, + { + "Category": "Other revenue", + "Units": 126.0, + "Metric": "Q2 2016" + }, + { + "Category": "Other revenue", + "Units": -0.43, + "Metric": "% Change" + }, + { + "Category": "Operating income", + "Units": 190.0, + "Metric": "Q2 2012" + }, + { + "Category": "Operating income", + "Units": 39.0, + "Metric": "Q2 2016" + }, + { + "Category": "Operating income", + "Units": -0.8, + "Metric": "% Change" + }, + { + "Category": "Stock price", + "Units": 15.83, + "Metric": "Q2 2012" + }, + { + "Category": "Stock price", + "Units": 37.56, + "Metric": "Q2 2016" + }, + { + "Category": "Stock price", + "Units": 1.37, + "Metric": "% Change" + }, + { + "Category": "Employees", + "Units": 12000.0, + "Metric": "Q2 2012" + }, + { + "Category": "Employees", + "Units": 8800.0, + "Metric": "Q2 2016" + }, + { + "Category": "Employees", + "Units": -0.27, + "Metric": "% Change" + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "The category of revenue, income, stock price, or employees", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding metric", + "role": "y" + }, + { + "name": "Metric", + "importance": "primary", + "description": "The metric of the data point", + "role": "group" + } + ] + } + }, + "1149": { + "metadata": { + "title": "The Countries With The Most Selfie-Related Deaths", + "description": "Deaths attributed to selfies worldwide since 2014", + "main_insight": "India has significantly more selfie-related deaths than any other country since 2014, with 19 reported deaths." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "India", + "Selfie-Related Deaths": 19 + } + ], + "score": 0.99, + "annotation": "The has a maximum value at India", + "reason": "The [{'Country': 'India', 'Selfie-Related Deaths': 19}] of has a maximum value of 19, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "India", + "Selfie-Related Deaths": 19 + } + ], + "score": 0.91, + "annotation": "The shows a sudden jump in Selfie-Related Deaths from Russia to India.", + "reason": "The Selfie-Related Deaths for increases from 7 at Russia to 19 at India, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "South Africa", + "Selfie-Related Deaths": 1 + }, + { + "Country": "Romania", + "Selfie-Related Deaths": 1 + }, + { + "Country": "Pakistan", + "Selfie-Related Deaths": 1 + }, + { + "Country": "Mexico", + "Selfie-Related Deaths": 1 + }, + { + "Country": "Italy", + "Selfie-Related Deaths": 1 + }, + { + "Country": "China", + "Selfie-Related Deaths": 1 + } + ], + "score": 0.38, + "annotation": "The has minimum values at South Africa, Romania, Pakistan, Mexico, Italy, China", + "reason": "The [{'Country': 'South Africa', 'Selfie-Related Deaths': 1}, {'Country': 'Romania', 'Selfie-Related Deaths': 1}, {'Country': 'Pakistan', 'Selfie-Related Deaths': 1}, {'Country': 'Mexico', 'Selfie-Related Deaths': 1}, {'Country': 'Italy', 'Selfie-Related Deaths': 1}, {'Country': 'China', 'Selfie-Related Deaths': 1}] of have minimum values of 1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "India", + "Selfie-Related Deaths": 19 + }, + { + "Country": "Russia", + "Selfie-Related Deaths": 7 + }, + { + "Country": "United States", + "Selfie-Related Deaths": 5 + }, + { + "Country": "Spain", + "Selfie-Related Deaths": 4 + }, + { + "Country": "Philippines", + "Selfie-Related Deaths": 4 + }, + { + "Country": "Portugal", + "Selfie-Related Deaths": 2 + }, + { + "Country": "Indonesia", + "Selfie-Related Deaths": 2 + }, + { + "Country": "South Africa", + "Selfie-Related Deaths": 1 + }, + { + "Country": "Romania", + "Selfie-Related Deaths": 1 + }, + { + "Country": "Pakistan", + "Selfie-Related Deaths": 1 + }, + { + "Country": "Mexico", + "Selfie-Related Deaths": 1 + }, + { + "Country": "Italy", + "Selfie-Related Deaths": 1 + }, + { + "Country": "China", + "Selfie-Related Deaths": 1 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where selfie-related deaths occurred", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Selfie-Related Deaths", + "importance": "primary", + "description": "Number of deaths attributed to selfies", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1940": { + "metadata": { + "title": "Rural Life Desire Rises in 2020", + "description": "Percentage of U.S. adults who said they'd prefer to live in a small town/rural area", + "main_insight": "The preference for living in a small town or rural area among U.S. adults increased significantly from 2018 to 2020 across various demographic groups." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Demographic": "Non-White", + "Units": 39.0, + "Year": "2020" + }, + { + "Demographic": "Non-White", + "Units": 27.0, + "Year": "2018" + } + ], + "score": 0.98, + "annotation": "The Units of 2020 and 2018 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2020 and 2018 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Demographic": "Non-White", + "Units": 27.0, + "Year": "2018" + } + ], + "score": 0.82, + "annotation": "The 2018 has a minimum value at Non-White", + "reason": "The [{'Demographic': 'Non-White', 'Units': 27.0, 'Year': '2018'}] of 2018 has a mainimum value of 27.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Demographic": "35-54", + "Units": 49.0, + "Year": "2020" + } + ], + "score": 0.79, + "annotation": "The 2020 accounts for the majority in 35-54.", + "reason": "The Units of 2020 in 35-54 accounts for 0.5268817204301075 (more than 50%) of the total of Units of all Year." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Demographic": "Men", + "Units": 52.0, + "Year": "2020" + } + ], + "score": 0.79, + "annotation": "The 2020 accounts for the majority in Men.", + "reason": "The Units of 2020 in Men accounts for 0.5360824742268041 (more than 50%) of the total of Units of all Year." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Demographic": "White", + "Units": 52.0, + "Year": "2020" + } + ], + "score": 0.79, + "annotation": "The 2020 accounts for the majority in White.", + "reason": "The Units of 2020 in White accounts for 0.5360824742268041 (more than 50%) of the total of Units of all Year." + } + ], + "data": { + "data": [ + { + "Demographic": "Total", + "Units": 48.0, + "Year": "2020" + }, + { + "Demographic": "Total", + "Units": 39.0, + "Year": "2018" + }, + { + "Demographic": "Men", + "Units": 52.0, + "Year": "2020" + }, + { + "Demographic": "Men", + "Units": 45.0, + "Year": "2018" + }, + { + "Demographic": "Women", + "Units": 44.0, + "Year": "2020" + }, + { + "Demographic": "Women", + "Units": 35.0, + "Year": "2018" + }, + { + "Demographic": "18-34", + "Units": 39.0, + "Year": "2020" + }, + { + "Demographic": "18-34", + "Units": 29.0, + "Year": "2018" + }, + { + "Demographic": "35-54", + "Units": 49.0, + "Year": "2020" + }, + { + "Demographic": "35-54", + "Units": 44.0, + "Year": "2018" + }, + { + "Demographic": "55+", + "Units": 52.0, + "Year": "2020" + }, + { + "Demographic": "55+", + "Units": 44.0, + "Year": "2018" + }, + { + "Demographic": "White", + "Units": 52.0, + "Year": "2020" + }, + { + "Demographic": "White", + "Units": 45.0, + "Year": "2018" + }, + { + "Demographic": "Non-White", + "Units": 39.0, + "Year": "2020" + }, + { + "Demographic": "Non-White", + "Units": 27.0, + "Year": "2018" + } + ], + "columns": [ + { + "name": "Demographic", + "importance": "primary", + "description": "Demographic groups", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "2331": { + "metadata": { + "title": "American Airlines Bumps Up Schedule as Passengers Return", + "description": "Average daily passengers and domestic flight schedule of American Airlines", + "main_insight": "American Airlines' domestic flight schedule and average daily passengers carried increased significantly from April 2020 to July 2020, showing a recovery in air travel." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "July 2020", + "Domestic Flight Schedule Compared to 2019": 55.0 + } + ], + "score": 0.87, + "annotation": "The shows a sudden jump in Domestic Flight Schedule Compared to 2019 from April 2020 to July 2020.", + "reason": "The Domestic Flight Schedule Compared to 2019 for increases from 35.0 at April 2020 to 55.0 at July 2020, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "July 2020", + "Domestic Flight Schedule Compared to 2019": 55.0 + } + ], + "score": 0.75, + "annotation": "The has a maximum value at July 2020", + "reason": "The [{'Month': 'July 2020', 'Domestic Flight Schedule Compared to 2019': 55.0}] of has a maximum value of 55.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "May 2020", + "Domestic Flight Schedule Compared to 2019": 20.0 + }, + { + "Month": "June 2020", + "Domestic Flight Schedule Compared to 2019": 20.0 + } + ], + "score": 0.48, + "annotation": "The has minimum values at May 2020, June 2020", + "reason": "The [{'Month': 'May 2020', 'Domestic Flight Schedule Compared to 2019': 20.0}, {'Month': 'June 2020', 'Domestic Flight Schedule Compared to 2019': 20.0}] of have minimum values of 20.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Month": "April 2020", + "Domestic Flight Schedule Compared to 2019": 35.0 + }, + { + "Month": "May 2020", + "Domestic Flight Schedule Compared to 2019": 20.0 + }, + { + "Month": "June 2020", + "Domestic Flight Schedule Compared to 2019": 20.0 + }, + { + "Month": "July 2020", + "Domestic Flight Schedule Compared to 2019": 55.0 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Time period", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Domestic Flight Schedule Compared to 2019", + "importance": "primary", + "description": "Percentage of domestic flights compared to the same period in 2019", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "20": { + "metadata": { + "title": "Direct Democracy: Best Bet or Mob Rule?", + "description": "Attitudes towards direct democracy in selected countries", + "main_insight": "A majority of respondents in all listed countries view direct democracy positively, with Germany showing the highest positive attitude and Sweden the highest negative view." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Sweden", + "Points": 57.0, + "Sentiment": "Good" + }, + { + "Country": "Sweden", + "Points": 41.0, + "Sentiment": "Bad" + } + ], + "score": 0.98, + "annotation": "The Points of Good and Bad show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Points between Good and Bad is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Points": 67.0, + "Sentiment": "Good" + } + ], + "score": 0.86, + "annotation": "The Good shows a sudden jump in Points from Sweden to United States.", + "reason": "The Points for Good increases from 57.0 at Sweden to 67.0 at United States, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Sweden", + "Points": 57.0, + "Sentiment": "Good" + } + ], + "score": 0.77, + "annotation": "The Good accounts for the majority in Sweden.", + "reason": "The Points of Good in Sweden accounts for 0.5816326530612245 (more than 50%) of the total of Points of all Sentiment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "United Kingdom", + "Points": 56.0, + "Sentiment": "Good" + } + ], + "score": 0.77, + "annotation": "The Good accounts for the majority in United Kingdom.", + "reason": "The Points of Good in United Kingdom accounts for 0.5957446808510638 (more than 50%) of the total of Points of all Sentiment." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Canada", + "Points": 30.0, + "Sentiment": "Bad" + }, + { + "Country": "United Kingdom", + "Points": 38.0, + "Sentiment": "Bad" + } + ], + "score": 0.76, + "annotation": "The Bad exhibits multiple sudden jumps in Points, transitioning from Germany, United States to Canada, United Kingdom.", + "reason": "Across these points, the Points for Bad increases from 23.0, 31.0 at Germany, United States to 30.0, 38.0 at Canada, United Kingdom, suggesting significant shifts in value." + } + ], + "data": { + "data": [ + { + "Country": "Germany", + "Points": 74.0, + "Sentiment": "Good" + }, + { + "Country": "Germany", + "Points": 23.0, + "Sentiment": "Bad" + }, + { + "Country": "United States", + "Points": 67.0, + "Sentiment": "Good" + }, + { + "Country": "United States", + "Points": 31.0, + "Sentiment": "Bad" + }, + { + "Country": "Canada", + "Points": 67.0, + "Sentiment": "Good" + }, + { + "Country": "Canada", + "Points": 30.0, + "Sentiment": "Bad" + }, + { + "Country": "United Kingdom", + "Points": 56.0, + "Sentiment": "Good" + }, + { + "Country": "United Kingdom", + "Points": 38.0, + "Sentiment": "Bad" + }, + { + "Country": "Sweden", + "Points": 57.0, + "Sentiment": "Good" + }, + { + "Country": "Sweden", + "Points": 41.0, + "Sentiment": "Bad" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "The country where the survey was conducted.", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "57": { + "metadata": { + "title": "Netflix's International Expansion", + "description": "Netflix's paid streaming subscribers at the end of the respective year (in millions)", + "main_insight": "Netflix's international subscribers have significantly increased from 2015 to 2019, surpassing the number of USA subscribers." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Units": 106.1, + "Region": "International" + }, + { + "Year": 2019, + "Units": 61.0, + "Region": "USA" + } + ], + "score": 0.98, + "annotation": "The Units of International and USA show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between International and USA is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2015, + "Units": 27.4, + "Region": "International" + }, + { + "Year": 2015, + "Units": 43.4, + "Region": "USA" + }, + { + "Year": 2016, + "Units": 41.2, + "Region": "International" + }, + { + "Year": 2016, + "Units": 47.9, + "Region": "USA" + }, + { + "Year": 2017, + "Units": 57.8, + "Region": "International" + }, + { + "Year": 2017, + "Units": 52.8, + "Region": "USA" + }, + { + "Year": 2018, + "Units": 80.8, + "Region": "International" + }, + { + "Year": 2018, + "Units": 58.5, + "Region": "USA" + }, + { + "Year": 2019, + "Units": 106.1, + "Region": "International" + }, + { + "Year": 2019, + "Units": 61.0, + "Region": "USA" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of subscriber count", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding region", + "role": "y" + }, + { + "name": "Region", + "importance": "primary", + "description": "The region of the data point", + "role": "group" + } + ] + } + }, + "209": { + "metadata": { + "title": "Over Half of U.S. Seniors Have Disruption in Care", + "description": "Percentage of U.S. adults aged 70 and older who have had the following delays to medical procedures", + "main_insight": "Over half (55%) of U.S. seniors have experienced some kind of disruption in care, with the highest percentage experiencing delays in essential medical procedures." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Procedure Type": "Non-essential", + "Units": 39.0, + "Response": "Yes" + }, + { + "Procedure Type": "Non-essential", + "Units": 52.0, + "Response": "No" + } + ], + "score": 1.0, + "annotation": "The Units of Yes and No show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Yes and No is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Procedure Type": "Essential", + "Units": 12.0, + "Response": "Not applicable/Other" + } + ], + "score": 0.93, + "annotation": "The Not applicable/Other shows a sudden jump in Units from Non-essential to Essential.", + "reason": "The Units for Not applicable/Other increases from 9.0 at Non-essential to 12.0 at Essential, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Procedure Type": "Preventative/primary", + "Units": 32.0, + "Response": "Yes" + } + ], + "score": 0.92, + "annotation": "The Yes shows a sudden jump in Units from Essential to Preventative/primary.", + "reason": "The Units for Yes increases from 15.0 at Essential to 32.0 at Preventative/primary, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Procedure Type": "Essential", + "Units": 72.0, + "Response": "No" + } + ], + "score": 0.9, + "annotation": "The No shows a sudden jump in Units from Preventative/primary to Essential.", + "reason": "The Units for No increases from 59.0 at Preventative/primary to 72.0 at Essential, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Procedure Type": "Non-essential", + "Units": 39.0, + "Response": "Yes" + }, + { + "Procedure Type": "Non-essential", + "Units": 9.0, + "Response": "Not applicable/Other" + } + ], + "score": 0.86, + "annotation": "The Units of Yes and Not applicable/Other show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Yes and Not applicable/Other is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Procedure Type": "Essential", + "Units": 15.0, + "Response": "Yes" + }, + { + "Procedure Type": "Essential", + "Units": 72.0, + "Response": "No" + }, + { + "Procedure Type": "Essential", + "Units": 12.0, + "Response": "Not applicable/Other" + }, + { + "Procedure Type": "Preventative/primary", + "Units": 32.0, + "Response": "Yes" + }, + { + "Procedure Type": "Preventative/primary", + "Units": 59.0, + "Response": "No" + }, + { + "Procedure Type": "Preventative/primary", + "Units": 8.0, + "Response": "Not applicable/Other" + }, + { + "Procedure Type": "Non-essential", + "Units": 39.0, + "Response": "Yes" + }, + { + "Procedure Type": "Non-essential", + "Units": 52.0, + "Response": "No" + }, + { + "Procedure Type": "Non-essential", + "Units": 9.0, + "Response": "Not applicable/Other" + } + ], + "columns": [ + { + "name": "Procedure Type", + "importance": "primary", + "description": "Type of medical procedure", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding response", + "role": "y" + }, + { + "name": "Response", + "importance": "primary", + "description": "The response of the data point", + "role": "group" + } + ] + } + }, + "2485": { + "metadata": { + "title": "Women Voters Favor Biden", + "description": "Percentage of U.S. women who would vote for each candidate if the election were today", + "main_insight": "Across multiple polls, a higher percentage of U.S. women voters prefer Biden over Trump in the 2020 election." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Poll": "Economist/YouGov", + "Units": 52.0, + "Vote Share": "Biden" + } + ], + "score": 0.78, + "annotation": "The Biden accounts for the majority in Economist/YouGov.", + "reason": "The Units of Biden in Economist/YouGov accounts for 0.5777777777777777 (more than 50%) of the total of Units of all Vote Share." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Poll": "Hill/Harris", + "Units": 33.0, + "Vote Share": "Trump" + } + ], + "score": 0.77, + "annotation": "The Trump has a minimum value at Hill/Harris", + "reason": "The [{'Poll': 'Hill/Harris', 'Units': 33.0, 'Vote Share': 'Trump'}] of Trump has a mainimum value of 33.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Poll": "Hill/Harris", + "Units": 47.0, + "Vote Share": "Biden" + } + ], + "score": 0.77, + "annotation": "The Biden accounts for the majority in Hill/Harris.", + "reason": "The Units of Biden in Hill/Harris accounts for 0.5875 (more than 50%) of the total of Units of all Vote Share." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Poll": "NPR/PBS/Marist", + "Units": 56.0, + "Vote Share": "Biden" + } + ], + "score": 0.77, + "annotation": "The Biden accounts for the majority in NPR/PBS/Marist.", + "reason": "The Units of Biden in NPR/PBS/Marist accounts for 0.5957446808510638 (more than 50%) of the total of Units of all Vote Share." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Vote Share": "Biden", + "Poll": "total", + "Units": 272.0 + } + ], + "score": 0.77, + "annotation": "The total value of Biden accounts for the majority in all Vote Share.", + "reason": "The total value of Units of Biden accounts for 0.6017699115044248 (more than 50%) of the total of Units of all Vote Share." + } + ], + "data": { + "data": [ + { + "Poll": "Economist/YouGov", + "Units": 52.0, + "Vote Share": "Biden" + }, + { + "Poll": "Economist/YouGov", + "Units": 38.0, + "Vote Share": "Trump" + }, + { + "Poll": "CNN", + "Units": 62.0, + "Vote Share": "Biden" + }, + { + "Poll": "CNN", + "Units": 35.0, + "Vote Share": "Trump" + }, + { + "Poll": "NPR/PBS/Marist", + "Units": 56.0, + "Vote Share": "Biden" + }, + { + "Poll": "NPR/PBS/Marist", + "Units": 38.0, + "Vote Share": "Trump" + }, + { + "Poll": "Emerson", + "Units": 55.0, + "Vote Share": "Biden" + }, + { + "Poll": "Emerson", + "Units": 36.0, + "Vote Share": "Trump" + }, + { + "Poll": "Hill/Harris", + "Units": 47.0, + "Vote Share": "Biden" + }, + { + "Poll": "Hill/Harris", + "Units": 33.0, + "Vote Share": "Trump" + } + ], + "columns": [ + { + "name": "Poll", + "importance": "primary", + "description": "Name of the polling organization", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding vote share", + "role": "y" + }, + { + "name": "Vote Share", + "importance": "primary", + "description": "The vote share of the data point", + "role": "group" + } + ] + } + }, + "104": { + "metadata": { + "title": "The most (and least) state school-friendly universities", + "description": "Share of university entrants from state schools in 2016-17*", + "main_insight": "There is a significant difference in the percentage of students admitted from state schools among top UK universities. Queen's University Belfast has the highest percentage at 98%, while the University of Oxford has the lowest at 58%." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "University": "University of Strathclyde", + "State School Entrants (%)": 90.0 + } + ], + "score": 0.9, + "annotation": "The shows a sudden jump in State School Entrants (%) from St Andrews University to University of Strathclyde.", + "reason": "The State School Entrants (%) for increases from 64.0 at St Andrews University to 90.0 at University of Strathclyde, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "University": "Queen's University Belfast", + "State School Entrants (%)": 98.0 + } + ], + "score": 0.74, + "annotation": "The has a maximum value at Queen's University Belfast", + "reason": "The [{'University': \"Queen's University Belfast\", 'State School Entrants (%)': 98.0}] of has a maximum value of 98.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "University": "University of Oxford", + "State School Entrants (%)": 58.0 + } + ], + "score": 0.69, + "annotation": "The has a minimum value at University of Oxford", + "reason": "The [{'University': 'University of Oxford', 'State School Entrants (%)': 58.0}] of has a mainimum value of 58.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "University": "Queen's University Belfast", + "State School Entrants (%)": 98.0 + }, + { + "University": "University of Lancaster", + "State School Entrants (%)": 91.0 + }, + { + "University": "University of Surrey", + "State School Entrants (%)": 91.0 + }, + { + "University": "University of Strathclyde", + "State School Entrants (%)": 90.0 + }, + { + "University": "Queen Mary, University of London", + "State School Entrants (%)": 90.0 + }, + { + "University": "Imperial College London", + "State School Entrants (%)": 64.0 + }, + { + "University": "St Andrews University", + "State School Entrants (%)": 64.0 + }, + { + "University": "University of Cambridge", + "State School Entrants (%)": 63.0 + }, + { + "University": "Durham University", + "State School Entrants (%)": 63.0 + }, + { + "University": "University of Oxford", + "State School Entrants (%)": 58.0 + } + ], + "columns": [ + { + "name": "University", + "importance": "primary", + "description": "Name of the university", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "State School Entrants (%)", + "importance": "primary", + "description": "Percentage of entrants from state schools", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1271": { + "metadata": { + "title": "Fireworks Made in China", + "description": "Total weight of fireworks exports by main exporters in 2023", + "main_insight": "China is by far the largest exporter of fireworks, exporting over 18 times more than the next largest exporter, the Netherlands, in 2023." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "China", + "Export Weight (t)": 363573.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Export Weight (t) from Netherlands to China.", + "reason": "The Export Weight (t) for increases from 20021.0 at Netherlands to 363573.0 at China, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "China", + "Export Weight (t)": 363573.0 + } + ], + "score": 0.92, + "annotation": "The has a maximum value at China", + "reason": "The [{'Country': 'China', 'Export Weight (t)': 363573.0}] of has a maximum value of 363573.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Cambodia", + "Export Weight (t)": 1599.0 + } + ], + "score": 0.33, + "annotation": "The has a minimum value at Cambodia", + "reason": "The [{'Country': 'Cambodia', 'Export Weight (t)': 1599.0}] of has a mainimum value of 1599.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "China", + "Export Weight (t)": 363573.0 + }, + { + "Country": "Netherlands", + "Export Weight (t)": 20021.0 + }, + { + "Country": "Germany", + "Export Weight (t)": 8423.0 + }, + { + "Country": "Czechia", + "Export Weight (t)": 3757.0 + }, + { + "Country": "Poland", + "Export Weight (t)": 3131.0 + }, + { + "Country": "Cambodia", + "Export Weight (t)": 1599.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of origin for firework exports", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Export Weight (t)", + "importance": "primary", + "description": "Total weight of firework exports in tonnes (t)", + "unit": "t", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2222": { + "metadata": { + "title": "Americans Refuse Contact Tracing", + "description": "Percentage of U.S. adults who are likely/unlikely to use a cell phone-based COVID-19 contact tracing system", + "main_insight": "A majority of U.S. adults surveyed in May 2020 were unlikely to use a cell phone-based COVID-19 contact tracing system, except when provided by CDC/Public health officials where the response was close to evenly split." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Entity": "Federal government", + "Units": 68.0, + "Probability": "Unlikely" + }, + { + "Entity": "Federal government", + "Units": 31.0, + "Probability": "Likely" + } + ], + "score": 1.0, + "annotation": "The Units of Unlikely and Likely show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Unlikely and Likely is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Entity": "CDC/Public health officials", + "Units": 51.0, + "Probability": "Likely" + } + ], + "score": 0.94, + "annotation": "The Likely shows a sudden jump in Units from Cell phone/Internet providers to CDC/Public health officials.", + "reason": "The Units for Likely increases from 35.0 at Cell phone/Internet providers to 51.0 at CDC/Public health officials, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Entity": "Cell phone/Internet providers", + "Units": 63.0, + "Probability": "Unlikely" + } + ], + "score": 0.93, + "annotation": "The Unlikely shows a sudden jump in Units from CDC/Public health officials to Cell phone/Internet providers.", + "reason": "The Units for Unlikely increases from 48.0 at CDC/Public health officials to 63.0 at Cell phone/Internet providers, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Entity": "CDC/Public health officials", + "Units": 51.0, + "Probability": "Likely" + } + ], + "score": 0.8, + "annotation": "The Likely accounts for the majority in CDC/Public health officials.", + "reason": "The Units of Likely in CDC/Public health officials accounts for 0.5151515151515151 (more than 50%) of the total of Units of all Probability." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Entity": "CDC/Public health officials", + "Units": 51.0, + "Probability": "Likely" + } + ], + "score": 0.79, + "annotation": "The Likely has a maximum value at CDC/Public health officials", + "reason": "The [{'Entity': 'CDC/Public health officials', 'Units': 51.0, 'Probability': 'Likely'}] of Likely has a maximum value of 51.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Entity": "CDC/Public health officials", + "Units": 48.0, + "Probability": "Unlikely" + }, + { + "Entity": "CDC/Public health officials", + "Units": 51.0, + "Probability": "Likely" + }, + { + "Entity": "Cell phone/Internet providers", + "Units": 63.0, + "Probability": "Unlikely" + }, + { + "Entity": "Cell phone/Internet providers", + "Units": 35.0, + "Probability": "Likely" + }, + { + "Entity": "Major tech companies", + "Units": 66.0, + "Probability": "Unlikely" + }, + { + "Entity": "Major tech companies", + "Units": 33.0, + "Probability": "Likely" + }, + { + "Entity": "Federal government", + "Units": 68.0, + "Probability": "Unlikely" + }, + { + "Entity": "Federal government", + "Units": 31.0, + "Probability": "Likely" + } + ], + "columns": [ + { + "name": "Entity", + "importance": "primary", + "description": "The entity providing the contact tracing system", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding probability", + "role": "y" + }, + { + "name": "Probability", + "importance": "primary", + "description": "The probability of the data point", + "role": "group" + } + ] + } + }, + "17": { + "metadata": { + "title": "After the Game: Professions of Former NFL Athletes", + "description": "Professions of ex-NFL players listed on LinkedIn (2017)", + "main_insight": "The most common professions for former NFL players on LinkedIn are small business/entrepreneurship and sales, while a significant portion (23%) fall under 'Other'." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Profession": "Other", + "Percentage": 23.0 + } + ], + "score": 0.86, + "annotation": "The has a maximum value at Other", + "reason": "The [{'Profession': 'Other', 'Percentage': 23.0}] of has a maximum value of 23.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Profession": "Sales", + "Percentage": 18.0 + } + ], + "score": 0.79, + "annotation": "The shows a sudden jump in Percentage from Coaching/fitness professional to Sales.", + "reason": "The Percentage for increases from 9.0 at Coaching/fitness professional to 18.0 at Sales, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Profession": "Nonprofit/board member", + "Percentage": 2.0 + } + ], + "score": 0.56, + "annotation": "The has a minimum value at Nonprofit/board member", + "reason": "The [{'Profession': 'Nonprofit/board member', 'Percentage': 2.0}] of has a mainimum value of 2.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Profession": "Small business/entrepreneur", + "Percentage": 20.0 + }, + { + "Profession": "Sales", + "Percentage": 18.0 + }, + { + "Profession": "Coaching/fitness professional", + "Percentage": 9.0 + }, + { + "Profession": "Finance/insurance/banking", + "Percentage": 8.0 + }, + { + "Profession": "Campus & professional athletics", + "Percentage": 5.0 + }, + { + "Profession": "Media & sports broadcasting", + "Percentage": 3.0 + }, + { + "Profession": "Real estate", + "Percentage": 3.0 + }, + { + "Profession": "Education", + "Percentage": 3.0 + }, + { + "Profession": "Nonprofit/board member", + "Percentage": 2.0 + }, + { + "Profession": "Other", + "Percentage": 23.0 + } + ], + "columns": [ + { + "name": "Profession", + "importance": "primary", + "description": "The profession of ex-NFL players", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of ex-NFL players in each profession", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1730": { + "metadata": { + "title": "British Adults Favour Physical Stores", + "description": "Primary shopping/purchasing channels among adults in Great Britain", + "main_insight": "An overwhelming majority of British adults prefer in-person/store shopping, with 90% indicating it as their primary purchasing channel." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Shopping Channel": "In-person/store", + "Percentage of Adults": 90.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Percentage of Adults from PC to In-person/store.", + "reason": "The Percentage of Adults for increases from 5.0 at PC to 90.0 at In-person/store, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Shopping Channel": "In-person/store", + "Percentage of Adults": 90.0 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at In-person/store", + "reason": "The [{'Shopping Channel': 'In-person/store', 'Percentage of Adults': 90.0}] of has a maximum value of 90.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Shopping Channel": "Other", + "Percentage of Adults": 1.0 + } + ], + "score": 0.35, + "annotation": "The has a minimum value at Other", + "reason": "The [{'Shopping Channel': 'Other', 'Percentage of Adults': 1.0}] of has a mainimum value of 1.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Shopping Channel": "In-person/store", + "Percentage of Adults": 90.0 + }, + { + "Shopping Channel": "PC", + "Percentage of Adults": 5.0 + }, + { + "Shopping Channel": "Smartphone", + "Percentage of Adults": 2.0 + }, + { + "Shopping Channel": "Tablet", + "Percentage of Adults": 2.0 + }, + { + "Shopping Channel": "Other", + "Percentage of Adults": 1.0 + } + ], + "columns": [ + { + "name": "Shopping Channel", + "importance": "primary", + "description": "Different methods of shopping/purchasing", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Adults", + "importance": "primary", + "description": "Percentage of adults in Great Britain who prefer the given shopping channel", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "448": { + "metadata": { + "title": "Female Fans First in Line for Concert Tickets", + "description": "Gender distribution of first-day visits to concert ticket pages of selected artists", + "main_insight": "Women are significantly more likely than men to be among the first-day visitors to online ticket pages for most of the listed artists, with the exception of Drake and Rolling Stones." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Artist": "Rolling Stones", + "Percentage": 44.0, + "Gender": "Women" + }, + { + "Artist": "Rolling Stones", + "Percentage": 56.0, + "Gender": "Men" + } + ], + "score": 1.0, + "annotation": "The Percentage of Women and Men show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Percentage between Women and Men is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Artist": "Rolling Stones", + "Percentage": 44.0, + "Gender": "Women" + } + ], + "score": 0.84, + "annotation": "The Women has a minimum value at Rolling Stones", + "reason": "The [{'Artist': 'Rolling Stones', 'Percentage': 44.0, 'Gender': 'Women'}] of Women has a mainimum value of 44.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Artist": "Rolling Stones", + "Percentage": 56.0, + "Gender": "Men" + } + ], + "score": 0.84, + "annotation": "The Men has a maximum value at Rolling Stones", + "reason": "The [{'Artist': 'Rolling Stones', 'Percentage': 56.0, 'Gender': 'Men'}] of Men has a maximum value of 56.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Artist": "Drake", + "Percentage": 57.0, + "Gender": "Women" + } + ], + "score": 0.78, + "annotation": "The Women shows a sudden jump in Percentage from Rolling Stones to Drake.", + "reason": "The Percentage for Women increases from 44.0 at Rolling Stones to 57.0 at Drake, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Artist": "Rolling Stones", + "Percentage": 56.0, + "Gender": "Men" + } + ], + "score": 0.78, + "annotation": "The Men shows a sudden jump in Percentage from Drake to Rolling Stones.", + "reason": "The Percentage for Men increases from 43.0 at Drake to 56.0 at Rolling Stones, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Artist": "Ariana Grande", + "Percentage": 75.0, + "Gender": "Women" + }, + { + "Artist": "Ariana Grande", + "Percentage": 25.0, + "Gender": "Men" + }, + { + "Artist": "BTS", + "Percentage": 73.0, + "Gender": "Women" + }, + { + "Artist": "BTS", + "Percentage": 27.0, + "Gender": "Men" + }, + { + "Artist": "Ed Sheeran", + "Percentage": 64.0, + "Gender": "Women" + }, + { + "Artist": "Ed Sheeran", + "Percentage": 36.0, + "Gender": "Men" + }, + { + "Artist": "Taylor Swift", + "Percentage": 62.0, + "Gender": "Women" + }, + { + "Artist": "Taylor Swift", + "Percentage": 38.0, + "Gender": "Men" + }, + { + "Artist": "Drake", + "Percentage": 57.0, + "Gender": "Women" + }, + { + "Artist": "Drake", + "Percentage": 43.0, + "Gender": "Men" + }, + { + "Artist": "Rolling Stones", + "Percentage": 44.0, + "Gender": "Women" + }, + { + "Artist": "Rolling Stones", + "Percentage": 56.0, + "Gender": "Men" + } + ], + "columns": [ + { + "name": "Artist", + "importance": "primary", + "description": "Name of the musical artist", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage for the corresponding gender", + "role": "y" + }, + { + "name": "Gender", + "importance": "primary", + "description": "The gender of the data point", + "role": "group" + } + ] + } + }, + "1604": { + "metadata": { + "title": "GP surgeries closing at record rate in the UK", + "description": "GP surgery closures in the UK from 2013 to 2018", + "main_insight": "The number of GP surgery closures in the UK increased significantly between 2013 and 2018, reaching a peak of 138 closures in 2018." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2013, + "Number of Closures": 18 + }, + { + "Year": 2014, + "Number of Closures": 62 + }, + { + "Year": 2015, + "Number of Closures": 95 + }, + { + "Year": 2016, + "Number of Closures": 136 + }, + { + "Year": 2017, + "Number of Closures": 134 + }, + { + "Year": 2018, + "Number of Closures": 138 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of surgery closure", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Number of Closures", + "importance": "primary", + "description": "Number of GP surgeries that closed", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "239": { + "metadata": { + "title": "iTunes Spending Is on the Decline", + "description": "Estimated quarterly billings per user of Apple's iTunes and App Store", + "main_insight": "iTunes quarterly billings per user have been steadily declining from Q1 2012 to Q1 2014, while App Store billings have generally increased during the same period." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Quarter": "Q1 2014", + "Ratio": 1.9, + "Platform": "iTunes" + }, + { + "Quarter": "Q1 2014", + "Ratio": 4.7, + "Platform": "App Store" + } + ], + "score": 0.89, + "annotation": "The Ratio of iTunes and App Store show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between iTunes and App Store is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Quarter": "Q1 2012", + "Ratio": 4.3, + "Platform": "iTunes" + } + ], + "score": 0.81, + "annotation": "The iTunes has a maximum value at Q1 2012", + "reason": "The [{'Quarter': 'Q1 2012', 'Ratio': 4.3, 'Platform': 'iTunes'}] of iTunes has a maximum value of 4.3, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q4 2012", + "Ratio": 3.5, + "Platform": "App Store" + } + ], + "score": 0.8, + "annotation": "The App Store accounts for the majority in Q4 2012.", + "reason": "The Ratio of App Store in Q4 2012 accounts for 0.5147058823529412 (more than 50%) of the total of Ratio of all Platform." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q3 2012", + "Ratio": 3.7, + "Platform": "iTunes" + } + ], + "score": 0.79, + "annotation": "The iTunes accounts for the majority in Q3 2012.", + "reason": "The Ratio of iTunes in Q3 2012 accounts for 0.5362318840579711 (more than 50%) of the total of Ratio of all Platform." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Quarter": "Q3 2013", + "Ratio": 4.3, + "Platform": "App Store" + } + ], + "score": 0.78, + "annotation": "The App Store shows a sudden jump in Ratio from Q4 2012 to Q3 2013.", + "reason": "The Ratio for App Store increases from 3.5 at Q4 2012 to 4.3 at Q3 2013, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Quarter": "Q1 2012", + "Ratio": 4.3, + "Platform": "iTunes" + }, + { + "Quarter": "Q1 2012", + "Ratio": 2.8, + "Platform": "App Store" + }, + { + "Quarter": "Q2 2012", + "Ratio": 3.8, + "Platform": "iTunes" + }, + { + "Quarter": "Q2 2012", + "Ratio": 3.0, + "Platform": "App Store" + }, + { + "Quarter": "Q3 2012", + "Ratio": 3.7, + "Platform": "iTunes" + }, + { + "Quarter": "Q3 2012", + "Ratio": 3.2, + "Platform": "App Store" + }, + { + "Quarter": "Q4 2012", + "Ratio": 3.3, + "Platform": "iTunes" + }, + { + "Quarter": "Q4 2012", + "Ratio": 3.5, + "Platform": "App Store" + }, + { + "Quarter": "Q1 2013", + "Ratio": 3.0, + "Platform": "iTunes" + }, + { + "Quarter": "Q1 2013", + "Ratio": 4.7, + "Platform": "App Store" + }, + { + "Quarter": "Q2 2013", + "Ratio": 2.6, + "Platform": "iTunes" + }, + { + "Quarter": "Q2 2013", + "Ratio": 4.5, + "Platform": "App Store" + }, + { + "Quarter": "Q3 2013", + "Ratio": 2.1, + "Platform": "iTunes" + }, + { + "Quarter": "Q3 2013", + "Ratio": 4.3, + "Platform": "App Store" + }, + { + "Quarter": "Q4 2013", + "Ratio": 2.0, + "Platform": "iTunes" + }, + { + "Quarter": "Q4 2013", + "Ratio": 4.5, + "Platform": "App Store" + }, + { + "Quarter": "Q1 2014", + "Ratio": 1.9, + "Platform": "iTunes" + }, + { + "Quarter": "Q1 2014", + "Ratio": 4.7, + "Platform": "App Store" + } + ], + "columns": [ + { + "name": "Quarter", + "importance": "primary", + "description": "Quarter and year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding platform", + "role": "y" + }, + { + "name": "Platform", + "importance": "primary", + "description": "The platform of the data point", + "role": "group" + } + ] + } + }, + "766": { + "metadata": { + "title": "Skyscraper Construction: Nobody Comes Close To China", + "description": "Number of buildings completed that are 200 meters in height or greater in 2020", + "main_insight": "China completed significantly more skyscrapers (200m or taller) in 2020 than any other country, with 56 completions, far surpassing the next highest, UAE, with 12." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "CHN", + "Buildings Completed": 56 + } + ], + "score": 0.99, + "annotation": "The has a maximum value at CHN", + "reason": "The [{'Country': 'CHN', 'Buildings Completed': 56}] of has a maximum value of 56, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "CHN", + "Buildings Completed": 56 + } + ], + "score": 0.95, + "annotation": "The shows a sudden jump in Buildings Completed from UAE to CHN.", + "reason": "The Buildings Completed for increases from 12 at UAE to 56 at CHN, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "RUS", + "Buildings Completed": 2 + }, + { + "Country": "AUS", + "Buildings Completed": 2 + }, + { + "Country": "MEX", + "Buildings Completed": 2 + }, + { + "Country": "THA", + "Buildings Completed": 2 + } + ], + "score": 0.33, + "annotation": "The has minimum values at RUS, AUS, MEX, THA", + "reason": "The [{'Country': 'RUS', 'Buildings Completed': 2}, {'Country': 'AUS', 'Buildings Completed': 2}, {'Country': 'MEX', 'Buildings Completed': 2}, {'Country': 'THA', 'Buildings Completed': 2}] of have minimum values of 2, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "CHN", + "Buildings Completed": 56 + }, + { + "Country": "UAE", + "Buildings Completed": 12 + }, + { + "Country": "USA", + "Buildings Completed": 10 + }, + { + "Country": "UK", + "Buildings Completed": 5 + }, + { + "Country": "KOR", + "Buildings Completed": 4 + }, + { + "Country": "IND", + "Buildings Completed": 3 + }, + { + "Country": "JPN", + "Buildings Completed": 3 + }, + { + "Country": "RUS", + "Buildings Completed": 2 + }, + { + "Country": "AUS", + "Buildings Completed": 2 + }, + { + "Country": "MEX", + "Buildings Completed": 2 + }, + { + "Country": "THA", + "Buildings Completed": 2 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where skyscrapers were completed", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Buildings Completed", + "importance": "primary", + "description": "Number of buildings 200m or taller completed in 2020", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "867": { + "metadata": { + "title": "Beer Companies: Big Output, Big Returns?", + "description": "Annual revenue and net income of the world's top 5 beer producers in 2023 (in billion U.S. dollars)", + "main_insight": "ABInBev leads the top 5 beer companies in both revenue and net income by a significant margin. There is a large difference in revenue and net income between the largest companies and smaller." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Company": "Molson Coors Beverage Company", + "Score": 0.9, + "Key Metrics": "Net Income" + }, + { + "Company": "Molson Coors Beverage Company", + "Score": 4.4, + "Key Metrics": "Share in global beer production volume (in %)" + } + ], + "score": 0.99, + "annotation": "The Score of Net Income and Share in global beer production volume (in %) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Net Income and Share in global beer production volume (in %) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Company": "Molson Coors Beverage Company", + "Score": 13.9, + "Key Metrics": "Revenue" + }, + { + "Company": "Molson Coors Beverage Company", + "Score": 0.9, + "Key Metrics": "Net Income" + } + ], + "score": 0.98, + "annotation": "The Score of Revenue and Net Income show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Revenue and Net Income is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Company": "Molson Coors Beverage Company", + "Score": 13.9, + "Key Metrics": "Revenue" + }, + { + "Company": "Molson Coors Beverage Company", + "Score": 4.4, + "Key Metrics": "Share in global beer production volume (in %)" + } + ], + "score": 0.97, + "annotation": "The Score of Revenue and Share in global beer production volume (in %) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Revenue and Share in global beer production volume (in %) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "ABInBev", + "Score": 6.9, + "Key Metrics": "Net Income" + } + ], + "score": 0.9, + "annotation": "The Net Income shows a sudden jump in Score from Heineken to ABInBev.", + "reason": "The Score for Net Income increases from 2.9 at Heineken to 6.9 at ABInBev, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "ABInBev", + "Score": 26.9, + "Key Metrics": "Share in global beer production volume (in %)" + } + ], + "score": 0.89, + "annotation": "The Share in global beer production volume (in %) shows a sudden jump in Score from Heineken to ABInBev.", + "reason": "The Score for Share in global beer production volume (in %) increases from 12.9 at Heineken to 26.9 at ABInBev, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Company": "ABInBev", + "Score": 59.4, + "Key Metrics": "Revenue" + }, + { + "Company": "ABInBev", + "Score": 6.9, + "Key Metrics": "Net Income" + }, + { + "Company": "ABInBev", + "Score": 26.9, + "Key Metrics": "Share in global beer production volume (in %)" + }, + { + "Company": "Heineken", + "Score": 33.5, + "Key Metrics": "Revenue" + }, + { + "Company": "Heineken", + "Score": 2.9, + "Key Metrics": "Net Income" + }, + { + "Company": "Heineken", + "Score": 12.9, + "Key Metrics": "Share in global beer production volume (in %)" + }, + { + "Company": "China Resources Enterprise, Limited", + "Score": 5.5, + "Key Metrics": "Revenue" + }, + { + "Company": "China Resources Enterprise, Limited", + "Score": 0.7, + "Key Metrics": "Net Income" + }, + { + "Company": "China Resources Enterprise, Limited", + "Score": 5.9, + "Key Metrics": "Share in global beer production volume (in %)" + }, + { + "Company": "Carlsberg Group", + "Score": 10.9, + "Key Metrics": "Revenue" + }, + { + "Company": "Carlsberg Group", + "Score": 1.2, + "Key Metrics": "Net Income" + }, + { + "Company": "Carlsberg Group", + "Score": 5.0, + "Key Metrics": "Share in global beer production volume (in %)" + }, + { + "Company": "Molson Coors Beverage Company", + "Score": 13.9, + "Key Metrics": "Revenue" + }, + { + "Company": "Molson Coors Beverage Company", + "Score": 0.9, + "Key Metrics": "Net Income" + }, + { + "Company": "Molson Coors Beverage Company", + "Score": 4.4, + "Key Metrics": "Share in global beer production volume (in %)" + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Name of the beer company", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding key metrics", + "role": "y" + }, + { + "name": "Key Metrics", + "importance": "primary", + "description": "The key metrics of the data point", + "role": "group" + } + ] + } + }, + "1172": { + "metadata": { + "title": "Congressional Reaction To Comey's Dismissal", + "description": "Lawmakers' reaction in the aftermath of Comey's firing*", + "main_insight": "Democrats overwhelmingly called for a 'special prosecutor' or independent investigation, while a large number of Republicans either remained neutral, supported, or had no statement yet." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Reaction": "No statement yet", + "Count": 174, + "Party Affiliation": "Republicans" + } + ], + "score": 0.88, + "annotation": "The Republicans shows a sudden jump in Count from Neutral or support to No statement yet.", + "reason": "The Count for Republicans increases from 73 at Neutral or support to 174 at No statement yet, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Reaction": "Call for independent investigation", + "Count": 80, + "Party Affiliation": "Democrats" + } + ], + "score": 0.85, + "annotation": "The Democrats shows a sudden jump in Count from No statement yet to Call for independent investigation.", + "reason": "The Count for Democrats increases from 21 at No statement yet to 80 at Call for independent investigation, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Reaction": "Call for independent investigation", + "Count": 5, + "Party Affiliation": "Republicans" + } + ], + "score": 0.84, + "annotation": "The Republicans has the smallest proportion in Call for 'special prosecutor' or similar.", + "reason": "The Count of Republicans in Call for 'special prosecutor' or similar accounts for 0.03875968992248062, smaller than all other Party Affiliation, of the total of Count of all Party Affiliation." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Reaction": "No statement yet", + "Count": 21, + "Party Affiliation": "Democrats" + }, + { + "Reaction": "No statement yet", + "Count": 174, + "Party Affiliation": "Republicans" + } + ], + "score": 0.79, + "annotation": "The Count of Democrats and Republicans show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Count between Democrats and Republicans is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Party Affiliation": "Republicans", + "Reaction": "total", + "Count": 286 + } + ], + "score": 0.79, + "annotation": "The total value of Republicans accounts for the majority in all Party Affiliation.", + "reason": "The total value of Count of Republicans accounts for 0.5437262357414449 (more than 50%) of the total of Count of all Party Affiliation." + } + ], + "data": { + "data": [ + { + "Reaction": "Call for 'special prosecutor' or similar", + "Count": 124, + "Party Affiliation": "Democrats" + }, + { + "Reaction": "Call for independent investigation", + "Count": 80, + "Party Affiliation": "Democrats" + }, + { + "Reaction": "Call for independent investigation", + "Count": 5, + "Party Affiliation": "Republicans" + }, + { + "Reaction": "Questions or concerns", + "Count": 15, + "Party Affiliation": "Democrats" + }, + { + "Reaction": "Questions or concerns", + "Count": 34, + "Party Affiliation": "Republicans" + }, + { + "Reaction": "Neutral or support", + "Count": 73, + "Party Affiliation": "Republicans" + }, + { + "Reaction": "No statement yet", + "Count": 21, + "Party Affiliation": "Democrats" + }, + { + "Reaction": "No statement yet", + "Count": 174, + "Party Affiliation": "Republicans" + } + ], + "columns": [ + { + "name": "Reaction", + "importance": "primary", + "description": "Type of reaction from lawmakers", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Count", + "importance": "primary", + "description": "Count for the corresponding party affiliation", + "role": "y" + }, + { + "name": "Party Affiliation", + "importance": "primary", + "description": "The party affiliation of the data point", + "role": "group" + } + ] + } + }, + "1129": { + "metadata": { + "title": "The Most-Followed FIFA World Cup Players on Twitter", + "description": "Number of Twitter followers as of June 5, 2014", + "main_insight": "Cristiano Ronaldo was by far the most-followed FIFA World Cup player on Twitter in 2014, with more than double the followers of the second most-followed player, Neymar Júnior." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Player": "Cristiano Ronaldo (@Cristiano)", + "Followers": 26.5 + } + ], + "score": 0.98, + "annotation": "The has a maximum value at Cristiano Ronaldo (@Cristiano)", + "reason": "The [{'Player': 'Cristiano Ronaldo (@Cristiano)', 'Followers': 26.5}] of has a maximum value of 26.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Player": "Cristiano Ronaldo (@Cristiano)", + "Followers": 26.5 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in Followers from Neymar Júnior (@neymarjr) to Cristiano Ronaldo (@Cristiano).", + "reason": "The Followers for increases from 10.7 at Neymar Júnior (@neymarjr) to 26.5 at Cristiano Ronaldo (@Cristiano), indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Player": "Sergio Aguero (@aguerosergiokun)", + "Followers": 5.6 + } + ], + "score": 0.41, + "annotation": "The has a minimum value at Sergio Aguero (@aguerosergiokun)", + "reason": "The [{'Player': 'Sergio Aguero (@aguerosergiokun)', 'Followers': 5.6}] of has a mainimum value of 5.6, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Player": "Cristiano Ronaldo (@Cristiano)", + "Followers": 26.5 + }, + { + "Player": "Neymar Júnior (@neymarjr)", + "Followers": 10.7 + }, + { + "Player": "Wayne Rooney (@WayneRooney)", + "Followers": 8.8 + }, + { + "Player": "Andrés Iniesta (@andresiniesta8)", + "Followers": 8.7 + }, + { + "Player": "Gerard Piqué (@3gerardpique)", + "Followers": 8.4 + }, + { + "Player": "Xabi Alonso (@XabiAlonso)", + "Followers": 7.3 + }, + { + "Player": "Cesc Fàbregas (@cesc4official)", + "Followers": 6.6 + }, + { + "Player": "David Villa (@Guaje7Villa)", + "Followers": 5.9 + }, + { + "Player": "Mesut Özil (@MesutOzil1088)", + "Followers": 5.8 + }, + { + "Player": "Sergio Aguero (@aguerosergiokun)", + "Followers": 5.6 + } + ], + "columns": [ + { + "name": "Player", + "importance": "primary", + "description": "Name and Twitter handle of the player", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Followers", + "importance": "primary", + "description": "Number of Twitter followers (in millions)", + "unit": "millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1360": { + "metadata": { + "title": "Most Support Sending Ground Troops To Fight ISIS", + "description": "% of people in the U.S. who support/oppose sending ground troops to fight ISIS", + "main_insight": "Republican respondents show the highest support (69%) for sending ground troops to fight ISIS, followed by Total (53%), Independent (50%) and lastly Democrat (44%)" + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Political Affiliation": "Independent", + "Units": 39.0, + "Sentiment": "Oppose" + } + ], + "score": 0.9, + "annotation": "The Oppose shows a sudden jump in Units from Total to Independent.", + "reason": "The Units for Oppose increases from 26.0 at Total to 39.0 at Independent, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Political Affiliation": "Republican", + "Units": 69.0, + "Sentiment": "Support" + } + ], + "score": 0.9, + "annotation": "The Support shows a sudden jump in Units from Total to Republican.", + "reason": "The Units for Support increases from 53.0 at Total to 69.0 at Republican, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Political Affiliation": "Total", + "Units": 21.0, + "Sentiment": "Unspecified" + } + ], + "score": 0.89, + "annotation": "The Unspecified shows a sudden jump in Units from Democrat to Total.", + "reason": "The Units for Unspecified increases from 13.0 at Democrat to 21.0 at Total, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Political Affiliation": "Democrat", + "Units": 44.0, + "Sentiment": "Support" + } + ], + "score": 0.87, + "annotation": "The Support has the largest proportion in Democrat.", + "reason": "The Units of Support in Democrat accounts for 0.44, larger than all other Sentiment, of the total of Units of all Sentiment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Political Affiliation": "Independent", + "Units": 50.0, + "Sentiment": "Support" + } + ], + "score": 0.86, + "annotation": "The Support has the largest proportion in Independent.", + "reason": "The Units of Support in Independent accounts for 0.5, larger than all other Sentiment, of the total of Units of all Sentiment." + } + ], + "data": { + "data": [ + { + "Political Affiliation": "Total", + "Units": 53.0, + "Sentiment": "Support" + }, + { + "Political Affiliation": "Total", + "Units": 26.0, + "Sentiment": "Oppose" + }, + { + "Political Affiliation": "Total", + "Units": 21.0, + "Sentiment": "Unspecified" + }, + { + "Political Affiliation": "Democrat", + "Units": 44.0, + "Sentiment": "Support" + }, + { + "Political Affiliation": "Democrat", + "Units": 43.0, + "Sentiment": "Oppose" + }, + { + "Political Affiliation": "Democrat", + "Units": 13.0, + "Sentiment": "Unspecified" + }, + { + "Political Affiliation": "Independent", + "Units": 50.0, + "Sentiment": "Support" + }, + { + "Political Affiliation": "Independent", + "Units": 39.0, + "Sentiment": "Oppose" + }, + { + "Political Affiliation": "Independent", + "Units": 11.0, + "Sentiment": "Unspecified" + }, + { + "Political Affiliation": "Republican", + "Units": 69.0, + "Sentiment": "Support" + }, + { + "Political Affiliation": "Republican", + "Units": 23.0, + "Sentiment": "Oppose" + }, + { + "Political Affiliation": "Republican", + "Units": 8.0, + "Sentiment": "Unspecified" + } + ], + "columns": [ + { + "name": "Political Affiliation", + "importance": "primary", + "description": "Categories of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "1372": { + "metadata": { + "title": "61% of Young Republicans Support Same-Sex Marriage", + "description": "Percent who favor allowing gays and lesbians to legally marry", + "main_insight": "Support for same-sex marriage decreases with age within both parties, and Democrats show significantly higher support than Republicans across all age groups." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Ages": "All", + "Score": 69.0, + "Party Affiliation": "Democrat/Lean Democrat" + }, + { + "Ages": "All", + "Score": 39.0, + "Party Affiliation": "Republican/Lean Republican" + } + ], + "score": 1.0, + "annotation": "The Score of Democrat/Lean Democrat and Republican/Lean Republican show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Democrat/Lean Democrat and Republican/Lean Republican is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Ages": "18-29", + "Score": 61.0, + "Party Affiliation": "Republican/Lean Republican" + } + ], + "score": 0.81, + "annotation": "The Republican/Lean Republican shows a sudden jump in Score from 30-49 to 18-29.", + "reason": "The Score for Republican/Lean Republican increases from 43.0 at 30-49 to 61.0 at 18-29, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Ages": "18-29", + "Score": 61.0, + "Party Affiliation": "Republican/Lean Republican" + } + ], + "score": 0.8, + "annotation": "The Republican/Lean Republican has a maximum value at 18-29", + "reason": "The [{'Ages': '18-29', 'Score': 61.0, 'Party Affiliation': 'Republican/Lean Republican'}] of Republican/Lean Republican has a maximum value of 61.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Ages": "18-29", + "Score": 77.0, + "Party Affiliation": "Democrat/Lean Democrat" + } + ], + "score": 0.78, + "annotation": "The Democrat/Lean Democrat accounts for the majority in 18-29.", + "reason": "The Score of Democrat/Lean Democrat in 18-29 accounts for 0.5579710144927537 (more than 50%) of the total of Score of all Party Affiliation." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Ages": "18-29", + "Score": 77.0, + "Party Affiliation": "Democrat/Lean Democrat" + } + ], + "score": 0.78, + "annotation": "The Democrat/Lean Democrat has a maximum value at 18-29", + "reason": "The [{'Ages': '18-29', 'Score': 77.0, 'Party Affiliation': 'Democrat/Lean Democrat'}] of Democrat/Lean Democrat has a maximum value of 77.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Ages": "18-29", + "Score": 77.0, + "Party Affiliation": "Democrat/Lean Democrat" + }, + { + "Ages": "18-29", + "Score": 61.0, + "Party Affiliation": "Republican/Lean Republican" + }, + { + "Ages": "30-49", + "Score": 71.0, + "Party Affiliation": "Democrat/Lean Democrat" + }, + { + "Ages": "30-49", + "Score": 43.0, + "Party Affiliation": "Republican/Lean Republican" + }, + { + "Ages": "50-64", + "Score": 66.0, + "Party Affiliation": "Democrat/Lean Democrat" + }, + { + "Ages": "50-64", + "Score": 30.0, + "Party Affiliation": "Republican/Lean Republican" + }, + { + "Ages": "65+", + "Score": 62.0, + "Party Affiliation": "Democrat/Lean Democrat" + }, + { + "Ages": "65+", + "Score": 22.0, + "Party Affiliation": "Republican/Lean Republican" + }, + { + "Ages": "All", + "Score": 69.0, + "Party Affiliation": "Democrat/Lean Democrat" + }, + { + "Ages": "All", + "Score": 39.0, + "Party Affiliation": "Republican/Lean Republican" + } + ], + "columns": [ + { + "name": "Ages", + "importance": "primary", + "description": "Age ranges and an overall category", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding party affiliation", + "role": "y" + }, + { + "name": "Party Affiliation", + "importance": "primary", + "description": "The party affiliation of the data point", + "role": "group" + } + ] + } + }, + "120": { + "metadata": { + "title": "Australia Plans to Use 50% Renewables by 2030", + "description": "Projected electricity generation in Australia by fuel type", + "main_insight": "Australia projects a significant shift in its electricity generation mix, with renewables increasing from 28% in 2020 to 51% in 2030, surpassing the combined contribution of coal and gas." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2030, + "Units": 51.0, + "Energy Sources": "Renewables" + }, + { + "Year": 2030, + "Units": 39.0, + "Energy Sources": "Coal" + } + ], + "score": 1.0, + "annotation": "The Units of Renewables and Coal show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Renewables and Coal is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2030, + "Units": 51.0, + "Energy Sources": "Renewables" + }, + { + "Year": 2030, + "Units": 10.0, + "Energy Sources": "Gas" + } + ], + "score": 0.93, + "annotation": "The Units of Renewables and Gas show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Renewables and Gas is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2030, + "Units": 39.0, + "Energy Sources": "Coal" + }, + { + "Year": 2030, + "Units": 10.0, + "Energy Sources": "Gas" + } + ], + "score": 0.89, + "annotation": "The Units of Coal and Gas show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Coal and Gas is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2020, + "Units": 28.0, + "Energy Sources": "Renewables" + }, + { + "Year": 2020, + "Units": 55.0, + "Energy Sources": "Coal" + }, + { + "Year": 2020, + "Units": 17.0, + "Energy Sources": "Gas" + }, + { + "Year": 2025, + "Units": 40.0, + "Energy Sources": "Renewables" + }, + { + "Year": 2025, + "Units": 48.0, + "Energy Sources": "Coal" + }, + { + "Year": 2025, + "Units": 11.0, + "Energy Sources": "Gas" + }, + { + "Year": 2030, + "Units": 51.0, + "Energy Sources": "Renewables" + }, + { + "Year": 2030, + "Units": 39.0, + "Energy Sources": "Coal" + }, + { + "Year": 2030, + "Units": 10.0, + "Energy Sources": "Gas" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of projected electricity generation", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding energy sources", + "role": "y" + }, + { + "name": "Energy Sources", + "importance": "primary", + "description": "The energy sources of the data point", + "role": "group" + } + ] + } + }, + "711": { + "metadata": { + "title": "India's Toilet-Building Revolution", + "description": "Share of India's population with access to a household toilet", + "main_insight": "India has dramatically increased household toilet access from 2014-2015 to 2019-2020, reaching 100% coverage." + }, + "chart_type": "Proportional Icon Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Year": "2017-2018", + "Toilet Access": 84.3 + } + ], + "score": 0.72, + "annotation": "The shows a sudden jump in Toilet Access from 2016-2017 to 2017-2018.", + "reason": "The Toilet Access for increases from 65.4 at 2016-2017 to 84.3 at 2017-2018, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Year": "2014-2015", + "Toilet Access": 43.4 + } + ], + "score": 0.72, + "annotation": "The has a minimum value at 2014-2015", + "reason": "The [{'Year': '2014-2015', 'Toilet Access': 43.4}] of has a mainimum value of 43.4, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Year": "2019-2020", + "Toilet Access": 100.0 + } + ], + "score": 0.64, + "annotation": "The has a maximum value at 2019-2020", + "reason": "The [{'Year': '2019-2020', 'Toilet Access': 100.0}] of has a maximum value of 100.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Year": "2014-2015", + "Toilet Access": 43.4 + }, + { + "Year": "2015-2016", + "Toilet Access": 51.4 + }, + { + "Year": "2016-2017", + "Toilet Access": 65.4 + }, + { + "Year": "2017-2018", + "Toilet Access": 84.3 + }, + { + "Year": "2018-2019", + "Toilet Access": 98.5 + }, + { + "Year": "2019-2020", + "Toilet Access": 100.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Fiscal year range", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Toilet Access", + "importance": "primary", + "description": "Percentage of India's population with access to a household toilet", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "336": { + "metadata": { + "title": "The most common cancers in England", + "description": "Share of new cancer diagnoses in England in 2017, by gender", + "main_insight": "Breast cancer was the most common cancer in women in England in 2017, while prostate cancer was the most prevalent in men." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Cancer Type": "Breast", + "Female": 30.7 + } + ], + "score": 0.89, + "annotation": "The has a maximum value at Breast", + "reason": "The [{'Cancer Type': 'Breast', 'Female': 30.7}] of has a maximum value of 30.7, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Cancer Type": "Breast", + "Female": 30.7 + } + ], + "score": 0.88, + "annotation": "The shows a sudden jump in Female from Lung to Breast.", + "reason": "The Female for increases from 12.4 at Lung to 30.7 at Breast, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Cancer Type": "Melanoma of skin", + "Female": 0 + }, + { + "Cancer Type": "Prostate", + "Female": 0 + } + ], + "score": 0.53, + "annotation": "The has minimum values at Melanoma of skin, Prostate", + "reason": "The [{'Cancer Type': 'Melanoma of skin', 'Female': 0}, {'Cancer Type': 'Prostate', 'Female': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Cancer Type": "Breast", + "Female": 30.7 + }, + { + "Cancer Type": "Lung", + "Female": 12.4 + }, + { + "Cancer Type": "Colorectal", + "Female": 10.3 + }, + { + "Cancer Type": "Uterus", + "Female": 5.3 + }, + { + "Cancer Type": "Melanoma of skin", + "Female": 0 + }, + { + "Cancer Type": "Prostate", + "Female": 0 + } + ], + "columns": [ + { + "name": "Cancer Type", + "importance": "primary", + "description": "Type of cancer", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Female", + "importance": "primary", + "description": "Percentage of new diagnoses in women", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1882": { + "metadata": { + "title": "Which Online Platforms Should Be Killed Off?", + "description": "Share of Americans who want the following platforms killed or kept alive", + "main_insight": "Twitter and Tinder are the platforms that the largest share of Americans want to see killed off, while Facebook has the largest share who want to keep it alive." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Online Platform": "Bumble", + "Units": 7.0, + "Action Taken": "Fuel it to keep it alive" + }, + { + "Online Platform": "Bumble", + "Units": 74.0, + "Action Taken": "Not familiar with it" + } + ], + "score": 0.93, + "annotation": "The Units of Fuel it to keep it alive and Not familiar with it show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Fuel it to keep it alive and Not familiar with it is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Action Taken": "Fuel it to keep it alive", + "Online Platform": "total", + "Units": 433.0 + } + ], + "score": 0.89, + "annotation": "The total value of Fuel it to keep it alive has the largest proportion.", + "reason": "The total value of Units of Fuel it to keep it alive accounts for 0.36083333333333334, larger than all other Action Taken, of the total of Units of all Action Taken." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Online Platform": "Snapchat", + "Units": 41.0, + "Action Taken": "Fuel it to keep it alive" + } + ], + "score": 0.88, + "annotation": "The Fuel it to keep it alive has the largest proportion in Snapchat.", + "reason": "The Units of Fuel it to keep it alive in Snapchat accounts for 0.41, larger than all other Action Taken, of the total of Units of all Action Taken." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Online Platform": "Tinder", + "Units": 43.0, + "Action Taken": "Kill it and hope it dies" + } + ], + "score": 0.87, + "annotation": "The Kill it and hope it dies has the largest proportion in Tinder.", + "reason": "The Units of Kill it and hope it dies in Tinder accounts for 0.43, larger than all other Action Taken, of the total of Units of all Action Taken." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Online Platform": "Twitter", + "Units": 46.0, + "Action 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hope it dies" + }, + { + "Online Platform": "Pinterest", + "Units": 57.0, + "Action Taken": "Fuel it to keep it alive" + }, + { + "Online Platform": "Pinterest", + "Units": 17.0, + "Action Taken": "Not familiar with it" + }, + { + "Online Platform": "Reddit", + "Units": 26.0, + "Action Taken": "Kill it and hope it dies" + }, + { + "Online Platform": "Reddit", + "Units": 30.0, + "Action Taken": "Fuel it to keep it alive" + }, + { + "Online Platform": "Reddit", + "Units": 44.0, + "Action Taken": "Not familiar with it" + }, + { + "Online Platform": "Spotify", + "Units": 22.0, + "Action Taken": "Kill it and hope it dies" + }, + { + "Online Platform": "Spotify", + "Units": 46.0, + "Action Taken": "Fuel it to keep it alive" + }, + { + "Online Platform": "Spotify", + "Units": 32.0, + "Action Taken": "Not familiar with it" + }, + { + "Online Platform": "Slack", + "Units": 20.0, + "Action Taken": "Kill it and hope it dies" + }, + { + "Online Platform": "Slack", + "Units": 8.0, + "Action Taken": "Fuel it to keep it alive" + }, + { + "Online Platform": "Slack", + "Units": 72.0, + "Action Taken": "Not familiar with it" + }, + { + "Online Platform": "Tidal", + "Units": 20.0, + "Action Taken": "Kill it and hope it dies" + }, + { + "Online Platform": "Tidal", + "Units": 8.0, + "Action Taken": "Fuel it to keep it alive" + }, + { + "Online Platform": "Tidal", + "Units": 72.0, + "Action Taken": "Not familiar with it" + }, + { + "Online Platform": "Pandora", + "Units": 19.0, + "Action Taken": "Kill it and hope it dies" + }, + { + "Online Platform": "Pandora", + "Units": 62.0, + "Action Taken": "Fuel it to keep it alive" + }, + { + "Online Platform": "Pandora", + "Units": 19.0, + "Action Taken": "Not familiar with it" + }, + { + "Online Platform": "Bumble", + "Units": 19.0, + "Action Taken": "Kill it and hope it dies" + }, + { + "Online Platform": "Bumble", + "Units": 7.0, + "Action Taken": "Fuel it to keep it alive" + }, + { + "Online Platform": "Bumble", + "Units": 74.0, + "Action Taken": "Not familiar with it" + } + ], + "columns": [ + { + "name": "Online Platform", + "importance": "primary", + "description": "Name of the online platform", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding action taken", + "role": "y" + }, + { + "name": "Action Taken", + "importance": "primary", + "description": "The action taken of the data point", + "role": "group" + } + ] + } + }, + "2006": { + "metadata": { + "title": "From Tape to Tidal: 4 Decades of U.S. Music Sales", + "description": "U.S. recorded music revenues from 1980-2021, by format (adjusted for inflation, 2021 dollars)", + "main_insight": "Streaming has become the dominant source of revenue in the U.S. music industry, surpassing all other formats. CD sales peaked in 1999 and have since greatly declined." + }, + "chart_type": "Stacked Area Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2021, + "Value": 14.9, + "Music Formats": "Streaming" + }, + { + "Year": 2021, + "Value": 0.1, + "Music Formats": "Ringtones" + } + ], + "score": 0.91, + "annotation": "The Value of Streaming and Ringtones show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Value between Streaming and Ringtones is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2021, + "Value": 14.9, + "Music Formats": "Streaming" + }, + { + "Year": 2021, + "Value": 0.5, + "Music Formats": "Downloads" + } + ], + "score": 0.55, + "annotation": "The Value of Streaming and Downloads show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Value between Streaming and Downloads is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Value": 0.5, + "Music Formats": "Downloads" + }, + { + "Year": 2021, + "Value": 0.1, + "Music Formats": "Ringtones" + } + ], + "score": 0.39, + "annotation": "The Value of Downloads and Ringtones show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Value between Downloads and Ringtones is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 1980, + "Value": 1.5, + "Music Formats": "Vinyl" + }, + { + "Year": 1980, + "Value": 2.5, + "Music Formats": "Cassette" + }, + { + "Year": 1980, + "Value": 2.5, + "Music Formats": "8-Track" + }, + { + "Year": 1980, + "Value": 0.1, + "Music Formats": "Others" + }, + { + "Year": 1985, + "Value": 0.5, + "Music Formats": "CD" + }, + { + "Year": 1985, + "Value": 1.7, + "Music Formats": "Vinyl" + }, + { + "Year": 1985, + "Value": 4.5, + "Music Formats": "Cassette" + }, + { + "Year": 1985, + "Value": 0.5, + "Music Formats": "8-Track" + }, + { + "Year": 1985, + "Value": 0.1, + "Music Formats": "Others" + }, + { + "Year": 1990, + "Value": 7.0, + "Music Formats": "CD" + }, + { + "Year": 1990, + "Value": 0.5, + "Music Formats": "Vinyl" + }, + { + "Year": 1990, + "Value": 8.0, + "Music Formats": "Cassette" + }, + { + "Year": 1990, + "Value": 0.1, + "Music Formats": "Others" + }, + { + "Year": 1995, + "Value": 16.0, + "Music Formats": "CD" + }, + { + "Year": 1995, + "Value": 0.3, + "Music Formats": "Vinyl" + }, + { + "Year": 1995, + "Value": 5.5, + "Music Formats": "Cassette" + }, + { + "Year": 1995, + "Value": 0.1, + "Music Formats": "Others" + }, + { + "Year": 1999, + "Value": 23.7, + "Music Formats": "CD" + }, + { + "Year": 1999, + "Value": 0.3, + "Music Formats": "Vinyl" + }, + { + "Year": 1999, + "Value": 3.0, + "Music Formats": "Cassette" + }, + { + "Year": 1999, + "Value": 0.1, + "Music Formats": "Others" + }, + { + "Year": 2000, + "Value": 23.0, + "Music Formats": "CD" + }, + { + "Year": 2000, + "Value": 0.4, + "Music Formats": "Vinyl" + }, + { + "Year": 2000, + "Value": 1.5, + "Music Formats": "Cassette" + }, + { + "Year": 2000, + "Value": 0.1, + "Music Formats": "Others" + }, + { + "Year": 2005, + "Value": 0.5, + "Music Formats": "Streaming" + }, + { + "Year": 2005, + "Value": 16.0, + "Music Formats": "CD" + }, + { + "Year": 2005, + "Value": 1.5, + "Music Formats": "Downloads" + }, + { + "Year": 2005, + "Value": 0.2, + "Music Formats": "Vinyl" + }, + { + "Year": 2005, + "Value": 0.1, + "Music Formats": "Cassette" + }, + { + "Year": 2005, + "Value": 1.5, + "Music Formats": "Ringtones" + }, + { + "Year": 2005, + "Value": 0.1, + "Music Formats": "Others" + }, + { + "Year": 2010, + "Value": 1.5, + "Music Formats": "Streaming" + }, + { + "Year": 2010, + "Value": 8.0, + "Music Formats": "CD" + }, + { + "Year": 2010, + "Value": 3.5, + "Music Formats": "Downloads" + }, + { + "Year": 2010, + "Value": 0.4, + "Music Formats": "Vinyl" + }, + { + "Year": 2010, + "Value": 0.1, + "Music Formats": "Cassette" + }, + { + "Year": 2010, + "Value": 2.0, + "Music Formats": "Ringtones" + }, + { + "Year": 2010, + "Value": 0.1, + "Music Formats": "Others" + }, + { + "Year": 2014, + "Value": 7.7, + "Music Formats": "Streaming" + }, + { + "Year": 2014, + "Value": 3.0, + "Music Formats": "CD" + }, + { + "Year": 2014, + "Value": 3.5, + "Music Formats": "Downloads" + }, + { + "Year": 2014, + "Value": 0.8, + "Music Formats": "Vinyl" + }, + { + "Year": 2014, + "Value": 0.4, + "Music Formats": "Ringtones" + }, + { + "Year": 2014, + "Value": 0.1, + "Music Formats": "Others" + }, + { + "Year": 2015, + "Value": 9.0, + "Music Formats": "Streaming" + }, + { + "Year": 2015, + "Value": 2.0, + "Music Formats": "CD" + }, + { + "Year": 2015, + "Value": 3.0, + "Music Formats": "Downloads" + }, + { + "Year": 2015, + "Value": 1.0, + "Music Formats": "Vinyl" + }, + { + "Year": 2015, + "Value": 0.1, + "Music Formats": "Cassette" + }, + { + "Year": 2015, + "Value": 0.2, + "Music Formats": "Ringtones" + }, + { + "Year": 2015, + "Value": 0.1, + "Music Formats": "Others" + }, + { + "Year": 2020, + "Value": 14.0, + "Music Formats": "Streaming" + }, + { + "Year": 2020, + "Value": 0.8, + "Music Formats": "CD" + }, + { + "Year": 2020, + "Value": 0.8, + "Music Formats": "Downloads" + }, + { + "Year": 2020, + "Value": 1.5, + "Music Formats": "Vinyl" + }, + { + "Year": 2020, + "Value": 0.1, + "Music Formats": "Ringtones" + }, + { + "Year": 2020, + "Value": 0.1, + "Music Formats": "Others" + }, + { + "Year": 2021, + "Value": 14.9, + "Music Formats": "Streaming" + }, + { + "Year": 2021, + "Value": 1.0, + "Music Formats": "CD" + }, + { + "Year": 2021, + "Value": 0.5, + "Music Formats": "Downloads" + }, + { + "Year": 2021, + "Value": 2.0, + "Music Formats": "Vinyl" + }, + { + "Year": 2021, + "Value": 0.1, + "Music Formats": "Ringtones" + }, + { + "Year": 2021, + "Value": 0.1, + "Music Formats": "Others" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of music sales", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding music formats", + "role": "y" + }, + { + "name": "Music Formats", + "importance": "primary", + "description": "The music formats of the data point", + "role": "group" + } + ] + } + }, + "2366": { + "metadata": { + "title": "Which Countries Have the Most Diplomatic Ties?", + "description": "Countries with the most diplomatic posts abroad as of 2016*", + "main_insight": "The United States had the most diplomatic posts abroad in 2016, with 271 posts, followed closely by France with 267." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United States", + "Diplomatic Posts": 271 + } + ], + "score": 0.76, + "annotation": "The has a maximum value at United States", + "reason": "The [{'Country': 'United States', 'Diplomatic Posts': 271}] of has a maximum value of 271, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Germany", + "Diplomatic Posts": 218 + } + ], + "score": 0.68, + "annotation": "The has a minimum value at Germany", + "reason": "The [{'Country': 'Germany', 'Diplomatic Posts': 218}] of has a mainimum value of 218, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "China", + "Diplomatic Posts": 258 + } + ], + "score": 0.68, + "annotation": "The shows a sudden jump in Diplomatic Posts from Russia to China.", + "reason": "The Diplomatic Posts for increases from 243 at Russia to 258 at China, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Diplomatic Posts": 271 + }, + { + "Country": "France", + "Diplomatic Posts": 267 + }, + { + "Country": "China", + "Diplomatic Posts": 258 + }, + { + "Country": "Russia", + "Diplomatic Posts": 243 + }, + { + "Country": "United Kingdom", + "Diplomatic Posts": 236 + }, + { + "Country": "Turkey", + "Diplomatic Posts": 225 + }, + { + "Country": "Brazil", + "Diplomatic Posts": 222 + }, + { + "Country": "Germany", + "Diplomatic Posts": 218 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Diplomatic Posts", + "importance": "primary", + "description": "Number of diplomatic posts abroad", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "139": { + "metadata": { + "title": "Majority of Americans in Favor of Breaking up Big Tech", + "description": "Support for breaking up big tech companies to level playing field for all content (in percent)", + "main_insight": "A majority of Americans, regardless of political affiliation, support breaking up big tech companies. The level of support is relatively consistent across Democrats, Republicans, and Independents/Others." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Opinion": "Not sure", + "Points": 14.0, + "Party Affiliation": "Republicans" + }, + { + "Opinion": "Not sure", + "Points": 13.0, + "Party Affiliation": "Independents/Others" + } + ], + "score": 1.0, + "annotation": "The Points of Republicans and Independents/Others show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Points between Republicans and Independents/Others is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Opinion": "Not sure", + "Points": 17.0, + "Party Affiliation": "Democrats" + }, + { + "Opinion": "Not sure", + "Points": 13.0, + "Party Affiliation": "Independents/Others" + } + ], + "score": 1.0, + "annotation": "The Points of Democrats and Independents/Others show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Points between Democrats and Independents/Others is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Opinion": "Not sure", + "Points": 17.0, + "Party Affiliation": "Democrats" + }, + { + "Opinion": "Not sure", + "Points": 14.0, + "Party Affiliation": "Republicans" + } + ], + "score": 1.0, + "annotation": "The Points of Democrats and Republicans show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Points between Democrats and Republicans is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Opinion": "Support", + "Points": 68.0, + "Party Affiliation": "Democrats" + } + ], + "score": 0.97, + "annotation": "The Democrats shows a sudden jump in Points from Not sure to Support.", + "reason": "The Points for Democrats increases from 17.0 at Not sure to 68.0 at Support, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Opinion": "Support", + "Points": 71.0, + "Party Affiliation": "Independents/Others" + } + ], + "score": 0.97, + "annotation": "The Independents/Others shows a sudden jump in Points from Oppose to Support.", + "reason": "The Points for Independents/Others increases from 16.0 at Oppose to 71.0 at Support, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Opinion": "Support", + "Points": 68.0, + "Party Affiliation": "Democrats" + }, + { + "Opinion": "Support", + "Points": 67.0, + "Party Affiliation": "Republicans" + }, + { + "Opinion": "Support", + "Points": 71.0, + "Party Affiliation": "Independents/Others" + }, + { + "Opinion": "Oppose", + "Points": 16.0, + "Party Affiliation": "Democrats" + }, + { + "Opinion": "Oppose", + "Points": 18.0, + "Party Affiliation": "Republicans" + }, + { + "Opinion": "Oppose", + "Points": 16.0, + "Party Affiliation": "Independents/Others" + }, + { + "Opinion": "Not sure", + "Points": 17.0, + "Party Affiliation": "Democrats" + }, + { + "Opinion": "Not sure", + "Points": 14.0, + "Party Affiliation": "Republicans" + }, + { + "Opinion": "Not sure", + "Points": 13.0, + "Party Affiliation": "Independents/Others" + } + ], + "columns": [ + { + "name": "Opinion", + "importance": "primary", + "description": "Type of voter opinion (Support, Oppose, Not sure)", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding party affiliation", + "role": "y" + }, + { + "name": "Party Affiliation", + "importance": "primary", + "description": "The party affiliation of the data point", + "role": "group" + } + ] + } + }, + "1899": { + "metadata": { + "title": "White Claw Sales Have Quadrupled", + "description": "U.S. sales of White Claw in the 52 weeks ending October 6 of the respective year", + "main_insight": "White Claw sales in the US have more than quadrupled from 2018 to 2019, increasing from $154.8 million to $627.2 million." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2018, + "Sales": 154.8 + }, + { + "Year": 2019, + "Sales": 627.2 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Respective year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Sales", + "importance": "primary", + "description": "U.S. sales of White Claw (in millions of dollars)", + "unit": "Million USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2472": { + "metadata": { + "title": "How Holiday Season Digital Spending Has Developed", + "description": "Holiday season online retail sales (in billion U.S. dollars)", + "main_insight": "Holiday season digital spending increased from 2015 to 2016, with both desktop and mobile spending contributing to the growth. Mobile spending saw a larger percentage increase." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2016, + "Score": 63.1, + "Traffic Source": "Desktop" + }, + { + "Year": 2016, + "Score": 17.1, + "Traffic Source": "Mobile" + } + ], + "score": 1.0, + "annotation": "The Score of Desktop and Mobile show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Desktop and Mobile is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2016, + "Score": 63.1, + "Traffic Source": "Desktop" + }, + { + "Year": 2016, + "Score": 80.2, + "Traffic Source": "Total" + } + ], + "score": 1.0, + "annotation": "The Score of Desktop and Total show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Desktop and Total is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2016, + "Score": 17.1, + "Traffic Source": "Mobile" + }, + { + "Year": 2016, + "Score": 80.2, + "Traffic Source": "Total" + } + ], + "score": 1.0, + "annotation": "The Score of Mobile and Total show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Mobile and Total is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2015, + "Score": 56.4, + "Traffic Source": "Desktop" + }, + { + "Year": 2015, + "Score": 11.9, + "Traffic Source": "Mobile" + }, + { + "Year": 2015, + "Score": 68.3, + "Traffic Source": "Total" + }, + { + "Year": 2016, + "Score": 63.1, + "Traffic Source": "Desktop" + }, + { + "Year": 2016, + "Score": 17.1, + "Traffic Source": "Mobile" + }, + { + "Year": 2016, + "Score": 80.2, + "Traffic Source": "Total" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of holiday season", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding traffic source", + "role": "y" + }, + { + "name": "Traffic Source", + "importance": "primary", + "description": "The traffic source of the data point", + "role": "group" + } + ] + } + }, + "645": { + "metadata": { + "title": "The Japanese Car Industry's COVID-19 Slump", + "description": "Monthly production, export and domestic sales of new cars in Japan (2019-2020)", + "main_insight": "The Japanese car industry experienced a significant slump in 2020, likely due to the COVID-19 pandemic, with production, exports, and sales all dropping considerably compared to 2019 levels, with a recovery towards the very end of 2020" + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "May 2020", + "Dollars": 90000.0, + "Business Functions": "Export" + } + ], + "score": 0.99, + "annotation": "The Export has a minimum value at May 2020", + "reason": "The [{'Month': 'May 2020', 'Dollars': 90000.0, 'Business Functions': 'Export'}] of Export has a mainimum value of 90000.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "May 2020", + "Dollars": 280000.0, + "Business Functions": "Production" + } + ], + "score": 0.94, + "annotation": "The Production has a minimum value at May 2020", + "reason": "The [{'Month': 'May 2020', 'Dollars': 280000.0, 'Business Functions': 'Production'}] of Production has a mainimum value of 280000.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Nov 2020", + "Dollars": 390000.0, + "Business Functions": "Sales" + } + ], + "score": 0.89, + "annotation": "The Sales has the largest proportion in Nov 2020.", + "reason": "The Dollars of Sales in Nov 2020 accounts for 0.36214792721383815, larger than all other Business Functions, of the total of Dollars of all Business Functions." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Oct 2020", + "Dollars": 382006.0, + "Business Functions": "Production" + } + ], + "score": 0.89, + "annotation": "The Production has the largest proportion in Oct 2020.", + "reason": "The Dollars of Production in Oct 2020 accounts for 0.3737805844584083, larger than all other Business Functions, of the total of Dollars of all Business Functions." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Mar 2020", + "Dollars": 490000.0, + "Business Functions": "Sales" + } + ], + "score": 0.88, + "annotation": "The Sales has a maximum value at Mar 2020", + "reason": "The [{'Month': 'Mar 2020', 'Dollars': 490000.0, 'Business Functions': 'Sales'}] of Sales has a maximum 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The global average of those who know how to ride a bike is only 63%." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "SA", + "I know how to ride a bicycle": 36.0 + } + ], + "score": 0.92, + "annotation": "The has a minimum value at SA", + "reason": "The [{'Country': 'SA', 'I know how to ride a bicycle': 36.0}] of has a mainimum value of 36.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "PL", + "I know how to ride a bicycle": 83.0 + } + ], + "score": 0.82, + "annotation": "The has a maximum value at PL", + "reason": "The [{'Country': 'PL', 'I know how to ride a bicycle': 83.0}] of has a maximum value of 83.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "GB", + "I know how to ride a bicycle": 55.0 + } + ], + "score": 0.77, 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"metadata": { + "title": "Where Does the Digital Divide Persist?", + "description": "% of U.S. adults with access to home broadband and devices by area", + "main_insight": "The digital divide persists across different types of technology, with rural areas consistently showing lower access rates compared to suburban and urban areas in 2019." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Device Type": "Desktop/laptop computer (2008)", + "Score": 69.0, + "Location": "Rural" + }, + { + "Device Type": "Desktop/laptop computer (2008)", + "Score": 80.0, + "Location": "Suburban" + } + ], + "score": 1.0, + "annotation": "The Score of Rural and Suburban show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Rural and Suburban is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Device Type": "Desktop/laptop computer (2008)", + "Score": 80.0, + "Location": "Suburban" + }, + { + "Device Type": "Desktop/laptop computer (2008)", + "Score": 73.0, + "Location": "Urban" + } + ], + "score": 0.97, + "annotation": "The Score of Suburban and Urban show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Suburban and Urban is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Device Type": "Desktop/laptop computer (2008)", + "Score": 69.0, + "Location": "Rural" + }, + { + "Device Type": "Desktop/laptop computer (2008)", + "Score": 73.0, + "Location": "Urban" + } + ], + "score": 0.95, + "annotation": "The Score of Rural and Urban show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Rural and Urban is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Device Type": "Desktop/laptop computer (2008)", + "Score": 73.0, + "Location": "Urban" + } + ], + "score": 0.92, + "annotation": "The Urban shows a sudden jump in Score from Tablet (2010) to Desktop/laptop computer (2008).", + "reason": "The Score for Urban increases from 49.0 at Tablet (2010) to 73.0 at Desktop/laptop computer (2008), indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Device Type": "Home broadband (2000)", + "Score": 75.0, + "Location": "Suburban" + } + ], + "score": 0.91, + "annotation": "The Suburban shows a sudden jump in Score from Tablet (2010) to Home broadband (2000).", + "reason": "The Score for Suburban increases from 58.0 at Tablet (2010) to 75.0 at Home broadband (2000), indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Device Type": "Home broadband (2000)", + "Score": 63.0, + "Location": "Rural" + }, + { + "Device Type": "Home broadband (2000)", + "Score": 75.0, + "Location": "Suburban" + }, + { + "Device Type": "Home broadband (2000)", + "Score": 75.0, + "Location": "Urban" + }, + { + "Device Type": "Smartphone (2011)", + "Score": 71.0, + "Location": "Rural" + }, + { + "Device Type": "Smartphone (2011)", + "Score": 83.0, + "Location": "Suburban" + }, + { + "Device Type": "Smartphone (2011)", + "Score": 83.0, + "Location": "Urban" + }, + { + "Device Type": "Tablet (2010)", + "Score": 49.0, + "Location": "Rural" + }, + { + "Device Type": "Tablet (2010)", + "Score": 58.0, + "Location": "Suburban" + }, + { + "Device Type": "Tablet (2010)", + "Score": 49.0, + "Location": "Urban" + }, + { + "Device Type": "Desktop/laptop computer (2008)", + "Score": 69.0, + "Location": "Rural" + }, + { + "Device Type": "Desktop/laptop computer (2008)", + "Score": 80.0, + "Location": "Suburban" + }, + { + "Device Type": "Desktop/laptop computer (2008)", + "Score": 73.0, + "Location": "Urban" + } + ], + "columns": [ + { + "name": "Device Type", + "importance": "primary", + "description": "Type of device and year of introduction", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding location", + "role": "y" + }, + { + "name": "Location", + "importance": "primary", + "description": "The location of the data point", + "role": "group" + } + ] + } + }, + "433": { + "metadata": { + "title": "EU Citizens Moving To UK Halved Since Brexit Vote", + "description": "Inflow of EU adults to the UK for work-related reasons*", + "main_insight": "The inflow of EU citizens to the UK for work peaked around the time of the Brexit referendum in 2016 and has significantly decreased since then, roughly halving by 2018/19." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2009, + "EU Citizens Moving to UK": 88000 + }, + { + "Year": 2010, + "EU Citizens Moving to UK": 95000 + }, + { + "Year": 2011, + "EU Citizens Moving to UK": 82000 + }, + { + "Year": 2012, + "EU Citizens Moving to UK": 98000 + }, + { + "Year": 2013, + "EU Citizens Moving to UK": 128000 + }, + { + "Year": 2014, + "EU Citizens Moving to UK": 141000 + }, + { + "Year": 2015, + "EU Citizens Moving to UK": 169000 + }, + { + "Year": 2016, + "EU Citizens Moving to UK": 190000 + }, + { + "Year": 2017, + "EU Citizens Moving to UK": 153000 + }, + { + "Year": 2018, + "EU Citizens Moving to UK": 107000 + }, + { + "Year": 2019, + "EU Citizens Moving to UK": 92000 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year ending June 2009 to year ending March 2019", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "EU Citizens Moving to UK", + "importance": "primary", + "description": "Number of EU citizens moving to the UK for work-related reasons", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "807": { + "metadata": { + "title": "To rent or to buy?", + "description": "Financial advantages of buying a home over renting one in Germany (in %)*", + "main_insight": "The financial advantage of buying versus renting varies significantly across major German cities, with Cologne, Dusseldorf, Frankfurt, Berlin, and Stuttgart showing an advantage for buying under specific loan conditions, while Hamburg and Munich show an advantage for renting." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "City": "Munich", + "Value": -10.1, + "Mortgage Term": "35-year term" + }, + { + "City": "Munich", + "Value": -69.0, + "Mortgage Term": "25-year term" + } + ], + "score": 1.0, + "annotation": "The Value of 35-year term and 25-year term show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Value between 35-year term and 25-year term is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "Munich", + "Value": -10.1, + "Mortgage Term": "35-year term" + } + ], + "score": 0.92, + "annotation": "The 35-year term has a minimum value at Munich", + "reason": "The [{'City': 'Munich', 'Value': -10.1, 'Mortgage Term': '35-year term'}] of 35-year term has a mainimum value of -10.1, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "Munich", + "Value": -69.0, + "Mortgage Term": "25-year term" + } + ], + "score": 0.92, + "annotation": "The 25-year term has a minimum value at Munich", + "reason": "The [{'City': 'Munich', 'Value': -69.0, 'Mortgage Term': '25-year term'}] of 25-year term has a mainimum value of -69.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Hamburg", + "Value": 11.1, + "Mortgage Term": "35-year term" + } + ], + "score": 0.86, + "annotation": "The 35-year term shows a sudden jump in Value from Munich to Hamburg.", + "reason": "The Value for 35-year term increases from -10.1 at Munich to 11.1 at Hamburg, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Hamburg", + "Value": -36.5, + "Mortgage Term": "25-year term" + } + ], + "score": 0.85, + "annotation": "The 25-year term shows a sudden jump in Value from Munich to Hamburg.", + "reason": "The Value for 25-year term increases from -69.0 at Munich to -36.5 at Hamburg, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "City": "Cologne", + "Value": 28.5, + "Mortgage Term": "35-year term" + }, + { + "City": "Cologne", + "Value": -9.1, + "Mortgage Term": "25-year term" + }, + { + "City": "Dusseldorf", + "Value": 24.9, + "Mortgage Term": "35-year term" + }, + { + "City": "Dusseldorf", + "Value": -15.2, + "Mortgage Term": "25-year term" + }, + { + "City": "Frankfurt am Main", + "Value": 20.7, + "Mortgage Term": "35-year term" + }, + { + "City": "Frankfurt am Main", + "Value": -21.7, + "Mortgage Term": "25-year term" + }, + { + "City": "Berlin", + "Value": 19.4, + "Mortgage Term": "35-year term" + }, + { + "City": "Berlin", + "Value": -23.6, + "Mortgage Term": "25-year term" + }, + { + "City": "Stuttgart", + "Value": 18.8, + "Mortgage Term": "35-year term" + }, + { + "City": "Stuttgart", + "Value": -24.6, + "Mortgage Term": "25-year term" + }, + { + "City": "Hamburg", + "Value": 11.1, + "Mortgage Term": "35-year term" + }, + { + "City": "Hamburg", + "Value": -36.5, + "Mortgage Term": "25-year term" + }, + { + "City": "Munich", + "Value": -10.1, + "Mortgage Term": "35-year term" + }, + { + "City": "Munich", + "Value": -69.0, + "Mortgage Term": "25-year term" + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "German cities", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding mortgage term", + "role": "y" + }, + { + "name": "Mortgage Term", + "importance": "primary", + "description": "The mortgage term of the data point", + "role": "group" + } + ] + } + }, + "1519": { + "metadata": { + "title": "Will the Gender Pay Gap Ever Be Closed?", + "description": "Female-to-male earnings ratio for full-time, year-round workers aged 15+ in the United States", + "main_insight": "The gender pay gap in the US has narrowed significantly since 1960, with the female-to-male earnings ratio increasing from around 60% to 84% in 2022. However, the rate of improvement has slowed down in recent years." + }, + "chart_type": "Spline Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1960, + "Female-to-male earnings ratio": 60.0 + }, + { + "Year": 1961, + "Female-to-male earnings ratio": 59.0 + }, + { + "Year": 1962, + "Female-to-male earnings ratio": 59.0 + }, + { + "Year": 1963, + "Female-to-male earnings ratio": 58.0 + }, + { + "Year": 1964, + "Female-to-male earnings ratio": 59.0 + }, + { + "Year": 1965, + "Female-to-male earnings ratio": 60.0 + }, + { + "Year": 1966, + "Female-to-male earnings ratio": 58.0 + }, + { + "Year": 1967, + "Female-to-male earnings ratio": 58.0 + }, + { + "Year": 1968, + "Female-to-male earnings ratio": 58.0 + }, + { + "Year": 1969, + "Female-to-male earnings ratio": 59.0 + }, + { + "Year": 1970, + "Female-to-male earnings ratio": 59.0 + }, + { + "Year": 1971, + "Female-to-male earnings ratio": 59.0 + }, + { + "Year": 1972, + "Female-to-male earnings ratio": 58.0 + }, + { + "Year": 1973, + "Female-to-male earnings ratio": 57.0 + }, + { + "Year": 1974, + "Female-to-male earnings ratio": 59.0 + }, + { + "Year": 1975, + "Female-to-male earnings ratio": 59.0 + }, + { + "Year": 1976, + "Female-to-male earnings ratio": 60.0 + }, + { + "Year": 1977, + "Female-to-male earnings ratio": 59.0 + }, + { + "Year": 1978, + "Female-to-male earnings ratio": 59.0 + }, + { + "Year": 1979, + "Female-to-male earnings ratio": 60.0 + }, + { + "Year": 1980, + "Female-to-male earnings ratio": 60.0 + }, + { + "Year": 1981, + "Female-to-male earnings ratio": 59.0 + }, + { + "Year": 1982, + "Female-to-male earnings ratio": 62.0 + }, + { + "Year": 1983, + "Female-to-male earnings ratio": 64.0 + }, + { + "Year": 1984, + "Female-to-male earnings ratio": 64.0 + }, + { + "Year": 1985, + "Female-to-male earnings ratio": 65.0 + }, + { + "Year": 1986, + "Female-to-male earnings ratio": 64.0 + }, + { + "Year": 1987, + "Female-to-male earnings ratio": 65.0 + }, + { + "Year": 1988, + "Female-to-male earnings ratio": 66.0 + }, + { + "Year": 1989, + "Female-to-male earnings ratio": 68.0 + }, + { + "Year": 1990, + "Female-to-male earnings ratio": 72.0 + }, + { + "Year": 1991, + "Female-to-male earnings ratio": 70.0 + }, + { + "Year": 1992, + "Female-to-male earnings ratio": 71.0 + }, + { + "Year": 1993, + "Female-to-male earnings ratio": 72.0 + }, + { + "Year": 1994, + "Female-to-male earnings ratio": 72.0 + }, + { + "Year": 1995, + "Female-to-male earnings ratio": 71.0 + }, + { + "Year": 1996, + "Female-to-male earnings ratio": 74.0 + }, + { + "Year": 1997, + "Female-to-male earnings ratio": 74.0 + }, + { + "Year": 1998, + "Female-to-male earnings ratio": 73.0 + }, + { + "Year": 1999, + "Female-to-male earnings ratio": 72.0 + }, + { + "Year": 2000, + "Female-to-male earnings ratio": 74.0 + }, + { + "Year": 2001, + "Female-to-male earnings ratio": 76.0 + }, + { + "Year": 2002, + "Female-to-male earnings ratio": 77.0 + }, + { + "Year": 2003, + "Female-to-male earnings ratio": 76.0 + }, + { + "Year": 2004, + "Female-to-male earnings ratio": 77.0 + }, + { + "Year": 2005, + "Female-to-male earnings ratio": 77.0 + }, + { + "Year": 2006, + "Female-to-male earnings ratio": 77.0 + }, + { + "Year": 2007, + "Female-to-male earnings ratio": 78.0 + }, + { + "Year": 2008, + "Female-to-male earnings ratio": 77.0 + }, + { + "Year": 2009, + "Female-to-male earnings ratio": 77.0 + }, + { + "Year": 2010, + "Female-to-male earnings ratio": 77.0 + }, + { + "Year": 2011, + "Female-to-male earnings ratio": 77.0 + }, + { + "Year": 2012, + "Female-to-male earnings ratio": 77.0 + }, + { + "Year": 2013, + "Female-to-male earnings ratio": 78.0 + }, + { + "Year": 2014, + "Female-to-male earnings ratio": 79.0 + }, + { + "Year": 2015, + "Female-to-male earnings ratio": 80.0 + }, + { + "Year": 2016, + "Female-to-male earnings ratio": 80.0 + }, + { + "Year": 2017, + "Female-to-male earnings ratio": 81.0 + }, + { + "Year": 2018, + "Female-to-male earnings ratio": 82.0 + }, + { + "Year": 2019, + "Female-to-male earnings ratio": 82.0 + }, + { + "Year": 2020, + "Female-to-male earnings ratio": 83.0 + }, + { + "Year": 2021, + "Female-to-male earnings ratio": 84.0 + }, + { + "Year": 2022, + "Female-to-male earnings ratio": 84.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the earnings ratio", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Female-to-male earnings ratio", + "importance": "primary", + "description": "Ratio of female earnings to male earnings, expressed as a percentage", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "64": { + "metadata": { + "title": "Social Media Silences NSA Debate", + "description": "Willingness to join conversations about government surveillance", + "main_insight": "People are more willing to discuss government surveillance in person than on social media platforms like Facebook and Twitter. A significant portion of respondents are unwilling to discuss the topic in any of the presented contexts." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Context": "On Twitter (based on Twitter users)", + "Value": 14.0, + "Willingness": "Very willing" + }, + { + "Context": "On Twitter (based on Twitter users)", + "Value": 40.0, + "Willingness": "Very unwilling" + } + ], + "score": 0.93, + "annotation": "The Value of Very willing and Very unwilling show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Value between Very willing and Very unwilling is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Willingness": "Somewhat willing", + "Context": "total", + "Value": 208.0 + } + ], + "score": 0.92, + "annotation": "The total value of Somewhat willing has the largest proportion.", + "reason": "The total value of Value of Somewhat willing accounts for 0.34608985024958405, larger than all other Willingness, of the total of Value of all Willingness." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Context": "On Facebook (based on Facebook users)", + "Value": 35.0, + "Willingness": "Very unwilling" + } + ], + "score": 0.92, + "annotation": "The Very unwilling has the largest proportion in On Facebook (based on Facebook users).", + "reason": "The Value of Very unwilling in On Facebook (based on Facebook users) accounts for 0.3465346534653465, larger than all other Willingness, of the total of Value of all Willingness." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Context": "On Twitter (based on Twitter users)", + "Value": 14.0, + "Willingness": "Very willing" + }, + { + "Context": "On Twitter (based on Twitter users)", + "Value": 19.0, + "Willingness": "Somewhat unwilling" + } + ], + "score": 0.92, + "annotation": "The Value of Very willing and Somewhat unwilling show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Value between Very willing and Somewhat unwilling is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Context": "At work", + "Value": 39.0, + "Willingness": "Somewhat willing" + } + ], + "score": 0.91, + "annotation": "The Somewhat willing has the largest proportion in At work.", + "reason": "The Value of Somewhat willing in At work accounts for 0.38613861386138615, larger than all other Willingness, of the total of Value of all Willingness." + } + ], + "data": { + "data": [ + { + "Context": "At a family dinner", + "Value": 40.0, + "Willingness": "Very willing" + }, + { + "Context": "At a family dinner", + "Value": 35.0, + "Willingness": "Somewhat willing" + }, + { + "Context": "At a family dinner", + "Value": 12.0, + "Willingness": "Somewhat unwilling" + }, + { + "Context": "At a family dinner", + "Value": 13.0, + "Willingness": "Very unwilling" + }, + { + "Context": "At a restaurant with friends", + "Value": 32.0, + "Willingness": "Very willing" + }, + { + "Context": "At a restaurant with friends", + "Value": 39.0, + "Willingness": "Somewhat willing" + }, + { + "Context": "At a restaurant with friends", + "Value": 14.0, + "Willingness": "Somewhat unwilling" + }, + { + "Context": "At a restaurant with friends", + "Value": 14.0, + "Willingness": "Very unwilling" + }, + { + "Context": "At a community meeting", + "Value": 27.0, + "Willingness": "Very willing" + }, + { + "Context": "At a community meeting", + "Value": 41.0, + "Willingness": "Somewhat willing" + }, + { + "Context": "At a community meeting", + "Value": 16.0, + "Willingness": "Somewhat unwilling" + }, + { + "Context": "At a community meeting", + "Value": 16.0, + "Willingness": "Very unwilling" + }, + { + "Context": "At work", + "Value": 27.0, + "Willingness": "Very willing" + }, + { + "Context": "At work", + "Value": 39.0, + "Willingness": "Somewhat willing" + }, + { + "Context": "At work", + "Value": 16.0, + "Willingness": "Somewhat unwilling" + }, + { + "Context": "At work", + "Value": 19.0, + "Willingness": "Very unwilling" + }, + { + "Context": "On Facebook (based on Facebook users)", + "Value": 16.0, + "Willingness": "Very willing" + }, + { + "Context": "On Facebook (based on Facebook users)", + "Value": 27.0, + "Willingness": "Somewhat willing" + }, + { + "Context": "On Facebook (based on Facebook users)", + "Value": 23.0, + "Willingness": "Somewhat unwilling" + }, + { + "Context": "On Facebook (based on Facebook users)", + "Value": 35.0, + "Willingness": "Very unwilling" + }, + { + "Context": "On Twitter (based on Twitter users)", + "Value": 14.0, + "Willingness": "Very willing" + }, + { + "Context": "On Twitter (based on Twitter users)", + "Value": 27.0, + "Willingness": "Somewhat willing" + }, + { + "Context": "On Twitter (based on Twitter users)", + "Value": 19.0, + "Willingness": "Somewhat unwilling" + }, + { + "Context": "On Twitter (based on Twitter users)", + "Value": 40.0, + "Willingness": "Very unwilling" + } + ], + "columns": [ + { + "name": "Context", + "importance": "primary", + "description": "The situation or platform for potential discussion", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding willingness", + "role": "y" + }, + { + "name": "Willingness", + "importance": "primary", + "description": "The willingness of the data point", + "role": "group" + } + ] + } + }, + "2138": { + "metadata": { + "title": "The Shape of Water Joins Oscars Most Nominated List", + "description": "Films with the most Academy Award nominations (as of 2018)", + "main_insight": "The chart lists films with the most Academy Award nominations, showing both the number of nominations and wins for each film. Titanic, La La Land, and All About Eve received the highest number of nominations (14 each)." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Film": "Titanic 1997", + "Nominations": 14.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Nominations from The Curious Case of Benjamin Button 2008 to Titanic 1997.", + "reason": "The Nominations for increases from 13.0 at The Curious Case of Benjamin Button 2008 to 14.0 at Titanic 1997, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Film": "Titanic 1997", + "Nominations": 14.0 + }, + { + "Film": "La La Land 2017", + "Nominations": 14.0 + }, + { + "Film": "All about Eve 1950", + "Nominations": 14.0 + } + ], + "score": 0.87, + "annotation": "The has maximum values at Titanic 1997, La La Land 2017, All about Eve 1950", + "reason": "The [{'Film': 'Titanic 1997', 'Nominations': 14.0}, {'Film': 'La La Land 2017', 'Nominations': 14.0}, {'Film': 'All about Eve 1950', 'Nominations': 14.0}] of have maximum values of 14.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Film": "The Shape of Water 2018", + "Nominations": 13.0 + }, + { + "Film": "Gone with the Wind 1939", + "Nominations": 13.0 + }, + { + "Film": "From Here to Eternity 1953", + "Nominations": 13.0 + }, + { + "Film": "Shakespeare in Love 1998", + "Nominations": 13.0 + }, + { + "Film": "Chicago 2002", + "Nominations": 13.0 + }, + { + "Film": "Forrest Gump 1994", + "Nominations": 13.0 + }, + { + "Film": "Mary Poppins 1964", + "Nominations": 13.0 + }, + { + "Film": "Who's Afraid of Virginia Woolf? 1966", + "Nominations": 13.0 + }, + { + "Film": "Lord of the Rings: The Fellowship of the Ring 2001", + "Nominations": 13.0 + }, + { + "Film": "The Curious Case of Benjamin Button 2008", + "Nominations": 13.0 + } + ], + "score": 0.37, + "annotation": "The has minimum values at The Shape of Water 2018, Gone with the Wind 1939, From Here to Eternity 1953, Shakespeare in Love 1998, Chicago 2002, Forrest Gump 1994, Mary Poppins 1964, Who's Afraid of Virginia Woolf? 1966, Lord of the Rings: The Fellowship of the Ring 2001, The Curious Case of Benjamin Button 2008", + "reason": "The [{'Film': 'The Shape of Water 2018', 'Nominations': 13.0}, {'Film': 'Gone with the Wind 1939', 'Nominations': 13.0}, {'Film': 'From Here to Eternity 1953', 'Nominations': 13.0}, {'Film': 'Shakespeare in Love 1998', 'Nominations': 13.0}, {'Film': 'Chicago 2002', 'Nominations': 13.0}, {'Film': 'Forrest Gump 1994', 'Nominations': 13.0}, {'Film': 'Mary Poppins 1964', 'Nominations': 13.0}, {'Film': \"Who's Afraid of Virginia Woolf? 1966\", 'Nominations': 13.0}, {'Film': 'Lord of the Rings: The Fellowship of the Ring 2001', 'Nominations': 13.0}, {'Film': 'The Curious Case of Benjamin Button 2008', 'Nominations': 13.0}] of have minimum values of 13.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Film": "Titanic 1997", + "Nominations": 14.0 + }, + { + "Film": "La La Land 2017", + "Nominations": 14.0 + }, + { + "Film": "All about Eve 1950", + "Nominations": 14.0 + }, + { + "Film": "The Shape of Water 2018", + "Nominations": 13.0 + }, + { + "Film": "Gone with the Wind 1939", + "Nominations": 13.0 + }, + { + "Film": "From Here to Eternity 1953", + "Nominations": 13.0 + }, + { + "Film": "Shakespeare in Love 1998", + "Nominations": 13.0 + }, + { + "Film": "Chicago 2002", + "Nominations": 13.0 + }, + { + "Film": "Forrest Gump 1994", + "Nominations": 13.0 + }, + { + "Film": "Mary Poppins 1964", + "Nominations": 13.0 + }, + { + "Film": "Who's Afraid of Virginia Woolf? 1966", + "Nominations": 13.0 + }, + { + "Film": "Lord of the Rings: The Fellowship of the Ring 2001", + "Nominations": 13.0 + }, + { + "Film": "The Curious Case of Benjamin Button 2008", + "Nominations": 13.0 + } + ], + "columns": [ + { + "name": "Film", + "importance": "primary", + "description": "Name and release year of the film", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Nominations", + "importance": "primary", + "description": "Number of Academy Award nominations", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "749": { + "metadata": { + "title": "Allure of Amazon", + "description": "How likely are U.S. adults by age to order a low cost item from Amazon Prime vs. buying in-store?", + "main_insight": "The likelihood of ordering low-cost items from Amazon Prime varies across age groups, with a significant portion of all age groups not likely to use the service, although those aged 25-34 are more likely use Amazon's service compared to the others." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "25-34", + "Score": 38.0, + "Likelihood": "Not at all likely" + } + ], + "score": 0.89, + "annotation": "The Not at all likely has the largest proportion in 25-34.", + "reason": "The Score of Not at all likely in 25-34 accounts for 0.37623762376237624, larger than all other Likelihood, of the total of Score of all Likelihood." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "35-54", + "Score": 40.0, + "Likelihood": "Somewhat Likely" + } + ], + "score": 0.88, + "annotation": "The Somewhat Likely has the largest proportion in 35-54.", + "reason": "The Score of Somewhat Likely in 35-54 accounts for 0.4, larger than all other Likelihood, of the total of Score of all Likelihood." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Age Group": "35-54", + "Score": 25.0, + "Likelihood": "Very Likely" + } + ], + "score": 0.88, + "annotation": "The Very Likely shows a sudden jump in Score from 55+ to 35-54.", + "reason": "The Score for Very Likely increases from 19.0 at 55+ to 25.0 at 35-54, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Likelihood": "Not at all likely", + "Age Group": "total", + "Score": 168.0 + } + ], + "score": 0.88, + "annotation": "The total value of Not at all likely has the largest proportion.", + "reason": "The total value of Score of Not at all likely accounts for 0.42, larger than all other Likelihood, of the total of Score of all Likelihood." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "18-24", + "Score": 44.0, + "Likelihood": "Not at all likely" + } + ], + "score": 0.87, + "annotation": "The Not at all likely has the largest proportion in 18-24.", + "reason": "The Score of Not at all likely in 18-24 accounts for 0.4444444444444444, larger than all other Likelihood, of the total of Score of all Likelihood." + } + ], + "data": { + "data": [ + { + "Age Group": "18-24", + "Score": 28.0, + "Likelihood": "Very Likely" + }, + { + "Age Group": "18-24", + "Score": 27.0, + "Likelihood": "Somewhat Likely" + }, + { + "Age Group": "18-24", + "Score": 44.0, + "Likelihood": "Not at all likely" + }, + { + "Age Group": "25-34", + "Score": 29.0, + "Likelihood": "Very Likely" + }, + { + "Age Group": "25-34", + "Score": 34.0, + "Likelihood": "Somewhat Likely" + }, + { + "Age Group": "25-34", + "Score": 38.0, + "Likelihood": "Not at all likely" + }, + { + "Age Group": "35-54", + "Score": 25.0, + "Likelihood": "Very Likely" + }, + { + "Age Group": "35-54", + "Score": 40.0, + "Likelihood": "Somewhat Likely" + }, + { + "Age Group": "35-54", + "Score": 35.0, + "Likelihood": "Not at all likely" + }, + { + "Age Group": "55+", + "Score": 19.0, + "Likelihood": "Very Likely" + }, + { + "Age Group": "55+", + "Score": 30.0, + "Likelihood": "Somewhat Likely" + }, + { + "Age Group": "55+", + "Score": 51.0, + "Likelihood": "Not at all likely" + } + ], + "columns": [ + { + "name": "Age Group", + "importance": "primary", + "description": "Age range of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding likelihood", + "role": "y" + }, + { + "name": "Likelihood", + "importance": "primary", + "description": "The likelihood of the data point", + "role": "group" + } + ] + } + }, + "541": { + "metadata": { + "title": "Who Has Donated Military Hardware to Ukraine?", + "description": "Military hardware allocated to Ukraine between Jan. 24, 2022 and Aug. 31, 2024 by selected countries", + "main_insight": "The United States has donated significantly more military hardware to Ukraine than any other country, with a diverse range of equipment including howitzers, infantry fighting vehicles, and tanks." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "US", + "Units": 20, + "Military Equipment": "Multiple launch rocket system" + } + ], + "score": 0.97, + "annotation": "The Multiple launch rocket system shows a sudden jump in Units from GB to US.", + "reason": "The Units for Multiple launch rocket system increases from 10 at GB to 20 at US, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Military Equipment": "Total Hardware", + "Country": "total", + "Units": 2620 + } + ], + "score": 0.97, + "annotation": "The total value of Total Hardware has the smallest proportion.", + "reason": "The total value of Units of Total Hardware accounts for 0.006635071090047393, smaller than all other Military Equipment, of the total of Units of all Military Equipment." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "US", + "Units": 10, + "Military Equipment": "Air defense systems" + } + ], + "score": 0.96, + "annotation": "The Air defense systems has the smallest proportion in US.", + "reason": "The Units of Air defense systems in US accounts for 0.007326007326007326, smaller than all other Military Equipment, of the total of Units of all Military Equipment." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "NL", + "Units": 10, + "Military Equipment": "Howitzers" + }, + { + "Country": "DE", + "Units": 10, + "Military Equipment": "Multiple launch rocket system" + } + ], + "score": 0.96, + "annotation": "The Howitzers, Multiple launch rocket system all have the smallest proportion in NL.", + "reason": "The Units of Howitzers, Multiple launch rocket system in NL all account for 0.008658008658008658, smaller than all other Military Equipment, of the total of Units of all Military Equipment." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "PL", + "Units": 325, + "Military Equipment": "Tanks" + } + ], + "score": 0.96, + "annotation": "The Tanks has a maximum value at PL", + "reason": "The [{'Country': 'PL', 'Units': 325, 'Military Equipment': 'Tanks'}] of Tanks has a maximum value of 325, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "US", + "Units": 670, + "Military Equipment": "Total Hardware" + }, + { + "Country": "US", + "Units": 75, + "Military Equipment": "Tanks" + }, + { + "Country": "US", + "Units": 450, + "Military Equipment": "Infantry fighting vehicles" + }, + { + "Country": "US", + "Units": 140, + "Military Equipment": "Howitzers" + }, + { + "Country": "US", + "Units": 20, + "Military Equipment": "Multiple launch rocket system" + }, + { + "Country": "US", + "Units": 10, + "Military Equipment": "Air defense systems" + }, + { + "Country": "NL", + "Units": 400, + "Military Equipment": "Total Hardware" + }, + { + "Country": "NL", + "Units": 390, + "Military Equipment": "Infantry fighting vehicles" + }, + { + "Country": "NL", + "Units": 10, + "Military Equipment": "Howitzers" + }, + { + "Country": "PL", + "Units": 380, + "Military Equipment": "Total Hardware" + }, + { + "Country": "PL", + "Units": 325, + "Military Equipment": "Tanks" + }, + { + "Country": "PL", + "Units": 25, + "Military Equipment": "Infantry fighting vehicles" + }, + { + "Country": "PL", + "Units": 30, + "Military Equipment": "Howitzers" + }, + { + "Country": "DE", + "Units": 310, + "Military Equipment": "Total Hardware" + }, + { + "Country": "DE", + "Units": 80, + "Military Equipment": "Tanks" + }, + { + "Country": "DE", + "Units": 150, + "Military Equipment": "Infantry fighting vehicles" + }, + { + "Country": "DE", + "Units": 60, + "Military Equipment": "Howitzers" + }, + { + "Country": "DE", + "Units": 10, + "Military Equipment": "Multiple launch rocket system" + }, + { + "Country": "DE", + "Units": 20, + "Military Equipment": "Air defense systems" + }, + { + "Country": "CZ", + "Units": 260, + "Military Equipment": "Total Hardware" + }, + { + "Country": "CZ", + "Units": 80, + "Military Equipment": "Tanks" + }, + { + "Country": "CZ", + "Units": 115, + "Military Equipment": "Infantry fighting vehicles" + }, + { + "Country": "CZ", + "Units": 40, + "Military Equipment": "Howitzers" + }, + { + "Country": "CZ", + "Units": 20, + "Military Equipment": "Multiple launch rocket system" + }, + { + "Country": "CZ", + "Units": 5, + "Military Equipment": "Air defense systems" + }, + { + "Country": "DK", + "Units": 210, + "Military Equipment": "Total Hardware" + }, + { + "Country": "DK", + "Units": 80, + "Military Equipment": "Tanks" + }, + { + "Country": "DK", + "Units": 120, + "Military Equipment": "Infantry fighting vehicles" + }, + { + "Country": "DK", + "Units": 10, + "Military Equipment": "Howitzers" + }, + { + "Country": "GB", + "Units": 190, + "Military Equipment": "Total Hardware" + }, + { + "Country": "GB", + "Units": 30, + "Military Equipment": "Tanks" + }, + { + "Country": "GB", + "Units": 50, + "Military Equipment": "Infantry fighting vehicles" + }, + { + "Country": "GB", + "Units": 100, + "Military Equipment": "Howitzers" + }, + { + "Country": "GB", + "Units": 10, + "Military Equipment": "Multiple launch rocket system" + }, + { + "Country": "IT", + "Units": 80, + "Military Equipment": "Total Hardware" + }, + { + "Country": "IT", + "Units": 40, + "Military Equipment": "Infantry fighting vehicles" + }, + { + "Country": "IT", + "Units": 40, + "Military Equipment": "Howitzers" + }, + { + "Country": "FR", + "Units": 70, + "Military Equipment": "Total Hardware" + }, + { + "Country": "FR", + "Units": 40, + "Military Equipment": "Infantry fighting vehicles" + }, + { + "Country": "FR", + "Units": 30, + "Military Equipment": "Howitzers" + }, + { + "Country": "NO", + "Units": 30, + "Military Equipment": "Total Hardware" + }, + { + "Country": "NO", + "Units": 20, + "Military Equipment": "Tanks" + }, + { + "Country": "NO", + "Units": 10, + "Military Equipment": "Howitzers" + }, + { + "Country": "ES", + "Units": 20, + "Military Equipment": "Total Hardware" + }, + { + "Country": "ES", + "Units": 10, + "Military Equipment": "Tanks" + }, + { + "Country": "ES", + "Units": 10, + "Military Equipment": "Howitzers" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country donating military hardware", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding military equipment", + "role": "y" + }, + { + "name": "Military Equipment", + "importance": "primary", + "description": "The military equipment of the data point", + "role": "group" + } + ] + } + }, + "1148": { + "metadata": { + "title": "Financial Future Worries", + "description": "Share of respondents saying they are worried about their financial future", + "main_insight": "There is a significant variation in the level of financial worry across different countries, with Greece having the highest percentage of respondents expressing concern and France the lowest." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country Code": "GR", + "Worried Respondents (%)": 60.0 + } + ], + "score": 0.91, + "annotation": "The has a maximum value at GR", + "reason": "The [{'Country Code': 'GR', 'Worried Respondents (%)': 60.0}] of has a maximum value of 60.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country Code": "GR", + "Worried Respondents (%)": 60.0 + } + ], + "score": 0.73, + "annotation": "The shows a sudden jump in Worried Respondents (%) from PT to GR.", + "reason": "The Worried Respondents (%) for increases from 49.0 at PT to 60.0 at GR, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country Code": "FR", + "Worried Respondents (%)": 29.0 + } + ], + "score": 0.71, + "annotation": "The has a minimum value at FR", + "reason": "The [{'Country Code': 'FR', 'Worried Respondents (%)': 29.0}] of has a mainimum value of 29.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country Code": "GR", + "Worried Respondents (%)": 60.0 + }, + { + "Country Code": "PT", + "Worried Respondents (%)": 49.0 + }, + { + "Country Code": "AR", + "Worried Respondents (%)": 48.0 + }, + { + "Country Code": "BR", + "Worried Respondents (%)": 46.0 + }, + { + "Country Code": "ES", + "Worried Respondents (%)": 43.0 + }, + { + "Country Code": "US", + "Worried Respondents (%)": 36.0 + }, + { + "Country Code": "DE", + "Worried Respondents (%)": 36.0 + }, + { + "Country Code": "GB", + "Worried Respondents (%)": 35.0 + }, + { + "Country Code": "CH", + "Worried Respondents (%)": 30.0 + }, + { + "Country Code": "FR", + "Worried Respondents (%)": 29.0 + } + ], + "columns": [ + { + "name": "Country Code", + "importance": "primary", + "description": "Two-letter country code", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Worried Respondents (%)", + "importance": "primary", + "description": "Percentage of respondents in each country who are worried about their financial future", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "484": { + "metadata": { + "title": "Alphabet Is Feeling the Ad Crisis Backlash", + "description": "Alphabet (GOOG) market capitalization Feb 27 to Mar 27, 2017 (in billion U.S. dollars)", + "main_insight": "Alphabet's market capitalization experienced a significant drop between March 17 and March 27, 2017, after initially rising throughout the month." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "Mar 17", + "Market Capitalization (Billion USD)": 589.2 + } + ], + "score": 0.86, + "annotation": "The has a maximum value at Mar 17", + "reason": "The [{'Date': 'Mar 17', 'Market Capitalization (Billion USD)': 589.2}] of has a maximum value of 589.2, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "Mar 25", + "Market Capitalization (Billion USD)": 563.1 + } + ], + "score": 0.85, + "annotation": "The has a minimum value at Mar 25", + "reason": "The [{'Date': 'Mar 25', 'Market Capitalization (Billion USD)': 563.1}] of has a mainimum value of 563.1, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "Mar 11", + "Market Capitalization (Billion USD)": 582.8 + } + ], + "score": 0.55, + "annotation": "The shows a sudden jump in Market Capitalization (Billion USD) from Mar 9 to Mar 11.", + "reason": "The Market Capitalization (Billion USD) for increases from 579.0 at Mar 9 to 582.8 at Mar 11, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Date": "Feb 27", + "Market Capitalization (Billion USD)": 573.4 + }, + { + "Date": "Mar 1", + "Market Capitalization (Billion USD)": 569.6 + }, + { + "Date": "Mar 3", + "Market Capitalization (Billion USD)": 572.6 + }, + { + "Date": "Mar 5", + "Market Capitalization (Billion USD)": 572.5 + }, + { + "Date": "Mar 7", + "Market Capitalization (Billion USD)": 572.8 + }, + { + "Date": "Mar 9", + "Market Capitalization (Billion USD)": 579.0 + }, + { + "Date": "Mar 11", + "Market Capitalization (Billion USD)": 582.8 + }, + { + "Date": "Mar 13", + "Market Capitalization (Billion USD)": 583.6 + }, + { + "Date": "Mar 15", + "Market Capitalization (Billion USD)": 584.5 + }, + { + "Date": "Mar 17", + "Market Capitalization (Billion USD)": 589.2 + }, + { + "Date": "Mar 19", + "Market Capitalization (Billion USD)": 586.0 + }, + { + "Date": "Mar 21", + "Market Capitalization (Billion USD)": 576.2 + }, + { + "Date": "Mar 23", + "Market Capitalization (Billion USD)": 569.0 + }, + { + "Date": "Mar 25", + "Market Capitalization (Billion USD)": 563.1 + }, + { + "Date": "Mar 27", + "Market Capitalization (Billion USD)": 566.6 + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Day in February/March 2017", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Market Capitalization (Billion USD)", + "importance": "primary", + "description": "Alphabet's market capitalization in billions of U.S. dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "233": { + "metadata": { + "title": "U.S. Economy Returns to Pre-Pandemic Growth Path", + "description": "U.S. real gross domestic product (in chained 2017 dollars)", + "main_insight": "The U.S. real GDP dropped in 2020 due to the pandemic but has since recovered and surpassed pre-pandemic levels by 2023." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2015, + "Real GDP": 18883.0 + }, + { + "Year": 2016, + "Real GDP": 19190.0 + }, + { + "Year": 2017, + "Real GDP": 19612.0 + }, + { + "Year": 2018, + "Real GDP": 20181.0 + }, + { + "Year": 2019, + "Real GDP": 20663.0 + }, + { + "Year": 2020, + "Real GDP": 20019.0 + }, + { + "Year": 2021, + "Real GDP": 21140.0 + }, + { + "Year": 2022, + "Real GDP": 21538.0 + }, + { + "Year": 2023, + "Real GDP": 22374.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 2015 to 2023", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Real GDP", + "importance": "primary", + "description": "U.S. real gross domestic product in chained 2017 dollars (in billions)", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "656": { + "metadata": { + "title": "Snowden Revelations Push DuckDuckGo", + "description": "The average number of daily searches on DuckDuckGo.com (in millions)", + "main_insight": "DuckDuckGo's average daily searches significantly increased following the start of the Snowden revelations in June 2013, nearly doubling by January 2014." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Jan 2014", + "Daily Searches": 4.38 + } + ], + "score": 0.73, + "annotation": "The has a maximum value at Jan 2014", + "reason": "The [{'Month': 'Jan 2014', 'Daily Searches': 4.38}] of has a maximum value of 4.38, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Mar 2013", + "Daily Searches": 1.59 + } + ], + "score": 0.69, + "annotation": "The has a minimum value at Mar 2013", + "reason": "The [{'Month': 'Mar 2013', 'Daily Searches': 1.59}] of has a mainimum value of 1.59, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Jun 2013", + "Daily Searches": 2.59 + } + ], + "score": 0.69, + "annotation": "The shows a sudden jump in Daily Searches from May 2013 to Jun 2013.", + "reason": "The Daily Searches for increases from 1.76 at May 2013 to 2.59 at Jun 2013, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Month": "Jan 2013", + "Daily Searches": 1.64 + }, + { + "Month": "Feb 2013", + "Daily Searches": 1.64 + }, + { + "Month": "Mar 2013", + "Daily Searches": 1.59 + }, + { + "Month": "Apr 2013", + "Daily Searches": 1.71 + }, + { + "Month": "May 2013", + "Daily Searches": 1.76 + }, + { + "Month": "Jun 2013", + "Daily Searches": 2.59 + }, + { + "Month": "Jul 2013", + "Daily Searches": 3.41 + }, + { + "Month": "Aug 2013", + "Daily Searches": 3.72 + }, + { + "Month": "Sep 2013", + "Daily Searches": 3.98 + }, + { + "Month": "Oct 2013", + "Daily Searches": 3.89 + }, + { + "Month": "Nov 2013", + "Daily Searches": 3.99 + }, + { + "Month": "Dec 2013", + "Daily Searches": 3.77 + }, + { + "Month": "Jan 2014", + "Daily Searches": 4.38 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Daily Searches", + "importance": "primary", + "description": "Average number of daily searches on DuckDuckGo (in millions)", + "unit": "millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1035": { + "metadata": { + "title": "6 in 10 Democrats Would Pick Harris, the Rest Is Not so Sure", + "description": "Share of respondents who say Kamala Harris/someone else should be nominated as the Dem. candidate for president", + "main_insight": "A majority of Democrats (60%) support Kamala Harris as the Democratic presidential nominee, while there is less certainty among other political affiliations and U.S. adults overall." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Political Affiliation": "Republicans", + "Units": 24.0, + "Preference": "Kamala Harris" + }, + { + "Political Affiliation": "Republicans", + "Units": 50.0, + "Preference": "Someone else" + } + ], + "score": 0.94, + "annotation": "The Units of Kamala Harris and Someone else show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Kamala Harris and Someone else is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Political Affiliation": "Democrats", + "Units": 60.0, + "Preference": "Kamala Harris" + } + ], + "score": 0.9, + "annotation": "The Kamala Harris shows a sudden jump in Units from U.S. adults to Democrats.", + "reason": "The Units for Kamala Harris increases from 37.0 at U.S. adults to 60.0 at Democrats, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Political Affiliation": "Independents", + "Units": 36.0, + "Preference": "Someone else" + } + ], + "score": 0.89, + "annotation": "The Someone else has the largest proportion in Independents.", + "reason": "The Units of Someone else in Independents accounts for 0.36, larger than all other Preference, of the total of Units of all Preference." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Political Affiliation": "U.S. adults", + "Units": 37.0, + "Preference": "Kamala Harris" + } + ], + "score": 0.89, + "annotation": "The Kamala Harris has the largest proportion in U.S. adults.", + "reason": "The Units of Kamala Harris in U.S. adults accounts for 0.37373737373737376, larger than all other Preference, of the total of Units of all Preference." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Preference": "Kamala Harris", + "Political Affiliation": "total", + "Units": 151.0 + } + ], + "score": 0.89, + "annotation": "The total value of Kamala Harris has the largest proportion.", + "reason": "The total value of Units of Kamala Harris accounts for 0.37844611528822053, larger than all other Preference, of the total of Units of all Preference." + } + ], + "data": { + "data": [ + { + "Political Affiliation": "U.S. adults", + "Units": 37.0, + "Preference": "Kamala Harris" + }, + { + "Political Affiliation": "U.S. adults", + "Units": 27.0, + "Preference": "Not sure" + }, + { + "Political Affiliation": "U.S. adults", + "Units": 35.0, + "Preference": "Someone else" + }, + { + "Political Affiliation": "Democrats", + "Units": 60.0, + "Preference": "Kamala Harris" + }, + { + "Political Affiliation": "Democrats", + "Units": 19.0, + "Preference": "Not sure" + }, + { + "Political Affiliation": "Democrats", + "Units": 21.0, + "Preference": "Someone else" + }, + { + "Political Affiliation": "Independents", + "Units": 30.0, + "Preference": "Kamala Harris" + }, + { + "Political Affiliation": "Independents", + "Units": 34.0, + "Preference": "Not sure" + }, + { + "Political Affiliation": "Independents", + "Units": 36.0, + "Preference": "Someone else" + }, + { + "Political Affiliation": "Republicans", + "Units": 24.0, + "Preference": "Kamala Harris" + }, + { + "Political Affiliation": "Republicans", + "Units": 26.0, + "Preference": "Not sure" + }, + { + "Political Affiliation": "Republicans", + "Units": 50.0, + "Preference": "Someone else" + } + ], + "columns": [ + { + "name": "Political Affiliation", + "importance": "primary", + "description": "Political affiliation of survey respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding preference", + "role": "y" + }, + { + "name": "Preference", + "importance": "primary", + "description": "The preference of the data point", + "role": "group" + } + ] + } + }, + "8": { + "metadata": { + "title": "The worst UK airports for security queues", + "description": "Average estimated waiting times for security checks at large UK airports (in minutes)", + "main_insight": "Manchester Terminal 1 has the longest average security queue wait time at 17 minutes, while Heathrow Terminal 4 has the shortest at 9.4 minutes." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Airport Terminal": "Manchester Terminal 1", + "Waiting Time (minutes)": 17.0 + } + ], + "score": 0.85, + "annotation": "The has a maximum value at Manchester Terminal 1", + "reason": "The [{'Airport Terminal': 'Manchester Terminal 1', 'Waiting Time (minutes)': 17.0}] of has a maximum value of 17.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Airport Terminal": "Stansted", + "Waiting Time (minutes)": 13.7 + } + ], + "score": 0.66, + "annotation": "The shows a sudden jump in Waiting Time (minutes) from Luton to Stansted.", + "reason": "The Waiting Time (minutes) for increases from 11.7 at Luton to 13.7 at Stansted, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Airport Terminal": "Heathrow Terminal 4", + "Waiting Time (minutes)": 9.4 + } + ], + "score": 0.63, + "annotation": "The has a minimum value at Heathrow Terminal 4", + "reason": "The [{'Airport Terminal': 'Heathrow Terminal 4', 'Waiting Time (minutes)': 9.4}] of has a mainimum value of 9.4, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Airport Terminal": "Manchester Terminal 1", + "Waiting Time (minutes)": 17.0 + }, + { + "Airport Terminal": "Manchester Terminal 2", + "Waiting Time (minutes)": 15.5 + }, + { + "Airport Terminal": "Manchester Terminal 3", + "Waiting Time (minutes)": 15.5 + }, + { + "Airport Terminal": "Stansted", + "Waiting Time (minutes)": 13.7 + }, + { + "Airport Terminal": "Luton", + "Waiting Time (minutes)": 11.7 + }, + { + "Airport Terminal": "Birmingham", + "Waiting Time (minutes)": 10.6 + }, + { + "Airport Terminal": "Edinburgh", + "Waiting Time (minutes)": 10.5 + }, + { + "Airport Terminal": "Heathrow Terminal 3", + "Waiting Time (minutes)": 10.3 + }, + { + "Airport Terminal": "Heathrow Terminal 2", + "Waiting Time (minutes)": 9.6 + }, + { + "Airport Terminal": "Heathrow Terminal 4", + "Waiting Time (minutes)": 9.4 + } + ], + "columns": [ + { + "name": "Airport Terminal", + "importance": "primary", + "description": "UK Airport and Terminal", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Waiting Time (minutes)", + "importance": "primary", + "description": "Average estimated waiting time in minutes", + "unit": "minutes", + "data_type": "number", + "role": "y" + } + ] + } + }, + "644": { + "metadata": { + "title": "Consumer Confidence Surges as Economic Outlook Brightens", + "description": "Monthly Index of Consumer Sentiment in the United States", + "main_insight": "Consumer confidence in the US has generally been increasing since 2022, though it remains below pre-2020 levels. Periods of recession are marked, showing a correlation between recessions and dips in consumer confidence." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2000, + "Consumer Sentiment Index": 110 + }, + { + "Year": 2001, + "Consumer Sentiment Index": 110 + }, + { + "Year": 2002, + "Consumer Sentiment Index": 90 + }, + { + "Year": 2003, + "Consumer Sentiment Index": 95 + }, + { + "Year": 2004, + "Consumer Sentiment Index": 100 + }, + { + "Year": 2005, + "Consumer Sentiment Index": 95 + }, + { + "Year": 2006, + "Consumer Sentiment Index": 95 + }, + { + "Year": 2007, + "Consumer Sentiment Index": 90 + }, + { + "Year": 2008, + "Consumer Sentiment Index": 60 + }, + { + "Year": 2009, + "Consumer Sentiment Index": 70 + }, + { + "Year": 2010, + "Consumer Sentiment Index": 75 + }, + { + "Year": 2011, + "Consumer Sentiment Index": 70 + }, + { + "Year": 2012, + "Consumer Sentiment Index": 80 + }, + { + "Year": 2013, + "Consumer Sentiment Index": 85 + }, + { + "Year": 2014, + "Consumer Sentiment Index": 85 + }, + { + "Year": 2015, + "Consumer Sentiment Index": 95 + }, + { + "Year": 2016, + "Consumer Sentiment Index": 95 + }, + { + "Year": 2017, + "Consumer Sentiment Index": 100 + }, + { + "Year": 2018, + "Consumer Sentiment Index": 100 + }, + { + "Year": 2019, + "Consumer Sentiment Index": 98 + }, + { + "Year": 2020, + "Consumer Sentiment Index": 80 + }, + { + "Year": 2021, + "Consumer Sentiment Index": 70 + }, + { + "Year": 2022, + "Consumer Sentiment Index": 60 + }, + { + "Year": 2023, + "Consumer Sentiment Index": 70 + }, + { + "Year": 2024, + "Consumer Sentiment Index": 80 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year, from 2000 to 2024", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Consumer Sentiment Index", + "importance": "primary", + "description": "Monthly Index of Consumer Sentiment", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1422": { + "metadata": { + "title": "The Gradual Death of EU Coal Production", + "description": "Share of EU hard coal production from 1990 to 2020, by member state", + "main_insight": "EU coal production has significantly decreased from 1990 to 2020, with Poland becoming the dominant producer, replacing Germany." + }, + "chart_type": "Multiple Pie Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2020, + "Units": 56.5, + "Production": "Total Production" + }, + { + "Year": 2020, + "Units": 96.0, + "Production": "Poland" + } + ], + "score": 0.9, + "annotation": "The Units of Total Production and Poland show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Production and Poland is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2020, + "Units": 96.0, + "Production": "Poland" + }, + { + "Year": 2020, + "Units": 4.0, + "Production": "Czechia" + } + ], + "score": 0.62, + "annotation": "The Units of Poland and Czechia show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Poland and Czechia is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 56.5, + "Production": "Total Production" + }, + { + "Year": 2020, + "Units": 4.0, + "Production": "Czechia" + } + ], + "score": 0.31, + "annotation": "The Units of Total Production and Czechia show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Production and Czechia is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2010, + "Units": 13.0, + "Production": "Germany" + }, + { + "Year": 2010, + "Units": 5.0, + "Production": "Spain" + } + ], + "score": 0.16, + "annotation": "The Units of Germany and Spain show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Germany and Spain is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 1990, + "Units": 277.4, + "Production": "Total Production" + }, + { + "Year": 1990, + "Units": 53.0, + "Production": "Poland" + }, + { + "Year": 1990, + "Units": 27.0, + "Production": "Germany" + }, + { + "Year": 1990, + "Units": 8.0, + "Production": "Czechia" + }, + { + "Year": 1990, + "Units": 5.0, + "Production": "Spain" + }, + { + "Year": 1990, + "Units": 4.0, + "Production": "France" + }, + { + "Year": 1990, + "Units": 2.0, + "Production": "Other EU" + }, + { + "Year": 2000, + "Units": 169.3, + "Production": "Total Production" + }, + { + "Year": 2000, + "Units": 60.0, + "Production": "Poland" + }, + { + "Year": 2000, + "Units": 22.0, + "Production": "Germany" + }, + { + "Year": 2000, + "Units": 8.0, + "Production": "Czechia" + }, + { + "Year": 2000, + "Units": 6.0, + "Production": "Spain" + }, + { + "Year": 2000, + "Units": 2.0, + "Production": "France" + }, + { + "Year": 2000, + "Units": 2.0, + "Production": "Other EU" + }, + { + "Year": 2010, + "Units": 107.8, + "Production": "Total Production" + }, + { + "Year": 2010, + "Units": 71.0, + "Production": "Poland" + }, + { + "Year": 2010, + "Units": 13.0, + "Production": "Germany" + }, + { + "Year": 2010, + "Units": 11.0, + "Production": "Czechia" + }, + { + "Year": 2010, + "Units": 5.0, + "Production": "Spain" + }, + { + "Year": 2020, + "Units": 56.5, + "Production": "Total Production" + }, + { + "Year": 2020, + "Units": 96.0, + "Production": "Poland" + }, + { + "Year": 2020, + "Units": 4.0, + "Production": "Czechia" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of coal production data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding production", + "role": "y" + }, + { + "name": "Production", + "importance": "primary", + "description": "The production of the data point", + "role": "group" + } + ] + } + }, + "2120": { + "metadata": { + "title": "Where People Are Warming up to Self-Driving Cars", + "description": "Percentage of consumers who think self-driving vehicles will not be safe", + "main_insight": "The percentage of consumers who believe self-driving cars are unsafe has significantly decreased in most countries from 2017 to 2020, indicating growing acceptance of the technology." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Germany", + "Score": 47.0, + "Years": "2019" + } + ], + "score": 0.96, + "annotation": "The 2019 shows a sudden jump in Score from China to Germany.", + "reason": "The Score for 2019 increases from 25.0 at China to 47.0 at Germany, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "India", + "Score": 64.0, + "Years": "2017" + } + ], + "score": 0.93, + "annotation": "The 2017 has the largest proportion in India.", + "reason": "The Score of 2017 in India accounts for 0.29493087557603687, larger than all other Years, of the total of Score of all Years." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "China", + "Score": 26.0, + "Years": "2018" + }, + { + "Country": "China", + "Score": 25.0, + "Years": "2019" + } + ], + "score": 0.93, + "annotation": "The Score of 2018 and 2019 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between 2018 and 2019 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "U.S.", + "Score": 74.0, + "Years": "2017" + } + ], + "score": 0.92, + "annotation": "The 2017 has the largest proportion in U.S..", + "reason": "The Score of 2017 in U.S. accounts for 0.3378995433789954, larger than all other Years, of the total of Score of all Years." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Japan", + "Score": 79.0, + "Years": "2017" + } + ], + "score": 0.92, + "annotation": "The 2017 has the largest proportion in Japan.", + "reason": "The Score of 2017 in Japan accounts for 0.33905579399141633, larger than all other Years, of the total of Score of all Years." + } + ], + "data": { + "data": [ + { + "Country": "South Korea", + "Score": 81.0, + "Years": "2017" + }, + { + "Country": "South Korea", + "Score": 54.0, + "Years": "2018" + }, + { + "Country": "South Korea", + "Score": 49.0, + "Years": "2019" + }, + { + "Country": "South Korea", + "Score": 46.0, + "Years": "2020" + }, + { + "Country": "Japan", + "Score": 79.0, + "Years": "2017" + }, + { + "Country": "Japan", + "Score": 57.0, + "Years": "2018" + }, + { + "Country": "Japan", + "Score": 50.0, + "Years": "2019" + }, + { + "Country": "Japan", + "Score": 47.0, + "Years": "2020" + }, + { + "Country": "U.S.", + "Score": 74.0, + "Years": "2017" + }, + { + "Country": "U.S.", + "Score": 47.0, + "Years": "2018" + }, + { + "Country": "U.S.", + "Score": 50.0, + "Years": "2019" + }, + { + "Country": "U.S.", + "Score": 48.0, + "Years": "2020" + }, + { + "Country": "Germany", + "Score": 72.0, + "Years": "2017" + }, + { + "Country": "Germany", + "Score": 45.0, + "Years": "2018" + }, + { + "Country": "Germany", + "Score": 47.0, + "Years": "2019" + }, + { + "Country": "Germany", + "Score": 45.0, + "Years": "2020" + }, + { + "Country": "India", + "Score": 64.0, + "Years": "2017" + }, + { + "Country": "India", + "Score": 47.0, + "Years": "2018" + }, + { + "Country": "India", + "Score": 48.0, + "Years": "2019" + }, + { + "Country": "India", + "Score": 58.0, + "Years": "2020" + }, + { + "Country": "China", + "Score": 62.0, + "Years": "2017" + }, + { + "Country": "China", + "Score": 26.0, + "Years": "2018" + }, + { + "Country": "China", + "Score": 25.0, + "Years": "2019" + }, + { + "Country": "China", + "Score": 35.0, + "Years": "2020" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where the survey was conducted", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding years", + "role": "y" + }, + { + "name": "Years", + "importance": "primary", + "description": "The years of the data point", + "role": "group" + } + ] + } + }, + "1833": { + "metadata": { + "title": "Queen of Pop Dethroned by Queen Bey", + "description": "Number of MTV VMA awards per artist", + "main_insight": "Beyoncé has won more MTV VMA awards (26) than any other artist, surpassing Madonna's previous record of 20 awards." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Artist": "Beyoncé", + "Awards Won": 26 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at Beyoncé", + "reason": "The [{'Artist': 'Beyoncé', 'Awards Won': 26}] of has a maximum value of 26, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Artist": "Cardi B", + "Awards Won": 0 + } + ], + "score": 0.91, + "annotation": "The has a minimum value at Cardi B", + "reason": "The [{'Artist': 'Cardi B', 'Awards Won': 0}] of has a mainimum value of 0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Artist": "Fatboy Slim", + "Awards Won": 9 + } + ], + "score": 0.73, + "annotation": "The shows a sudden jump in Awards Won from Cardi B to Fatboy Slim.", + "reason": "The Awards Won for increases from 0 at Cardi B to 9 at Fatboy Slim, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Artist": "Beyoncé", + "Awards Won": 26 + }, + { + "Artist": "Madonna", + "Awards Won": 20 + }, + { + "Artist": "Lady Gaga", + "Awards Won": 13 + }, + { + "Artist": "Peter Gabriel", + "Awards Won": 13 + }, + { + "Artist": "Eminem", + "Awards Won": 12 + }, + { + "Artist": "Justin Timberlake", + "Awards Won": 11 + }, + { + "Artist": "R.E.M", + "Awards Won": 12 + }, + { + "Artist": "Cardi B", + "Awards Won": 0 + }, + { + "Artist": "Green Day", + "Awards Won": 11 + }, + { + "Artist": "Aerosmith", + "Awards Won": 10 + }, + { + "Artist": "Fatboy Slim", + "Awards Won": 9 + }, + { + "Artist": "Janet Jackson", + "Awards Won": 9 + } + ], + "columns": [ + { + "name": "Artist", + "importance": "primary", + "description": "Name of the recording artist", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Awards Won", + "importance": "primary", + "description": "Total number of MTV VMA awards won by the artist, including awards with Destiny's Child", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1693": { + "metadata": { + "title": "Accommodation Barometer: What's on the minds of European hoteliers?", + "description": null, + "main_insight": "European hoteliers show varying levels of agreement on different topics, with the highest positive responses for the importance of government policies and preparation for digital transformation." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Topic": "Preparation for sustainability & decarbonization", + "Units": 39.0, + "Sentiment": "Positive Response" + }, + { + "Topic": "Preparation for sustainability & decarbonization", + "Units": 18.0, + "Sentiment": "Negative Response" + } + ], + "score": 0.94, + "annotation": "The Units of Positive Response and Negative Response show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Positive Response and Negative Response is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Topic": "Expected impact of government policies", + "Units": 29.0, + "Sentiment": "Negative Response" + } + ], + "score": 0.9, + "annotation": "The Negative Response shows a sudden jump in Units from Preparation for sustainability & decarbonization to Expected impact of government policies.", + "reason": "The Units for Negative Response increases from 18.0 at Preparation for sustainability & decarbonization to 29.0 at Expected impact of government policies, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Topic": "Preparation for digital transformation", + "Units": 59.0, + "Sentiment": "Positive Response" + } + ], + "score": 0.83, + "annotation": "The Positive Response shows a sudden jump in Units from Preparation for sustainability & decarbonization to Preparation for digital transformation.", + "reason": "The Units for Positive Response increases from 39.0 at Preparation for sustainability & decarbonization to 59.0 at Preparation for digital transformation, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Topic": "Expected impact of government policies", + "Units": 29.0, + "Sentiment": "Negative Response" + } + ], + "score": 0.78, + "annotation": "The Negative Response accounts for the majority in Expected impact of government policies.", + "reason": "The Units of Negative Response in Expected impact of government policies accounts for 0.5686274509803921 (more than 50%) of the total of Units of all Sentiment." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Topic": "Expected impact of government policies", + "Units": 29.0, + "Sentiment": "Negative Response" + } + ], + "score": 0.77, + "annotation": "The Negative Response has a maximum value at Expected impact of government policies", + "reason": "The [{'Topic': 'Expected impact of government policies', 'Units': 29.0, 'Sentiment': 'Negative Response'}] of Negative Response has a maximum value of 29.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Topic": "Importance of government policies", + "Units": 62.0, + "Sentiment": "Positive Response" + }, + { + "Topic": "Importance of government policies", + "Units": 15.0, + "Sentiment": "Negative Response" + }, + { + "Topic": "Expected impact of government policies", + "Units": 22.0, + "Sentiment": "Positive Response" + }, + { + "Topic": "Expected impact of government policies", + "Units": 29.0, + "Sentiment": "Negative Response" + }, + { + "Topic": "Preparation for digital transformation", + "Units": 59.0, + "Sentiment": "Positive Response" + }, + { + "Topic": "Preparation for digital transformation", + "Units": 12.0, + "Sentiment": "Negative Response" + }, + { + "Topic": "Preparation for sustainability & decarbonization", + "Units": 39.0, + "Sentiment": "Positive Response" + }, + { + "Topic": "Preparation for sustainability & decarbonization", + "Units": 18.0, + "Sentiment": "Negative Response" + } + ], + "columns": [ + { + "name": "Topic", + "importance": "primary", + "description": "The topic being assessed", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "1695": { + "metadata": { + "title": "German Carmakers to Profit Most From Trade Truce", + "description": "Top 4 automakers exporting American-made vehicles to China in 2018", + "main_insight": "German automakers BMW Group and Daimler Group exported the most American-made vehicles to China in 2018, significantly more than Ford Motor and Tesla." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Automaker": "Ford Motor", + "Exports": 46300.0 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Exports from Tesla to Ford Motor.", + "reason": "The Exports for increases from 16400.0 at Tesla to 46300.0 at Ford Motor, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Automaker": "Tesla", + "Exports": 16400.0 + } + ], + "score": 0.73, + "annotation": "The has a minimum value at Tesla", + "reason": "The [{'Automaker': 'Tesla', 'Exports': 16400.0}] of has a mainimum value of 16400.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Automaker": "BMW Group", + "Exports": 77000.0 + } + ], + "score": 0.66, + "annotation": "The has a maximum value at BMW Group", + "reason": "The [{'Automaker': 'BMW Group', 'Exports': 77000.0}] of has a maximum value of 77000.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Automaker": "BMW Group", + "Exports": 77000.0 + }, + { + "Automaker": "Daimler Group", + "Exports": 54900.0 + }, + { + "Automaker": "Ford Motor", + "Exports": 46300.0 + }, + { + "Automaker": "Tesla", + "Exports": 16400.0 + } + ], + "columns": [ + { + "name": "Automaker", + "importance": "primary", + "description": "Name of the Automaker", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Exports", + "importance": "primary", + "description": "Number of vehicles exported to China in 2018", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1180": { + "metadata": { + "title": "E-Commerce Growth is Fastest in China", + "description": "E-commerce sales as a share of total retail sales in selected countries", + "main_insight": "China has experienced the fastest growth in e-commerce sales as a share of total retail sales, reaching 44% in 2020, significantly outpacing the UK, US, and Germany." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 27.5, + "Countries": "U.K." + }, + { + "Year": 2020, + "Units": 14.5, + "Countries": "U.S." + } + ], + "score": 1.0, + "annotation": "The Units of U.K. and U.S. show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between U.K. and U.S. is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 14.5, + "Countries": "U.S." + }, + { + "Year": 2020, + "Units": 11.2, + "Countries": "Germany" + } + ], + "score": 0.99, + "annotation": "The Units of U.S. and Germany show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between U.S. and Germany is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 27.5, + "Countries": "U.K." + }, + { + "Year": 2020, + "Units": 11.2, + "Countries": "Germany" + } + ], + "score": 0.99, + "annotation": "The Units of U.K. and Germany show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between U.K. and Germany is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 44.0, + "Countries": "China" + }, + { + "Year": 2020, + "Units": 27.5, + "Countries": "U.K." + } + ], + "score": 0.99, + "annotation": "The Units of China and U.K. show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between China and U.K. is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 44.0, + "Countries": "China" + }, + { + "Year": 2020, + "Units": 14.5, + "Countries": "U.S." + } + ], + "score": 0.98, + "annotation": "The Units of China and U.S. show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between China and U.S. is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2014, + "Units": 17.0, + "Countries": "China" + }, + { + "Year": 2014, + "Units": 14.0, + "Countries": "U.K." + }, + { + "Year": 2014, + "Units": 7.0, + "Countries": "U.S." + }, + { + "Year": 2014, + "Units": 5.0, + "Countries": "Germany" + }, + { + "Year": 2015, + "Units": 18.0, + "Countries": "China" + }, + { + "Year": 2015, + "Units": 15.0, + "Countries": "U.K." + }, + { + "Year": 2015, + "Units": 8.0, + "Countries": "U.S." + }, + { + "Year": 2015, + "Units": 6.0, + "Countries": "Germany" + }, + { + "Year": 2016, + "Units": 20.0, + "Countries": "China" + }, + { + "Year": 2016, + "Units": 17.0, + "Countries": "U.K." + }, + { + "Year": 2016, + "Units": 9.0, + "Countries": "U.S." + }, + { + "Year": 2016, + "Units": 7.0, + "Countries": "Germany" + }, + { + "Year": 2017, + "Units": 23.0, + "Countries": "China" + }, + { + "Year": 2017, + "Units": 19.0, + "Countries": "U.K." + }, + { + "Year": 2017, + "Units": 10.0, + "Countries": "U.S." + }, + { + "Year": 2017, + "Units": 8.0, + "Countries": "Germany" + }, + { + "Year": 2018, + "Units": 28.0, + "Countries": "China" + }, + { + "Year": 2018, + "Units": 21.0, + "Countries": "U.K." + }, + { + "Year": 2018, + "Units": 11.0, + "Countries": "U.S." + }, + { + "Year": 2018, + "Units": 9.0, + "Countries": "Germany" + }, + { + "Year": 2019, + "Units": 35.0, + "Countries": "China" + }, + { + "Year": 2019, + "Units": 23.0, + "Countries": "U.K." + }, + { + "Year": 2019, + "Units": 12.0, + "Countries": "U.S." + }, + { + "Year": 2019, + "Units": 10.0, + "Countries": "Germany" + }, + { + "Year": 2020, + "Units": 44.0, + "Countries": "China" + }, + { + "Year": 2020, + "Units": 27.5, + "Countries": "U.K." + }, + { + "Year": 2020, + "Units": 14.5, + "Countries": "U.S." + }, + { + "Year": 2020, + "Units": 11.2, + "Countries": "Germany" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding countries", + "role": "y" + }, + { + "name": "Countries", + "importance": "primary", + "description": "The countries of the data point", + "role": "group" + } + ] + } + }, + "109": { + "metadata": { + "title": "The Most Trafficked Animals", + "description": "Share of all seizure records by species, 2015-2021", + "main_insight": "Rhinoceros and Pangolins account for the largest share of trafficked animal seizure records between 2015 and 2021, almost double the seizure records of Elephants." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Species": "Rhinoceros", + "Seizure Share": 29.0 + } + ], + "score": 0.9, + "annotation": "The has a maximum value at Rhinoceros", + "reason": "The [{'Species': 'Rhinoceros', 'Seizure Share': 29.0}] of has a maximum value of 29.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Species": "Pangolins", + "Seizure Share": 28.0 + } + ], + "score": 0.82, + "annotation": "The shows a sudden jump in Seizure Share from Elephants to Pangolins.", + "reason": "The Seizure Share for increases from 15.0 at Elephants to 28.0 at Pangolins, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Species": "Parrots and cockatoos", + "Seizure Share": 2.0 + }, + { + "Species": "Carnivores", + "Seizure Share": 2.0 + }, + { + "Species": "Turtles and tortoises", + "Seizure Share": 2.0 + }, + { + "Species": "Snakes", + "Seizure Share": 2.0 + }, + { + "Species": "Seahorses", + "Seizure Share": 2.0 + } + ], + "score": 0.44, + "annotation": "The has minimum values at Parrots and cockatoos, Carnivores, Turtles and tortoises, Snakes, Seahorses", + "reason": "The [{'Species': 'Parrots and cockatoos', 'Seizure Share': 2.0}, {'Species': 'Carnivores', 'Seizure Share': 2.0}, {'Species': 'Turtles and tortoises', 'Seizure Share': 2.0}, {'Species': 'Snakes', 'Seizure Share': 2.0}, {'Species': 'Seahorses', 'Seizure Share': 2.0}] of have minimum values of 2.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Species": "Rhinoceros", + "Seizure Share": 29.0 + }, + { + "Species": "Pangolins", + "Seizure Share": 28.0 + }, + { + "Species": "Elephants", + "Seizure Share": 15.0 + }, + { + "Species": "Eels", + "Seizure Share": 5.0 + }, + { + "Species": "Crocodilians", + "Seizure Share": 5.0 + }, + { + "Species": "Parrots and cockatoos", + "Seizure Share": 2.0 + }, + { + "Species": "Carnivores", + "Seizure Share": 2.0 + }, + { + "Species": "Turtles and tortoises", + "Seizure Share": 2.0 + }, + { + "Species": "Snakes", + "Seizure Share": 2.0 + }, + { + "Species": "Seahorses", + "Seizure Share": 2.0 + }, + { + "Species": "Others", + "Seizure Share": 8.0 + } + ], + "columns": [ + { + "name": "Species", + "importance": "primary", + "description": "The animal species", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Seizure Share", + "importance": "primary", + "description": "Percentage of seizure records", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1888": { + "metadata": { + "title": "Majority of Windows Users Has Yet to Upgrade", + "description": "Worldwide market share of the latest Windows versions", + "main_insight": "Windows 7 still held the largest market share in April 2016, despite a steady decline since April 2015. Windows 10's market share increased significantly over the same period, surpassing Windows 8/8.1." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "Apr 2016", + "Score": 45.0, + "Operating System": "Windows 7" + }, + { + "Month": "Apr 2016", + "Score": 14.0, + "Operating System": "Windows 8 / 8.1" + } + ], + "score": 0.89, + "annotation": "The Score of Windows 7 and Windows 8 / 8.1 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Windows 7 and Windows 8 / 8.1 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Apr 2016", + "Score": 45.0, + "Operating System": "Windows 7" + } + ], + "score": 0.88, + "annotation": "The Windows 7 has a minimum value at Apr 2016", + "reason": "The [{'Month': 'Apr 2016', 'Score': 45.0, 'Operating System': 'Windows 7'}] of Windows 7 has a mainimum value of 45.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Apr 2016", + "Score": 14.0, + "Operating System": "Windows 8 / 8.1" + } + ], + "score": 0.85, + "annotation": "The Windows 8 / 8.1 has a minimum value at Apr 2016", + "reason": "The [{'Month': 'Apr 2016', 'Score': 14.0, 'Operating System': 'Windows 8 / 8.1'}] of Windows 8 / 8.1 has a mainimum value of 14.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Jul 2015", + "Score": 21.0, + "Operating System": "Windows 8 / 8.1" + } + ], + "score": 0.84, + "annotation": "The Windows 8 / 8.1 has a maximum value at Jul 2015", + "reason": "The [{'Month': 'Jul 2015', 'Score': 21.0, 'Operating System': 'Windows 8 / 8.1'}] of Windows 8 / 8.1 has a maximum value of 21.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Dez 2015", + "Score": 49.0, + "Operating System": "Windows 7" + } + ], + "score": 0.84, + "annotation": "The Windows 7 accounts for the majority in Dez 2015.", + "reason": "The Score of Windows 7 in Dez 2015 accounts for 0.5833333333333334 (more than 50%) of the total of Score of all Operating System." + } + ], + "data": { + "data": [ + { + "Month": "Apr 2015", + "Score": 53.0, + "Operating System": "Windows 7" + }, + { + "Month": "Apr 2015", + "Score": 20.0, + "Operating System": "Windows 8 / 8.1" + }, + { + "Month": "May 2015", + "Score": 53.0, + "Operating System": "Windows 7" + }, + { + "Month": "May 2015", + "Score": 20.0, + "Operating System": "Windows 8 / 8.1" + }, + { + "Month": "Jun 2015", + "Score": 54.0, + "Operating System": "Windows 7" + }, + { + "Month": "Jun 2015", + "Score": 20.0, + "Operating System": "Windows 8 / 8.1" + }, + { + "Month": "Jul 2015", + "Score": 55.0, + "Operating System": "Windows 7" + }, + { + "Month": "Jul 2015", + "Score": 21.0, + "Operating System": "Windows 8 / 8.1" + }, + { + "Month": "Aug 2015", + "Score": 53.0, + "Operating System": "Windows 7" + }, + { + "Month": "Aug 2015", + "Score": 3.0, + "Operating System": "Windows 10" + }, + { + "Month": "Aug 2015", + "Score": 18.0, + "Operating System": "Windows 8 / 8.1" + }, + { + "Month": "Sep 2015", + "Score": 53.0, + "Operating System": "Windows 7" + }, + { + "Month": "Sep 2015", + "Score": 6.0, + "Operating System": "Windows 10" + }, + { + "Month": "Sep 2015", + "Score": 17.0, + "Operating System": "Windows 8 / 8.1" + }, + { + "Month": "Okt 2015", + "Score": 52.0, + "Operating System": "Windows 7" + }, + { + "Month": "Okt 2015", + "Score": 8.0, + "Operating System": "Windows 10" + }, + { + "Month": "Okt 2015", + "Score": 17.0, + "Operating System": "Windows 8 / 8.1" + }, + { + "Month": "Nov 2015", + "Score": 50.0, + "Operating System": "Windows 7" + }, + { + "Month": "Nov 2015", + "Score": 10.0, + "Operating System": "Windows 10" + }, + { + "Month": "Nov 2015", + "Score": 17.0, + "Operating System": "Windows 8 / 8.1" + }, + { + "Month": "Dez 2015", + "Score": 49.0, + "Operating System": "Windows 7" + }, + { + "Month": "Dez 2015", + "Score": 12.0, + "Operating System": "Windows 10" + }, + { + "Month": "Dez 2015", + "Score": 16.0, + "Operating System": "Windows 8 / 8.1" + }, + { + "Month": "Jan 2016", + "Score": 48.0, + "Operating System": "Windows 7" + }, + { + "Month": "Jan 2016", + "Score": 14.0, + "Operating System": "Windows 10" + }, + { + "Month": "Jan 2016", + "Score": 16.0, + "Operating System": "Windows 8 / 8.1" + }, + { + "Month": "Feb 2016", + "Score": 47.0, + "Operating System": "Windows 7" + }, + { + "Month": "Feb 2016", + "Score": 16.0, + "Operating System": "Windows 10" + }, + { + "Month": "Feb 2016", + "Score": 16.0, + "Operating System": "Windows 8 / 8.1" + }, + { + "Month": "Mar 2016", + "Score": 47.0, + "Operating System": "Windows 7" + }, + { + "Month": "Mar 2016", + "Score": 18.0, + "Operating System": "Windows 10" + }, + { + "Month": "Mar 2016", + "Score": 16.0, + "Operating System": "Windows 8 / 8.1" + }, + { + "Month": "Apr 2016", + "Score": 45.0, + "Operating System": "Windows 7" + }, + { + "Month": "Apr 2016", + "Score": 19.0, + "Operating System": "Windows 10" + }, + { + "Month": "Apr 2016", + "Score": 14.0, + "Operating System": "Windows 8 / 8.1" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month and Year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding operating system", + "role": "y" + }, + { + "name": "Operating System", + "importance": "primary", + "description": "The operating system of the data point", + "role": "group" + } + ] + } + }, + "714": { + "metadata": { + "title": "Americans Mob Website Shipping Free At-Home Covid-19 Tests", + "description": "Percent of U.S. government web traffic, by webpage (on Jan 18, 2022 at 1 p.m. EST)", + "main_insight": "On January 18, 2022, at 1 p.m. EST, COVIDtests.gov accounted for 49.2% of U.S. government web traffic, indicating high demand for free at-home Covid-19 tests." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Webpage": "Other government", + "Value": 40.3, + "Traffic Source": "Percent of Traffic" + } + ], + "score": 0.94, + "annotation": "The Percent of Traffic shows a sudden jump in Value from Other USPS to Other government.", + "reason": "The Value for Percent of Traffic increases from 10.5 at Other USPS to 40.3 at Other government, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Webpage": "Other USPS", + "Value": 10.5, + "Traffic Source": "Percent of Traffic" + } + ], + "score": 0.66, + "annotation": "The Percent of Traffic has a minimum value at Other USPS", + "reason": "The [{'Webpage': 'Other USPS', 'Value': 10.5, 'Traffic Source': 'Percent of Traffic'}] of Percent of Traffic has a mainimum value of 10.5, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Webpage": "COVIDtests.gov", + "Value": 49.2, + "Traffic Source": "Percent of Traffic" + } + ], + "score": 0.49, + "annotation": "The Percent of Traffic has a maximum value at COVIDtests.gov", + "reason": "The [{'Webpage': 'COVIDtests.gov', 'Value': 49.2, 'Traffic Source': 'Percent of Traffic'}] of Percent of Traffic has a maximum value of 49.2, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Webpage": "COVIDtests.gov", + "Value": 49.2, + "Traffic Source": "Percent of Traffic" + }, + { + "Webpage": "Other USPS", + "Value": 10.5, + "Traffic Source": "Percent of Traffic" + }, + { + "Webpage": "Other government", + "Value": 40.3, + "Traffic Source": "Percent of Traffic" + } + ], + "columns": [ + { + "name": "Webpage", + "importance": "primary", + "description": "Name of the website", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding traffic source", + "role": "y" + }, + { + "name": "Traffic Source", + "importance": "primary", + "description": "The traffic source of the data point", + "role": "group" + } + ] + } + }, + "1438": { + "metadata": { + "title": "Where Stalin Beats Gorbachev in Popularity", + "description": "% saying Stalin/Gorbachev played a very or mostly positive role in history (2016)", + "main_insight": "Stalin has significantly higher positive historical perception than Gorbachev in several Eastern European and former Soviet countries, most notably Russia and Georgia." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Russia", + "Units": 58.0, + "Leader": "Stalin" + } + ], + "score": 0.88, + "annotation": "The Stalin has a maximum value at Russia", + "reason": "The [{'Country': 'Russia', 'Units': 58.0, 'Leader': 'Stalin'}] of Stalin has a maximum value of 58.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Armenia", + "Units": 13.0, + "Leader": "Gorbachev" + } + ], + "score": 0.83, + "annotation": "The Gorbachev has a minimum value at Armenia", + "reason": "The [{'Country': 'Armenia', 'Units': 13.0, 'Leader': 'Gorbachev'}] of Gorbachev has a mainimum value of 13.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Bulgaria", + "Units": 33.0, + "Leader": "Stalin" + } + ], + "score": 0.8, + "annotation": "The Stalin accounts for the majority in Bulgaria.", + "reason": "The Units of Stalin in Bulgaria accounts for 0.5076923076923077 (more than 50%) of the total of Units of all Leader." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Ukraine", + "Units": 22.0, + "Leader": "Gorbachev" + } + ], + "score": 0.78, + "annotation": "The Gorbachev accounts for the majority in Ukraine.", + "reason": "The Units of Gorbachev in Ukraine accounts for 0.5789473684210527 (more than 50%) of the total of Units of all Leader." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Poland", + "Units": 6.0, + "Leader": "Stalin" + }, + { + "Country": "Poland", + "Units": 51.0, + "Leader": "Gorbachev" + } + ], + "score": 0.77, + "annotation": "The Units of Stalin and Gorbachev show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Stalin and Gorbachev is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Country": "Russia", + "Units": 58.0, + "Leader": "Stalin" + }, + { + "Country": "Russia", + "Units": 22.0, + "Leader": "Gorbachev" + }, + { + "Country": "Georgia", + "Units": 57.0, + "Leader": "Stalin" + }, + { + "Country": "Georgia", + "Units": 18.0, + "Leader": "Gorbachev" + }, + { + "Country": "Armenia", + "Units": 38.0, + "Leader": "Stalin" + }, + { + "Country": "Armenia", + "Units": 13.0, + "Leader": "Gorbachev" + }, + { + "Country": "Bulgaria", + "Units": 33.0, + "Leader": "Stalin" + }, + { + "Country": "Bulgaria", + "Units": 32.0, + "Leader": "Gorbachev" + }, + { + "Country": "Romania", + "Units": 29.0, + "Leader": "Stalin" + }, + { + "Country": "Romania", + "Units": 47.0, + "Leader": "Gorbachev" + }, + { + "Country": "Ukraine", + "Units": 16.0, + "Leader": "Stalin" + }, + { + "Country": "Ukraine", + "Units": 22.0, + "Leader": "Gorbachev" + }, + { + "Country": "Czech Republic", + "Units": 11.0, + "Leader": "Stalin" + }, + { + "Country": "Czech Republic", + "Units": 53.0, + "Leader": "Gorbachev" + }, + { + "Country": "Latvia", + "Units": 11.0, + "Leader": "Stalin" + }, + { + "Country": "Latvia", + "Units": 39.0, + "Leader": "Gorbachev" + }, + { + "Country": "Estonia", + "Units": 9.0, + "Leader": "Stalin" + }, + { + "Country": "Estonia", + "Units": 56.0, + "Leader": "Gorbachev" + }, + { + "Country": "Hungary", + "Units": 9.0, + "Leader": "Stalin" + }, + { + "Country": "Hungary", + "Units": 54.0, + "Leader": "Gorbachev" + }, + { + "Country": "Lithuania", + "Units": 8.0, + "Leader": "Stalin" + }, + { + "Country": "Lithuania", + "Units": 48.0, + "Leader": "Gorbachev" + }, + { + "Country": "Poland", + "Units": 6.0, + "Leader": "Stalin" + }, + { + "Country": "Poland", + "Units": 51.0, + "Leader": "Gorbachev" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where the survey was conducted", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding leader", + "role": "y" + }, + { + "name": "Leader", + "importance": "primary", + "description": "The leader of the data point", + "role": "group" + } + ] + } + }, + "1004": { + "metadata": { + "title": "Europe Has Six Times As Many Weapon Systems As The U.S.", + "description": "Number of different weapon systems in service in 2016", + "main_insight": "European NATO members have significantly more variety in weapon systems than the U.S. across multiple categories, with a total of 178 compared to 30 for the US." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Weapon System": "Fighter planes", + "Count": 8, + "NATO": "USA" + } + ], + "score": 0.95, + "annotation": "The USA has a maximum value at Fighter planes", + "reason": "The [{'Weapon System': 'Fighter planes', 'Count': 8, 'NATO': 'USA'}] of USA has a maximum value of 8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Weapon System": "152 & 155 mm howitzers", + "Count": 29, + "NATO": "European NATO Members" + } + ], + "score": 0.85, + "annotation": "The European NATO Members has a maximum value at 152 & 155 mm howitzers", + "reason": "The [{'Weapon System': '152 & 155 mm howitzers', 'Count': 29, 'NATO': 'European NATO Members'}] of European NATO Members has a maximum value of 29, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Weapon System": "Torpedoes", + "Count": 1, + "NATO": "USA" + } + ], + "score": 0.79, + "annotation": "The USA has the smallest proportion in Torpedoes.", + "reason": "The Count of USA in Torpedoes accounts for 0.05263157894736842, smaller than all other NATO, of the total of Count of all NATO." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Weapon System": "Fighter planes", + "Count": 8, + "NATO": "USA" + } + ], + "score": 0.79, + "annotation": "The USA shows a sudden jump in Count from Air-to-air missiles to Fighter planes.", + "reason": "The Count for USA increases from 5 at Air-to-air missiles to 8 at Fighter planes, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Weapon System": "152 & 155 mm howitzers", + "Count": 2, + "NATO": "USA" + } + ], + "score": 0.76, + "annotation": "The USA has the smallest proportion in 152 & 155 mm howitzers.", + "reason": "The Count of USA in 152 & 155 mm howitzers accounts for 0.06451612903225806, smaller than all other NATO, of the total of Count of all NATO." + } + ], + "data": { + "data": [ + { + "Weapon System": "Main Battle Tanks", + "Count": 12, + "NATO": "European NATO Members" + }, + { + "Weapon System": "Main Battle Tanks", + "Count": 1, + "NATO": "USA" + }, + { + "Weapon System": "Infantry fighting vehicles", + "Count": 21, + "NATO": "European NATO Members" + }, + { + "Weapon System": "Infantry fighting vehicles", + "Count": 3, + "NATO": "USA" + }, + { + "Weapon System": "152 & 155 mm howitzers", + "Count": 29, + "NATO": "European NATO Members" + }, + { + "Weapon System": "152 & 155 mm howitzers", + "Count": 2, + "NATO": "USA" + }, + { + "Weapon System": "Fighter planes", + "Count": 28, + "NATO": "European NATO Members" + }, + { + "Weapon System": "Fighter planes", + "Count": 8, + "NATO": "USA" + }, + { + "Weapon System": "Attack helicopters", + "Count": 7, + "NATO": "European NATO Members" + }, + { + "Weapon System": "Attack helicopters", + "Count": 3, + "NATO": "USA" + }, + { + "Weapon System": "Anti-ship missiles", + "Count": 8, + "NATO": "European NATO Members" + }, + { + "Weapon System": "Anti-ship missiles", + "Count": 3, + "NATO": "USA" + }, + { + "Weapon System": "Air-to-air missiles", + "Count": 21, + "NATO": "European NATO Members" + }, + { + "Weapon System": "Air-to-air missiles", + "Count": 5, + "NATO": "USA" + }, + { + "Weapon System": "Destroyers/frigates", + "Count": 13, + "NATO": "European NATO Members" + }, + { + "Weapon System": "Destroyers/frigates", + "Count": 3, + "NATO": "USA" + }, + { + "Weapon System": "Torpedoes", + "Count": 18, + "NATO": "European NATO Members" + }, + { + "Weapon System": "Torpedoes", + "Count": 1, + "NATO": "USA" + }, + { + "Weapon System": "Conventional submarines", + "Count": 11, + "NATO": "European NATO Members" + }, + { + "Weapon System": "Nuclear-powered submarines", + "Count": 10, + "NATO": "European NATO Members" + }, + { + "Weapon System": "Nuclear-powered submarines", + "Count": 1, + "NATO": "USA" + } + ], + "columns": [ + { + "name": "Weapon System", + "importance": "primary", + "description": "Type of weapon system", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Count", + "importance": "primary", + "description": "Count for the corresponding nato", + "role": "y" + }, + { + "name": "NATO", + "importance": "primary", + "description": "The nato of the data point", + "role": "group" + } + ] + } + }, + "1462": { + "metadata": { + "title": "Americans and Facebook - It's Complicated", + "description": "% of U.S. Facebook users who have/haven't done the following in the past twelve months", + "main_insight": "A significant percentage of U.S. Facebook users have taken steps to manage their privacy or reduce their Facebook usage in the past year, but a small minority have downloaded their personal data." + }, + "chart_type": "Multiple Semi Circle Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Action": "Downloaded the personal data Facebook has collected about them", + "Units": 9.0, + "Response": "Yes" + }, + { + "Action": "Downloaded the personal data Facebook has collected about them", + "Units": 91.0, + "Response": "No" + } + ], + "score": 1.0, + "annotation": "The Units of Yes and No show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Yes and No is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Action": "Adjusted their Facebook privacy settings", + "Units": 54.0, + "Response": "Yes" + } + ], + "score": 0.79, + "annotation": "The Yes accounts for the majority in Adjusted their Facebook privacy settings.", + "reason": "The Units of Yes in Adjusted their Facebook privacy settings accounts for 0.54 (more than 50%) of the total of Units of all Response." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Action": "Taken a Facebook break for several weeks or more", + "Units": 58.0, + "Response": "No" + } + ], + "score": 0.78, + "annotation": "The No accounts for the majority in Taken a Facebook break for several weeks or more.", + "reason": "The Units of No in Taken a Facebook break for several weeks or more accounts for 0.58 (more than 50%) of the total of Units of all Response." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Action": "Deleted the Facebook app", + "Units": 26.0, + "Response": "Yes" + } + ], + "score": 0.75, + "annotation": "The Yes shows a sudden jump in Units from Downloaded the personal data Facebook has collected about them to Deleted the Facebook app.", + "reason": "The Units for Yes increases from 9.0 at Downloaded the personal data Facebook has collected about them to 26.0 at Deleted the Facebook app, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Action": "Downloaded the personal data Facebook has collected about them", + "Units": 91.0, + "Response": "No" + } + ], + "score": 0.75, + "annotation": "The No shows a sudden jump in Units from Deleted the Facebook app to Downloaded the personal data Facebook has collected about them.", + "reason": "The Units for No increases from 74.0 at Deleted the Facebook app to 91.0 at Downloaded the personal data Facebook has collected about them, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Action": "Adjusted their Facebook privacy settings", + "Units": 54.0, + "Response": "Yes" + }, + { + "Action": "Adjusted their Facebook privacy settings", + "Units": 46.0, + "Response": "No" + }, + { + "Action": "Taken a Facebook break for several weeks or more", + "Units": 42.0, + "Response": "Yes" + }, + { + "Action": "Taken a Facebook break for several weeks or more", + "Units": 58.0, + "Response": "No" + }, + { + "Action": "Deleted the Facebook app", + "Units": 26.0, + "Response": "Yes" + }, + { + "Action": "Deleted the Facebook app", + "Units": 74.0, + "Response": "No" + }, + { + "Action": "Downloaded the personal data Facebook has collected about them", + "Units": 9.0, + "Response": "Yes" + }, + { + "Action": "Downloaded the personal data Facebook has collected about them", + "Units": 91.0, + "Response": "No" + } + ], + "columns": [ + { + "name": "Action", + "importance": "primary", + "description": "Actions taken by Facebook users", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding response", + "role": "y" + }, + { + "name": "Response", + "importance": "primary", + "description": "The response of the data point", + "role": "group" + } + ] + } + }, + "1728": { + "metadata": { + "title": "The UK's busiest airports", + "description": "Number of terminal passengers in 2018", + "main_insight": "Heathrow is by far the busiest airport in the UK, with almost double the number of passengers compared to Gatwick, the second busiest." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Airport": "Heathrow", + "Passengers": 79.96 + } + ], + "score": 0.96, + "annotation": "The has a maximum value at Heathrow", + "reason": "The [{'Airport': 'Heathrow', 'Passengers': 79.96}] of has a maximum value of 79.96, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Airport": "Heathrow", + "Passengers": 79.96 + } + ], + "score": 0.81, + "annotation": "The shows a sudden jump in Passengers from Gatwick to Heathrow.", + "reason": "The Passengers for increases from 46.08 at Gatwick to 79.96 at Heathrow, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Airport": "Belfast International", + "Passengers": 6.27 + } + ], + "score": 0.51, + "annotation": "The has a minimum value at Belfast International", + "reason": "The [{'Airport': 'Belfast International', 'Passengers': 6.27}] of has a mainimum value of 6.27, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Airport": "Heathrow", + "Passengers": 79.96 + }, + { + "Airport": "Gatwick", + "Passengers": 46.08 + }, + { + "Airport": "Manchester", + "Passengers": 28.26 + }, + { + "Airport": "Stansted", + "Passengers": 28.0 + }, + { + "Airport": "Luton", + "Passengers": 16.77 + }, + { + "Airport": "Edinburgh", + "Passengers": 14.29 + }, + { + "Airport": "Birmingham", + "Passengers": 12.46 + }, + { + "Airport": "Glasgow", + "Passengers": 9.65 + }, + { + "Airport": "Bristol", + "Passengers": 8.7 + }, + { + "Airport": "Belfast International", + "Passengers": 6.27 + } + ], + "columns": [ + { + "name": "Airport", + "importance": "primary", + "description": "Name of the UK airport", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Passengers", + "importance": "primary", + "description": "Number of terminal passengers in millions", + "unit": "Millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1155": { + "metadata": { + "title": "Facebook Dwarfs Competition in Terms of Referral Traffic", + "description": "% of visits to sampled websites coming from social media referrals*", + "main_insight": "Facebook is the dominant source of referral traffic from social media, significantly exceeding all other platforms. Referral traffic from all platforms increased from September 13 to September 14." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Social Media Platform": "LinkedIn", + "Units": 0.07, + "Date": "Sep 13" + }, + { + "Social Media Platform": "LinkedIn", + "Units": 0.04, + "Date": "Sep 14" + } + ], + "score": 0.99, + "annotation": "The Units of Sep 13 and Sep 14 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Sep 13 and Sep 14 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Social Media Platform": "Facebook", + "Units": 22.4, + "Date": "Sep 14" + } + ], + "score": 0.96, + "annotation": "The Sep 14 has a maximum value at Facebook", + "reason": "The [{'Social Media Platform': 'Facebook', 'Units': 22.4, 'Date': 'Sep 14'}] of Sep 14 has a maximum value of 22.4, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Social Media Platform": "Facebook", + "Units": 10.4, + "Date": "Sep 13" + } + ], + "score": 0.96, + "annotation": "The Sep 13 has a maximum value at Facebook", + "reason": "The [{'Social Media Platform': 'Facebook', 'Units': 10.4, 'Date': 'Sep 13'}] of Sep 13 has a maximum value of 10.4, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Social Media Platform": "Facebook", + "Units": 22.4, + "Date": "Sep 14" + } + ], + "score": 0.93, + "annotation": "The Sep 14 shows a sudden jump in Units from Pinterest to Facebook.", + "reason": "The Units for Sep 14 increases from 5.5 at Pinterest to 22.4 at Facebook, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Social Media Platform": "Facebook", + "Units": 10.4, + "Date": "Sep 13" + } + ], + "score": 0.9, + "annotation": "The Sep 13 shows a sudden jump in Units from Pinterest to Facebook.", + "reason": "The Units for Sep 13 increases from 3.7 at Pinterest to 10.4 at Facebook, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Social Media Platform": "Facebook", + "Units": 10.4, + "Date": "Sep 13" + }, + { + "Social Media Platform": "Facebook", + "Units": 22.4, + "Date": "Sep 14" + }, + { + "Social Media Platform": "Pinterest", + "Units": 3.7, + "Date": "Sep 13" + }, + { + "Social Media Platform": "Pinterest", + "Units": 5.5, + "Date": "Sep 14" + }, + { + "Social Media Platform": "Twitter", + "Units": 1.2, + "Date": "Sep 13" + }, + { + "Social Media Platform": "Twitter", + "Units": 0.9, + "Date": "Sep 14" + }, + { + "Social Media Platform": "StumbleUpon", + "Units": 0.6, + "Date": "Sep 13" + }, + { + "Social Media Platform": "StumbleUpon", + "Units": 0.4, + "Date": "Sep 14" + }, + { + "Social Media Platform": "Reddit", + "Units": 0.3, + "Date": "Sep 13" + }, + { + "Social Media Platform": "Reddit", + "Units": 0.2, + "Date": "Sep 14" + }, + { + "Social Media Platform": "Google+", + "Units": 0.04, + "Date": "Sep 13" + }, + { + "Social Media Platform": "Google+", + "Units": 0.07, + "Date": "Sep 14" + }, + { + "Social Media Platform": "YouTube", + "Units": 0.29, + "Date": "Sep 13" + }, + { + "Social Media Platform": "YouTube", + "Units": 0.04, + "Date": "Sep 14" + }, + { + "Social Media Platform": "LinkedIn", + "Units": 0.07, + "Date": "Sep 13" + }, + { + "Social Media Platform": "LinkedIn", + "Units": 0.04, + "Date": "Sep 14" + } + ], + "columns": [ + { + "name": "Social Media Platform", + "importance": "primary", + "description": "The social media platform referring traffic", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding date", + "role": "y" + }, + { + "name": "Date", + "importance": "primary", + "description": "The date of the data point", + "role": "group" + } + ] + } + }, + "497": { + "metadata": { + "title": "Two Decades of CEO Pay in the United States", + "description": "An overview of chief executives' pay in the United States between 1989 and 2012 (in million dollars)", + "main_insight": "CEO pay in the United States increased significantly between 1989 and 2012, with the most substantial growth occurring in stock gains and other compensation, rather than just salary and bonus." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2012, + "Ratio": 10.6, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2012, + "Ratio": 4.2, + "Compensation Components": "Stock Gains" + } + ], + "score": 0.98, + "annotation": "The Ratio of Total Compensation and Stock Gains show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Total Compensation and Stock Gains is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2012, + "Ratio": 10.6, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2012, + "Ratio": 2.4, + "Compensation Components": "Other Compensation" + } + ], + "score": 0.95, + "annotation": "The Ratio of Total Compensation and Other Compensation show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Total Compensation and Other Compensation is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2012, + "Ratio": 4.0, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 2012, + "Ratio": 2.4, + "Compensation Components": "Other Compensation" + } + ], + "score": 0.95, + "annotation": "The Ratio of Salary + Bonus and Other Compensation show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Salary + Bonus and Other Compensation is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2012, + "Ratio": 10.6, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2012, + "Ratio": 4.0, + "Compensation Components": "Salary + Bonus" + } + ], + "score": 0.94, + "annotation": "The Ratio of Total Compensation and Salary + Bonus show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Total Compensation and Salary + Bonus is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2012, + "Ratio": 4.2, + "Compensation Components": "Stock Gains" + }, + { + "Year": 2012, + "Ratio": 2.4, + "Compensation Components": "Other Compensation" + } + ], + "score": 0.88, + "annotation": "The Ratio of Stock Gains and Other Compensation show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Stock Gains and Other Compensation is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 1989, + "Ratio": 2.2, + "Compensation Components": "Total Compensation" + }, + { + "Year": 1989, + "Ratio": 1.4, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 1989, + "Ratio": 0.5, + "Compensation Components": "Stock Gains" + }, + { + "Year": 1989, + "Ratio": 0.3, + "Compensation Components": "Other Compensation" + }, + { + "Year": 1990, + "Ratio": 2.3, + "Compensation Components": "Total Compensation" + }, + { + "Year": 1990, + "Ratio": 1.5, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 1990, + "Ratio": 0.5, + "Compensation Components": "Stock Gains" + }, + { + "Year": 1990, + "Ratio": 0.3, + "Compensation Components": "Other Compensation" + }, + { + "Year": 1991, + "Ratio": 2.5, + "Compensation Components": "Total Compensation" + }, + { + "Year": 1991, + "Ratio": 1.7, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 1991, + "Ratio": 0.6, + "Compensation Components": "Stock Gains" + }, + { + "Year": 1991, + "Ratio": 0.2, + "Compensation Components": "Other Compensation" + }, + { + "Year": 1992, + "Ratio": 3.3, + "Compensation Components": "Total Compensation" + }, + { + "Year": 1992, + "Ratio": 1.8, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 1992, + "Ratio": 1.1, + "Compensation Components": "Stock Gains" + }, + { + "Year": 1992, + "Ratio": 0.4, + "Compensation Components": "Other Compensation" + }, + { + "Year": 1993, + "Ratio": 3.8, + "Compensation Components": "Total Compensation" + }, + { + "Year": 1993, + "Ratio": 1.9, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 1993, + "Ratio": 1.4, + "Compensation Components": "Stock Gains" + }, + { + "Year": 1993, + "Ratio": 0.5, + "Compensation Components": "Other Compensation" + }, + { + "Year": 1994, + "Ratio": 4.1, + "Compensation Components": "Total Compensation" + }, + { + "Year": 1994, + "Ratio": 2.1, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 1994, + "Ratio": 1.5, + "Compensation Components": "Stock Gains" + }, + { + "Year": 1994, + "Ratio": 0.5, + "Compensation Components": "Other Compensation" + }, + { + "Year": 1995, + "Ratio": 4.5, + "Compensation Components": "Total Compensation" + }, + { + "Year": 1995, + "Ratio": 2.1, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 1995, + "Ratio": 1.8, + "Compensation Components": "Stock Gains" + }, + { + "Year": 1995, + "Ratio": 0.6, + "Compensation Components": "Other Compensation" + }, + { + "Year": 1996, + "Ratio": 5.4, + "Compensation Components": "Total Compensation" + }, + { + "Year": 1996, + "Ratio": 2.3, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 1996, + "Ratio": 2.3, + "Compensation Components": "Stock Gains" + }, + { + "Year": 1996, + "Ratio": 0.8, + "Compensation Components": "Other Compensation" + }, + { + "Year": 1997, + "Ratio": 6.4, + "Compensation Components": "Total Compensation" + }, + { + "Year": 1997, + "Ratio": 2.5, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 1997, + "Ratio": 2.9, + "Compensation Components": "Stock Gains" + }, + { + "Year": 1997, + "Ratio": 1.0, + "Compensation Components": "Other Compensation" + }, + { + "Year": 1998, + "Ratio": 8.1, + "Compensation Components": "Total Compensation" + }, + { + "Year": 1998, + "Ratio": 2.7, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 1998, + "Ratio": 4.1, + "Compensation Components": "Stock Gains" + }, + { + "Year": 1998, + "Ratio": 1.3, + "Compensation Components": "Other Compensation" + }, + { + "Year": 1999, + "Ratio": 12.5, + "Compensation Components": "Total Compensation" + }, + { + "Year": 1999, + "Ratio": 3.0, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 1999, + "Ratio": 7.4, + "Compensation Components": "Stock Gains" + }, + { + "Year": 1999, + "Ratio": 2.1, + "Compensation Components": "Other Compensation" + }, + { + "Year": 2000, + "Ratio": 13.7, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2000, + "Ratio": 3.5, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 2000, + "Ratio": 7.9, + "Compensation Components": "Stock Gains" + }, + { + "Year": 2000, + "Ratio": 2.3, + "Compensation Components": "Other Compensation" + }, + { + "Year": 2001, + "Ratio": 11.4, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2001, + "Ratio": 3.2, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 2001, + "Ratio": 6.1, + "Compensation Components": "Stock Gains" + }, + { + "Year": 2001, + "Ratio": 2.1, + "Compensation Components": "Other Compensation" + }, + { + "Year": 2002, + "Ratio": 8.0, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2002, + "Ratio": 3.0, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 2002, + "Ratio": 3.4, + "Compensation Components": "Stock Gains" + }, + { + "Year": 2002, + "Ratio": 1.6, + "Compensation Components": "Other Compensation" + }, + { + "Year": 2003, + "Ratio": 8.6, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2003, + "Ratio": 3.2, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 2003, + "Ratio": 3.9, + "Compensation Components": "Stock Gains" + }, + { + "Year": 2003, + "Ratio": 1.5, + "Compensation Components": "Other Compensation" + }, + { + "Year": 2004, + "Ratio": 11.6, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2004, + "Ratio": 3.6, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 2004, + "Ratio": 6.4, + "Compensation Components": "Stock Gains" + }, + { + "Year": 2004, + "Ratio": 1.6, + "Compensation Components": "Other Compensation" + }, + { + "Year": 2005, + "Ratio": 13.0, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2005, + "Ratio": 3.9, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 2005, + "Ratio": 6.8, + "Compensation Components": "Stock Gains" + }, + { + "Year": 2005, + "Ratio": 2.3, + "Compensation Components": "Other Compensation" + }, + { + "Year": 2006, + "Ratio": 12.7, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2006, + "Ratio": 4.1, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 2006, + "Ratio": 6.1, + "Compensation Components": "Stock Gains" + }, + { + "Year": 2006, + "Ratio": 2.5, + "Compensation Components": "Other Compensation" + }, + { + "Year": 2007, + "Ratio": 17.5, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2007, + "Ratio": 4.3, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 2007, + "Ratio": 9.6, + "Compensation Components": "Stock Gains" + }, + { + "Year": 2007, + "Ratio": 3.6, + "Compensation Components": "Other Compensation" + }, + { + "Year": 2008, + "Ratio": 13.3, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2008, + "Ratio": 3.9, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 2008, + "Ratio": 6.6, + "Compensation Components": "Stock Gains" + }, + { + "Year": 2008, + "Ratio": 2.8, + "Compensation Components": "Other Compensation" + }, + { + "Year": 2009, + "Ratio": 9.7, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2009, + "Ratio": 3.3, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 2009, + "Ratio": 3.8, + "Compensation Components": "Stock Gains" + }, + { + "Year": 2009, + "Ratio": 2.6, + "Compensation Components": "Other Compensation" + }, + { + "Year": 2010, + "Ratio": 11.4, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2010, + "Ratio": 3.8, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 2010, + "Ratio": 5.2, + "Compensation Components": "Stock Gains" + }, + { + "Year": 2010, + "Ratio": 2.4, + "Compensation Components": "Other Compensation" + }, + { + "Year": 2011, + "Ratio": 9.8, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2011, + "Ratio": 3.6, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 2011, + "Ratio": 3.8, + "Compensation Components": "Stock Gains" + }, + { + "Year": 2011, + "Ratio": 2.4, + "Compensation Components": "Other Compensation" + }, + { + "Year": 2012, + "Ratio": 10.6, + "Compensation Components": "Total Compensation" + }, + { + "Year": 2012, + "Ratio": 4.0, + "Compensation Components": "Salary + Bonus" + }, + { + "Year": 2012, + "Ratio": 4.2, + "Compensation Components": "Stock Gains" + }, + { + "Year": 2012, + "Ratio": 2.4, + "Compensation Components": "Other Compensation" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of CEO compensation", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding compensation components", + "role": "y" + }, + { + "name": "Compensation Components", + "importance": "primary", + "description": "The compensation components of the data point", + "role": "group" + } + ] + } + }, + "719": { + "metadata": { + "title": "Two thirds of the money goes to Berlin start-ups", + "description": "Berlin's share of VC investment in Germany", + "main_insight": "Berlin start-ups receive a significant majority (69%) of the total VC investment in Germany, with E-commerce and Media & Entertainment sectors receiving the largest shares within Berlin." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Industry": "Life Sciences", + "Berlin Share of VC Investment": 21.0 + } + ], + "score": 0.82, + "annotation": "The has a minimum value at Life Sciences", + "reason": "The [{'Industry': 'Life Sciences', 'Berlin Share of VC Investment': 21.0}] of has a mainimum value of 21.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Industry": "Mobility", + "Berlin Share of VC Investment": 48.0 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Berlin Share of VC Investment from Life Sciences to Mobility.", + "reason": "The Berlin Share of VC Investment for increases from 21.0 at Life Sciences to 48.0 at Mobility, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Industry": "E-commerce", + "Berlin Share of VC Investment": 94.0 + } + ], + "score": 0.71, + "annotation": "The has a maximum value at E-commerce", + "reason": "The [{'Industry': 'E-commerce', 'Berlin Share of VC Investment': 94.0}] of has a maximum value of 94.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Industry": "E-commerce", + "Berlin Share of VC Investment": 94.0 + }, + { + "Industry": "Media & Entertainment", + "Berlin Share of VC Investment": 92.0 + }, + { + "Industry": "FinTech", + "Berlin Share of VC Investment": 55.0 + }, + { + "Industry": "Software & Analytics", + "Berlin Share of VC Investment": 52.0 + }, + { + "Industry": "Mobility", + "Berlin Share of VC Investment": 48.0 + }, + { + "Industry": "Life Sciences", + "Berlin Share of VC Investment": 21.0 + }, + { + "Industry": "Total", + "Berlin Share of VC Investment": 69.0 + } + ], + "columns": [ + { + "name": "Industry", + "importance": "primary", + "description": "Start-up industry sector", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Berlin Share of VC Investment", + "importance": "primary", + "description": "Percentage of German VC investment going to Berlin-based start-ups in each sector", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "264": { + "metadata": { + "title": "Demand Growing For Smart Home Security Systems", + "description": "Forecast revenue growth of the U.S. smart home security sector", + "main_insight": "The revenue of the U.S. smart home security sector has been steadily increasing from 2020 to 2023, and is projected to continue increasing through 2025." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2020, + "Revenue": 4.1 + }, + { + "Year": 2021, + "Revenue": 5.0 + }, + { + "Year": 2022, + "Revenue": 5.8 + }, + { + "Year": 2023, + "Revenue": 6.6 + }, + { + "Year": 2024, + "Revenue": 7.4 + }, + { + "Year": 2025, + "Revenue": 8.2 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of revenue", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Revenue", + "importance": "primary", + "description": "Forecast revenue in billions of US dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2144": { + "metadata": { + "title": "Recycling Lags For Some Green Energy Minerals", + "description": "Recycled input rate for selected metals (in percent)", + "main_insight": "Recycling rates for green energy minerals vary significantly. While Aluminum, Copper and Nickel have higher recycling rates and show some growth, Cobalt and especially Lithium have very low recycling rates in 2015, 2019, and 2024." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Value": 35.4, + "Metal Types": "Aluminium" + }, + { + "Year": 2024, + "Value": 10.4, + "Metal Types": "Cobalt" + } + ], + "score": 1.0, + "annotation": "The Value of Aluminium and Cobalt show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Value between Aluminium and Cobalt is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Value": 25.6, + "Metal Types": "Nickel" + }, + { + "Year": 2024, + "Value": 24.2, + "Metal Types": "Nickel_scrap" + } + ], + "score": 1.0, + "annotation": "The Value of Nickel and Nickel_scrap show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Value between Nickel and Nickel_scrap is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Value": 16.1, + "Metal Types": "Copper_scrap" + }, + { + "Year": 2024, + "Value": 33.3, + "Metal Types": "Aluminium_scrap" + } + ], + "score": 1.0, + "annotation": "The Value of Copper_scrap and Aluminium_scrap show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Value between Copper_scrap and Aluminium_scrap is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Value": 10.4, + "Metal Types": "Cobalt" + }, + { + "Year": 2024, + "Value": 2.9, + "Metal Types": "Lithium" + } + ], + "score": 1.0, + "annotation": "The Value of Cobalt and Lithium show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Value between Cobalt and Lithium is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2024, + "Value": 2.9, + "Metal Types": "Lithium" + }, + { + "Year": 2024, + "Value": 24.2, + "Metal Types": "Nickel_scrap" + } + ], + "score": 1.0, + "annotation": "The Value of Lithium and Nickel_scrap show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Value between Lithium and Nickel_scrap is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2015, + "Value": 31.7, + "Metal Types": "Aluminium" + }, + { + "Year": 2015, + "Value": 17.7, + "Metal Types": "Copper" + }, + { + "Year": 2015, + "Value": 33.3, + "Metal Types": "Nickel" + }, + { + "Year": 2015, + "Value": 6.7, + "Metal Types": "Cobalt" + }, + { + "Year": 2015, + "Value": 0.3, + "Metal Types": "Lithium" + }, + { + "Year": 2015, + "Value": 19.0, + "Metal Types": "Copper_scrap" + }, + { + "Year": 2015, + "Value": 36.7, + "Metal Types": "Aluminium_scrap" + }, + { + "Year": 2015, + "Value": 33.3, + "Metal Types": "Nickel_scrap" + }, + { + "Year": 2019, + "Value": 32.5, + "Metal Types": "Aluminium" + }, + { + "Year": 2019, + "Value": 17.2, + "Metal Types": "Copper" + }, + { + "Year": 2019, + "Value": 29.9, + "Metal Types": "Nickel" + }, + { + "Year": 2019, + "Value": 7.6, + "Metal Types": "Cobalt" + }, + { + "Year": 2019, + "Value": 1.1, + "Metal Types": "Lithium" + }, + { + "Year": 2019, + "Value": 17.3, + "Metal Types": "Copper_scrap" + }, + { + "Year": 2019, + "Value": 34.5, + "Metal Types": "Aluminium_scrap" + }, + { + "Year": 2019, + "Value": 29.8, + "Metal Types": "Nickel_scrap" + }, + { + "Year": 2024, + "Value": 35.4, + "Metal Types": "Aluminium" + }, + { + "Year": 2024, + "Value": 17.2, + "Metal Types": "Copper" + }, + { + "Year": 2024, + "Value": 25.6, + "Metal Types": "Nickel" + }, + { + "Year": 2024, + "Value": 10.4, + "Metal Types": "Cobalt" + }, + { + "Year": 2024, + "Value": 2.9, + "Metal Types": "Lithium" + }, + { + "Year": 2024, + "Value": 16.1, + "Metal Types": "Copper_scrap" + }, + { + "Year": 2024, + "Value": 33.3, + "Metal Types": "Aluminium_scrap" + }, + { + "Year": 2024, + "Value": 24.2, + "Metal Types": "Nickel_scrap" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding metal types", + "role": "y" + }, + { + "name": "Metal Types", + "importance": "primary", + "description": "The metal types of the data point", + "role": "group" + } + ] + } + }, + "483": { + "metadata": { + "title": "Dutch continue to save money, despite low interest rates", + "description": "Total value of household savings in banks in the Netherlands from '08 to '17 (in billion euros)", + "main_insight": "Household savings in the Netherlands have steadily increased from 2008 to 2017, despite low interest rates." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2008, + "Household Savings": 251.0 + }, + { + "Year": 2009, + "Household Savings": 281.0 + }, + { + "Year": 2010, + "Household Savings": 293.0 + }, + { + "Year": 2011, + "Household Savings": 304.0 + }, + { + "Year": 2012, + "Household Savings": 318.0 + }, + { + "Year": 2013, + "Household Savings": 331.0 + }, + { + "Year": 2014, + "Household Savings": 330.0 + }, + { + "Year": 2015, + "Household Savings": 338.0 + }, + { + "Year": 2016, + "Household Savings": 342.0 + }, + { + "Year": 2017, + "Household Savings": 342.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 2008 to 2017", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Household Savings", + "importance": "primary", + "description": "Total value of household savings in banks (in billion euros)", + "unit": "Billion euros", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2112": { + "metadata": { + "title": "Second-hand Clothes Shopping Sees Slight Uptick in 2022", + "description": "Share of respondents that have bought second hand clothing in the prior 12 months since 2018", + "main_insight": "The percentage of respondents who bought second-hand clothing increased in all surveyed countries between 2018 and 2022, with the U.S. and UK showing the largest increases." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 22.0, + "Country": "Germany" + }, + { + "Year": 2022, + "Units": 16.0, + "Country": "Spain" + } + ], + "score": 1.0, + "annotation": "The Units of Germany and Spain show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Germany and Spain is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 29.0, + "Country": "France" + }, + { + "Year": 2022, + "Units": 22.0, + "Country": "Germany" + } + ], + "score": 0.94, + "annotation": "The Units of France and Germany show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between France and Germany is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 29.0, + "Country": "France" + }, + { + "Year": 2022, + "Units": 16.0, + "Country": "Spain" + } + ], + "score": 0.94, + "annotation": "The Units of France and Spain show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between France and Spain is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 32.0, + "Country": "U.S." + }, + { + "Year": 2022, + "Units": 29.0, + "Country": "France" + } + ], + "score": 0.83, + "annotation": "The Units of U.S. and France show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between U.S. and France is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 31.0, + "Country": "UK" + }, + { + "Year": 2022, + "Units": 22.0, + "Country": "Germany" + } + ], + "score": 0.82, + "annotation": "The Units of UK and Germany show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between UK and Germany is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2018, + "Units": 29.0, + "Country": "U.S." + }, + { + "Year": 2018, + "Units": 23.0, + "Country": "UK" + }, + { + "Year": 2018, + "Units": 18.0, + "Country": "France" + }, + { + "Year": 2018, + "Units": 18.0, + "Country": "Germany" + }, + { + "Year": 2018, + "Units": 12.0, + "Country": "Spain" + }, + { + "Year": 2019, + "Units": 25.0, + "Country": "U.S." + }, + { + "Year": 2019, + "Units": 25.0, + "Country": "UK" + }, + { + "Year": 2019, + "Units": 15.0, + "Country": "France" + }, + { + "Year": 2019, + "Units": 18.0, + "Country": "Germany" + }, + { + "Year": 2019, + "Units": 12.0, + "Country": "Spain" + }, + { + "Year": 2020, + "Units": 26.0, + "Country": "U.S." + }, + { + "Year": 2020, + "Units": 21.0, + "Country": "UK" + }, + { + "Year": 2020, + "Units": 21.0, + "Country": "France" + }, + { + "Year": 2020, + "Units": 19.0, + "Country": "Germany" + }, + { + "Year": 2020, + "Units": 13.0, + "Country": "Spain" + }, + { + "Year": 2021, + "Units": 29.0, + "Country": "U.S." + }, + { + "Year": 2021, + "Units": 27.0, + "Country": "UK" + }, + { + "Year": 2021, + "Units": 23.0, 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"description": "Percentage decline in remittance inflows to low/middle-income countries by world region", + "main_insight": "The projected decline in remittances in 2020 is greater than the decline seen in 2009 for most regions, except for Sub-Saharan Africa and South Asia." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "South Asia", + "Units": 5.0, + "Year": "2009" + } + ], + "score": 0.83, + "annotation": "The 2009 shows a sudden jump in Units from East Asia and Pacific to South Asia.", + "reason": "The Units for 2009 increases from -5.0 at East Asia and Pacific to 5.0 at South Asia, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "East Asia and Pacific", + "Units": -13.0, + "Year": "2020" + } + ], + "score": 0.83, + "annotation": "The 2020 has a maximum value at East Asia and Pacific", + "reason": "The [{'Region': 'East Asia and Pacific', 'Units': -13.0, 'Year': '2020'}] of 2020 has a maximum value of -13.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "South Asia", + "Units": 5.0, + "Year": "2009" + } + ], + "score": 0.81, + "annotation": "The 2009 has a maximum value at South Asia", + "reason": "The [{'Region': 'South Asia', 'Units': 5.0, 'Year': '2009'}] of 2009 has a maximum value of 5.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Region": "Europe and Central Asia", + "Units": -28.0, + "Year": "2020" + } + ], + "score": 0.78, + "annotation": "The 2020 has a minimum value at Europe and Central Asia", + "reason": "The [{'Region': 'Europe and Central Asia', 'Units': -28.0, 'Year': '2020'}] of 2020 has a mainimum value of -28.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "East Asia and Pacific", + "Units": -13.0, + "Year": "2020" + } + ], + "score": 0.77, + "annotation": "The 2020 shows a sudden jump in Units from Latin America and Caribbean to East Asia and Pacific.", + "reason": "The Units for 2020 increases from -19.0 at Latin America and Caribbean to -13.0 at East Asia and Pacific, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Region": "East Asia and Pacific", + "Units": -13.0, + "Year": "2020" + }, + { + "Region": "East Asia and Pacific", + "Units": -5.0, + "Year": "2009" + }, + { + "Region": "Latin America and Caribbean", + "Units": -19.0, + "Year": "2020" + }, + { + "Region": "Latin America and Caribbean", + "Units": -11.0, + "Year": "2009" + }, + { + "Region": "Middle East and North Africa", + "Units": -20.0, + "Year": "2020" + }, + { + "Region": "Middle East and North Africa", + "Units": -6.0, + "Year": "2009" + }, + { + "Region": "South Asia", + "Units": -22.0, + "Year": "2020" + }, + { + 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since 2017, while the Conservative share has increased, leading to a Conservative victory in 2021." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2021, + "Units": 28.7, + "Party": "Labour" + }, + { + "Year": 2021, + "Units": 51.9, + "Party": "Conservatives" + } + ], + "score": 0.63, + "annotation": "The Units of Labour and Conservatives show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Labour and Conservatives is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2017, + "Units": 52.5, + "Party": "Labour" + }, + { + "Year": 2017, + "Units": 34.2, + "Party": "Conservatives" + }, + { + "Year": 2019, + "Units": 37.7, + "Party": "Labour" + }, + { + "Year": 2019, + "Units": 28.9, + "Party": "Conservatives" + }, + { + "Year": 2021, + "Units": 28.7, + "Party": "Labour" + }, + { + "Year": 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"title": "The most heavily policed countries in the world", + "description": "Police personnel per 100,000 of the population", + "main_insight": "Russia has the highest number of police personnel per 100,000 of the population among the listed countries, significantly exceeding the others." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Russia", + "Police Personnel per 100,000": 564.6 + } + ], + "score": 0.95, + "annotation": "The has a maximum value at Russia", + "reason": "The [{'Country': 'Russia', 'Police Personnel per 100,000': 564.6}] of has a maximum value of 564.6, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Russia", + "Police Personnel per 100,000": 564.6 + } + ], + "score": 0.85, + "annotation": "The shows a sudden jump in Police Personnel per 100,000 from Turkey to Russia.", + "reason": "The Police Personnel per 100,000 for increases from 474.8 at Turkey to 564.6 at Russia, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Czech Republic", + "Police Personnel per 100,000": 393.8 + } + ], + "score": 0.62, + "annotation": "The has a minimum value at Czech Republic", + "reason": "The [{'Country': 'Czech Republic', 'Police Personnel per 100,000': 393.8}] of has a mainimum value of 393.8, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Russia", + "Police Personnel per 100,000": 564.6 + }, + { + "Country": "Turkey", + "Police Personnel per 100,000": 474.8 + }, + { + "Country": "Italy", + "Police Personnel per 100,000": 467.2 + }, + { + "Country": "Portugal", + "Police Personnel per 100,000": 454.2 + }, + { + "Country": "Hong Kong", + "Police Personnel per 100,000": 450.7 + }, + { + "Country": "Kazakhstan", + "Police Personnel per 100,000": 444.8 + }, + { + "Country": 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affected group by a significant margin." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Animal Type": "Reptiles", + "Number Impacted": 2460000000.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Number Impacted from Birds to Reptiles.", + "reason": "The Number Impacted for increases from 180000000.0 at Birds to 2460000000.0 at Reptiles, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Animal Type": "Reptiles", + "Number Impacted": 2460000000.0 + } + ], + "score": 0.8, + "annotation": "The has a maximum value at Reptiles", + "reason": "The [{'Animal Type': 'Reptiles', 'Number Impacted': 2460000000.0}] of has a maximum value of 2460000000.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Animal Type": "Frogs", + "Number Impacted": 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Nokia's stock also declined, but less dramatically than HTC and RIM." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Jan. 2011", + "Units": 100.0, + "Brands": "HTC" + }, + { + "Month": "Jan. 2011", + "Units": 100.0, + "Brands": "Apple" + }, + { + "Month": "Jan. 2011", + "Units": 100.0, + "Brands": "Samsung" + }, + { + "Month": "Jan. 2011", + "Units": 100.0, + "Brands": "Nokia" + }, + { + "Month": "Jan. 2011", + "Units": 100.0, + "Brands": "RIM" + } + ], + "score": 0.96, + "annotation": "The HTC, Apple, Samsung, Nokia, RIM all have the largest proportion in Jan. 2011.", + "reason": "The Units of HTC, Apple, Samsung, Nokia, RIM in Jan. 2011 all account for 0.2, larger than all other Brands, of the total of Units of all Brands." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Feb. 2011", + "Units": 110.0, + "Brands": "HTC" + }, + { 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+ }, + { + "Month": "Jul. 2011", + "Units": 60.0, + "Brands": "RIM" + }, + { + "Month": "Aug. 2011", + "Units": 90.0, + "Brands": "HTC" + }, + { + "Month": "Aug. 2011", + "Units": 120.0, + "Brands": "Apple" + }, + { + "Month": "Aug. 2011", + "Units": 75.0, + "Brands": "Samsung" + }, + { + "Month": "Aug. 2011", + "Units": 75.0, + "Brands": "Nokia" + }, + { + "Month": "Aug. 2011", + "Units": 40.0, + "Brands": "RIM" + }, + { + "Month": "Sep. 2011", + "Units": 80.0, + "Brands": "HTC" + }, + { + "Month": "Sep. 2011", + "Units": 125.0, + "Brands": "Apple" + }, + { + "Month": "Sep. 2011", + "Units": 80.0, + "Brands": "Samsung" + }, + { + "Month": "Sep. 2011", + "Units": 60.0, + "Brands": "Nokia" + }, + { + "Month": "Sep. 2011", + "Units": 30.0, + "Brands": "RIM" + }, + { + "Month": "Oct. 2011", + "Units": 75.0, + "Brands": "HTC" + }, + { + "Month": "Oct. 2011", + "Units": 115.0, + "Brands": "Apple" + }, + { + "Month": "Oct. 2011", + "Units": 85.0, + "Brands": "Samsung" + }, + { + "Month": "Oct. 2011", + "Units": 50.0, + "Brands": "Nokia" + }, + { + "Month": "Oct. 2011", + "Units": 45.0, + "Brands": "RIM" + }, + { + "Month": "Nov. 2011", + "Units": 60.0, + "Brands": "HTC" + }, + { + "Month": "Nov. 2011", + "Units": 115.0, + "Brands": "Apple" + }, + { + "Month": "Nov. 2011", + "Units": 95.0, + "Brands": "Samsung" + }, + { + "Month": "Nov. 2011", + "Units": 65.0, + "Brands": "Nokia" + }, + { + "Month": "Nov. 2011", + "Units": 30.0, + "Brands": "RIM" + }, + { + "Month": "Dec. 2011", + "Units": 50.0, + "Brands": "HTC" + }, + { + "Month": "Dec. 2011", + "Units": 110.0, + "Brands": "Apple" + }, + { + "Month": "Dec. 2011", + "Units": 105.0, + "Brands": "Samsung" + }, + { + "Month": "Dec. 2011", + "Units": 50.0, + "Brands": "Nokia" + }, + { + "Month": "Dec. 2011", + "Units": 30.0, + "Brands": "RIM" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year 2011", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding brands", + "role": "y" + }, + { + "name": "Brands", + "importance": "primary", + "description": "The brands of the data point", + "role": "group" + } + ] + } + }, + "1278": { + "metadata": { + "title": "Europe's cheapest city break destinations", + "description": "Typical cost of a two night stay in the following cities in 2018 (in GBP)", + "main_insight": "Krakow and Vilnius are the cheapest European city break destinations in 2018, with a two-night stay costing around £165, while Athens is the most expensive among the top 8, costing just over £200." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Moscow", + "Cost (GBP)": 199.64 + } + ], + "score": 0.8, + "annotation": "The shows a sudden jump in Cost (GBP) from Budapest to Moscow.", + "reason": "The Cost (GBP) for increases from 183.51 at Budapest to 199.64 at Moscow, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "Krakow", + "Cost (GBP)": 164.8 + } + ], + "score": 0.7, + "annotation": "The has a minimum value at Krakow", + "reason": "The [{'City': 'Krakow', 'Cost (GBP)': 164.8}] of has a mainimum value of 164.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Athens", + "Cost (GBP)": 200.42 + } + ], + "score": 0.66, + "annotation": "The has a maximum value at Athens", + "reason": "The [{'City': 'Athens', 'Cost (GBP)': 200.42}] of has a maximum value of 200.42, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "City": "Krakow", + "Cost (GBP)": 164.8 + }, + { + "City": "Vilnius", + "Cost (GBP)": 165.53 + }, + { + "City": "Riga", + "Cost (GBP)": 172.17 + }, + { + "City": "Warsaw", + "Cost (GBP)": 180.53 + }, + { + "City": "Budapest", + "Cost (GBP)": 183.51 + }, + { + "City": "Moscow", + "Cost (GBP)": 199.64 + }, + { + "City": "Prague", + "Cost (GBP)": 199.86 + }, + { + "City": "Athens", + "Cost (GBP)": 200.42 + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "Name of the European city", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Cost (GBP)", + "importance": "primary", + "description": "Typical cost of a two-night stay in 2018 in British Pounds", + "unit": "GBP", + "data_type": "number", + "role": "y" + } + ] + } + }, + "762": { + "metadata": { + "title": "Is Airbnb Really Cheaper Than A Hotel Room?", + "description": "Average room price per night in selected major cities in January 2018", + "main_insight": "Airbnb is significantly cheaper than hotels in all the major cities presented, with the largest savings being in New York." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "New York", + "Units": 306.0, + "Booking Type": "Hotel" + } + ], + "score": 0.86, + "annotation": "The Hotel has a maximum value at New York", + "reason": "The [{'City': 'New York', 'Units': 306.0, 'Booking Type': 'Hotel'}] of Hotel has a maximum value of 306.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "City": "New York", + "Units": 306.0, + "Booking Type": "Hotel" + } + ], + "score": 0.86, + "annotation": "The Hotel has the largest proportion in New York.", + "reason": "The Units of Hotel in New York accounts for 0.5, larger than all other Booking Type, of the total of Units of all Booking Type." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "City": "Sydney", + "Units": 240.0, + "Booking Type": "Hotel" + } + ], + "score": 0.86, + "annotation": "The Hotel has the largest proportion in Sydney.", + "reason": "The Units of Hotel in Sydney accounts for 0.5, larger than all other Booking Type, of the total of Units of all Booking Type." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "City": "Tokyo", + "Units": 220.0, + "Booking Type": "Hotel" + } + ], + "score": 0.86, + "annotation": "The Hotel has the largest proportion in Tokyo.", + "reason": "The Units of Hotel in Tokyo accounts for 0.5, larger than all other Booking Type, of the total of Units of all Booking Type." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "City": "London", + "Units": 217.0, + "Booking Type": "Hotel" + } + ], + "score": 0.86, + "annotation": "The Hotel has the largest proportion in London.", + "reason": "The Units of Hotel in London accounts for 0.5, larger than all other Booking Type, of the total of Units of all Booking Type." + } + ], + "data": { + "data": [ + { + "City": "New York", + "Units": 306.0, + "Booking Type": "Hotel" + }, + { + "City": "New York", + "Units": 187.0, + "Booking Type": "Airbnb" + }, + { + "City": "New York", + "Units": 119.0, + "Booking Type": "Saved through Airbnb" + }, + { + "City": "Sydney", + "Units": 240.0, + "Booking Type": "Hotel" + }, + { + "City": "Sydney", + "Units": 191.0, + "Booking Type": "Airbnb" + }, + { + "City": "Sydney", + "Units": 49.0, + "Booking Type": "Saved through Airbnb" + }, + { + "City": "Tokyo", + "Units": 220.0, + "Booking Type": "Hotel" + }, + { + "City": "Tokyo", + "Units": 93.0, + "Booking Type": "Airbnb" + }, + { + "City": "Tokyo", + "Units": 127.0, + "Booking Type": "Saved through Airbnb" + }, + { + "City": "London", + "Units": 217.0, + "Booking Type": "Hotel" + }, + { + "City": "London", + "Units": 179.0, + "Booking Type": "Airbnb" + }, + { + "City": "London", + "Units": 38.0, + "Booking Type": "Saved through Airbnb" + }, + { + "City": "Toronto", + "Units": 193.0, + "Booking Type": "Hotel" + }, + { + "City": "Toronto", + "Units": 114.0, + "Booking Type": "Airbnb" + }, + { + "City": "Toronto", + "Units": 79.0, + "Booking Type": "Saved through Airbnb" + }, + { + "City": "Paris", + "Units": 167.0, + "Booking Type": "Hotel" + }, + { + "City": "Paris", + "Units": 110.0, + "Booking Type": "Airbnb" + }, + { + "City": "Paris", + "Units": 57.0, + "Booking Type": "Saved through Airbnb" + }, + { + "City": "Moscow", + "Units": 118.0, + "Booking Type": "Hotel" + }, + { + "City": "Moscow", + "Units": 65.0, + "Booking Type": "Airbnb" + }, + { + "City": "Moscow", + "Units": 53.0, + "Booking Type": "Saved through Airbnb" + }, + { + "City": "Berlin", + "Units": 114.0, + "Booking Type": "Hotel" + }, + { + "City": "Berlin", + "Units": 92.0, + "Booking Type": "Airbnb" + }, + { + "City": "Berlin", + "Units": 22.0, + "Booking Type": "Saved through Airbnb" + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "Name of the city", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding booking type", + "role": "y" + }, + { + "name": "Booking Type", + "importance": "primary", + "description": "The booking type of the data point", + "role": "group" + } + ] + } + }, + "1139": { + "metadata": { + "title": "How Much Do Americans Pay For Pet Dogs?", + "description": "Share of U.S. dog owners who paid the following for their most recent pet in 2020", + "main_insight": "A significant portion of US dog owners (38%) paid no costs for their most recent pet in 2020, while the remaining owners paid varying amounts, with the largest paid categories being 'Up to $100' and '$251 - $500', both at 16%." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Cost Range": "No costs", + "Percentage of Dog Owners": 38.0 + } + ], + "score": 0.91, + "annotation": "The shows a sudden jump in Percentage of Dog Owners from $251 - $500 to No costs.", + "reason": "The Percentage of Dog Owners for increases from 16.0 at $251 - $500 to 38.0 at No costs, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Cost Range": "No costs", + "Percentage of Dog Owners": 38.0 + } + ], + "score": 0.9, + "annotation": "The has a maximum value at No costs", + "reason": "The [{'Cost Range': 'No costs', 'Percentage of Dog Owners': 38.0}] of has a maximum value of 38.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Cost Range": "$751 - $1,000", + "Percentage of Dog Owners": 6.0 + } + ], + "score": 0.58, + "annotation": "The has a minimum value at $751 - $1,000", + "reason": "The [{'Cost Range': '$751 - $1,000', 'Percentage of Dog Owners': 6.0}] of has a mainimum value of 6.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Cost Range": "No costs", + "Percentage of Dog Owners": 38.0 + }, + { + "Cost Range": "Up to $100", + "Percentage of Dog Owners": 16.0 + }, + { + "Cost Range": "$101 - $250", + "Percentage of Dog Owners": 14.0 + }, + { + "Cost Range": "$251 - $500", + "Percentage of Dog Owners": 16.0 + }, + { + "Cost Range": "$501 - $750", + "Percentage of Dog Owners": 10.0 + }, + { + "Cost Range": "$751 - $1,000", + "Percentage of Dog Owners": 6.0 + } + ], + "columns": [ + { + "name": "Cost Range", + "importance": "primary", + "description": "The cost range for acquiring a pet dog", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Dog Owners", + "importance": "primary", + "description": "The percentage of U.S. dog owners who paid the corresponding amount for their most recent pet", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "488": { + "metadata": { + "title": "Deaths at the Berlin Wall", + "description": "Number of fatalities at the Berlin Wall (1961-1989)", + "main_insight": "The number of fatalities at the Berlin Wall was highest in the early 1960s, immediately after its construction, and decreased significantly over the following decades. The total number of fatalities across the entire period was 140." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1961, + "Fatalities": 11 + }, + { + "Year": 1962, + "Fatalities": 23 + }, + { + "Year": 1963, + "Fatalities": 10 + }, + { + "Year": 1964, + "Fatalities": 10 + }, + { + "Year": 1965, + "Fatalities": 12 + }, + { + "Year": 1966, + "Fatalities": 12 + }, + { + "Year": 1967, + "Fatalities": 2 + }, + { + "Year": 1968, + "Fatalities": 6 + }, + { + "Year": 1969, + "Fatalities": 11 + }, + { + "Year": 1970, + "Fatalities": 9 + }, + { + "Year": 1971, + "Fatalities": 4 + }, + { + "Year": 1972, + "Fatalities": 5 + }, + { + "Year": 1973, + "Fatalities": 4 + }, + { + "Year": 1974, + "Fatalities": 0 + }, + { + "Year": 1975, + "Fatalities": 0 + }, + { + "Year": 1976, + "Fatalities": 1 + }, + { + "Year": 1977, + "Fatalities": 3 + }, + { + "Year": 1978, + "Fatalities": 0 + }, + { + "Year": 1979, + "Fatalities": 1 + }, + { + "Year": 1980, + "Fatalities": 0 + }, + { + "Year": 1981, + "Fatalities": 4 + }, + { + "Year": 1982, + "Fatalities": 1 + }, + { + "Year": 1983, + "Fatalities": 0 + }, + { + "Year": 1984, + "Fatalities": 0 + }, + { + "Year": 1985, + "Fatalities": 0 + }, + { + "Year": 1986, + "Fatalities": 4 + }, + { + "Year": 1989, + "Fatalities": 3 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year ranges grouped by decades", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Fatalities", + "importance": "primary", + "description": "Number of deaths at the Berlin Wall", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1055": { + "metadata": { + "title": "Northwest Europe Sees Surge In Wildfires", + "description": "Estimated number of hectares burnt in 2018 compared with 2008-17 average", + "main_insight": "Northwest European countries experienced a significant increase in hectares burnt by wildfires in 2018 compared to the 2008-2017 average." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Latvia", + "Units": 10.0, + "Timeframe": "2008-2017 average" + } + ], + "score": 0.98, + "annotation": "The 2008-2017 average has the smallest proportion in Latvia.", + "reason": "The Units of 2008-2017 average in Latvia accounts for 0.004574565416285453, smaller than all other Timeframe, of the total of Units of all Timeframe." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Sweden", + "Units": 454.0, + "Timeframe": "2008-2017 average" + } + ], + "score": 0.89, + "annotation": "The 2008-2017 average has the smallest proportion in Sweden.", + "reason": "The Units of 2008-2017 average in Sweden accounts for 0.02362737444704658, smaller than all other Timeframe, of the total of Units of all Timeframe." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Germany", + "Units": 40.0, + "Timeframe": "2008-2017 average" + } + ], + "score": 0.89, + "annotation": "The 2008-2017 average has the smallest proportion in Germany.", + "reason": "The Units of 2008-2017 average in Germany accounts for 0.02449479485609308, smaller than all other Timeframe, of the total of Units of all Timeframe." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Greece", + "Units": 8361.0, + "Timeframe": "2008-2017 average" + } + ], + "score": 0.88, + "annotation": "The 2008-2017 average has a maximum value at Greece", + "reason": "The [{'Country': 'Greece', 'Units': 8361.0, 'Timeframe': '2008-2017 average'}] of 2008-2017 average has a maximum value of 8361.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Spain", + "Units": 32639.0, + "Timeframe": "2018 Season" + } + ], + "score": 0.83, + "annotation": "The 2018 Season has a maximum value at Spain", + "reason": "The [{'Country': 'Spain', 'Units': 32639.0, 'Timeframe': '2018 Season'}] of 2018 Season has a maximum value of 32639.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Germany", + "Units": 1593.0, + "Timeframe": "2018 Season" + }, + { + "Country": "Germany", + "Units": 40.0, + "Timeframe": "2008-2017 average" + }, + { + "Country": "Latvia", + "Units": 2176.0, + "Timeframe": "2018 Season" + }, + { + "Country": "Latvia", + "Units": 10.0, + "Timeframe": "2008-2017 average" + }, + { + "Country": "Ireland", + "Units": 2505.0, + "Timeframe": "2018 Season" + }, + { + "Country": "Ireland", + "Units": 4024.0, + "Timeframe": "2008-2017 average" + }, + { + "Country": "United Kingdom", + "Units": 13888.0, + "Timeframe": "2018 Season" + }, + { + "Country": "United Kingdom", + "Units": 3113.0, + "Timeframe": "2008-2017 average" + }, + { + "Country": "Sweden", + "Units": 18761.0, + "Timeframe": "2018 Season" + }, + { + "Country": "Sweden", + "Units": 454.0, + "Timeframe": "2008-2017 average" + }, + { + "Country": "Portugal", + "Units": 29523.0, + "Timeframe": "2018 Season" + }, + { + "Country": "Portugal", + "Units": 1327.0, + "Timeframe": "2008-2017 average" + }, + { + "Country": "France", + "Units": 2296.0, + "Timeframe": "2018 Season" + }, + { + "Country": "France", + "Units": 3257.0, + "Timeframe": "2008-2017 average" + }, + { + "Country": "Spain", + "Units": 32639.0, + "Timeframe": "2018 Season" + }, + { + "Country": "Spain", + "Units": 3639.0, + "Timeframe": "2008-2017 average" + }, + { + "Country": "Greece", + "Units": 6884.0, + "Timeframe": "2018 Season" + }, + { + "Country": "Greece", + "Units": 8361.0, + "Timeframe": "2008-2017 average" + }, + { + "Country": "Italy", + "Units": 23925.0, + "Timeframe": "2018 Season" + }, + { + "Country": "Italy", + "Units": 7358.0, + "Timeframe": "2008-2017 average" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country in Northwest Europe", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding timeframe", + "role": "y" + }, + { + "name": "Timeframe", + "importance": "primary", + "description": "The timeframe of the data point", + "role": "group" + } + ] + } + }, + "979": { + "metadata": { + "title": "Marvel and DC Dominate The Comic Market", + "description": "Revenue share of comic book publishers in specialty store sales worldwide in Q1 2023 (in %)", + "main_insight": "Marvel and DC combined account for over 60% of comic book sales in specialty stores worldwide in Q1 2023, significantly outperforming other publishers." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Publisher": "Marvel", + "Revenue Share": 36.5 + } + ], + "score": 0.91, + "annotation": "The has a maximum value at Marvel", + "reason": "The [{'Publisher': 'Marvel', 'Revenue Share': 36.5}] of has a maximum value of 36.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Publisher": "DC", + "Revenue Share": 25.5 + } + ], + "score": 0.76, + "annotation": "The shows a sudden jump in Revenue Share from Others to DC.", + "reason": "The Revenue Share for increases from 12.5 at Others to 25.5 at DC, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Publisher": "VIZ Media", + "Revenue Share": 2.6 + } + ], + "score": 0.5, + "annotation": "The has a minimum value at VIZ Media", + "reason": "The [{'Publisher': 'VIZ Media', 'Revenue Share': 2.6}] of has a mainimum value of 2.6, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Publisher": "Marvel", + "Revenue Share": 36.5 + }, + { + "Publisher": "DC", + "Revenue Share": 25.5 + }, + { + "Publisher": "Image", + "Revenue Share": 10.6 + }, + { + "Publisher": "BOOM! Studios", + "Revenue Share": 4.7 + }, + { + "Publisher": "Dark Horse Comics", + "Revenue Share": 4.1 + }, + { + "Publisher": "IDW Publishing", + "Revenue Share": 3.6 + }, + { + "Publisher": "VIZ Media", + "Revenue Share": 2.6 + }, + { + "Publisher": "Others", + "Revenue Share": 12.5 + } + ], + "columns": [ + { + "name": "Publisher", + "importance": "primary", + "description": "Name of the comic book publisher", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Revenue Share", + "importance": "primary", + "description": "Percentage of revenue share in Q1 2023", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "261": { + "metadata": { + "title": "Chinese GDP Growth Dips Once More in 2022", + "description": "China's real GDP growth since 1980", + "main_insight": "China's real GDP growth in 2022 was 3.0%, significantly lower than its peak of 14.2% in 2007 and generally showing a declining trend since the mid-2000s." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1980, + "Real GDP Growth": 7.9 + }, + { + "Year": 1981, + "Real GDP Growth": 5.1 + }, + { + "Year": 1982, + "Real GDP Growth": 9.0 + }, + { + "Year": 1983, + "Real GDP Growth": 10.8 + }, + { + "Year": 1984, + "Real GDP Growth": 15.2 + }, + { + "Year": 1985, + "Real GDP Growth": 13.5 + }, + { + "Year": 1986, + "Real GDP Growth": 8.9 + }, + { + "Year": 1987, + "Real GDP Growth": 11.6 + }, + { + "Year": 1988, + "Real GDP Growth": 11.3 + }, + { + "Year": 1989, + "Real GDP Growth": 4.2 + }, + { + "Year": 1990, + "Real GDP Growth": 3.9 + }, + { + "Year": 1991, + "Real GDP Growth": 9.3 + }, + { + "Year": 1992, + "Real GDP Growth": 14.2 + }, + { + "Year": 1993, + "Real GDP Growth": 14.0 + }, + { + "Year": 1994, + "Real GDP Growth": 13.1 + }, + { + "Year": 1995, + "Real GDP Growth": 10.9 + }, + { + "Year": 1996, + "Real GDP Growth": 10.0 + }, + { + "Year": 1997, + "Real GDP Growth": 9.3 + }, + { + "Year": 1998, + "Real GDP Growth": 7.8 + }, + { + "Year": 1999, + "Real GDP Growth": 7.7 + }, + { + "Year": 2000, + "Real GDP Growth": 8.5 + }, + { + "Year": 2001, + "Real GDP Growth": 8.3 + }, + { + "Year": 2002, + "Real GDP Growth": 9.1 + }, + { + "Year": 2003, + "Real GDP Growth": 10.0 + }, + { + "Year": 2004, + "Real GDP Growth": 10.1 + }, + { + "Year": 2005, + "Real GDP Growth": 11.4 + }, + { + "Year": 2006, + "Real GDP Growth": 12.7 + }, + { + "Year": 2007, + "Real GDP Growth": 14.2 + }, + { + "Year": 2008, + "Real GDP Growth": 9.7 + }, + { + "Year": 2009, + "Real GDP Growth": 9.4 + }, + { + "Year": 2010, + "Real GDP Growth": 10.6 + }, + { + "Year": 2011, + "Real GDP Growth": 9.6 + }, + { + "Year": 2012, + "Real GDP Growth": 7.9 + }, + { + "Year": 2013, + "Real GDP Growth": 7.8 + }, + { + "Year": 2014, + "Real GDP Growth": 7.4 + }, + { + "Year": 2015, + "Real GDP Growth": 7.0 + }, + { + "Year": 2016, + "Real GDP Growth": 6.9 + }, + { + "Year": 2017, + "Real GDP Growth": 6.9 + }, + { + "Year": 2018, + "Real GDP Growth": 6.8 + }, + { + "Year": 2019, + "Real GDP Growth": 6.0 + }, + { + "Year": 2020, + "Real GDP Growth": 2.2 + }, + { + "Year": 2021, + "Real GDP Growth": 8.4 + }, + { + "Year": 2022, + "Real GDP Growth": 3.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of GDP growth measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Real GDP Growth", + "importance": "primary", + "description": "China's real GDP growth in percentage", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2394": { + "metadata": { + "title": "Where Shadow Banking is Growing", + "description": "Non-bank financial intermediation as a share of all financial assets in selected countries in 2013 and 2018", + "main_insight": "Shadow banking, represented as non-bank financial intermediation, increased as a share of total financial assets between 2013 and 2018 in all selected countries. Brazil saw the largest increase." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Italy", + "Value": 8.8, + "Year": "2013" + } + ], + "score": 0.84, + "annotation": "The 2013 has a minimum value at Italy", + "reason": "The [{'Country': 'Italy', 'Value': 8.8, 'Year': '2013'}] of 2013 has a mainimum value of 8.8, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "South Korea", + "Value": 11.8, + "Year": "2013" + } + ], + "score": 0.73, + "annotation": "The 2013 shows a sudden jump in Value from Italy to South Korea.", + "reason": "The Value for 2013 increases from 8.8 at Italy to 11.8 at South Korea, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Brazil", + "Value": 17.3, + "Year": "2013" + } + ], + "score": 0.73, + "annotation": "The 2013 has a maximum value at Brazil", + "reason": "The [{'Country': 'Brazil', 'Value': 17.3, 'Year': '2013'}] of 2013 has a maximum value of 17.3, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Italy", + "Value": 8.8, + "Year": "2013" + }, + { + "Country": "South Korea", + "Value": 11.8, + "Year": "2013" + }, + { + "Country": "China", + "Value": 14.0, + "Year": "2013" + }, + { + "Country": "India", + "Value": 14.5, + "Year": "2013" + }, + { + "Country": "Canada", + "Value": 14.7, + "Year": "2013" + }, + { + "Country": "Brazil", + "Value": 17.3, + "Year": "2013" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Selected countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "1480": { + "metadata": { + "title": "Is The U.S. Government Hiding Something About UFOs?", + "description": "Share believing the government has UFO information which isn't being shared with the public", + "main_insight": "A majority of U.S. adults, across all political affiliations, believe the government is withholding information about UFOs. Democrats are most likely to believe this." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Political Affiliation": "Independent", + "Units": 18.0, + "Likelihood": "Don't know" + } + ], + "score": 0.93, + "annotation": "The Don't know shows a sudden jump in Units from Republican to Independent.", + "reason": "The Units for Don't know increases from 15.0 at Republican to 18.0 at Independent, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Political Affiliation": "Independent", + "Units": 50.0, + "Likelihood": "Very/somewhat likely" + }, + { + "Political Affiliation": "Independent", + "Units": 32.0, + "Likelihood": "Very/somewhat unlikely" + } + ], + "score": 0.93, + "annotation": "The Units of Very/somewhat likely and Very/somewhat unlikely show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Very/somewhat likely and Very/somewhat unlikely is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Political Affiliation": "Democrat", + "Units": 62.0, + "Likelihood": "Very/somewhat likely" + } + ], + "score": 0.91, + "annotation": "The Very/somewhat likely shows a sudden jump in Units from Total to Democrat.", + "reason": "The Units for Very/somewhat likely increases from 54.0 at Total to 62.0 at Democrat, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Political Affiliation": "Independent", + "Units": 50.0, + "Likelihood": "Very/somewhat likely" + } + ], + "score": 0.86, + "annotation": "The Very/somewhat likely has the largest proportion in Independent.", + "reason": "The Units of Very/somewhat likely in Independent accounts for 0.5, larger than all other Likelihood, of the total of Units of all Likelihood." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Political Affiliation": "Republican", + "Units": 51.0, + "Likelihood": "Very/somewhat likely" + } + ], + "score": 0.85, + "annotation": "The Very/somewhat likely accounts for the majority in Republican.", + "reason": "The Units of Very/somewhat likely in Republican accounts for 0.51 (more than 50%) of the total of Units of all Likelihood." + } + ], + "data": { + "data": [ + { + "Political Affiliation": "Total", + "Units": 54.0, + "Likelihood": "Very/somewhat likely" + }, + { + "Political Affiliation": "Total", + "Units": 27.0, + "Likelihood": "Very/somewhat unlikely" + }, + { + "Political Affiliation": "Total", + "Units": 19.0, + "Likelihood": "Don't know" + }, + { + "Political Affiliation": "Democrat", + "Units": 62.0, + "Likelihood": "Very/somewhat likely" + }, + { + "Political Affiliation": "Democrat", + "Units": 23.0, + "Likelihood": "Very/somewhat unlikely" + }, + { + "Political Affiliation": "Democrat", + "Units": 15.0, + "Likelihood": "Don't know" + }, + { + "Political Affiliation": "Republican", + "Units": 51.0, + "Likelihood": "Very/somewhat likely" + }, + { + "Political Affiliation": "Republican", + "Units": 34.0, + "Likelihood": "Very/somewhat unlikely" + }, + { + "Political Affiliation": "Republican", + "Units": 15.0, + "Likelihood": "Don't know" + }, + { + "Political Affiliation": "Independent", + "Units": 50.0, + "Likelihood": "Very/somewhat likely" + }, + { + "Political Affiliation": "Independent", + "Units": 32.0, + "Likelihood": "Very/somewhat unlikely" + }, + { + "Political Affiliation": "Independent", + "Units": 18.0, + "Likelihood": "Don't know" + } + ], + "columns": [ + { + "name": "Political Affiliation", + "importance": "primary", + "description": "Political affiliation of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding 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"subtype": "max", + "data_points": [ + { + "U.S. Region": "Texas", + "Value": 23.73, + "Timeframe": "2020" + } + ], + "score": 0.83, + "annotation": "The 2020 has a maximum value at Texas", + "reason": "The [{'U.S. Region': 'Texas', 'Value': 23.73, 'Timeframe': '2020'}] of 2020 has a maximum value of 23.73, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "U.S. Region": "California", + "Value": 25.44, + "Timeframe": "5-year average" + } + ], + "score": 0.82, + "annotation": "The 5-year average shows a sudden jump in Value from Northwest to California.", + "reason": "The Value for 5-year average increases from 15.82 at Northwest to 25.44 at California, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "U.S. Region": "Midcontinent", + "Value": 23.09, + "Timeframe": "2020" + } + ], + "score": 0.79, + "annotation": "The 2020 shows a sudden jump in Value from New England to Midcontinent.", + "reason": "The Value for 2020 increases from 20.13 at New England to 23.09 at Midcontinent, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "U.S. Region": "Texas", + "Value": 26.62, + "Timeframe": "5-year average" + } + ], + "score": 0.79, + "annotation": "The 5-year average accounts for the majority in Texas.", + "reason": "The Value of 5-year average in Texas accounts for 0.5286991062562065 (more than 50%) of the total of Value of all Timeframe." + } + ], + "data": { + "data": [ + { + "U.S. Region": "NYC", + "Value": 16.94, + "Timeframe": "2020" + }, + { + "U.S. Region": "NYC", + "Value": 36.03, + "Timeframe": "5-year average" + }, + { + "U.S. Region": "PJM", + "Value": 18.83, + "Timeframe": "2020" + }, + { + "U.S. Region": "PJM", + "Value": 35.03, + "Timeframe": "5-year average" + }, + { + "U.S. Region": "New England", + "Value": 20.13, + "Timeframe": "2020" + }, + { + "U.S. Region": "New England", + "Value": 35.92, + "Timeframe": "5-year average" + }, + { + "U.S. Region": "Southwest", + "Value": 17.37, + "Timeframe": "2020" + }, + { + "U.S. Region": "Southwest", + "Value": 28.08, + "Timeframe": "5-year average" + }, + { + "U.S. Region": "Midcontinent", + "Value": 23.09, + "Timeframe": "2020" + }, + { + "U.S. Region": "Midcontinent", + "Value": 32.68, + "Timeframe": "5-year average" + }, + { + "U.S. Region": "California", + "Value": 17.11, + "Timeframe": "2020" + }, + { + "U.S. Region": "California", + "Value": 25.44, + "Timeframe": "5-year average" + }, + { + "U.S. Region": "Texas", + "Value": 23.73, + "Timeframe": "2020" + }, + { + "U.S. Region": "Texas", + "Value": 26.62, + "Timeframe": "5-year average" + }, + { + "U.S. Region": "Northwest", + "Value": 18.41, + "Timeframe": "2020" + }, + { + "U.S. Region": "Northwest", + "Value": 15.82, + "Timeframe": "5-year average" + } + ], + "columns": [ + { + "name": "U.S. Region", + "importance": "primary", + "description": "U.S. geographical region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding timeframe", + "role": "y" + }, + { + "name": "Timeframe", + "importance": "primary", + "description": "The timeframe of the data point", + "role": "group" + } + ] + } + }, + "265": { + "metadata": { + "title": "Journalism sees a rise in employment", + "description": "Number of journalists in Great Britain (in 1,000)", + "main_insight": "The number of journalists in Great Britain has generally increased from 2001 to 2018, with a notable rise in self-employed journalists in recent years." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2018, + "Points": 90.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2018, + "Points": 61.0, + "Employment Type": "Employees" + } + ], + "score": 0.86, + "annotation": "The Points of Total Journalists and Employees show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Points between Total Journalists and Employees is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2018, + "Points": 90.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2018, + "Points": 28.0, + "Employment Type": "Self-Employed" + } + ], + "score": 0.79, + "annotation": "The Points of Total Journalists and Self-Employed show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Points between Total Journalists and Self-Employed is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2018, + "Points": 61.0, + "Employment Type": "Employees" + }, + { + "Year": 2018, + "Points": 28.0, + "Employment Type": "Self-Employed" + } + ], + "score": 0.38, + "annotation": "The Points of Employees and Self-Employed show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Points between Employees and Self-Employed is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2001, + "Points": 59.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2001, + "Points": 42.0, + "Employment Type": "Employees" + }, + { + "Year": 2001, + "Points": 15.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2002, + "Points": 56.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2002, + "Points": 38.0, + "Employment Type": "Employees" + }, + { + "Year": 2002, + "Points": 18.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2003, + "Points": 63.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2003, + "Points": 45.0, + "Employment Type": "Employees" + }, + { + "Year": 2003, + "Points": 18.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2004, + "Points": 55.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2004, + "Points": 38.0, + "Employment Type": "Employees" + }, + { + "Year": 2004, + "Points": 18.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2005, + "Points": 52.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2005, + "Points": 38.0, + "Employment Type": "Employees" + }, + { + "Year": 2005, + "Points": 14.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2006, + "Points": 79.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2006, + "Points": 50.0, + "Employment Type": "Employees" + }, + { + "Year": 2006, + "Points": 29.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2007, + "Points": 57.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2007, + "Points": 35.0, + "Employment Type": "Employees" + }, + { + "Year": 2007, + "Points": 22.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2008, + "Points": 67.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2008, + "Points": 45.0, + "Employment Type": "Employees" + }, + { + "Year": 2008, + "Points": 21.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2009, + "Points": 58.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2009, + "Points": 38.0, + "Employment Type": "Employees" + }, + { + "Year": 2009, + "Points": 20.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2010, + "Points": 63.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2010, + "Points": 46.0, + "Employment Type": "Employees" + }, + { + "Year": 2010, + "Points": 17.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2011, + "Points": 62.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2011, + "Points": 44.0, + "Employment Type": "Employees" + }, + { + "Year": 2011, + "Points": 17.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2012, + "Points": 65.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2012, + "Points": 43.0, + "Employment Type": "Employees" + }, + { + "Year": 2012, + "Points": 21.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2013, + "Points": 70.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2013, + "Points": 43.0, + "Employment Type": "Employees" + }, + { + "Year": 2013, + "Points": 27.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2014, + "Points": 60.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2014, + "Points": 35.0, + "Employment Type": "Employees" + }, + { + "Year": 2014, + "Points": 25.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2015, + "Points": 64.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2015, + "Points": 45.0, + "Employment Type": "Employees" + }, + { + "Year": 2015, + "Points": 18.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2016, + "Points": 84.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2016, + "Points": 47.0, + "Employment Type": "Employees" + }, + { + "Year": 2016, + "Points": 34.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2017, + "Points": 73.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2017, + "Points": 53.0, + "Employment Type": "Employees" + }, + { + "Year": 2017, + "Points": 20.0, + "Employment Type": "Self-Employed" + }, + { + "Year": 2018, + "Points": 90.0, + "Employment Type": "Total Journalists" + }, + { + "Year": 2018, + "Points": 61.0, + "Employment Type": "Employees" + }, + { + "Year": 2018, + "Points": 28.0, + "Employment Type": "Self-Employed" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding employment type", + "role": "y" + }, + { + "name": "Employment Type", + "importance": "primary", + "description": "The employment type of the data point", + "role": "group" + } + ] + } + }, + "114": { + "metadata": { + "title": "59% Oppose Trump's Decision To Exit Paris Agreement", + "description": "% of Americans who support/oppose Trump's decision to withdraw from the Paris agreement", + "main_insight": "A majority of Americans (59%) oppose Trump's decision to withdraw from the Paris Agreement, with 46% strongly opposing it." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Opinion": "Strongly oppose", + "Percentage": 46.0 + } + ], + "score": 0.94, + "annotation": "The shows a sudden jump in Percentage from Strongly support to Strongly oppose.", + "reason": "The Percentage for increases from 18.0 at Strongly support to 46.0 at Strongly oppose, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Opinion": "Strongly oppose", + "Percentage": 46.0 + } + ], + "score": 0.87, + "annotation": "The has a maximum value at Strongly oppose", + "reason": "The [{'Opinion': 'Strongly oppose', 'Percentage': 46.0}] of has a maximum value of 46.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Opinion": "Somewhat support", + "Percentage": 10.0 + } + ], + "score": 0.44, + "annotation": "The has a minimum value at Somewhat support", + "reason": "The [{'Opinion': 'Somewhat support', 'Percentage': 10.0}] of has a mainimum value of 10.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Opinion": "Strongly oppose", + "Percentage": 46.0 + }, + { + "Opinion": "Somewhat oppose", + "Percentage": 13.0 + }, + { + "Opinion": "No opinion", + "Percentage": 13.0 + }, + { + "Opinion": "Somewhat support", + "Percentage": 10.0 + }, + { + "Opinion": "Strongly support", + "Percentage": 18.0 + } + ], + "columns": [ + { + "name": "Opinion", + "importance": "primary", + "description": "Level of support or opposition to Trump's decision", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of Americans holding that opinion", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "278": { + "metadata": { + "title": "Cars Increasingly Ready for Autonomous Driving", + "description": "Estimated share of newly registered cars worldwide by autonomous driving level (in %)", + "main_insight": "The share of newly registered cars with at least Level 1 autonomous driving capabilities is projected to significantly increase between 2018 and 2028, with Level 1 becoming dominant and higher levels also seeing substantial growth." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2028, + "Score": 5.0, + "Hierarchy Levels": "Level 0" + }, + { + "Year": 2028, + "Score": 62.0, + "Hierarchy Levels": "Level 1" + } + ], + "score": 0.77, + "annotation": "The Score of Level 0 and Level 1 show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Level 0 and Level 1 is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2028, + "Score": 5.0, + "Hierarchy Levels": "Level 0" + }, + { + "Year": 2028, + "Score": 31.0, + "Hierarchy Levels": "Level 2" + } + ], + "score": 0.65, + "annotation": "The Score of Level 0 and Level 2 show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Level 0 and Level 2 is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2028, + "Score": 62.0, + "Hierarchy Levels": "Level 1" + }, + { + "Year": 2028, + "Score": 31.0, + "Hierarchy Levels": "Level 2" + } + ], + "score": 0.02, + "annotation": "The Score of Level 1 and Level 2 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Level 1 and Level 2 is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2018, + "Score": 24.0, + "Hierarchy Levels": "Level 0" + }, + { + "Year": 2018, + "Score": 55.0, + "Hierarchy Levels": "Level 1" + }, + { + "Year": 2018, + "Score": 21.0, + "Hierarchy Levels": "Level 2" + }, + { + "Year": 2023, + "Score": 7.0, + "Hierarchy Levels": "Level 0" + }, + { + "Year": 2023, + "Score": 71.0, + "Hierarchy Levels": "Level 1" + }, + { + "Year": 2023, + "Score": 22.0, + "Hierarchy Levels": "Level 2" + }, + { + "Year": 2028, + "Score": 5.0, + "Hierarchy Levels": "Level 0" + }, + { + "Year": 2028, + "Score": 62.0, + "Hierarchy Levels": "Level 1" + }, + { + "Year": 2028, + "Score": 31.0, + "Hierarchy Levels": "Level 2" + }, + { + "Year": 2028, + "Score": 2.0, + "Hierarchy Levels": "Level 3" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of estimated share", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding hierarchy levels", + "role": "y" + }, + { + "name": "Hierarchy Levels", + "importance": "primary", + "description": "The hierarchy levels of the data point", + "role": "group" + } + ] + } + }, + "1489": { + "metadata": { + "title": "Nearly 30% of Hispanics voted for Trump", + "description": "US election voter share by race", + "main_insight": "A significant percentage of Hispanic/Latino voters (29%) voted for Trump in the 2016 US election, while the majority (65%) voted for Clinton." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Race": "White", + "Score": 58.0, + "Vote Share": "Trump" + }, + { + "Race": "White", + "Score": 37.0, + "Vote Share": "Clinton" + } + ], + "score": 1.0, + "annotation": "The Score of Trump and Clinton show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Trump and Clinton is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Race": "Black", + "Score": 88.0, + "Vote Share": "Clinton" + } + ], + "score": 0.8, + "annotation": "The Clinton shows a sudden jump in Score from Hispanic/Latino to Black.", + "reason": "The Score for Clinton increases from 65.0 at Hispanic/Latino to 88.0 at Black, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Race": "Asian", + "Score": 29.0, + "Vote Share": "Trump" + }, + { + "Race": "White", + "Score": 58.0, + "Vote Share": "Trump" + } + ], + "score": 0.78, + "annotation": "The Trump exhibits multiple sudden jumps in Score, transitioning from Black, Other to Asian, White.", + "reason": "Across these points, the Score for Trump increases from 8.0, 37.0 at Black, Other to 29.0, 58.0 at Asian, White, suggesting significant shifts in value." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Race": "White", + "Score": 58.0, + "Vote Share": "Trump" + } + ], + "score": 0.78, + "annotation": "The Trump has a maximum value at White", + "reason": "The [{'Race': 'White', 'Score': 58.0, 'Vote Share': 'Trump'}] of Trump has a maximum value of 58.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Race": "Black", + "Score": 88.0, + "Vote Share": "Clinton" + } + ], + "score": 0.77, + "annotation": "The Clinton has a maximum value at Black", + "reason": "The [{'Race': 'Black', 'Score': 88.0, 'Vote Share': 'Clinton'}] of Clinton has a maximum value of 88.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Race": "Black", + "Score": 8.0, + "Vote Share": "Trump" + }, + { + "Race": "Black", + "Score": 88.0, + "Vote Share": "Clinton" + }, + { + "Race": "Asian", + "Score": 29.0, + "Vote Share": "Trump" + }, + { + "Race": "Asian", + "Score": 65.0, + "Vote Share": "Clinton" + }, + { + "Race": "Hispanic/Latino", + "Score": 29.0, + "Vote Share": "Trump" + }, + { + "Race": "Hispanic/Latino", + "Score": 65.0, + "Vote Share": "Clinton" + }, + { + "Race": "Other", + "Score": 37.0, + "Vote Share": "Trump" + }, + { + "Race": "Other", + "Score": 56.0, + "Vote Share": "Clinton" + }, + { + "Race": "White", + "Score": 58.0, + "Vote Share": "Trump" + }, + { + "Race": "White", + "Score": 37.0, + "Vote Share": "Clinton" + } + ], + "columns": [ + { + "name": "Race", + "importance": "primary", + "description": "Racial/Ethnic group of voters", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding vote share", + "role": "y" + }, + { + "name": "Vote Share", + "importance": "primary", + "description": "The vote share of the data point", + "role": "group" + } + ] + } + }, + "191": { + "metadata": { + "title": "The Evolution Of America's Federal Prison Population", + "description": "Federal inmates in custody on December 31 (excluding private prisons). Inmates serving sentences of more than one year.", + "main_insight": "The US federal prison population has significantly increased since 1980, peaking around 2010 and then slightly decreasing." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1925, + "Federal Inmates": 6430 + }, + { + "Year": 1930, + "Federal Inmates": 12500 + }, + { + "Year": 1940, + "Federal Inmates": 17500 + }, + { + "Year": 1950, + "Federal Inmates": 18000 + }, + { + "Year": 1960, + "Federal Inmates": 22500 + }, + { + "Year": 1970, + "Federal Inmates": 20000 + }, + { + "Year": 1980, + "Federal Inmates": 24000 + }, + { + "Year": 1990, + "Federal Inmates": 58000 + }, + { + "Year": 2000, + "Federal Inmates": 133000 + }, + { + "Year": 2010, + "Federal Inmates": 175000 + }, + { + "Year": 2020, + "Federal Inmates": 133135 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year on December 31", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Federal Inmates", + "importance": "primary", + "description": "Number of federal inmates in custody", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "520": { + "metadata": { + "title": "Over Half of Young Voters Certain to Vote", + "description": "Percentage of young voters and their likelihood to vote in the 2020 election", + "main_insight": "A majority (56%) of young voters surveyed in November 2019 were certain to vote in the 2020 election, with an additional 21% indicating they probably would vote." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Likelihood to Vote": "Certain to vote", + "Percentage": 56.0 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in Percentage from Probably will vote to Certain to vote.", + "reason": "The Percentage for increases from 21.0 at Probably will vote to 56.0 at Certain to vote, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Likelihood to Vote": "Certain to vote", + "Percentage": 56.0 + } + ], + "score": 0.86, + "annotation": "The has a maximum value at Certain to vote", + "reason": "The [{'Likelihood to Vote': 'Certain to vote', 'Percentage': 56.0}] of has a maximum value of 56.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Likelihood to Vote": "Probably will not vote", + "Percentage": 7.0 + } + ], + "score": 0.41, + "annotation": "The has a minimum value at Probably will not vote", + "reason": "The [{'Likelihood to Vote': 'Probably will not vote', 'Percentage': 7.0}] of has a mainimum value of 7.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Likelihood to Vote": "Certain to vote", + "Percentage": 56.0 + }, + { + "Likelihood to Vote": "Probably will vote", + "Percentage": 21.0 + }, + { + "Likelihood to Vote": "Probably will not vote", + "Percentage": 7.0 + }, + { + "Likelihood to Vote": "Certain to not vote", + "Percentage": 8.0 + }, + { + "Likelihood to Vote": "Don't know", + "Percentage": 8.0 + } + ], + "columns": [ + { + "name": "Likelihood to Vote", + "importance": "primary", + "description": "Categories describing the likelihood of young voters participating in the 2020 election", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of young voters falling into each likelihood category", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2001": { + "metadata": { + "title": "Olympic Games Bring Hotel Construction Boom to Japan", + "description": "Construction orders for hotels in Japan (in billion Japanese yen)", + "main_insight": "Hotel construction orders in Japan have significantly increased in the years leading up to the Olympic Games, peaking in 2017/18." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Fiscal Year": "2017/18", + "Construction Orders (billion yen)": 833.0 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at 2017/18", + "reason": "The [{'Fiscal Year': '2017/18', 'Construction Orders (billion yen)': 833.0}] of has a maximum value of 833.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Fiscal Year": "2016/17", + "Construction Orders (billion yen)": 633.0 + } + ], + "score": 0.79, + "annotation": "The shows a sudden jump in Construction Orders (billion yen) from 2015/16 to 2016/17.", + "reason": "The Construction Orders (billion yen) for increases from 315.0 at 2015/16 to 633.0 at 2016/17, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Fiscal Year": "2010/11", + "Construction Orders (billion yen)": 98.0 + } + ], + "score": 0.51, + "annotation": "The has a minimum value at 2010/11", + "reason": "The [{'Fiscal Year': '2010/11', 'Construction Orders (billion yen)': 98.0}] of has a mainimum value of 98.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Fiscal Year": "2008/09", + "Construction Orders (billion yen)": 214.0 + }, + { + "Fiscal Year": "2009/10", + "Construction Orders (billion yen)": 159.0 + }, + { + "Fiscal Year": "2010/11", + "Construction Orders (billion yen)": 98.0 + }, + { + "Fiscal Year": "2011/12", + "Construction Orders (billion yen)": 107.0 + }, + { + "Fiscal Year": "2012/13", + "Construction Orders (billion yen)": 129.0 + }, + { + "Fiscal Year": "2013/14", + "Construction Orders (billion yen)": 227.0 + }, + { + "Fiscal Year": "2014/15", + "Construction Orders (billion yen)": 249.0 + }, + { + "Fiscal Year": "2015/16", + "Construction Orders (billion yen)": 315.0 + }, + { + "Fiscal Year": "2016/17", + "Construction Orders (billion yen)": 633.0 + }, + { + "Fiscal Year": "2017/18", + "Construction Orders (billion yen)": 833.0 + } + ], + "columns": [ + { + "name": "Fiscal Year", + "importance": "primary", + "description": "Fiscal year starting in April and ending in March of the following year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Construction Orders (billion yen)", + "importance": "primary", + "description": "Value of construction orders for hotels in Japan", + "unit": "billion yen", + "data_type": "number", + "role": "y" + } + ] + } + }, + "896": { + "metadata": { + "title": "The gender balance in the Irish Defence Forces", + "description": "Male and female personnel in the Irish Defence Forces in 2017", + "main_insight": "The Irish Defence Forces are heavily male-dominated across all branches in 2017. The Army has the largest number of personnel, and also the largest gender imbalance." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Branch": "Air Corps", + "Units": 669, + "Gender": "Male" + }, + { + "Branch": "Air Corps", + "Units": 36, + "Gender": "Female" + } + ], + "score": 0.94, + "annotation": "The Units of Male and Female show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Male and Female is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Branch": "Army", + "Units": 6797, + "Gender": "Male" + } + ], + "score": 0.94, + "annotation": "The Male shows a sudden jump in Units from Army Reserve to Army.", + "reason": "The Units for Male increases from 1530 at Army Reserve to 6797 at Army, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Branch": "Army", + "Units": 6797, + "Gender": "Male" + } + ], + "score": 0.87, + "annotation": "The Male has a maximum value at Army", + "reason": "The [{'Branch': 'Army', 'Units': 6797, 'Gender': 'Male'}] of Male has a maximum value of 6797, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Branch": "Army", + "Units": 485, + "Gender": "Female" + } + ], + "score": 0.82, + "annotation": "The Female shows a sudden jump in Units from Army Reserve to Army.", + "reason": "The Units for Female increases from 259 at Army Reserve to 485 at Army, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Branch": "Army", + "Units": 485, + "Gender": "Female" + } + ], + "score": 0.82, + "annotation": "The Female has a maximum value at Army", + "reason": "The [{'Branch': 'Army', 'Units': 485, 'Gender': 'Female'}] of Female has a maximum value of 485, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Branch": "Army", + "Units": 6797, + "Gender": "Male" + }, + { + "Branch": "Army", + "Units": 485, + "Gender": "Female" + }, + { + "Branch": "Army Reserve", + "Units": 1530, + "Gender": "Male" + }, + { + "Branch": "Army Reserve", + "Units": 259, + "Gender": "Female" + }, + { + "Branch": "Naval Service", + "Units": 1009, + "Gender": "Male" + }, + { + "Branch": "Naval Service", + "Units": 66, + "Gender": "Female" + }, + { + "Branch": "Naval Service Reserve", + "Units": 107, + "Gender": "Male" + }, + { + "Branch": "Naval Service Reserve", + "Units": 20, + "Gender": "Female" + }, + { + "Branch": "Air Corps", + "Units": 669, + "Gender": "Male" + }, + { + "Branch": "Air Corps", + "Units": 36, + "Gender": "Female" + } + ], + "columns": [ + { + "name": "Branch", + "importance": "primary", + "description": "Branch of the Irish Defence Forces", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding gender", + "role": "y" + }, + { + "name": "Gender", + "importance": "primary", + "description": "The gender of the data point", + "role": "group" + } + ] + } + }, + "1893": { + "metadata": { + "title": "Trust in Platforms vs Journalism", + "description": "Extent to which users trust in journalism or platforms for general news and information", + "main_insight": "Trust in journalism has generally increased since 2012 and surpassed trust in platforms (search engines and social media) in 2018." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2018, + "Units": 59.0, + "Source": "Journalism" + }, + { + "Year": 2018, + "Units": 51.0, + "Source": "Platforms" + } + ], + "score": 0.34, + "annotation": "The Units of Journalism and Platforms show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Journalism and Platforms is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2012, + "Units": 54.0, + "Source": "Journalism" + }, + { + "Year": 2012, + "Units": 53.0, + "Source": "Platforms" + }, + { + "Year": 2013, + "Units": 52.0, + "Source": "Journalism" + }, + { + "Year": 2013, + "Units": 50.0, + "Source": "Platforms" + }, + { + "Year": 2014, + "Units": 54.0, + "Source": "Journalism" + }, + { + "Year": 2014, + "Units": 52.0, + "Source": "Platforms" + }, + { + "Year": 2015, + "Units": 51.0, + "Source": "Journalism" + }, + { + "Year": 2015, + "Units": 55.0, + "Source": "Platforms" + }, + { + "Year": 2016, + "Units": 54.0, + "Source": "Journalism" + }, + { + "Year": 2016, + "Units": 55.0, + "Source": "Platforms" + }, + { + "Year": 2017, + "Units": 55.0, + "Source": "Journalism" + }, + { + "Year": 2017, + "Units": 53.0, + "Source": "Platforms" + }, + { + "Year": 2018, + "Units": 59.0, + "Source": "Journalism" + }, + { + "Year": 2018, + "Units": 51.0, + "Source": "Platforms" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding source", + "role": "y" + }, + { + "name": "Source", + "importance": "primary", + "description": "The source of the data point", + "role": "group" + } + ] + } + }, + "1350": { + "metadata": { + "title": "Flying High on Business", + "description": "Aspects of business travel most commonly booked by companies", + "main_insight": "Hotels and flights are the most commonly booked aspects of business travel, with 93% and 83% of companies booking them, respectively. Other travel aspects like taxis, house rentals, and buses are much less frequently booked." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Travel Aspect": "Hotel", + "Booking Percentage": 93.0 + } + ], + "score": 0.81, + "annotation": "The has a maximum value at Hotel", + "reason": "The [{'Travel Aspect': 'Hotel', 'Booking Percentage': 93.0}] of has a maximum value of 93.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Travel Aspect": "Buses", + "Booking Percentage": 12.0 + } + ], + "score": 0.74, + "annotation": "The has a minimum value at Buses", + "reason": "The [{'Travel Aspect': 'Buses', 'Booking Percentage': 12.0}] of has a mainimum value of 12.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Travel Aspect": "Flights", + "Booking Percentage": 83.0 + } + ], + "score": 0.64, + "annotation": "The shows a sudden jump in Booking Percentage from Car rentals to Flights.", + "reason": "The Booking Percentage for increases from 63.0 at Car rentals to 83.0 at Flights, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Travel Aspect": "Hotel", + "Booking Percentage": 93.0 + }, + { + "Travel Aspect": "Flights", + "Booking Percentage": 83.0 + }, + { + "Travel Aspect": "Car rentals", + "Booking Percentage": 63.0 + }, + { + "Travel Aspect": "Trains", + "Booking Percentage": 52.0 + }, + { + "Travel Aspect": "Travel insurance", + "Booking Percentage": 37.0 + }, + { + "Travel Aspect": "Taxis", + "Booking Percentage": 33.0 + }, + { + "Travel Aspect": "House rentals", + "Booking Percentage": 25.0 + }, + { + "Travel Aspect": "Buses", + "Booking Percentage": 12.0 + } + ], + "columns": [ + { + "name": "Travel Aspect", + "importance": "primary", + "description": "The category of business travel expense.", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Booking Percentage", + "importance": "primary", + "description": "Percentage of companies booking this travel aspect.", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "467": { + "metadata": { + "title": "Police Officers Seldomly Convicted on Murder Charges", + "description": "Outcomes for all non-federal law enforcement officers arrested for murder or manslaughter in the U.S. 2005-2019", + "main_insight": "Out of 80 non-federal law enforcement officers arrested for murder or manslaughter between 2005 and 2019, only 4 were convicted of murder, while a larger number were convicted of a lesser crime or manslaughter." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Outcome": "Arrested for murder/manslaughter shooting death", + "Number of Officers": 80 + } + ], + "score": 0.88, + "annotation": "The shows a sudden jump in Number of Officers from Convicted of a crime to Arrested for murder/manslaughter shooting death.", + "reason": "The Number of Officers for increases from 35 at Convicted of a crime to 80 at Arrested for murder/manslaughter shooting death, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Outcome": "Arrested for murder/manslaughter shooting death", + "Number of Officers": 80 + } + ], + "score": 0.76, + "annotation": "The has a maximum value at Arrested for murder/manslaughter shooting death", + "reason": "The [{'Outcome': 'Arrested for murder/manslaughter shooting death', 'Number of Officers': 80}] of has a maximum value of 80, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Outcome": "Convicted of murder", + "Number of Officers": 4 + } + ], + "score": 0.56, + "annotation": "The has a minimum value at Convicted of murder", + "reason": "The [{'Outcome': 'Convicted of murder', 'Number of Officers': 4}] of has a mainimum value of 4, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Outcome": "Arrested for murder/manslaughter shooting death", + "Number of Officers": 80 + }, + { + "Outcome": "Convicted of a crime", + "Number of Officers": 35 + }, + { + "Outcome": "Convicted of manslaughter", + "Number of Officers": 18 + }, + { + "Outcome": "Convicted of murder", + "Number of Officers": 4 + } + ], + "columns": [ + { + "name": "Outcome", + "importance": "primary", + "description": "The outcome of cases against officers arrested for murder or manslaughter", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Officers", + "importance": "primary", + "description": "The number of officers corresponding to each outcome", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "207": { + "metadata": { + "title": "The Only One in the Room", + "description": "Share who said that at work they were often/almost always the only one of their gender/race/ethnicity/sexual orientation", + "main_insight": "LGBTQ+ women of color are most likely to report being the only one of their identity in the workplace, while straight white men are the least likely." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Group": "LGBTQ+ women", + "Percentage": 58.0 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Percentage from Women to LGBTQ+ women.", + "reason": "The Percentage for increases from 29.0 at Women to 58.0 at LGBTQ+ women, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Group": "LGBTQ+ women of color", + "Percentage": 66.0 + } + ], + "score": 0.69, + "annotation": "The has a maximum value at LGBTQ+ women of color", + "reason": "The [{'Group': 'LGBTQ+ women of color', 'Percentage': 66.0}] of has a maximum value of 66.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Group": "Straight white men", + "Percentage": 8.0 + } + ], + "score": 0.65, + "annotation": "The has a minimum value at Straight white men", + "reason": "The [{'Group': 'Straight white men', 'Percentage': 8.0}] of has a mainimum value of 8.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Group": "Straight white men", + "Percentage": 8.0 + }, + { + "Group": "Men", + "Percentage": 18.0 + }, + { + "Group": "Women", + "Percentage": 29.0 + }, + { + "Group": "LGBTQ+ women", + "Percentage": 58.0 + }, + { + "Group": "LGBTQ+ women of color", + "Percentage": 66.0 + } + ], + "columns": [ + { + "name": "Group", + "importance": "primary", + "description": "Demographic group", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents who said they were often/almost always the only one of their gender/race/ethnicity/sexual orientation at work", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1050": { + "metadata": { + "title": "Germany's Perspective on World Leaders", + "description": "In your opinion, which of these world leaders is the most...?", + "main_insight": "Germans surveyed view Angela Merkel as the most competent world leader, while Barack Obama is considered the most powerful." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "World Leader": "Angela Merkel", + "Units": 53.9, + "Perception": "Competent" + } + ], + "score": 0.86, + "annotation": "The Competent has a maximum value at Angela Merkel", + "reason": "The [{'World Leader': 'Angela Merkel', 'Units': 53.9, 'Perception': 'Competent'}] of Competent has a maximum value of 53.9, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "World Leader": "Barack Obama", + "Units": 32.4, + "Perception": "Competent" + } + ], + "score": 0.85, + "annotation": "The Competent shows a sudden jump in Units from David Cameron to Barack Obama.", + "reason": "The Units for Competent increases from 4.7 at David Cameron to 32.4 at Barack Obama, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "World Leader": "Barack Obama", + "Units": 45.5, + "Perception": "Powerful" + } + ], + "score": 0.83, + "annotation": "The Powerful has a maximum value at Barack Obama", + "reason": "The [{'World Leader': 'Barack Obama', 'Units': 45.5, 'Perception': 'Powerful'}] of Powerful has a maximum value of 45.5, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "World Leader": "David Cameron", + "Units": 0.2, + "Perception": "Powerful" + } + ], + "score": 0.83, + "annotation": "The Powerful has the smallest proportion in David Cameron.", + "reason": "The Units of Powerful in David Cameron accounts for 0.04081632653061224, smaller than all other Perception, of the total of Units of all Perception." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Perception": "Powerful", + "World Leader": "total", + "Units": 100.00000000000001 + } + ], + "score": 0.8, + "annotation": "The total value of Powerful accounts for the majority in all Perception.", + "reason": "The total value of Units of Powerful accounts for 0.5000000000000001 (more than 50%) of the total of Units of all Perception." + } + ], + "data": { + "data": [ + { + "World Leader": "Barack Obama", + "Units": 32.4, + "Perception": "Competent" + }, + { + "World Leader": "Barack Obama", + "Units": 45.5, + "Perception": "Powerful" + }, + { + "World Leader": "Vladimir Putin", + "Units": 4.3, + "Perception": "Competent" + }, + { + "World Leader": "Vladimir Putin", + "Units": 27.7, + "Perception": "Powerful" + }, + { + "World Leader": "Xi Jinping", + "Units": 2.9, + "Perception": "Competent" + }, + { + "World Leader": "Xi Jinping", + "Units": 20.1, + "Perception": "Powerful" + }, + { + "World Leader": "Angela Merkel", + "Units": 53.9, + "Perception": "Competent" + }, + { + "World Leader": "Angela Merkel", + "Units": 6.1, + "Perception": "Powerful" + }, + { + "World Leader": "François Hollande", + "Units": 1.8, + "Perception": "Competent" + }, + { + "World Leader": "François Hollande", + "Units": 0.4, + "Perception": "Powerful" + }, + { + "World Leader": "David Cameron", + "Units": 4.7, + "Perception": "Competent" + }, + { + "World Leader": "David Cameron", + "Units": 0.2, + "Perception": "Powerful" + } + ], + "columns": [ + { + "name": "World Leader", + "importance": "primary", + "description": "Name of the world leader", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding perception", + "role": "y" + }, + { + "name": "Perception", + "importance": "primary", + "description": "The perception of the data point", + "role": "group" + } + ] + } + }, + "2433": { + "metadata": { + "title": "Football's Biggest (Mis)spenders", + "description": "Big-5 league football clubs with the highest net transfer spending since summer 2012", + "main_insight": "Manchester United has the highest net transfer spending since 2012, exceeding one billion euros, despite winning only one domestic league title in that period. Several clubs with high net spend have had limited domestic success." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Club": "Paris Saint-Germain", + "Net Transfer Spending": 941.0 + } + ], + "score": 0.8, + "annotation": "The shows a sudden jump in Net Transfer Spending from FC Barcelona to Paris Saint-Germain.", + "reason": "The Net Transfer Spending for increases from 650.0 at FC Barcelona to 941.0 at Paris Saint-Germain, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Club": "Manchester United", + "Net Transfer Spending": 1075.0 + } + ], + "score": 0.78, + "annotation": "The has a maximum value at Manchester United", + "reason": "The [{'Club': 'Manchester United', 'Net Transfer Spending': 1075.0}] of has a maximum value of 1075.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Club": "Everton", + "Net Transfer Spending": 429.0 + } + ], + "score": 0.64, + "annotation": "The has a minimum value at Everton", + "reason": "The [{'Club': 'Everton', 'Net Transfer Spending': 429.0}] of has a mainimum value of 429.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Club": "Manchester United", + "Net Transfer Spending": 1075.0 + }, + { + "Club": "Manchester City", + "Net Transfer Spending": 984.0 + }, + { + "Club": "Paris Saint-Germain", + "Net Transfer Spending": 941.0 + }, + { + "Club": "FC Barcelona", + "Net Transfer Spending": 650.0 + }, + { + "Club": "Arsenal", + "Net Transfer Spending": 583.0 + }, + { + "Club": "Juventus", + "Net Transfer Spending": 561.0 + }, + { + "Club": "AC Milan", + "Net Transfer Spending": 432.0 + }, + { + "Club": "Everton", + "Net Transfer Spending": 429.0 + } + ], + "columns": [ + { + "name": "Club", + "importance": "primary", + "description": "Football club name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Net Transfer Spending", + "importance": "primary", + "description": "Total spent on transfers minus total earned through transfers out, in millions of euros", + "unit": "Million EUR", + "data_type": "number", + "role": "y" + } + ] + } + }, + "689": { + "metadata": { + "title": "Where British passports get lost or stolen the most", + "description": "Total UK passports lost or stolen and reported to HM Passport Office in 2014/15*", + "main_insight": "Spain is by far the country where the most UK passports are reported lost or stolen, with over 5,000 cases in 2014/15. The United States and France rank second and third, respectively, but with significantly fewer reported incidents." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Spain", + "Lost or Stolen Passports": 5132 + } + ], + "score": 0.99, + "annotation": "The has a maximum value at Spain", + "reason": "The [{'Country': 'Spain', 'Lost or Stolen Passports': 5132}] of has a maximum value of 5132, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Spain", + "Lost or Stolen Passports": 5132 + } + ], + "score": 0.91, + "annotation": "The shows a sudden jump in Lost or Stolen Passports from United States to Spain.", + "reason": "The Lost or Stolen Passports for increases from 1880 at United States to 5132 at Spain, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Greece", + "Lost or Stolen Passports": 387 + } + ], + "score": 0.42, + "annotation": "The has a minimum value at Greece", + "reason": "The [{'Country': 'Greece', 'Lost or Stolen Passports': 387}] of has a mainimum value of 387, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Spain", + "Lost or Stolen Passports": 5132 + }, + { + "Country": "United States", + "Lost or Stolen Passports": 1880 + }, + { + "Country": "France", + "Lost or Stolen Passports": 1487 + }, + { + "Country": "Italy", + "Lost or Stolen Passports": 1268 + }, + { + "Country": "Australia", + "Lost or Stolen Passports": 1043 + }, + { + "Country": "Netherlands", + "Lost or Stolen Passports": 883 + }, + { + "Country": "Germany", + "Lost or Stolen Passports": 758 + }, + { + "Country": "Portugal", + "Lost or Stolen Passports": 470 + }, + { + "Country": "Thailand", + "Lost or Stolen Passports": 456 + }, + { + "Country": "Turkey", + "Lost or Stolen Passports": 444 + }, + { + "Country": "Switzerland", + "Lost or Stolen Passports": 422 + }, + { + "Country": "Greece", + "Lost or Stolen Passports": 387 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where passports were lost or stolen", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Lost or Stolen Passports", + "importance": "primary", + "description": "Number of UK passports lost or stolen", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "597": { + "metadata": { + "title": "Petrol is the Most Expensive Here", + "description": "Average price per liter of Euro-super 95 in different European countries* *As of May 8, 2017", + "main_insight": "Italy, The Netherlands, and Greece had the highest petrol prices among the listed European countries in May 2017, while the prices, including taxes, are relatively similar." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "United Kingdom", + "Ratio": 0.469, + "Taxation": "Excluding taxes" + } + ], + "score": 0.86, + "annotation": "The Excluding taxes has a minimum value at United Kingdom", + "reason": "The [{'Country': 'United Kingdom', 'Ratio': 0.469, 'Taxation': 'Excluding taxes'}] of Excluding taxes has a mainimum value of 0.469, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Denmark", + "Ratio": 0.56, + "Taxation": "Excluding taxes" + } + ], + "score": 0.8, + "annotation": "The Excluding taxes has a maximum value at Denmark", + "reason": "The [{'Country': 'Denmark', 'Ratio': 0.56, 'Taxation': 'Excluding taxes'}] of Excluding taxes has a maximum value of 0.56, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Italy", + "Ratio": 1.54, + "Taxation": "Including taxes" + } + ], + "score": 0.76, + "annotation": "The Including taxes has a maximum value at Italy", + "reason": "The [{'Country': 'Italy', 'Ratio': 1.54, 'Taxation': 'Including taxes'}] of Including taxes has a maximum value of 1.54, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Belgium", + "Ratio": 1.399, + "Taxation": "Including taxes" + } + ], + "score": 0.74, + "annotation": "The Including taxes accounts for the majority in Belgium.", + "reason": "The Ratio of Including taxes in Belgium accounts for 0.7174358974358974 (more than 50%) of the total of Ratio of all Taxation." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Denmark", + "Ratio": 1.472, + "Taxation": "Including taxes" + } + ], + "score": 0.73, + "annotation": "The Including taxes accounts for the majority in Denmark.", + "reason": "The Ratio of Including taxes in Denmark accounts for 0.7244094488188976 (more than 50%) of the total of Ratio of all Taxation." + } + ], + "data": { + "data": [ + { + "Country": "Italy", + "Ratio": 1.54, + "Taxation": "Including taxes" + }, + { + "Country": "Italy", + "Ratio": 0.534, + "Taxation": "Excluding taxes" + }, + { + "Country": "The Netherlands", + "Ratio": 1.535, + "Taxation": "Including taxes" + }, + { + "Country": "The Netherlands", + "Ratio": 0.488, + "Taxation": "Excluding taxes" + }, + { + "Country": "Greece", + "Ratio": 1.524, + "Taxation": "Including taxes" + }, + { + "Country": "Greece", + "Ratio": 0.519, + "Taxation": "Excluding taxes" + }, + { + "Country": "Denmark", + "Ratio": 1.472, + "Taxation": "Including taxes" + }, + { + "Country": "Denmark", + "Ratio": 0.56, + "Taxation": "Excluding taxes" + }, + { + "Country": "Finland", + "Ratio": 1.462, + "Taxation": "Including taxes" + }, + { + "Country": "Finland", + "Ratio": 0.526, + "Taxation": "Excluding taxes" + }, + { + "Country": "Portugal", + "Ratio": 1.446, + "Taxation": "Including taxes" + }, + { + "Country": "Portugal", + "Ratio": 0.524, + "Taxation": "Excluding taxes" + }, + { + "Country": "Sweden", + "Ratio": 1.432, + "Taxation": "Including taxes" + }, + { + "Country": "Sweden", + "Ratio": 0.492, + "Taxation": "Excluding taxes" + }, + { + "Country": "Belgium", + "Ratio": 1.399, + "Taxation": "Including taxes" + }, + { + "Country": "Belgium", + "Ratio": 0.551, + "Taxation": "Excluding taxes" + }, + { + "Country": "United Kingdom", + "Ratio": 1.386, + "Taxation": "Including taxes" + }, + { + "Country": "United Kingdom", + "Ratio": 0.469, + "Taxation": "Excluding taxes" + }, + { + "Country": "Ireland", + "Ratio": 1.379, + "Taxation": "Including taxes" + }, + { + "Country": "Ireland", + "Ratio": 0.513, + "Taxation": "Excluding taxes" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "European country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding taxation", + "role": "y" + }, + { + "name": "Taxation", + "importance": "primary", + "description": "The taxation of the data point", + "role": "group" + } + ] + } + }, + "626": { + "metadata": { + "title": "Claims under the UK's job retention scheme", + "description": "Value of claims made by companies under the UK's job retention scheme (in billion GBP)", + "main_insight": "The value of claims under the UK's job retention scheme increased significantly from April to August 2020, rising from 4.5 billion GBP to 34.7 billion GBP." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "April 23", + "Claim Value (billion GBP)": 4.5 + } + ], + "score": 0.88, + "annotation": "The has a minimum value at April 23", + "reason": "The [{'Date': 'April 23', 'Claim Value (billion GBP)': 4.5}] of has a mainimum value of 4.5, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "August 9", + "Claim Value (billion GBP)": 34.7 + } + ], + "score": 0.77, + "annotation": "The has a maximum value at August 9", + "reason": "The [{'Date': 'August 9', 'Claim Value (billion GBP)': 34.7}] of has a maximum value of 34.7, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "May 11", + "Claim Value (billion GBP)": 11.0 + } + ], + "score": 0.52, + "annotation": "The shows a sudden jump in Claim Value (billion GBP) from May 3 to May 11.", + "reason": "The Claim Value (billion GBP) for increases from 7.5 at May 3 to 11.0 at May 11, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Date": "April 23", + "Claim Value (billion GBP)": 4.5 + }, + { + "Date": "May 3", + "Claim Value (billion GBP)": 7.5 + }, + { + "Date": "May 11", + "Claim Value (billion GBP)": 11.0 + }, + { + "Date": "May 17", + "Claim Value (billion GBP)": 13.5 + }, + { + "Date": "May 24", + "Claim Value (billion GBP)": 15.5 + }, + { + "Date": "May 31", + "Claim Value (billion GBP)": 17.5 + }, + { + "Date": "June 7", + "Claim Value (billion GBP)": 19.5 + }, + { + "Date": "June 14", + "Claim Value (billion GBP)": 21.0 + }, + { + "Date": "June 21", + "Claim Value (billion GBP)": 23.0 + }, + { + "Date": "June 28", + "Claim Value (billion GBP)": 25.5 + }, + { + "Date": "July 5", + "Claim Value (billion GBP)": 27.0 + }, + { + "Date": "July 12", + "Claim Value (billion GBP)": 28.5 + }, + { + "Date": "July 19", + "Claim Value (billion GBP)": 30.0 + }, + { + "Date": "July 26", + "Claim Value (billion GBP)": 31.7 + }, + { + "Date": "August 2", + "Claim Value (billion GBP)": 33.5 + }, + { + "Date": "August 9", + "Claim Value (billion GBP)": 34.7 + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Date in 2020 (April 20 - August 09)", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Claim Value (billion GBP)", + "importance": "primary", + "description": "Value of claims in billion GBP", + "unit": "billion GBP", + "data_type": "number", + "role": "y" + } + ] + } + }, + "112": { + "metadata": { + "title": "Are the Taliban Reclaiming Afghanistan?", + "description": "Taliban presence in Afghanistan by grade of control over districts", + "main_insight": "The Taliban either fully controls or has an active and open presence in a significant majority (70%) of Afghan districts, while the government controls only 30%." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Control Type": "Government controlled", + "Units": 120, + "Metrics": "Districts" + }, + { + "Control Type": "Government controlled", + "Units": 30.0, + "Metrics": "Percentage" + } + ], + "score": 1.0, + "annotation": "The Units of Districts and Percentage show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Districts and Percentage is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Control Type": "Active and open presence of Taliban", + "Units": 66.0, + "Metrics": "Percentage" + } + ], + "score": 0.87, + "annotation": "The Percentage shows a sudden jump in Units from Government controlled to Active and open presence of Taliban.", + "reason": "The Units for Percentage increases from 30.0 at Government controlled to 66.0 at Active and open presence of Taliban, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Control Type": "Active and open presence of Taliban", + "Units": 263, + "Metrics": "Districts" + } + ], + "score": 0.87, + "annotation": "The Districts shows a sudden jump in Units from Government controlled to Active and open presence of Taliban.", + "reason": "The Units for Districts increases from 120 at Government controlled to 263 at Active and open presence of Taliban, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Control Type": "In full control of Taliban", + "Units": 14, + "Metrics": "Districts" + } + ], + "score": 0.72, + "annotation": "The Districts accounts for the majority in In full control of Taliban.", + "reason": "The Units of Districts in In full control of Taliban accounts for 0.7777777777777778 (more than 50%) of the total of Units of all Metrics." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Metrics": "Districts", + "Control Type": "total", + "Units": 397 + } + ], + "score": 0.71, + "annotation": "The total value of Districts accounts for the majority in all Metrics.", + "reason": "The total value of Units of Districts accounts for 0.7987927565392354 (more than 50%) of the total of Units of all Metrics." + } + ], + "data": { + "data": [ + { + "Control Type": "In full control of Taliban", + "Units": 14, + "Metrics": "Districts" + }, + { + "Control Type": "In full control of Taliban", + "Units": 4.0, + "Metrics": "Percentage" + }, + { + "Control Type": "Active and open presence of Taliban", + "Units": 263, + "Metrics": "Districts" + }, + { + "Control Type": "Active and open presence of Taliban", + "Units": 66.0, + "Metrics": "Percentage" + }, + { + "Control Type": "Government controlled", + "Units": 120, + "Metrics": "Districts" + }, + { + "Control Type": "Government controlled", + "Units": 30.0, + "Metrics": "Percentage" + } + ], + "columns": [ + { + "name": "Control Type", + "importance": "primary", + "description": "Categories of control over Afghan districts", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding metrics", + "role": "y" + }, + { + "name": "Metrics", + "importance": "primary", + "description": "The metrics of the data point", + "role": "group" + } + ] + } + }, + "1641": { + "metadata": { + "title": "E-Books Set to Surpass Print in the U.S.", + "description": "Book market development forecast (in billion U.S. dollars)", + "main_insight": "E-book sales are projected to surpass print sales in the US, while in other countries the print book market is still expected to be higher than e-books in 2018." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Britain", + "Units": 1.8, + "Format": "eBook 2018" + }, + { + "Country": "Britain", + "Units": 1.0, + "Format": "eBook 2014" + } + ], + "score": 0.99, + "annotation": "The Units of eBook 2018 and eBook 2014 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between eBook 2018 and eBook 2014 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Britain", + "Units": 2.0, + "Format": "Print 2018" + }, + { + "Country": "Britain", + "Units": 2.7, + "Format": "Print 2014" + } + ], + "score": 0.99, + "annotation": "The Units of Print 2018 and Print 2014 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Print 2018 and Print 2014 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Units": 5.8, + "Format": "eBook 2014" + } + ], + "score": 0.94, + "annotation": "The eBook 2014 shows a sudden jump in Units from Japan to United States.", + "reason": "The Units for eBook 2014 increases from 1.3 at Japan to 5.8 at United States, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Units": 8.8, + "Format": "eBook 2018" + } + ], + "score": 0.94, + "annotation": "The eBook 2018 shows a sudden jump in Units from Japan to United States.", + "reason": "The Units for eBook 2018 increases from 2.3 at Japan to 8.8 at United States, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "United States", + "Units": 10.2, + "Format": "Print 2014" + } + ], + "score": 0.93, + "annotation": "The Print 2014 has the largest proportion in United States.", + "reason": "The Units of Print 2014 in United States accounts for 0.31097560975609756, larger than all other Format, of the total of Units of all Format." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Units": 8.8, + "Format": "eBook 2018" + }, + { + "Country": "United States", + "Units": 8.0, + "Format": "Print 2018" + }, + { + "Country": "United States", + "Units": 10.2, + "Format": "Print 2014" + }, + { + "Country": "United States", + "Units": 5.8, + "Format": "eBook 2014" + }, + { + "Country": "Japan", + "Units": 2.3, + "Format": "eBook 2018" + }, + { + "Country": "Japan", + "Units": 6.1, + "Format": "Print 2018" + }, + { + "Country": "Japan", + "Units": 6.9, + "Format": "Print 2014" + }, + { + "Country": "Japan", + "Units": 1.3, + "Format": "eBook 2014" + }, + { + "Country": "Germany", + "Units": 1.2, + "Format": "eBook 2018" + }, + { + "Country": "Germany", + "Units": 5.9, + "Format": "Print 2018" + }, + { + "Country": "Germany", + "Units": 6.5, + "Format": "Print 2014" + }, + { + "Country": "Germany", + "Units": 1.2, + "Format": "eBook 2014" + }, + { + "Country": "China", + "Units": 0.5, + "Format": "eBook 2018" + }, + { + "Country": "China", + "Units": 4.3, + "Format": "Print 2018" + }, + { + "Country": "China", + "Units": 4.6, + "Format": "Print 2014" + }, + { + "Country": "China", + "Units": 0.2, + "Format": "eBook 2014" + }, + { + "Country": "Italy", + "Units": 0.5, + "Format": "eBook 2018" + }, + { + "Country": "Italy", + "Units": 2.7, + "Format": "Print 2018" + }, + { + "Country": "Italy", + "Units": 3.0, + "Format": "Print 2014" + }, + { + "Country": "Italy", + "Units": 0.5, + "Format": "eBook 2014" + }, + { + "Country": "Britain", + "Units": 1.8, + "Format": "eBook 2018" + }, + { + "Country": "Britain", + "Units": 2.0, + "Format": "Print 2018" + }, + { + "Country": "Britain", + "Units": 2.7, + "Format": "Print 2014" + }, + { + "Country": "Britain", + "Units": 1.0, + "Format": "eBook 2014" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country for which the data is presented", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding format", + "role": "y" + }, + { + "name": "Format", + "importance": "primary", + "description": "The format of the data point", + "role": "group" + } + ] + } + }, + "1166": { + "metadata": { + "title": "Socialism Increasingly Popular With Young U.S. Adults", + "description": "Share of Americans with a positive view of capitalism/socialism by generation", + "main_insight": "Positive views of socialism have increased among Millennials/Gen Zers between 2010 and 2019, while positive views of capitalism decreased, closing the gap between the two ideologies within that age group." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Generation": "Baby boomers/Traditionalists", + "Score": 58.0, + "Economic System": "Capitalism 2010" + }, + { + "Generation": "Baby boomers/Traditionalists", + "Score": 31.0, + "Economic System": "Socialism 2010" + } + ], + "score": 1.0, + "annotation": "The Score of Capitalism 2010 and Socialism 2010 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Capitalism 2010 and Socialism 2010 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Generation": "Baby boomers/Traditionalists", + "Score": 68.0, + "Economic System": "Capitalism 2019" + }, + { + "Generation": "Baby boomers/Traditionalists", + "Score": 32.0, + "Economic System": "Socialism 2019" + } + ], + "score": 1.0, + "annotation": "The Score of Capitalism 2019 and Socialism 2019 show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Capitalism 2019 and Socialism 2019 is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Generation": "Millennials/Gen Zers", + "Score": 66.0, + "Economic System": "Capitalism 2010" + } + ], + "score": 0.97, + "annotation": "The Capitalism 2010 shows a sudden jump in Score from Baby boomers/Traditionalists to Millennials/Gen Zers.", + "reason": "The Score for Capitalism 2010 increases from 58.0 at Baby boomers/Traditionalists to 66.0 at Millennials/Gen Zers, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Generation": "Millennials/Gen Zers", + "Score": 48.0, + "Economic System": "Socialism 2010" + } + ], + "score": 0.97, + "annotation": "The Socialism 2010 shows a sudden jump in Score from Baby boomers/Traditionalists to Millennials/Gen Zers.", + "reason": "The Score for Socialism 2010 increases from 31.0 at Baby boomers/Traditionalists to 48.0 at Millennials/Gen Zers, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Economic System": "Capitalism 2010", + "Generation": "total", + "Score": 182.0 + } + ], + "score": 0.93, + "annotation": "The total value of Capitalism 2010 has the largest proportion.", + "reason": "The total value of Score of Capitalism 2010 accounts for 0.30743243243243246, larger than all other Economic System, of the total of Score of all Economic System." + } + ], + "data": { + "data": [ + { + "Generation": "Millennials/Gen Zers", + "Score": 66.0, + "Economic System": "Capitalism 2010" + }, + { + "Generation": "Millennials/Gen Zers", + "Score": 48.0, + "Economic System": "Socialism 2010" + }, + { + "Generation": "Millennials/Gen Zers", + "Score": 51.0, + "Economic System": "Capitalism 2019" + }, + { + "Generation": "Millennials/Gen Zers", + "Score": 49.0, + "Economic System": "Socialism 2019" + }, + { + "Generation": "Gen Xers", + "Score": 58.0, + "Economic System": "Capitalism 2010" + }, + { + "Generation": "Gen Xers", + "Score": 31.0, + "Economic System": "Socialism 2010" + }, + { + "Generation": "Gen Xers", + "Score": 61.0, + "Economic System": "Capitalism 2019" + }, + { + "Generation": "Gen Xers", + "Score": 39.0, + "Economic System": "Socialism 2019" + }, + { + "Generation": "Baby boomers/Traditionalists", + "Score": 58.0, + "Economic System": "Capitalism 2010" + }, + { + "Generation": "Baby boomers/Traditionalists", + "Score": 31.0, + "Economic System": "Socialism 2010" + }, + { + "Generation": "Baby boomers/Traditionalists", + "Score": 68.0, + "Economic System": "Capitalism 2019" + }, + { + "Generation": "Baby boomers/Traditionalists", + "Score": 32.0, + "Economic System": "Socialism 2019" + } + ], + "columns": [ + { + "name": "Generation", + "importance": "primary", + "description": "Generational cohort", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding economic system", + "role": "y" + }, + { + "name": "Economic System", + "importance": "primary", + "description": "The economic system of the data point", + "role": "group" + } + ] + } + }, + "598": { + "metadata": { + "title": "London Dominates The Global Luxury Property Market", + "description": "Number of properties valued at $5+ million sold in 2009 and 2014", + "main_insight": "London has the highest number of luxury property sales, and the number of sales increased significantly in most cities between 2009 and 2014." + }, + "chart_type": "Multiple Semi Circle Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "London", + "Count": 1638, + "Sales Year": "2014 Sales" + } + ], + "score": 0.93, + "annotation": "The 2014 Sales has a maximum value at London", + "reason": "The [{'City': 'London', 'Count': 1638, 'Sales Year': '2014 Sales'}] of 2014 Sales has a maximum value of 1638, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "City": "Los Angeles", + "Count": 21, + "Sales Year": "2014 Sales" + }, + { + "City": "Los Angeles", + "Count": 5, + "Sales Year": "2009 Sales" + } + ], + "score": 0.91, + "annotation": "The Count of 2014 Sales and 2009 Sales show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Count between 2014 Sales and 2009 Sales is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "London", + "Count": 1638, + "Sales Year": "2014 Sales" + } + ], + "score": 0.84, + "annotation": "The 2014 Sales shows a sudden jump in Count from Manhattan to London.", + "reason": "The Count for 2014 Sales increases from 796 at Manhattan to 1638 at London, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Hong Kong", + "Count": 388, + "Sales Year": "2009 Sales" + } + ], + "score": 0.81, + "annotation": "The 2009 Sales shows a sudden jump in Count from Sydney to Hong Kong.", + "reason": "The Count for 2009 Sales increases from 151 at Sydney to 388 at Hong Kong, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "London", + "Count": 523, + "Sales Year": "2009 Sales" + } + ], + "score": 0.78, + "annotation": "The 2009 Sales has a maximum value at London", + "reason": "The [{'City': 'London', 'Count': 523, 'Sales Year': '2009 Sales'}] of 2009 Sales has a maximum value of 523, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "City": "London", + "Count": 1638, + "Sales Year": "2014 Sales" + }, + { + "City": "London", + "Count": 523, + "Sales Year": "2009 Sales" + }, + { + "City": "Manhattan", + "Count": 796, + "Sales Year": "2014 Sales" + }, + { + "City": "Manhattan", + "Count": 476, + "Sales Year": "2009 Sales" + }, + { + "City": "Hong Kong", + "Count": 596, + "Sales Year": "2014 Sales" + }, + { + "City": "Hong Kong", + "Count": 388, + "Sales Year": "2009 Sales" + }, + { + "City": "Sydney", + "Count": 258, + "Sales Year": "2014 Sales" + }, + { + "City": "Sydney", + "Count": 151, + "Sales Year": "2009 Sales" + }, + { + "City": "Singapore", + "Count": 74, + "Sales Year": "2014 Sales" + }, + { + "City": "Singapore", + "Count": 107, + "Sales Year": "2009 Sales" + }, + { + "City": "Dubai", + "Count": 46, + "Sales Year": "2014 Sales" + }, + { + "City": "Dubai", + "Count": 12, + "Sales Year": "2009 Sales" + }, + { + "City": "Miami", + "Count": 55, + "Sales Year": "2014 Sales" + }, + { + "City": "Miami", + "Count": 12, + "Sales Year": "2009 Sales" + }, + { + "City": "Los Angeles", + "Count": 21, + "Sales Year": "2014 Sales" + }, + { + "City": "Los Angeles", + "Count": 5, + "Sales Year": "2009 Sales" + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "City where properties were sold", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Count", + "importance": "primary", + "description": "Count for the corresponding sales year", + "role": "y" + }, + { + "name": "Sales Year", + "importance": "primary", + "description": "The sales year of the data point", + "role": "group" + } + ] + } + }, + "2412": { + "metadata": { + "title": "How Beer Drinking Habits Have Changed Since Covid", + "description": "Estimated distribution of beer sales in selected countries (in %)", + "main_insight": "The visualization shows a shift in beer consumption habits between 2019 and 2022 across several countries, with a general trend towards increased at-home consumption and decreased out-of-home consumption." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "U.S.", + "Score": 55.0, + "Location": "2022_At_Home" + }, + { + "Country": "U.S.", + "Score": 45.0, + "Location": "2022_Out_of_Home" + } + ], + "score": 1.0, + "annotation": "The Score of 2022_At_Home and 2022_Out_of_Home show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between 2022_At_Home and 2022_Out_of_Home is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "U.S.", + "Score": 65.0, + "Location": "2019_At_Home" + }, + { + "Country": "U.S.", + "Score": 35.0, + "Location": "2019_Out_of_Home" + } + ], + "score": 1.0, + "annotation": "The Score of 2019_At_Home and 2019_Out_of_Home show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between 2019_At_Home and 2019_Out_of_Home is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "U.S.", + "Score": 65.0, + "Location": "2019_At_Home" + }, + { + "Country": "U.S.", + "Score": 55.0, + "Location": "2022_At_Home" + } + ], + "score": 1.0, + "annotation": "The Score of 2019_At_Home and 2022_At_Home show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between 2019_At_Home and 2022_At_Home is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "U.S.", + "Score": 65.0, + "Location": "2019_At_Home" + }, + { + "Country": "U.S.", + "Score": 45.0, + "Location": "2022_Out_of_Home" + } + ], + "score": 1.0, + "annotation": "The Score of 2019_At_Home and 2022_Out_of_Home show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between 2019_At_Home and 2022_Out_of_Home is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "U.S.", + "Score": 55.0, + "Location": "2022_At_Home" + }, + { + "Country": "U.S.", + "Score": 35.0, + "Location": "2019_Out_of_Home" + } + ], + "score": 1.0, + "annotation": "The Score of 2022_At_Home and 2019_Out_of_Home show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between 2022_At_Home and 2019_Out_of_Home is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Country": "Spain", + "Score": 17.0, + "Location": "2019_At_Home" + }, + { + "Country": "Spain", + "Score": 15.0, + "Location": "2022_At_Home" + }, + { + "Country": "Spain", + "Score": 83.0, + "Location": "2019_Out_of_Home" + }, + { + "Country": "Spain", + "Score": 85.0, + "Location": "2022_Out_of_Home" + }, + { + "Country": "UK", + "Score": 37.0, + "Location": "2019_At_Home" + }, + { + "Country": "UK", + "Score": 28.0, + "Location": "2022_At_Home" + }, + { + "Country": "UK", + "Score": 63.0, + "Location": "2019_Out_of_Home" + }, + { + "Country": "UK", + "Score": 72.0, + "Location": "2022_Out_of_Home" + }, + { + "Country": "Germany", + "Score": 48.0, + "Location": "2019_At_Home" + }, + { + "Country": "Germany", + "Score": 39.0, + "Location": "2022_At_Home" + }, + { + "Country": "Germany", + "Score": 52.0, + "Location": "2019_Out_of_Home" + }, + { + "Country": "Germany", + "Score": 61.0, + "Location": "2022_Out_of_Home" + }, + { + "Country": "Italy", + "Score": 54.0, + "Location": "2019_At_Home" + }, + { + "Country": "Italy", + "Score": 45.0, + "Location": "2022_At_Home" + }, + { + "Country": "Italy", + "Score": 46.0, + "Location": "2019_Out_of_Home" + }, + { + "Country": "Italy", + "Score": 55.0, + "Location": "2022_Out_of_Home" + }, + { + "Country": "U.S.", + "Score": 65.0, + "Location": "2019_At_Home" + }, + { + "Country": "U.S.", + "Score": 55.0, + "Location": "2022_At_Home" + }, + { + "Country": "U.S.", + "Score": 35.0, + "Location": "2019_Out_of_Home" + }, + { + "Country": "U.S.", + "Score": 45.0, + "Location": "2022_Out_of_Home" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where beer sales were recorded", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding location", + "role": "y" + }, + { + "name": "Location", + "importance": "primary", + "description": "The location of the data point", + "role": "group" + } + ] + } + }, + "625": { + "metadata": { + "title": "EU Train Accident Deaths Continue To Decline", + "description": "Total number of fatalities in railway accidents in the EU-27 by year", + "main_insight": "The number of fatalities in railway accidents in the EU-27 has generally decreased from 2010 to 2019." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2010, + "Fatalities": 1245 + }, + { + "Year": 2011, + "Fatalities": 1140 + }, + { + "Year": 2012, + "Fatalities": 1104 + }, + { + "Year": 2013, + "Fatalities": 1110 + }, + { + "Year": 2014, + "Fatalities": 1019 + }, + { + "Year": 2015, + "Fatalities": 938 + }, + { + "Year": 2016, + "Fatalities": 946 + }, + { + "Year": 2017, + "Fatalities": 916 + }, + { + "Year": 2018, + "Fatalities": 840 + }, + { + "Year": 2019, + "Fatalities": 802 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of railway accident fatalities", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Fatalities", + "importance": "primary", + "description": "Number of fatalities in railway accidents", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1761": { + "metadata": { + "title": "Polls: Macron Highly Likely To Win The Second Round", + "description": "Second round voting intention in the French presidential election*", + "main_insight": "According to the most recent polls published between April 21 and April 23, 2017, Emmanuel Macron consistently leads Marine Le Pen in voting intention for the second round of the French presidential election." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Poll Source": "Opinion Way", + "Score": 64.0, + "Vote Share": "Emmanuel Macron" + }, + { + "Poll Source": "Opinion Way", + "Score": 36.0, + "Vote Share": "Marine Le Pen" + } + ], + "score": 1.0, + "annotation": "The Score of Emmanuel Macron and Marine Le Pen show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Emmanuel Macron and Marine Le Pen is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Poll Source": "Harris Interactive", + "Score": 64.0, + "Vote Share": "Emmanuel Macron" + } + ], + "score": 0.8, + "annotation": "The Emmanuel Macron shows a sudden jump in Score from Ipsos Sopra Steria to Harris Interactive.", + "reason": "The Score for Emmanuel Macron increases from 62.0 at Ipsos Sopra Steria to 64.0 at Harris Interactive, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Poll Source": "Ipsos Sopra Steria", + "Score": 38.0, + "Vote Share": "Marine Le Pen" + } + ], + "score": 0.8, + "annotation": "The Marine Le Pen shows a sudden jump in Score from Opinion Way to Ipsos Sopra Steria.", + "reason": "The Score for Marine Le Pen increases from 36.0 at Opinion Way to 38.0 at Ipsos Sopra Steria, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Poll Source": "Ifop", + "Score": 60.5, + "Vote Share": "Emmanuel Macron" + } + ], + "score": 0.78, + "annotation": "The Emmanuel Macron has a minimum value at Ifop", + "reason": "The [{'Poll Source': 'Ifop', 'Score': 60.5, 'Vote Share': 'Emmanuel Macron'}] of Emmanuel Macron has a mainimum value of 60.5, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Poll Source": "Ifop", + "Score": 39.5, + "Vote Share": "Marine Le Pen" + } + ], + "score": 0.78, + "annotation": "The Marine Le Pen has a maximum value at Ifop", + "reason": "The [{'Poll Source': 'Ifop', 'Score': 39.5, 'Vote Share': 'Marine Le Pen'}] of Marine Le Pen has a maximum value of 39.5, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Poll Source": "BVA", + "Score": 65.0, + "Vote Share": "Emmanuel Macron" + }, + { + "Poll Source": "BVA", + "Score": 35.0, + "Vote Share": "Marine Le Pen" + }, + { + "Poll Source": "Harris Interactive", + "Score": 64.0, + "Vote Share": "Emmanuel Macron" + }, + { + "Poll Source": "Harris Interactive", + "Score": 36.0, + "Vote Share": "Marine Le Pen" + }, + { + "Poll Source": "Ifop", + "Score": 60.5, + "Vote Share": "Emmanuel Macron" + }, + { + "Poll Source": "Ifop", + "Score": 39.5, + "Vote Share": "Marine Le Pen" + }, + { + "Poll Source": "Ipsos Sopra Steria", + "Score": 62.0, + "Vote Share": "Emmanuel Macron" + }, + { + "Poll Source": "Ipsos Sopra Steria", + "Score": 38.0, + "Vote Share": "Marine Le Pen" + }, + { + "Poll Source": "Opinion Way", + "Score": 64.0, + "Vote Share": "Emmanuel Macron" + }, + { + "Poll Source": "Opinion Way", + "Score": 36.0, + "Vote Share": "Marine Le Pen" + } + ], + "columns": [ + { + "name": "Poll Source", + "importance": "primary", + "description": "The polling organization", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding vote share", + "role": "y" + }, + { + "name": "Vote Share", + "importance": "primary", + "description": "The vote share of the data point", + "role": "group" + } + ] + } + }, + "1423": { + "metadata": { + "title": "The U.S. Wireless Headphone Market", + "description": "Wireless headphone market share in the United States in December 2016", + "main_insight": "In December 2016, Beats and Apple dominated the U.S. wireless headphone market, with Beats having the highest unit sales, and the two combined (since Beats is a subsidiary of Apple) capturing almost half the market share in terms of dollar sales." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "Beats", + "Units": 46.0, + "Sales Metrics": "% of dollar sales" + } + ], + "score": 0.89, + "annotation": "The % of dollar sales shows a sudden jump in Units from Others to Beats.", + "reason": "The Units for % of dollar sales increases from 19.0 at Others to 46.0 at Beats, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Company": "Beats", + "Units": 46.0, + "Sales Metrics": "% of dollar sales" + } + ], + "score": 0.89, + "annotation": "The % of dollar sales has a maximum value at Beats", + "reason": "The [{'Company': 'Beats', 'Units': 46.0, 'Sales Metrics': '% of dollar sales'}] of % of dollar sales has a maximum value of 46.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Company": "Others", + "Units": 48.0, + "Sales Metrics": "% of unit sales" + } + ], + "score": 0.89, + "annotation": "The % of unit sales has a maximum value at Others", + "reason": "The [{'Company': 'Others', 'Units': 48.0, 'Sales Metrics': '% of unit sales'}] of % of unit sales has a maximum value of 48.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "Others", + "Units": 48.0, + "Sales Metrics": "% of unit sales" + } + ], + "score": 0.83, + "annotation": "The % of unit sales shows a sudden jump in Units from Beats to Others.", + "reason": "The Units for % of unit sales increases from 25.0 at Beats to 48.0 at Others, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Sales Metrics": "% of dollar sales", + "Company": "total", + "Units": 100.0 + }, + { + "Sales Metrics": "% of unit sales", + "Company": "total", + "Units": 100.0 + } + ], + "score": 0.8, + "annotation": "The total value of % of dollar sales, % of unit sales all have the largest proportion.", + "reason": "The total value of Units of % of dollar sales, % of unit sales all account for 0.5, larger than all other Sales Metrics, of the total of Units of all Sales Metrics." + } + ], + "data": { + "data": [ + { + "Company": "Beats", + "Units": 46.0, + "Sales Metrics": "% of dollar sales" + }, + { + "Company": "Beats", + "Units": 25.0, + "Sales Metrics": "% of unit sales" + }, + { + "Company": "Apple", + "Units": 3.0, + "Sales Metrics": "% of dollar sales" + }, + { + "Company": "Apple", + "Units": 2.0, + "Sales Metrics": "% of unit sales" + }, + { + "Company": "Bose", + "Units": 19.0, + "Sales Metrics": "% of dollar sales" + }, + { + "Company": "Bose", + "Units": 8.0, + "Sales Metrics": "% of unit sales" + }, + { + "Company": "LG", + "Units": 7.0, + "Sales Metrics": "% of dollar sales" + }, + { + "Company": "LG", + "Units": 10.0, + "Sales Metrics": "% of unit sales" + }, + { + "Company": "Sony", + "Units": 6.0, + "Sales Metrics": "% of dollar sales" + }, + { + "Company": "Sony", + "Units": 7.0, + "Sales Metrics": "% of unit sales" + }, + { + "Company": "Others", + "Units": 19.0, + "Sales Metrics": "% of dollar sales" + }, + { + "Company": "Others", + "Units": 48.0, + "Sales Metrics": "% of unit sales" + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Brand of wireless headphones", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sales metrics", + "role": "y" + }, + { + "name": "Sales Metrics", + "importance": "primary", + "description": "The sales metrics of the data point", + "role": "group" + } + ] + } + }, + "982": { + "metadata": { + "title": "Public Debt", + "description": "U.S. public debt outstanding from 1990 to 2011 (in trillion U.S. dollars)", + "main_insight": "U.S. public debt increased significantly from 1990 to 2011, more than quadrupling in that time period, with a sharp rise between 2008 and 2011" + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1990, + "Public Debt (trillions)": 3.2 + }, + { + "Year": 1991, + "Public Debt (trillions)": 3.6 + }, + { + "Year": 1992, + "Public Debt (trillions)": 4.0 + }, + { + "Year": 1993, + "Public Debt (trillions)": 4.3 + }, + { + "Year": 1994, + "Public Debt (trillions)": 4.6 + }, + { + "Year": 1995, + "Public Debt (trillions)": 4.9 + }, + { + "Year": 1996, + "Public Debt (trillions)": 5.1 + }, + { + "Year": 1997, + "Public Debt (trillions)": 5.3 + }, + { + "Year": 1998, + "Public Debt (trillions)": 5.4 + }, + { + "Year": 1999, + "Public Debt (trillions)": 5.6 + }, + { + "Year": 2000, + "Public Debt (trillions)": 5.6 + }, + { + "Year": 2001, + "Public Debt (trillions)": 5.7 + }, + { + "Year": 2002, + "Public Debt (trillions)": 6.1 + }, + { + "Year": 2003, + "Public Debt (trillions)": 6.7 + }, + { + "Year": 2004, + "Public Debt (trillions)": 7.3 + }, + { + "Year": 2005, + "Public Debt (trillions)": 7.9 + }, + { + "Year": 2006, + "Public Debt (trillions)": 8.4 + }, + { + "Year": 2007, + "Public Debt (trillions)": 8.9 + }, + { + "Year": 2008, + "Public Debt (trillions)": 9.9 + }, + { + "Year": 2009, + "Public Debt (trillions)": 11.9 + }, + { + "Year": 2010, + "Public Debt (trillions)": 13.5 + }, + { + "Year": 2011, + "Public Debt (trillions)": 14.7 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 1990 to 2011", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Public Debt (trillions)", + "importance": "primary", + "description": "U.S. public debt outstanding in trillions of U.S. dollars", + "unit": "trillions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1041": { + "metadata": { + "title": "Financial Prosperity Has Plummeted For Millennials", + "description": "% growth in household disposable income above or below national average growth rate", + "main_insight": "The chart shows that the growth in household disposable income for millennials (age 25-29) is significantly lower than that of older age groups (65-69 and 70-74) across various countries, indicating a decline in financial prosperity for this generation." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "UK (1979-2010)", + "Score": 70.0, + "Age Group": "Age 70-74" + } + ], + "score": 0.94, + "annotation": "The Age 70-74 has a maximum value at UK (1979-2010)", + "reason": "The [{'Country': 'UK (1979-2010)', 'Score': 70.0, 'Age Group': 'Age 70-74'}] of Age 70-74 has a maximum value of 70.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Australia (1985-2010)", + "Score": 15.0, + "Age Group": "Age 65-69" + }, + { + "Country": "Australia (1985-2010)", + "Score": 22.0, + "Age Group": "Age 70-74" + } + ], + "score": 0.88, + "annotation": "The Score of Age 65-69 and Age 70-74 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Age 65-69 and Age 70-74 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "UK (1979-2010)", + "Score": 60.0, + "Age Group": "Age 65-69" + } + ], + "score": 0.88, + "annotation": "The Age 65-69 has a maximum value at UK (1979-2010)", + "reason": "The [{'Country': 'UK (1979-2010)', 'Score': 60.0, 'Age Group': 'Age 65-69'}] of Age 65-69 has a maximum value of 60.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "UK (1979-2010)", + "Score": 70.0, + "Age Group": "Age 70-74" + } + ], + "score": 0.87, + "annotation": "The Age 70-74 shows a sudden jump in Score from USA (1979-2010) to UK (1979-2010).", + "reason": "The Score for Age 70-74 increases from 35.0 at USA (1979-2010) to 70.0 at UK (1979-2010), indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Australia (1985-2010)", + "Score": 1.0, + "Age Group": "Age 25-29" + } + ], + "score": 0.83, + "annotation": "The Age 25-29 has a maximum value at Australia (1985-2010)", + "reason": "The [{'Country': 'Australia (1985-2010)', 'Score': 1.0, 'Age Group': 'Age 25-29'}] of Age 25-29 has a maximum value of 1.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "UK (1979-2010)", + "Score": -5.0, + "Age Group": "Age 25-29" + }, + { + "Country": "UK (1979-2010)", + "Score": 60.0, + "Age Group": "Age 65-69" + }, + { + "Country": "UK (1979-2010)", + "Score": 70.0, + "Age Group": "Age 70-74" + }, + { + "Country": "Canada (1987-2010)", + "Score": -7.0, + "Age Group": "Age 25-29" + }, + { + "Country": "Canada (1987-2010)", + "Score": 12.0, + "Age Group": "Age 65-69" + }, + { + "Country": "Canada (1987-2010)", + "Score": 8.0, + "Age Group": "Age 70-74" + }, + { + "Country": "Germany (1978-2010)", + "Score": -8.0, + "Age Group": "Age 25-29" + }, + { + "Country": "Germany (1978-2010)", + "Score": 10.0, + "Age Group": "Age 65-69" + }, + { + "Country": "Germany (1978-2010)", + "Score": 20.0, + "Age Group": "Age 70-74" + }, + { + "Country": "France (1978-2010)", + "Score": -10.0, + "Age Group": "Age 25-29" + }, + { + "Country": "France (1978-2010)", + "Score": 45.0, + "Age Group": "Age 65-69" + }, + { + "Country": "France (1978-2010)", + "Score": 30.0, + "Age Group": "Age 70-74" + }, + { + "Country": "USA (1979-2010)", + "Score": -15.0, + "Age Group": "Age 25-29" + }, + { + "Country": "USA (1979-2010)", + "Score": 30.0, + "Age Group": "Age 65-69" + }, + { + "Country": "USA (1979-2010)", + "Score": 35.0, + "Age Group": "Age 70-74" + }, + { + "Country": "Spain (1980-2010)", + "Score": -20.0, + "Age Group": "Age 25-29" + }, + { + "Country": "Spain (1980-2010)", + "Score": 35.0, + "Age Group": "Age 65-69" + }, + { + "Country": "Spain (1980-2010)", + "Score": 30.0, + "Age Group": "Age 70-74" + }, + { + "Country": "Italy (1986-2010)", + "Score": -18.0, + "Age Group": "Age 25-29" + }, + { + "Country": "Italy (1986-2010)", + "Score": 12.0, + "Age Group": "Age 65-69" + }, + { + "Country": "Italy (1986-2010)", + "Score": 20.0, + "Age Group": "Age 70-74" + }, + { + "Country": "Australia (1985-2010)", + "Score": 1.0, + "Age Group": "Age 25-29" + }, + { + "Country": "Australia (1985-2010)", + "Score": 15.0, + "Age Group": "Age 65-69" + }, + { + "Country": "Australia (1985-2010)", + "Score": 22.0, + "Age Group": "Age 70-74" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country and time period", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding age group", + "role": "y" + }, + { + "name": "Age Group", + "importance": "primary", + "description": "The age group of the data point", + "role": "group" + } + ] + } + }, + "721": { + "metadata": { + "title": "Who Imports and Exports Ag Products in the U.S.?", + "description": "Sources of imports and destinations of exports for agricultural products in the U.S. in 2023 (in billion U.S. dollars)", + "main_insight": "Mexico and Canada are the largest importers of U.S. agricultural products, while China, Mexico, and Canada are the biggest export destinations. There is significant trade between the US, Mexico and Canada for agricultural products." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "EU", + "Biggest Importers to U.S.": 32.9 + } + ], + "score": 0.88, + "annotation": "The shows a sudden jump in Biggest Importers to U.S. from Brazil to EU.", + "reason": "The Biggest Importers to U.S. for increases from 6.2 at Brazil to 32.9 at EU, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Mexico", + "Biggest Importers to U.S.": 45.4 + } + ], + "score": 0.68, + "annotation": "The has a maximum value at Mexico", + "reason": "The [{'Country': 'Mexico', 'Biggest Importers to U.S.': 45.4}] of has a maximum value of 45.4, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Japan", + "Biggest Importers to U.S.": 0 + } + ], + "score": 0.63, + "annotation": "The has a minimum value at Japan", + "reason": "The [{'Country': 'Japan', 'Biggest Importers to U.S.': 0}] of has a mainimum value of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Mexico", + "Biggest Importers to U.S.": 45.4 + }, + { + "Country": "Canada", + "Biggest Importers to U.S.": 40.1 + }, + { + "Country": "EU", + "Biggest Importers to U.S.": 32.9 + }, + { + "Country": "Brazil", + "Biggest Importers to U.S.": 6.2 + }, + { + "Country": "China", + "Biggest Importers to U.S.": 4.7 + }, + { + "Country": "Japan", + "Biggest Importers to U.S.": 0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country or region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Biggest Importers to U.S.", + "importance": "primary", + "description": "Value of agricultural imports to the U.S. in billions of U.S. dollars", + "unit": "Billion U.S. dollars", + "data_type": "number", + "role": "y" + } + ] + } + }, + "461": { + "metadata": { + "title": "Disney's 7-Billion Dollar Year", + "description": "Worldwide box office revenue of Disney's Top 10 movie releases in 2016", + "main_insight": "Disney's top 10 movies in 2016 generated significant revenue, with 'Captain America: Civil War' leading at over $1.1 billion, and four movies exceeding $900 million. This contributed to Disney studios earning over $7 Billion at the box office that year" + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Movie Title": "Captain America: Civil War", + "Box Office Revenue": 1153.0 + } + ], + "score": 0.75, + "annotation": "The has a maximum value at Captain America: Civil War", + "reason": "The [{'Movie Title': 'Captain America: Civil War', 'Box Office Revenue': 1153.0}] of has a maximum value of 1153.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Movie Title": "Doctor Strange", + "Box Office Revenue": 653.0 + } + ], + "score": 0.71, + "annotation": "The shows a sudden jump in Box Office Revenue from Rogue One: A Star Wars Story to Doctor Strange.", + "reason": "The Box Office Revenue for increases from 323.0 at Rogue One: A Star Wars Story to 653.0 at Doctor Strange, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Movie Title": "Pete's Dragon", + "Box Office Revenue": 142.0 + } + ], + "score": 0.67, + "annotation": "The has a minimum value at Pete's Dragon", + "reason": "The [{'Movie Title': \"Pete's Dragon\", 'Box Office Revenue': 142.0}] of has a mainimum value of 142.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Movie Title": "Captain America: Civil War", + "Box Office Revenue": 1153.0 + }, + { + "Movie Title": "Finding Dory", + "Box Office Revenue": 1028.0 + }, + { + "Movie Title": "Zootopia", + "Box Office Revenue": 1024.0 + }, + { + "Movie Title": "The Jungle Book", + "Box Office Revenue": 967.0 + }, + { + "Movie Title": "Doctor Strange", + "Box Office Revenue": 653.0 + }, + { + "Movie Title": "Rogue One: A Star Wars Story", + "Box Office Revenue": 323.0 + }, + { + "Movie Title": "Alice Through the Looking Glass", + "Box Office Revenue": 299.0 + }, + { + "Movie Title": "Moana", + "Box Office Revenue": 284.0 + }, + { + "Movie Title": "The BFG", + "Box Office Revenue": 178.0 + }, + { + "Movie Title": "Pete's Dragon", + "Box Office Revenue": 142.0 + } + ], + "columns": [ + { + "name": "Movie Title", + "importance": "primary", + "description": "Name of the Disney movie released in 2016", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Box Office Revenue", + "importance": "primary", + "description": "Worldwide box office revenue in millions of US dollars", + "unit": "Millions USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "362": { + "metadata": { + "title": "Where Most People Lack Access to the Financial System", + "description": "Countries with the highest share of adults (15+) without a bank account in 2017*", + "main_insight": "South Sudan, Central African Republic, and Afghanistan had the highest percentage of adults without bank accounts in 2017, all exceeding 85%. Globally, about 1.7 billion adults remained unbanked in 2017." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "South Sudan", + "Unbanked Adults (%)": 91.0 + } + ], + "score": 0.93, + "annotation": "The has a maximum value at South Sudan", + "reason": "The [{'Country': 'South Sudan', 'Unbanked Adults (%)': 91.0}] of has a maximum value of 91.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "South Sudan", + "Unbanked Adults (%)": 91.0 + } + ], + "score": 0.76, + "annotation": "The shows a sudden jump in Unbanked Adults (%) from Central African Republic to South Sudan.", + "reason": "The Unbanked Adults (%) for increases from 86.0 at Central African Republic to 91.0 at South Sudan, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Cambodia", + "Unbanked Adults (%)": 78.0 + }, + { + "Country": "Chad", + "Unbanked Adults (%)": 78.0 + } + ], + "score": 0.59, + "annotation": "The has minimum values at Cambodia, Chad", + "reason": "The [{'Country': 'Cambodia', 'Unbanked Adults (%)': 78.0}, {'Country': 'Chad', 'Unbanked Adults (%)': 78.0}] of have minimum values of 78.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "South Sudan", + "Unbanked Adults (%)": 91.0 + }, + { + "Country": "Central African Republic", + "Unbanked Adults (%)": 86.0 + }, + { + "Country": "Afghanistan", + "Unbanked Adults (%)": 85.0 + }, + { + "Country": "Niger", + "Unbanked Adults (%)": 84.0 + }, + { + "Country": "Madagascar", + "Unbanked Adults (%)": 82.0 + }, + { + "Country": "Sierra Leone", + "Unbanked Adults (%)": 80.0 + }, + { + "Country": "Mauritania", + "Unbanked Adults (%)": 79.0 + }, + { + "Country": "Pakistan", + "Unbanked Adults (%)": 79.0 + }, + { + "Country": "Cambodia", + "Unbanked Adults (%)": 78.0 + }, + { + "Country": "Chad", + "Unbanked Adults (%)": 78.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country Name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Unbanked Adults (%)", + "importance": "primary", + "description": "Percentage of adults (15+) without a bank account", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2516": { + "metadata": { + "title": "Champagne Champions", + "description": "Champagne exports to the largest markets outside of France in 2023 (in million bottles)", + "main_insight": "The US and Great Britain are by far the largest export markets for champagne, importing 26.9 and 25.5 million bottles respectively in 2023. Total champagne exports reached 172 million bottles." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "US", + "Exports (million bottles)": 26.9 + } + ], + "score": 0.83, + "annotation": "The has a maximum value at US", + "reason": "The [{'Country': 'US', 'Exports (million bottles)': 26.9}] of has a maximum value of 26.9, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "GB", + "Exports (million bottles)": 25.5 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Exports (million bottles) from JP to GB.", + "reason": "The Exports (million bottles) for increases from 15.3 at JP to 25.5 at GB, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "CH", + "Exports (million bottles)": 6.1 + } + ], + "score": 0.6, + "annotation": "The has a minimum value at CH", + "reason": "The [{'Country': 'CH', 'Exports (million bottles)': 6.1}] of has a mainimum value of 6.1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "US", + "Exports (million bottles)": 26.9 + }, + { + "Country": "GB", + "Exports (million bottles)": 25.5 + }, + { + "Country": "JP", + "Exports (million bottles)": 15.3 + }, + { + "Country": "DE", + "Exports (million bottles)": 11.7 + }, + { + "Country": "IT", + "Exports (million bottles)": 9.9 + }, + { + "Country": "AU", + "Exports (million bottles)": 8.9 + }, + { + "Country": "BE", + "Exports (million bottles)": 8.0 + }, + { + "Country": "CH", + "Exports (million bottles)": 6.1 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of export", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Exports (million bottles)", + "importance": "primary", + "description": "Number of champagne bottles exported in millions", + "unit": "million bottles", + "data_type": "number", + "role": "y" + } + ] + } + }, + "427": { + "metadata": { + "title": "China's Steady Growth Takes a Hit", + "description": "Annual growth rate of GDP in China Q2 2015 to Q2 2018", + "main_insight": "China's annual GDP growth rate has generally declined from Q2 2015 to Q2 2018, reaching 6.7% in Q2 2018." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Quarter": "Q2 2015", + "GDP Growth Rate": 6.9 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in GDP Growth Rate from Q1 2018 to Q2 2015.", + "reason": "The GDP Growth Rate for increases from 6.8 at Q1 2018 to 6.9 at Q2 2015, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Quarter": "Q2 2015", + "GDP Growth Rate": 6.9 + }, + { + "Quarter": "Q1 2017", + "GDP Growth Rate": 6.9 + }, + { + "Quarter": "Q2 2017", + "GDP Growth Rate": 6.9 + } + ], + "score": 0.78, + "annotation": "The has maximum values at Q2 2015, Q1 2017, Q2 2017", + "reason": "The [{'Quarter': 'Q2 2015', 'GDP Growth Rate': 6.9}, {'Quarter': 'Q1 2017', 'GDP Growth Rate': 6.9}, {'Quarter': 'Q2 2017', 'GDP Growth Rate': 6.9}] of have maximum values of 6.9, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Quarter": "Q4 2015", + "GDP Growth Rate": 6.7 + }, + { + "Quarter": "Q1 2016", + "GDP Growth Rate": 6.7 + }, + { + "Quarter": "Q2 2016", + "GDP Growth Rate": 6.7 + }, + { + "Quarter": "Q3 2016", + "GDP Growth Rate": 6.7 + }, + { + "Quarter": "Q2 2018", + "GDP Growth Rate": 6.7 + } + ], + "score": 0.63, + "annotation": "The has minimum values at Q4 2015, Q1 2016, Q2 2016, Q3 2016, Q2 2018", + "reason": "The [{'Quarter': 'Q4 2015', 'GDP Growth Rate': 6.7}, {'Quarter': 'Q1 2016', 'GDP Growth Rate': 6.7}, {'Quarter': 'Q2 2016', 'GDP Growth Rate': 6.7}, {'Quarter': 'Q3 2016', 'GDP Growth Rate': 6.7}, {'Quarter': 'Q2 2018', 'GDP Growth Rate': 6.7}] of have minimum values of 6.7, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Quarter": "Q2 2015", + "GDP Growth Rate": 6.9 + }, + { + "Quarter": "Q3 2015", + "GDP Growth Rate": 6.8 + }, + { + "Quarter": "Q4 2015", + "GDP Growth Rate": 6.7 + }, + { + "Quarter": "Q1 2016", + "GDP Growth Rate": 6.7 + }, + { + "Quarter": "Q2 2016", + "GDP Growth Rate": 6.7 + }, + { + "Quarter": "Q3 2016", + "GDP Growth Rate": 6.7 + }, + { + "Quarter": "Q4 2016", + "GDP Growth Rate": 6.8 + }, + { + "Quarter": "Q1 2017", + "GDP Growth Rate": 6.9 + }, + { + "Quarter": "Q2 2017", + "GDP Growth Rate": 6.9 + }, + { + "Quarter": "Q3 2017", + "GDP Growth Rate": 6.8 + }, + { + "Quarter": "Q4 2017", + "GDP Growth Rate": 6.8 + }, + { + "Quarter": "Q1 2018", + "GDP Growth Rate": 6.8 + }, + { + "Quarter": "Q2 2018", + "GDP Growth Rate": 6.7 + } + ], + "columns": [ + { + "name": "Quarter", + "importance": "primary", + "description": "Represents the quarter and year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "GDP Growth Rate", + "importance": "primary", + "description": "Annual growth rate of GDP", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "892": { + "metadata": { + "title": "The Limited Effects of Taxes on the Overall Economy", + "description": "Average federal tax revenue and average annual real GDP per capita growth rate in U.S.", + "main_insight": "Despite significant increases in average federal tax revenue as a percentage of GDP over different time periods, the average per capita GDP growth has remained relatively stable." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time Period": "1913-1950", + "Ratio": 10.7, + "Economic Indicators": "Avg. federal tax revenue (as % of GDP)" + } + ], + "score": 0.86, + "annotation": "The Avg. federal tax revenue (as % of GDP) shows a sudden jump in Ratio from 1868-1912 to 1913-1950.", + "reason": "The Ratio for Avg. federal tax revenue (as % of GDP) increases from 2.4 at 1868-1912 to 10.7 at 1913-1950, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time Period": "1913-1950", + "Ratio": 2.0, + "Economic Indicators": "Avg. per capita GDP growth (in %)" + } + ], + "score": 0.83, + "annotation": "The Avg. per capita GDP growth (in %) shows a sudden jump in Ratio from 1951-2014 to 1913-1950.", + "reason": "The Ratio for Avg. per capita GDP growth (in %) increases from 1.9 at 1951-2014 to 2.0 at 1913-1950, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Time Period": "1868-1912", + "Ratio": 2.4, + "Economic Indicators": "Avg. federal tax revenue (as % of GDP)" + } + ], + "score": 0.78, + "annotation": "The Avg. federal tax revenue (as % of GDP) accounts for the majority in 1868-1912.", + "reason": "The Ratio of Avg. federal tax revenue (as % of GDP) in 1868-1912 accounts for 0.5714285714285714 (more than 50%) of the total of Ratio of all Economic Indicators." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Time Period": "1913-1950", + "Ratio": 10.7, + "Economic Indicators": "Avg. federal tax revenue (as % of GDP)" + } + ], + "score": 0.7, + "annotation": "The Avg. federal tax revenue (as % of GDP) accounts for the majority in 1913-1950.", + "reason": "The Ratio of Avg. federal tax revenue (as % of GDP) in 1913-1950 accounts for 0.84251968503937 (more than 50%) of the total of Ratio of all Economic Indicators." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Economic Indicators": "Avg. federal tax revenue (as % of GDP)", + "Time Period": "total", + "Ratio": 30.5 + } + ], + "score": 0.7, + "annotation": "The total value of Avg. federal tax revenue (as % of GDP) accounts for the majority in all Economic Indicators.", + "reason": "The total value of Ratio of Avg. federal tax revenue (as % of GDP) accounts for 0.8425414364640883 (more than 50%) of the total of Ratio of all Economic Indicators." + } + ], + "data": { + "data": [ + { + "Time Period": "1868-1912", + "Ratio": 2.4, + "Economic Indicators": "Avg. federal tax revenue (as % of GDP)" + }, + { + "Time Period": "1868-1912", + "Ratio": 1.8, + "Economic Indicators": "Avg. per capita GDP growth (in %)" + }, + { + "Time Period": "1913-1950", + "Ratio": 10.7, + "Economic Indicators": "Avg. federal tax revenue (as % of GDP)" + }, + { + "Time Period": "1913-1950", + "Ratio": 2.0, + "Economic Indicators": "Avg. per capita GDP growth (in %)" + }, + { + "Time Period": "1951-2014", + "Ratio": 17.4, + "Economic Indicators": "Avg. federal tax revenue (as % of GDP)" + }, + { + "Time Period": "1951-2014", + "Ratio": 1.9, + "Economic Indicators": "Avg. per capita GDP growth (in %)" + } + ], + "columns": [ + { + "name": "Time Period", + "importance": "primary", + "description": "Ranges of years", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding economic indicators", + "role": "y" + }, + { + "name": "Economic Indicators", + "importance": "primary", + "description": "The economic indicators of the data point", + "role": "group" + } + ] + } + }, + "1839": { + "metadata": { + "title": "Most Ukrainians Want Their Country to Remain United", + "description": "Should Ukraine...", + "main_insight": "A majority of Ukrainians across different regions want their country to remain united, with the highest support in Western Ukraine." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Region": "Crimea", + "Score": 54.0, + "Referendum Options": "Remain united" + }, + { + "Region": "Crimea", + "Score": 34.0, + "Referendum Options": "Allow regions to secede" + } + ], + "score": 0.99, + "annotation": "The Score of Remain united and Allow regions to secede show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Remain united and Allow regions to secede is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Region": "Western Ukraine", + "Score": 2.0, + "Referendum Options": "Don't know" + } + ], + "score": 0.91, + "annotation": "The Don't know has the smallest proportion in Western Ukraine.", + "reason": "The Score of Don't know in Western Ukraine accounts for 0.020202020202020204, smaller than all other Referendum Options, of the total of Score of all Referendum Options." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Region": "Crimea", + "Score": 54.0, + "Referendum Options": "Remain united" + }, + { + "Region": "Crimea", + "Score": 12.0, + "Referendum Options": "Don't know" + } + ], + "score": 0.89, + "annotation": "The Score of Remain united and Don't know show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Remain united and Don't know is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Ukraine", + "Score": 9.0, + "Referendum Options": "Don't know" + } + ], + "score": 0.85, + "annotation": "The Don't know shows a sudden jump in Score from Western Ukraine to Ukraine.", + "reason": "The Score for Don't know increases from 2.0 at Western Ukraine to 9.0 at Ukraine, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Crimea", + "Score": 54.0, + "Referendum Options": "Remain united" + } + ], + "score": 0.85, + "annotation": "The Remain united accounts for the majority in Crimea.", + "reason": "The Score of Remain united in Crimea accounts for 0.54 (more than 50%) of the total of Score of all Referendum Options." + } + ], + "data": { + "data": [ + { + "Region": "Ukraine", + "Score": 77.0, + "Referendum Options": "Remain united" + }, + { + "Region": "Ukraine", + "Score": 14.0, + "Referendum Options": "Allow regions to secede" + }, + { + "Region": "Ukraine", + "Score": 9.0, + "Referendum Options": "Don't know" + }, + { + "Region": "Western Ukraine", + "Score": 93.0, + "Referendum Options": "Remain united" + }, + { + "Region": "Western Ukraine", + "Score": 4.0, + "Referendum Options": "Allow regions to secede" + }, + { + "Region": "Western Ukraine", + "Score": 2.0, + "Referendum Options": "Don't know" + }, + { + "Region": "Eastern Ukraine", + "Score": 70.0, + "Referendum Options": "Remain united" + }, + { + "Region": "Eastern Ukraine", + "Score": 18.0, + "Referendum Options": "Allow regions to secede" + }, + { + "Region": "Eastern Ukraine", + "Score": 13.0, + "Referendum Options": "Don't know" + }, + { + "Region": "Russian Speakers", + "Score": 58.0, + "Referendum Options": "Remain united" + }, + { + "Region": "Russian Speakers", + "Score": 27.0, + "Referendum Options": "Allow regions to secede" + }, + { + "Region": "Russian Speakers", + "Score": 15.0, + "Referendum Options": "Don't know" + }, + { + "Region": "Crimea", + "Score": 54.0, + "Referendum Options": "Remain united" + }, + { + "Region": "Crimea", + "Score": 34.0, + "Referendum Options": "Allow regions to secede" + }, + { + "Region": "Crimea", + "Score": 12.0, + "Referendum Options": "Don't know" + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Different regions or groups within Ukraine", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding referendum options", + "role": "y" + }, + { + "name": "Referendum Options", + "importance": "primary", + "description": "The referendum options of the data point", + "role": "group" + } + ] + } + }, + "2527": { + "metadata": { + "title": "Liverpool Edged Manchester City In Season Earnings", + "description": "Premier League's payments to clubs in the 2018/19 season", + "main_insight": "Liverpool earned the most in payments from the Premier League in the 2018/19 season, slightly edging out Manchester City, with a difference of around 1.5 million pounds." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Team": "Arsenal", + "Payments": 142193180.0 + } + ], + "score": 0.81, + "annotation": "The shows a sudden jump in Payments from Everton to Arsenal.", + "reason": "The Payments for increases from 128603905.0 at Everton to 142193180.0 at Arsenal, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Team": "West Ham United", + "Payments": 122528663.0 + } + ], + "score": 0.75, + "annotation": "The has a minimum value at West Ham United", + "reason": "The [{'Team': 'West Ham United', 'Payments': 122528663.0}] of has a mainimum value of 122528663.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Team": "Liverpool", + "Payments": 152425146.0 + } + ], + "score": 0.71, + "annotation": "The has a maximum value at Liverpool", + "reason": "The [{'Team': 'Liverpool', 'Payments': 152425146.0}] of has a maximum value of 152425146.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Team": "Liverpool", + "Payments": 152425146.0 + }, + { + "Team": "Manchester City", + "Payments": 150986355.0 + }, + { + "Team": "Chelsea", + "Payments": 146030216.0 + }, + { + "Team": "Tottenham Hotspur", + "Payments": 145230801.0 + }, + { + "Team": "Manchester United", + "Payments": 142512868.0 + }, + { + "Team": "Arsenal", + "Payments": 142193180.0 + }, + { + "Team": "Everton", + "Payments": 128603905.0 + }, + { + "Team": "Wolverhampton Wanderers", + "Payments": 127165114.0 + }, + { + "Team": "Leicester City", + "Payments": 123328078.0 + }, + { + "Team": "West Ham United", + "Payments": 122528663.0 + } + ], + "columns": [ + { + "name": "Team", + "importance": "primary", + "description": "Premier League Team", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Payments", + "importance": "primary", + "description": "Payments received by each team (in British Pounds)", + "unit": "GBP", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1713": { + "metadata": { + "title": "Investor Activist Demands Fall After 2018 Record", + "description": "Number of U.S. based companies publicly subjected to activist demands", + "main_insight": "The number of U.S. companies facing activist demands peaked in 2018 and declined in 2019. The full-year numbers consistently exceed the Q1 numbers, showing that activist demands happen throughout the year." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2013, + "Units": 328, + "Reporting Period": "Full Year" + }, + { + "Year": 2013, + "Units": 121, + "Reporting Period": "Q1" + }, + { + "Year": 2014, + "Units": 378, + "Reporting Period": "Full Year" + }, + { + "Year": 2014, + "Units": 144, + "Reporting Period": "Q1" + }, + { + "Year": 2015, + "Units": 432, + "Reporting Period": "Full Year" + }, + { + "Year": 2015, + "Units": 174, + "Reporting Period": "Q1" + }, + { + "Year": 2016, + "Units": 491, + "Reporting Period": "Full Year" + }, + { + "Year": 2016, + "Units": 221, + "Reporting Period": "Q1" + }, + { + "Year": 2017, + "Units": 470, + "Reporting Period": "Full Year" + }, + { + "Year": 2017, + "Units": 191, + "Reporting Period": "Q1" + }, + { + "Year": 2018, + "Units": 493, + "Reporting Period": "Full Year" + }, + { + "Year": 2018, + "Units": 227, + "Reporting Period": "Q1" + }, + { + "Year": 2019, + "Units": 198, + "Reporting Period": "Full Year" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the data", + "unit": "none", + "data_type": "time", + "discarded_count": 0, + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding reporting period", + "role": "y" + }, + { + "name": "Reporting Period", + "importance": "primary", + "description": "The reporting period of the data point", + "role": "group" + } + ] + } + }, + "1927": { + "metadata": { + "title": "Federer's Brand Pays Off Even When He's Not Playing", + "description": "Estimated earnings of the highest-paid tennis players in the 12 months since August 2021", + "main_insight": "Roger Federer earned significantly more than any other tennis player in the 12 months since August 2021, and the vast majority of his earnings came from off-court sources." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Player": "Serena Williams", + "Score": 0.1, + "Court Status": "On-court" + } + ], + "score": 0.99, + "annotation": "The On-court has the smallest proportion in Naomi Osaka.", + "reason": "The Score of On-court in Naomi Osaka accounts for 0.0018148820326678767, smaller than all other Court Status, of the total of Score of all Court Status." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Player": "Naomi Osaka", + "Score": 1.2, + "Court Status": "On-court" + } + ], + "score": 0.94, + "annotation": "The On-court has the smallest proportion in Roger Federer.", + "reason": "The Score of On-court in Roger Federer accounts for 0.013157894736842105, smaller than all other Court Status, of the total of Score of all Court Status." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Player": "Roger Federer", + "Score": 90.0, + "Court Status": "Off-court" + } + ], + "score": 0.93, + "annotation": "The Off-court has a maximum value at Roger Federer", + "reason": "The [{'Player': 'Roger Federer', 'Score': 90.0, 'Court Status': 'Off-court'}] of Off-court has a maximum value of 90.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Player": "Kei Nishikori", + "Score": 0.2, + "Court Status": "On-court" + } + ], + "score": 0.92, + "annotation": "The On-court has the smallest proportion in Daniil Medvedev.", + "reason": "The Score of On-court in Daniil Medvedev accounts for 0.01639344262295082, smaller than all other Court Status, of the total of Score of all Court Status." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Player": "Rafael Nadal", + "Score": 6.4, + "Court Status": "On-court" + } + ], + "score": 0.81, + "annotation": "The On-court shows a sudden jump in Score from Emma Raducanu to Rafael Nadal.", + "reason": "The Score for On-court increases from 3.1 at Emma Raducanu to 6.4 at Rafael Nadal, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Player": "Roger Federer", + "Score": 90.0, + "Court Status": "Off-court" + }, + { + "Player": "Naomi Osaka", + "Score": 55.0, + "Court Status": "Off-court" + }, + { + "Player": "Naomi Osaka", + "Score": 1.2, + "Court Status": "On-court" + }, + { + "Player": "Serena Williams", + "Score": 35.0, + "Court Status": "Off-court" + }, + { + "Player": "Serena Williams", + "Score": 0.1, + "Court Status": "On-court" + }, + { + "Player": "Rafael Nadal", + "Score": 25.0, + "Court Status": "Off-court" + }, + { + "Player": "Rafael Nadal", + "Score": 6.4, + "Court Status": "On-court" + }, + { + "Player": "Novak Djokovic", + "Score": 20.0, + "Court Status": "Off-court" + }, + { + "Player": "Novak Djokovic", + "Score": 7.1, + "Court Status": "On-court" + }, + { + "Player": "Emma Raducanu", + "Score": 18.0, + "Court Status": "Off-court" + }, + { + "Player": "Emma Raducanu", + "Score": 3.1, + "Court Status": "On-court" + }, + { + "Player": "Daniil Medvedev", + "Score": 12.0, + "Court Status": "Off-court" + }, + { + "Player": "Daniil Medvedev", + "Score": 7.3, + "Court Status": "On-court" + }, + { + "Player": "Kei Nishikori", + "Score": 13.0, + "Court Status": "Off-court" + }, + { + "Player": "Kei Nishikori", + "Score": 0.2, + "Court Status": "On-court" + } + ], + "columns": [ + { + "name": "Player", + "importance": "primary", + "description": "Name of the tennis player", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding court status", + "role": "y" + }, + { + "name": "Court Status", + "importance": "primary", + "description": "The court status of the data point", + "role": "group" + } + ] + } + }, + "582": { + "metadata": { + "title": "Majority of Americans Not Ready to Reopen", + "description": "Percentage of U.S. adults who have each opinion related to removing COVID-19 restrictions", + "main_insight": "A majority of Americans prioritize controlling the spread of COVID-19 over restarting the economy and are not willing to go to stores and restaurants, deeming it too early." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [], + "data": { + "data": [ + { + "Question": "What is more important?", + "Score": 57.0, + "Priorities": "Controlling the spread" + }, + { + "Question": "What is more important?", + "Score": 37.0, + "Priorities": "Restarting the economy" + }, + { + "Question": "What is more important?", + "Score": 6.0, + "Priorities": "No opinion (What is more important?)" + }, + { + "Question": "Are you now willing to go to stores, restaurants and other public places?", + "Score": 40.0, + "Priorities": "Yes" + }, + { + "Question": "Are you now willing to go to stores, restaurants and other public places?", + "Score": 58.0, + "Priorities": "No, too early" + }, + { + "Question": "Are you now willing to go to stores, restaurants and other public places?", + "Score": 2.0, + "Priorities": "No opinion (Willing to go to stores)" + } + ], + "columns": [ + { + "name": "Question", + "importance": "primary", + "description": "The question being asked to gauge public opinion", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding priorities", + "role": "y" + }, + { + "name": "Priorities", + "importance": "primary", + "description": "The priorities of the data point", + "role": "group" + } + ] + } + }, + "2302": { + "metadata": { + "title": "Less Japanese Women Give \"Giri Choco\" on Valentine's Day", + "description": "Percentage of people Japanese women said they would give chocolates to on Feb 14", + "main_insight": "The percentage of Japanese women giving 'Giri Choco' to colleagues, friends, and themselves decreased from 2017 to 2019, while the percentage giving to family and partners remained relatively stable." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Recipient": "Partner", + "Ratio": 0.65, + "Year": "2017" + } + ], + "score": 0.87, + "annotation": "The 2017 shows a sudden jump in Ratio from Colleagues to Partner.", + "reason": "The Ratio for 2017 increases from 0.41 at Colleagues to 0.65 at Partner, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Recipient": "Partner", + "Ratio": 0.66, + "Year": "2019" + } + ], + "score": 0.84, + "annotation": "The 2019 shows a sudden jump in Ratio from Family to Partner.", + "reason": "The Ratio for 2019 increases from 0.39 at Family to 0.66 at Partner, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Recipient": "Partner", + "Ratio": 0.66, + "Year": "2019" + } + ], + "score": 0.82, + "annotation": "The 2019 has a maximum value at Partner", + "reason": "The [{'Recipient': 'Partner', 'Ratio': 0.66, 'Year': '2019'}] of 2019 has a maximum value of 0.66, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Recipient": "Partner", + "Ratio": 0.66, + "Year": "2019" + } + ], + "score": 0.8, + "annotation": "The 2019 accounts for the majority in Partner.", + "reason": "The Ratio of 2019 in Partner accounts for 0.5038167938931297 (more than 50%) of the total of Ratio of all Year." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "2019", + "Recipient": "total", + "Ratio": 1.6400000000000001 + } + ], + "score": 0.8, + "annotation": "The total value of 2019 accounts for the majority in all Year.", + "reason": "The total value of Ratio of 2019 accounts for 0.5061728395061729 (more than 50%) of the total of Ratio of all Year." + } + ], + "data": { + "data": [ + { + "Recipient": "Partner", + "Ratio": 0.65, + "Year": "2017" + }, + { + "Recipient": "Partner", + "Ratio": 0.66, + "Year": "2019" + }, + { + "Recipient": "Colleagues", + "Ratio": 0.41, + "Year": "2017" + }, + { + "Recipient": "Colleagues", + "Ratio": 0.31, + "Year": "2019" + }, + { + "Recipient": "Family", + "Ratio": 0.39, + "Year": "2019" + }, + { + "Recipient": "Friends", + "Ratio": 0.24, + "Year": "2017" + }, + { + "Recipient": "Friends", + "Ratio": 0.14, + "Year": "2019" + }, + { + "Recipient": "Myself", + "Ratio": 0.3, + "Year": "2017" + }, + { + "Recipient": "Myself", + "Ratio": 0.14, + "Year": "2019" + } + ], + "columns": [ + { + "name": "Recipient", + "importance": "primary", + "description": "Who the chocolates are given to", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "510": { + "metadata": { + "title": "Fitbit Retains Wearables Market Lead", + "description": "Global wearable device shipments by vendor (in million units)", + "main_insight": "Fitbit maintained its lead in the wearables market in Q3 2015, although shipments from other vendors, including Apple and Xiaomi, increased significantly." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Vendor": "Fitbit", + "Units": 2.3, + "Quarter": "Q3 2014" + } + ], + "score": 0.84, + "annotation": "The Q3 2014 shows a sudden jump in Units from Garmin to Fitbit.", + "reason": "The Units for Q3 2014 increases from 0.5 at Garmin to 2.3 at Fitbit, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Vendor": "Others", + "Units": 7.3, + "Quarter": "Q3 2015" + } + ], + "score": 0.81, + "annotation": "The Q3 2015 has a maximum value at Others", + "reason": "The [{'Vendor': 'Others', 'Units': 7.3, 'Quarter': 'Q3 2015'}] of Q3 2015 has a maximum value of 7.3, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Vendor": "Xiaomi", + "Units": 3.7, + "Quarter": "Q3 2015" + } + ], + "score": 0.79, + "annotation": "The Q3 2015 shows a sudden jump in Units from Garmin to Xiaomi.", + "reason": "The Units for Q3 2015 increases from 0.9 at Garmin to 3.7 at Xiaomi, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Vendor": "Fitbit", + "Units": 4.7, + "Quarter": "Q3 2015" + } + ], + "score": 0.75, + "annotation": "The Q3 2015 accounts for the majority in Fitbit.", + "reason": "The Units of Q3 2015 in Fitbit accounts for 0.6714285714285715 (more than 50%) of the total of Units of all Quarter." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q3 2015", + "Vendor": "total", + "Units": 21.2 + } + ], + "score": 0.73, + "annotation": "The total value of Q3 2015 accounts for the majority in all Quarter.", + "reason": "The total value of Units of Q3 2015 accounts for 0.7491166077738517 (more than 50%) of the total of Units of all Quarter." + } + ], + "data": { + "data": [ + { + "Vendor": "Fitbit", + "Units": 4.7, + "Quarter": "Q3 2015" + }, + { + "Vendor": "Fitbit", + "Units": 2.3, + "Quarter": "Q3 2014" + }, + { + "Vendor": "Apple", + "Units": 3.9, + "Quarter": "Q3 2015" + }, + { + "Vendor": "Xiaomi", + "Units": 3.7, + "Quarter": "Q3 2015" + }, + { + "Vendor": "Xiaomi", + "Units": 0.4, + "Quarter": "Q3 2014" + }, + { + "Vendor": "Garmin", + "Units": 0.9, + "Quarter": "Q3 2015" + }, + { + "Vendor": "Garmin", + "Units": 0.5, + "Quarter": "Q3 2014" + }, + { + "Vendor": "BBK", + "Units": 0.7, + "Quarter": "Q3 2015" + }, + { + "Vendor": "Others", + "Units": 7.3, + "Quarter": "Q3 2015" + }, + { + "Vendor": "Others", + "Units": 3.9, + "Quarter": "Q3 2014" + } + ], + "columns": [ + { + "name": "Vendor", + "importance": "primary", + "description": "Wearable device vendor", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding quarter", + "role": "y" + }, + { + "name": "Quarter", + "importance": "primary", + "description": "The quarter of the data point", + "role": "group" + } + ] + } + }, + "1426": { + "metadata": { + "title": "Meituan Holds Power over Chinese Food Delivery Market", + "description": "Market share of food delivery services in China (2020)", + "main_insight": "Meituan dominates the Chinese food delivery market in 2020 with a 67.3% market share, significantly ahead of its closest competitor, Ele.me." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "Meituan", + "Market Share": 67.3 + } + ], + "score": 0.89, + "annotation": "The shows a sudden jump in Market Share from Ele.me to Meituan.", + "reason": "The Market Share for increases from 26.9 at Ele.me to 67.3 at Meituan, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Company": "Meituan", + "Market Share": 67.3 + } + ], + "score": 0.77, + "annotation": "The has a maximum value at Meituan", + "reason": "The [{'Company': 'Meituan', 'Market Share': 67.3}] of has a maximum value of 67.3, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Company": "Others", + "Market Share": 1.8 + } + ], + "score": 0.48, + "annotation": "The has a minimum value at Others", + "reason": "The [{'Company': 'Others', 'Market Share': 1.8}] of has a mainimum value of 1.8, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Company": "Meituan", + "Market Share": 67.3 + }, + { + "Company": "Ele.me", + "Market Share": 26.9 + }, + { + "Company": "Star.ele.me", + "Market Share": 4.0 + }, + { + "Company": "Others", + "Market Share": 1.8 + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Name of the food delivery service", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Market Share", + "importance": "primary", + "description": "Percentage of the market share", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2065": { + "metadata": { + "title": "Then & Now China's Military Modernization", + "description": "Share of modern and older equipment in the Chinese military", + "main_insight": "China's military has significantly modernized from 1999 to 2019, with a substantial increase in the proportion of modern fighter jets, attack submarines, and surface warships." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Equipment Type": "Attack submarines", + "Units": 3, + "Timeframe": "Modern" + } + ], + "score": 0.99, + "annotation": "The Modern has the smallest proportion in Attack submarines.", + "reason": "The Units of Modern in Attack submarines accounts for 0.0014506769825918763, smaller than all other Timeframe, of the total of Units of all Timeframe." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Equipment Type": "Fighter jets", + "Units": 2019, + "Timeframe": "Year" + } + ], + "score": 0.97, + "annotation": "The Year shows a sudden jump in Units from Surface warships to Fighter jets.", + "reason": "The Units for Year increases from 1999 at Surface warships to 2019 at Fighter jets, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Equipment Type": "Fighter jets", + "Units": 837, + "Timeframe": "Modern" + } + ], + "score": 0.97, + "annotation": "The Modern shows a sudden jump in Units from Fighter jets to Fighter jets.", + "reason": "The Units for Modern increases from 52 at Fighter jets to 837 at Fighter jets, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Equipment Type": "Attack submarines", + "Units": 19, + "Timeframe": "Older" + } + ], + "score": 0.96, + "annotation": "The Older has the smallest proportion in Attack submarines.", + "reason": "The Units of Older in Attack submarines accounts for 0.009161041465766635, smaller than all other Timeframe, of the total of Units of all Timeframe." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Equipment Type": "Fighter jets", + "Units": 52, + "Timeframe": "Modern" + } + ], + "score": 0.95, + "annotation": "The Modern has the smallest proportion in Fighter jets.", + "reason": "The Units of Modern in Fighter jets accounts for 0.009631413224671235, smaller than all other Timeframe, of the total of Units of all Timeframe." + } + ], + "data": { + "data": [ + { + "Equipment Type": "Fighter jets", + "Units": 1999, + "Timeframe": "Year" + }, + { + "Equipment Type": "Fighter jets", + "Units": 3348, + "Timeframe": "Older" + }, + { + "Equipment Type": "Fighter jets", + "Units": 52, + "Timeframe": "Modern" + }, + { + "Equipment Type": "Fighter jets", + "Units": 2019, + "Timeframe": "Year" + }, + { + "Equipment Type": "Fighter jets", + "Units": 852, + "Timeframe": "Older" + }, + { + "Equipment Type": "Fighter jets", + "Units": 837, + "Timeframe": "Modern" + }, + { + "Equipment Type": "Attack submarines", + "Units": 1999, + "Timeframe": "Year" + }, + { + "Equipment Type": "Attack submarines", + "Units": 66, + "Timeframe": "Older" + }, + { + "Equipment Type": "Attack submarines", + "Units": 3, + "Timeframe": "Modern" + }, + { + "Equipment Type": "Attack submarines", + "Units": 2019, + "Timeframe": "Year" + }, + { + "Equipment Type": "Attack submarines", + "Units": 19, + "Timeframe": "Older" + }, + { + "Equipment Type": "Attack submarines", + "Units": 36, + "Timeframe": "Modern" + }, + { + "Equipment Type": "Surface warships", + "Units": 1999, + "Timeframe": "Year" + }, + { + "Equipment Type": "Surface warships", + "Units": 53, + "Timeframe": "Older" + }, + { + "Equipment Type": "Surface warships", + "Units": 2019, + "Timeframe": "Year" + }, + { + "Equipment Type": "Surface warships", + "Units": 39, + "Timeframe": "Older" + }, + { + "Equipment Type": "Surface warships", + "Units": 48, + "Timeframe": "Modern" + } + ], + "columns": [ + { + "name": "Equipment Type", + "importance": "primary", + "description": "Category of military equipment", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding 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"primary", + "description": "Score for the corresponding time", + "role": "y" + }, + { + "name": "Time", + "importance": "primary", + "description": "The time of the data point", + "role": "group" + } + ] + } + }, + "627": { + "metadata": { + "title": "Brazil Stops Publishing COVID-19 Figures As Deaths Soar", + "description": "Total confirmed COVID-19 deaths in Brazil", + "main_insight": "Confirmed COVID-19 deaths in Brazil increased significantly from March to June 2020, exceeding 30,000 by early June. Brazil ceased publication of total deaths and cases figures on June 6." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "01 June", + "Total Deaths": 29000 + } + ], + "score": 0.91, + "annotation": "The shows a sudden jump in Total Deaths from 01 May to 01 June.", + "reason": "The Total Deaths for increases from 6000 at 01 May to 29000 at 01 June, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "05 June", + "Total Deaths": 34000 + } + ], + "score": 0.7, + "annotation": "The has a maximum value at 05 June", + "reason": "The [{'Date': '05 June', 'Total Deaths': 34000}] of has a maximum value of 34000, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "18 March", + "Total Deaths": 0 + } + ], + "score": 0.52, + "annotation": "The has a minimum value at 18 March", + "reason": "The [{'Date': '18 March', 'Total Deaths': 0}] of has a mainimum value of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Date": "18 March", + "Total Deaths": 0 + }, + { + "Date": "01 April", + "Total Deaths": 200 + }, + { + "Date": "01 May", + "Total Deaths": 6000 + }, + { + "Date": "01 June", + "Total Deaths": 29000 + }, + { + "Date": "05 June", + "Total Deaths": 34000 + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Date in 2020, ranging from March to June", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Total Deaths", + "importance": "primary", + "description": "Total confirmed COVID-19 deaths", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1978": { + "metadata": { + "title": "International Student Enrollment Declining In The U.S.", + "description": "Undergraduate and graduate level enrollment of international students at U.S. universities", + "main_insight": "International student enrollment in U.S. universities, both undergraduate and graduate, increased from 2012 to 2016 and then showed a slight decline in 2017. Graduate student numbers are significantly higher than undergraduate numbers in all years." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2012, + "Units": 633070, + "Education Level": "Graduate" + }, + { + "Year": 2013, + "Units": 673480, + "Education Level": "Graduate" + }, + { + "Year": 2014, + "Units": 747400, + "Education Level": "Graduate" + }, + { + "Year": 2015, + "Units": 776720, + "Education Level": "Graduate" + }, + { + "Year": 2016, + "Units": 840160, + "Education Level": "Graduate" + }, + { + "Year": 2017, + "Units": 367920, + "Education Level": "Graduate" + }, + { + "Year": 2017, + "Units": 440720, + "Education Level": "Undergraduate" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Academic Year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding education level", + 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Agricultural Bank of China Limited had the highest emission in 2022." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Bank Name": "Agricultural Bank of China Limited", + "CO2 Emissions (1,000 tonnes)": 2153.91 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at Agricultural Bank of China Limited", + "reason": "The [{'Bank Name': 'Agricultural Bank of China Limited', 'CO2 Emissions (1,000 tonnes)': 2153.91}] of has a maximum value of 2153.91, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Bank Name": "Bank of China Limited", + "CO2 Emissions (1,000 tonnes)": 1537.55 + } + ], + "score": 0.67, + "annotation": "The shows a sudden jump in CO2 Emissions (1,000 tonnes) from JPMorgan Chase to Bank of China Limited.", + "reason": "The CO2 Emissions (1,000 tonnes) for increases from 961.4 at JPMorgan Chase to 1537.55 at Bank of China Limited, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Bank Name": "Bank of Communications Co. 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Ltd.*', 'CO2 Emissions (1,000 tonnes)': 58.8}] of has a mainimum value of 58.8, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Bank Name": "Agricultural Bank of China Limited", + "CO2 Emissions (1,000 tonnes)": 2153.91 + }, + { + "Bank Name": "Industrial & Commerical Bank of China Limited", + "CO2 Emissions (1,000 tonnes)": 2021.3 + }, + { + "Bank Name": "China Construction Bank Corporation", + "CO2 Emissions (1,000 tonnes)": 1682.81 + }, + { + "Bank Name": "Bank of China Limited", + "CO2 Emissions (1,000 tonnes)": 1537.55 + }, + { + "Bank Name": "JPMorgan Chase", + "CO2 Emissions (1,000 tonnes)": 961.4 + }, + { + "Bank Name": "Bank of America", + "CO2 Emissions (1,000 tonnes)": 773.03 + }, + { + "Bank Name": "Citigroup Inc", + "CO2 Emissions (1,000 tonnes)": 562.79 + }, + { + "Bank Name": "BNP Paribas", + "CO2 Emissions (1,000 tonnes)": 318.49 + }, + { + "Bank Name": "HSBC Holdings", + "CO2 Emissions (1,000 tonnes)": 285.0 + }, + { + "Bank Name": "MUFG Bank", + "CO2 Emissions (1,000 tonnes)": 249.0 + }, + { + "Bank Name": "Postal Savings Bank of China Ltd.", + "CO2 Emissions (1,000 tonnes)": 214.02 + }, + { + "Bank Name": "Sumitomo Mitsui Financial Group", + "CO2 Emissions (1,000 tonnes)": 177.19 + }, + { + "Bank Name": "Mizuho Bank Ltd)", + "CO2 Emissions (1,000 tonnes)": 108.88 + }, + { + "Bank Name": "Bank of Communications Co. 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"data_points": [ + { + "Age Group": "65+", + "Count": 7, + "Social Media": "Twitter" + }, + { + "Age Group": "65+", + "Count": 9, + "Social Media": "Facebook" + } + ], + "score": 0.92, + "annotation": "The Count of Twitter and Facebook show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Count between Twitter and Facebook is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Social Media": "Twitter", + "Age Group": "total", + "Count": 101 + }, + { + "Social Media": "Facebook", + "Age Group": "total", + "Count": 101 + } + ], + "score": 0.9, + "annotation": "The total value of Twitter, Facebook all have the largest proportion.", + "reason": "The total value of Count of Twitter, Facebook all account for 0.3344370860927152, larger than all other Social Media, of the total of Count of all Social Media." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "45 to 54", + "Count": 17, + "Social Media": "Facebook" + }, + { + "Age Group": "55 to 64", + "Count": 17, + "Social Media": "LinkedIn" + } + ], + "score": 0.9, + "annotation": "The Facebook, LinkedIn all have the largest proportion in 45 to 54.", + "reason": "The Count of Facebook, LinkedIn in 45 to 54 all account for 0.34, larger than all other Social Media, of the total of Count of all Social Media." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "13 to 17", + "Count": 10, + "Social Media": "Twitter" + } + ], + "score": 0.89, + "annotation": "The Twitter has the largest proportion in 13 to 17.", + "reason": "The Count of Twitter in 13 to 17 accounts for 0.37037037037037035, larger than all other Social Media, of the total of Count of all Social Media." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "55 to 64", + "Count": 13, + "Social Media": "Facebook" + } + ], + "score": 0.89, + "annotation": "The Facebook has the largest proportion in 55 to 64.", + "reason": "The Count of Facebook in 55 to 64 accounts for 0.37142857142857144, larger than all other Social Media, of the total of Count of all Social Media." + } + ], + "data": { + "data": [ + { + "Age Group": "2 to 12", + "Count": 1, + "Social Media": "Twitter" + }, + { + "Age Group": "2 to 12", + "Count": 2, + "Social Media": "Facebook" + }, + { + "Age Group": "13 to 17", + "Count": 10, + "Social Media": "Twitter" + }, + { + "Age Group": "13 to 17", + "Count": 8, + "Social Media": "Facebook" + }, + { + "Age Group": "13 to 17", + "Count": 4, + "Social Media": "LinkedIn" + }, + { + "Age Group": "18 to 24", + "Count": 18, + "Social Media": "Twitter" + }, + { + "Age Group": "18 to 24", + "Count": 14, + "Social Media": "Facebook" + }, + { + "Age Group": "18 to 24", + "Count": 9, + "Social Media": "LinkedIn" + }, + { + "Age Group": "25 to 34", + "Count": 22, + "Social Media": "Twitter" + }, + { + "Age Group": "25 to 34", + "Count": 19, + "Social Media": "Facebook" + }, + { + "Age Group": "25 to 34", + "Count": 20, + "Social Media": "LinkedIn" + }, + { + "Age Group": "35 to 44", + "Count": 16, + "Social Media": "Twitter" + }, + { + "Age Group": "35 to 44", + "Count": 19, + "Social Media": "Facebook" + }, + { + "Age Group": "35 to 44", + "Count": 18, + "Social Media": "LinkedIn" + }, + { + "Age Group": "45 to 54", + "Count": 16, + "Social Media": "Twitter" + }, + { + "Age Group": "45 to 54", + "Count": 17, + "Social Media": "Facebook" + }, + { + "Age Group": "45 to 54", + "Count": 21, + "Social Media": "LinkedIn" + }, + { + "Age Group": "55 to 64", + "Count": 11, + "Social Media": "Twitter" + }, + { + "Age Group": "55 to 64", + "Count": 13, + "Social Media": "Facebook" + }, + { + "Age Group": "55 to 64", + "Count": 17, + "Social Media": "LinkedIn" + }, + { + "Age Group": "65+", + "Count": 7, + "Social Media": "Twitter" + }, + { + "Age Group": "65+", + "Count": 9, + "Social Media": "Facebook" + }, + { + "Age Group": "65+", + "Count": 11, + "Social Media": "LinkedIn" + } + ], + "columns": [ + { + "name": "Age Group", + "importance": "primary", + "description": "Age ranges of users", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Count", + "importance": "primary", + "description": "Count for the corresponding social media", + "role": "y" + }, + { + "name": "Social Media", + "importance": "primary", + "description": "The social media of the data point", + "role": "group" + } + ] + } + }, + "2036": { + "metadata": { + "title": "Where U.S. Companies Are Reshoring Jobs From", + "description": "Estimated share of reshored U.S. manufacturing jobs between 2010 and 2021, by source country", + "main_insight": "The majority of reshored U.S. manufacturing jobs between 2010 and 2021 came from China, followed by Mexico and Canada." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Source Country": "China", + "Reshored Jobs Share": 0.44 + } + ], + "score": 0.95, + "annotation": "The has a maximum value at China", + "reason": "The [{'Source Country': 'China', 'Reshored Jobs Share': 0.44}] of has a maximum value of 0.44, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Source Country": "China", + "Reshored Jobs Share": 0.44 + } + ], + "score": 0.85, + "annotation": "The shows a sudden jump in Reshored Jobs Share from Mexico to China.", + "reason": "The Reshored Jobs Share for increases from 0.21 at Mexico to 0.44 at China, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Source Country": "Honduras", + "Reshored Jobs Share": 0.01 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at Honduras", + "reason": "The [{'Source Country': 'Honduras', 'Reshored Jobs Share': 0.01}] of has a mainimum value of 0.01, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Source Country": "China", + "Reshored Jobs Share": 0.44 + }, + { + "Source Country": "Mexico", + "Reshored Jobs Share": 0.21 + }, + { + "Source Country": "Canada", + "Reshored Jobs Share": 0.1 + }, + { + "Source Country": "India", + "Reshored Jobs Share": 0.05 + }, + { + "Source Country": "Japan", + "Reshored Jobs Share": 0.05 + }, + { + "Source Country": "Singapore", + "Reshored Jobs Share": 0.03 + }, + { + "Source Country": "Germany", + "Reshored Jobs Share": 0.02 + }, + { + "Source Country": "Honduras", + "Reshored Jobs Share": 0.01 + } + ], + "columns": [ + { + "name": "Source Country", + "importance": "primary", + "description": "Country from which jobs were reshored", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Reshored Jobs Share", + "importance": "primary", + "description": "Estimated share of reshored U.S. manufacturing jobs", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1574": { + "metadata": { + "title": "UK Covid-19 cases per 1,000 tests", + "description": "Positive Covid-19 cases per 1,000 tests conducted in the United Kingdom", + "main_insight": "The rate of positive COVID-19 cases per 1,000 tests in the UK peaked in early January 2021 and has significantly decreased since then." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Jan 2021", + "Positive Cases per 1000 Tests": 140.0 + } + ], + "score": 0.9, + "annotation": "The has a maximum value at Jan 2021", + "reason": "The [{'Month': 'Jan 2021', 'Positive Cases per 1000 Tests': 140.0}] of has a maximum value of 140.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Jan 2021", + "Positive Cases per 1000 Tests": 140.0 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Positive Cases per 1000 Tests from Dec 2020 to Jan 2021.", + "reason": "The Positive Cases per 1000 Tests for increases from 90.0 at Dec 2020 to 140.0 at Jan 2021, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Jul 2020", + "Positive Cases per 1000 Tests": 5.0 + } + ], + "score": 0.58, + "annotation": "The has a minimum value at Jul 2020", + "reason": "The [{'Month': 'Jul 2020', 'Positive Cases per 1000 Tests': 5.0}] of has a mainimum value of 5.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Month": "Jul 2020", + "Positive Cases per 1000 Tests": 5.0 + }, + { + "Month": "Aug 2020", + "Positive Cases per 1000 Tests": 7.0 + }, + { + "Month": "Sep 2020", + "Positive Cases per 1000 Tests": 25.0 + }, + { + "Month": "Oct 2020", + "Positive Cases per 1000 Tests": 65.0 + }, + { + "Month": "Nov 2020", + "Positive Cases per 1000 Tests": 55.0 + }, + { + "Month": "Dec 2020", + "Positive Cases per 1000 Tests": 90.0 + }, + { + "Month": "Jan 2021", + "Positive Cases per 1000 Tests": 140.0 + }, + { + "Month": "Feb 2021", + "Positive Cases per 1000 Tests": 18.1 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Positive Cases per 1000 Tests", + "importance": "primary", + "description": "Seven-day average of positive Covid-19 cases per 1,000 tests", + "unit": "cases per 1000 Tests", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2502": { + "metadata": { + "title": "American & European Sanctions Against Russia", + "description": "Number of sanctions levied by the US and EU against Russia (2014-2018)", + "main_insight": "The US and EU imposed a large number of sanctions against Russia in 2014, with the number of sanctions decreasing in subsequent years. The EU imposed more sanctions than the US in 2014, but in other years the numbers are mostly the same, or the US levied more." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2018, + "Units": 7, + "Region": "US" + }, + { + "Year": 2018, + "Units": 7, + "Region": "EU" + } + ], + "score": 0.81, + "annotation": "The Units of US and EU show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between US and EU is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2014, + "Units": 13, + "Region": "US" + }, + { + "Year": 2014, + "Units": 14, + "Region": "EU" + }, + { + "Year": 2015, + "Units": 6, + "Region": "US" + }, + { + "Year": 2015, + "Units": 5, + "Region": "EU" + }, + { + "Year": 2016, + "Units": 6, + "Region": "US" + }, + { + "Year": 2016, + "Units": 6, + "Region": "EU" + }, + { + "Year": 2017, + "Units": 2, + "Region": "US" + }, + { + "Year": 2017, + "Units": 7, + "Region": "EU" + }, + { + "Year": 2018, + "Units": 7, + "Region": "US" + }, + { + "Year": 2018, + "Units": 7, + "Region": "EU" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of sanctions", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding region", + "role": "y" + }, + { + "name": "Region", + "importance": "primary", + "description": "The region of the data point", + "role": "group" + } + ] + } + }, + "1162": { + "metadata": { + "title": "Airbnb Heats Up Housing Market in The Melting Pot", + "description": "Change in monthly rental prices in selected New York neighborhoods due to Airbnb ('09-'16)", + "main_insight": "Airbnb significantly increased monthly rental prices in several New York neighborhoods, with the largest dollar increase in Greenpoint & Williamsburg and the largest percentage increase in Chelsea, Clinton & Midtown Business District." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Neighborhood": "Greenpoint & Williamsburg", + "Units": 123.0, + "Airbnb Impact": "Change due to Airbnb" + } + ], + "score": 0.92, + "annotation": "The Change due to Airbnb has a maximum value at Greenpoint & Williamsburg", + "reason": "The [{'Neighborhood': 'Greenpoint & Williamsburg', 'Units': 123.0, 'Airbnb Impact': 'Change due to Airbnb'}] of Change due to Airbnb has a maximum value of 123.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Neighborhood": "Brooklyn Heights & Fort Greene", + "Units": 10.7, + "Airbnb Impact": "Change due to Airbnb in %" + } + ], + "score": 0.79, + "annotation": "The Change due to Airbnb in % has a minimum value at Brooklyn Heights & Fort Greene", + "reason": "The [{'Neighborhood': 'Brooklyn Heights & Fort Greene', 'Units': 10.7, 'Airbnb Impact': 'Change due to Airbnb in %'}] of Change due to Airbnb in % has a mainimum value of 10.7, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Neighborhood": "Chinatown & Lower East Side", + "Units": 47.0, + "Airbnb Impact": "Change due to Airbnb" + } + ], + "score": 0.74, + "annotation": "The Change due to Airbnb accounts for the majority in Chinatown & Lower East Side.", + "reason": "The Units of Change due to Airbnb in Chinatown & Lower East Side accounts for 0.7057057057057058 (more than 50%) of the total of Units of all Airbnb Impact." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Neighborhood": "Hamilton Heights, Manhattanville & West Harlem", + "Units": 39.0, + "Airbnb Impact": "Change due to Airbnb" + } + ], + "score": 0.74, + "annotation": "The Change due to Airbnb accounts for the majority in Hamilton Heights, Manhattanville & West Harlem.", + "reason": "The Units of Change due to Airbnb in Hamilton Heights, Manhattanville & West Harlem accounts for 0.7103825136612022 (more than 50%) of the total of Units of all Airbnb Impact." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Neighborhood": "Chelsea, Clinton & Midtown Business District", + "Units": 21.6, + "Airbnb Impact": "Change due to Airbnb in %" + } + ], + "score": 0.73, + "annotation": "The Change due to Airbnb in % has a maximum value at Chelsea, Clinton & Midtown Business District", + "reason": "The [{'Neighborhood': 'Chelsea, Clinton & Midtown Business District', 'Units': 21.6, 'Airbnb Impact': 'Change due to Airbnb in %'}] of Change due to Airbnb in % has a maximum value of 21.6, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Neighborhood": "Greenpoint & Williamsburg", + "Units": 123.0, + "Airbnb Impact": "Change due to Airbnb" + }, + { + "Neighborhood": "Greenpoint & Williamsburg", + "Units": 18.6, + "Airbnb Impact": "Change due to Airbnb in %" + }, + { + "Neighborhood": "Murray Hill, Gramercy & Stuyvesant Town", + "Units": 105.0, + "Airbnb Impact": "Change due to Airbnb" + }, + { + "Neighborhood": "Murray Hill, Gramercy & Stuyvesant Town", + "Units": 21.5, + "Airbnb Impact": "Change due to Airbnb in %" + }, + { + "Neighborhood": "Chelsea, Clinton & Midtown Business District", + "Units": 86.0, + "Airbnb Impact": "Change due to Airbnb" + }, + { + "Neighborhood": "Chelsea, Clinton & Midtown Business District", + "Units": 21.6, + "Airbnb Impact": "Change due to Airbnb in %" + }, + { + "Neighborhood": "Battery Park City, Greenwich Village & Soho", + "Units": 79.0, + "Airbnb Impact": "Change due to Airbnb" + }, + { + "Neighborhood": "Battery Park City, Greenwich Village & Soho", + "Units": 19.3, + "Airbnb Impact": "Change due to Airbnb in %" + }, + { + "Neighborhood": "Bedford-Stuyvesant", + "Units": 59.0, + "Airbnb Impact": "Change due to Airbnb" + }, + { + "Neighborhood": "Bedford-Stuyvesant", + "Units": 14.4, + "Airbnb Impact": "Change due to Airbnb in %" + }, + { + "Neighborhood": "Bushwick", + "Units": 58.0, + "Airbnb Impact": "Change due to Airbnb" + }, + { + "Neighborhood": "Bushwick", + "Units": 15.6, + "Airbnb Impact": "Change due to Airbnb in %" + }, + { + "Neighborhood": "Brooklyn Heights & Fort Greene", + "Units": 54.0, + "Airbnb Impact": "Change due to Airbnb" + }, + { + "Neighborhood": "Brooklyn Heights & Fort Greene", + "Units": 10.7, + "Airbnb Impact": "Change due to Airbnb in %" + }, + { + "Neighborhood": "Chinatown & Lower East Side", + "Units": 47.0, + "Airbnb Impact": "Change due to Airbnb" + }, + { + "Neighborhood": "Chinatown & Lower East Side", + "Units": 19.6, + "Airbnb Impact": "Change due to Airbnb in %" + }, + { + "Neighborhood": "Upper East Side", + "Units": 43.0, + "Airbnb Impact": "Change due to Airbnb" + }, + { + "Neighborhood": "Upper East Side", + "Units": 11.3, + "Airbnb Impact": "Change due to Airbnb in %" + }, + { + "Neighborhood": "Hamilton Heights, Manhattanville & West Harlem", + "Units": 39.0, + "Airbnb Impact": "Change due to Airbnb" + }, + { + "Neighborhood": "Hamilton Heights, Manhattanville & West Harlem", + "Units": 15.9, + "Airbnb Impact": "Change due to Airbnb in %" + }, + { + "Neighborhood": "Park Slope, Carroll Gardens & Red Hook", + "Units": 39.0, + "Airbnb Impact": "Change due to Airbnb" + }, + { + "Neighborhood": "Park Slope, Carroll Gardens & Red Hook", + "Units": 12.4, + "Airbnb Impact": "Change due to Airbnb in %" + } + ], + "columns": [ + { + "name": "Neighborhood", + "importance": "primary", + "description": "New York City neighborhoods", + "unit": "none", + "data_type": "string", + "discarded_count": 0, + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding airbnb impact", + "role": "y" + }, + { + "name": "Airbnb Impact", + "importance": "primary", + "description": "The airbnb impact of the data point", + "role": "group" + } + ] + } + }, + "928": { + "metadata": { + "title": "Where VR & AR Buzz Takes Hold (And Where It Doesn't)", + "description": "Share of respondents who get excited about VR & AR headsets", + "main_insight": "India shows the highest excitement for VR & AR headsets among the surveyed countries, while the UK shows the lowest." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "India", + "Excitement Level": 0.47 + } + ], + "score": 0.81, + "annotation": "The has a maximum value at India", + "reason": "The [{'Country': 'India', 'Excitement Level': 0.47}] of has a maximum value of 0.47, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Mexico", + "Excitement Level": 0.33 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Excitement Level from United States to Mexico.", + "reason": "The Excitement Level for increases from 0.22 at United States to 0.33 at Mexico, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "United Kingdom", + "Excitement Level": 0.18 + } + ], + "score": 0.6, + "annotation": "The has a minimum value at United Kingdom", + "reason": "The [{'Country': 'United Kingdom', 'Excitement Level': 0.18}] of has a mainimum value of 0.18, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "India", + "Excitement Level": 0.47 + }, + { + "Country": "Brazil", + "Excitement Level": 0.37 + }, + { + "Country": "Mexico", + "Excitement Level": 0.33 + }, + { + "Country": "United States", + "Excitement Level": 0.22 + }, + { + "Country": "Germany", + "Excitement Level": 0.2 + }, + { + "Country": "United Kingdom", + "Excitement Level": 0.18 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Excitement Level", + "importance": "primary", + "description": "Percentage of respondents who get excited about VR & AR headsets", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1124": { + "metadata": { + "title": "Where The Mo Is Raising The Most Dough", + "description": "Total amount raised for 'Movember' in 2016, by country (in U.S. dollars)", + "main_insight": "Australia raised the most money for 'Movember' in 2016, followed by the United States and Canada. The total raised across all countries listed was $43,814,019." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Australia", + "Amount Raised": 13392967.0 + } + ], + "score": 0.87, + "annotation": "The has a maximum value at Australia", + "reason": "The [{'Country': 'Australia', 'Amount Raised': 13392967.0}] of has a maximum value of 13392967.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Canada", + "Amount Raised": 9453874.0 + } + ], + "score": 0.74, + "annotation": "The shows a sudden jump in Amount Raised from UK to Canada.", + "reason": "The Amount Raised for increases from 4746744.0 at UK to 9453874.0 at Canada, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "France", + "Amount Raised": 336673.0 + } + ], + "score": 0.47, + "annotation": "The has a minimum value at France", + "reason": "The [{'Country': 'France', 'Amount Raised': 336673.0}] of has a mainimum value of 336673.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Australia", + "Amount Raised": 13392967.0 + }, + { + "Country": "United States", + "Amount Raised": 11478289.0 + }, + { + "Country": "Canada", + "Amount Raised": 9453874.0 + }, + { + "Country": "UK", + "Amount Raised": 4746744.0 + }, + { + "Country": "New Zealand", + "Amount Raised": 789319.0 + }, + { + "Country": "Ireland", + "Amount Raised": 595330.0 + }, + { + "Country": "Norway", + "Amount Raised": 363631.0 + }, + { + "Country": "Germany", + "Amount Raised": 346484.0 + }, + { + "Country": "Switzerland", + "Amount Raised": 338418.0 + }, + { + "Country": "France", + "Amount Raised": 336673.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country participating in Movember", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Amount Raised", + "importance": "primary", + "description": "Total amount raised in U.S. dollars", + "unit": "USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2097": { + "metadata": { + "title": "Irish Households Lost The Most In The Financial Crisis", + "description": "Household net worth across countries in the 4 years up to Q1 2015 (Euro per capita)", + "main_insight": "Irish households experienced the largest loss in net worth per capita among the listed countries during the specified period, while Netherlands households saw the largest gain." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Netherlands", + "Net Worth Change": 33621.0 + } + ], + "score": 0.84, + "annotation": "The has a maximum value at Netherlands", + "reason": "The [{'Country': 'Netherlands', 'Net Worth Change': 33621.0}] of has a maximum value of 33621.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Ireland", + "Net Worth Change": -18474.0 + } + ], + "score": 0.73, + "annotation": "The has a minimum value at Ireland", + "reason": "The [{'Country': 'Ireland', 'Net Worth Change': -18474.0}] of has a mainimum value of -18474.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Italy", + "Net Worth Change": -1386.0 + } + ], + "score": 0.62, + "annotation": "The shows a sudden jump in Net Worth Change from Spain to Italy.", + "reason": "The Net Worth Change for increases from -12780.0 at Spain to -1386.0 at Italy, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Netherlands", + "Net Worth Change": 33621.0 + }, + { + "Country": "Belgium", + "Net Worth Change": 24005.0 + }, + { + "Country": "Germany", + "Net Worth Change": 19277.0 + }, + { + "Country": "Finland", + "Net Worth Change": 10206.0 + }, + { + "Country": "France", + "Net Worth Change": 7297.0 + }, + { + "Country": "Portugal", + "Net Worth Change": -307.0 + }, + { + "Country": "Italy", + "Net Worth Change": -1386.0 + }, + { + "Country": "Spain", + "Net Worth Change": -12780.0 + }, + { + "Country": "Greece", + "Net Worth Change": -16909.0 + }, + { + "Country": "Ireland", + "Net Worth Change": -18474.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Net Worth Change", + "importance": "primary", + "description": "Change in household net worth per capita (Euro)", + "unit": "Euro", + "data_type": "number", + "role": "y" + } + ] + } + }, + "426": { + "metadata": { + "title": "U.S. Heating Costs Expected To Tick up This Winter", + "description": "Average U.S. household expenditure on heating per winter season, by fuel type (in U.S. dollars)", + "main_insight": "Heating costs, particularly for heating oil, are expected to increase significantly in the 2022/23 winter season compared to previous years. Natural gas also shows an increase, while electricity costs, which rose until 21/22, show a slower increase in the last period." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Winter Season": "2022/23", + "Units": 2354.0, + "Energy Source": "Heating Oil" + }, + { + "Winter Season": "2022/23", + "Units": 1359.0, + "Energy Source": "Electricity" + } + ], + "score": 0.95, + "annotation": "The Units of Heating Oil and Electricity show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Heating Oil and Electricity is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Winter Season": "2022/23", + "Units": 931.0, + "Energy Source": "Natural gas" + } + ], + "score": 0.92, + "annotation": "The Natural gas has a maximum value at 2022/23", + "reason": "The [{'Winter Season': '2022/23', 'Units': 931.0, 'Energy Source': 'Natural gas'}] of Natural gas has a maximum value of 931.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Winter Season": "2022/23", + "Units": 2354.0, + "Energy Source": "Heating Oil" + } + ], + "score": 0.92, + "annotation": "The Heating Oil has a maximum value at 2022/23", + "reason": "The [{'Winter Season': '2022/23', 'Units': 2354.0, 'Energy Source': 'Heating Oil'}] of Heating Oil has a maximum value of 2354.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Winter Season": "2022/23", + "Units": 2354.0, + "Energy Source": "Heating Oil" + }, + { + "Winter Season": "2022/23", + "Units": 931.0, + "Energy Source": "Natural gas" + } + ], + "score": 0.91, + "annotation": "The Units of Heating Oil and Natural gas show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Heating Oil and Natural gas is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Winter Season": "2022/23", + "Units": 931.0, + "Energy Source": "Natural gas" + }, + { + "Winter Season": "2022/23", + "Units": 1359.0, + "Energy Source": "Electricity" + } + ], + "score": 0.9, + "annotation": "The Units of Natural gas and Electricity show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Natural gas and Electricity is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Winter Season": "2015/16", + "Units": 955.0, + "Energy Source": "Heating Oil" + }, + { + "Winter Season": "2015/16", + "Units": 570.0, + "Energy Source": "Natural gas" + }, + { + "Winter Season": "2015/16", + "Units": 1013.0, + "Energy Source": "Electricity" + }, + { + "Winter Season": "2016/17", + "Units": 1208.0, + "Energy Source": "Heating Oil" + }, + { + "Winter Season": "2016/17", + "Units": 513.0, + "Energy Source": "Natural gas" + }, + { + "Winter Season": "2016/17", + "Units": 1083.0, + "Energy Source": "Electricity" + }, + { + "Winter Season": "2017/18", + "Units": 1459.0, + "Energy Source": "Heating Oil" + }, + { + "Winter Season": "2017/18", + "Units": 578.0, + "Energy Source": "Natural gas" + }, + { + "Winter Season": "2017/18", + "Units": 1141.0, + "Energy Source": "Electricity" + }, + { + "Winter Season": "2018/19", + "Units": 1569.0, + "Energy Source": "Heating Oil" + }, + { + "Winter Season": "2018/19", + "Units": 613.0, + "Energy Source": "Natural gas" + }, + { + "Winter Season": "2018/19", + "Units": 1184.0, + "Energy Source": "Electricity" + }, + { + "Winter Season": "2019/20", + "Units": 1391.0, + "Energy Source": "Heating Oil" + }, + { + "Winter Season": "2019/20", + "Units": 571.0, + "Energy Source": "Natural gas" + }, + { + "Winter Season": "2019/20", + "Units": 1147.0, + "Energy Source": "Electricity" + }, + { + "Winter Season": "2020/21", + "Units": 1173.0, + "Energy Source": "Heating Oil" + }, + { + "Winter Season": "2020/21", + "Units": 573.0, + "Energy Source": "Natural gas" + }, + { + "Winter Season": "2020/21", + "Units": 1165.0, + "Energy Source": "Electricity" + }, + { + "Winter Season": "2021/22", + "Units": 1767.0, + "Energy Source": "Heating Oil" + }, + { + "Winter Season": "2021/22", + "Units": 792.0, + "Energy Source": "Natural gas" + }, + { + "Winter Season": "2021/22", + "Units": 1268.0, + "Energy Source": "Electricity" + }, + { + "Winter Season": "2022/23", + "Units": 2354.0, + "Energy Source": "Heating Oil" + }, + { + "Winter Season": "2022/23", + "Units": 931.0, + "Energy Source": "Natural gas" + }, + { + "Winter Season": "2022/23", + "Units": 1359.0, + "Energy Source": "Electricity" + } + ], + "columns": [ + { + "name": "Winter Season", + "importance": "primary", + "description": "Winter season from 2015/16 to 2022/23", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding energy source", + "role": "y" + }, + { + "name": "Energy Source", + "importance": "primary", + "description": "The energy source of the data point", + "role": "group" + } + ] + } + }, + "2197": { + "metadata": { + "title": "The Extent of the Youth's Climate Fear", + "description": "Feelings about climate change among those aged 16-25 in selected countries in 2021 (in %)", + "main_insight": "A significant proportion of young people aged 16-25 across various countries express high levels of worry about climate change, with the Philippines showing the highest combined percentage of 'Extremely worried' and 'Very worried' responses." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "United States", + "Points": 17.0, + "Worry Level": "Extremely worried" + }, + { + "Country": "United States", + "Points": 49.0, + "Worry Level": "Other answers" + } + ], + "score": 0.92, + "annotation": "The Points of Extremely worried and Other answers show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Points between Extremely worried and Other answers is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Philippines", + "Points": 16.0, + "Worry Level": "Other answers" + } + ], + "score": 0.91, + "annotation": "The Other answers has a minimum value at Philippines", + "reason": "The [{'Country': 'Philippines', 'Points': 16.0, 'Worry Level': 'Other answers'}] of Other answers has a mainimum value of 16.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Philippines", + "Points": 48.0, + "Worry Level": "Extremely worried" + } + ], + "score": 0.9, + "annotation": "The Extremely worried has a maximum value at Philippines", + "reason": "The [{'Country': 'Philippines', 'Points': 48.0, 'Worry Level': 'Extremely worried'}] of Extremely worried has a maximum value of 48.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Brazil", + "Points": 35.0, + "Worry Level": "Very worried" + } + ], + "score": 0.9, + "annotation": "The Very worried has the largest proportion in Brazil.", + "reason": "The Points of Very worried in Brazil accounts for 0.35, larger than all other Worry Level, of the total of Points of all Worry Level." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "India", + "Points": 36.0, + "Worry Level": "Extremely worried" + } + ], + "score": 0.89, + "annotation": "The Extremely worried has the largest proportion in India.", + "reason": "The Points of Extremely worried in India accounts for 0.36, larger than all other Worry Level, of the total of Points of all Worry Level." + } + ], + "data": { + "data": [ + { + "Country": "Philippines", + "Points": 48.0, + "Worry Level": "Extremely worried" + }, + { + "Country": "Philippines", + "Points": 36.0, + "Worry Level": "Very worried" + }, + { + "Country": "Philippines", + "Points": 16.0, + "Worry Level": "Other answers" + }, + { + "Country": "India", + "Points": 36.0, + "Worry Level": "Extremely worried" + }, + { + "Country": "India", + "Points": 32.0, + "Worry Level": "Very worried" + }, + { + "Country": "India", + "Points": 32.0, + "Worry Level": "Other answers" + }, + { + "Country": "Brazil", + "Points": 33.0, + "Worry Level": "Extremely worried" + }, + { + "Country": "Brazil", + "Points": 35.0, + "Worry Level": "Very worried" + }, + { + "Country": "Brazil", + "Points": 32.0, + "Worry Level": "Other answers" + }, + { + "Country": "Portugal", + "Points": 26.0, + "Worry Level": "Extremely worried" + }, + { + "Country": "Portugal", + "Points": 40.0, + "Worry Level": "Very worried" + }, + { + "Country": "Portugal", + "Points": 34.0, + "Worry Level": "Other answers" + }, + { + "Country": "France", + "Points": 24.0, + "Worry Level": "Extremely worried" + }, + { + "Country": "France", + "Points": 41.0, + "Worry Level": "Very worried" + }, + { + "Country": "France", + "Points": 35.0, + "Worry Level": "Other answers" + }, + { + "Country": "Nigeria", + "Points": 20.0, + "Worry Level": "Extremely worried" + }, + { + "Country": "Nigeria", + "Points": 44.0, + "Worry Level": "Very worried" + }, + { + "Country": "Nigeria", + "Points": 36.0, + "Worry Level": "Other answers" + }, + { + "Country": "United Kingdom", + "Points": 19.0, + "Worry Level": "Extremely worried" + }, + { + "Country": "United Kingdom", + "Points": 39.0, + "Worry Level": "Very worried" + }, + { + "Country": "United Kingdom", + "Points": 42.0, + "Worry Level": "Other answers" + }, + { + "Country": "United States", + "Points": 17.0, + "Worry Level": "Extremely worried" + }, + { + "Country": "United States", + "Points": 34.0, + "Worry Level": "Very worried" + }, + { + "Country": "United States", + "Points": 49.0, + "Worry Level": "Other answers" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding worry level", + "role": "y" + }, + { + "name": "Worry Level", + "importance": "primary", + "description": "The worry level of the data point", + "role": "group" + } + ] + } + }, + "1767": { + "metadata": { + "title": "Carbon Emissions Rebound", + "description": "Percent year-over-year change in global CO₂ emissions in 2020", + "main_insight": "Global CO₂ emissions experienced a significant drop in early 2020, but rebounded sharply, ending the year with a 2.1% increase compared to the previous year." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Feb", + "CO2 Emission Change": -14.0 + } + ], + "score": 0.97, + "annotation": "The has a minimum value at Feb", + "reason": "The [{'Month': 'Feb', 'CO2 Emission Change': -14.0}] of has a mainimum value of -14.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Jan", + "CO2 Emission Change": -6.0 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in CO2 Emission Change from Feb to Jan.", + "reason": "The CO2 Emission Change for increases from -14.0 at Feb to -6.0 at Jan, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Dec", + "CO2 Emission Change": 2.1 + } + ], + "score": 0.73, + "annotation": "The has a maximum value at Dec", + "reason": "The [{'Month': 'Dec', 'CO2 Emission Change': 2.1}] of has a maximum value of 2.1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Month": "Jan", + "CO2 Emission Change": -6.0 + }, + { + "Month": "Feb", + "CO2 Emission Change": -14.0 + }, + { + "Month": "Mar", + "CO2 Emission Change": -6.0 + }, + { + "Month": "Apr", + "CO2 Emission Change": -5.0 + }, + { + "Month": "May", + "CO2 Emission Change": -4.0 + }, + { + "Month": "Jun", + "CO2 Emission Change": -3.0 + }, + { + "Month": "Jul", + "CO2 Emission Change": -3.0 + }, + { + "Month": "Aug", + "CO2 Emission Change": -2.0 + }, + { + "Month": "Sep", + "CO2 Emission Change": -1.0 + }, + { + "Month": "Oct", + "CO2 Emission Change": 0.0 + }, + { + "Month": "Nov", + "CO2 Emission Change": 1.0 + }, + { + "Month": "Dec", + "CO2 Emission Change": 2.1 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year 2020", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "CO2 Emission Change", + "importance": "primary", + "description": "Percent year-over-year change in global CO₂ emissions", + "unit": "percent", + "data_type": "number", + "role": "y" + } + ] + } + }, + "927": { + "metadata": { + "title": "Where Ad Fraud is Rife", + "description": "Share of desktop ad impressions that are fraudulent, in selected countries (Q1 2017)*", + "main_insight": "Japan has by far the highest rate of fraudulent ad impressions among the selected countries, at 80%, more than double the next highest country, Brazil." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Japan", + "Fraudulent Ad Impressions": 80.0 + } + ], + "score": 0.92, + "annotation": "The has a maximum value at Japan", + "reason": "The [{'Country': 'Japan', 'Fraudulent Ad Impressions': 80.0}] of has a maximum value of 80.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Japan", + "Fraudulent Ad Impressions": 80.0 + } + ], + "score": 0.9, + "annotation": "The shows a sudden jump in Fraudulent Ad Impressions from Brazil to Japan.", + "reason": "The Fraudulent Ad Impressions for increases from 38.0 at Brazil to 80.0 at Japan, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Spain", + "Fraudulent Ad Impressions": 16.0 + } + ], + "score": 0.51, + "annotation": "The has a minimum value at Spain", + "reason": "The [{'Country': 'Spain', 'Fraudulent Ad Impressions': 16.0}] of has a mainimum value of 16.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Japan", + "Fraudulent Ad Impressions": 80.0 + }, + { + "Country": "Brazil", + "Fraudulent Ad Impressions": 38.0 + }, + { + "Country": "United States", + "Fraudulent Ad Impressions": 37.0 + }, + { + "Country": "Germany", + "Fraudulent Ad Impressions": 35.0 + }, + { + "Country": "UK", + "Fraudulent Ad Impressions": 18.0 + }, + { + "Country": "France", + "Fraudulent Ad Impressions": 17.0 + }, + { + "Country": "Spain", + "Fraudulent Ad Impressions": 16.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Selected countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Fraudulent Ad Impressions", + "importance": "primary", + "description": "Share of desktop ad impressions that are fraudulent", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "276": { + "metadata": { + "title": "South Korean Exports Maintain Growth", + "description": "Change in the value of monthly exports from South Korea (in percent)", + "main_insight": "South Korean export growth experienced significant volatility between 2019 and 2021, dropping sharply in 2020 but rebounding to positive growth in 2021." + }, + "chart_type": "Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2019, + "Export Change": -10.0 + }, + { + "Year": 2019, + "Export Change": -12.0 + }, + { + "Year": 2019, + "Export Change": -14.0 + }, + { + "Year": 2019, + "Export Change": -6.0 + }, + { + "Year": 2019, + "Export Change": -4.0 + }, + { + "Year": 2019, + "Export Change": 4.0 + }, + { + "Year": 2019, + "Export Change": 1.0 + }, + { + "Year": 2019, + "Export Change": -1.0 + }, + { + "Year": 2019, + "Export Change": -9.0 + }, + { + "Year": 2019, + "Export Change": -24.3 + }, + { + "Year": 2020, + "Export Change": -20.0 + }, + { + "Year": 2020, + "Export Change": -10.0 + }, + { + "Year": 2020, + "Export Change": -8.0 + }, + { + "Year": 2020, + "Export Change": -15.0 + }, + { + "Year": 2020, + "Export Change": -9.0 + }, + { + "Year": 2020, + "Export Change": -2.0 + }, + { + "Year": 2020, + "Export Change": 7.0 + }, + { + "Year": 2020, + "Export Change": -4.0 + }, + { + "Year": 2020, + "Export Change": -1.0 + }, + { + "Year": 2020, + "Export Change": 9.0 + }, + { + "Year": 2020, + "Export Change": 12.6 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of export data", + "unit": "none", + "data_type": "time", + "discarded_count": 0, + "role": "x" + }, + { + "name": "Export Change", + "importance": "primary", + "description": "Year-over-year change in the value of monthly exports (in percent)", + "unit": "percent", + "data_type": "number", + "discarded_count": 0, + "role": "y" + } + ] + } + }, + "214": { + "metadata": { + "title": "The Economic Cost of Violence Against Women", + "description": "Share of societal costs of gender-based violence against women in the EU-27, by sector", + "main_insight": "The estimated annual societal costs of gender-based violence against women in the EU is €290 billion, with the largest share (67%) attributed to criminal justice." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Sector": "Criminal justice", + "Percentage of Costs": 67.0 + } + ], + "score": 0.94, + "annotation": "The shows a sudden jump in Percentage of Costs from Health services to Criminal justice.", + "reason": "The Percentage of Costs for increases from 14.0 at Health services to 67.0 at Criminal justice, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Sector": "Criminal justice", + "Percentage of Costs": 67.0 + } + ], + "score": 0.92, + "annotation": "The has a maximum value at Criminal justice", + "reason": "The [{'Sector': 'Criminal justice', 'Percentage of Costs': 67.0}] of has a maximum value of 67.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Sector": "Specialized services", + "Percentage of Costs": 1.0 + } + ], + "score": 0.41, + "annotation": "The has a minimum value at Specialized services", + "reason": "The [{'Sector': 'Specialized services', 'Percentage of Costs': 1.0}] of has a mainimum value of 1.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Sector": "Criminal justice", + "Percentage of Costs": 67.0 + }, + { + "Sector": "Health services", + "Percentage of Costs": 14.0 + }, + { + "Sector": "Social welfare", + "Percentage of Costs": 12.0 + }, + { + "Sector": "Personal cost", + "Percentage of Costs": 4.0 + }, + { + "Sector": "Civil justice", + "Percentage of Costs": 2.0 + }, + { + "Sector": "Specialized services", + "Percentage of Costs": 1.0 + } + ], + "columns": [ + { + "name": "Sector", + "importance": "primary", + "description": "Sector of societal costs", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Costs", + "importance": "primary", + "description": "Percentage of total societal costs attributed to each sector", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "202": { + "metadata": { + "title": "Britons sceptical Brexit will occur by 31 October", + "description": "Share who think it's likely/unlikely the UK will leave the EU by Oct 31st, 2019", + "main_insight": "A majority of Britons across most groups surveyed believe it is unlikely the UK will leave the EU by October 31st, 2019, with Leave voters and 2017 Conservative voters having the smallest 'unlikely' percentages." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Group": "2017 Lib Dem voters", + "Units": 64.0, + "Likelihood": "Unlikely" + }, + { + "Group": "2017 Lib Dem voters", + "Units": 21.0, + "Likelihood": "Likely" + } + ], + "score": 0.98, + "annotation": "The Units of Unlikely and Likely show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Unlikely and Likely is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Group": "2017 Conservative voters", + "Units": 48.0, + "Likelihood": "Unlikely" + } + ], + "score": 0.86, + "annotation": "The Unlikely has the largest proportion in 2017 Conservative voters.", + "reason": "The Units of Unlikely in 2017 Conservative voters accounts for 0.48, larger than all other Likelihood, of the total of Units of all Likelihood." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Group": "Leave voters", + "Units": 38.0, + "Likelihood": "Likely" + } + ], + "score": 0.86, + "annotation": "The Likely shows a sudden jump in Units from General population to Leave voters.", + "reason": "The Units for Likely increases from 27.0 at General population to 38.0 at Leave voters, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Group": "Leave voters", + "Units": 50.0, + "Likelihood": "Unlikely" + } + ], + "score": 0.86, + "annotation": "The Unlikely has the largest proportion in Leave voters.", + "reason": "The Units of Unlikely in Leave voters accounts for 0.5, larger than all other Likelihood, of the total of Units of all Likelihood." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Group": "General population", + "Units": 56.0, + "Likelihood": "Unlikely" + } + ], + "score": 0.84, + "annotation": "The Unlikely accounts for the majority in General population.", + "reason": "The Units of Unlikely in General population accounts for 0.56 (more than 50%) of the total of Units of all Likelihood." + } + ], + "data": { + "data": [ + { + "Group": "General population", + "Units": 56.0, + "Likelihood": "Unlikely" + }, + { + "Group": "General population", + "Units": 27.0, + "Likelihood": "Likely" + }, + { + "Group": "General population", + "Units": 17.0, + "Likelihood": "Don't know" + }, + { + "Group": "Remain voters", + "Units": 63.0, + "Likelihood": "Unlikely" + }, + { + "Group": "Remain voters", + "Units": 22.0, + "Likelihood": "Likely" + }, + { + "Group": "Remain voters", + "Units": 15.0, + "Likelihood": "Don't know" + }, + { + "Group": "Leave voters", + "Units": 50.0, + "Likelihood": "Unlikely" + }, + { + "Group": "Leave voters", + "Units": 38.0, + "Likelihood": "Likely" + }, + { + "Group": "Leave voters", + "Units": 12.0, + "Likelihood": "Don't know" + }, + { + "Group": "2017 Conservative voters", + "Units": 48.0, + "Likelihood": "Unlikely" + }, + { + "Group": "2017 Conservative voters", + "Units": 41.0, + "Likelihood": "Likely" + }, + { + "Group": "2017 Conservative voters", + "Units": 11.0, + "Likelihood": "Don't know" + }, + { + "Group": "2017 Labour voters", + "Units": 63.0, + "Likelihood": "Unlikely" + }, + { + "Group": "2017 Labour voters", + "Units": 22.0, + "Likelihood": "Likely" + }, + { + "Group": "2017 Labour voters", + "Units": 15.0, + "Likelihood": "Don't know" + }, + { + "Group": "2017 Lib Dem voters", + "Units": 64.0, + "Likelihood": "Unlikely" + }, + { + "Group": "2017 Lib Dem voters", + "Units": 21.0, + "Likelihood": "Likely" + }, + { + "Group": "2017 Lib Dem voters", + "Units": 15.0, + "Likelihood": "Don't know" + } + ], + "columns": [ + { + "name": "Group", + "importance": "primary", + "description": "The group of people surveyed", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding likelihood", + "role": "y" + }, + { + "name": "Likelihood", + "importance": "primary", + "description": "The likelihood of the data point", + "role": "group" + } + ] + } + }, + "1573": { + "metadata": { + "title": "In Unusual Trend, U.S. Trade Deficit Widens in 2020 Recession", + "description": "Monthly U.S. trade deficit in goods and services on a balance of payments basis, seasonally adjusted", + "main_insight": "The U.S. trade deficit significantly widened during the 2020 recession, reaching a low of -$63.12 billion, which bucks typical trends during economic downturns." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2000, + "Trade Deficit": -30.0 + }, + { + "Year": 2002, + "Trade Deficit": -35.0 + }, + { + "Year": 2004, + "Trade Deficit": -50.0 + }, + { + "Year": 2006, + "Trade Deficit": -65.0 + }, + { + "Year": 2008, + "Trade Deficit": -60.0 + }, + { + "Year": 2010, + "Trade Deficit": -40.0 + }, + { + "Year": 2012, + "Trade Deficit": -45.0 + }, + { + "Year": 2014, + "Trade Deficit": -40.0 + }, + { + "Year": 2016, + "Trade Deficit": -45.0 + }, + { + "Year": 2018, + "Trade Deficit": -50.0 + }, + { + "Year": 2020, + "Trade Deficit": -63.12 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year, ranging from 2000 to 2020", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Trade Deficit", + "importance": "primary", + "description": "Monthly U.S. trade deficit in billions of dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1566": { + "metadata": { + "title": "Negative Views of China Rise Sharply", + "description": "Percentage of Americans who have an unfavorable opinion of China", + "main_insight": "Unfavorable views of China among Americans have increased significantly since 2018, with a notable sharp rise in 2020 across all political affiliations." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 66.0, + "Political Affiliation": "Total" + }, + { + "Year": 2020, + "Units": 72.0, + "Political Affiliation": "Republicans" + } + ], + "score": 0.97, + "annotation": "The Units of Total and Republicans show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total and Republicans is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 66.0, + "Political Affiliation": "Total" + }, + { + "Year": 2020, + "Units": 62.0, + "Political Affiliation": "Democrats" + } + ], + "score": 0.96, + "annotation": "The Units of Total and Democrats show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total and Democrats is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 72.0, + "Political Affiliation": "Republicans" + }, + { + "Year": 2020, + "Units": 62.0, + "Political Affiliation": "Democrats" + } + ], + "score": 0.91, + "annotation": "The Units of Republicans and Democrats show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Republicans and Democrats is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2005, + "Units": 35.0, + "Political Affiliation": "Total" + }, + { + "Year": 2005, + "Units": 39.0, + "Political Affiliation": "Republicans" + }, + { + "Year": 2005, + "Units": 30.0, + "Political Affiliation": "Democrats" + }, + { + "Year": 2006, + "Units": 29.0, + "Political Affiliation": "Total" + }, + { + "Year": 2006, + "Units": 35.0, + "Political Affiliation": "Republicans" + }, + { + "Year": 2006, + "Units": 23.0, + "Political Affiliation": "Democrats" + }, + { + "Year": 2007, + "Units": 39.0, + "Political Affiliation": "Total" + }, + { + "Year": 2007, + "Units": 45.0, + "Political Affiliation": "Republicans" + }, + { + "Year": 2007, + "Units": 38.0, + "Political Affiliation": "Democrats" + }, + { + "Year": 2008, + "Units": 42.0, + "Political Affiliation": "Total" + }, + { + "Year": 2008, + "Units": 45.0, + "Political Affiliation": "Republicans" + }, + { + "Year": 2008, + "Units": 40.0, + "Political Affiliation": "Democrats" + }, + { + "Year": 2009, + "Units": 39.0, + "Political Affiliation": "Total" + }, + { + "Year": 2009, + "Units": 43.0, + "Political Affiliation": "Republicans" + }, + { + "Year": 2009, + "Units": 33.0, + "Political Affiliation": "Democrats" + }, + { + "Year": 2010, + "Units": 37.0, + "Political Affiliation": "Total" + }, + { + "Year": 2010, + "Units": 45.0, + "Political Affiliation": "Republicans" + }, + { + "Year": 2010, + "Units": 34.0, + "Political Affiliation": "Democrats" + }, + { + "Year": 2011, + "Units": 36.0, + "Political Affiliation": "Total" + }, + { + "Year": 2011, + "Units": 45.0, + "Political Affiliation": "Republicans" + }, + { + "Year": 2011, + "Units": 31.0, + "Political Affiliation": "Democrats" + }, + { + "Year": 2012, + "Units": 40.0, + "Political Affiliation": "Total" + }, + { + "Year": 2012, + "Units": 52.0, + "Political Affiliation": "Republicans" + }, + { + "Year": 2012, + "Units": 35.0, + "Political Affiliation": "Democrats" + }, + { + "Year": 2013, + "Units": 53.0, + "Political Affiliation": "Total" + }, + { + "Year": 2013, + "Units": 61.0, + "Political 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+ "Year": 2017, + "Units": 41.0, + "Political Affiliation": "Democrats" + }, + { + "Year": 2018, + "Units": 47.0, + "Political Affiliation": "Total" + }, + { + "Year": 2018, + "Units": 51.0, + "Political Affiliation": "Republicans" + }, + { + "Year": 2018, + "Units": 52.0, + "Political Affiliation": "Democrats" + }, + { + "Year": 2019, + "Units": 60.0, + "Political Affiliation": "Total" + }, + { + "Year": 2019, + "Units": 70.0, + "Political Affiliation": "Republicans" + }, + { + "Year": 2019, + "Units": 53.0, + "Political Affiliation": "Democrats" + }, + { + "Year": 2020, + "Units": 66.0, + "Political Affiliation": "Total" + }, + { + "Year": 2020, + "Units": 72.0, + "Political Affiliation": "Republicans" + }, + { + "Year": 2020, + "Units": 62.0, + "Political Affiliation": "Democrats" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + 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], + "score": 0.62, + "annotation": "The has a minimum value at Champagne toast at midnight", + "reason": "The [{\"New Year's Celebration Element\": 'Champagne toast at midnight', 'Percentage of Respondents': 0.22}] of has a mainimum value of 0.22, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "New Year's Celebration Element": "Wishing friends and family a happy new year", + "Percentage of Respondents": 0.47 + }, + { + "New Year's Celebration Element": "New Year's countdown on TV", + "Percentage of Respondents": 0.36 + }, + { + "New Year's Celebration Element": "Watching the ball drop in New York", + "Percentage of Respondents": 0.31 + }, + { + "New Year's Celebration Element": "Kiss at midnight", + "Percentage of Respondents": 0.31 + }, + { + "New Year's Celebration Element": "Fireworks and firecrackers", + "Percentage of Respondents": 0.23 + }, + { + "New Year's Celebration Element": "Champagne toast at midnight", + "Percentage of Respondents": 0.22 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"data_points": [ + { + "Country": "DE", + "Interest Level": 35.0 + } + ], + "score": 0.81, + "annotation": "The shows a sudden jump in Interest Level from IN to DE.", + "reason": "The Interest Level for increases from 27.0 at IN to 35.0 at DE, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "JP", + "Interest Level": 19.0 + }, + { + "Country": "FR", + "Interest Level": 19.0 + } + ], + "score": 0.72, + "annotation": "The has minimum values at JP, FR", + "reason": "The [{'Country': 'JP', 'Interest Level': 19.0}, {'Country': 'FR', 'Interest Level': 19.0}] of have minimum values of 19.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "FI", + "Interest Level": 36.0 + }, + { + "Country": "BR", + "Interest Level": 36.0 + } + ], + "score": 0.67, + "annotation": "The has maximum values at FI, BR", + "reason": "The [{'Country': 'FI', 'Interest Level': 36.0}, {'Country': 'BR', 'Interest Level': 36.0}] of have maximum values of 36.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "DE", + "Interest Level": 35.0 + }, + { + "Country": "FI", + "Interest Level": 36.0 + }, + { + "Country": "JP", + "Interest Level": 19.0 + }, + { + "Country": "CN", + "Interest Level": 22.0 + }, + { + "Country": "US", + "Interest Level": 24.0 + }, + { + "Country": "BR", + "Interest Level": 36.0 + }, + { + "Country": "ZA", + "Interest Level": 35.0 + }, + { + "Country": "FR", + "Interest Level": 19.0 + }, + { + "Country": "MX", + "Interest Level": 26.0 + }, + { + "Country": "IN", + "Interest Level": 27.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Interest Level", + "importance": "primary", + "description": "Percentage of respondents who ranked politics among their 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Bush, and Bill Clinton." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "President": "Donald Trump", + "Days at Getaway": 21.0 + } + ], + "score": 0.79, + "annotation": "The shows a sudden jump in Days at Getaway from George W. Bush to Donald Trump.", + "reason": "The Days at Getaway for increases from 12.0 at George W. Bush to 21.0 at Donald Trump, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "President": "Donald Trump", + "Days at Getaway": 21.0 + } + ], + "score": 0.72, + "annotation": "The has a maximum value at Donald Trump", + "reason": "The [{'President': 'Donald Trump', 'Days at Getaway': 21.0}] of has a maximum value of 21.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "President": "Bill Clinton", + "Days at Getaway": 0.0 + } + ], + "score": 0.6, + "annotation": "The has a minimum value at Bill Clinton", + "reason": "The [{'President': 'Bill Clinton', 'Days at Getaway': 0.0}] of has a mainimum value of 0.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "President": "Donald Trump", + "Days at Getaway": 21.0 + }, + { + "President": "Barack Obama", + "Days at Getaway": 4.0 + }, + { + "President": "George W. 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Soccer is a popular sport for billionaires to own teams in across all observed regions." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Sport": "Football", + "USA": 23.0 + }, + { + "Sport": "Basketball", + "USA": 23.0 + } + ], + "score": 0.8, + "annotation": "The has maximum values at Football, Basketball", + "reason": "The [{'Sport': 'Football', 'USA': 23.0}, {'Sport': 'Basketball', 'USA': 23.0}] of have maximum values of 23.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Sport": "Football", + "USA": 23.0 + } + ], + "score": 0.73, + "annotation": "The shows a sudden jump in USA from Baseball to Football.", + "reason": "The USA for increases from 15.0 at Baseball to 23.0 at Football, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Sport": "Ice Hockey", + "USA": 0 + }, + { + "Sport": "Cricket", + "USA": 0 + } + ], + "score": 0.56, + "annotation": "The has minimum values at Ice Hockey, Cricket", + "reason": "The [{'Sport': 'Ice Hockey', 'USA': 0}, {'Sport': 'Cricket', 'USA': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Sport": "Soccer", + "USA": 10.0 + }, + { + "Sport": "Ice Hockey", + "USA": 0 + }, + { + "Sport": "Rugby", + "USA": 2.0 + }, + { + "Sport": "Car Racing", + "USA": 3.0 + }, + { + "Sport": "Football", + "USA": 23.0 + }, + { + "Sport": "Basketball", + "USA": 23.0 + }, + { + "Sport": "Baseball", + "USA": 15.0 + }, + { + "Sport": "Lacrosse", + "USA": 2.0 + }, + { + "Sport": "Cricket", + "USA": 0 + } + ], + "columns": [ + { + "name": "Sport", + "importance": "primary", + "description": "Type of sport", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "USA", + "importance": "primary", + "description": "Number of sports teams owned by billionaires 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"The Pearson correlation coefficient of the Units between Agree and Neutral is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Entity": "Brands should set up a hardship fund", + "Units": 54.0, + "Agreement": "Agree" + }, + { + "Entity": "Brands should set up a hardship fund", + "Units": 11.0, + "Agreement": "Disagree" + } + ], + "score": 1.0, + "annotation": "The Units of Agree and Disagree show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Agree and Disagree is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Entity": "Brands should set up a hardship fund", + "Units": 54.0, + "Agreement": "Agree" + }, + { + "Entity": "Brands should set up a hardship fund", + "Units": 10.0, + "Agreement": "Don't know" + } + ], + "score": 1.0, + "annotation": "The Units of Agree and Don't know show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Agree and Don't know is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Entity": "Brands should set up a hardship fund", + "Units": 25.0, + "Agreement": "Neutral" + }, + { + "Entity": "Brands should set up a hardship fund", + "Units": 11.0, + "Agreement": "Disagree" + } + ], + "score": 1.0, + "annotation": "The Units of Neutral and Disagree show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Neutral and Disagree is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Entity": "Brands should set up a hardship fund", + "Units": 25.0, + "Agreement": "Neutral" + }, + { + "Entity": "Brands should set up a hardship fund", + "Units": 10.0, + "Agreement": 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September 2017, with the largest decreases in perception of her as a capable leader and having sound judgment." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Attribute": "Out of touch with ordinary people", + "Score": 23.0, + "Daily Sales": "Change" + } + ], + "score": 0.93, + "annotation": "The Change has a maximum value at Out of touch with ordinary people", + "reason": "The [{'Attribute': 'Out of touch with ordinary people', 'Score': 23.0, 'Daily Sales': 'Change'}] of Change has a maximum value of 23.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Attribute": "Patriotic", + "Score": 72.0, + "Daily Sales": "Sep 17" + } + ], + "score": 0.89, + "annotation": "The Sep 17 has a maximum value at Patriotic", + "reason": "The [{'Attribute': 'Patriotic', 'Score': 72.0, 'Daily Sales': 'Sep 17'}] of Sep 17 has a maximum value of 72.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Attribute": "More style than substance", + "Score": 25.0, + "Daily Sales": "Sep 16" + } + ], + "score": 0.88, + "annotation": "The Sep 16 has a minimum value at More style than substance", + "reason": "The [{'Attribute': 'More style than substance', 'Score': 25.0, 'Daily Sales': 'Sep 16'}] of Sep 16 has a mainimum value of 25.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Attribute": "Patriotic", + "Score": 75.0, + "Daily Sales": "Sep 16" + } + ], + "score": 0.82, + "annotation": "The Sep 16 has a maximum value at Patriotic", + "reason": "The [{'Attribute': 'Patriotic', 'Score': 75.0, 'Daily Sales': 'Sep 16'}] of Sep 16 has a maximum value of 75.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Attribute": "Has got a lot of 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"Attribute": "Understands the problems facing Britain", + "Score": 49.0, + "Daily Sales": "Sep 17" + }, + { + "Attribute": "Understands the problems facing Britain", + "Score": -12.0, + "Daily Sales": "Change" + }, + { + "Attribute": "Has sound judgement", + "Score": 56.0, + "Daily Sales": "Sep 16" + }, + { + "Attribute": "Has sound judgement", + "Score": 36.0, + "Daily Sales": "Sep 17" + }, + { + "Attribute": "Has sound judgement", + "Score": -20.0, + "Daily Sales": "Change" + }, + { + "Attribute": "Has a clear vision for Britain", + "Score": 55.0, + "Daily Sales": "Sep 16" + }, + { + "Attribute": "Has a clear vision for Britain", + "Score": 45.0, + "Daily Sales": "Sep 17" + }, + { + "Attribute": "Has a clear vision for Britain", + "Score": -10.0, + "Daily Sales": "Change" + }, + { + "Attribute": "Good in a crisis", + "Score": 52.0, + "Daily Sales": "Sep 16" + }, + { + "Attribute": "Good in a crisis", + "Score": 40.0, + "Daily Sales": "Sep 17" + }, + { + "Attribute": "Good in a crisis", + "Score": -12.0, + "Daily Sales": "Change" + }, + { + "Attribute": "More honest than most politicians", + "Score": 44.0, + "Daily Sales": "Sep 16" + }, + { + "Attribute": "More honest than most politicians", + "Score": 36.0, + "Daily Sales": "Sep 17" + }, + { + "Attribute": "More honest than most politicians", + "Score": -8.0, + "Daily Sales": "Change" + }, + { + "Attribute": "Out of touch with ordinary people", + "Score": 43.0, + "Daily Sales": "Sep 16" + }, + { + "Attribute": "Out of touch with ordinary people", + "Score": 66.0, + "Daily Sales": "Sep 17" + }, + { + "Attribute": "Out of touch with ordinary people", + "Score": 23.0, + "Daily Sales": "Change" + }, + { + "Attribute": "Has got a lot of personality", + "Score": 37.0, + "Daily Sales": "Sep 16" + }, + { + "Attribute": "Has got a lot of personality", + "Score": 21.0, + "Daily Sales": "Sep 17" + }, + { + "Attribute": "Has got a lot of personality", + "Score": -16.0, + "Daily Sales": "Change" + }, + { + "Attribute": "More style than substance", + "Score": 25.0, + "Daily Sales": "Sep 16" + }, + { + "Attribute": "More style than substance", + "Score": 36.0, + "Daily Sales": "Sep 17" + }, + { + "Attribute": "More style than substance", + "Score": 11.0, + "Daily Sales": "Change" + } + ], + "columns": [ + { + "name": "Attribute", + "importance": "primary", + "description": "The characteristic or trait attributed to Theresa May", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding daily sales", + "role": "y" + }, + { + "name": "Daily Sales", + "importance": "primary", + "description": "The daily sales of the data point", + "role": "group" + } + ] + } + }, + "2473": { + "metadata": { + "title": "Electronic Devices Are Becoming Less Power Hungry", + "description": "Estimated overall annual energy consumption in U.S. households in TWh", + "main_insight": "Most electronic devices consumed less energy in 2013 compared to 2010, with the exception of Set Top Boxes, which saw no change." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Device": "Speaker Dock", + "Units": 1.9, + "Year": "2013" + }, + { + "Device": "Speaker Dock", + "Units": 1.2, + "Year": "2010" + } + ], + "score": 0.96, + "annotation": "The Units of 2013 and 2010 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2013 and 2010 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Device": "Television", + "Units": 65.0, + "Year": "2010" + } + ], + "score": 0.91, + "annotation": "The 2010 has a maximum value at Television", + "reason": "The [{'Device': 'Television', 'Units': 65.0, 'Year': '2010'}] of 2010 has a maximum value of 65.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Device": "Television", + "Units": 50.0, + "Year": "2013" + } + ], + "score": 0.89, + "annotation": "The 2013 has a maximum value at Television", + "reason": "The [{'Device': 'Television', 'Units': 50.0, 'Year': '2013'}] of 2013 has a maximum value of 50.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Device": "Television", + "Units": 65.0, + "Year": "2010" + } + ], + "score": 0.85, + "annotation": "The 2010 shows a sudden jump in Units from Personal Computer to Television.", + "reason": "The Units for 2010 increases from 31.0 at Personal Computer to 65.0 at Television, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Device": "Set Top Box", + "Units": 31.0, + "Year": "2013" + } + ], + "score": 0.79, + "annotation": "The 2013 accounts for the majority in Set Top Box.", + "reason": "The Units of 2013 in Set Top Box accounts for 0.543859649122807 (more than 50%) of the total of Units of all Year." + } + ], + "data": { + "data": [ + { + "Device": "Television", + "Units": 50.0, + "Year": "2013" + }, + { + "Device": "Television", + "Units": 65.0, + "Year": "2010" + }, + { + "Device": "Personal Computer", + "Units": 21.0, + "Year": "2013" + }, + { + "Device": "Personal Computer", + "Units": 31.0, + "Year": "2010" + }, + { + "Device": "Set Top Box", + "Units": 31.0, + "Year": "2013" + }, + { + "Device": "Set Top Box", + "Units": 26.0, + "Year": "2010" + }, + { + "Device": "Video Game Console", + "Units": 11.0, + "Year": "2013" + }, + { + "Device": "Video Game Console", + "Units": 15.0, + "Year": "2010" + }, + { + "Device": "Computer Monitor", + "Units": 5.6, + "Year": "2013" + }, + { + "Device": "Computer Monitor", + "Units": 13.0, + "Year": "2010" + }, + { + "Device": "Stereo System", + "Units": 4.8, + "Year": "2013" + }, + { + "Device": "Stereo System", + "Units": 6.6, + "Year": "2010" + }, + { + "Device": "Speaker Dock", + "Units": 1.9, + "Year": "2013" + }, + { + "Device": "Speaker Dock", + "Units": 1.2, + "Year": "2010" + } + ], + "columns": [ + { + "name": "Device", + "importance": "primary", + "description": "Type of electronic device", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "1735": { + "metadata": { + "title": "Soccer Superpowers That Missed the World Cup", + "description": "Number of times men's national soccer teams failed to qualify for the FIFA World Cup", + "main_insight": "The Netherlands has failed to qualify for the FIFA World Cup the most out of the listed soccer superpowers, with 8 missed qualifications between 1930 and 2018. Germany and Brazil have never failed to qualify." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Netherlands", + "Missed_Qualifications": 8 + } + ], + "score": 0.89, + "annotation": "The has a maximum value at Netherlands", + "reason": "The [{'Country': 'Netherlands', 'Missed_Qualifications': 8}] of has a maximum value of 8, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Netherlands", + "Missed_Qualifications": 8 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Missed_Qualifications from France to Netherlands.", + "reason": "The Missed_Qualifications for increases from 5 at France to 8 at Netherlands, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Germany", + "Missed_Qualifications": 0 + }, + { + "Country": "Brazil", + "Missed_Qualifications": 0 + } + ], + "score": 0.62, + "annotation": "The has minimum values at Germany, Brazil", + "reason": "The [{'Country': 'Germany', 'Missed_Qualifications': 0}, {'Country': 'Brazil', 'Missed_Qualifications': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Netherlands", + "Missed_Qualifications": 8 + }, + { + "Country": "France", + "Missed_Qualifications": 5 + }, + { + "Country": "Spain", + "Missed_Qualifications": 4 + }, + { + "Country": "England", + "Missed_Qualifications": 3 + }, + { + "Country": "Italy", + "Missed_Qualifications": 2 + }, + { + "Country": "Argentina", + "Missed_Qualifications": 1 + }, + { + "Country": "Germany", + "Missed_Qualifications": 0 + }, + { + "Country": "Brazil", + "Missed_Qualifications": 0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "National soccer team", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Missed_Qualifications", + "importance": "primary", + "description": "Number of times the team failed to qualify for the FIFA World Cup from 1930 to 2018", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "542": { + "metadata": { + "title": "Indians Rate Chance of Financial Success Highly", + "description": "Share of respondents who rate their personal chance of accumulating a fortune in the following ways*", + "main_insight": "Indians are significantly more optimistic about their chances of financial success compared to other surveyed countries. France shows the lowest optimism." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "France", + "Score": 15.0, + "Likelihood": "Very good/rather good chance" + }, + { + "Country": "France", + "Score": 44.0, + "Likelihood": "Rather bad/very bad chance" + } + ], + "score": 0.95, + "annotation": "The Score of Very good/rather good chance and Rather bad/very bad chance show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Very good/rather good chance and Rather bad/very bad chance is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "India", + "Score": 60.0, + "Likelihood": "Very good/rather good chance" + } + ], + "score": 0.92, + "annotation": "The Very good/rather good chance has a maximum value at India", + "reason": "The [{'Country': 'India', 'Score': 60.0, 'Likelihood': 'Very good/rather good chance'}] of Very good/rather good chance has a maximum value of 60.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "United States", + "Score": 37.0, + "Likelihood": "Moderate chance" + } + ], + "score": 0.92, + "annotation": "The Moderate chance has the largest proportion in United States.", + "reason": "The Score of Moderate chance in United States accounts for 0.37, larger than all other Likelihood, of the total of Score of all Likelihood." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Likelihood": "Moderate chance", + "Country": "total", + "Score": 312.0 + } + ], + "score": 0.91, + "annotation": "The total value of Moderate chance has the largest proportion.", + "reason": "The total value of Score of Moderate chance accounts for 0.39, larger than all other Likelihood, of the total of Score of all Likelihood." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "India", + "Score": 2.0, + "Likelihood": "Don't know" + } + ], + "score": 0.91, + "annotation": "The Don't know has the smallest proportion in India.", + "reason": "The Score of Don't know in India accounts for 0.02, smaller than all other Likelihood, of the total of Score of all Likelihood." + } + ], + "data": { + "data": [ + { + "Country": "India", + "Score": 60.0, + "Likelihood": "Very good/rather good chance" + }, + { + "Country": "India", + "Score": 33.0, + "Likelihood": "Moderate chance" + }, + { + "Country": "India", + "Score": 5.0, + "Likelihood": "Rather bad/very bad chance" + }, + { + "Country": "India", + "Score": 2.0, + "Likelihood": "Don't know" + }, + { + "Country": "Mexico", + "Score": 41.0, + "Likelihood": "Very good/rather good chance" + }, + { + "Country": "Mexico", + "Score": 36.0, + "Likelihood": "Moderate chance" + }, + { + "Country": "Mexico", + "Score": 18.0, + "Likelihood": "Rather bad/very bad chance" + }, + { + "Country": "Mexico", + "Score": 5.0, + "Likelihood": "Don't know" + }, + { + "Country": "Australia", + "Score": 32.0, + "Likelihood": "Very good/rather good chance" + }, + { + "Country": "Australia", + "Score": 43.0, + "Likelihood": "Moderate chance" + }, + { + "Country": "Australia", + "Score": 22.0, + "Likelihood": "Rather bad/very bad chance" + }, + { + "Country": "Australia", + "Score": 3.0, + "Likelihood": "Don't know" + }, + { + "Country": "United States", + "Score": 31.0, + "Likelihood": "Very good/rather good chance" + }, + { + "Country": "United States", + "Score": 37.0, + "Likelihood": "Moderate chance" + }, + { + "Country": "United States", + "Score": 28.0, + "Likelihood": "Rather bad/very bad chance" + }, + { + "Country": "United States", + "Score": 4.0, + "Likelihood": "Don't know" + }, + { + "Country": "Germany", + "Score": 26.0, + "Likelihood": "Very good/rather good chance" + }, + { + "Country": "Germany", + "Score": 43.0, + "Likelihood": "Moderate chance" + }, + { + "Country": "Germany", + "Score": 26.0, + "Likelihood": "Rather bad/very bad chance" + }, + { + "Country": "Germany", + "Score": 5.0, + "Likelihood": "Don't know" + }, + { + "Country": "Canada", + "Score": 25.0, + "Likelihood": "Very good/rather good chance" + }, + { + "Country": "Canada", + "Score": 42.0, + "Likelihood": "Moderate chance" + }, + { + "Country": "Canada", + "Score": 28.0, + "Likelihood": "Rather bad/very bad chance" + }, + { + "Country": "Canada", + "Score": 5.0, + "Likelihood": "Don't know" + }, + { + "Country": "United Kingdom", + "Score": 19.0, + "Likelihood": "Very good/rather good chance" + }, + { + "Country": "United Kingdom", + "Score": 43.0, + "Likelihood": "Moderate chance" + }, + { + "Country": "United Kingdom", + "Score": 31.0, + "Likelihood": "Rather bad/very bad chance" + }, + { + "Country": "United Kingdom", + "Score": 7.0, + "Likelihood": "Don't know" + }, + { + "Country": "France", + "Score": 15.0, + "Likelihood": "Very good/rather good chance" + }, + { + "Country": "France", + "Score": 35.0, + "Likelihood": "Moderate chance" + }, + { + "Country": "France", + "Score": 44.0, + "Likelihood": "Rather bad/very bad chance" + }, + { + "Country": "France", + "Score": 6.0, + "Likelihood": "Don't know" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding likelihood", + "role": "y" + }, + { + "name": "Likelihood", + "importance": "primary", + "description": "The likelihood of the data point", + "role": "group" + } + ] + } + }, + "221": { + "metadata": { + "title": "Yellowstone's Steamboat Geyser Eruptions Break Record", + "description": "Number of major annual eruptions of the Steamboat geyser (1961-2019)", + "main_insight": "Steamboat Geyser in Yellowstone National Park had a record-breaking number of eruptions in 2019, far exceeding any year since 1961." + }, + "chart_type": "Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1961, + "Eruptions": 7 + }, + { + "Year": 1962, + "Eruptions": 26 + }, + { + "Year": 1963, + "Eruptions": 29 + }, + { + "Year": 1964, + "Eruptions": 11 + }, + { + "Year": 1965, + "Eruptions": 22 + }, + { + "Year": 1966, + "Eruptions": 7 + }, + { + "Year": 1967, + "Eruptions": 17 + }, + { + "Year": 1968, + "Eruptions": 6 + }, + { + "Year": 1969, + "Eruptions": 8 + }, + { + "Year": 1970, + "Eruptions": 1 + }, + { + "Year": 1971, + "Eruptions": 1 + }, + { + "Year": 1972, + "Eruptions": 0 + }, + { + "Year": 1973, + "Eruptions": 0 + }, + { + "Year": 1974, + "Eruptions": 0 + }, + { + "Year": 1975, + "Eruptions": 0 + }, + { + "Year": 1976, + "Eruptions": 0 + }, + { + "Year": 1977, + "Eruptions": 0 + }, + { + "Year": 1978, + "Eruptions": 0 + }, + { + "Year": 1979, + "Eruptions": 0 + }, + { + "Year": 1980, + "Eruptions": 0 + }, + { + "Year": 1981, + "Eruptions": 2 + }, + { + "Year": 1982, + "Eruptions": 26 + }, + { + "Year": 1983, + "Eruptions": 23 + }, + { + "Year": 1984, + "Eruptions": 9 + }, + { + "Year": 1985, + "Eruptions": 1 + }, + { + "Year": 1986, + "Eruptions": 0 + }, + { + "Year": 1987, + "Eruptions": 0 + }, + { + "Year": 1988, + "Eruptions": 0 + }, + { + "Year": 1989, + "Eruptions": 4 + }, + { + "Year": 1990, + "Eruptions": 4 + }, + { + "Year": 1991, + "Eruptions": 2 + }, + { + "Year": 1992, + "Eruptions": 0 + }, + { + "Year": 1993, + "Eruptions": 0 + }, + { + "Year": 1994, + "Eruptions": 1 + }, + { + "Year": 1995, + "Eruptions": 0 + }, + { + "Year": 1996, + "Eruptions": 0 + }, + { + "Year": 1997, + "Eruptions": 0 + }, + { + "Year": 1998, + "Eruptions": 0 + }, + { + "Year": 1999, + "Eruptions": 1 + }, + { + "Year": 2000, + "Eruptions": 0 + }, + { + "Year": 2001, + "Eruptions": 0 + }, + { + "Year": 2002, + "Eruptions": 3 + }, + { + "Year": 2003, + "Eruptions": 3 + }, + { + "Year": 2004, + "Eruptions": 0 + }, + { + "Year": 2005, + "Eruptions": 0 + }, + { + "Year": 2006, + "Eruptions": 0 + }, + { + "Year": 2007, + "Eruptions": 0 + }, + { + "Year": 2008, + "Eruptions": 0 + }, + { + "Year": 2009, + "Eruptions": 0 + }, + { + "Year": 2010, + "Eruptions": 0 + }, + { + "Year": 2011, + "Eruptions": 0 + }, + { + "Year": 2012, + "Eruptions": 0 + }, + { + "Year": 2013, + "Eruptions": 2 + }, + { + "Year": 2014, + "Eruptions": 2 + }, + { + "Year": 2015, + "Eruptions": 0 + }, + { + "Year": 2016, + "Eruptions": 0 + }, + { + "Year": 2017, + "Eruptions": 0 + }, + { + "Year": 2018, + "Eruptions": 32 + }, + { + "Year": 2019, + "Eruptions": 48 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of observation", + "unit": "none", + "data_type": "time", + "discarded_count": 0, + "role": "x" + }, + { + "name": "Eruptions", + "importance": "primary", + "description": "Number of major annual eruptions", + "unit": "none", + "data_type": "number", + "discarded_count": 0, + "role": "y" + } + ] + } + }, + "1239": { + "metadata": { + "title": "Global Coal Power Falls in 2020", + "description": "Growth in global coal power generation in the last five years (in Gigawatts)", + "main_insight": "Global coal power generation growth decreased significantly from 2015 to 2020, with 2020 showing negative growth." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2015, + "Growth (GW)": 68.1 + }, + { + "Year": 2016, + "Growth (GW)": 51.8 + }, + { + "Year": 2017, + "Growth (GW)": 36.0 + }, + { + "Year": 2018, + "Growth (GW)": 16.8 + }, + { + "Year": 2019, + "Growth (GW)": 37.8 + }, + { + "Year": 2020, + "Growth (GW)": -2.9 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Growth (GW)", + "importance": "primary", + "description": "Growth in global coal power generation in Gigawatts", + "unit": "GW", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1733": { + "metadata": { + "title": "The Short Life Of A Trump Communications Director", + "description": "Number of days worked as communications director since Trump's election", + "main_insight": "The tenure of Trump's Communications Directors has been remarkably short, with Anthony Scaramucci serving the shortest term at only 11 days, and even the longest tenures being relatively brief compared to similar roles in past administrations." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Communications Director": "Sean Spicer", + "Days Worked": 182 + } + ], + "score": 0.84, + "annotation": "The shows a sudden jump in Days Worked from Michael Dubke to Sean Spicer.", + "reason": "The Days Worked for increases from 86 at Michael Dubke to 182 at Sean Spicer, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Communications Director": "Anthony Scaramucci", + "Days Worked": 11 + } + ], + "score": 0.71, + "annotation": "The has a minimum value at Anthony Scaramucci", + "reason": "The [{'Communications Director': 'Anthony Scaramucci', 'Days Worked': 11}] of has a mainimum value of 11, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Communications Director": "Hope Hicks", + "Days Worked": 197 + } + ], + "score": 0.55, + "annotation": "The has a maximum value at Hope Hicks", + "reason": "The [{'Communications Director': 'Hope Hicks', 'Days Worked': 197}] of has a maximum value of 197, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Communications Director": "Hope Hicks", + "Days Worked": 197 + }, + { + "Communications Director": "Sean Spicer", + "Days Worked": 182 + }, + { + "Communications Director": "Michael Dubke", + "Days Worked": 86 + }, + { + "Communications Director": "Anthony Scaramucci", + "Days Worked": 11 + } + ], + "columns": [ + { + "name": "Communications Director", + "importance": "primary", + "description": "Name of the Communications Director", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Days Worked", + "importance": "primary", + "description": "Number of days worked as communications director", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1822": { + "metadata": { + "title": "Boxing for Millions", + "description": "Highest-grossing boxing fights of all time by total revenue", + "main_insight": "The Mayweather vs McGregor fight in 2017 generated the highest revenue among all boxing fights, reaching $500 million. Mayweather's fights consistently dominate the highest-grossing matches." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Fight": "Mayweather vs Pacquiao May 2015", + "Revenue": 410.0 + } + ], + "score": 0.9, + "annotation": "The shows a sudden jump in Revenue from Mayweather vs Alvarez Sep 2013 to Mayweather vs Pacquiao May 2015.", + "reason": "The Revenue for increases from 150.0 at Mayweather vs Alvarez Sep 2013 to 410.0 at Mayweather vs Pacquiao May 2015, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Fight": "Mayweather vs McGregor Aug 2017", + "Revenue": 500.0 + } + ], + "score": 0.81, + "annotation": "The has a maximum value at Mayweather vs McGregor Aug 2017", + "reason": "The [{'Fight': 'Mayweather vs McGregor Aug 2017', 'Revenue': 500.0}] of has a maximum value of 500.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Fight": "Tyson vs Holyfield Jun 1997", + "Revenue": 100.0 + } + ], + "score": 0.49, + "annotation": "The has a minimum value at Tyson vs Holyfield Jun 1997", + "reason": "The [{'Fight': 'Tyson vs Holyfield Jun 1997', 'Revenue': 100.0}] of has a mainimum value of 100.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Fight": "Mayweather vs McGregor Aug 2017", + "Revenue": 500.0 + }, + { + "Fight": "Mayweather vs Pacquiao May 2015", + "Revenue": 410.0 + }, + { + "Fight": "Mayweather vs Alvarez Sep 2013", + "Revenue": 150.0 + }, + { + "Fight": "Mayweather vs De La Hoya May 2007", + "Revenue": 140.0 + }, + { + "Fight": "Tyson vs Lewis Jun 2002", + "Revenue": 110.0 + }, + { + "Fight": "Tyson vs Holyfield Jun 1997", + "Revenue": 100.0 + } + ], + "columns": [ + { + "name": "Fight", + "importance": "primary", + "description": "The boxing match and date", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Revenue", + "importance": "primary", + "description": "Total revenue generated by the fight in millions of dollars", + "unit": "Million USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "326": { + "metadata": { + "title": "Old Habits Don't Stick For Churchgoing", + "description": "Share of U.S. adults by frequency of church/synagogue/mosque/temple visits as a child and as an adult", + "main_insight": "The frequency of church/synagogue/mosque/temple visits significantly decreases from childhood to adulthood among U.S. adults. A much larger percentage of adults seldom/never attend compared to their childhood attendance." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Life Stage": "As an adult", + "Score": 22.0, + "Frequency": "Every week" + }, + { + "Life Stage": "As an adult", + "Score": 9.0, + "Frequency": "Almost every week" + } + ], + "score": 1.0, + "annotation": "The Score of Every week and Almost every week show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Every week and Almost every week is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Frequency": "Seldom/never", + "Life Stage": "total", + "Score": 81.0 + } + ], + "score": 0.91, + "annotation": "The total value of Seldom/never has the largest proportion.", + "reason": "The total value of Score of Seldom/never accounts for 0.40703517587939697, larger than all other Frequency, of the total of Score of all Frequency." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Life Stage": "As a child", + "Score": 50.0, + "Frequency": "Every week" + } + ], + "score": 0.89, + "annotation": "The Every week has the largest proportion in As a child.", + "reason": "The Score of Every week in As a child accounts for 0.5, larger than all other Frequency, of the total of Score of all Frequency." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Life Stage": "As an adult", + "Score": 58.0, + "Frequency": "Seldom/never" + } + ], + "score": 0.87, + "annotation": "The Seldom/never accounts for the majority in As an adult.", + "reason": "The Score of Seldom/never in As an adult accounts for 0.5858585858585859 (more than 50%) of the total of Score of all Frequency." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Life Stage": "As a child", + "Score": 50.0, + "Frequency": "Every week" + } + ], + "score": 0.45, + "annotation": "The Every week has a maximum value at As a child", + "reason": "The [{'Life Stage': 'As a child', 'Score': 50.0, 'Frequency': 'Every week'}] of Every week has a maximum value of 50.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Life Stage": "As a child", + "Score": 50.0, + "Frequency": "Every week" + }, + { + "Life Stage": "As a child", + "Score": 17.0, + "Frequency": "Almost every week" + }, + { + "Life Stage": "As a child", + "Score": 10.0, + "Frequency": "About once a month" + }, + { + "Life Stage": "As a child", + "Score": 23.0, + "Frequency": "Seldom/never" + }, + { + "Life Stage": "As an adult", + "Score": 22.0, + "Frequency": "Every week" + }, + { + "Life Stage": "As an adult", + "Score": 9.0, + "Frequency": "Almost every week" + }, + { + "Life Stage": "As an adult", + "Score": 10.0, + "Frequency": "About once a month" + }, + { + "Life Stage": "As an adult", + "Score": 58.0, + "Frequency": "Seldom/never" + } + ], + "columns": [ + { + "name": "Life Stage", + "importance": "primary", + "description": "Whether the frequency of visits is reported for childhood or adulthood", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding frequency", + "role": "y" + }, + { + "name": "Frequency", + "importance": "primary", + "description": "The frequency of the data point", + "role": "group" + } + ] + } + }, + "612": { + "metadata": { + "title": "Millennials Rack Up 18 Hours of Media Use Per Day", + "description": "Average time millennials in the U.S. spend interacting with media per day (hh:mm)", + "main_insight": "Millennials spend a significant amount of time interacting with media each day, with browsing the internet, social networking, and watching live TV being the top activities." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Activity": "Browse the internet", + "Time (hh:mm)": 3.57 + } + ], + "score": 0.89, + "annotation": "The has a maximum value at Browse the internet", + "reason": "The [{'Activity': 'Browse the internet', 'Time (hh:mm)': 3.57}] of has a maximum value of 3.57, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Activity": "Read print magazines/newspapers", + "Time (hh:mm)": 0.53 + } + ], + "score": 0.72, + "annotation": "The has a minimum value at Read print magazines/newspapers", + "reason": "The [{'Activity': 'Read print magazines/newspapers', 'Time (hh:mm)': 0.53}] of has a mainimum value of 0.53, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Activity": "Social networking", + "Time (hh:mm)": 3.2 + } + ], + "score": 0.68, + "annotation": "The shows a sudden jump in Time (hh:mm) from Watch live TV to Social networking.", + "reason": "The Time (hh:mm) for increases from 2.32 at Watch live TV to 3.2 at Social networking, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Activity": "Browse the internet", + "Time (hh:mm)": 3.57 + }, + { + "Activity": "Social networking", + "Time (hh:mm)": 3.2 + }, + { + "Activity": "Watch live TV", + "Time (hh:mm)": 2.32 + }, + { + "Activity": "Play video games", + "Time (hh:mm)": 1.78 + }, + { + "Activity": "Watch timeshifted TV", + "Time (hh:mm)": 1.78 + }, + { + "Activity": "Go to the movies", + "Time (hh:mm)": 1.25 + }, + { + "Activity": "Listen to the radio", + "Time (hh:mm)": 1.25 + }, + { + "Activity": "Use email, text, texting apps", + "Time (hh:mm)": 1.07 + }, + { + "Activity": "Talk about news/products/brands", + "Time (hh:mm)": 1.07 + }, + { + "Activity": "Read print magazines/newspapers", + "Time (hh:mm)": 0.53 + } + ], + "columns": [ + { + "name": "Activity", + "importance": "primary", + "description": "Type of media activity", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Time (hh:mm)", + "importance": "primary", + "description": "Average time spent on each activity", + "unit": "hh:mm", + "data_type": "number", + "role": "y" + } + ] + } + }, + "91": { + "metadata": { + "title": "Online Travel Platforms Accounted for 15% of the Australian Accommodation Market Prepandemic", + "description": "Room nights in all accommodation types in Australia (in millions)", + "main_insight": "Online travel platforms represented a significant but minority share of the Australian accommodation market before the pandemic, and both online and other booking avenues saw a sharp decline in 2020 and 2021." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Units": 81.0, + "Booking Source": "Booked using other avenues" + }, + { + "Year": 2021, + "Units": 8.0, + "Booking Source": "Booked using online travel platforms" + } + ], + "score": 1.0, + "annotation": "The Units of Booked using other avenues and Booked using online travel platforms show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Booked using other avenues and Booked using online travel platforms is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2017, + "Units": 203.0, + "Booking Source": "Booked using other avenues" + }, + { + "Year": 2017, + "Units": 37.0, + "Booking Source": "Booked using online travel platforms" + }, + { + "Year": 2018, + "Units": 215.0, + "Booking Source": "Booked using other avenues" + }, + { + "Year": 2018, + "Units": 39.0, + "Booking Source": "Booked using online travel platforms" + }, + { + "Year": 2019, + "Units": 219.0, + "Booking Source": "Booked using other avenues" + }, + { + "Year": 2019, + "Units": 40.0, + "Booking Source": "Booked using online travel platforms" + }, + { + "Year": 2020, + "Units": 100.0, + "Booking Source": "Booked using other avenues" + }, + { + "Year": 2020, + "Units": 14.0, + "Booking Source": "Booked using online travel platforms" + }, + { + "Year": 2021, + "Units": 81.0, + "Booking Source": "Booked using other avenues" + }, + { + "Year": 2021, + "Units": 8.0, + "Booking Source": "Booked using online travel platforms" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of booking", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding booking source", + "role": "y" + }, + { + "name": "Booking Source", + "importance": "primary", + "description": "The booking source of the data point", + "role": "group" + } + ] + } + }, + "1469": { + "metadata": { + "title": "No Hands Land", + "description": "Percent of surveyed jeans that can fit the average hand size broken down by gender", + "main_insight": "There is a significant difference in the functionality of jean pockets based on gender, with men's jean pockets being far more likely to fit an average hand than women's jean pockets." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Gender": "Men", + "Percent_Fit": 100.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Percent_Fit from Women to Men.", + "reason": "The Percent_Fit for increases from 10.0 at Women to 100.0 at Men, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Gender": "Men", + "Percent_Fit": 100.0 + } + ], + "score": 0.45, + "annotation": "The has a maximum value at Men", + "reason": "The [{'Gender': 'Men', 'Percent_Fit': 100.0}] of has a maximum value of 100.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Gender": "Women", + "Percent_Fit": 10.0 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at Women", + "reason": "The [{'Gender': 'Women', 'Percent_Fit': 10.0}] of has a mainimum value of 10.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Gender": "Women", + "Percent_Fit": 10.0 + }, + { + "Gender": "Men", + "Percent_Fit": 100.0 + } + ], + "columns": [ + { + "name": "Gender", + "importance": "primary", + "description": "Category of jeans: Men's or Women's", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percent_Fit", + "importance": "primary", + "description": "Percentage of jean pockets that can fit the average hand", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2073": { + "metadata": { + "title": "Should your Facebook page be deleted after you die?", + "description": "% of people in the UK on what should happen to their Facebook accounts after they die", + "main_insight": "Opinions on what should happen to Facebook accounts after death vary significantly by age group. A large majority (71%) of people aged 60+ believe the account should be deleted, while younger demographics are more divided between deletion, freezing, and legacy contact options." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "25-39", + "Units": 32.0, + "Account Status": "Deleted" + } + ], + "score": 0.93, + "annotation": "The Deleted has the largest proportion in 25-39.", + "reason": "The Units of Deleted in 25-39 accounts for 0.32, larger than all other Account Status, of the total of Units of all Account Status." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Age Group": "60+", + "Units": 71.0, + "Account Status": "Deleted" + }, + { + "Age Group": "60+", + "Units": 8.0, + "Account Status": "Frozen" + } + ], + "score": 0.92, + "annotation": "The Units of Deleted and Frozen show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Deleted and Frozen is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "18-24", + "Units": 38.0, + "Account Status": "Frozen" + } + ], + "score": 0.91, + "annotation": "The Frozen has the largest proportion in 18-24.", + "reason": "The Units of Frozen in 18-24 accounts for 0.38, larger than all other Account Status, of the total of Units of all Account Status." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Age Group": "60+", + "Units": 71.0, + "Account Status": "Deleted" + }, + { + "Age Group": "60+", + "Units": 10.0, + "Account Status": "Legacy contact" + } + ], + "score": 0.91, + "annotation": "The Units of Deleted and Legacy contact show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Deleted and Legacy contact is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Account Status": "Deleted", + "Age Group": "total", + "Units": 180.0 + } + ], + "score": 0.9, + "annotation": "The total value of Deleted has the largest proportion.", + "reason": "The total value of Units of Deleted accounts for 0.45, larger than all other Account Status, of the total of Units of all Account Status." + } + ], + "data": { + "data": [ + { + "Age Group": "18-24", + "Units": 26.0, + "Account Status": "Deleted" + }, + { + "Age Group": "18-24", + "Units": 38.0, + "Account Status": "Frozen" + }, + { + "Age Group": "18-24", + "Units": 23.0, + "Account Status": "Legacy contact" + }, + { + "Age Group": "18-24", + "Units": 13.0, + "Account Status": "Don't know" + }, + { + "Age Group": "25-39", + "Units": 32.0, + "Account Status": "Deleted" + }, + { + "Age Group": "25-39", + "Units": 23.0, + "Account Status": "Frozen" + }, + { + "Age Group": "25-39", + "Units": 29.0, + "Account Status": "Legacy contact" + }, + { + "Age Group": "25-39", + "Units": 16.0, + "Account Status": "Don't know" + }, + { + "Age Group": "40-59", + "Units": 51.0, + "Account Status": "Deleted" + }, + { + "Age Group": "40-59", + "Units": 16.0, + "Account Status": "Frozen" + }, + { + "Age Group": "40-59", + "Units": 18.0, + "Account Status": "Legacy contact" + }, + { + "Age Group": "40-59", + "Units": 15.0, + "Account Status": "Don't know" + }, + { + "Age Group": "60+", + "Units": 71.0, + "Account Status": "Deleted" + }, + { + "Age Group": "60+", + "Units": 8.0, + "Account Status": "Frozen" + }, + { + "Age Group": "60+", + "Units": 10.0, + "Account Status": "Legacy contact" + }, + { + "Age Group": "60+", + "Units": 11.0, + "Account Status": "Don't know" + } + ], + "columns": [ + { + "name": "Age Group", + "importance": "primary", + "description": "Age ranges of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding account status", + "role": "y" + }, + { + "name": "Account Status", + "importance": "primary", + "description": "The account status of the data point", + "role": "group" + } + ] + } + }, + "1999": { + "metadata": { + "title": "Osborne's policy changes will yield an extra 5 billion in tax", + "description": "Extra tax to be generated from policy changes in the 2015 Autumn Statement", + "main_insight": "Policy changes introduced in the 2015 Autumn Statement were projected to significantly increase tax revenue, rising from 0.34 billion GBP in 2015-16 to a peak of 5.5 billion GBP in 2019-20." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Fiscal Year": "2017-18", + "Extra Tax Revenue (billion GBP)": 4.5 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in Extra Tax Revenue (billion GBP) from 2016-17 to 2017-18.", + "reason": "The Extra Tax Revenue (billion GBP) for increases from 0.59 at 2016-17 to 4.5 at 2017-18, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Fiscal Year": "2015-16", + "Extra Tax Revenue (billion GBP)": 0.34 + } + ], + "score": 0.74, + "annotation": "The has a minimum value at 2015-16", + "reason": "The [{'Fiscal Year': '2015-16', 'Extra Tax Revenue (billion GBP)': 0.34}] of has a mainimum value of 0.34, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Fiscal Year": "2019-20", + "Extra Tax Revenue (billion GBP)": 5.5 + } + ], + "score": 0.53, + "annotation": "The has a maximum value at 2019-20", + "reason": "The [{'Fiscal Year': '2019-20', 'Extra Tax Revenue (billion GBP)': 5.5}] of has a maximum value of 5.5, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Fiscal Year": "2015-16", + "Extra Tax Revenue (billion GBP)": 0.34 + }, + { + "Fiscal Year": "2016-17", + "Extra Tax Revenue (billion GBP)": 0.59 + }, + { + "Fiscal Year": "2017-18", + "Extra Tax Revenue (billion GBP)": 4.5 + }, + { + "Fiscal Year": "2018-19", + "Extra Tax Revenue (billion GBP)": 4.6 + }, + { + "Fiscal Year": "2019-20", + "Extra Tax Revenue (billion GBP)": 5.5 + }, + { + "Fiscal Year": "2020-21", + "Extra Tax Revenue (billion GBP)": 5.3 + } + ], + "columns": [ + { + "name": "Fiscal Year", + "importance": "primary", + "description": "Financial year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Extra Tax Revenue (billion GBP)", + "importance": "primary", + "description": "Extra tax revenue generated in billions of British pounds", + "unit": "Billion GBP", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1903": { + "metadata": { + "title": "Climate Fight Now in Courts Across the World", + "description": "Quantity of legal climate cases between May 2018 to May 2019", + "main_insight": "The visualization shows the number of legal climate cases in different countries and regions between May 2018 and May 2019, highlighting that the U.S. had the highest number with 1,023 cases, and Australia is second with 94." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country/Region": "United States", + "Number of Cases": 1023 + } + ], + "score": 0.99, + "annotation": "The has a maximum value at United States", + "reason": "The [{'Country/Region': 'United States', 'Number of Cases': 1023}] of has a maximum value of 1023, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country/Region": "United States", + "Number of Cases": 1023 + } + ], + "score": 0.96, + "annotation": "The shows a sudden jump in Number of Cases from Australia to United States.", + "reason": "The Number of Cases for increases from 94 at Australia to 1023 at United States, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country/Region": "Germany", + "Number of Cases": 5 + }, + { + "Country/Region": "Brazil", + "Number of Cases": 5 + } + ], + "score": 0.3, + "annotation": "The has minimum values at Germany, Brazil", + "reason": "The [{'Country/Region': 'Germany', 'Number of Cases': 5}, {'Country/Region': 'Brazil', 'Number of Cases': 5}] of have minimum values of 5, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country/Region": "United States", + "Number of Cases": 1023 + }, + { + "Country/Region": "Australia", + "Number of Cases": 94 + }, + { + "Country/Region": "European Union", + "Number of Cases": 55 + }, + { + "Country/Region": "United Kingdom", + "Number of Cases": 53 + }, + { + "Country/Region": "New Zealand", + "Number of Cases": 17 + }, + { + "Country/Region": "Canada", + "Number of Cases": 16 + }, + { + "Country/Region": "Spain", + "Number of Cases": 13 + }, + { + "Country/Region": "India", + "Number of Cases": 10 + }, + { + "Country/Region": "France", + "Number of Cases": 6 + }, + { + "Country/Region": "Germany", + "Number of Cases": 5 + }, + { + "Country/Region": "Brazil", + "Number of Cases": 5 + } + ], + "columns": [ + { + "name": "Country/Region", + "importance": "primary", + "description": "The country or region where the legal climate cases were filed.", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Cases", + "importance": "primary", + "description": "The quantity of legal climate cases in each country/region.", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1531": { + "metadata": { + "title": "Greece Enters A Small And Unenviable Club", + "description": "Funds overdue to the IMF as of July 2015 (SDR)*", + "main_insight": "Greece joined a small group of countries with overdue funds to the IMF in July 2015, with significantly larger amounts owed in 2015 and over the next decade compared to the arrears of other countries." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Country": "Zimbabwe", + "Date entered arrears": "February 2001" + }, + { + "Country": "Somalia", + "Date entered arrears": "July 1987" + }, + { + "Country": "Sudan", + "Date entered arrears": "December 1984" + }, + { + "Country": "Greece", + "Date entered arrears": "July 2015" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Countries with overdue funds to the IMF", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Date entered arrears", + "importance": "primary", + "description": "The date each country entered arrears", + "unit": "none", + "data_type": "time", + "role": "y" + } + ] + } + }, + "2206": { + "metadata": { + "title": "The EU's Biggest Chocolate Producers", + "description": "EU chocolate production in 2017 (million tonnes)*", + "main_insight": "Germany is by far the largest chocolate producer in the EU, producing almost double the amount of the second largest producer, Italy." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Germany", + "Production (million tonnes)": 1.3 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at Germany", + "reason": "The [{'Country': 'Germany', 'Production (million tonnes)': 1.3}] of has a maximum value of 1.3, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Germany", + "Production (million tonnes)": 1.3 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Production (million tonnes) from Italy to Germany.", + "reason": "The Production (million tonnes) for increases from 0.7 at Italy to 1.3 at Germany, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Spain", + "Production (million tonnes)": 0.1 + } + ], + "score": 0.57, + "annotation": "The has a minimum value at Spain", + "reason": "The [{'Country': 'Spain', 'Production (million tonnes)': 0.1}] of has a mainimum value of 0.1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Germany", + "Production (million tonnes)": 1.3 + }, + { + "Country": "Italy", + "Production (million tonnes)": 0.7 + }, + { + "Country": "France", + "Production (million tonnes)": 0.4 + }, + { + "Country": "Netherlands", + "Production (million tonnes)": 0.4 + }, + { + "Country": "United Kingdom", + "Production (million tonnes)": 0.3 + }, + { + "Country": "Belgium", + "Production (million tonnes)": 0.3 + }, + { + "Country": "Poland", + "Production (million tonnes)": 0.2 + }, + { + "Country": "Spain", + "Production (million tonnes)": 0.1 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "EU Country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Production (million tonnes)", + "importance": "primary", + "description": "Chocolate production in million tonnes", + "unit": "million tonnes", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2233": { + "metadata": { + "title": "Scandinavia: First For Public Sector Employment", + "description": "Employment in general government as a percentage of total employment (2015)", + "main_insight": "Scandinavian countries lead in public sector employment as a percentage of total employment, with Norway, Denmark, and Sweden having the highest percentages." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Norway", + "Public Sector Employment": 30.0 + } + ], + "score": 0.83, + "annotation": "The has a maximum value at Norway", + "reason": "The [{'Country': 'Norway', 'Public Sector Employment': 30.0}] of has a maximum value of 30.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Japan", + "Public Sector Employment": 5.9 + } + ], + "score": 0.82, + "annotation": "The has a minimum value at Japan", + "reason": "The [{'Country': 'Japan', 'Public Sector Employment': 5.9}] of has a mainimum value of 5.9, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Sweden", + "Public Sector Employment": 28.6 + } + ], + "score": 0.55, + "annotation": "The shows a sudden jump in Public Sector Employment from Finland to Sweden.", + "reason": "The Public Sector Employment for increases from 24.9 at Finland to 28.6 at Sweden, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Norway", + "Public Sector Employment": 30.0 + }, + { + "Country": "Denmark", + "Public Sector Employment": 29.1 + }, + { + "Country": "Sweden", + "Public Sector Employment": 28.6 + }, + { + "Country": "Finland", + "Public Sector Employment": 24.9 + }, + { + "Country": "France", + "Public Sector Employment": 21.4 + }, + { + "Country": "Canada", + "Public Sector Employment": 18.2 + }, + { + "Country": "Greece", + "Public Sector Employment": 18.0 + }, + { + "Country": "United Kingdom", + "Public Sector Employment": 16.4 + }, + { + "Country": "Spain", + "Public Sector Employment": 15.7 + }, + { + "Country": "United States", + "Public Sector Employment": 15.3 + }, + { + "Country": "Italy", + "Public Sector Employment": 13.6 + }, + { + "Country": "Turkey", + "Public Sector Employment": 12.4 + }, + { + "Country": "Germany", + "Public Sector Employment": 10.6 + }, + { + "Country": "South Korea", + "Public Sector Employment": 7.6 + }, + { + "Country": "Japan", + "Public Sector Employment": 5.9 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country Name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Public Sector Employment", + "importance": "primary", + "description": "Percentage of total employment in general government", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "262": { + "metadata": { + "title": "Democrats Flip Gubernatorial Seats", + "description": "Number of Republican and Democratic governors in the U.S. (1990-2022)", + "main_insight": "The number of Republican and Democratic governors has fluctuated over time, with Democrats currently holding 24 governorships and Republicans holding 26 as of 2023. There were more democratic governors before 2010 and there are more republican governors after." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2023, + "Units": 26, + "Party Affiliation": "Republican" + }, + { + "Year": 2023, + "Units": 24, + "Party Affiliation": "Democrat" + } + ], + "score": 0.93, + "annotation": "The Units of Republican and Democrat show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Republican and Democrat is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 1990, + "Units": 22, + "Party Affiliation": "Republican" + }, + { + "Year": 1990, + "Units": 28, + "Party Affiliation": "Democrat" + }, + { + "Year": 1991, + "Units": 23, + "Party Affiliation": "Republican" + }, + { + "Year": 1991, + "Units": 27, + "Party Affiliation": "Democrat" + }, + { + "Year": 1992, + "Units": 22, + "Party Affiliation": "Republican" + }, + { + "Year": 1992, + "Units": 28, + "Party Affiliation": "Democrat" + }, + { + "Year": 1993, + "Units": 24, + "Party Affiliation": "Republican" + }, + { + "Year": 1993, + "Units": 24, + "Party Affiliation": "Democrat" + }, + { + "Year": 1994, + "Units": 24, + "Party Affiliation": "Republican" + }, + { + "Year": 1994, + "Units": 25, + "Party Affiliation": "Democrat" + }, + { + "Year": 1995, + "Units": 29, + "Party Affiliation": "Republican" + }, + { + "Year": 1995, + "Units": 21, + "Party Affiliation": "Democrat" + }, + { + "Year": 1996, + "Units": 31, + "Party Affiliation": "Republican" + }, + { + "Year": 1996, + "Units": 19, + "Party Affiliation": "Democrat" + }, + { + "Year": 1997, + "Units": 32, + "Party Affiliation": "Republican" + }, + { + "Year": 1997, + "Units": 18, + "Party Affiliation": "Democrat" + }, + { + "Year": 1998, + "Units": 31, + "Party Affiliation": "Republican" + }, + { + "Year": 1998, + "Units": 18, + "Party Affiliation": "Democrat" + }, + { + "Year": 1999, + "Units": 30, + "Party Affiliation": "Republican" + }, + { + "Year": 1999, + "Units": 18, + "Party Affiliation": "Democrat" + }, + { + "Year": 2000, + "Units": 29, + "Party Affiliation": "Republican" + }, + { + "Year": 2000, + "Units": 19, + "Party Affiliation": "Democrat" + }, + { + "Year": 2001, + "Units": 28, + "Party Affiliation": "Republican" + }, + { + "Year": 2001, + "Units": 20, + "Party Affiliation": "Democrat" + }, + { + "Year": 2002, + "Units": 27, + "Party Affiliation": "Republican" + }, + { + "Year": 2002, + "Units": 21, + "Party Affiliation": "Democrat" + }, + { + "Year": 2003, + "Units": 26, + "Party Affiliation": "Republican" + }, + { + "Year": 2003, + "Units": 24, + "Party Affiliation": "Democrat" + }, + { + "Year": 2004, + "Units": 28, + "Party Affiliation": "Republican" + }, + { + "Year": 2004, + "Units": 22, + "Party Affiliation": "Democrat" + }, + { + "Year": 2005, + "Units": 28, + "Party Affiliation": "Republican" + }, + { + "Year": 2005, + "Units": 22, + "Party Affiliation": "Democrat" + }, + { + "Year": 2006, + "Units": 28, + "Party Affiliation": "Republican" + }, + { + "Year": 2006, + "Units": 22, + "Party Affiliation": "Democrat" + }, + { + "Year": 2007, + "Units": 22, + "Party Affiliation": "Republican" + }, + { + "Year": 2007, + "Units": 28, + "Party Affiliation": "Democrat" + }, + { + "Year": 2008, + "Units": 22, + "Party Affiliation": "Republican" + }, + { + "Year": 2008, + "Units": 28, + "Party Affiliation": "Democrat" + }, + { + "Year": 2009, + "Units": 24, + "Party Affiliation": "Republican" + }, + { + "Year": 2009, + "Units": 26, + "Party Affiliation": "Democrat" + }, + { + "Year": 2010, + "Units": 23, + "Party Affiliation": "Republican" + }, + { + "Year": 2010, + "Units": 20, + "Party Affiliation": "Democrat" + }, + { + "Year": 2011, + "Units": 29, + "Party Affiliation": "Republican" + }, + { + "Year": 2011, + "Units": 19, + "Party Affiliation": "Democrat" + }, + { + "Year": 2012, + "Units": 30, + "Party Affiliation": "Republican" + }, + { + "Year": 2012, + "Units": 19, + "Party Affiliation": "Democrat" + }, + { + "Year": 2013, + "Units": 30, + "Party Affiliation": "Republican" + }, + { + "Year": 2013, + "Units": 19, + "Party Affiliation": "Democrat" + }, + { + "Year": 2014, + "Units": 31, + "Party Affiliation": "Republican" + }, + { + "Year": 2014, + "Units": 18, + "Party Affiliation": "Democrat" + }, + { + "Year": 2015, + "Units": 31, + "Party Affiliation": "Republican" + }, + { + "Year": 2015, + "Units": 18, + "Party Affiliation": "Democrat" + }, + { + "Year": 2016, + "Units": 33, + "Party Affiliation": "Republican" + }, + { + "Year": 2016, + "Units": 16, + "Party Affiliation": "Democrat" + }, + { + "Year": 2017, + "Units": 34, + "Party Affiliation": "Republican" + }, + { + "Year": 2017, + "Units": 15, + "Party Affiliation": "Democrat" + }, + { + "Year": 2018, + "Units": 33, + "Party Affiliation": "Republican" + }, + { + "Year": 2018, + "Units": 16, + "Party Affiliation": "Democrat" + }, + { + "Year": 2019, + "Units": 27, + "Party Affiliation": "Republican" + }, + { + "Year": 2019, + "Units": 23, + "Party Affiliation": "Democrat" + }, + { + "Year": 2020, + "Units": 26, + "Party Affiliation": "Republican" + }, + { + "Year": 2020, + "Units": 24, + "Party Affiliation": "Democrat" + }, + { + "Year": 2021, + "Units": 27, + "Party Affiliation": "Republican" + }, + { + "Year": 2021, + "Units": 23, + "Party Affiliation": "Democrat" + }, + { + "Year": 2022, + "Units": 28, + "Party Affiliation": "Republican" + }, + { + "Year": 2022, + "Units": 22, + "Party Affiliation": "Democrat" + }, + { + "Year": 2023, + "Units": 26, + "Party Affiliation": "Republican" + }, + { + "Year": 2023, + "Units": 24, + "Party Affiliation": "Democrat" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of governorships", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding party affiliation", + "role": "y" + }, + { + "name": "Party Affiliation", + "importance": "primary", + "description": "The party affiliation of the data point", + "role": "group" + } + ] + } + }, + "365": { + "metadata": { + "title": "Investing In Italy: An Offer You Can't Refuse?", + "description": "FDI stocks inflow in Italy in 2016, by country of origin (in million euros)", + "main_insight": "Luxembourg and the Netherlands were the largest sources of FDI stock inflow into Italy in 2016, with over 64,000 million euros each." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Luxembourg", + "FDI Inflow (million euros)": 64542.0 + } + ], + "score": 0.81, + "annotation": "The has a maximum value at Luxembourg", + "reason": "The [{'Country': 'Luxembourg', 'FDI Inflow (million euros)': 64542.0}] of has a maximum value of 64542.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "France", + "FDI Inflow (million euros)": 59807.0 + } + ], + "score": 0.66, + "annotation": "The shows a sudden jump in FDI Inflow (million euros) from United Kingdom to France.", + "reason": "The FDI Inflow (million euros) for increases from 43022.0 at United Kingdom to 59807.0 at France, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Denmark", + "FDI Inflow (million euros)": 2663.0 + } + ], + "score": 0.57, + "annotation": "The has a minimum value at Denmark", + "reason": "The [{'Country': 'Denmark', 'FDI Inflow (million euros)': 2663.0}] of has a mainimum value of 2663.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Luxembourg", + "FDI Inflow (million euros)": 64542.0 + }, + { + "Country": "Netherlands", + "FDI Inflow (million euros)": 64464.0 + }, + { + "Country": "France", + "FDI Inflow (million euros)": 59807.0 + }, + { + "Country": "United Kingdom", + "FDI Inflow (million euros)": 43022.0 + }, + { + "Country": "Germany", + "FDI Inflow (million euros)": 28005.0 + }, + { + "Country": "Switzerland", + "FDI Inflow (million euros)": 16778.0 + }, + { + "Country": "Belgium", + "FDI Inflow (million euros)": 10294.0 + }, + { + "Country": "United States", + "FDI Inflow (million euros)": 8286.0 + }, + { + "Country": "Spain", + "FDI Inflow (million euros)": 7658.0 + }, + { + "Country": "Austria", + "FDI Inflow (million euros)": 4900.0 + }, + { + "Country": "Japan", + "FDI Inflow (million euros)": 2750.0 + }, + { + "Country": "Denmark", + "FDI Inflow (million euros)": 2663.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of origin of FDI stocks", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "FDI Inflow (million euros)", + "importance": "primary", + "description": "FDI stocks inflow in Italy in 2016, in millions of euros", + "unit": "million euros", + "data_type": "number", + "role": "y" + } + ] + } + }, + "193": { + "metadata": { + "title": "Timeline of Attacks in Israel and Gaza", + "description": "Overview of military attacks since July 8th 2014", + "main_insight": "The number of attacks peaked on July 9th, with a significant number of targets struck by Israel. While rockets were fired from Gaza every day, a varying proportion were intercepted by the Iron Dome." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "2014-07-10", + "Units": 192, + "Activity Type": "Rockets Fired from Gaza" + } + ], + "score": 0.93, + "annotation": "The Rockets Fired from Gaza has a maximum value at 2014-07-10", + "reason": "The [{'Date': '2014-07-10', 'Units': 192, 'Activity Type': 'Rockets Fired from Gaza'}] of Rockets Fired from Gaza has a maximum value of 192, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "2014-07-10", + "Units": 47, + "Activity Type": "Rockets Fired from Gaza Intercepted by Iron Dome" + } + ], + "score": 0.9, + "annotation": "The Rockets Fired from Gaza Intercepted by Iron Dome has a maximum value at 2014-07-10", + "reason": "The [{'Date': '2014-07-10', 'Units': 47, 'Activity Type': 'Rockets Fired from Gaza Intercepted by Iron Dome'}] of Rockets Fired from Gaza Intercepted by Iron Dome has a maximum value of 47, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "2014-07-09", + "Units": 320, + "Activity Type": "Targets struck by Israel" + } + ], + "score": 0.87, + "annotation": "The Targets struck by Israel has a maximum value at 2014-07-09", + "reason": "The [{'Date': '2014-07-09', 'Units': 320, 'Activity Type': 'Targets struck by Israel'}] of Targets struck by Israel has a maximum value of 320, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Date": "2014-07-10", + "Units": 210, + "Activity Type": "Targets struck by Israel" + } + ], + "score": 0.87, + "annotation": "The Targets struck by Israel has the largest proportion in 2014-07-10.", + "reason": "The Units of Targets struck by Israel in 2014-07-10 accounts for 0.46770601336302897, larger than all other Activity Type, of the total of Units of all Activity Type." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Date": "2014-07-12", + "Units": 129, + "Activity Type": "Rockets Fired from Gaza" + } + ], + "score": 0.86, + "annotation": "The Rockets Fired from Gaza has the largest proportion in 2014-07-12.", + "reason": "The Units of Rockets Fired from Gaza in 2014-07-12 accounts for 0.48134328358208955, larger than all other Activity Type, of the total of Units of all Activity Type." + } + ], + "data": { + "data": [ + { + "Date": "2014-07-08", + "Units": 150, + "Activity Type": "Targets struck by Israel" + }, + { + "Date": "2014-07-08", + "Units": 117, + "Activity Type": "Rockets Fired from Gaza" + }, + { + "Date": "2014-07-08", + "Units": 33, + "Activity Type": "Rockets Fired from Gaza Intercepted by Iron Dome" + }, + { + "Date": "2014-07-09", + "Units": 320, + "Activity Type": "Targets struck by Israel" + }, + { + "Date": "2014-07-09", + "Units": 113, + "Activity Type": "Rockets Fired from Gaza" + }, + { + "Date": "2014-07-09", + "Units": 23, + "Activity Type": "Rockets Fired from Gaza Intercepted by Iron Dome" + }, + { + "Date": "2014-07-10", + "Units": 210, + "Activity Type": "Targets struck by Israel" + }, + { + "Date": "2014-07-10", + "Units": 192, + "Activity Type": "Rockets Fired from Gaza" + }, + { + "Date": "2014-07-10", + "Units": 47, + "Activity Type": "Rockets Fired from Gaza Intercepted by Iron Dome" + }, + { + "Date": "2014-07-11", + "Units": 235, + "Activity Type": "Targets struck by Israel" + }, + { + "Date": "2014-07-11", + "Units": 140, + "Activity Type": "Rockets Fired from Gaza" + }, + { + "Date": "2014-07-11", + "Units": 38, + "Activity Type": "Rockets Fired from Gaza Intercepted by Iron Dome" + }, + { + "Date": "2014-07-12", + "Units": 120, + "Activity Type": "Targets struck by Israel" + }, + { + "Date": "2014-07-12", + "Units": 129, + "Activity 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+ { + "name": "Date", + "importance": "primary", + "description": "Date of attacks", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding activity type", + "role": "y" + }, + { + "name": "Activity Type", + "importance": "primary", + "description": "The activity type of the data point", + "role": "group" + } + ] + } + }, + "1852": { + "metadata": { + "title": "To Keep or Remove Confederate Monuments?", + "description": "Americans who think Confederate monuments should remain in all public spaces or not", + "main_insight": "A majority of Americans polled believe Confederate monuments should remain in public spaces, while a smaller percentage believe they should be removed, and some are unsure." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Opinion": "Should remain", + "Percentage": 54.0 + } + ], + "score": 0.94, + "annotation": "The shows a sudden jump in Percentage from Should be removed to Should remain.", + "reason": "The Percentage for increases from 27.0 at Should be removed to 54.0 at Should remain, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Opinion": "Should remain", + "Percentage": 54.0 + } + ], + "score": 0.66, + "annotation": "The has a maximum value at Should remain", + "reason": "The [{'Opinion': 'Should remain', 'Percentage': 54.0}] of has a maximum value of 54.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Opinion": "Don't know", + "Percentage": 19.0 + } + ], + "score": 0.49, + "annotation": "The has a minimum value at Don't know", + "reason": "The [{'Opinion': \"Don't know\", 'Percentage': 19.0}] of has a mainimum value of 19.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Opinion": "Should remain", + "Percentage": 54.0 + }, + { + "Opinion": "Should be removed", + "Percentage": 27.0 + }, + { + "Opinion": "Don't know", + "Percentage": 19.0 + } + ], + "columns": [ + { + "name": "Opinion", + "importance": "primary", + "description": "Category of opinion on Confederate monuments", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents holding that opinion", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2089": { + "metadata": { + "title": "Online Hotel Reviews are More Important to Consumers than Star Classification", + "description": "Main influencing factors in European customers' decision to book online accommodation in 2022", + "main_insight": "Online customer reviews and ratings are more influential than star ratings for European customers when booking online accommodation." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Factor": "Star ratings", + "Units": 44.0, + "Agreement": "Agree/Strongly agree" + }, + { + "Factor": "Star ratings", + "Units": 28.0, + "Agreement": "Disagree/strongly disagree" + } + ], + "score": 0.99, + "annotation": "The Units of Agree/Strongly agree and Disagree/strongly disagree show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Agree/Strongly agree and Disagree/strongly disagree is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Factor": "Online customer reviews", + "Units": 54.0, + "Agreement": "Agree/Strongly agree" + } + ], + "score": 0.97, + "annotation": "The Agree/Strongly agree shows a sudden jump in Units from Star ratings to Online customer reviews.", + "reason": "The Units for Agree/Strongly agree increases from 44.0 at Star ratings to 54.0 at Online customer reviews, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Factor": "Star ratings", + "Units": 28.0, + "Agreement": "Disagree/strongly disagree" + } + ], + "score": 0.96, + "annotation": "The Disagree/strongly disagree shows a sudden jump in Units from Online customer reviews to Star ratings.", + "reason": "The Units for Disagree/strongly disagree increases from 20.0 at Online customer reviews to 28.0 at Star ratings, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Factor": "Star ratings", + "Units": 44.0, + "Agreement": "Agree/Strongly agree" + } + ], + "score": 0.87, + "annotation": "The Agree/Strongly agree has the largest proportion in Star ratings.", + "reason": "The Units of Agree/Strongly agree in Star ratings accounts for 0.44, larger than all other Agreement, of the total of Units of all Agreement." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Factor": "Star ratings", + "Units": 44.0, + "Agreement": "Agree/Strongly agree" + }, + { + "Factor": "Star ratings", + "Units": 28.0, + "Agreement": "Neither agree nor disagree/no answer" + } + ], + "score": 0.87, + "annotation": "The Units of Agree/Strongly agree and Neither agree nor disagree/no answer show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Agree/Strongly agree and Neither agree nor disagree/no answer is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Factor": "Online customer reviews", + "Units": 54.0, + "Agreement": "Agree/Strongly agree" + }, + { + "Factor": "Online customer reviews", + "Units": 26.0, + "Agreement": "Neither agree nor disagree/no answer" + }, + { + "Factor": "Online customer reviews", + "Units": 20.0, + "Agreement": "Disagree/strongly disagree" + }, + { + "Factor": "Online ratings", + "Units": 54.0, + "Agreement": "Agree/Strongly agree" + }, + { + "Factor": "Online ratings", + "Units": 27.0, + "Agreement": "Neither agree nor disagree/no answer" + }, + { + "Factor": "Online ratings", + "Units": 19.0, + "Agreement": "Disagree/strongly disagree" + }, + { + "Factor": "Star ratings", + "Units": 44.0, + "Agreement": "Agree/Strongly agree" + }, + { + "Factor": "Star ratings", + "Units": 28.0, + "Agreement": "Neither agree nor disagree/no answer" + }, + { + "Factor": "Star ratings", + "Units": 28.0, + "Agreement": "Disagree/strongly disagree" + } + ], + "columns": [ + { + "name": "Factor", + "importance": "primary", + "description": "The factor influencing booking decisions", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding agreement", + "role": "y" + }, + { + "name": "Agreement", + "importance": "primary", + "description": "The agreement of the data point", + "role": "group" + } + ] + } + }, + "332": { + "metadata": { + "title": "The Most Successful Actors at the Emmys", + "description": "Actors and actresses with the most Primetime Emmy Award wins", + "main_insight": "Carl Reiner has won the most Primetime Emmy Awards (9), followed by Cloris Leachman (8). Several actors are tied with 7 and 6 wins." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Actor": "Carl Reiner", + "Wins": 9 + } + ], + "score": 0.91, + "annotation": "The has a maximum value at Carl Reiner", + "reason": "The [{'Actor': 'Carl Reiner', 'Wins': 9}] of has a maximum value of 9, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Actor": "Don Knotts", + "Wins": 5 + } + ], + "score": 0.79, + "annotation": "The has a minimum value at Don Knotts", + "reason": "The [{'Actor': 'Don Knotts', 'Wins': 5}] of has a mainimum value of 5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Actor": "Art Carney", + "Wins": 6 + }, + { + "Actor": "Ed Asner", + "Wins": 7 + }, + { + "Actor": "Cloris Leachman", + "Wins": 8 + }, + { + "Actor": "Carl Reiner", + "Wins": 9 + } + ], + "score": 0.65, + "annotation": "The exhibits multiple sudden jumps in Wins, transitioning from Don Knotts, Billy Crystal, Tracey Ullman, Cloris Leachman to Art Carney, Ed Asner, Cloris Leachman, Carl Reiner.", + "reason": "Across these points, the Wins for increases from 5, 6, 7, 8 at Don Knotts, Billy Crystal, Tracey Ullman, Cloris Leachman to 6, 7, 8, 9 at Art Carney, Ed Asner, Cloris Leachman, Carl Reiner, suggesting significant shifts in value." + } + ], + "data": { + "data": [ + { + "Actor": "Carl Reiner", + "Wins": 9 + }, + { + "Actor": "Cloris Leachman", + "Wins": 8 + }, + { + "Actor": "Ed Asner", + "Wins": 7 + }, + { + "Actor": "Mary Tyler Moore", + "Wins": 7 + }, + { + "Actor": "Tracey Ullman", + "Wins": 7 + }, + { + "Actor": "Art Carney", + "Wins": 6 + }, + { + "Actor": "Tim Conway", + "Wins": 6 + }, + { + "Actor": "Tyne Daly", + "Wins": 6 + }, + { + "Actor": "Billy Crystal", + "Wins": 6 + }, + { + "Actor": "Don Knotts", + "Wins": 5 + } + ], + "columns": [ + { + "name": "Actor", + "importance": "primary", + "description": "Name of the actor/actress", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Wins", + "importance": "primary", + "description": "Number of Primetime Emmy Awards won", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "48": { + "metadata": { + "title": "Africa and Asia Are Embracing Mobile Payments", + "description": "Forecast: mobile payment users (in millions)", + "main_insight": "Mobile payment users have significantly increased from 2012 to 2016 across all regions, with Asia/Pacific and Africa showing the largest number of users in 2016." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Region": "Middle East", + "Units": 6.0, + "Cohort": "2016 Users" + }, + { + "Region": "Middle East", + "Units": 1.5, + "Cohort": "2012 Users" + } + ], + "score": 0.98, + "annotation": "The Units of 2016 Users and 2012 Users show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2016 Users and 2012 Users is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "Asia/Pacific", + "Units": 85.0, + "Cohort": "2012 Users" + } + ], + "score": 0.83, + "annotation": "The 2012 Users has a maximum value at Asia/Pacific", + "reason": "The [{'Region': 'Asia/Pacific', 'Units': 85.0, 'Cohort': '2012 Users'}] of 2012 Users has a maximum value of 85.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "Asia/Pacific", + "Units": 163.6, + "Cohort": "2016 Users" + } + ], + "score": 0.82, + "annotation": "The 2016 Users has a maximum value at Asia/Pacific", + "reason": "The [{'Region': 'Asia/Pacific', 'Units': 163.6, 'Cohort': '2016 Users'}] of 2016 Users has a maximum value of 163.6, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Asia/Pacific", + "Units": 163.6, + "Cohort": "2016 Users" + } + ], + "score": 0.77, + "annotation": "The 2016 Users shows a sudden jump in Units from Africa to Asia/Pacific.", + "reason": "The Units for 2016 Users increases from 101.3 at Africa to 163.6 at Asia/Pacific, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Africa", + "Units": 101.3, + "Cohort": "2016 Users" + } + ], + "score": 0.76, + "annotation": "The 2016 Users accounts for the majority in Africa.", + "reason": "The Units of 2016 Users in Africa accounts for 0.6367064739157763 (more than 50%) of the total of Units of all Cohort." + } + ], + "data": { + "data": [ + { + "Region": "Asia/Pacific", + "Units": 163.6, + "Cohort": "2016 Users" + }, + { + "Region": "Asia/Pacific", + "Units": 85.0, + "Cohort": "2012 Users" + }, + { + "Region": "Africa", + "Units": 101.3, + "Cohort": "2016 Users" + }, + { + "Region": "Africa", + "Units": 57.8, + "Cohort": "2012 Users" + }, + { + "Region": "North America", + "Units": 90.7, + "Cohort": "2016 Users" + }, + { + "Region": "North America", + "Units": 32.8, + "Cohort": "2012 Users" + }, + { + "Region": "Europe", + "Units": 64.0, + "Cohort": "2016 Users" + }, + { + "Region": "Europe", + "Units": 26.7, + "Cohort": "2012 Users" + }, + { + "Region": "Latin America", + "Units": 22.3, + "Cohort": "2016 Users" + }, + { + "Region": "Latin America", + "Units": 8.5, + "Cohort": "2012 Users" + }, + { + "Region": "Middle East", + "Units": 6.0, + "Cohort": "2016 Users" + }, + { + "Region": "Middle East", + "Units": 1.5, + "Cohort": "2012 Users" + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Geographic region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding cohort", + "role": "y" + }, + { + "name": "Cohort", + "importance": "primary", + "description": "The cohort of the data point", + "role": "group" + } + ] + } + }, + "268": { + "metadata": { + "title": "German Elections Since 1990", + "description": "Share of vote received in German federal elections, by party", + "main_insight": "The chart shows the fluctuating vote shares of German political parties since 1990. The most notable trend is the decline of CDU/CSU and the recent rise of SPD, with other parties showing smaller variations over time." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2021, + "Units": 24.1, + "Party Affiliation": "CDU/CSU" + }, + { + "Year": 2021, + "Units": 14.8, + "Party Affiliation": "Green" + } + ], + "score": 0.86, + "annotation": "The Units of CDU/CSU and Green show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between CDU/CSU and Green is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2021, + "Units": 25.7, + "Party Affiliation": "SPD" + }, + { + "Year": 2021, + "Units": 4.9, + "Party Affiliation": "Left" + } + ], + "score": 0.61, + "annotation": "The Units of SPD and Left show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between SPD and Left is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2021, + "Units": 25.7, + "Party Affiliation": "SPD" + }, + { + "Year": 2021, + "Units": 11.5, + "Party Affiliation": "FDP" + } + ], + "score": 0.53, + "annotation": "The Units of SPD and FDP show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between SPD and FDP is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2021, + "Units": 25.7, + "Party Affiliation": "SPD" + }, + { + "Year": 2021, + "Units": 14.8, + "Party Affiliation": "Green" + } + ], + "score": 0.52, + "annotation": "The Units of SPD and Green show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between SPD and Green is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2021, + "Units": 24.1, + "Party Affiliation": "CDU/CSU" + }, + { + "Year": 2021, + "Units": 11.5, + "Party Affiliation": "FDP" + } + ], + "score": 0.47, + "annotation": "The Units of CDU/CSU and FDP show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between CDU/CSU and FDP is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 1990, + "Units": 33.5, + "Party Affiliation": "SPD" + }, + { + "Year": 1990, + "Units": 43.8, + "Party Affiliation": "CDU/CSU" + }, + { + "Year": 1990, + "Units": 3.8, + "Party Affiliation": "Green" + }, + { + "Year": 1990, + "Units": 11.0, + "Party Affiliation": "FDP" + }, + { + "Year": 1990, + "Units": 0.3, + "Party Affiliation": "Left" + }, + { + "Year": 1990, + "Units": 7.6, + "Party Affiliation": "Others" + }, + { + "Year": 1994, + "Units": 36.4, + "Party Affiliation": "SPD" + }, + { + "Year": 1994, + "Units": 41.4, + "Party Affiliation": "CDU/CSU" + }, + { + "Year": 1994, + "Units": 7.3, + "Party Affiliation": "Green" + }, + { + "Year": 1994, + "Units": 6.9, + "Party Affiliation": "FDP" + }, + { + "Year": 1994, + "Units": 4.4, + "Party Affiliation": "Left" + }, + { + "Year": 1994, + "Units": 3.6, + "Party Affiliation": "Others" + }, + { + "Year": 1998, + "Units": 40.9, + "Party Affiliation": "SPD" + }, + { + "Year": 1998, + "Units": 35.1, + "Party Affiliation": "CDU/CSU" + }, + { + "Year": 1998, + "Units": 6.7, + "Party Affiliation": "Green" + }, + { + "Year": 1998, + "Units": 6.2, + "Party Affiliation": "FDP" + }, + { + "Year": 1998, + "Units": 5.1, + "Party Affiliation": "Left" + }, + { + "Year": 1998, + "Units": 6.0, + "Party Affiliation": "Others" + }, + { + "Year": 2002, + "Units": 38.5, + "Party Affiliation": "SPD" + }, + { + "Year": 2002, + "Units": 38.5, + "Party Affiliation": "CDU/CSU" + }, + { + "Year": 2002, + "Units": 8.6, + "Party Affiliation": "Green" + }, + { + "Year": 2002, + "Units": 7.4, + "Party Affiliation": "FDP" + }, + { + "Year": 2002, + "Units": 4.0, + "Party Affiliation": "Left" + }, + { + "Year": 2002, + "Units": 3.0, + "Party Affiliation": "Others" + }, + { + "Year": 2005, + "Units": 34.2, + "Party Affiliation": "SPD" + }, + { + "Year": 2005, + "Units": 35.2, + "Party Affiliation": "CDU/CSU" + }, + { + "Year": 2005, + "Units": 8.1, + "Party Affiliation": "Green" + }, + { + "Year": 2005, + "Units": 9.8, + "Party Affiliation": "FDP" + }, + { + "Year": 2005, + "Units": 8.7, + "Party Affiliation": "Left" + }, + { + "Year": 2005, + "Units": 4.0, + "Party Affiliation": "Others" + }, + { + "Year": 2009, + "Units": 23.0, + "Party Affiliation": "SPD" + }, + { + "Year": 2009, + "Units": 33.8, + "Party Affiliation": "CDU/CSU" + }, + { + "Year": 2009, + "Units": 10.7, + "Party Affiliation": "Green" + }, + { + "Year": 2009, + "Units": 14.6, + "Party Affiliation": "FDP" + }, + { + "Year": 2009, + "Units": 11.9, + "Party Affiliation": "Left" + }, + { + "Year": 2009, + "Units": 6.0, + "Party Affiliation": "Others" + }, + { + "Year": 2013, + "Units": 25.7, + "Party Affiliation": "SPD" + }, + { + "Year": 2013, + "Units": 41.5, + "Party Affiliation": "CDU/CSU" + }, + { + "Year": 2013, + "Units": 8.4, + "Party Affiliation": "Green" + }, + { + "Year": 2013, + "Units": 4.8, + "Party Affiliation": "FDP" + }, + { + "Year": 2013, + "Units": 4.7, + "Party Affiliation": "AfD" + }, + { + "Year": 2013, + "Units": 8.6, + "Party Affiliation": "Left" + }, + { + "Year": 2013, + "Units": 6.2, + "Party Affiliation": "Others" + }, + { + "Year": 2017, + "Units": 20.5, + "Party Affiliation": "SPD" + }, + { + "Year": 2017, + "Units": 32.9, + "Party Affiliation": "CDU/CSU" + }, + { + "Year": 2017, + "Units": 8.9, + "Party Affiliation": "Green" + }, + { + "Year": 2017, + "Units": 10.7, + "Party Affiliation": "FDP" + }, + { + "Year": 2017, + "Units": 12.6, + "Party Affiliation": "AfD" + }, + { + "Year": 2017, + "Units": 9.2, + "Party Affiliation": "Left" + }, + { + "Year": 2017, + "Units": 5.2, + "Party Affiliation": "Others" + }, + { + "Year": 2021, + "Units": 25.7, + "Party Affiliation": "SPD" + }, + { + "Year": 2021, + "Units": 24.1, + "Party Affiliation": "CDU/CSU" + }, + { + "Year": 2021, + "Units": 14.8, + "Party Affiliation": "Green" + }, + { + "Year": 2021, + "Units": 11.5, + "Party Affiliation": "FDP" + }, + { + "Year": 2021, + "Units": 10.3, + "Party Affiliation": "AfD" + }, + { + "Year": 2021, + "Units": 4.9, + "Party Affiliation": "Left" + }, + { + "Year": 2021, + "Units": 8.6, + "Party Affiliation": "Others" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Election Year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding party affiliation", + "role": "y" + }, + { + "name": "Party Affiliation", + "importance": "primary", + "description": "The party affiliation of the data point", + "role": "group" + } + ] + } + }, + "1437": { + "metadata": { + "title": "COVID-19 Travel: Many Europeans Holiday At Home", + "description": "% of trips that are domestic or abroad in selected EU countries in 2017", + "main_insight": "In 2017, a significant majority of trips taken by residents of many European countries were domestic, with Spain, France, and Italy having the highest percentages." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Belgium", + "Percentage": 20.1, + "Location": "Domestic" + }, + { + "Country": "Belgium", + "Percentage": 79.9, + "Location": "Abroad" + } + ], + "score": 1.0, + "annotation": "The Percentage of Domestic and Abroad show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Percentage between Domestic and Abroad is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Belgium", + "Percentage": 20.1, + "Location": "Domestic" + } + ], + "score": 0.9, + "annotation": "The Domestic has a minimum value at Belgium", + "reason": "The [{'Country': 'Belgium', 'Percentage': 20.1, 'Location': 'Domestic'}] of Domestic has a mainimum value of 20.1, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Belgium", + "Percentage": 79.9, + "Location": "Abroad" + } + ], + "score": 0.9, + "annotation": "The Abroad has a maximum value at Belgium", + "reason": "The [{'Country': 'Belgium', 'Percentage': 79.9, 'Location': 'Abroad'}] of Abroad has a maximum value of 79.9, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Belgium", + "Percentage": 79.9, + "Location": "Abroad" + } + ], + "score": 0.8, + "annotation": "The Abroad shows a sudden jump in Percentage from Ireland to Belgium.", + "reason": "The Percentage for Abroad increases from 48.0 at Ireland to 79.9 at Belgium, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Ireland", + "Percentage": 52.0, + "Location": "Domestic" + } + ], + "score": 0.8, + "annotation": "The Domestic shows a sudden jump in Percentage from Belgium to Ireland.", + "reason": "The Percentage for Domestic increases from 20.1 at Belgium to 52.0 at Ireland, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Spain", + "Percentage": 90.6, + "Location": "Domestic" + }, + { + "Country": "Spain", + "Percentage": 9.4, + "Location": "Abroad" + }, + { + "Country": "France", + "Percentage": 87.1, + "Location": "Domestic" + }, + { + "Country": "France", + "Percentage": 12.9, + "Location": "Abroad" + }, + { + "Country": "Italy", + "Percentage": 79.8, + "Location": "Domestic" + }, + { + "Country": "Italy", + "Percentage": 20.2, + "Location": "Abroad" + }, + { + "Country": "UK", + "Percentage": 67.2, + "Location": "Domestic" + }, + { + "Country": "UK", + "Percentage": 32.8, + "Location": "Abroad" + }, + { + "Country": "Germany", + "Percentage": 62.1, + "Location": "Domestic" + }, + { + "Country": "Germany", + "Percentage": 37.9, + "Location": "Abroad" + }, + { + "Country": "Netherlands", + "Percentage": 53.9, + "Location": "Domestic" + }, + { + "Country": "Netherlands", + "Percentage": 46.1, + "Location": "Abroad" + }, + { + "Country": "Ireland", + "Percentage": 52.0, + "Location": "Domestic" + }, + { + "Country": "Ireland", + "Percentage": 48.0, + "Location": "Abroad" + }, + { + "Country": "Belgium", + "Percentage": 20.1, + "Location": "Domestic" + }, + { + "Country": "Belgium", + "Percentage": 79.9, + "Location": "Abroad" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Selected EU countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage for the corresponding location", + "role": "y" + }, + { + "name": "Location", + "importance": "primary", + "description": "The location of the data point", + "role": "group" + } + ] + } + }, + "416": { + "metadata": { + "title": "How Important Is Cruelty-Free?", + "description": "Share of respondents who said animal welfare was important when making the following purchases (in %)", + "main_insight": "German respondents place a significantly higher importance on animal welfare when buying food, personal care products, and apparel than respondents in India, the United States, and China." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "China", + "Units": 10.0, + "Spending Categories": "Personal care products" + }, + { + "Country": "China", + "Units": 10.0, + "Spending Categories": "Apparel" + } + ], + "score": 0.99, + "annotation": "The Units of Personal care products and Apparel show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Personal care products and Apparel is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Germany", + "Units": 33.0, + "Spending Categories": "Food" + } + ], + "score": 0.92, + "annotation": "The Food shows a sudden jump in Units from India to Germany.", + "reason": "The Units for Food increases from 17.0 at India to 33.0 at Germany, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "China", + "Units": 10.0, + "Spending Categories": "Food" + }, + { + "Country": "China", + "Units": 10.0, + "Spending Categories": "Personal care products" + } + ], + "score": 0.9, + "annotation": "The Units of Food and Personal care products show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Food and Personal care products is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "China", + "Units": 10.0, + "Spending Categories": "Food" + }, + { + "Country": "China", + "Units": 10.0, + "Spending Categories": "Personal care products" + }, + { + "Country": "China", + "Units": 10.0, + "Spending Categories": "Apparel" + } + ], + "score": 0.9, + "annotation": "The Food, Personal care products, Apparel all have the largest proportion in China.", + "reason": "The Units of Food, Personal care products, Apparel in China all account for 0.3333333333333333, larger than all other Spending Categories, of the total of Units of all Spending Categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Spending Categories": "Food", + "Country": "total", + "Units": 72.0 + } + ], + "score": 0.9, + "annotation": "The total value of Food has the largest proportion.", + "reason": "The total value of Units of Food accounts for 0.3364485981308411, larger than all other Spending Categories, of the total of Units of all Spending Categories." + } + ], + "data": { + "data": [ + { + "Country": "Germany", + "Units": 33.0, + "Spending Categories": "Food" + }, + { + "Country": "Germany", + "Units": 25.0, + "Spending Categories": "Personal care products" + }, + { + "Country": "Germany", + "Units": 24.0, + "Spending Categories": "Apparel" + }, + { + "Country": "India", + "Units": 17.0, + "Spending Categories": "Food" + }, + { + "Country": "India", + "Units": 20.0, + "Spending Categories": "Personal care products" + }, + { + "Country": "India", + "Units": 21.0, + "Spending Categories": "Apparel" + }, + { + "Country": "United States", + "Units": 12.0, + "Spending Categories": "Food" + }, + { + "Country": "United States", + "Units": 16.0, + "Spending Categories": "Personal care products" + }, + { + "Country": "United States", + "Units": 16.0, + "Spending Categories": "Apparel" + }, + { + "Country": "China", + "Units": 10.0, + "Spending Categories": "Food" + }, + { + "Country": "China", + "Units": 10.0, + "Spending Categories": "Personal care products" + }, + { + "Country": "China", + "Units": 10.0, + "Spending Categories": "Apparel" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding spending categories", + "role": "y" + }, + { + "name": "Spending Categories", + "importance": "primary", + "description": "The spending categories of the data point", + "role": "group" + } + ] + } + }, + "1570": { + "metadata": { + "title": "Instagram. You Just Don't Get It", + "description": "Share of U.S. adults that regularly use Facebook and Instagram, by age group (in %)", + "main_insight": "Instagram usage decreases significantly with age, while Facebook usage is more prevalent in older demographics compared to Instagram. There's a clear crossover point where Facebook usage surpasses Instagram usage, occurring between the 35-44 and 45-54 age groups." + }, + "chart_type": "Dot Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Age Group": "35-44", + "Instagram": 58.0 + } + ], + "score": 0.77, + "annotation": "The shows a sudden jump in Instagram from 45-54 to 35-44.", + "reason": "The Instagram for increases from 33.0 at 45-54 to 58.0 at 35-44, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Age Group": "65+", + "Instagram": 13.0 + } + ], + "score": 0.73, + "annotation": "The has a minimum value at 65+", + "reason": "The [{'Age Group': '65+', 'Instagram': 13.0}] of has a mainimum value of 13.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Age Group": "25-34", + "Instagram": 75.0 + } + ], + "score": 0.66, + "annotation": "The has a maximum value at 25-34", + "reason": "The [{'Age Group': '25-34', 'Instagram': 75.0}] of has a maximum value of 75.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Age Group": "18-24", + "Instagram": 71.0 + }, + { + "Age Group": "25-34", + "Instagram": 75.0 + }, + { + "Age Group": "35-44", + "Instagram": 58.0 + }, + { + "Age Group": "45-54", + "Instagram": 33.0 + }, + { + "Age Group": "55-64", + "Instagram": 27.0 + }, + { + "Age Group": "65+", + "Instagram": 13.0 + } + ], + "columns": [ + { + "name": "Age Group", + "importance": "primary", + "description": "Age ranges of U.S. adults", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Instagram", + "importance": "primary", + "description": "Percentage of U.S. adults in each age group who regularly use Instagram", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1019": { + "metadata": { + "title": "Wyoming Still Leading in Coal Production", + "description": "Leading U.S. states by coal production (in 1,000 short tons)", + "main_insight": "Wyoming leads U.S. states in coal production by a significant margin, although its production decreased from 2015 to 2016. Most states saw declines, with Texas being a notable exception." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "State": "Ohio", + "Dollars": 17000.0, + "Production Metrics": "2015 Production" + }, + { + "State": "Ohio", + "Dollars": 12600.0, + "Production Metrics": "2016 Production" + } + ], + "score": 1.0, + "annotation": "The Dollars of 2015 Production and 2016 Production show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between 2015 Production and 2016 Production is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "State": "Wyoming", + "Dollars": 375800.0, + "Production Metrics": "2015 Production" + } + ], + "score": 0.99, + "annotation": "The 2015 Production has a maximum value at Wyoming", + "reason": "The [{'State': 'Wyoming', 'Dollars': 375800.0, 'Production Metrics': '2015 Production'}] of 2015 Production has a maximum value of 375800.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "State": "Wyoming", + "Dollars": 297200.0, + "Production Metrics": "2016 Production" + } + ], + "score": 0.99, + "annotation": "The 2016 Production has a maximum value at Wyoming", + "reason": "The [{'State': 'Wyoming', 'Dollars': 297200.0, 'Production Metrics': '2016 Production'}] of 2016 Production has a maximum value of 297200.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "State": "Wyoming", + "Dollars": 375800.0, + "Production Metrics": "2015 Production" + } + ], + "score": 0.94, + "annotation": "The 2015 Production shows a sudden jump in Dollars from West Virginia to Wyoming.", + "reason": "The Dollars for 2015 Production increases from 95600.0 at West Virginia to 375800.0 at Wyoming, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "State": "Wyoming", + "Dollars": 297200.0, + "Production Metrics": "2016 Production" + } + ], + "score": 0.93, + "annotation": "The 2016 Production shows a sudden jump in Dollars from West Virginia to Wyoming.", + "reason": "The Dollars for 2016 Production increases from 79800.0 at West Virginia to 297200.0 at Wyoming, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "State": "Wyoming", + "Dollars": 375800.0, + "Production Metrics": "2015 Production" + }, + { + "State": "Wyoming", + "Dollars": 297200.0, + "Production Metrics": "2016 Production" + }, + { + "State": "Wyoming", + "Dollars": -20.9, + "Production Metrics": "Percent Change" + }, + { + "State": "West Virginia", + "Dollars": 95600.0, + "Production Metrics": "2015 Production" + }, + { + "State": "West Virginia", + "Dollars": 79800.0, + "Production Metrics": "2016 Production" + }, + { + "State": "West Virginia", + "Dollars": -16.6, + "Production Metrics": "Percent Change" + }, + { + "State": "Pennsylvania", + "Dollars": 50000.0, + "Production Metrics": "2015 Production" + }, + { + "State": "Pennsylvania", + "Dollars": 45700.0, + "Production Metrics": "2016 Production" + }, + { + "State": "Pennsylvania", + "Dollars": -8.6, + "Production Metrics": "Percent Change" + }, + { + "State": "Illinois", + "Dollars": 56100.0, + "Production Metrics": "2015 Production" + }, + { + "State": "Illinois", + "Dollars": 43400.0, + "Production Metrics": "2016 Production" + }, + { + "State": "Illinois", + "Dollars": -22.6, + "Production Metrics": "Percent Change" + }, + { + "State": "Kentucky", + "Dollars": 61400.0, + "Production Metrics": "2015 Production" + }, + { + "State": "Kentucky", + "Dollars": 42900.0, + "Production Metrics": "2016 Production" + }, + { + "State": "Kentucky", + "Dollars": -30.2, + "Production Metrics": "Percent Change" + }, + { + "State": "Texas", + "Dollars": 35900.0, + "Production Metrics": "2015 Production" + }, + { + "State": "Texas", + "Dollars": 39000.0, + "Production Metrics": "2016 Production" + }, + { + "State": "Texas", + "Dollars": 8.6, + "Production Metrics": "Percent Change" + }, + { + "State": "Montana", + "Dollars": 41900.0, + "Production Metrics": "2015 Production" + }, + { + "State": "Montana", + "Dollars": 32300.0, + "Production Metrics": "2016 Production" + }, + { + "State": "Montana", + "Dollars": -22.8, + "Production Metrics": "Percent Change" + }, + { + "State": "Indiana", + "Dollars": 34300.0, + "Production Metrics": "2015 Production" + }, + { + "State": "Indiana", + "Dollars": 28800.0, + "Production Metrics": "2016 Production" + }, + { + "State": "Indiana", + "Dollars": -16.1, + "Production Metrics": "Percent Change" + }, + { + "State": "North Dakota", + "Dollars": 28800.0, + "Production Metrics": "2015 Production" + }, + { + "State": "North Dakota", + "Dollars": 28100.0, + "Production Metrics": "2016 Production" + }, + { + "State": "North Dakota", + "Dollars": -2.4, + "Production Metrics": "Percent Change" + }, + { + "State": "Colorado", + "Dollars": 18900.0, + "Production Metrics": "2015 Production" + }, + { + "State": "Colorado", + "Dollars": 12600.0, + "Production Metrics": "2016 Production" + }, + { + "State": "Colorado", + "Dollars": -33.1, + "Production Metrics": "Percent Change" + }, + { + "State": "Ohio", + "Dollars": 17000.0, + "Production Metrics": "2015 Production" + }, + { + "State": "Ohio", + "Dollars": 12600.0, + "Production Metrics": "2016 Production" + }, + { + "State": "Ohio", + "Dollars": -26.3, + "Production Metrics": "Percent Change" + } + ], + "columns": [ + { + "name": "State", + "importance": "primary", + "description": "U.S. State", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding production metrics", + "role": "y" + }, + { + "name": "Production Metrics", + "importance": "primary", + "description": "The production metrics of the data point", + "role": "group" + } + ] + } + }, + "2395": { + "metadata": { + "title": "The Biggest (Non-EU) Guzzlers of Spanish Olive Oil", + "description": "Exports of olive oil from Spain to countries outside the EU (thousands of tons)*\n* October 2018 to July 2019. Includes virgin olive oil and its subtypes, extra virgin and lampante virgin.", + "main_insight": "The United States is by far the largest importer of Spanish olive oil outside of the EU, importing more than triple the amount of the next largest importer, Japan." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United States", + "Exports (thousands of tons)": 104.705 + } + ], + "score": 0.84, + "annotation": "The has a maximum value at United States", + "reason": "The [{'Country': 'United States', 'Exports (thousands of tons)': 104.705}] of has a maximum value of 104.705, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Others", + "Exports (thousands of tons)": 85.743 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Exports (thousands of tons) from Japan to Others.", + "reason": "The Exports (thousands of tons) for increases from 36.255 at Japan to 85.743 at Others, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Canada", + "Exports (thousands of tons)": 5.96 + } + ], + "score": 0.56, + "annotation": "The has a minimum value at Canada", + "reason": "The [{'Country': 'Canada', 'Exports (thousands of tons)': 5.96}] of has a mainimum value of 5.96, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Exports (thousands of tons)": 104.705 + }, + { + "Country": "Japan", + "Exports (thousands of tons)": 36.255 + }, + { + "Country": "China", + "Exports (thousands of tons)": 32.894 + }, + { + "Country": "Australia", + "Exports (thousands of tons)": 16.605 + }, + { + "Country": "Brazil", + "Exports (thousands of tons)": 10.437 + }, + { + "Country": "Canada", + "Exports (thousands of tons)": 5.96 + }, + { + "Country": "Others", + "Exports (thousands of tons)": 85.743 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Importing country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Exports (thousands of tons)", + "importance": "primary", + "description": "Exports of olive oil from Spain in thousands of tons", + "unit": "thousands of tons", + "data_type": "number", + "role": "y" + } + ] + } + }, + "621": { + "metadata": { + "title": "Opposition or Support for Trump Generally Remains Steady", + "description": "Projected percent of U.S. adults who approve or disapprove of President Donald Trump", + "main_insight": "Public opinion on President Trump remained relatively stable between 2017 and 2019, with disapproval consistently higher than approval." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Time": 0, + "Units": 51.9, + "Approval Rating": "Disapprove" + }, + { + "Time": 0, + "Units": 43.3, + "Approval Rating": "Approve" + } + ], + "score": 0.26, + "annotation": "The Units of Disapprove and Approve show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Disapprove and Approve is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Time": "2017-01-01", + "Units": 45.5, + "Approval Rating": "Disapprove" + }, + { + "Time": "2017-01-01", + "Units": 41.3, + "Approval Rating": "Approve" + }, + { + "Time": 0, + "Units": 55.0, + "Approval Rating": "Disapprove" + }, + { + "Time": 0, + "Units": 40.0, + "Approval Rating": "Approve" + }, + { + "Time": "2018-01-01", + "Units": 55.0, + "Approval Rating": "Disapprove" + }, + { + "Time": "2018-01-01", + "Units": 40.0, + "Approval Rating": "Approve" + }, + { + "Time": 0, + "Units": 52.5, + "Approval Rating": "Disapprove" + }, + { + "Time": 0, + "Units": 42.5, + "Approval Rating": "Approve" + }, + { + "Time": "2019-01-01", + "Units": 52.5, + "Approval Rating": "Disapprove" + }, + { + "Time": "2019-01-01", + "Units": 42.5, + "Approval Rating": "Approve" + }, + { + "Time": 0, + "Units": 52.5, + "Approval Rating": "Disapprove" + }, + { + "Time": 0, + "Units": 42.5, + "Approval Rating": "Approve" + }, + { + "Time": 0, + "Units": 51.9, + "Approval Rating": "Disapprove" + }, + { + "Time": 0, + "Units": 43.3, + "Approval Rating": "Approve" + } + ], + "columns": [ + { + "name": "Time", + "importance": "primary", + "description": "Time periods from January 2017 to December 2019", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding approval rating", + "role": "y" + }, + { + "name": "Approval Rating", + "importance": "primary", + "description": "The approval rating of the data point", + "role": "group" + } + ] + } + }, + "774": { + "metadata": { + "title": "Apple Sheds 4 Facebooks Worth of Market Cap in 5 Months", + "description": "Apple's loss of market share since Sept. '12 compared to the current market cap of tech companies", + "main_insight": "Apple lost a significant amount of market capitalization between September 21, 2012, and February 21, 2013, equivalent to four times Facebook's market cap at that time." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Company": "Apple", + "Units": 235.4, + "Market Cap": "Loss of market cap since Sept. 21, 2012" + }, + { + "Company": "Apple", + "Units": 419.2, + "Market Cap": "Market cap on Feb. 21, 2013" + }, + { + "Company": "Apple", + "Units": 654.6, + "Market Cap": "Market cap on Sept. 21, 2012" + }, + { + "Company": "Google", + "Units": 261.4, + "Market Cap": "Market cap on Feb. 21, 2013" + }, + { + "Company": "amazon", + "Units": 120.4, + "Market Cap": "Market cap on Feb. 21, 2013" + }, + { + "Company": "facebook.", + "Units": 59.2, + "Market Cap": "Market cap on Feb. 21, 2013" + }, + { + "Company": "Linked in", + "Units": 16.9, + "Market Cap": "Market cap on Feb. 21, 2013" + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Name of the tech company", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding market cap", + "role": "y" + }, + { + "name": "Market Cap", + "importance": "primary", + "description": "The market cap of the data point", + "role": "group" + } + ] + } + }, + "1366": { + "metadata": { + "title": "Majority of Americans Support Military Action in NK", + "description": "Share of U.S. adults that would favor military action against North Korea (September 2017)", + "main_insight": "In September 2017, a majority of Americans (58%) favored military action against North Korea, with significant support from Republicans (82%) and lower support from Democrats (37%)." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Political Affiliation": "Republicans", + "Support for Military Action": 82.0 + } + ], + "score": 0.85, + "annotation": "The shows a sudden jump in Support for Military Action from All to Republicans.", + "reason": "The Support for Military Action for increases from 58.0 at All to 82.0 at Republicans, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Political Affiliation": "Republicans", + "Support for Military Action": 82.0 + } + ], + "score": 0.61, + "annotation": "The has a maximum value at Republicans", + "reason": "The [{'Political Affiliation': 'Republicans', 'Support for Military Action': 82.0}] of has a maximum value of 82.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Political Affiliation": "Democrats", + "Support for Military Action": 37.0 + } + ], + "score": 0.59, + "annotation": "The has a minimum value at 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"data_type": "number", + "role": "y" + } + ] + } + }, + "1628": { + "metadata": { + "title": "Meta's Most Valuable Acquisitions", + "description": "Highest acquisition prices for companies bought by Meta (in million U.S. dollars)*", + "main_insight": "Meta's acquisition of WhatsApp for $16 billion stands out as significantly more valuable than any of its other acquisitions, highlighting a strong investment in the messaging segment." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Company": "WhatsApp", + "Acquisition Price (Millions)": 16000.0 + } + ], + "score": 0.97, + "annotation": "The has a maximum value at WhatsApp", + "reason": "The [{'Company': 'WhatsApp', 'Acquisition Price (Millions)': 16000.0}] of has a maximum value of 16000.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "WhatsApp", + "Acquisition 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"importance": "primary", + "description": "The screen size of the data point", + "role": "group" + } + ] + } + }, + "2529": { + "metadata": { + "title": "Humans Threatening 1 Million Species With Extinction", + "description": "Key takeaways from the \"summary for policymakers\" report on human impact on nature", + "main_insight": "The infographic highlights various statistics demonstrating the significant negative impact of human activities on the environment and biodiversity, showing high percentages of loss and threat across multiple ecosystems and species." + }, + "chart_type": "Multiple Pie Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Statistic Description": "Share of wetlands present in 1700 that had been lost by 2000", + "Score": 85.0, + "Percentage": "Percentage/Share" + } + ], + "score": 0.9, + "annotation": "The Percentage/Share has a maximum value at Share of wetlands present in 1700 that had been lost by 2000", + "reason": 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"The shows a sudden jump in Pay-TV Subscribers from Verizon FiOS to DISH.", + "reason": "The Pay-TV Subscribers for increases from 4.67 at Verizon FiOS to 13.64 at DISH, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Provider": "Comcast", + "Pay-TV Subscribers": 22.43 + } + ], + "score": 0.73, + "annotation": "The has a maximum value at Comcast", + "reason": "The [{'Provider': 'Comcast', 'Pay-TV Subscribers': 22.43}] of has a maximum value of 22.43, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Provider": "AT&T", + "Pay-TV Subscribers": 0 + }, + { + "Provider": "Verizon", + "Pay-TV Subscribers": 0 + }, + { + "Provider": "CenturyLink", + "Pay-TV Subscribers": 0 + } + ], + "score": 0.6, + "annotation": "The has minimum values at AT&T, Verizon, CenturyLink", + "reason": "The [{'Provider': 'AT&T', 'Pay-TV Subscribers': 0}, {'Provider': 'Verizon', 'Pay-TV Subscribers': 0}, {'Provider': 'CenturyLink', 'Pay-TV Subscribers': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Provider": "Comcast", + "Pay-TV Subscribers": 22.43 + }, + { + "Provider": "DirecTV", + "Pay-TV Subscribers": 20.78 + }, + { + "Provider": "Charter", + "Pay-TV Subscribers": 17.28 + }, + { + "Provider": "DISH", + "Pay-TV Subscribers": 13.64 + }, + { + "Provider": "Verizon FiOS", + "Pay-TV Subscribers": 4.67 + }, + { + "Provider": "AT&T", + "Pay-TV Subscribers": 0 + }, + { + "Provider": "Verizon", + "Pay-TV Subscribers": 0 + }, + { + "Provider": "CenturyLink", + "Pay-TV Subscribers": 0 + } + ], + "columns": [ + { + "name": "Provider", + "importance": "primary", + "description": "Name of the Pay-TV/Broadband provider", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Pay-TV Subscribers", + "importance": "primary", + "description": "Number of Pay-TV subscribers (in millions)", + "unit": "Millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "637": { + "metadata": { + "title": "U.S. Surges Past China in COVID-19 Cases", + "description": "Total confirmed COVID-19 cases in the United States and China (as of March 26, 2020)", + "main_insight": "The number of confirmed COVID-19 cases in the US grew rapidly in March 2020, surpassing the total number of cases in China." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "March", + "Units": 83836, + "Location": "USA" + } + ], + "score": 0.97, + "annotation": "The USA shows a sudden jump in Units from February to March.", + "reason": "The Units for USA increases from 1000 at February to 83836 at March, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "March", + "Units": 81782, + "Location": "China" + } + ], + "score": 0.84, + "annotation": "The China shows a sudden jump in Units from February to March.", + "reason": "The Units for China increases from 40000 at February to 81782 at March, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Location": "China", + "Month": "total", + "Units": 123782 + } + ], + "score": 0.77, + "annotation": "The total value of China accounts for the majority in all Location.", + "reason": "The total value of Units of China accounts for 0.5933428563211228 (more than 50%) of the total of Units of all Location." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "January", + "Units": 2000, + "Location": "China" + } + ], + "score": 0.75, + "annotation": "The China accounts for the majority in January.", + "reason": "The Units of China in January accounts for 0.6666666666666666 (more than 50%) of the total of Units of all Location." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "March", + "Units": 83836, + "Location": "USA" + } + ], + "score": 0.75, + "annotation": "The USA accounts for the majority in February.", + "reason": "The Units of USA in February accounts for 0.6769921509092671 (more than 50%) of the total of Units of all Location." + } + ], + "data": { + "data": [ + { + "Month": "January", + "Units": 2000, + "Location": "China" + }, + { + "Month": "February", + "Units": 1000, + "Location": "USA" + }, + { + "Month": "February", + "Units": 40000, + "Location": "China" + }, + { + "Month": "March", + "Units": 83836, + "Location": "USA" + }, + { + "Month": "March", + "Units": 81782, + "Location": "China" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year 2020", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding location", + "role": "y" + }, + { + "name": "Location", + "importance": "primary", + "description": "The location of the data point", + "role": "group" + } + ] + } + }, + "613": { + "metadata": { + "title": "Rugby World Cup: Second Only To Soccer In Attendance", + "description": "Attendance at selected major single sports events", + "main_insight": "The Rugby World Cup has the second-highest attendance among major single sports events, surpassed only by the FIFA World Cup." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Event": "2014 FIFA World Cup Brazil", + "Attendance": 3.43 + } + ], + "score": 0.92, + "annotation": "The has a maximum value at 2014 FIFA World Cup Brazil", + "reason": "The [{'Event': '2014 FIFA World Cup Brazil', 'Attendance': 3.43}] of has a maximum value of 3.43, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Event": "2010 FIFA World Cup South Africa", + "Attendance": 3.18 + } + ], + "score": 0.73, + "annotation": "The shows a sudden jump in Attendance from 2015 Rugby World Cup England to 2010 FIFA World Cup South Africa.", + "reason": "The Attendance for increases from 2.22 at 2015 Rugby World Cup England to 3.18 at 2010 FIFA World Cup South Africa, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Event": "FIBA Basketball World Cup", + "Attendance": 0.675473 + } + ], + "score": 0.55, + "annotation": "The has a minimum value at FIBA Basketball World Cup", + "reason": "The [{'Event': 'FIBA Basketball World Cup', 'Attendance': 0.675473}] of has a mainimum value of 0.675473, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Event": "2014 FIFA World Cup Brazil", + "Attendance": 3.43 + }, + { + "Event": "2010 FIFA World Cup South Africa", + "Attendance": 3.18 + }, + { + "Event": "2015 Rugby World Cup England", + "Attendance": 2.22 + }, + { + "Event": "2011 Rugby World Cup New Zealand", + "Attendance": 1.47 + }, + { + "Event": "UEFA Euro 2012 Poland/Ukraine", + "Attendance": 1.44 + }, + { + "Event": "FIFA Women's World Cup", + "Attendance": 1.35 + }, + { + "Event": "2015 Cricket World Cup", + "Attendance": 1.2 + }, + { + "Event": "2015 Six Nations", + "Attendance": 1.04 + }, + { + "Event": "2010-11 Ashes Series", + "Attendance": 0.764094 + }, + { + "Event": "2015 Men's World Ice Hockey Championships", + "Attendance": 0.74169 + }, + { + "Event": "2014 US Open (Tennis)", + "Attendance": 0.713642 + }, + { + "Event": "FIBA Basketball World Cup", + "Attendance": 0.675473 + } + ], + "columns": [ + { + "name": "Event", + "importance": "primary", + "description": "Name of the sporting event", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Attendance", + "importance": "primary", + "description": "Attendance figures for each event", + "unit": "m", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1368": { + "metadata": { + "title": "US military aid: Israel gets more than everyone else combined", + "description": "U.S. foreign military aid to countries in 2014 (in billion U.S. dollars)", + "main_insight": "In 2014, Israel received more US military aid than all other countries combined, accounting for 54% of the total." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Recipient": "Total", + "Foreign military aid": 5.7 + } + ], + "score": 0.88, + "annotation": "The shows a sudden jump in Foreign military aid from Israel to Total.", + "reason": "The Foreign military aid for increases from 3.1 at Israel to 5.7 at Total, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Recipient": "Total", + "Foreign military aid": 5.7 + } + ], + "score": 0.76, + "annotation": "The has a maximum value at Total", + "reason": "The [{'Recipient': 'Total', 'Foreign military aid': 5.7}] of has a maximum value of 5.7, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Recipient": "Egypt", + "Foreign military aid": 1.3 + }, + { + "Recipient": "73 other countries", + "Foreign military aid": 1.3 + } + ], + "score": 0.47, + "annotation": "The has minimum values at Egypt, 73 other countries", + "reason": "The [{'Recipient': 'Egypt', 'Foreign military aid': 1.3}, {'Recipient': '73 other countries', 'Foreign military aid': 1.3}] of have minimum values of 1.3, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Recipient": "Israel", + "Foreign military aid": 3.1 + }, + { + "Recipient": "Egypt", + "Foreign military aid": 1.3 + }, + { + "Recipient": "73 other countries", + "Foreign military aid": 1.3 + }, + { + "Recipient": "Total", + "Foreign military aid": 5.7 + } + ], + "columns": [ + { + "name": "Recipient", + "importance": "primary", + "description": "Country or group receiving US military aid", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Foreign military aid", + "importance": "primary", + "description": "Amount of aid in billion US dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "741": { + "metadata": { + "title": "How America's Middle Class Is Shrinking", + "description": "Share of people and aggregate income associated with U.S. income classes in 1970/71 and 2021 (in percent)", + "main_insight": "The share of people in the middle class has decreased from 1970/71 to 2021, while the share of people in both low and high income classes has increased. The share of aggregate income for middle class has decreased more significantly." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Income Class": "High income", + "Units": 14.0, + "Share": "Share of people (1970/71)" + }, + { + "Income Class": "High income", + "Units": 21.0, + "Share": "Share of people (2021)" + } + ], + "score": 1.0, + "annotation": "The Units of Share of people (1970/71) and Share of people (2021) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Share of people (1970/71) and Share of people (2021) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Income Class": "Middle income", + "Units": 42.0, + "Share": "Share of aggregate income (2021)" + } + ], + "score": 0.94, + "annotation": "The Share of aggregate income (2021) shows a sudden jump in Units from Low income to Middle income.", + "reason": "The Units for Share of aggregate income (2021) increases from 8.0 at Low income to 42.0 at Middle income, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Share": "Share of aggregate income (1970/71)", + "Income Class": "total", + "Units": 101.0 + } + ], + "score": 0.94, + "annotation": "The total value of Share of aggregate income (1970/71) has the largest proportion.", + "reason": "The total value of Units of Share of aggregate income (1970/71) accounts for 0.2518703241895262, larger than all other Share, of the total of Units of all Share." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Income Class": "Middle income", + "Units": 61.0, + "Share": "Share of people (1970/71)" + } + ], + "score": 0.93, + "annotation": "The Share of people (1970/71) shows a sudden jump in Units from Low income to Middle income.", + "reason": "The Units for Share of people (1970/71) increases from 25.0 at Low income to 61.0 at Middle income, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Income Class": "Middle income", + "Units": 62.0, + "Share": "Share of aggregate income (1970/71)" + } + ], + "score": 0.93, + "annotation": "The Share of aggregate income (1970/71) has the largest proportion in Middle income.", + "reason": "The Units of Share of aggregate income (1970/71) in Middle income accounts for 0.28837209302325584, larger than all other Share, of the total of Units of all Share." + } + ], + "data": { + "data": [ + { + "Income Class": "Low income", + "Units": 25.0, + "Share": "Share of people (1970/71)" + }, + { + "Income Class": "Low income", + "Units": 29.0, + "Share": "Share of people (2021)" + }, + { + "Income Class": "Low income", + "Units": 10.0, + "Share": "Share of aggregate income (1970/71)" + }, + { + "Income Class": "Low income", + "Units": 8.0, + "Share": "Share of aggregate income (2021)" + }, + { + "Income Class": "Middle income", + "Units": 61.0, + "Share": "Share of people (1970/71)" + }, + { + "Income Class": "Middle income", + "Units": 50.0, + "Share": "Share of people (2021)" + }, + { + "Income Class": "Middle income", + "Units": 62.0, + "Share": "Share of aggregate income (1970/71)" + }, + { + "Income Class": "Middle income", + "Units": 42.0, + "Share": "Share of aggregate income (2021)" + }, + { + "Income Class": "High income", + "Units": 14.0, + "Share": "Share of people (1970/71)" + }, + { + "Income Class": "High income", + "Units": 21.0, + "Share": "Share of people (2021)" + }, + { + "Income Class": "High income", + "Units": 29.0, + "Share": "Share of aggregate income (1970/71)" + }, + { + "Income Class": "High income", + "Units": 50.0, + "Share": "Share of aggregate income (2021)" + } + ], + "columns": [ + { + "name": "Income Class", + "importance": "primary", + "description": "Income class category (Low, Middle, High)", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding share", + "role": "y" + }, + { + "name": "Share", + "importance": "primary", + "description": "The share of the data point", + "role": "group" + } + ] + } + }, + "2012": { + "metadata": { + "title": "(Not) Everybody Loves Chocolate", + "description": "Estimated chocolate consumption per capita in selected countries in 2022", + "main_insight": "Switzerland has the highest per capita chocolate consumption and revenue, significantly exceeding other countries like the US, Germany, and France. India and China have notably lower consumption and revenue figures." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "CH", + "Per-capita consumption": 11.8 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at CH", + "reason": "The [{'Country': 'CH', 'Per-capita consumption': 11.8}] of has a maximum value of 11.8, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "US", + "Per-capita consumption": 9.0 + } + ], + "score": 0.67, + "annotation": "The shows a sudden jump in Per-capita consumption from DE to US.", + "reason": "The Per-capita consumption for increases from 5.8 at DE to 9.0 at US, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "CN", + "Per-capita consumption": 0.2 + } + ], + "score": 0.61, + "annotation": "The has a minimum value at CN", + "reason": "The [{'Country': 'CN', 'Per-capita consumption': 0.2}] of has a mainimum value of 0.2, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "CH", + "Per-capita consumption": 11.8 + }, + { + "Country": "US", + "Per-capita consumption": 9.0 + }, + { + "Country": "DE", + "Per-capita consumption": 5.8 + }, + { + "Country": "FR", + "Per-capita consumption": 3.6 + }, + { + "Country": "GB", + "Per-capita consumption": 2.9 + }, + { + "Country": "BR", + "Per-capita consumption": 1.4 + }, + { + "Country": "IN", + "Per-capita consumption": 1.0 + }, + { + "Country": "CN", + "Per-capita consumption": 0.2 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country code", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Per-capita consumption", + "importance": "primary", + "description": "Estimated chocolate consumption per capita in kilograms (kg)", + "unit": "kg", + "data_type": "number", + "role": "y" + } + ] + } + }, + "981": { + "metadata": { + "title": "Military Drone Crashes Are Climbing", + "description": "U.S. military drone crashes by year*", + "main_insight": "The number of U.S. military drone crashes, specifically Class A accidents, generally increased from 2001 to 2013, peaking in 2012." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2001, + "Crashes": 6 + }, + { + "Year": 2002, + "Crashes": 7 + }, + { + "Year": 2003, + "Crashes": 4 + }, + { + "Year": 2004, + "Crashes": 9 + }, + { + "Year": 2005, + "Crashes": 12 + }, + { + "Year": 2006, + "Crashes": 12 + }, + { + "Year": 2007, + "Crashes": 16 + }, + { + "Year": 2008, + "Crashes": 21 + }, + { + "Year": 2009, + "Crashes": 23 + }, + { + "Year": 2010, + "Crashes": 14 + }, + { + "Year": 2011, + "Crashes": 23 + }, + { + "Year": 2012, + "Crashes": 26 + }, + { + "Year": 2013, + "Crashes": 21 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of drone crash", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Crashes", + "importance": "primary", + "description": "Number of Class A drone crashes that destroyed the aircraft or caused $2 million of damage", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "569": { + "metadata": { + "title": "How Snapchat Compares to Facebook and Twitter Pre-IPO", + "description": "Selected company metrics for the four quarters prior to the respective IPO", + "main_insight": "Facebook had significantly higher revenue and daily active users than Snapchat and Twitter in the four quarters leading up to their respective IPOs, though it also showed a substantial net loss." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Metric": "Daily Active Users", + "Snap": 158.0 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in Snap from Net Income/Loss to Daily Active Users.", + "reason": "The Snap for increases from -515.0 at Net Income/Loss to 158.0 at Daily Active Users, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Metric": "Net Income/Loss", + "Snap": -515.0 + } + ], + "score": 0.65, + "annotation": "The has a minimum value at Net Income/Loss", + "reason": "The [{'Metric': 'Net Income/Loss', 'Snap': -515.0}] of has a mainimum value of -515.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Metric": "Revenue", + "Snap": 404.0 + } + ], + "score": 0.51, + "annotation": "The has a maximum value at Revenue", + "reason": "The [{'Metric': 'Revenue', 'Snap': 404.0}] of has a maximum value of 404.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Metric": "Revenue", + "Snap": 404.0 + }, + { + "Metric": "Net Income/Loss", + "Snap": -515.0 + }, + { + "Metric": "Daily Active Users", + "Snap": 158.0 + } + ], + "columns": [ + { + "name": "Metric", + "importance": "primary", + "description": "The financial or usage metric being compared", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Snap", + "importance": "primary", + "description": "Value for Snap (IPO in March 2017)", + "unit": "Million USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "580": { + "metadata": { + "title": "Police shootings in perspective", + "description": "Reported fatal police shootings since 2009 in selected countries", + "main_insight": "The US has significantly more fatal police shootings than other developed countries, with thousands of incidents since 2009 compared to single or double digits in countries like Iceland, UK, Germany, and Australia." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "US - FBI justifiable homicides", + "Fatal_Shootings": 2085 + } + ], + "score": 0.87, + "annotation": "The shows a sudden jump in Fatal_Shootings from Australia to US - FBI justifiable homicides.", + "reason": "The Fatal_Shootings for increases from 15 at Australia to 2085 at US - FBI justifiable homicides, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "US - total fatal police shootings", + "Fatal_Shootings": 3667 + } + ], + "score": 0.87, + "annotation": "The has a maximum value at US - total fatal police shootings", + "reason": "The [{'Country': 'US - total fatal police shootings', 'Fatal_Shootings': 3667}] of has a maximum value of 3667, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Iceland", + "Fatal_Shootings": 1 + } + ], + "score": 0.41, + "annotation": "The has a minimum value at Iceland", + "reason": "The [{'Country': 'Iceland', 'Fatal_Shootings': 1}] of has a mainimum value of 1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "US - total fatal police shootings", + "Fatal_Shootings": 3667 + }, + { + "Country": "US - FBI justifiable homicides", + "Fatal_Shootings": 2085 + }, + { + "Country": "Australia", + "Fatal_Shootings": 15 + }, + { + "Country": "Germany", + "Fatal_Shootings": 13 + }, + { + "Country": "UK", + "Fatal_Shootings": 7 + }, + { + "Country": "Iceland", + "Fatal_Shootings": 1 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country or category", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Fatal_Shootings", + "importance": "primary", + "description": "Number of fatal police shootings", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2132": { + "metadata": { + "title": "The U.S. Cities With The Worst Traffic Problems", + "description": "Average hours lost to congestion per driver in major U.S. cities in 2018", + "main_insight": "Boston had the worst traffic congestion in the U.S. in 2018, with drivers losing an average of 164 hours to traffic." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Boston", + "Hours Lost": 164.0 + } + ], + "score": 0.89, + "annotation": "The has a maximum value at Boston", + "reason": "The [{'City': 'Boston', 'Hours Lost': 164.0}] of has a maximum value of 164.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Washington D.C.", + "Hours Lost": 155.0 + } + ], + "score": 0.71, + "annotation": "The shows a sudden jump in Hours Lost from Seattle to Washington D.C..", + "reason": "The Hours Lost for increases from 138.0 at Seattle to 155.0 at Washington D.C., indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "Philadelphia", + "Hours Lost": 112.0 + } + ], + "score": 0.7, + "annotation": "The has a minimum value at Philadelphia", + "reason": "The [{'City': 'Philadelphia', 'Hours Lost': 112.0}] of has a mainimum value of 112.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "City": "Boston", + "Hours Lost": 164.0 + }, + { + "City": "Washington D.C.", + "Hours Lost": 155.0 + }, + { + "City": "Chicago", + "Hours Lost": 138.0 + }, + { + "City": "Seattle", + "Hours Lost": 138.0 + }, + { + "City": "New York City", + "Hours Lost": 133.0 + }, + { + "City": "Los Angeles", + "Hours Lost": 128.0 + }, + { + "City": "Pittsburgh", + "Hours Lost": 127.0 + }, + { + "City": "San Francisco", + "Hours Lost": 116.0 + }, + { + "City": "Portland", + "Hours Lost": 116.0 + }, + { + "City": "Philadelphia", + "Hours Lost": 112.0 + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "U.S. City", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Hours Lost", + "importance": "primary", + "description": "Average hours lost to congestion per driver in 2018", + "unit": "hours", + "data_type": "number", + "role": "y" + } + ] + } + }, + "98": { + "metadata": { + "title": "Back-to-School: E-Learning to Push Spending Past $100B", + "description": "Planned back-to-school/back-to-college spending in the United States", + "main_insight": "Total back-to-school spending in the US is projected to exceed $100 billion in 2020, driven primarily by back-to-college spending." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Score": 101.6, + "Spending Category": "Total Spending" + }, + { + "Year": 2020, + "Score": 67.7, + "Spending Category": "Back-to-college" + } + ], + "score": 0.99, + "annotation": "The Score of Total Spending and Back-to-college show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Total Spending and Back-to-college is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Score": 101.6, + "Spending Category": "Total Spending" + }, + { + "Year": 2020, + "Score": 33.9, + "Spending Category": "Back-to-school" + } + ], + "score": 0.94, + "annotation": "The Score of Total Spending and Back-to-school show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Total Spending and Back-to-school is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Score": 67.7, + "Spending Category": "Back-to-college" + }, + { + "Year": 2020, + "Score": 33.9, + "Spending Category": "Back-to-school" + } + ], + "score": 0.87, + "annotation": "The Score of Back-to-college and Back-to-school show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Back-to-college and Back-to-school is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2007, + "Score": 55.2, + "Spending Category": "Total Spending" + }, + { + "Year": 2007, + "Score": 33.7, + "Spending Category": "Back-to-college" + }, + { + "Year": 2007, + "Score": 21.5, + "Spending Category": "Back-to-school" + }, + { + "Year": 2008, + "Score": 55.3, + "Spending Category": "Total Spending" + }, + { + "Year": 2008, + "Score": 35.7, + "Spending Category": "Back-to-college" + }, + { + "Year": 2008, + "Score": 19.6, + "Spending Category": "Back-to-school" + }, + { + "Year": 2009, + "Score": 52.8, + "Spending Category": "Total Spending" + }, + { + "Year": 2009, + "Score": 33.5, + "Spending Category": "Back-to-college" + }, + { + "Year": 2009, + "Score": 19.3, + "Spending Category": "Back-to-school" + }, + { + "Year": 2010, + "Score": 68.8, + "Spending Category": "Total Spending" + }, + { + "Year": 2010, + "Score": 48.4, + "Spending Category": "Back-to-college" + }, + { + "Year": 2010, + "Score": 20.4, + "Spending Category": "Back-to-school" + }, + { + "Year": 2011, + "Score": 68.4, + "Spending Category": "Total Spending" + }, + { + "Year": 2011, + "Score": 45.9, + "Spending Category": "Back-to-college" + }, + { + "Year": 2011, + "Score": 22.5, + "Spending Category": "Back-to-school" + }, + { + "Year": 2012, + "Score": 83.8, + "Spending Category": "Total Spending" + }, + { + "Year": 2012, + "Score": 53.4, + "Spending Category": "Back-to-college" + }, + { + "Year": 2012, + "Score": 30.4, + "Spending Category": "Back-to-school" + }, + { + "Year": 2013, + "Score": 72.5, + "Spending Category": "Total Spending" + }, + { + "Year": 2013, + "Score": 45.5, + "Spending Category": "Back-to-college" + }, + { + "Year": 2013, + "Score": 27.0, + "Spending Category": "Back-to-school" + }, + { + "Year": 2014, + "Score": 68.0, + "Spending Category": "Total Spending" + }, + { + "Year": 2014, + "Score": 43.2, + "Spending Category": "Back-to-college" + }, + { + "Year": 2014, + "Score": 24.8, + "Spending Category": "Back-to-school" + }, + { + "Year": 2015, + "Score": 68.0, + "Spending Category": "Total Spending" + }, + { + "Year": 2015, + "Score": 43.7, + "Spending Category": "Back-to-college" + }, + { + "Year": 2015, + "Score": 24.3, + "Spending Category": "Back-to-school" + }, + { + "Year": 2016, + "Score": 75.8, + "Spending Category": "Total Spending" + }, + { + "Year": 2016, + "Score": 48.5, + "Spending Category": "Back-to-college" + }, + { + "Year": 2016, + "Score": 27.3, + "Spending Category": "Back-to-school" + }, + { + "Year": 2017, + "Score": 83.6, + "Spending Category": "Total Spending" + }, + { + "Year": 2017, + "Score": 54.9, + "Spending Category": "Back-to-college" + }, + { + "Year": 2017, + "Score": 28.7, + "Spending Category": "Back-to-school" + }, + { + "Year": 2018, + "Score": 82.8, + "Spending Category": "Total Spending" + }, + { + "Year": 2018, + "Score": 55.3, + "Spending Category": "Back-to-college" + }, + { + "Year": 2018, + "Score": 27.5, + "Spending Category": "Back-to-school" + }, + { + "Year": 2019, + "Score": 80.7, + "Spending Category": "Total Spending" + }, + { + "Year": 2019, + "Score": 54.5, + "Spending Category": "Back-to-college" + }, + { + "Year": 2019, + "Score": 26.2, + "Spending Category": "Back-to-school" + }, + { + "Year": 2020, + "Score": 101.6, + "Spending Category": "Total Spending" + }, + { + "Year": 2020, + "Score": 67.7, + "Spending Category": "Back-to-college" + }, + { + "Year": 2020, + "Score": 33.9, + "Spending Category": "Back-to-school" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Academic Year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding spending category", + "role": "y" + }, + { + "name": "Spending Category", + "importance": "primary", + "description": "The spending category of the data point", + "role": "group" + } + ] + } + }, + "176": { + "metadata": { + "title": "A Super League Denies Football's Fluid Reality", + "description": "Football clubs with the most top-6 finishes in the English top flight in given timeframes", + "main_insight": "The chart shows the top-performing English football clubs across different decades, highlighting how the dominance of specific teams has shifted over time. Some teams, like Liverpool, Man Utd, and Arsenal show consistent high performance, while others have more sporadic appearances." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Team": "Liverpool", + "70/71 - 79/80": 10.0 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at Liverpool", + "reason": "The [{'Team': 'Liverpool', '70/71 - 79/80': 10.0}] of has a maximum value of 10.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Team": "Notts Forest", + "70/71 - 79/80": 3.0 + }, + { + "Team": "Liverpool", + "70/71 - 79/80": 10.0 + } + ], + "score": 0.69, + "annotation": "The exhibits multiple sudden jumps in 70/71 - 79/80, transitioning from Man City, Ipswich to Notts Forest, Liverpool.", + "reason": "Across these points, the 70/71 - 79/80 for increases from 0, 7.0 at Man City, Ipswich to 3.0, 10.0 at Notts Forest, Liverpool, suggesting significant shifts in value." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Team": "Man Utd", + "70/71 - 79/80": 0 + }, + { + "Team": "Spurs", + "70/71 - 79/80": 0 + }, + { + "Team": "Everton", + "70/71 - 79/80": 0 + }, + { + "Team": "Aston Villa", + "70/71 - 79/80": 0 + }, + { + "Team": "Blackburn", + "70/71 - 79/80": 0 + }, + { + "Team": "Chelsea", + "70/71 - 79/80": 0 + }, + { + "Team": "Man City", + "70/71 - 79/80": 0 + } + ], + "score": 0.49, + "annotation": "The has minimum values at Man Utd, Spurs, Everton, Aston Villa, Blackburn, Chelsea, Man City", + "reason": "The [{'Team': 'Man Utd', '70/71 - 79/80': 0}, {'Team': 'Spurs', '70/71 - 79/80': 0}, {'Team': 'Everton', '70/71 - 79/80': 0}, {'Team': 'Aston Villa', '70/71 - 79/80': 0}, {'Team': 'Blackburn', '70/71 - 79/80': 0}, {'Team': 'Chelsea', '70/71 - 79/80': 0}, {'Team': 'Man City', '70/71 - 79/80': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Team": "Liverpool", + "70/71 - 79/80": 10.0 + }, + { + "Team": "Ipswich", + "70/71 - 79/80": 7.0 + }, + { + "Team": "Leeds", + "70/71 - 79/80": 6.0 + }, + { + "Team": "Arsenal", + "70/71 - 79/80": 5.0 + }, + { + "Team": "Derby County", + "70/71 - 79/80": 4.0 + }, + { + "Team": "Notts Forest", + "70/71 - 79/80": 3.0 + }, + { + "Team": "Man Utd", + "70/71 - 79/80": 0 + }, + { + "Team": "Spurs", + "70/71 - 79/80": 0 + }, + { + "Team": "Everton", + "70/71 - 79/80": 0 + }, + { + "Team": "Aston Villa", + "70/71 - 79/80": 0 + }, + { + "Team": "Blackburn", + "70/71 - 79/80": 0 + }, + { + "Team": "Chelsea", + "70/71 - 79/80": 0 + }, + { + "Team": "Man City", + "70/71 - 79/80": 0 + } + ], + "columns": [ + { + "name": "Team", + "importance": "primary", + "description": "Football club", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "70/71 - 79/80", + "importance": "secondary", + "description": "Number of top-6 finishes", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "476": { + "metadata": { + "title": "VPN Use By Gender", + "description": "The graphic presents two donut charts showing VPN usage by gender. The first chart shows the share of internet users worldwide who have used a VPN in the past month as of Q1 2018, by gender. The second chart shows the share of all VPN users, by gender.", + "main_insight": "The infographic shows that while a smaller proportion of female internet users have used a VPN compared to males, among all VPN users there is a higher proportion of females than of those who have used it in the past month" + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Gender": "Female", + "Score": 20.0, + "User Group": "Internet_Users_Used_VPN" + }, + { + "Gender": "Female", + "Score": 38.0, + "User Group": "All_VPN_Users" + } + ], + "score": 1.0, + "annotation": "The Score of Internet_Users_Used_VPN and All_VPN_Users show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Internet_Users_Used_VPN and All_VPN_Users is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Gender": "Male", + "Score": 30.0, + "User Group": "Internet_Users_Used_VPN" + } + ], + "score": 0.97, + "annotation": "The Internet_Users_Used_VPN shows a sudden jump in Score from Female to Male.", + "reason": "The Score for Internet_Users_Used_VPN increases from 20.0 at Female to 30.0 at Male, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Gender": "Male", + "Score": 62.0, + "User Group": "All_VPN_Users" + } + ], + "score": 0.97, + "annotation": "The All_VPN_Users shows a sudden jump in Score from Female to Male.", + "reason": "The Score for All_VPN_Users increases from 38.0 at Female to 62.0 at Male, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Gender": "Female", + "Score": 38.0, + "User Group": "All_VPN_Users" + } + ], + "score": 0.75, + "annotation": "The All_VPN_Users accounts for the majority in Female.", + "reason": "The Score of All_VPN_Users in Female accounts for 0.6551724137931034 (more than 50%) of the total of Score of all User Group." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "User Group": "All_VPN_Users", + "Gender": "total", + "Score": 100.0 + } + ], + "score": 0.75, + "annotation": "The total value of All_VPN_Users accounts for the majority in all User Group.", + "reason": "The total value of Score of All_VPN_Users accounts for 0.6666666666666666 (more than 50%) of the total of Score of all User Group." + } + ], + "data": { + "data": [ + { + "Gender": "Male", + "Score": 30.0, + "User Group": "Internet_Users_Used_VPN" + }, + { + "Gender": "Male", + "Score": 62.0, + "User Group": "All_VPN_Users" + }, + { + "Gender": "Female", + "Score": 20.0, + "User Group": "Internet_Users_Used_VPN" + }, + { + "Gender": "Female", + "Score": 38.0, + "User Group": "All_VPN_Users" + } + ], + "columns": [ + { + "name": "Gender", + "importance": "primary", + "description": "Gender Category", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding user group", + "role": "y" + }, + { + "name": "User Group", + "importance": "primary", + "description": "The user group of the data point", + "role": "group" + } + ] + } + }, + "1783": { + "metadata": { + "title": "FTSE 100 CEOs already outearned the average worker in 2021", + "description": null, + "main_insight": "FTSE 100 CEOs earn significantly more than the average UK worker, requiring only 3 working days (34 hours) to make the average worker's annual salary." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Metric": "Median FTSE 100 CEO remuneration (FYE 2019)", + "Value": 3610000 + }, + { + "Metric": "Median earnings for full-time workers in the UK", + "Value": 30353 + }, + { + "Metric": "Time needed for FTSE 100 CEOs to make the average worker's annual earnings", + "Value": "3 working days (34 hours)" + }, + { + "Metric": "Time needed for FTSE 100 CEOs to make the average worker's annual earnings in 2020", + "Value": "33 hours" + } + ], + "columns": [ + { + "name": "Metric", + "importance": "primary", + "description": "The different earnings metrics being compared", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "The corresponding numerical value for each metric", + "unit": "none", + "data_type": "string", + "role": "y" + } + ] + } + }, + "84": { + "metadata": { + "title": "Transport Construction Spending on the Rise Again", + "description": "Value of transportation infrastructure construction in Australia 1986-2018 (in billion AU$)", + "main_insight": "Transportation infrastructure construction spending in Australia has significantly increased since 1986, with roads and bridges consistently representing the largest share. Total spending saw a large increase between 2006 and 2012, then fell until 2016 and has increased slightly since then." + }, + "chart_type": "Stacked Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Year": "2010-2011", + "Units": 9.0, + "Infrastructure": "Ports and habours" + } + ], + "score": 0.99, + "annotation": "The Ports and habours has a maximum value at 2010-2011", + "reason": "The [{'Year': '2010-2011', 'Units': 9.0, 'Infrastructure': 'Ports and habours'}] of Ports and habours has a maximum value of 9.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Year": "2008-2009", + "Units": 6.5, + "Infrastructure": "Railways" + } + ], + "score": 0.93, + "annotation": "The Railways has a maximum value at 2008-2009", + "reason": "The [{'Year': '2008-2009', 'Units': 6.5, 'Infrastructure': 'Railways'}] of Railways has a maximum value of 6.5, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": "2017-2018", + "Units": 22.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "2017-2018", + "Units": 4.5, + "Infrastructure": "Railways" + } + ], + "score": 0.85, + "annotation": "The Units of Roads and bridges and Railways show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Roads and bridges and Railways is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "2010-2011", + "Units": 18.0, + "Infrastructure": "Roads and bridges" + } + ], + "score": 0.85, + "annotation": "The Roads and bridges accounts for the majority in 2010-2011.", + "reason": "The Units of Roads and bridges in 2010-2011 accounts for 0.5454545454545454 (more than 50%) of the total of Units of all Infrastructure." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Year": "2017-2018", + "Units": 22.0, + "Infrastructure": "Roads and bridges" + } + ], + "score": 0.84, + "annotation": "The Roads and bridges has a maximum value at 2017-2018", + "reason": "The [{'Year': '2017-2018', 'Units': 22.0, 'Infrastructure': 'Roads and bridges'}] of Roads and bridges has a maximum value of 22.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Year": "1986-1987", + "Units": 5.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "1986-1987", + "Units": 1.5, + "Infrastructure": "Railways" + }, + { + "Year": "1986-1987", + "Units": 1.0, + "Infrastructure": "Ports and habours" + }, + { + "Year": "1988-1989", + "Units": 6.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "1988-1989", + "Units": 1.5, + "Infrastructure": "Railways" + }, + { + "Year": "1988-1989", + "Units": 1.0, + "Infrastructure": "Ports and habours" + }, + { + "Year": "1990-1991", + "Units": 7.5, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "1990-1991", + "Units": 2.0, + "Infrastructure": "Railways" + }, + { + "Year": "1990-1991", + "Units": 1.0, + "Infrastructure": "Ports and habours" + }, + { + "Year": "1992-1993", + "Units": 8.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "1992-1993", + "Units": 2.0, + "Infrastructure": "Railways" + }, + { + "Year": "1992-1993", + "Units": 1.5, + "Infrastructure": "Ports and habours" + }, + { + "Year": "1994-1995", + "Units": 9.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "1994-1995", + "Units": 2.5, + "Infrastructure": "Railways" + }, + { + "Year": "1994-1995", + "Units": 1.5, + "Infrastructure": "Ports and habours" + }, + { + "Year": "1996-1997", + "Units": 9.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "1996-1997", + "Units": 3.0, + "Infrastructure": "Railways" + }, + { + "Year": "1996-1997", + "Units": 1.0, + "Infrastructure": "Ports and habours" + }, + { + "Year": "1998-1999", + "Units": 8.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "1998-1999", + "Units": 2.5, + "Infrastructure": "Railways" + }, + { + "Year": "1998-1999", + "Units": 1.0, + "Infrastructure": "Ports and habours" + }, + { + "Year": "2000-2001", + "Units": 11.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "2000-2001", + "Units": 2.0, + "Infrastructure": "Railways" + }, + { + "Year": "2000-2001", + "Units": 0.5, + "Infrastructure": "Ports and habours" + }, + { + "Year": "2002-2003", + "Units": 12.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "2002-2003", + "Units": 2.5, + "Infrastructure": "Railways" + }, + { + "Year": "2002-2003", + "Units": 1.0, + "Infrastructure": "Ports and habours" + }, + { + "Year": "2004-2005", + "Units": 14.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "2004-2005", + "Units": 3.5, + "Infrastructure": "Railways" + }, + { + "Year": "2004-2005", + "Units": 1.0, + "Infrastructure": "Ports and habours" + }, + { + "Year": "2006-2007", + "Units": 16.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "2006-2007", + "Units": 4.5, + "Infrastructure": "Railways" + }, + { + "Year": "2006-2007", + "Units": 2.0, + "Infrastructure": "Ports and habours" + }, + { + "Year": "2008-2009", + "Units": 19.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "2008-2009", + "Units": 6.5, + "Infrastructure": "Railways" + }, + { + "Year": "2008-2009", + "Units": 2.5, + "Infrastructure": "Ports and habours" + }, + { + "Year": "2010-2011", + "Units": 18.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "2010-2011", + "Units": 6.0, + "Infrastructure": "Railways" + }, + { + "Year": "2010-2011", + "Units": 9.0, + "Infrastructure": "Ports and habours" + }, + { + "Year": "2012-2013", + "Units": 18.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "2012-2013", + "Units": 5.0, + "Infrastructure": "Railways" + }, + { + "Year": "2012-2013", + "Units": 6.0, + "Infrastructure": "Ports and habours" + }, + { + "Year": "2014-2015", + "Units": 18.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "2014-2015", + "Units": 3.5, + "Infrastructure": "Railways" + }, + { + "Year": "2014-2015", + "Units": 4.0, + "Infrastructure": "Ports and habours" + }, + { + "Year": "2016-2017", + "Units": 20.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "2016-2017", + "Units": 4.0, + "Infrastructure": "Railways" + }, + { + "Year": "2016-2017", + "Units": 2.5, + "Infrastructure": "Ports and habours" + }, + { + "Year": "2017-2018", + "Units": 22.0, + "Infrastructure": "Roads and bridges" + }, + { + "Year": "2017-2018", + "Units": 4.5, + "Infrastructure": "Railways" + }, + { + "Year": "2017-2018", + "Units": 4.0, + "Infrastructure": "Ports and habours" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of construction spending", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding infrastructure", + "role": "y" + }, + { + "name": "Infrastructure", + "importance": "primary", + "description": "The infrastructure of the data point", + "role": "group" + } + ] + } + }, + "1951": { + "metadata": { + "title": "The United States' Biggest Charities", + "description": "U.S. charities/nonprofit organizations with the highest revenue in 2022", + "main_insight": "Lutheran Services in America and Mayo Clinic had the highest revenue among US charities in 2022, with $23.3 billion and $19.8 billion respectively. A significant portion of the revenue for all shown charities came from sources other than Private Support." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Charity": "Salvation Army", + "Units": 6.0, + "Revenue Source": "Total Revenue" + }, + { + "Charity": "Salvation Army", + "Units": 4.7, + "Revenue Source": "Other*" + } + ], + "score": 1.0, + "annotation": "The Units of Total Revenue and Other* show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Revenue and Other* is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Charity": "Salvation Army", + "Units": 1.3, + "Revenue Source": "Private Support" + } + ], + "score": 0.93, + "annotation": "The Private Support shows a sudden jump in Units from YMCA of the USA to Salvation Army.", + "reason": "The Units for Private Support increases from 1.0 at YMCA of the USA to 1.3 at Salvation Army, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Revenue Source": "Total Revenue", + "Charity": "total", + "Units": 92.10000000000002 + } + ], + "score": 0.92, + "annotation": "The total value of Total Revenue has the smallest proportion.", + "reason": "The total value of Units of Total Revenue accounts for 0.017372421281216067, smaller than all other Revenue Source, of the total of Units of all Revenue Source." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Charity": "YMCA of the USA", + "Units": 1.0, + "Revenue Source": "Private Support" + } + ], + "score": 0.9, + "annotation": "The Private Support has the smallest proportion in Lutheran Services in America.", + "reason": "The Units of Private Support in Lutheran Services in America accounts for 0.021008403361344536, smaller than all other Revenue Source, of the total of Units of all Revenue Source." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Charity": "Goodwill Industries International", + "Units": 0.9, + "Revenue Source": "Private Support" + } + ], + "score": 0.9, + "annotation": "The Private Support has the smallest proportion in Mayo Clinic.", + "reason": "The Units of Private Support in Mayo Clinic accounts for 0.022222222222222223, smaller than all other Revenue Source, of the total of Units of all Revenue Source." + } + ], + "data": { + "data": [ + { + "Charity": "Lutheran Services in America", + "Units": 23.3, + "Revenue Source": "Total Revenue" + }, + { + "Charity": "Lutheran Services in America", + "Units": 23.3, + "Revenue Source": "Other*" + }, + { + "Charity": "Mayo Clinic", + "Units": 19.8, + "Revenue Source": "Total Revenue" + }, + { + "Charity": "Mayo Clinic", + "Units": 19.8, + "Revenue Source": "Other*" + }, + { + "Charity": "New York-Presbyterian Hospital", + "Units": 11.3, + "Revenue Source": "Total Revenue" + }, + { + "Charity": "New York-Presbyterian Hospital", + "Units": 11.3, + "Revenue Source": "Other*" + }, + { + "Charity": "Mount Sinai Health Systems", + "Units": 9.2, + "Revenue Source": "Total Revenue" + }, + { + "Charity": "Mount Sinai Health Systems", + "Units": 9.2, + "Revenue Source": "Other*" + }, + { + "Charity": "Memorial Sloan Kettering Cancer Center", + "Units": 7.7, + "Revenue Source": "Total Revenue" + }, + { + "Charity": "Memorial Sloan Kettering Cancer Center", + "Units": 7.7, + "Revenue Source": "Other*" + }, + { + "Charity": "YMCA of the USA", + "Units": 7.4, + "Revenue Source": "Total Revenue" + }, + { + "Charity": "YMCA of the USA", + "Units": 6.4, + "Revenue Source": "Other*" + }, + { + "Charity": "YMCA of the USA", + "Units": 1.0, + "Revenue Source": "Private Support" + }, + { + "Charity": "Goodwill Industries International", + "Units": 7.4, + "Revenue Source": "Total Revenue" + }, + { + "Charity": "Goodwill Industries International", + "Units": 6.5, + "Revenue Source": "Other*" + }, + { + "Charity": "Goodwill Industries International", + "Units": 0.9, + "Revenue Source": "Private Support" + }, + { + "Charity": "Salvation Army", + "Units": 6.0, + "Revenue Source": "Total Revenue" + }, + { + "Charity": "Salvation Army", + "Units": 4.7, + "Revenue Source": "Other*" + }, + { + "Charity": "Salvation Army", + "Units": 1.3, + "Revenue Source": "Private Support" + } + ], + "columns": [ + { + "name": "Charity", + "importance": "primary", + "description": "Name of the Charity/Nonprofit Organization", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding revenue source", + "role": "y" + }, + { + "name": "Revenue Source", + "importance": "primary", + "description": "The revenue source of the data point", + "role": "group" + } + ] + } + }, + "2055": { + "metadata": { + "title": "The Electric Cars That Will Get You the Furthest", + "description": "Electric cars ranked by range in 2019 (in km)", + "main_insight": "The Tesla Model S has the longest range among the listed electric cars in 2019, at 600 km. There is significant variation in range among the different models." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Car Model": "Hyundai Ioniq Electric", + "Range (km)": 280.0 + } + ], + "score": 0.78, + "annotation": "The has a minimum value at Hyundai Ioniq Electric", + "reason": "The [{'Car Model': 'Hyundai Ioniq Electric', 'Range (km)': 280.0}] of has a mainimum value of 280.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Car Model": "Tesla Model S", + "Range (km)": 600.0 + } + ], + "score": 0.78, + "annotation": "The has a maximum value at Tesla Model S", + "reason": "The [{'Car Model': 'Tesla Model S', 'Range (km)': 600.0}] of has a maximum value of 600.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Car Model": "Nissan Leaf", + "Range (km)": 380.0 + } + ], + "score": 0.62, + "annotation": "The shows a sudden jump in Range (km) from BMW i3/i3s to Nissan Leaf.", + "reason": "The Range (km) for increases from 310.0 at BMW i3/i3s to 380.0 at Nissan Leaf, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Car Model": "Tesla Model S", + "Range (km)": 600.0 + }, + { + "Car Model": "Tesla Model 3", + "Range (km)": 580.0 + }, + { + "Car Model": "Tesla Model X", + "Range (km)": 565.0 + }, + { + "Car Model": "Opel Ampera", + "Range (km)": 520.0 + }, + { + "Car Model": "Hyundai Kona Electric", + "Range (km)": 480.0 + }, + { + "Car Model": "Kia e-Niro", + "Range (km)": 455.0 + }, + { + "Car Model": "Jaguar I-Pace", + "Range (km)": 420.0 + }, + { + "Car Model": "Renault Zoe", + "Range (km)": 400.0 + }, + { + "Car Model": "Nissan Leaf", + "Range (km)": 380.0 + }, + { + "Car Model": "BMW i3/i3s", + "Range (km)": 310.0 + }, + { + "Car Model": "Volkswagen e-Golf", + "Range (km)": 300.0 + }, + { + "Car Model": "Hyundai Ioniq Electric", + "Range (km)": 280.0 + } + ], + "columns": [ + { + "name": "Car Model", + "importance": "primary", + "description": "Electric car model", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Range (km)", + "importance": "primary", + "description": "Range of the electric car in kilometers", + "unit": "km", + "data_type": "number", + "role": "y" + } + ] + } + }, + "822": { + "metadata": { + "title": "Which Products does America Export to China?", + "description": "Major exports from the U.S. to China by goods category (in million U.S. dollar)", + "main_insight": "Oil & gas exports experienced a dramatic increase (373.3%) between 2016 and 2017, while aerospace products and parts, along with oilseeds and grains remain the top categories in terms of total export value in 2017." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Product Category": "Oil & gas", + "Dollars": 373.3, + "Revenue Metrics": "YOY change (in %)" + } + ], + "score": 0.98, + "annotation": "The YOY change (in %) has a maximum value at Oil & gas", + "reason": "The [{'Product Category': 'Oil & gas', 'Dollars': 373.3, 'Revenue Metrics': 'YOY change (in %)'}] of YOY change (in %) has a maximum value of 373.3, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Product Category": "Oil & gas", + "Dollars": 373.3, + "Revenue Metrics": "YOY change (in %)" + } + ], + "score": 0.96, + "annotation": "The YOY change (in %) shows a sudden jump in Dollars from Motor vehicles to Oil & gas.", + "reason": "The Dollars for YOY change (in %) increases from 21.1 at Motor vehicles to 373.3 at Oil & gas, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Product Category": "Pharmaceuticals & medicines", + "Dollars": 3000.0, + "Revenue Metrics": "2017" + }, + { + "Product Category": "Pharmaceuticals & medicines", + "Dollars": 2500.0, + "Revenue Metrics": "2016" + } + ], + "score": 0.94, + "annotation": "The Dollars of 2017 and 2016 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between 2017 and 2016 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Product Category": "Aerospace products & parts", + "Dollars": 18000.0, + "Revenue Metrics": "2017" + } + ], + "score": 0.92, + "annotation": "The 2017 has a maximum value at Aerospace products & parts", + "reason": "The [{'Product Category': 'Aerospace products & parts', 'Dollars': 18000.0, 'Revenue Metrics': '2017'}] of 2017 has a maximum value of 18000.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Product Category": "Aerospace products & parts", + "Dollars": 16000.0, + "Revenue Metrics": "2016" + }, + { + "Product Category": "Oilseeds & grains", + "Dollars": 16000.0, + "Revenue Metrics": "2016" + } + ], + "score": 0.85, + "annotation": "The 2016 has maximum values at Aerospace products & parts, Oilseeds & grains", + "reason": "The [{'Product Category': 'Aerospace products & parts', 'Dollars': 16000.0, 'Revenue Metrics': '2016'}, {'Product Category': 'Oilseeds & grains', 'Dollars': 16000.0, 'Revenue Metrics': '2016'}] of 2016 have maximum values of 16000.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Product Category": "Aerospace products & parts", + "Dollars": 18000.0, + "Revenue Metrics": "2017" + }, + { + "Product Category": "Aerospace products & parts", + "Dollars": 16000.0, + "Revenue Metrics": "2016" + }, + { + "Product Category": "Aerospace products & parts", + "Dollars": 11.6, + "Revenue Metrics": "YOY change (in %)" + }, + { + "Product Category": "Oilseeds & grains", + "Dollars": 14000.0, + "Revenue Metrics": "2017" + }, + { + "Product Category": "Oilseeds & grains", + "Dollars": 16000.0, + "Revenue Metrics": "2016" + }, + { + "Product Category": "Oilseeds & grains", + "Dollars": -11.6, + "Revenue Metrics": "YOY change (in %)" + }, + { + "Product Category": "Motor vehicles", + "Dollars": 12000.0, + "Revenue Metrics": "2017" + }, + { + "Product Category": "Motor vehicles", + "Dollars": 10000.0, + "Revenue Metrics": "2016" + }, + { + "Product Category": "Motor vehicles", + "Dollars": 21.1, + "Revenue Metrics": "YOY change (in %)" + }, + { + "Product Category": "Semiconductors & other electronic components", + "Dollars": 7000.0, + "Revenue Metrics": "2017" + }, + { + "Product Category": "Semiconductors & other electronic components", + "Dollars": 7000.0, + "Revenue Metrics": "2016" + }, + { + "Product Category": "Semiconductors & other electronic components", + "Dollars": 3.0, + "Revenue Metrics": "YOY change (in %)" + }, + { + "Product Category": "Oil & gas", + "Dollars": 6000.0, + "Revenue Metrics": "2017" + }, + { + "Product Category": "Oil & gas", + "Dollars": 1000.0, + "Revenue Metrics": "2016" + }, + { + "Product Category": "Oil & gas", + "Dollars": 373.3, + "Revenue Metrics": "YOY change (in %)" + }, + { + "Product Category": "Waste & scrap", + "Dollars": 5500.0, + "Revenue Metrics": "2017" + }, + { + "Product Category": "Waste & scrap", + "Dollars": 5000.0, + "Revenue Metrics": "2016" + }, + { + "Product Category": "Waste & scrap", + "Dollars": 8.5, + "Revenue Metrics": "YOY change (in %)" + }, + { + "Product Category": "Navigational, measuring, medical, control instruments", + "Dollars": 5000.0, + "Revenue Metrics": "2017" + }, + { + "Product Category": "Navigational, measuring, medical, control instruments", + "Dollars": 5000.0, + "Revenue Metrics": "2016" + }, + { + "Product Category": "Navigational, measuring, medical, control instruments", + "Dollars": 2.1, + "Revenue Metrics": "YOY change (in %)" + }, + { + "Product Category": "Basic chemicals", + "Dollars": 4500.0, + "Revenue Metrics": "2017" + }, + { + "Product Category": "Basic chemicals", + "Dollars": 4000.0, + "Revenue Metrics": "2016" + }, + { + "Product Category": "Basic chemicals", + "Dollars": 6.6, + "Revenue Metrics": "YOY change (in %)" + }, + { + "Product Category": "Resin, synthetic rubber, artificial & synthetic fiber", + "Dollars": 4000.0, + "Revenue Metrics": "2017" + }, + { + "Product Category": "Resin, synthetic rubber, artificial & synthetic fiber", + "Dollars": 3500.0, + "Revenue Metrics": "2016" + }, + { + "Product Category": "Resin, synthetic rubber, artificial & synthetic fiber", + "Dollars": 15.3, + "Revenue Metrics": "YOY change (in %)" + }, + { + "Product Category": "Pharmaceuticals & medicines", + "Dollars": 3000.0, + "Revenue Metrics": "2017" + }, + { + "Product Category": "Pharmaceuticals & medicines", + "Dollars": 2500.0, + "Revenue Metrics": "2016" + }, + { + "Product Category": "Pharmaceuticals & medicines", + "Dollars": 20.7, + "Revenue Metrics": "YOY change (in %)" + } + ], + "columns": [ + { + "name": "Product Category", + "importance": "primary", + "description": "Category of goods exported", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding revenue metrics", + "role": "y" + }, + { + "name": "Revenue Metrics", + "importance": "primary", + "description": "The revenue metrics of the data point", + "role": "group" + } + ] + } + }, + "1851": { + "metadata": { + "title": "Younger generations less proud to be English", + "description": "Share proud/embarrassed to identify as English in March 2018", + "main_insight": "Pride in being English increases significantly with age, with older generations much more likely to express pride compared to younger generations." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Age Group": "65+", + "Units": 72.0, + "Pride Level": "Proud" + }, + { + "Age Group": "65+", + "Units": 20.0, + "Pride Level": "Neither proud nor embarrassed" + } + ], + "score": 1.0, + "annotation": "The Units of Proud and Neither proud nor embarrassed show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Proud and Neither proud nor embarrassed is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Age Group": "65+", + "Units": 20.0, + "Pride Level": "Neither proud nor embarrassed" + }, + { + "Age Group": "65+", + "Units": 3.0, + "Pride Level": "Embarrassed" + } + ], + "score": 0.98, + "annotation": "The Units of Neither proud nor embarrassed and Embarrassed show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Neither proud nor embarrassed and Embarrassed is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Age Group": "65+", + "Units": 72.0, + "Pride Level": "Proud" + }, + { + "Age Group": "65+", + "Units": 3.0, + "Pride Level": "Embarrassed" + } + ], + "score": 0.98, + "annotation": "The Units of Proud and Embarrassed show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Proud and Embarrassed is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Age Group": "65+", + "Units": 3.0, + "Pride Level": "Embarrassed" + } + ], + "score": 0.86, + "annotation": "The Embarrassed has the smallest proportion in 65+.", + "reason": "The Units of Embarrassed in 65+ accounts for 0.031578947368421054, smaller than all other Pride Level, of the total of Units of all Pride Level." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "18-24", + "Units": 45.0, + "Pride Level": "Proud" + } + ], + "score": 0.85, + "annotation": "The Proud accounts for the majority in 18-24.", + "reason": "The Units of Proud in 18-24 accounts for 0.5113636363636364 (more than 50%) of the total of Units of all Pride Level." + } + ], + "data": { + "data": [ + { + "Age Group": "18-24", + "Units": 45.0, + "Pride Level": "Proud" + }, + { + "Age Group": "18-24", + "Units": 34.0, + "Pride Level": "Neither proud nor embarrassed" + }, + { + "Age Group": "18-24", + "Units": 9.0, + "Pride Level": "Embarrassed" + }, + { + "Age Group": "25-49", + "Units": 51.0, + "Pride Level": "Proud" + }, + { + "Age Group": "25-49", + "Units": 30.0, + "Pride Level": "Neither proud nor embarrassed" + }, + { + "Age Group": "25-49", + "Units": 8.0, + "Pride Level": "Embarrassed" + }, + { + "Age Group": "50-64", + "Units": 63.0, + "Pride Level": "Proud" + }, + { + "Age Group": "50-64", + "Units": 25.0, + "Pride Level": "Neither proud nor embarrassed" + }, + { + "Age Group": "50-64", + "Units": 6.0, + "Pride Level": "Embarrassed" + }, + { + "Age Group": "65+", + "Units": 72.0, + "Pride Level": "Proud" + }, + { + "Age Group": "65+", + "Units": 20.0, + "Pride Level": "Neither proud nor embarrassed" + }, + { + "Age Group": "65+", + "Units": 3.0, + "Pride Level": "Embarrassed" + } + ], + "columns": [ + { + "name": "Age Group", + "importance": "primary", + "description": "Age ranges of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding pride level", + "role": "y" + }, + { + "name": "Pride Level", + "importance": "primary", + "description": "The pride level of the data point", + "role": "group" + } + ] + } + }, + "1122": { + "metadata": { + "title": "Modern Supreme Court Nominees Quizzed on More Topics", + "description": "Number of unique issues brought up during select Supreme Court nominee hearings", + "main_insight": "Recent Supreme Court nominees have been questioned on a significantly greater number of unique issues during their hearings compared to nominees from the mid-20th century." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Nominee and Year": "Kennedy 1987", + "Unique Issues": 19 + } + ], + "score": 0.95, + "annotation": "The shows a sudden jump in Unique Issues from Fortas 1968 to Kennedy 1987.", + "reason": "The Unique Issues for increases from 4 at Fortas 1968 to 19 at Kennedy 1987, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Nominee and Year": "Jackson 1941", + "Unique Issues": 3 + }, + { + "Nominee and Year": "Whittaker 1957", + "Unique Issues": 3 + } + ], + "score": 0.6, + "annotation": "The has minimum values at Jackson 1941, Whittaker 1957", + "reason": "The [{'Nominee and Year': 'Jackson 1941', 'Unique Issues': 3}, {'Nominee and Year': 'Whittaker 1957', 'Unique Issues': 3}] of have minimum values of 3, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Nominee and Year": "Ginsburg 1993", + "Unique Issues": 21 + }, + { + "Nominee and Year": "Breyer 1994", + "Unique Issues": 21 + }, + { + "Nominee and Year": "Roberts 2005", + "Unique Issues": 21 + }, + { + "Nominee and Year": "Kagan 2010", + "Unique Issues": 21 + } + ], + "score": 0.59, + "annotation": "The has maximum values at Ginsburg 1993, Breyer 1994, Roberts 2005, Kagan 2010", + "reason": "The [{'Nominee and Year': 'Ginsburg 1993', 'Unique Issues': 21}, {'Nominee and Year': 'Breyer 1994', 'Unique Issues': 21}, {'Nominee and Year': 'Roberts 2005', 'Unique Issues': 21}, {'Nominee and Year': 'Kagan 2010', 'Unique Issues': 21}] of have maximum values of 21, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Nominee and Year": "Ginsburg 1993", + "Unique Issues": 21 + }, + { + "Nominee and Year": "Breyer 1994", + "Unique Issues": 21 + }, + { + "Nominee and Year": "Roberts 2005", + "Unique Issues": 21 + }, + { + "Nominee and Year": "Kagan 2010", + "Unique Issues": 21 + }, + { + "Nominee and Year": "Kennedy 1987", + "Unique Issues": 19 + }, + { + "Nominee and Year": "White 1962", + "Unique Issues": 4 + }, + { + "Nominee and Year": "Fortas 1965", + "Unique Issues": 4 + }, + { + "Nominee and Year": "Fortas 1968", + "Unique Issues": 4 + }, + { + "Nominee and Year": "Jackson 1941", + "Unique Issues": 3 + }, + { + "Nominee and Year": "Whittaker 1957", + "Unique Issues": 3 + } + ], + "columns": [ + { + "name": "Nominee and Year", + "importance": "primary", + "description": "Supreme Court nominee and the year of their hearing", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Unique Issues", + "importance": "primary", + "description": "Number of unique issues discussed", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1388": { + "metadata": { + "title": "Conservative and Labour party images in perspective", + "description": "\"How close is the Labour/Conservative party to the following groups in society?\" (%)", + "main_insight": "The chart compares public perception of how close the Labour and Conservative parties are to various groups in society. The results show a significant difference in perceived closeness, with the Conservative Party generally seen as closer to most societal groups, except for Trade Unions." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Groups in Society": "Older people", + "Score": 47.0, + "Vote Outcome": "Conservative Close" + } + ], + "score": 0.94, + "annotation": "The Conservative Close has the largest proportion in Older people.", + "reason": "The Score of Conservative Close in Older people accounts for 0.26857142857142857, larger than all other Vote Outcome, of the total of Score of all Vote Outcome." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Groups in Society": "Women", + "Score": 51.0, + "Vote Outcome": "Conservative Close" + } + ], + "score": 0.93, + "annotation": "The Conservative Close has the largest proportion in Women.", + "reason": "The Score of Conservative Close in Women accounts for 0.30538922155688625, larger than all other Vote Outcome, of the total of Score of all Vote Outcome." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Groups in Society": "Homeowners", + "Score": 60.0, + "Vote Outcome": "Conservative Close" + } + ], + "score": 0.92, + "annotation": "The Conservative Close has the largest proportion in Homeowners.", + "reason": "The Score of Conservative Close in Homeowners accounts for 0.3468208092485549, larger than all other Vote Outcome, of the total of Score of all Vote Outcome." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Groups in Society": "People with families", + "Score": 67.0, + "Vote Outcome": "Conservative Close" + } + ], + "score": 0.92, + "annotation": "The Conservative Close has the largest proportion in People with families.", + "reason": "The Score of Conservative Close in People with families accounts for 0.3507853403141361, larger than all other Vote Outcome, of the total of Score of all Vote Outcome." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Groups in Society": "Immigrants", + "Score": 56.0, + "Vote Outcome": "Labour Not Close" + } + ], + "score": 0.92, + "annotation": "The Labour Not Close has the largest proportion in Immigrants.", + "reason": "The Score of Labour Not Close in Immigrants accounts for 0.35668789808917195, larger than all other Vote Outcome, of the total of Score of all Vote Outcome." + } + ], + "data": { + "data": [ + { + "Groups in Society": "Rich people", + "Score": 67.0, + "Vote Outcome": "Labour Not Close" + }, + { + "Groups in Society": "Rich people", + "Score": 17.0, + "Vote Outcome": "Labour Close" + }, + { + "Groups in Society": "Rich people", + "Score": 4.0, + "Vote Outcome": "Conservative Not Close" + }, + { + "Groups in Society": "Rich people", + "Score": 88.0, + "Vote Outcome": "Conservative Close" + }, + { + "Groups in Society": "Businessmen and the City", + "Score": 72.0, + "Vote Outcome": "Labour Not Close" + }, + { + "Groups in Society": "Businessmen and the City", + "Score": 21.0, + "Vote Outcome": "Labour Close" + }, + { + "Groups in Society": "Businessmen and the City", + "Score": 5.0, + "Vote Outcome": "Conservative Not Close" + }, + { + "Groups in Society": "Businessmen and the City", + "Score": 88.0, + "Vote Outcome": "Conservative Close" + }, + { + "Groups in Society": "The middle class", + "Score": 54.0, + "Vote Outcome": "Labour Not Close" + }, + { + "Groups in Society": "The middle class", + "Score": 34.0, + "Vote Outcome": "Labour Close" + }, + { + "Groups in Society": "The middle class", + "Score": 19.0, + "Vote Outcome": "Conservative Not Close" + }, + { + "Groups in Society": "The middle class", + "Score": 72.0, + "Vote Outcome": "Conservative Close" + }, + { + "Groups in Society": "Homeowners", + "Score": 49.0, + "Vote Outcome": "Labour Not Close" + }, + { + "Groups in Society": "Homeowners", + "Score": 36.0, + "Vote Outcome": "Labour Close" + }, + { + "Groups in Society": "Homeowners", + "Score": 28.0, + "Vote Outcome": "Conservative Not Close" + }, + { + "Groups in Society": "Homeowners", + "Score": 60.0, + "Vote Outcome": "Conservative Close" + }, + { + "Groups in Society": "Older people", + "Score": 42.0, + "Vote Outcome": "Labour Not Close" + }, + { + "Groups in Society": "Older people", + "Score": 44.0, + "Vote Outcome": "Labour Close" + }, + { + "Groups in Society": "Older people", + "Score": 42.0, + "Vote Outcome": "Conservative Not Close" + }, + { + "Groups in Society": "Older people", + "Score": 47.0, + "Vote Outcome": "Conservative Close" + }, + { + "Groups in Society": "Women", + "Score": 35.0, + "Vote Outcome": "Labour Not Close" + }, + { + "Groups in Society": "Women", + "Score": 48.0, + "Vote Outcome": "Labour Close" + }, + { + "Groups in Society": "Women", + "Score": 33.0, + "Vote Outcome": "Conservative Not Close" + }, + { + "Groups in Society": "Women", + "Score": 51.0, + "Vote Outcome": "Conservative Close" + }, + { + "Groups in Society": "Immigrants", + "Score": 56.0, + "Vote Outcome": "Labour Not Close" + }, + { + "Groups in Society": "Immigrants", + "Score": 29.0, + "Vote Outcome": "Labour Close" + }, + { + "Groups in Society": "Immigrants", + "Score": 20.0, + "Vote Outcome": "Conservative Not Close" + }, + { + "Groups in Society": "Immigrants", + "Score": 52.0, + "Vote Outcome": "Conservative Close" + }, + { + "Groups in Society": "People with families", + "Score": 59.0, + "Vote Outcome": "Labour Not Close" + }, + { + "Groups in Society": "People with families", + "Score": 28.0, + "Vote Outcome": "Labour Close" + }, + { + "Groups in Society": "People with families", + "Score": 37.0, + "Vote Outcome": "Conservative Not Close" + }, + { + "Groups in Society": "People with families", + "Score": 67.0, + "Vote Outcome": "Conservative Close" + }, + { + "Groups in Society": "Ordinary working people", + "Score": 61.0, + "Vote Outcome": "Labour Not Close" + }, + { + "Groups in Society": "Ordinary working people", + "Score": 29.0, + "Vote Outcome": "Labour Close" + }, + { + "Groups in Society": "Ordinary working people", + "Score": 25.0, + "Vote Outcome": "Conservative Not Close" + }, + { + "Groups in Society": "Ordinary working people", + "Score": 67.0, + "Vote Outcome": "Conservative Close" + }, + { + "Groups in Society": "The working class", + "Score": 68.0, + "Vote Outcome": "Labour Not Close" + }, + { + "Groups in Society": "The working class", + "Score": 22.0, + "Vote Outcome": "Labour Close" + }, + { + "Groups in Society": "The working class", + "Score": 19.0, + "Vote Outcome": "Conservative Not Close" + }, + { + "Groups in Society": "The working class", + "Score": 72.0, + "Vote Outcome": "Conservative Close" + }, + { + "Groups in Society": "Benefit claimants", + "Score": 69.0, + "Vote Outcome": "Labour Not Close" + }, + { + "Groups in Society": "Benefit claimants", + "Score": 8.0, + "Vote Outcome": "Labour Close" + }, + { + "Groups in Society": "Benefit claimants", + "Score": 7.0, + "Vote Outcome": "Conservative Not Close" + }, + { + "Groups in Society": "Benefit claimants", + "Score": 83.0, + "Vote Outcome": "Conservative Close" + }, + { + "Groups in Society": "The trade unions", + "Score": 81.0, + "Vote Outcome": "Labour Not Close" + }, + { + "Groups in Society": "The trade unions", + "Score": 19.0, + "Vote Outcome": "Labour Close" + }, + { + "Groups in Society": "The trade unions", + "Score": 6.0, + "Vote Outcome": "Conservative Not Close" + }, + { + "Groups in Society": "The trade unions", + "Score": 83.0, + "Vote Outcome": "Conservative Close" + } + ], + "columns": [ + { + "name": "Groups in Society", + "importance": "primary", + "description": "Different societal groups", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding vote outcome", + "role": "y" + }, + { + "name": "Vote Outcome", + "importance": "primary", + "description": "The vote outcome of the data point", + "role": "group" + } + ] + } + }, + "321": { + "metadata": { + "title": "Mental Health: The Impact of Social Media on Young People", + "description": "Ranking of social media platforms according to impact on youth mental health", + "main_insight": "YouTube is ranked as having the most net positive impact on young people's mental health, while Instagram is ranked as having the most net negative impact, according to a survey conducted in the United Kingdom." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Social Media Platform": "YouTube", + "Ratio": 1.0, + "Sentiment": "Net positive" + }, + { + "Social Media Platform": "Twitter", + "Ratio": 2.0, + "Sentiment": "Net negative" + }, + { + "Social Media Platform": "Facebook", + "Ratio": 3.0, + "Sentiment": "Net negative" + }, + { + "Social Media Platform": "Snapchat", + "Ratio": 4.0, + "Sentiment": "Net negative" + }, + { + "Social Media Platform": "Instagram", + "Ratio": 5.0, + "Sentiment": "Net negative" + } + ], + "columns": [ + { + "name": "Social Media Platform", + "importance": "primary", + "description": "The social media platform being ranked.", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "770": { + "metadata": { + "title": "Apple Is Lagging Behind in Terms of Smartphone Growth", + "description": "Global smartphone shipments (in million units)", + "main_insight": "Apple's smartphone shipment growth from 2012 to 2013 was significantly lower than that of its competitors, including Samsung, Huawei, LG, Lenovo, and others." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Company": "Others", + "Units": 290.5, + "Shipment Metrics": "2012 Shipments" + }, + { + "Company": "Others", + "Units": 394.9, + "Shipment Metrics": "2013 Shipments" + } + ], + "score": 0.99, + "annotation": "The Units of 2012 Shipments and 2013 Shipments show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2012 Shipments and 2013 Shipments is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Company": "Huawei", + "Units": 67.5, + "Shipment Metrics": "Growth" + } + ], + "score": 0.87, + "annotation": "The Growth has the largest proportion in Huawei.", + "reason": "The Units of Growth in Huawei accounts for 0.4642365887207703, larger than all other Shipment Metrics, of the total of Units of all Shipment Metrics." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Shipment Metrics": "2013 Shipments", + "Company": "total", + "Units": 1004.1999999999999 + } + ], + "score": 0.86, + "annotation": "The total value of 2013 Shipments has the largest proportion.", + "reason": "The total value of Units of 2013 Shipments accounts for 0.4871446589696322, larger than all other Shipment Metrics, of the total of Units of all Shipment Metrics." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Company": "Apple", + "Units": 153.4, + "Shipment Metrics": "2013 Shipments" + } + ], + "score": 0.86, + "annotation": "The 2013 Shipments accounts for the majority in Apple.", + "reason": "The Units of 2013 Shipments in Apple accounts for 0.5076108537392455 (more than 50%) of the total of Units of all Shipment Metrics." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Company": "LG", + "Units": 81.1, + "Shipment Metrics": "Growth" + } + ], + "score": 0.85, + "annotation": "The Growth accounts for the majority in LG.", + "reason": "The Units of Growth in LG accounts for 0.5228884590586718 (more than 50%) of the total of Units of all Shipment Metrics." + } + ], + "data": { + "data": [ + { + "Company": "Samsung", + "Units": 219.7, + "Shipment Metrics": "2012 Shipments" + }, + { + "Company": "Samsung", + "Units": 313.9, + "Shipment Metrics": "2013 Shipments" + }, + { + "Company": "Samsung", + "Units": 42.9, + "Shipment Metrics": "Growth" + }, + { + "Company": "Apple", + "Units": 135.9, + "Shipment Metrics": "2012 Shipments" + }, + { + "Company": "Apple", + "Units": 153.4, + "Shipment Metrics": "2013 Shipments" + }, + { + "Company": "Apple", + "Units": 12.9, + "Shipment Metrics": "Growth" + }, + { + "Company": "Huawei", + "Units": 29.1, + "Shipment Metrics": "2012 Shipments" + }, + { + "Company": "Huawei", + "Units": 48.8, + "Shipment Metrics": "2013 Shipments" + }, + { + "Company": "Huawei", + "Units": 67.5, + "Shipment Metrics": "Growth" + }, + { + "Company": "LG", + "Units": 26.3, + "Shipment Metrics": "2012 Shipments" + }, + { + "Company": "LG", + "Units": 47.7, + "Shipment Metrics": "2013 Shipments" + }, + { + "Company": "LG", + "Units": 81.1, + "Shipment Metrics": "Growth" + }, + { + "Company": "Lenovo", + "Units": 23.7, + "Shipment Metrics": "2012 Shipments" + }, + { + "Company": "Lenovo", + "Units": 45.5, + "Shipment Metrics": "2013 Shipments" + }, + { + "Company": "Lenovo", + "Units": 91.7, + "Shipment Metrics": "Growth" + }, + { + "Company": "Others", + "Units": 290.5, + "Shipment Metrics": "2012 Shipments" + }, + { + "Company": "Others", + "Units": 394.9, + "Shipment Metrics": "2013 Shipments" + }, + { + "Company": "Others", + "Units": 35.9, + "Shipment Metrics": "Growth" + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Smartphone manufacturer", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding shipment metrics", + "role": "y" + }, + { + "name": "Shipment Metrics", + "importance": "primary", + "description": "The shipment metrics of the data point", + "role": "group" + } + ] + } + }, + "1174": { + "metadata": { + "title": "European Airlines Had A Tough First Quarter", + "description": "Operating profits of selected airlines (million euro)*", + "main_insight": "Most European airlines saw a decrease in operating profits from Q1 2018 to Q1 2019, with some exceptions like IAG Group and Wizz Air showing significant profit increases." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Airline": "IAG Group", + "Units": 340.0, + "Quarter": "Q1 2019" + } + ], + "score": 0.95, + "annotation": "The Q1 2019 has a maximum value at IAG Group", + "reason": "The [{'Airline': 'IAG Group', 'Units': 340.0, 'Quarter': 'Q1 2019'}] of Q1 2019 has a maximum value of 340.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Airline": "IAG Group", + "Units": 135.0, + "Quarter": "Q1 2018" + } + ], + "score": 0.84, + "annotation": "The Q1 2018 has a maximum value at IAG Group", + "reason": "The [{'Airline': 'IAG Group', 'Units': 135.0, 'Quarter': 'Q1 2018'}] of Q1 2018 has a maximum value of 135.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Airline": "IAG Group", + "Units": 340.0, + "Quarter": "Q1 2019" + } + ], + "score": 0.82, + "annotation": "The Q1 2019 shows a sudden jump in Units from Wizz Air to IAG Group.", + "reason": "The Units for Q1 2019 increases from 107.0 at Wizz Air to 340.0 at IAG Group, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Airline": "Norwegian", + "Units": -149.0, + "Quarter": "Q1 2019" + } + ], + "score": 0.69, + "annotation": "The Q1 2019 has a minimum value at Norwegian", + "reason": "The [{'Airline': 'Norwegian', 'Units': -149.0, 'Quarter': 'Q1 2019'}] of Q1 2019 has a mainimum value of -149.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Airline": "Ryanair", + "Units": -245.0, + "Quarter": "Q1 2018" + }, + { + "Airline": "Ryanair", + "Units": 51.0, + "Quarter": "Q1 2019" + } + ], + "score": 0.66, + "annotation": "The Units of Q1 2018 and Q1 2019 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Q1 2018 and Q1 2019 is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Airline": "AFR/KLM Group", + "Units": -336.0, + "Quarter": "Q1 2018" + }, + { + "Airline": "AFR/KLM Group", + "Units": -118.0, + "Quarter": "Q1 2019" + }, + { + "Airline": "IAG Group", + "Units": 135.0, + "Quarter": "Q1 2018" + }, + { + "Airline": "IAG Group", + "Units": 340.0, + "Quarter": "Q1 2019" + }, + { + "Airline": "Lufthansa Group", + "Units": -336.0, + "Quarter": "Q1 2018" + }, + { + "Airline": "Lufthansa Group", + "Units": 52.0, + "Quarter": "Q1 2019" + }, + { + "Airline": "SAS Group", + "Units": -29.0, + "Quarter": "Q1 2018" + }, + { + "Airline": "SAS Group", + "Units": -53.0, + "Quarter": "Q1 2019" + }, + { + "Airline": "Turkish Airlines", + "Units": -130.0, + "Quarter": "Q1 2018" + }, + { + "Airline": "Turkish Airlines", + "Units": 52.0, + "Quarter": "Q1 2019" + }, + { + "Airline": "Pegasus", + "Units": -28.0, + "Quarter": "Q1 2018" + }, + { + "Airline": "Pegasus", + "Units": -8.0, + "Quarter": "Q1 2019" + }, + { + "Airline": "Norwegian", + "Units": -227.0, + "Quarter": "Q1 2018" + }, + { + "Airline": "Norwegian", + "Units": -149.0, + "Quarter": "Q1 2019" + }, + { + "Airline": "EasyJet", + "Units": -310.0, + "Quarter": "Q1 2018" + }, + { + "Airline": "EasyJet", + "Units": -77.0, + "Quarter": "Q1 2019" + }, + { + "Airline": "Wizz Air", + "Units": 64.0, + "Quarter": "Q1 2018" + }, + { + "Airline": "Wizz Air", + "Units": 107.0, + "Quarter": "Q1 2019" + }, + { + "Airline": "Ryanair", + "Units": -245.0, + "Quarter": "Q1 2018" + }, + { + "Airline": "Ryanair", + "Units": 51.0, + "Quarter": "Q1 2019" + } + ], + "columns": [ + { + "name": "Airline", + "importance": "primary", + "description": "Name of the airline", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding quarter", + "role": "y" + }, + { + "name": "Quarter", + "importance": "primary", + "description": "The quarter of the data point", + "role": "group" + } + ] + } + }, + "1848": { + "metadata": { + "title": "British public opposes weapons shipments to Ukraine", + "description": "Level of support for the following in the UK in February 2015*", + "main_insight": "In February 2015, the British public showed more support for current and additional sanctions on Russia than for providing large-scale military equipment to Ukraine. A significant portion of respondents opposed providing military equipment." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Action": "Providing large scale military equipment to Ukraine", + "Units": 26.0, + "Opinion": "Support" + }, + { + "Action": "Providing large scale military equipment to Ukraine", + "Units": 34.0, + "Opinion": "Oppose" + } + ], + "score": 1.0, + "annotation": "The Units of Support and Oppose show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Support and Oppose is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Action": "Providing large scale military equipment to Ukraine", + "Units": 26.0, + "Opinion": "Support" + }, + { + "Action": "Providing large scale military equipment to Ukraine", + "Units": 40.0, + "Opinion": "Don't know" + } + ], + "score": 0.98, + "annotation": "The Units of Support and Don't know show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Support and Don't know is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Action": "Providing large scale military equipment to Ukraine", + "Units": 34.0, + "Opinion": "Oppose" + }, + { + "Action": "Providing large scale military equipment to Ukraine", + "Units": 40.0, + "Opinion": "Don't know" + } + ], + "score": 0.96, + "annotation": "The Units of Oppose and Don't know show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Oppose and Don't know is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Action": "Providing large scale military equipment to Ukraine", + "Units": 34.0, + "Opinion": "Oppose" + } + ], + "score": 0.95, + "annotation": "The Oppose shows a sudden jump in Units from Imposing additional sanctions on Russia to Providing large scale military equipment to Ukraine.", + "reason": "The Units for Oppose increases from 15.0 at Imposing additional sanctions on Russia to 34.0 at Providing large scale military equipment to Ukraine, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Action": "Imposing additional sanctions on Russia", + "Units": 54.0, + "Opinion": "Support" + } + ], + "score": 0.93, + "annotation": "The Support shows a sudden jump in Units from Providing large scale military equipment to Ukraine to Imposing additional sanctions on Russia.", + "reason": "The Units for Support increases from 26.0 at Providing large scale military equipment to Ukraine to 54.0 at Imposing additional sanctions on Russia, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Action": "Current sanctions on Russia", + "Units": 64.0, + "Opinion": "Support" + }, + { + "Action": "Current sanctions on Russia", + "Units": 11.0, + "Opinion": "Oppose" + }, + { + "Action": "Current sanctions on Russia", + "Units": 26.0, + "Opinion": "Don't know" + }, + { + "Action": "Imposing additional sanctions on Russia", + "Units": 54.0, + "Opinion": "Support" + }, + { + "Action": "Imposing additional sanctions on Russia", + "Units": 15.0, + "Opinion": "Oppose" + }, + { + "Action": "Imposing additional sanctions on Russia", + "Units": 32.0, + "Opinion": "Don't know" + }, + { + "Action": "Providing large scale military equipment to Ukraine", + "Units": 26.0, + "Opinion": "Support" + }, + { + "Action": "Providing large scale military equipment to Ukraine", + "Units": 34.0, + "Opinion": "Oppose" + }, + { + "Action": "Providing large scale military equipment to Ukraine", + "Units": 40.0, + "Opinion": "Don't know" + } + ], + "columns": [ + { + "name": "Action", + "importance": "primary", + "description": "The action being considered regarding Russia and Ukraine", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding opinion", + "role": "y" + }, + { + "name": "Opinion", + "importance": "primary", + "description": "The opinion of the data point", + "role": "group" + } + ] + } + }, + "1560": { + "metadata": { + "title": "The Rise of U.S. Energy Costs", + "description": "Average energy prices in the United States (in U.S. dollars)", + "main_insight": "Energy costs in the US, particularly for gasoline and electricity, have significantly increased since 2020, with electricity reaching $5.19 per 30 kWh in January 2024." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Score": 5.19, + "Energy Prices": "30 kWh of electricity" + }, + { + "Year": 2024, + "Score": 1.45, + "Energy Prices": "Piped gas per therm" + } + ], + "score": 0.87, + "annotation": "The Score of 30 kWh of electricity and Piped gas per therm show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between 30 kWh of electricity and Piped gas per therm is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Score": 3.35, + "Energy Prices": "Gasoline per gallon" + }, + { + "Year": 2024, + "Score": 1.45, + "Energy Prices": "Piped gas per therm" + } + ], + "score": 0.85, + "annotation": "The Score of Gasoline per gallon and Piped gas per therm show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Gasoline per gallon and Piped gas per therm is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Score": 5.19, + "Energy Prices": "30 kWh of electricity" + }, + { + "Year": 2024, + "Score": 3.35, + "Energy Prices": "Gasoline per gallon" + } + ], + "score": 0.5, + "annotation": "The Score of 30 kWh of electricity and Gasoline per gallon show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between 30 kWh of electricity and Gasoline per gallon is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2020, + "Score": 4.02, + "Energy Prices": "30 kWh of electricity" + }, + { + "Year": 2020, + "Score": 2.63, + "Energy Prices": "Gasoline per gallon" + }, + { + "Year": 2020, + "Score": 1.07, + "Energy Prices": "Piped gas per therm" + }, + { + "Year": 2021, + "Score": 4.25, + "Energy Prices": "30 kWh of electricity" + }, + { + "Year": 2021, + "Score": 3.25, + "Energy Prices": "Gasoline per gallon" + }, + { + "Year": 2021, + "Score": 1.25, + "Energy Prices": "Piped gas per therm" + }, + { + "Year": 2022, + "Score": 4.75, + "Energy Prices": "30 kWh of electricity" + }, + { + "Year": 2022, + "Score": 4.5, + "Energy Prices": "Gasoline per gallon" + }, + { + "Year": 2022, + "Score": 1.5, + "Energy Prices": "Piped gas per therm" + }, + { + "Year": 2023, + "Score": 5.0, + "Energy Prices": "30 kWh of electricity" + }, + { + "Year": 2023, + "Score": 3.5, + "Energy Prices": "Gasoline per gallon" + }, + { + "Year": 2023, + "Score": 1.4, + "Energy Prices": "Piped gas per therm" + }, + { + "Year": 2024, + "Score": 5.19, + "Energy Prices": "30 kWh of electricity" + }, + { + "Year": 2024, + "Score": 3.35, + "Energy Prices": "Gasoline per gallon" + }, + { + "Year": 2024, + "Score": 1.45, + "Energy Prices": "Piped gas per therm" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year, ranging from 2020 to 2024", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding energy prices", + "role": "y" + }, + { + "name": "Energy Prices", + "importance": "primary", + "description": "The energy prices of the data point", + "role": "group" + } + ] + } + }, + "1982": { + "metadata": { + "title": "Heat Islands Have City Dwellers Swelter in a Concrete Jungle", + "description": "U.S. cities with the biggest populations in heat island areas (>8° F hotter than surrounding areas)", + "main_insight": "A significant portion of the population in major U.S. cities lives in heat island areas that are considerably hotter than surrounding areas. New York has the largest population share (78%) living in these hotter zones." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "City": "San Diego", + "Score": 0.1, + "Heat Exposure": ">10-12°F hotter" + } + ], + "score": 0.99, + "annotation": "The >10-12°F hotter has the smallest proportion in Dallas.", + "reason": "The Score of >10-12°F hotter in Dallas accounts for 0.0012919896640826876, smaller than all other Heat Exposure, of the total of Score of all Heat Exposure." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "City": "Los Angeles", + "Score": 0.3, + "Heat Exposure": ">10-12°F hotter" + } + ], + "score": 0.98, + "annotation": "The >10-12°F hotter has the smallest proportion in Houston.", + "reason": "The Score of >10-12°F hotter in Houston accounts for 0.0038560411311053984, smaller than all other Heat Exposure, of the total of Score of all Heat Exposure." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "New York", + "Score": 3.7, + "Heat Exposure": ">10-12°F hotter" + } + ], + "score": 0.96, + "annotation": "The >10-12°F hotter shows a sudden jump in Score from Chicago to New York.", + "reason": "The Score for >10-12°F hotter increases from 0.4 at Chicago to 3.7 at New York, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "City": "Chicago", + "Score": 0.4, + "Heat Exposure": ">10-12°F hotter" + } + ], + "score": 0.96, + "annotation": "The >10-12°F hotter has the smallest proportion in Los Angeles.", + "reason": "The Score of >10-12°F hotter in Los Angeles accounts for 0.008350730688935283, smaller than all other Heat Exposure, of the total of Score of all Heat Exposure." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Heat Exposure": "Share of city's population (in %)", + "City": "total", + "Score": 490.0 + } + ], + "score": 0.96, + "annotation": "The total value of Share of city's population (in %) has the smallest proportion.", + "reason": "The total value of Score of Share of city's population (in %) accounts for 0.008777062609713282, smaller than all other Heat Exposure, of the total of Score of all Heat Exposure." + } + ], + "data": { + "data": [ + { + "City": "New York", + "Score": 78.0, + "Heat Exposure": "Share of city's population (in %)" + }, + { + "City": "New York", + "Score": 3.7, + "Heat Exposure": ">8-10°F hotter" + }, + { + "City": "New York", + "Score": 3.7, + "Heat Exposure": ">10-12°F hotter" + }, + { + "City": "Houston", + "Score": 73.0, + "Heat Exposure": "Share of city's population (in %)" + }, + { + "City": "Houston", + "Score": 4.5, + "Heat Exposure": ">8-10°F hotter" + }, + { + "City": "Los Angeles", + "Score": 45.0, + "Heat Exposure": "Share of city's population (in %)" + }, + { + "City": "Los Angeles", + "Score": 2.5, + "Heat Exposure": ">8-10°F hotter" + }, + { + "City": "Los Angeles", + "Score": 0.3, + "Heat Exposure": ">10-12°F hotter" + }, + { + "City": "Dallas", + "Score": 75.0, + "Heat Exposure": "Share of city's population (in %)" + }, + { + "City": "Dallas", + "Score": 2.3, + "Heat Exposure": ">8-10°F hotter" + }, + { + "City": "Chicago", + "Score": 52.0, + "Heat Exposure": "Share of city's population (in %)" + }, + { + "City": "Chicago", + "Score": 1.4, + "Heat Exposure": ">8-10°F hotter" + }, + { + "City": "Chicago", + "Score": 0.4, + "Heat Exposure": ">10-12°F hotter" + }, + { + "City": "San Antonio", + "Score": 67.0, + "Heat Exposure": "Share of city's population (in %)" + }, + { + "City": "San Antonio", + "Score": 1.3, + "Heat Exposure": ">8-10°F hotter" + }, + { + "City": "San Diego", + "Score": 55.0, + "Heat Exposure": "Share of city's population (in %)" + }, + { + "City": "San Diego", + "Score": 1.1, + "Heat Exposure": ">8-10°F hotter" + }, + { + "City": "San Diego", + "Score": 0.1, + "Heat Exposure": ">10-12°F hotter" + }, + { + "City": "Phoenix", + "Score": 45.0, + "Heat Exposure": "Share of city's population (in %)" + }, + { + "City": "Phoenix", + "Score": 1.4, + "Heat Exposure": ">8-10°F hotter" + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "Name of the U.S. city", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding heat exposure", + "role": "y" + }, + { + "name": "Heat Exposure", + "importance": "primary", + "description": "The heat exposure of the data point", + "role": "group" + } + ] + } + }, + "539": { + "metadata": { + "title": "Which Covid-19 Vaccines Are Most Widely Used?", + "description": "Number of countries using selected Covid-19 vaccines as of February 16, 2021", + "main_insight": "As of February 16, 2021, the Pfizer-BioNTech vaccine was the most widely used Covid-19 vaccine, followed by the Oxford-AstraZeneca vaccine, based on the number of countries using them." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Vaccine": "Pfizer BioNTech", + "Number of Countries": 61.0 + } + ], + "score": 0.9, + "annotation": "The has a maximum value at Pfizer BioNTech", + "reason": "The [{'Vaccine': 'Pfizer BioNTech', 'Number of Countries': 61.0}] of has a maximum value of 61.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Vaccine": "Pfizer BioNTech", + "Number of Countries": 61.0 + } + ], + "score": 0.72, + "annotation": "The shows a sudden jump in Number of Countries from Oxford-AstraZeneca to Pfizer BioNTech.", + "reason": "The Number of Countries for increases from 41.0 at Oxford-AstraZeneca to 61.0 at Pfizer BioNTech, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Vaccine": "Bharat Biotech (Covaxin)", + "Number of Countries": 1.0 + } + ], + "score": 0.53, + "annotation": "The has a minimum value at Bharat Biotech (Covaxin)", + "reason": "The [{'Vaccine': 'Bharat Biotech (Covaxin)', 'Number of Countries': 1.0}] of has a mainimum value of 1.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Vaccine": "Pfizer BioNTech", + "Number of Countries": 61.0 + }, + { + "Vaccine": "Oxford-AstraZeneca", + "Number of Countries": 41.0 + }, + { + "Vaccine": "Moderna", + "Number of Countries": 27.0 + }, + { + "Vaccine": "Sinopharm Beijing", + "Number of Countries": 10.0 + }, + { + "Vaccine": "Gamaleya (Sputnik)", + "Number of Countries": 9.0 + }, + { + "Vaccine": "Sinovac", + "Number of Countries": 6.0 + }, + { + "Vaccine": "Sinopharm-Wuhan", + "Number of Countries": 2.0 + }, + { + "Vaccine": "Bharat Biotech (Covaxin)", + "Number of Countries": 1.0 + } + ], + "columns": [ + { + "name": "Vaccine", + "importance": "primary", + "description": "Name of the Covid-19 vaccine", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Countries", + "importance": "primary", + "description": "Number of countries using the specified vaccine", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "82": { + "metadata": { + "title": "S&P 500 Tech Stocks Surpass Dot-Com Peak", + "description": "S&P 500 Information Technology sector from July 19, 2007 to July 19, 2017", + "main_insight": "The S&P 500 Information Technology sector index surpassed its previous high from March 2000 (dot-com peak) in 2017." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2007, + "S&P 500 Information Technology Sector Index": 400.0 + }, + { + "Year": 2008, + "S&P 500 Information Technology Sector Index": 300.0 + }, + { + "Year": 2009, + "S&P 500 Information Technology Sector Index": 350.0 + }, + { + "Year": 2010, + "S&P 500 Information Technology Sector Index": 400.0 + }, + { + "Year": 2011, + "S&P 500 Information Technology Sector Index": 450.0 + }, + { + "Year": 2012, + "S&P 500 Information Technology Sector Index": 500.0 + }, + { + "Year": 2013, + "S&P 500 Information Technology Sector Index": 600.0 + }, + { + "Year": 2014, + "S&P 500 Information Technology Sector Index": 700.0 + }, + { + "Year": 2015, + "S&P 500 Information Technology Sector Index": 750.0 + }, + { + "Year": 2016, + "S&P 500 Information Technology Sector Index": 800.0 + }, + { + "Year": 2017, + "S&P 500 Information Technology Sector Index": 992.29 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 2007 to 2017", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "S&P 500 Information Technology Sector Index", + "importance": "primary", + "description": "Index value of the S&P 500 Information Technology sector", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "3": { + "metadata": { + "title": "The United States of Emergency", + "description": "Number of national emergencies declared in the United States since 1976, by current status", + "main_insight": "The number of national emergencies declared in the US has generally increased over time, with a significant number of those declared since 2000 still active." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time Period": "'90-'99", + "Binary": 14, + "Status": "Ended" + } + ], + "score": 0.85, + "annotation": "The Ended shows a sudden jump in Binary from '80-'89 to '90-'99.", + "reason": "The Binary for Ended increases from 7 at '80-'89 to 14 at '90-'99, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Time Period": "'90-'99", + "Binary": 14, + "Status": "Ended" + } + ], + "score": 0.83, + "annotation": "The Ended has a maximum value at '90-'99", + "reason": "The [{'Time Period': \"'90-'99\", 'Binary': 14, 'Status': 'Ended'}] of Ended has a maximum value of 14, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Status": "Still active", + "Time Period": "total", + "Binary": 41 + } + ], + "score": 0.78, + "annotation": "The total value of Still active accounts for the majority in all Status.", + "reason": "The total value of Binary of Still active accounts for 0.5774647887323944 (more than 50%) of the total of Binary of all Status." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Time Period": "'76-'79", + "Binary": 1, + "Status": "Still active" + } + ], + "score": 0.76, + "annotation": "The Still active has a minimum value at '76-'79", + "reason": "The [{'Time Period': \"'76-'79\", 'Binary': 1, 'Status': 'Still active'}] of Still active has a mainimum value of 1, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Time Period": "'00-'09", + "Binary": 11, + "Status": "Still active" + } + ], + "score": 0.74, + "annotation": "The Still active accounts for the majority in '00-'09.", + "reason": "The Binary of Still active in '00-'09 accounts for 0.6875 (more than 50%) of the total of Binary of all Status." + } + ], + "data": { + "data": [ + { + "Time Period": "'76-'79", + "Binary": 1, + "Status": "Still active" + }, + { + "Time Period": "'80-'89", + "Binary": 7, + "Status": "Ended" + }, + { + "Time Period": "'90-'99", + "Binary": 6, + "Status": "Still active" + }, + { + "Time Period": "'90-'99", + "Binary": 14, + "Status": "Ended" + }, + { + "Time Period": "'00-'09", + "Binary": 11, + "Status": "Still active" + }, + { + "Time Period": "'00-'09", + "Binary": 5, + "Status": "Ended" + }, + { + "Time Period": "'10-'19", + "Binary": 15, + "Status": "Still active" + }, + { + "Time Period": "'10-'19", + "Binary": 3, + "Status": "Ended" + }, + { + "Time Period": "'20-'22", + "Binary": 8, + "Status": "Still active" + }, + { + "Time Period": "'20-'22", + "Binary": 1, + "Status": "Ended" + } + ], + "columns": [ + { + "name": "Time Period", + "importance": "primary", + "description": "Time periods of national emergencies declared", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Binary", + "importance": "primary", + "description": "Binary for the corresponding status", + "role": "y" + }, + { + "name": "Status", + "importance": "primary", + "description": "The status of the data point", + "role": "group" + } + ] + } + }, + "1541": { + "metadata": { + "title": "One-third of Australian data breaches down to human error", + "description": "Instances of human error data breaches by category", + "main_insight": "The most common cause of human error data breaches in Australia is sending personal information to the wrong recipient via email, followed by unauthorized disclosure." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Personal Information sent to wrong recipient (email)", + "Instances": 22 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at Personal Information sent to wrong recipient (email)", + "reason": "The [{'Category': 'Personal Information sent to wrong recipient (email)', 'Instances': 22}] of has a maximum value of 22, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "Personal Information sent to wrong recipient (email)", + "Instances": 22 + } + ], + "score": 0.82, + "annotation": "The shows a sudden jump in Instances from Other to Personal Information sent to wrong recipient (email).", + "reason": "The Instances for increases from 12 at Other to 22 at Personal Information sent to wrong recipient (email), indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Category": "Unauthorised disclosure (verbal)", + "Instances": 1 + } + ], + "score": 0.73, + "annotation": "The has a minimum value at Unauthorised disclosure (verbal)", + "reason": "The [{'Category': 'Unauthorised disclosure (verbal)', 'Instances': 1}] of has a mainimum value of 1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Category": "Personal Information sent to wrong recipient (email)", + "Instances": 22 + }, + { + "Category": "Unauthorised disclosure (unintended release or publication)", + "Instances": 12 + }, + { + "Category": "Personal Information sent to wrong recipient (mail)", + "Instances": 10 + }, + { + "Category": "Loss of paperwork/data storage device", + "Instances": 9 + }, + { + "Category": "Personal Information sent to wrong recipient (other)", + "Instances": 8 + }, + { + "Category": "Failure to use BCC when sending email", + "Instances": 7 + }, + { + "Category": "Insecure disposal", + "Instances": 5 + }, + { + "Category": "Unauthorised disclosure (failure to redact)", + "Instances": 2 + }, + { + "Category": "Unauthorised disclosure (verbal)", + "Instances": 1 + }, + { + "Category": "Other", + "Instances": 12 + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Type of human error", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Instances", + "importance": "primary", + "description": "Number of data breaches", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "44": { + "metadata": { + "title": "Apple rules U.S. Smartphone Market", + "description": "Q1 2012 U.S. smartphone sales by carrier (in million units)", + "main_insight": "In Q1 2012, Apple's iPhone had significant sales across major US carriers, often outselling other smartphones, especially on Verizon Wireless and AT&T." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Carrier": "Verizon Wireless", + "Value": 3.1, + "Phone Type": "Other Smartphones" + } + ], + "score": 0.81, + "annotation": "The Other Smartphones has a maximum value at Verizon Wireless", + "reason": "The [{'Carrier': 'Verizon Wireless', 'Value': 3.1, 'Phone Type': 'Other Smartphones'}] of Other Smartphones has a maximum value of 3.1, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Carrier": "Verizon Wireless", + "Value": 3.2, + "Phone Type": "iPhone" + } + ], + "score": 0.8, + "annotation": "The iPhone accounts for the majority in Verizon Wireless.", + "reason": "The Value of iPhone in Verizon Wireless accounts for 0.5079365079365079 (more than 50%) of the total of Value of all Phone Type." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Carrier": "Verizon Wireless", + "Value": 3.2, + "Phone Type": "iPhone" + } + ], + "score": 0.79, + "annotation": "The iPhone shows a sudden jump in Value from Sprint* to Verizon Wireless.", + "reason": "The Value for iPhone increases from 1.5 at Sprint* to 3.2 at Verizon Wireless, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Carrier": "Verizon Wireless", + "Value": 3.1, + "Phone Type": "Other Smartphones" + } + ], + "score": 0.78, + "annotation": "The Other Smartphones shows a sudden jump in Value from T-Mobile to Verizon Wireless.", + "reason": "The Value for Other Smartphones increases from 2.0 at T-Mobile to 3.1 at Verizon Wireless, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Phone Type": "iPhone", + "Carrier": "total", + "Value": 9.9 + } + ], + "score": 0.78, + "annotation": "The total value of iPhone accounts for the majority in all Phone Type.", + "reason": "The total value of Value of iPhone accounts for 0.553072625698324 (more than 50%) of the total of Value of all Phone Type." + } + ], + "data": { + "data": [ + { + "Carrier": "Verizon Wireless", + "Value": 3.2, + "Phone Type": "iPhone" + }, + { + "Carrier": "Verizon Wireless", + "Value": 3.1, + "Phone Type": "Other Smartphones" + }, + { + "Carrier": "AT&T", + "Value": 4.3, + "Phone Type": "iPhone" + }, + { + "Carrier": "AT&T", + "Value": 1.2, + "Phone Type": "Other Smartphones" + }, + { + "Carrier": "Sprint*", + "Value": 1.5, + "Phone Type": "iPhone" + }, + { + "Carrier": "Sprint*", + "Value": 1.2, + "Phone Type": "Other Smartphones" + }, + { + "Carrier": "T-Mobile", + "Value": 0.5, + "Phone Type": "iPhone" + }, + { + "Carrier": "T-Mobile", + "Value": 2.0, + "Phone Type": "Other Smartphones" + }, + { + "Carrier": "Other operators*", + "Value": 0.4, + "Phone Type": "iPhone" + }, + { + "Carrier": "Other operators*", + "Value": 0.5, + "Phone Type": "Other Smartphones" + } + ], + "columns": [ + { + "name": "Carrier", + "importance": "primary", + "description": "US Wireless Carrier", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding phone type", + "role": "y" + }, + { + "name": "Phone Type", + "importance": "primary", + "description": "The phone type of the data point", + "role": "group" + } + ] + } + }, + "2505": { + "metadata": { + "title": "Online Travel Platforms Generate Additional Bookings for Australian Accommodations", + "description": "Hotel room night increases attributable to online travel platforms in Australia", + "main_insight": "Online travel platforms have significantly contributed to additional hotel bookings in Australia, with the largest increase in room nights seen in 2019." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2019, + "Room nights": 4.1 + }, + { + "Year": 2020, + "Room nights": 1.7 + }, + { + "Year": 2021, + "Room nights": 0.8 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "The year of the recorded data.", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Room nights", + "importance": "primary", + "description": "Room nights in millions", + "unit": "Millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "534": { + "metadata": { + "title": "What Are the Most Popular American Deli Meats?", + "description": "Category share of bulk meat sales at delis in the United States in 2017", + "main_insight": "Turkey and Ham are the most popular deli meats in the US, making up over 65% of bulk meat sales at delis in 2017." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Deli Meat": "Ham", + "Sales Share": 30.9 + } + ], + "score": 0.89, + "annotation": "The shows a sudden jump in Sales Share from Beef to Ham.", + "reason": "The Sales Share for increases from 11.7 at Beef to 30.9 at Ham, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Deli Meat": "Turkey", + "Sales Share": 34.6 + } + ], + "score": 0.82, + "annotation": "The has a maximum value at Turkey", + "reason": "The [{'Deli Meat': 'Turkey', 'Sales Share': 34.6}] of has a maximum value of 34.6, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Deli Meat": "Bologna", + "Sales Share": 3.6 + } + ], + "score": 0.51, + "annotation": "The has a minimum value at Bologna", + "reason": "The [{'Deli Meat': 'Bologna', 'Sales Share': 3.6}] of has a mainimum value of 3.6, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Deli Meat": "Turkey", + "Sales Share": 34.6 + }, + { + "Deli Meat": "Ham", + "Sales Share": 30.9 + }, + { + "Deli Meat": "Beef", + "Sales Share": 11.7 + }, + { + "Deli Meat": "Chicken", + "Sales Share": 7.8 + }, + { + "Deli Meat": "Salami", + "Sales Share": 5.7 + }, + { + "Deli Meat": "Other", + "Sales Share": 5.7 + }, + { + "Deli Meat": "Bologna", + "Sales Share": 3.6 + } + ], + "columns": [ + { + "name": "Deli Meat", + "importance": "primary", + "description": "Type of deli meat", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Sales Share", + "importance": "primary", + "description": "Percentage of total bulk meat sales", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "794": { + "metadata": { + "title": "The Tech Giants Led by Indian CEOs", + "description": "Indian-born CEOs of major American tech companies and their market caps as of Nov 30, 2021, 8 a.m. EST", + "main_insight": "Major American tech companies led by Indian-born CEOs have significantly different market capitalizations, with Microsoft and Alphabet having the largest." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Company": "Microsoft", + "CEO": "Satya Nadella" + }, + { + "Company": "Alphabet", + "CEO": "Sundar Pichai" + }, + { + "Company": "Adobe", + "CEO": "Shantanu Narayen" + }, + { + "Company": "IBM", + "CEO": "Arvind Krishna" + }, + { + "Company": "Twitter", + "CEO": "Parag Agrawal" + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Name of the tech company", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "CEO", + "importance": "primary", + "description": "Name of the CEO", + "unit": "none", + "data_type": "string", + "role": "y" + } + ] + } + }, + "673": { + "metadata": { + "title": "Rising reports of sexual offences on British railways", + "description": "Number of sexual offences reported on British railways and rail stations*", + "main_insight": "The number of sexual offenses reported on British railways and rail stations has been steadily increasing from 2013 to 2017." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2013, + "Sexual Offences Reported": 1049 + }, + { + "Year": 2014, + "Sexual Offences Reported": 1307 + }, + { + "Year": 2015, + "Sexual Offences Reported": 1795 + }, + { + "Year": 2016, + "Sexual Offences Reported": 2070 + }, + { + "Year": 2017, + "Sexual Offences Reported": 2382 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of reported offences", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Sexual Offences Reported", + "importance": "primary", + "description": "Number of sexual offences reported", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "481": { + "metadata": { + "title": "A Bad Year For Twitter Investors", + "description": "Stock performance of Twitter and Facebook in 2014 (as of December 17)", + "main_insight": "Twitter's stock performance significantly declined in 2014, while Facebook's stock performance increased." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Feb", + "Score": 65.0, + "Platform": "Twitter" + } + ], + "score": 0.91, + "annotation": "The Twitter has a maximum value at Feb", + "reason": "The [{'Month': 'Feb', 'Score': 65.0, 'Platform': 'Twitter'}] of Twitter has a maximum value of 65.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Jan", + "Score": 55.0, + "Platform": "Facebook" + } + ], + "score": 0.87, + "annotation": "The Facebook has a minimum value at Jan", + "reason": "The [{'Month': 'Jan', 'Score': 55.0, 'Platform': 'Facebook'}] of Facebook has a mainimum value of 55.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Feb", + "Score": 65.0, + "Platform": "Twitter" + }, + { + "Month": "Feb", + "Score": 65.0, + "Platform": "Facebook" + } + ], + "score": 0.8, + "annotation": "The Twitter, Facebook all have the largest proportion in Feb.", + "reason": "The Score of Twitter, Facebook in Feb all account for 0.5, larger than all other Platform, of the total of Score of all Platform." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Jan", + "Score": 60.0, + "Platform": "Twitter" + } + ], + "score": 0.79, + "annotation": "The Twitter accounts for the majority in Jan.", + "reason": "The Score of Twitter in Jan accounts for 0.5217391304347826 (more than 50%) of the total of Score of all Platform." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Apr", + "Score": 60.0, + "Platform": "Facebook" + } + ], + "score": 0.78, + "annotation": "The Facebook accounts for the majority in Apr.", + "reason": "The Score of Facebook in Apr accounts for 0.5714285714285714 (more than 50%) of the total of Score of all Platform." + } + ], + "data": { + "data": [ + { + "Month": "Jan", + "Score": 60.0, + "Platform": "Twitter" + }, + { + "Month": "Jan", + "Score": 55.0, + "Platform": "Facebook" + }, + { + "Month": "Feb", + "Score": 65.0, + "Platform": "Twitter" + }, + { + "Month": "Feb", + "Score": 65.0, + "Platform": "Facebook" + }, + { + "Month": "Mar", + "Score": 55.0, + "Platform": "Twitter" + }, + { + "Month": "Mar", + "Score": 75.0, + "Platform": "Facebook" + }, + { + "Month": "Apr", + "Score": 45.0, + "Platform": "Twitter" + }, + { + "Month": "Apr", + "Score": 60.0, + "Platform": "Facebook" + }, + { + "Month": "May", + "Score": 40.0, + "Platform": "Twitter" + }, + { + "Month": "May", + "Score": 60.0, + "Platform": "Facebook" + }, + { + "Month": "Jun", + "Score": 45.0, + "Platform": "Twitter" + }, + { + "Month": "Jun", + "Score": 65.0, + "Platform": "Facebook" + }, + { + "Month": "Jul", + "Score": 48.0, + "Platform": "Twitter" + }, + { + "Month": "Jul", + "Score": 70.0, + "Platform": "Facebook" + }, + { + "Month": "Aug", + "Score": 52.0, + "Platform": "Twitter" + }, + { + "Month": "Aug", + "Score": 75.0, + "Platform": "Facebook" + }, + { + "Month": "Sep", + "Score": 50.0, + "Platform": "Twitter" + }, + { + "Month": "Sep", + "Score": 78.0, + "Platform": "Facebook" + }, + { + "Month": "Oct", + "Score": 45.0, + "Platform": "Twitter" + }, + { + "Month": "Oct", + "Score": 75.0, + "Platform": "Facebook" + }, + { + "Month": "Nov", + "Score": 38.0, + "Platform": "Twitter" + }, + { + "Month": "Nov", + "Score": 75.0, + "Platform": "Facebook" + }, + { + "Month": "Dec", + "Score": 38.0, + "Platform": "Twitter" + }, + { + "Month": "Dec", + "Score": 80.0, + "Platform": "Facebook" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year 2014", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding platform", + "role": "y" + }, + { + "name": "Platform", + "importance": "primary", + "description": "The platform of the data point", + "role": "group" + } + ] + } + }, + "136": { + "metadata": { + "title": "42 U.S. Children Died In Hot Cars Last Year", + "description": "Number of child heatstroke deaths in vehicles from 2008 to 2018", + "main_insight": "The number of child heatstroke deaths in vehicles fluctuates yearly, with 2018 having a significantly lower number compared to prior years and 2010 exhibiting the highest." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2008, + "Child Heatstroke Deaths": 43 + }, + { + "Year": 2009, + "Child Heatstroke Deaths": 33 + }, + { + "Year": 2010, + "Child Heatstroke Deaths": 49 + }, + { + "Year": 2011, + "Child Heatstroke Deaths": 33 + }, + { + "Year": 2012, + "Child Heatstroke Deaths": 34 + }, + { + "Year": 2013, + "Child Heatstroke Deaths": 44 + }, + { + "Year": 2014, + "Child Heatstroke Deaths": 31 + }, + { + "Year": 2015, + "Child Heatstroke Deaths": 24 + }, + { + "Year": 2016, + "Child Heatstroke Deaths": 39 + }, + { + "Year": 2017, + "Child Heatstroke Deaths": 42 + }, + { + "Year": 2018, + "Child Heatstroke Deaths": 9 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 2008 to 2018", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Child Heatstroke Deaths", + "importance": "primary", + "description": "Number of child heatstroke deaths in vehicles", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1410": { + "metadata": { + "title": "Economic Slowdown Felt Around the World", + "description": "Global GDP growth 2010-2023 by region (in percent)", + "main_insight": "Global GDP growth has generally slowed down since 2010 across all regions, with Asia and Pacific consistently showing the highest growth, while Europe and Americas showing significantly lower and relatively flat/declining growth." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Units": 5.2, + "Region": "Asia and Pacific" + }, + { + "Year": 2023, + "Units": 1.7, + "Region": "Americas" + } + ], + "score": 0.79, + "annotation": "The Units of Asia and Pacific and Americas show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Asia and Pacific and Americas is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2023, + "Units": 2.1, + "Region": "Africa" + }, + { + "Year": 2023, + "Units": 4.1, + "Region": "Europe" + } + ], + "score": 0.65, + "annotation": "The Units of Africa and Europe show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Africa and Europe is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Units": 2.1, + "Region": "Africa" + }, + { + "Year": 2023, + "Units": 1.7, + "Region": "Americas" + } + ], + "score": 0.55, + "annotation": "The Units of Africa and Americas show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Africa and Americas is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Units": 5.2, + "Region": "Asia and Pacific" + }, + { + "Year": 2023, + "Units": 2.1, + "Region": "Africa" + } + ], + "score": 0.4, + "annotation": "The Units of Asia and Pacific and Africa show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Asia and Pacific and Africa is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2023, + "Units": 1.7, + "Region": "Americas" + }, + { + "Year": 2023, + "Units": 4.1, + "Region": "Europe" + } + ], + "score": 0.28, + "annotation": "The Units of Americas and Europe show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Americas and Europe is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2010, + "Units": 8.4, + "Region": "Asia and Pacific" + }, + { + "Year": 2010, + "Units": 5.9, + "Region": "Africa" + }, + { + "Year": 2010, + "Units": 3.7, + "Region": "Americas" + }, + { + "Year": 2010, + "Units": 2.4, + "Region": "Europe" + }, + { + "Year": 2011, + "Units": 7.0, + "Region": "Asia and Pacific" + }, + { + "Year": 2011, + "Units": 3.5, + "Region": "Africa" + }, + { + "Year": 2011, + "Units": 3.9, + "Region": "Americas" + }, + { + "Year": 2011, + "Units": 2.2, + "Region": "Europe" + }, + { + "Year": 2012, + "Units": 5.6, + "Region": "Asia and Pacific" + }, + { + "Year": 2012, + "Units": 6.8, + "Region": "Africa" + }, + { + "Year": 2012, + "Units": 2.7, + "Region": "Americas" + }, + { + "Year": 2012, + "Units": 0.8, + "Region": "Europe" + }, + { + "Year": 2013, + "Units": 5.7, + "Region": "Asia and Pacific" + }, + { + "Year": 2013, + "Units": 5.5, + "Region": "Africa" + }, + { + "Year": 2013, + "Units": 2.8, + "Region": "Americas" + }, + { + "Year": 2013, + "Units": 0.4, + "Region": "Europe" + }, + { + "Year": 2014, + "Units": 5.7, + "Region": "Asia and Pacific" + }, + { + "Year": 2014, + "Units": 5.7, + "Region": "Africa" + }, + { + "Year": 2014, + "Units": 1.9, + "Region": "Americas" + }, + { + "Year": 2014, + "Units": 1.7, + "Region": "Europe" + }, + { + "Year": 2015, + "Units": 5.7, + "Region": "Asia and Pacific" + }, + { + "Year": 2015, + "Units": 5.8, + "Region": "Africa" + }, + { + "Year": 2015, + "Units": 2.1, + "Region": "Americas" + }, + { + "Year": 2015, + "Units": 2.5, + "Region": "Europe" + }, + { + "Year": 2016, + "Units": 5.7, + "Region": "Asia and Pacific" + }, + { + "Year": 2016, + "Units": 2.2, + "Region": "Africa" + }, + { + "Year": 2016, + "Units": 0.9, + "Region": "Americas" + }, + { + "Year": 2016, + "Units": 2.1, + "Region": "Europe" + }, + { + "Year": 2017, + "Units": 5.8, + "Region": "Asia and Pacific" + }, + { + "Year": 2017, + "Units": 3.7, + "Region": "Africa" + }, + { + "Year": 2017, + "Units": 2.0, + "Region": "Americas" + }, + { + "Year": 2017, + "Units": 3.0, + "Region": "Europe" + }, + { + "Year": 2018, + "Units": 5.7, + "Region": "Asia and Pacific" + }, + { + "Year": 2018, + "Units": 3.8, + "Region": "Africa" + }, + { + "Year": 2018, + "Units": 1.9, + "Region": "Americas" + }, + { + "Year": 2018, + "Units": 2.1, + "Region": "Europe" + }, + { + "Year": 2019, + "Units": 5.3, + "Region": "Asia and Pacific" + }, + { + "Year": 2019, + "Units": 3.4, + "Region": "Africa" + }, + { + "Year": 2019, + "Units": 1.4, + "Region": "Americas" + }, + { + "Year": 2019, + "Units": 1.8, + "Region": "Europe" + }, + { + "Year": 2020, + "Units": 5.3, + "Region": "Asia and Pacific" + }, + { + "Year": 2020, + "Units": 2.9, + "Region": "Africa" + }, + { + "Year": 2020, + "Units": 1.6, + "Region": "Americas" + }, + { + "Year": 2020, + "Units": 2.4, + "Region": "Europe" + }, + { + "Year": 2021, + "Units": 5.2, + "Region": "Asia and Pacific" + }, + { + "Year": 2021, + "Units": 2.7, + "Region": "Africa" + }, + { + "Year": 2021, + "Units": 1.7, + "Region": "Americas" + }, + { + "Year": 2021, + "Units": 2.8, + "Region": "Europe" + }, + { + "Year": 2022, + "Units": 5.2, + "Region": "Asia and Pacific" + }, + { + "Year": 2022, + "Units": 2.5, + "Region": "Africa" + }, + { + "Year": 2022, + "Units": 1.7, + "Region": "Americas" + }, + { + "Year": 2022, + "Units": 3.2, + "Region": "Europe" + }, + { + "Year": 2023, + "Units": 5.2, + "Region": "Asia and Pacific" + }, + { + "Year": 2023, + "Units": 2.1, + "Region": "Africa" + }, + { + "Year": 2023, + "Units": 1.7, + "Region": "Americas" + }, + { + "Year": 2023, + "Units": 4.1, + "Region": "Europe" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 2010 to 2023", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding region", + "role": "y" + }, + { + "name": "Region", + "importance": "primary", + "description": "The region of the data point", + "role": "group" + } + ] + } + }, + "1270": { + "metadata": { + "title": "Always On: Media Usage Amounts to 10+ Hours a Day", + "description": "Average time U.S. adults (18+) spent with electronic media per day in Q2 2018 (hours:minutes)", + "main_insight": "U.S. adults spent over 10 hours per day consuming media in Q2 2018, with Live TV being the largest single component, followed by App/Web usage on smartphones." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Media Type": "Total", + "Time Spent (hh:mm)": 10.4 + } + ], + "score": 0.98, + "annotation": "The has a maximum value at Total", + "reason": "The [{'Media Type': 'Total', 'Time Spent (hh:mm)': 10.4}] of has a maximum value of 10.4, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Media Type": "Total", + "Time Spent (hh:mm)": 10.4 + } + ], + "score": 0.9, + "annotation": "The shows a sudden jump in Time Spent (hh:mm) from Live TV to Total.", + "reason": "The Time Spent (hh:mm) for increases from 3.82 at Live TV to 10.4 at Total, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Media Type": "DVD/Blu-ray", + "Time Spent (hh:mm)": 0.08 + } + ], + "score": 0.42, + "annotation": "The has a minimum value at DVD/Blu-ray", + "reason": "The [{'Media Type': 'DVD/Blu-ray', 'Time Spent (hh:mm)': 0.08}] of has a mainimum value of 0.08, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Media Type": "Live TV", + "Time Spent (hh:mm)": 3.82 + }, + { + "Media Type": "App/Web on Smartphone", + "Time Spent (hh:mm)": 2.32 + }, + { + "Media Type": "Radio", + "Time Spent (hh:mm)": 1.75 + }, + { + "Media Type": "App/Web on Tablet", + "Time Spent (hh:mm)": 0.72 + }, + { + "Media Type": "Timeshifted TV", + "Time Spent (hh:mm)": 0.53 + }, + { + "Media Type": "Internet on a PC", + "Time Spent (hh:mm)": 0.53 + }, + { + "Media Type": "Internet Connected Device", + "Time Spent (hh:mm)": 0.43 + }, + { + "Media Type": "Game Console", + "Time Spent (hh:mm)": 0.22 + }, + { + "Media Type": "DVD/Blu-ray", + "Time Spent (hh:mm)": 0.08 + }, + { + "Media Type": "Total", + "Time Spent (hh:mm)": 10.4 + } + ], + "columns": [ + { + "name": "Media Type", + "importance": "primary", + "description": "Different types of media platforms", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Time Spent 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ZTE was the only company of the top 5 whose shipments reduced." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Vendor": "ZTE", + "Units": 10.1, + "Quarter": "Q2 2013" + }, + { + "Vendor": "ZTE", + "Units": 6.4, + "Quarter": "Q2 2012" + } + ], + "score": 0.99, + "annotation": "The Units of Q2 2013 and Q2 2012 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Q2 2013 and Q2 2012 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Vendor": "Samsung", + "Units": 72.4, + "Quarter": "Q2 2013" + } + ], + "score": 0.9, + "annotation": "The Q2 2013 shows a sudden jump in Units from Apple to Samsung.", + "reason": "The Units for Q2 2013 increases from 31.2 at Apple to 72.4 at Samsung, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Vendor": "Samsung", + "Units": 72.4, + "Quarter": "Q2 2013" + } + ], + "score": 0.86, + "annotation": "The Q2 2013 has a maximum value at Samsung", + "reason": "The [{'Vendor': 'Samsung', 'Units': 72.4, 'Quarter': 'Q2 2013'}] of Q2 2013 has a maximum value of 72.4, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Vendor": "Samsung", + "Units": 50.3, + "Quarter": "Q2 2012" + } + ], + "score": 0.85, + "annotation": "The Q2 2012 shows a sudden jump in Units from Apple to Samsung.", + "reason": "The Units for Q2 2012 increases from 26.0 at Apple to 50.3 at Samsung, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Vendor": "Samsung", + "Units": 50.3, + "Quarter": "Q2 2012" + } + ], + "score": 0.83, + "annotation": "The Q2 2012 has a maximum value at Samsung", + "reason": "The [{'Vendor': 'Samsung', 'Units': 50.3, 'Quarter': 'Q2 2012'}] of Q2 2012 has a maximum value of 50.3, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Vendor": "Samsung", + "Units": 72.4, + "Quarter": "Q2 2013" + }, + { + "Vendor": "Samsung", + "Units": 50.3, + "Quarter": "Q2 2012" + }, + { + "Vendor": "Apple", + "Units": 31.2, + "Quarter": "Q2 2013" + }, + { + "Vendor": "Apple", + "Units": 26.0, + "Quarter": "Q2 2012" + }, + { + "Vendor": "LG", + "Units": 12.1, + "Quarter": "Q2 2013" + }, + { + "Vendor": "LG", + "Units": 5.8, + "Quarter": "Q2 2012" + }, + { + "Vendor": "Lenovo", + "Units": 11.3, + "Quarter": "Q2 2013" + }, + { + "Vendor": "Lenovo", + "Units": 4.9, + "Quarter": "Q2 2012" + }, + { + "Vendor": "ZTE", + "Units": 10.1, + "Quarter": "Q2 2013" + }, + { + "Vendor": "ZTE", + "Units": 6.4, + "Quarter": "Q2 2012" + } + ], + "columns": [ + { + "name": "Vendor", + "importance": "primary", + "description": "Smartphone vendor", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + 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coefficient of the Ratio between Revenue and Net income is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 1997, + "Ratio": 0.1, + "Financial Metric": "Revenue" + }, + { + "Year": 1997, + "Ratio": -0.03, + "Financial Metric": "Net income" + }, + { + "Year": 1998, + "Ratio": 0.6, + "Financial Metric": "Revenue" + }, + { + "Year": 1998, + "Ratio": -0.1, + "Financial Metric": "Net income" + }, + { + "Year": 1999, + "Ratio": 1.6, + "Financial Metric": "Revenue" + }, + { + "Year": 1999, + "Ratio": -0.7, + "Financial Metric": "Net income" + }, + { + "Year": 2000, + "Ratio": 2.8, + "Financial Metric": "Revenue" + }, + { + "Year": 2000, + "Ratio": -1.4, + "Financial Metric": "Net income" + }, + { + "Year": 2001, + "Ratio": 3.1, + "Financial Metric": "Revenue" + }, + { + "Year": 2001, + "Ratio": -0.6, + "Financial Metric": "Net income" + }, + { + "Year": 2002, + "Ratio": 3.9, + "Financial Metric": "Revenue" + }, + { + "Year": 2002, + "Ratio": -0.1, + "Financial Metric": "Net income" + }, + { + "Year": 2003, + "Ratio": 5.3, + "Financial Metric": "Revenue" + }, + { + "Year": 2003, + "Ratio": 0.04, + "Financial Metric": "Net income" + }, + { + "Year": 2004, + "Ratio": 6.9, + "Financial Metric": "Revenue" + }, + { + "Year": 2004, + "Ratio": 0.6, + "Financial Metric": "Net income" + }, + { + "Year": 2005, + "Ratio": 8.5, + "Financial Metric": "Revenue" + }, + { + "Year": 2005, + "Ratio": 0.4, + "Financial Metric": "Net income" + }, + { + "Year": 2006, + "Ratio": 10.7, + "Financial Metric": "Revenue" + }, + { + "Year": 2006, + "Ratio": 0.2, + "Financial Metric": "Net income" + }, + { + "Year": 2007, + "Ratio": 14.8, + "Financial Metric": "Revenue" + }, + { + "Year": 2007, + "Ratio": 0.5, + "Financial Metric": "Net income" + }, + { + "Year": 2008, + "Ratio": 19.2, + "Financial Metric": "Revenue" + }, + { + "Year": 2008, + "Ratio": 0.7, + "Financial Metric": "Net income" + }, + { + "Year": 2009, + "Ratio": 24.5, + "Financial Metric": "Revenue" + }, + { + "Year": 2009, + "Ratio": 0.9, + "Financial Metric": "Net income" + }, + { + "Year": 2010, + "Ratio": 34.2, + "Financial Metric": "Revenue" + }, + { + "Year": 2010, + "Ratio": 1.2, + "Financial Metric": "Net income" + }, + { + "Year": 2011, + "Ratio": 48.1, + "Financial Metric": "Revenue" + }, + { + "Year": 2011, + "Ratio": 0.6, + "Financial Metric": "Net income" + }, + { + "Year": 2012, + "Ratio": 61.1, + "Financial Metric": "Revenue" + }, + { + "Year": 2012, + "Ratio": -0.04, + "Financial Metric": "Net income" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 1997 to 2012", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding financial metric", + "role": "y" + }, + { + "name": "Financial Metric", + "importance": "primary", + "description": "The financial metric of the data point", + "role": "group" + } + ] + } + }, + "1417": { + "metadata": { + "title": "Beyond the iPhone", + "description": "Apple's non-iPhone revenue by product group (fiscal years ending in September)", + "main_insight": "Apple's Services revenue has significantly grown from 2015 to 2018, surpassing Mac, iPad and Other Products to become the largest non-iPhone revenue stream." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Product Group": "Mac", + "Units": 25.5, + "Revenue": "2018 Revenue" + } + ], + "score": 0.94, + "annotation": "The 2018 Revenue has the largest proportion in Mac.", + "reason": "The Units of 2018 Revenue in Mac accounts for 0.2604698672114402, larger than all other Revenue, of the total of Units of all Revenue." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Product Group": "iPad", + "Units": 23.2, + "Revenue": "2015 Revenue" + } + ], + "score": 0.93, + "annotation": "The 2015 Revenue has the largest proportion in iPad.", + "reason": "The Units of 2015 Revenue in iPad accounts for 0.28361858190709044, larger than all other Revenue, of the total of Units of all Revenue." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Revenue": "2018 Revenue", + "Product Group": "total", + "Units": 98.9 + } + ], + "score": 0.93, + "annotation": "The total value of 2018 Revenue has the largest proportion.", + "reason": "The total value of Units of 2018 Revenue accounts for 0.2885906040268456, larger than all other Revenue, of the total of Units of all Revenue." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Product Group": "Other Products", + "Units": 11.1, + "Revenue": "2016 Revenue" + }, + { + "Product Group": "Other Products", + "Units": 12.9, + "Revenue": "2017 Revenue" + } + ], + "score": 0.93, + "annotation": "The Units of 2016 Revenue and 2017 Revenue show a positive 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"Product Group": "Services", + "Units": 37.2, + "Revenue": "2018 Revenue" + }, + { + "Product Group": "Mac", + "Units": 25.3, + "Revenue": "2015 Revenue" + }, + { + "Product Group": "Mac", + "Units": 21.9, + "Revenue": "2016 Revenue" + }, + { + "Product Group": "Mac", + "Units": 25.2, + "Revenue": "2017 Revenue" + }, + { + "Product Group": "Mac", + "Units": 25.5, + "Revenue": "2018 Revenue" + }, + { + "Product Group": "iPad", + "Units": 23.2, + "Revenue": "2015 Revenue" + }, + { + "Product Group": "iPad", + "Units": 20.6, + "Revenue": "2016 Revenue" + }, + { + "Product Group": "iPad", + "Units": 19.2, + "Revenue": "2017 Revenue" + }, + { + "Product Group": "iPad", + "Units": 18.8, + "Revenue": "2018 Revenue" + }, + { + "Product Group": "Other Products", + "Units": 10.1, + "Revenue": "2015 Revenue" + }, + { + "Product Group": "Other Products", + "Units": 11.1, + "Revenue": "2016 Revenue" + }, + { + "Product Group": "Other Products", + "Units": 12.9, + "Revenue": "2017 Revenue" + }, + { + "Product Group": "Other Products", + "Units": 17.4, + "Revenue": "2018 Revenue" + } + ], + "columns": [ + { + "name": "Product Group", + "importance": "primary", + "description": "Category of Apple product", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding revenue", + "role": "y" + }, + { + "name": "Revenue", + "importance": "primary", + "description": "The revenue of the data point", + "role": "group" + } + ] + } + }, + "746": { + "metadata": { + "title": "2024 Election: Will the Loser Accept Defeat?", + "description": "Share of U.S. registered voters who think Trump/Harris will concede if they lose the election", + "main_insight": "A significant majority of U.S. registered voters believe that neither Donald Trump nor Kamala Harris will accept the results and concede if they lose the 2024 election." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Candidate": "Kamala Harris", + "Units": 71.0, + "Response Category": "Yes" + }, + { + "Candidate": "Kamala Harris", + "Units": 28.0, + "Response Category": "No" + } + ], + "score": 1.0, + "annotation": "The Units of Yes and No show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Yes and No is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Candidate": "Kamala Harris", + "Units": 71.0, + "Response Category": "Yes" + }, + { + "Candidate": "Kamala Harris", + "Units": 61.0, + "Response Category": "Supporters_Importance" + } + ], + "score": 1.0, + "annotation": "The Units of Yes and Supporters_Importance show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Yes and Supporters_Importance is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Candidate": "Kamala Harris", + "Units": 28.0, + "Response Category": "No" + }, + { + "Candidate": "Kamala Harris", + "Units": 61.0, + "Response Category": "Supporters_Importance" + } + ], + "score": 1.0, + "annotation": "The Units of No and Supporters_Importance show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between No and Supporters_Importance is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Candidate": "Kamala Harris", + "Units": 71.0, + "Response Category": "Yes" + } + ], + "score": 0.97, + "annotation": "The Yes shows a sudden jump in Units from Donald Trump to Kamala Harris.", + "reason": "The Units for Yes increases from 24.0 at Donald Trump to 71.0 at Kamala Harris, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Candidate": "Donald Trump", + "Units": 74.0, + "Response Category": "No" + } + ], + "score": 0.97, + "annotation": "The No shows a sudden jump in Units from Kamala Harris to Donald Trump.", + "reason": "The Units for No increases from 28.0 at Kamala Harris to 74.0 at Donald Trump, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Candidate": "Donald Trump", + "Units": 24.0, + "Response Category": "Yes" + }, + { + "Candidate": "Donald Trump", + "Units": 74.0, + "Response Category": "No" + }, + { + "Candidate": "Donald Trump", + "Units": 32.0, + "Response Category": "Supporters_Importance" + }, + { + "Candidate": "Kamala Harris", + "Units": 71.0, + "Response Category": "Yes" + }, + { + "Candidate": "Kamala Harris", + "Units": 28.0, + "Response Category": "No" + }, + { + "Candidate": "Kamala Harris", + "Units": 61.0, + "Response Category": "Supporters_Importance" + } + ], + "columns": [ + { + "name": "Candidate", + "importance": "primary", + "description": "Which candidate's potential reaction is being assessed", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding response category", + "role": "y" + }, + { + "name": "Response Category", + "importance": "primary", + "description": "The response category of the data point", + "role": "group" + } + ] + } + }, + "1506": { + "metadata": { + "title": "European Transshipments Head to Kyrgyzstan", + "description": "Monthly exports of selected European countries to Kyrgyzstan (in million U.S. dollars)", + "main_insight": "Exports from Germany, Italy, Lithuania, and Poland to Kyrgyzstan have increased dramatically since 2022. Germany has the highest export value among the four countries." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Score": 65.0, + "Country": "Germany" + }, + { + "Year": 2024, + "Score": 22.0, + "Country": "Poland" + } + ], + "score": 0.89, + "annotation": "The Score of Germany and Poland show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Germany and Poland is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Score": 65.0, + "Country": "Germany" + }, + { + "Year": 2024, + "Score": 40.0, + "Country": "Italy" + } + ], + "score": 0.84, + "annotation": "The Score of Germany and Italy show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Germany and Italy is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Score": 18.0, + "Country": "Lithuania" + }, + { + "Year": 2024, + "Score": 22.0, + "Country": "Poland" + } + ], + "score": 0.8, + "annotation": "The Score of Lithuania and Poland show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Lithuania and Poland is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Score": 65.0, + "Country": "Germany" + }, + { + "Year": 2024, + "Score": 18.0, + "Country": "Lithuania" + } + ], + "score": 0.78, + "annotation": "The Score of Germany and Lithuania show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Germany and Lithuania is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Score": 22.0, + "Country": "Poland" + }, + { + "Year": 2024, + "Score": 40.0, + "Country": "Italy" + } + ], + "score": 0.63, + "annotation": "The Score of Poland and Italy show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Poland and Italy is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2020, + "Score": 2.0, + "Country": "Germany" + }, + { + "Year": 2020, + "Score": 1.0, + "Country": "Lithuania" + }, + { + "Year": 2020, + "Score": 2.0, + "Country": "Poland" + }, + { + "Year": 2020, + "Score": 3.0, + "Country": "Italy" + }, + { + "Year": 2021, + "Score": 5.0, + "Country": "Germany" + }, + { + "Year": 2021, + "Score": 3.0, + "Country": "Lithuania" + }, + { + "Year": 2021, + "Score": 4.0, + "Country": "Poland" + }, + { + "Year": 2021, + "Score": 4.0, + "Country": "Italy" + }, + { + "Year": 2022, + "Score": 45.0, + "Country": "Germany" + }, + { + "Year": 2022, + "Score": 50.0, + "Country": "Lithuania" + }, + { + "Year": 2022, + "Score": 15.0, + "Country": "Poland" + }, + { + "Year": 2022, + "Score": 10.0, + "Country": "Italy" + }, + { + "Year": 2023, + "Score": 75.0, + "Country": "Germany" + }, + { + "Year": 2023, + "Score": 58.0, + "Country": "Lithuania" + }, + { + "Year": 2023, + "Score": 50.0, + "Country": "Poland" + }, + { + "Year": 2023, + "Score": 25.0, + "Country": "Italy" + }, + { + "Year": 2024, + "Score": 65.0, + "Country": "Germany" + }, + { + "Year": 2024, + "Score": 18.0, + "Country": "Lithuania" + }, + { + "Year": 2024, + "Score": 22.0, + "Country": "Poland" + }, + { + "Year": 2024, + "Score": 40.0, + "Country": "Italy" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the export", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding country", + "role": "y" + }, + { + "name": "Country", + "importance": "primary", + "description": "The country of the data point", + "role": "group" + } + ] + } + }, + "649": { + "metadata": { + "title": "Brazil's Rare Trade Surplus With China", + "description": "Brazil's goods imports from and exports to China", + "main_insight": "Brazil's exports to China have consistently exceeded imports, resulting in a trade surplus. This surplus has grown significantly since 2016, with a notable increase in 2021 and 2022." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 88.0, + "Trade Flows": "Exports" + }, + { + "Year": 2022, + "Units": 61.0, + "Trade Flows": "Imports" + } + ], + "score": 0.91, + "annotation": "The Units of Exports and Imports show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Exports and Imports is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2016, + "Units": 36.0, + "Trade Flows": "Exports" + }, + { + "Year": 2016, + "Units": 24.0, + "Trade Flows": "Imports" + }, + { + "Year": 2016, + "Units": -0.1, + "Trade Flows": "Change in bilateral trade volume" + }, + { + "Year": 2017, + "Units": 48.0, + "Trade Flows": "Exports" + }, + { + "Year": 2017, + "Units": 28.0, + "Trade Flows": "Imports" + }, + { + "Year": 2017, + "Units": 0.29, + "Trade Flows": "Change in bilateral trade volume" + }, + { + "Year": 2018, + "Units": 64.0, + "Trade Flows": "Exports" + }, + { + "Year": 2018, + "Units": 35.0, + "Trade Flows": "Imports" + }, + { + "Year": 2018, + "Units": 0.31, + "Trade Flows": "Change in bilateral trade volume" + }, + { + "Year": 2019, + "Units": 63.0, + "Trade Flows": "Exports" + }, + { + "Year": 2019, + "Units": 35.0, + "Trade Flows": "Imports" + }, + { + "Year": 2020, + "Units": 68.0, + "Trade Flows": "Exports" + }, + { + "Year": 2020, + "Units": 34.0, + "Trade Flows": "Imports" + }, + { + "Year": 2020, + "Units": 0.04, + "Trade Flows": "Change in bilateral trade volume" + }, + { + "Year": 2021, + "Units": 90.0, + "Trade Flows": "Exports" + }, + { + "Year": 2021, + "Units": 48.0, + "Trade Flows": "Imports" + }, + { + "Year": 2021, + "Units": 0.35, + "Trade Flows": "Change in bilateral trade volume" + }, + { + "Year": 2022, + "Units": 88.0, + "Trade Flows": "Exports" + }, + { + "Year": 2022, + "Units": 61.0, + "Trade Flows": "Imports" + }, + { + "Year": 2022, + "Units": 0.11, + "Trade Flows": "Change in bilateral trade volume" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of trade", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding trade flows", + "role": "y" + }, + { + "name": "Trade Flows", + "importance": "primary", + "description": "The trade flows of the data point", + "role": "group" + } + ] + } + }, + "1702": { + "metadata": { + "title": "Where Brands Matter Most", + "description": "\"In which of these categories do you pay particular attention to brands?\"", + "main_insight": "Consumers pay the most attention to brands when purchasing smartphones, followed by clothing & shoes. Brand attention is significantly lower for categories like furniture and toys." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Product Category": "Toys & baby products", + "Brand Attention": 18.0 + } + ], + "score": 0.88, + "annotation": "The has a minimum value at Toys & baby products", + "reason": "The [{'Product Category': 'Toys & baby products', 'Brand Attention': 18.0}] of has a mainimum value of 18.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Product Category": "Smartphones", + "Brand Attention": 63.0 + } + ], + "score": 0.84, + "annotation": "The has a maximum value at Smartphones", + "reason": "The [{'Product Category': 'Smartphones', 'Brand Attention': 63.0}] of has a maximum value of 63.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Product Category": "Alcoholic drinks", + "Brand Attention": 36.0 + } + ], + "score": 0.64, + "annotation": "The shows a sudden jump in Brand Attention from Furniture & household goods to Alcoholic drinks.", + "reason": "The Brand Attention for increases from 25.0 at Furniture & household goods to 36.0 at Alcoholic drinks, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Product Category": "Smartphones", + "Brand Attention": 63.0 + }, + { + "Product Category": "Clothing & shoes", + "Brand Attention": 53.0 + }, + { + "Product Category": "TV & HiFi", + "Brand Attention": 48.0 + }, + { + "Product Category": "Cars, motorcycles, bicycles", + "Brand Attention": 47.0 + }, + { + "Product Category": "Household appliances", + "Brand Attention": 44.0 + }, + { + "Product Category": "Food & non-alcoholic drinks", + "Brand Attention": 42.0 + }, + { + "Product Category": "Cosmetics & body care", + "Brand Attention": 39.0 + }, + { + "Product Category": "Alcoholic drinks", + "Brand Attention": 36.0 + }, + { + "Product Category": "Furniture & household goods", + "Brand Attention": 25.0 + }, + { + "Product Category": "Toys & baby products", + "Brand Attention": 18.0 + } + ], + "columns": [ + { + "name": "Product Category", + "importance": "primary", + "description": "The product category for which brand attention was surveyed", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Brand Attention", + "importance": "primary", + "description": "Percentage of respondents who pay particular attention to brands in that category", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "728": { + "metadata": { + "title": "Eating Meat Is the Norm Almost Everywhere", + "description": "Share of respondents in selected countries who eat meat and share who regularly buy meat substitutes", + "main_insight": "A large majority of respondents in most of the surveyed countries eat meat, while the purchase of meat substitutes is significantly less common, except in India." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "India", + "Score": 53.0, + "Dietary Habits": "% eating meat" + } + ], + "score": 0.96, + "annotation": "The % eating meat has a minimum value at India", + "reason": "The [{'Country': 'India', 'Score': 53.0, 'Dietary Habits': '% eating meat'}] of % eating meat has a mainimum value of 53.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "South Korea", + "Score": 9.0, + "Dietary Habits": "% buying meat substitutes" + } + ], + "score": 0.9, + "annotation": "The % buying meat substitutes has a minimum value at South Korea", + "reason": "The [{'Country': 'South Korea', 'Score': 9.0, 'Dietary Habits': '% buying meat substitutes'}] of % buying meat substitutes has a mainimum value of 9.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United Kingdom", + "Score": 21.0, + "Dietary Habits": "% buying meat substitutes" + } + ], + "score": 0.82, + "annotation": "The % buying meat substitutes has a maximum value at United Kingdom", + "reason": "The [{'Country': 'United Kingdom', 'Score': 21.0, 'Dietary Habits': '% buying meat substitutes'}] of % buying meat substitutes has a maximum value of 21.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Switzerland", + "Score": 72.0, + "Dietary Habits": "% eating meat" + } + ], + "score": 0.8, + "annotation": "The % eating meat shows a sudden jump in Score from India to Switzerland.", + "reason": "The Score for % eating meat increases from 53.0 at India to 72.0 at Switzerland, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "India", + "Score": 53.0, + "Dietary Habits": "% eating meat" + } + ], + "score": 0.72, + "annotation": "The % eating meat accounts for the majority in India.", + "reason": "The Score of % eating meat in India accounts for 0.7681159420289855 (more than 50%) of the total of Score of all Dietary Habits." + } + ], + "data": { + "data": [ + { + "Country": "South Korea", + "Score": 95.0, + "Dietary Habits": "% eating meat" + }, + { + "Country": "South Korea", + "Score": 9.0, + "Dietary Habits": "% buying meat substitutes" + }, + { + "Country": "Spain", + "Score": 94.0, + "Dietary Habits": "% eating meat" + }, + { + "Country": "Spain", + "Score": 12.0, + "Dietary Habits": "% buying meat substitutes" + }, + { + "Country": "Brazil", + "Score": 92.0, + "Dietary Habits": "% eating meat" + }, + { + "Country": "Brazil", + "Score": 15.0, + "Dietary Habits": "% buying meat substitutes" + }, + { + "Country": "Italy", + "Score": 91.0, + "Dietary Habits": "% eating meat" + }, + { + "Country": "Italy", + "Score": 17.0, + "Dietary Habits": "% buying meat substitutes" + }, + { + "Country": "Germany", + "Score": 88.0, + "Dietary Habits": "% eating meat" + }, + { + "Country": "Germany", + "Score": 16.0, + "Dietary Habits": "% buying meat substitutes" + }, + { + "Country": "United States", + "Score": 87.0, + "Dietary Habits": "% eating meat" + }, + { + "Country": "United States", + "Score": 14.0, + "Dietary Habits": "% buying meat substitutes" + }, + { + "Country": "United Kingdom", + "Score": 86.0, + "Dietary Habits": "% eating meat" + }, + { + "Country": "United Kingdom", + "Score": 21.0, + "Dietary Habits": "% buying meat substitutes" + }, + { + "Country": "China", + "Score": 79.0, + "Dietary Habits": "% eating meat" + }, + { + "Country": "China", + "Score": 20.0, + "Dietary Habits": "% buying meat substitutes" + }, + { + "Country": "Switzerland", + "Score": 72.0, + "Dietary Habits": "% eating meat" + }, + { + "Country": "Switzerland", + "Score": 16.0, + "Dietary Habits": "% buying meat substitutes" + }, + { + "Country": "India", + "Score": 53.0, + "Dietary Habits": "% eating meat" + }, + { + "Country": "India", + "Score": 16.0, + "Dietary Habits": "% buying meat substitutes" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding dietary habits", + "role": "y" + }, + { + "name": "Dietary Habits", + "importance": "primary", + "description": "The dietary habits of the data point", + "role": "group" + } + ] + } + }, + "2137": { + "metadata": { + "title": "America Runs on... Bottled Water", + "description": "Most common beverages U.S. adults said they consumed regularly*", + "main_insight": "Bottled water is the most commonly consumed beverage among U.S. adults, with 63% saying they consume it regularly, followed by coffee (57%) and soft drinks (56%)." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Beverage": "Tea", + "Percentage of Respondents": 48.0 + } + ], + "score": 0.85, + "annotation": "The shows a sudden jump in Percentage of Respondents from Energy drinks to Tea.", + "reason": "The Percentage of Respondents for increases from 27.0 at Energy drinks to 48.0 at Tea, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Beverage": "Wine", + "Percentage of Respondents": 24.0 + } + ], + "score": 0.71, + "annotation": "The has a minimum value at Wine", + "reason": "The [{'Beverage': 'Wine', 'Percentage of Respondents': 24.0}] of has a mainimum value of 24.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Beverage": "Bottled water", + "Percentage of Respondents": 63.0 + } + ], + "score": 0.69, + "annotation": "The has a maximum value at Bottled water", + "reason": "The [{'Beverage': 'Bottled water', 'Percentage of Respondents': 63.0}] of has a maximum value of 63.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Beverage": "Bottled water", + "Percentage of Respondents": 63.0 + }, + { + "Beverage": "Coffee", + "Percentage of Respondents": 57.0 + }, + { + "Beverage": "Soft drinks", + "Percentage of Respondents": 56.0 + }, + { + "Beverage": "Juice", + "Percentage of Respondents": 50.0 + }, + { + "Beverage": "Tea", + "Percentage of Respondents": 48.0 + }, + { + "Beverage": "Energy drinks", + "Percentage of Respondents": 27.0 + }, + { + "Beverage": "Beer", + "Percentage of Respondents": 25.0 + }, + { + "Beverage": "Wine", + "Percentage of Respondents": 24.0 + } + ], + "columns": [ + { + "name": "Beverage", + "importance": "primary", + "description": "Type of beverage", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Respondents", + "importance": "primary", + "description": "Percentage of U.S. adults (18-64 y/o) who said they consumed the beverage regularly", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2347": { + "metadata": { + "title": "The Militaries With The Most Firearms", + "description": "Estimated number of military-owned firearms in 2018", + "main_insight": "The Russian Federation and China have the highest number of military-owned firearms, significantly exceeding other countries." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Russian Federation", + "Firearms": 30.3 + } + ], + "score": 0.92, + "annotation": "The has a maximum value at Russian Federation", + "reason": "The [{'Country': 'Russian Federation', 'Firearms': 30.3}] of has a maximum value of 30.3, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "China", + "Firearms": 27.5 + } + ], + "score": 0.91, + "annotation": "The shows a sudden jump in Firearms from North Korea to China.", + "reason": "The Firearms for increases from 8.4 at North Korea to 27.5 at China, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Pakistan", + "Firearms": 2.3 + } + ], + "score": 0.44, + "annotation": "The has a minimum value at Pakistan", + "reason": "The [{'Country': 'Pakistan', 'Firearms': 2.3}] of has a mainimum value of 2.3, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Russian Federation", + "Firearms": 30.3 + }, + { + "Country": "China", + "Firearms": 27.5 + }, + { + "Country": "North Korea", + "Firearms": 8.4 + }, + { + "Country": "Ukraine", + "Firearms": 6.6 + }, + { + "Country": "United States", + "Firearms": 4.5 + }, + { + "Country": "India", + "Firearms": 3.9 + }, + { + "Country": "Vietnam", + "Firearms": 3.8 + }, + { + "Country": "Iran", + "Firearms": 3.3 + }, + { + "Country": "South Korea", + "Firearms": 2.7 + }, + { + "Country": "Pakistan", + "Firearms": 2.3 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Firearms", + "importance": "primary", + "description": "Estimated number of military-owned firearms (millions)", + "unit": "millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "667": { + "metadata": { + "title": "Majority of Americans Favor Popular Vote", + "description": "Percentage of U.S. adults who are in favor of abolishing the electoral college system", + "main_insight": "A majority of Americans (61%) favor amending the Constitution to abolish the electoral college, with a significant partisan divide: 89% of Democrats support it, while only 23% of Republicans do." + }, + "chart_type": "Multiple Pie Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Political Affiliation": "Democrats", + "Units": 89.0, + "Reform Stance": "Amend the Constitution" + }, + { + "Political Affiliation": "Democrats", + "Units": 11.0, + "Reform Stance": "Keep current system" + } + ], + "score": 1.0, + "annotation": "The Units of Amend the Constitution and Keep current system show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Amend the Constitution and Keep current system is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Political Affiliation": "Total", + "Units": 61.0, + "Reform Stance": "Amend the Constitution" + } + ], + "score": 0.87, + "annotation": "The Amend the Constitution shows a sudden jump in Units from Republicans to Total.", + "reason": "The Units for Amend the Constitution increases from 23.0 at Republicans to 61.0 at Total, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Political Affiliation": "Republicans", + "Units": 77.0, + "Reform Stance": "Keep current system" + } + ], + "score": 0.87, + "annotation": "The Keep current system shows a sudden jump in Units from Total to Republicans.", + "reason": "The Units for Keep current system increases from 39.0 at Total to 77.0 at Republicans, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Reform Stance": "Amend the Constitution", + "Political Affiliation": "total", + "Units": 173.0 + } + ], + "score": 0.78, + "annotation": "The total value of Amend the Constitution accounts for the majority in all Reform Stance.", + "reason": "The total value of Units of Amend the Constitution accounts for 0.5766666666666667 (more than 50%) of the total of Units of all Reform Stance." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Political Affiliation": "Total", + "Units": 61.0, + "Reform Stance": "Amend the Constitution" + } + ], + "score": 0.77, + "annotation": "The Amend the Constitution accounts for the majority in Total.", + "reason": "The Units of Amend the Constitution in Total accounts for 0.61 (more than 50%) of the total of Units of all Reform Stance." + } + ], + "data": { + "data": [ + { + "Political Affiliation": "Total", + "Units": 61.0, + "Reform Stance": "Amend the Constitution" + }, + { + "Political Affiliation": "Total", + "Units": 39.0, + "Reform Stance": "Keep current system" + }, + { + "Political Affiliation": "Republicans", + "Units": 23.0, + "Reform Stance": "Amend the Constitution" + }, + { + "Political Affiliation": "Republicans", + "Units": 77.0, + "Reform Stance": "Keep current system" + }, + { + "Political Affiliation": "Democrats", + "Units": 89.0, + "Reform Stance": "Amend the Constitution" + }, + { + "Political Affiliation": "Democrats", + "Units": 11.0, + "Reform Stance": "Keep current system" + } + ], + "columns": [ + { + "name": "Political Affiliation", + "importance": "primary", + "description": "Political party affiliation of survey respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding reform stance", + "role": "y" + }, + { + "name": "Reform Stance", + "importance": "primary", + "description": "The reform stance of the data point", + "role": "group" + } + ] + } + }, + "1002": { + "metadata": { + "title": "America's Coldest Cities", + "description": "Record low and minimum average temperature in the coldest month (in °F)*", + "main_insight": "Fairbanks, Alaska, has the lowest recorded temperature and the lowest minimum average temperature among the listed cities, making it the coldest city overall." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "Fairbanks, Alaska", + "Units": -16.9, + "Temperature": "Minimum average temperature (coldest month)" + } + ], + "score": 0.97, + "annotation": "The Minimum average temperature (coldest month) has a minimum value at Fairbanks, Alaska", + "reason": "The [{'City': 'Fairbanks, Alaska', 'Units': -16.9, 'Temperature': 'Minimum average temperature (coldest month)'}] of Minimum average temperature (coldest month) has a mainimum value of -16.9, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "Fairbanks, Alaska", + "Units": -66.0, + "Temperature": "Lowest temperature on record" + } + ], + "score": 0.97, + "annotation": "The Lowest temperature on record has a minimum value at Fairbanks, Alaska", + "reason": "The [{'City': 'Fairbanks, Alaska', 'Units': -66.0, 'Temperature': 'Lowest temperature on record'}] of Lowest temperature on record has a mainimum value of -66.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Grand Forks, North Dakota", + "Units": -3.1, + "Temperature": "Minimum average temperature (coldest month)" + } + ], + "score": 0.88, + "annotation": "The Minimum average temperature (coldest month) shows a sudden jump in Units from Fairbanks, Alaska to Grand Forks, North Dakota.", + "reason": "The Units for Minimum average temperature (coldest month) increases from -16.9 at Fairbanks, Alaska to -3.1 at Grand Forks, North Dakota, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "City": "Huron, South Dakota", + "Units": -43.0, + "Temperature": "Lowest temperature on record" + }, + { + "City": "Huron, South Dakota", + "Units": 6.6, + "Temperature": "Minimum average temperature (coldest month)" + } + ], + "score": 0.86, + "annotation": "The Units of Lowest temperature on record and Minimum average temperature (coldest month) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Lowest temperature on record and Minimum average temperature (coldest month) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Williston, North Dakota", + "Units": -50.0, + "Temperature": "Lowest temperature on record" + } + ], + "score": 0.83, + "annotation": "The Lowest temperature on record shows a sudden jump in Units from Fairbanks, Alaska to Williston, North Dakota.", + "reason": "The Units for Lowest temperature on record increases from -66.0 at Fairbanks, Alaska to -50.0 at Williston, North Dakota, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "City": "Fairbanks, Alaska", + "Units": -66.0, + "Temperature": "Lowest temperature on record" + }, + { + "City": "Fairbanks, Alaska", + "Units": -16.9, + "Temperature": "Minimum average temperature (coldest month)" + }, + { + "City": "Grand Forks, North Dakota", + "Units": -43.0, + "Temperature": "Lowest temperature on record" + }, + { + "City": "Grand Forks, North Dakota", + "Units": -3.1, + "Temperature": "Minimum average temperature (coldest month)" + }, + { + "City": "Williston, North Dakota", + "Units": -50.0, + "Temperature": "Lowest temperature on record" + }, + { + "City": "Williston, North Dakota", + "Units": 0.1, + "Temperature": "Minimum average temperature (coldest month)" + }, + { + "City": "Fargo, North Dakota", + "Units": -39.0, + "Temperature": "Lowest temperature on record" + }, + { + "City": "Fargo, North Dakota", + "Units": 0.1, + "Temperature": "Minimum average temperature (coldest month)" + }, + { + "City": "Duluth, Minnesota", + "Units": -39.0, + "Temperature": "Lowest temperature on record" + }, + { + "City": "Duluth, Minnesota", + "Units": 1.5, + "Temperature": "Minimum average temperature (coldest month)" + }, + { + "City": "Aberdeen, South Dakota", + "Units": -46.0, + "Temperature": "Lowest temperature on record" + }, + { + "City": "Aberdeen, South Dakota", + "Units": 1.5, + "Temperature": "Minimum average temperature (coldest month)" + }, + { + "City": "St. Cloud, Minnesota", + "Units": -43.0, + "Temperature": "Lowest temperature on record" + }, + { + "City": "St. Cloud, Minnesota", + "Units": 1.8, + "Temperature": "Minimum average temperature (coldest month)" + }, + { + "City": "Bismarck, North Dakota", + "Units": -44.0, + "Temperature": "Lowest temperature on record" + }, + { + "City": "Bismarck, North Dakota", + "Units": 2.2, + "Temperature": "Minimum average temperature (coldest month)" + }, + { + "City": "Marquette, Michigan", + "Units": -34.0, + "Temperature": "Lowest temperature on record" + }, + { + "City": "Marquette, Michigan", + "Units": 5.2, + "Temperature": "Minimum average temperature (coldest month)" + }, + { + "City": "Huron, South Dakota", + "Units": -43.0, + "Temperature": "Lowest temperature on record" + }, + { + "City": "Huron, South Dakota", + "Units": 6.6, + "Temperature": "Minimum average temperature (coldest month)" + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "Name of the city", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding temperature", + "role": "y" + }, + { + "name": "Temperature", + "importance": "primary", + "description": "The temperature of the data point", + "role": "group" + } + ] + } + }, + "352": { + "metadata": { + "title": "Rather German Than Brexit", + "description": "Number of UK citizens granted German citizenship", + "main_insight": "The number of UK citizens granted German citizenship has significantly increased since 2016, with a dramatic spike in 2019, suggesting a correlation with Brexit." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Year": "2019", + "UK Citizens Granted German Citizenship": 14600.0 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in UK Citizens Granted German Citizenship from 2017 to 2019.", + "reason": "The UK Citizens Granted German Citizenship for increases from 7493.0 at 2017 to 14600.0 at 2019, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Year": "2019", + "UK Citizens Granted German Citizenship": 14600.0 + } + ], + "score": 0.81, + "annotation": "The has a maximum value at 2019", + "reason": "The [{'Year': '2019', 'UK Citizens Granted German Citizenship': 14600.0}] of has a maximum value of 14600.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Year": "2008", + "UK Citizens Granted German Citizenship": 318.0 + } + ], + "score": 0.65, + "annotation": "The has a minimum value at 2008", + "reason": "The [{'Year': '2008', 'UK Citizens Granted German Citizenship': 318.0}] of has a mainimum value of 318.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Year": "2008", + "UK Citizens Granted German Citizenship": 318.0 + }, + { + "Year": "2016", + "UK Citizens Granted German Citizenship": 2865.0 + }, + { + "Year": "2017", + "UK Citizens Granted German Citizenship": 7493.0 + }, + { + "Year": "2018", + "UK Citizens Granted German Citizenship": 6640.0 + }, + { + "Year": "2019", + "UK Citizens Granted German Citizenship": 14600.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year or period", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "UK Citizens Granted German Citizenship", + "importance": "primary", + "description": "Number of UK citizens who received German citizenship", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "409": { + "metadata": { + "title": "Undocumented Encounters at Both U.S. Borders", + "description": "Top five nationalities encountered by the CBP and OFO at the U.S. land borders between Oct. 2023-Aug. 2024", + "main_insight": "Undocumented encounters at the northern border increased 101% between 2022 and 2024, while southern border encounters slightly decreased (-2%). The top nationality at the Northern border is India and on the South border is Mexico." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Nationality": "IN", + "Northern Land Border Encounters": 41000.0 + } + ], + "score": 0.9, + "annotation": "The has a maximum value at IN", + "reason": "The [{'Nationality': 'IN', 'Northern Land Border Encounters': 41000.0}] of has a maximum value of 41000.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Nationality": "CA", + "Northern Land Border Encounters": 33000.0 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Northern Land Border Encounters from CN to CA.", + "reason": "The Northern Land Border Encounters for increases from 13000.0 at CN to 33000.0 at CA, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Nationality": "VE", + "Northern Land Border Encounters": 0 + }, + { + "Nationality": "GT", + "Northern Land Border Encounters": 0 + }, + { + "Nationality": "CU", + "Northern Land Border Encounters": 0 + }, + { + "Nationality": "HN", + "Northern Land Border Encounters": 0 + } + ], + "score": 0.48, + "annotation": "The has minimum values at VE, GT, CU, HN", + "reason": "The [{'Nationality': 'VE', 'Northern Land Border Encounters': 0}, {'Nationality': 'GT', 'Northern Land Border Encounters': 0}, {'Nationality': 'CU', 'Northern Land Border Encounters': 0}, {'Nationality': 'HN', 'Northern Land Border Encounters': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Nationality": "IN", + "Northern Land Border Encounters": 41000.0 + }, + { + "Nationality": "CA", + "Northern Land Border Encounters": 33000.0 + }, + { + "Nationality": "CN", + "Northern Land Border Encounters": 13000.0 + }, + { + "Nationality": "PH", + "Northern Land Border Encounters": 9000.0 + }, + { + "Nationality": "MX", + "Northern Land Border Encounters": 8000.0 + }, + { + "Nationality": "VE", + "Northern Land Border Encounters": 0 + }, + { + "Nationality": "GT", + "Northern Land Border Encounters": 0 + }, + { + "Nationality": "CU", + "Northern Land Border Encounters": 0 + }, + { + "Nationality": "HN", + "Northern Land Border Encounters": 0 + } + ], + "columns": [ + { + "name": "Nationality", + "importance": "primary", + "description": "Country of origin", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Northern Land Border Encounters", + "importance": "primary", + "description": "Number of encounters at the Northern Land Border", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "755": { + "metadata": { + "title": "Early Primary States Don't Favor Biden", + "description": "Polling averages for Iowa, New Hampshire and the U.S. ahead of the 2020 Democratic presidential primaries (in %)", + "main_insight": "In early 2020, polling data indicated that Joe Biden was not the front-runner in Iowa and New Hampshire, and was only slightly leading in national polls." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Poll Location": "Iowa polls", + "Units": 24.2, + "Candidates": "Bernie Sanders" + } + ], + "score": 0.95, + "annotation": "The Bernie Sanders has the largest proportion in Iowa polls.", + "reason": "The Units of Bernie Sanders in Iowa polls accounts for 0.242, larger than all other Candidates, of the total of Units of all Candidates." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Poll Location": "N.H. polls", + "Units": 14.8, + "Candidates": "Pete Buttigieg" + } + ], + "score": 0.95, + "annotation": "The Pete Buttigieg shows a sudden jump in Units from National polls to N.H. polls.", + "reason": "The Units for Pete Buttigieg increases from 6.7 at National polls to 14.8 at N.H. polls, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Candidates": "Others/Not sure", + "Poll Location": "total", + "Units": 79.80000000000001 + } + ], + "score": 0.95, + "annotation": "The total value of Others/Not sure has the largest proportion.", + "reason": "The total value of Units of Others/Not sure accounts for 0.266, larger than all other Candidates, of the total of Units of all Candidates." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Poll Location": "National polls", + "Units": 27.6, + "Candidates": "Others/Not sure" + } + ], + "score": 0.95, + "annotation": "The Others/Not sure has the largest proportion in National polls.", + "reason": "The Units of Others/Not sure in National polls accounts for 0.276, larger than all other Candidates, of the total of Units of all Candidates." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Poll Location": "N.H. polls", + "Units": 28.6, + "Candidates": "Others/Not sure" + } + ], + "score": 0.95, + "annotation": "The Others/Not sure has the largest proportion in N.H. polls.", + "reason": "The Units of Others/Not sure in N.H. polls accounts for 0.28600000000000003, larger than all other Candidates, of the total of Units of all Candidates." + } + ], + "data": { + "data": [ + { + "Poll Location": "Iowa polls", + "Units": 20.2, + "Candidates": "Joe Biden" + }, + { + "Poll Location": "Iowa polls", + "Units": 24.2, + "Candidates": "Bernie Sanders" + }, + { + "Poll Location": "Iowa polls", + "Units": 15.6, + "Candidates": "Elizabeth Warren" + }, + { + "Poll Location": "Iowa polls", + "Units": 16.4, + "Candidates": "Pete Buttigieg" + }, + { + "Poll Location": "Iowa polls", + "Units": 23.6, + "Candidates": "Others/Not sure" + }, + { + "Poll Location": "N.H. polls", + "Units": 16.8, + "Candidates": "Joe Biden" + }, + { + "Poll Location": "N.H. polls", + "Units": 26.3, + "Candidates": "Bernie Sanders" + }, + { + "Poll Location": "N.H. polls", + "Units": 13.5, + "Candidates": "Elizabeth Warren" + }, + { + "Poll Location": "N.H. polls", + "Units": 14.8, + "Candidates": "Pete Buttigieg" + }, + { + "Poll Location": "N.H. polls", + "Units": 28.6, + "Candidates": "Others/Not sure" + }, + { + "Poll Location": "National polls", + "Units": 27.2, + "Candidates": "Joe Biden" + }, + { + "Poll Location": "National polls", + "Units": 23.5, + "Candidates": "Bernie Sanders" + }, + { + "Poll Location": "National polls", + "Units": 15.0, + "Candidates": "Elizabeth Warren" + }, + { + "Poll Location": "National polls", + "Units": 6.7, + "Candidates": "Pete Buttigieg" + }, + { + "Poll Location": "National polls", + "Units": 27.6, + "Candidates": "Others/Not sure" + } + ], + "columns": [ + { + "name": "Poll Location", + "importance": "primary", + "description": "Location of the poll", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding candidates", + "role": "y" + }, + { + "name": "Candidates", + "importance": "primary", + "description": "The candidates of the data point", + "role": "group" + } + ] + } + }, + "1832": { + "metadata": { + "title": "Americans Love the NFL, But Change Is Looming", + "description": "% of U.S. sports fans saying they followed the following leagues", + "main_insight": "The NFL is the most popular sports league among U.S. sports fans, but its popularity is significantly lower among the younger demographic (16-25 year-olds) compared to the overall population, while other sports like the NBA are much closer." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "League": "NHL", + "Units": 9.0, + "Audience": "All respondents" + }, + { + "League": "NHL", + "Units": 6.0, + "Audience": "Ages 16-25" + } + ], + "score": 0.93, + "annotation": "The Units of All respondents and Ages 16-25 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between All respondents and Ages 16-25 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "League": "NBA", + "Units": 42.0, + "Audience": "All respondents" + } + ], + "score": 0.8, + "annotation": "The All respondents accounts for the majority in NBA.", + "reason": "The Units of All respondents in NBA accounts for 0.5121951219512195 (more than 50%) of the total of Units of all Audience." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "League": "MLS", + "Units": 13.0, + "Audience": "All respondents" + } + ], + "score": 0.79, + "annotation": "The All respondents accounts for the majority in MLS.", + "reason": "The Units of All respondents in MLS accounts for 0.52 (more than 50%) of the total of Units of all Audience." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "League": "MLB", + "Units": 31.0, + "Audience": "All respondents" + } + ], + "score": 0.78, + "annotation": "The All respondents accounts for the majority in MLB.", + "reason": "The Units of All respondents in MLB accounts for 0.5535714285714286 (more than 50%) of the total of Units of all Audience." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Audience": "All respondents", + "League": "total", + "Units": 147.0 + } + ], + "score": 0.78, + "annotation": "The total value of All respondents accounts for the majority in all Audience.", + "reason": "The total value of Units of All respondents accounts for 0.55893536121673 (more than 50%) of the total of Units of all Audience." + } + ], + "data": { + "data": [ + { + "League": "NFL", + "Units": 52.0, + "Audience": "All respondents" + }, + { + "League": "NFL", + "Units": 33.0, + "Audience": "Ages 16-25" + }, + { + "League": "NBA", + "Units": 42.0, + "Audience": "All respondents" + }, + { + "League": "NBA", + "Units": 40.0, + "Audience": "Ages 16-25" + }, + { + "League": "MLB", + "Units": 31.0, + "Audience": "All respondents" + }, + { + "League": "MLB", + "Units": 25.0, + "Audience": "Ages 16-25" + }, + { + "League": "MLS", + "Units": 13.0, + "Audience": "All respondents" + }, + { + "League": "MLS", + "Units": 12.0, + "Audience": "Ages 16-25" + }, + { + "League": "NHL", + "Units": 9.0, + "Audience": "All respondents" + }, + { + "League": "NHL", + "Units": 6.0, + "Audience": "Ages 16-25" + } + ], + "columns": [ + { + "name": "League", + "importance": "primary", + "description": "Name of the sports league", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding audience", + "role": "y" + }, + { + "name": "Audience", + "importance": "primary", + "description": "The audience of the data point", + "role": "group" + } + ] + } + }, + "75": { + "metadata": { + "title": "What's in the Pipeline?", + "description": "U.S. pipeline mileage broken down by hazardous liquids or CO2 (2005 to 2015)", + "main_insight": "U.S. pipeline mileage increased from 2005 to 2015, with Petroleum/Refined Product and Crude Oil consistently making up the largest portions." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2015, + "Units": 77171, + "Substances": "Crude Oil" + }, + { + "Year": 2015, + "Units": 54192, + "Substances": "HVLS" + } + ], + "score": 0.98, + "annotation": "The Units of Crude Oil and HVLS show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Crude Oil and HVLS is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2015, + "Units": 54192, + "Substances": "HVLS" + }, + { + "Year": 2015, + "Units": 12706, + "Substances": "CO2 or Other" + } + ], + "score": 0.93, + "annotation": "The Units of HVLS and CO2 or Other show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between HVLS and CO2 or Other is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2015, + "Units": 77171, + "Substances": "Crude Oil" + }, + { + "Year": 2015, + "Units": 12706, + "Substances": "CO2 or Other" + } + ], + "score": 0.92, + "annotation": "The Units of Crude Oil and CO2 or Other show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Crude Oil and CO2 or Other is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2015, + "Units": 64643, + "Substances": "Petroleum/Refined Product" + }, + { + "Year": 2015, + "Units": 12706, + "Substances": "CO2 or Other" + } + ], + "score": 0.1, + "annotation": "The Units of Petroleum/Refined Product and CO2 or Other show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Petroleum/Refined Product and CO2 or Other is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2015, + "Units": 64643, + "Substances": "Petroleum/Refined Product" + }, + { + "Year": 2015, + "Units": 77171, + "Substances": "Crude Oil" + } + ], + "score": 0.09, + "annotation": "The Units of Petroleum/Refined Product and Crude Oil show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Petroleum/Refined Product and Crude Oil is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2005, + "Units": 64501, + "Substances": "Petroleum/Refined Product" + }, + { + "Year": 2005, + "Units": 54515, + "Substances": "Crude Oil" + }, + { + "Year": 2005, + "Units": 41752, + "Substances": "HVLS" + }, + { + "Year": 2005, + "Units": 5992, + "Substances": "CO2 or Other" + }, + { + "Year": 2006, + "Units": 64210, + "Substances": "Petroleum/Refined Product" + }, + { + "Year": 2006, + "Units": 54881, + "Substances": "Crude Oil" + }, + { + "Year": 2006, + "Units": 43115, + "Substances": "HVLS" + }, + { + "Year": 2006, + "Units": 6218, + "Substances": "CO2 or Other" + }, + { + "Year": 2007, + "Units": 64032, + "Substances": "Petroleum/Refined Product" + }, + { + "Year": 2007, + "Units": 55999, + "Substances": "Crude Oil" + }, + { + "Year": 2007, + "Units": 44634, + "Substances": "HVLS" + }, + { + "Year": 2007, + "Units": 6532, + "Substances": "CO2 or Other" + }, + { + "Year": 2008, + "Units": 64023, + "Substances": "Petroleum/Refined Product" + }, + { + "Year": 2008, + "Units": 57662, + "Substances": "Crude Oil" + }, + { + "Year": 2008, + "Units": 44924, + "Substances": "HVLS" + }, + { + "Year": 2008, + "Units": 6927, + "Substances": "CO2 or Other" + }, + { + "Year": 2009, + "Units": 65028, + "Substances": "Petroleum/Refined Product" + }, + { + "Year": 2009, + "Units": 57874, + "Substances": "Crude Oil" + }, + { + "Year": 2009, + "Units": 45198, + "Substances": "HVLS" + }, + { + "Year": 2009, + "Units": 7142, + "Substances": "CO2 or Other" + }, + { + "Year": 2010, + "Units": 64714, + "Substances": "Petroleum/Refined Product" + }, + { + "Year": 2010, + "Units": 58185, + "Substances": "Crude Oil" + }, + { + "Year": 2010, + "Units": 45772, + "Substances": "HVLS" + }, + { + "Year": 2010, + "Units": 7620, + "Substances": "CO2 or Other" + }, + { + "Year": 2011, + "Units": 63567, + "Substances": "Petroleum/Refined Product" + }, + { + "Year": 2011, + "Units": 60873, + "Substances": "Crude Oil" + }, + { + "Year": 2011, + "Units": 46114, + "Substances": "HVLS" + }, + { + "Year": 2011, + "Units": 7854, + "Substances": "CO2 or Other" + }, + { + "Year": 2012, + "Units": 63819, + "Substances": "Petroleum/Refined Product" + }, + { + "Year": 2012, + "Units": 63990, + "Substances": "Crude Oil" + }, + { + "Year": 2012, + "Units": 47210, + "Substances": "HVLS" + }, + { + "Year": 2012, + "Units": 8084, + "Substances": "CO2 or Other" + }, + { + "Year": 2013, + "Units": 64044, + "Substances": "Petroleum/Refined Product" + }, + { + "Year": 2013, + "Units": 68256, + "Substances": "Crude Oil" + }, + { + "Year": 2013, + "Units": 49009, + "Substances": "HVLS" + }, + { + "Year": 2013, + "Units": 8344, + "Substances": "CO2 or Other" + }, + { + "Year": 2014, + "Units": 63899, + "Substances": "Petroleum/Refined Product" + }, + { + "Year": 2014, + "Units": 71736, + "Substances": "Crude Oil" + }, + { + "Year": 2014, + "Units": 51501, + "Substances": "HVLS" + }, + { + "Year": 2014, + "Units": 8656, + "Substances": "CO2 or Other" + }, + { + "Year": 2015, + "Units": 64643, + "Substances": "Petroleum/Refined Product" + }, + { + "Year": 2015, + "Units": 77171, + "Substances": "Crude Oil" + }, + { + "Year": 2015, + "Units": 54192, + "Substances": "HVLS" + }, + { + "Year": 2015, + "Units": 12706, + "Substances": "CO2 or Other" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding substances", + "role": "y" + }, + { + "name": "Substances", + "importance": "primary", + "description": "The substances of the data point", + "role": "group" + } + ] + } + }, + "1156": { + "metadata": { + "title": "Techlash – Is Facebook Doing More Harm Than Good?", + "description": "In ten years, which company will we say was a net negative for society?", + "main_insight": "A majority of respondents (59%) believe that Facebook will be a 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"Inflation Takes a Bite Out of Americans' Savings", + "description": "Personal saving as a percentage of disposable personal income in the United States", + "main_insight": "Personal savings as a percentage of disposable income spiked dramatically around 2020, but has since dropped significantly, indicating the impact of recent economic changes." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2000, + "Personal Saving Rate": 5.0 + }, + { + "Year": 2001, + "Personal Saving Rate": 5.0 + }, + { + "Year": 2002, + "Personal Saving Rate": 5.5 + }, + { + "Year": 2003, + "Personal Saving Rate": 5.0 + }, + { + "Year": 2004, + "Personal Saving Rate": 5.0 + }, + { + "Year": 2005, + "Personal Saving Rate": 3.5 + }, + { + "Year": 2006, + "Personal Saving Rate": 3.0 + }, + { + "Year": 2007, + "Personal Saving Rate": 3.5 + }, + { + "Year": 2008, + "Personal Saving Rate": 5.0 + }, + { + "Year": 2009, + "Personal Saving Rate": 6.5 + }, + { + "Year": 2010, + "Personal Saving Rate": 7.0 + }, + { + "Year": 2011, + "Personal Saving Rate": 7.5 + }, + { + "Year": 2012, + "Personal Saving Rate": 10.0 + }, + { + "Year": 2013, + "Personal Saving Rate": 6.5 + }, + { + "Year": 2014, + "Personal Saving Rate": 7.0 + }, + { + "Year": 2015, + "Personal Saving Rate": 8.0 + }, + { + "Year": 2016, + "Personal Saving Rate": 7.0 + }, + { + "Year": 2017, + "Personal Saving Rate": 7.5 + }, + { + "Year": 2018, + "Personal Saving Rate": 8.5 + }, + { + "Year": 2019, + "Personal Saving Rate": 8.0 + }, + { + "Year": 2020, + "Personal Saving Rate": 32.5 + }, + { + "Year": 2021, + "Personal Saving Rate": 12.0 + }, + { + "Year": 2022, + "Personal Saving Rate": 4.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 2000 to 2022", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Personal Saving Rate", + "importance": "primary", + "description": "Personal saving as a percentage of disposable 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"Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "1342": { + "metadata": { + "title": "Which Countries' Soccer Fans Bought the Most World Cup Tickets", + "description": "Number of World Cup tickets sold to fans from the following countries", + "main_insight": "Brazilian fans bought the most World Cup tickets by a large margin, followed by fans from the U.S. The top 10 list shows the countries with most tickets bought, as of June 5." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Brazil", + "Tickets Sold": 1363179.0 + } + ], + "score": 0.98, + "annotation": "The has a maximum value at Brazil", + "reason": "The [{'Country': 'Brazil', 'Tickets Sold': 1363179.0}] of has a maximum value of 1363179.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + 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"Tickets Sold": 54477.0 + }, + { + "Country": "Australia", + "Tickets Sold": 52289.0 + }, + { + "Country": "Chile", + "Tickets Sold": 38638.0 + }, + { + "Country": "France", + "Tickets Sold": 34865.0 + }, + { + "Country": "Mexico", + "Tickets Sold": 33694.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of origin of World Cup ticket purchasers", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Tickets Sold", + "importance": "primary", + "description": "Number of World Cup tickets sold to fans from that country", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "513": { + "metadata": { + "title": "European Views On Muslim Women's Religious Clothing", + "description": "% saying Muslim women in their country should/shouldn't face clothing restrictions*", + "main_insight": "A majority of respondents in most European countries surveyed believe there should be some restrictions on Muslim women's religious clothing, specifically items covering the face." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Spain", + "Score": 92.0, + "Restriction Level": "Total" + } + ], + "score": 0.99, + "annotation": "The Total has a minimum value at Spain", + "reason": "The [{'Country': 'Spain', 'Score': 92.0, 'Restriction Level': 'Total'}] of Total has a mainimum value of 92.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Portugal", + "Score": 52.0, + "Restriction Level": "No restrictions" + }, + { + "Country": "Portugal", + "Score": 44.0, + "Restriction Level": "Some restrictions" + } + ], + "score": 0.99, + "annotation": "The Score of No restrictions and Some restrictions show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between No restrictions and Some restrictions is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Portugal", + "Score": 44.0, + "Restriction Level": "Some restrictions" + } + ], + "score": 0.92, + "annotation": "The Some restrictions has a minimum value at Portugal", + "reason": "The [{'Country': 'Portugal', 'Score': 44.0, 'Restriction Level': 'Some restrictions'}] of Some restrictions has a mainimum value of 44.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Portugal", + "Score": 52.0, + "Restriction Level": "No restrictions" + } + ], + "score": 0.92, + "annotation": "The No restrictions has a maximum value at Portugal", + "reason": "The [{'Country': 'Portugal', 'Score': 52.0, 'Restriction Level': 'No restrictions'}] of No restrictions has a maximum value of 52.0, which is the largest in all categories." + }, + { + "type": "difference", + 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"No restrictions" + }, + { + "Country": "Belgium", + "Score": 78.0, + "Restriction Level": "Some restrictions" + }, + { + "Country": "Belgium", + "Score": 97.0, + "Restriction Level": "Total" + }, + { + "Country": "Italy", + "Score": 21.0, + "Restriction Level": "No restrictions" + }, + { + "Country": "Italy", + "Score": 78.0, + "Restriction Level": "Some restrictions" + }, + { + "Country": "Italy", + "Score": 99.0, + "Restriction Level": "Total" + }, + { + "Country": "Austria", + "Score": 21.0, + "Restriction Level": "No restrictions" + }, + { + "Country": "Austria", + "Score": 77.0, + "Restriction Level": "Some restrictions" + }, + { + "Country": "Austria", + "Score": 98.0, + "Restriction Level": "Total" + }, + { + "Country": "Germany", + "Score": 24.0, + "Restriction Level": "No restrictions" + }, + { + "Country": "Germany", + "Score": 75.0, + "Restriction Level": "Some restrictions" + }, + { + "Country": "Germany", + "Score": 99.0, + "Restriction Level": "Total" + }, + { + "Country": "France", + "Score": 23.0, + "Restriction Level": "No restrictions" + }, + { + "Country": "France", + "Score": 74.0, + "Restriction Level": "Some restrictions" + }, + { + "Country": "France", + "Score": 97.0, + "Restriction Level": "Total" + }, + { + "Country": "Norway", + "Score": 25.0, + "Restriction Level": "No restrictions" + }, + { + "Country": "Norway", + "Score": 72.0, + "Restriction Level": "Some restrictions" + }, + { + "Country": "Norway", + "Score": 97.0, + "Restriction Level": "Total" + }, + { + "Country": "United Kingdom", + "Score": 27.0, + "Restriction Level": "No restrictions" + }, + { + "Country": "United Kingdom", + "Score": 72.0, + "Restriction Level": "Some restrictions" + }, + { + "Country": "United Kingdom", + "Score": 99.0, + "Restriction Level": "Total" + }, + { + "Country": "Ireland", + "Score": 31.0, + "Restriction Level": "No restrictions" + }, + { + "Country": "Ireland", + "Score": 67.0, + "Restriction Level": "Some restrictions" + }, + { + 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"Restriction Level": "Some restrictions" + }, + { + "Country": "Sweden", + "Score": 98.0, + "Restriction Level": "Total" + }, + { + "Country": "Portugal", + "Score": 52.0, + "Restriction Level": "No restrictions" + }, + { + "Country": "Portugal", + "Score": 44.0, + "Restriction Level": "Some restrictions" + }, + { + "Country": "Portugal", + "Score": 96.0, + "Restriction Level": "Total" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "European countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding restriction level", + "role": "y" + }, + { + "name": "Restriction Level", + "importance": "primary", + "description": "The restriction level of the data point", + "role": "group" + } + ] + } + }, + "2195": { + "metadata": { + "title": "U.S. Congress: Most Used Hashtags", + "description": "Most used hashtags on Twitter by congressional Republicans and Democrats in Q1 2018", + "main_insight": "In Q1 2018, #taxreform was the most used hashtag by Republicans, while #sotu was the most used by Democrats. Both parties used #sotu frequently." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Hashtag": "#taxreform", + "Republicans": 2015 + } + ], + "score": 0.96, + "annotation": "The has a maximum value at #taxreform", + "reason": "The [{'Hashtag': '#taxreform', 'Republicans': 2015}] of has a maximum value of 2015, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Hashtag": "#schumershutdown", + "Republicans": 1219 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Republicans from #utpol to #schumershutdown.", + "reason": "The Republicans for increases from 467 at #utpol to 1219 at #schumershutdown, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Hashtag": "#trumpshutdown", + "Republicans": 0 + }, + { + "Hashtag": "#dreamers", + "Republicans": 0 + }, + { + "Hashtag": "#goptaxscam", + "Republicans": 0 + }, + { + "Hashtag": "#neveragain", + "Republicans": 0 + }, + { + "Hashtag": "#netneutrality", + "Republicans": 0 + }, + { + "Hashtag": "#protectdreamers", + "Republicans": 0 + }, + { + "Hashtag": "#dreamactnow", + "Republicans": 0 + }, + { + "Hashtag": "#blackhistorymonth", + "Republicans": 0 + } + ], + "score": 0.44, + "annotation": "The has minimum values at #trumpshutdown, #dreamers, #goptaxscam, #neveragain, #netneutrality, #protectdreamers, #dreamactnow, #blackhistorymonth", + "reason": "The [{'Hashtag': '#trumpshutdown', 'Republicans': 0}, {'Hashtag': '#dreamers', 'Republicans': 0}, {'Hashtag': '#goptaxscam', 'Republicans': 0}, {'Hashtag': '#neveragain', 'Republicans': 0}, {'Hashtag': '#netneutrality', 'Republicans': 0}, {'Hashtag': '#protectdreamers', 'Republicans': 0}, {'Hashtag': '#dreamactnow', 'Republicans': 0}, {'Hashtag': '#blackhistorymonth', 'Republicans': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Hashtag": "#taxreform", + "Republicans": 2015 + }, + { + "Hashtag": "#sotu", + "Republicans": 1601 + }, + { + "Hashtag": "#taxcutsandjobsact", + "Republicans": 1361 + }, + { + "Hashtag": "#schumershutdown", + "Republicans": 1219 + }, + { + "Hashtag": "#utpol", + "Republicans": 467 + }, + { + "Hashtag": "#puertorico", + "Republicans": 265 + }, + { + "Hashtag": "#releasethememo", + "Republicans": 265 + }, + { + "Hashtag": "#chip", + "Republicans": 258 + }, + { + "Hashtag": "#daca", + "Republicans": 251 + }, + { + "Hashtag": "#az05", + "Republicans": 225 + }, + { + "Hashtag": "#trumpshutdown", + "Republicans": 0 + }, + { + "Hashtag": "#dreamers", + "Republicans": 0 + }, + { + "Hashtag": "#goptaxscam", + "Republicans": 0 + }, + { + "Hashtag": "#neveragain", + "Republicans": 0 + }, + { + "Hashtag": "#netneutrality", + "Republicans": 0 + }, + { + "Hashtag": "#protectdreamers", + "Republicans": 0 + }, + { + "Hashtag": "#dreamactnow", + "Republicans": 0 + }, + { + "Hashtag": "#blackhistorymonth", + "Republicans": 0 + } + ], + "columns": [ + { + "name": "Hashtag", + "importance": "primary", + "description": "Hashtag used on Twitter", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Republicans", + "importance": "primary", + "description": "Number of times the hashtag was used by Republicans", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "475": { + "metadata": { + "title": "Hoppy With a Hint of Malt", + "description": "Share of U.S. beer drinkers who like the following taste characteristics in beer", + "main_insight": "Hoppy is the most preferred taste characteristic among US beer drinkers, followed by Malty, Sweet and Citrusy." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Taste Characteristic": "Earthy", + "Preference Share": 0.25 + } + ], + "score": 0.86, + "annotation": "The shows a sudden jump in Preference Share from Don't know to Earthy.", + "reason": "The Preference Share for increases from 0.15 at Don't know to 0.25 at Earthy, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Taste Characteristic": "Hoppy", + "Preference Share": 0.32 + } + ], + "score": 0.71, + "annotation": "The has a maximum value at Hoppy", + "reason": "The [{'Taste Characteristic': 'Hoppy', 'Preference Share': 0.32}] of has a maximum value of 0.32, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Taste Characteristic": "Bitter", + "Preference Share": 0.14 + }, + { + "Taste Characteristic": "Chocolatey", + "Preference Share": 0.14 + } + ], + "score": 0.69, + "annotation": "The has minimum values at Bitter, Chocolatey", + "reason": "The [{'Taste Characteristic': 'Bitter', 'Preference Share': 0.14}, {'Taste Characteristic': 'Chocolatey', 'Preference Share': 0.14}] of have minimum values of 0.14, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Taste Characteristic": "Hoppy", + "Preference Share": 0.32 + }, + { + "Taste Characteristic": "Malty", + "Preference Share": 0.3 + }, + { + "Taste Characteristic": "Sweet", + "Preference Share": 0.28 + }, + { + "Taste Characteristic": "Citrusy", + "Preference Share": 0.28 + }, + { + "Taste Characteristic": "Fruity", + "Preference Share": 0.27 + }, + { + "Taste Characteristic": "Earthy", + "Preference Share": 0.25 + }, + { + "Taste Characteristic": "Smoky", + "Preference Share": 0.15 + }, + { + "Taste Characteristic": "Don't know", + "Preference Share": 0.15 + }, + { + "Taste Characteristic": "Bitter", + "Preference Share": 0.14 + }, + { + "Taste Characteristic": "Chocolatey", + "Preference Share": 0.14 + } + ], + "columns": [ + { + "name": "Taste Characteristic", + "importance": "primary", + "description": "Different taste characteristics of beer", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Preference Share", + "importance": "primary", + "description": "Percentage of U.S. beer drinkers who like the corresponding taste characteristic", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "238": { + "metadata": { + "title": "Getting Into the Holiday Spirit, One Stream at a Time", + "description": "% of Spotify streams in the U.S. that were Christmas songs on given days in 2017", + "main_insight": "The percentage of Spotify streams that were Christmas songs in the U.S. remained low in November 2017, but increased dramatically in December, peaking at 22% on December 25th." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "December 25", + "Christmas Song Stream Percentage": 22.0 + } + ], + "score": 0.92, + "annotation": "The has a maximum value at December 25", + "reason": "The [{'Date': 'December 25', 'Christmas Song Stream Percentage': 22.0}] of has a maximum value of 22.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "December 24", + "Christmas Song Stream Percentage": 18.0 + } + ], + "score": 0.69, + "annotation": "The shows a sudden jump in Christmas Song Stream Percentage from December 23 to December 24.", + "reason": "The Christmas Song Stream Percentage for increases from 12.0 at December 23 to 18.0 at December 24, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "Early November", + "Christmas Song Stream Percentage": 2.0 + }, + { + "Date": "Mid November", + "Christmas Song Stream Percentage": 2.0 + }, + { + "Date": "Late December", + "Christmas Song Stream Percentage": 2.0 + } + ], + "score": 0.57, + "annotation": "The has minimum values at Early November, Mid November, Late December", + "reason": "The [{'Date': 'Early November', 'Christmas Song Stream Percentage': 2.0}, {'Date': 'Mid November', 'Christmas Song Stream Percentage': 2.0}, {'Date': 'Late December', 'Christmas Song Stream Percentage': 2.0}] of have minimum values of 2.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Date": "Early November", + "Christmas Song Stream Percentage": 2.0 + }, + { + "Date": "Mid November", + "Christmas Song Stream Percentage": 2.0 + }, + { + "Date": "Late November", + "Christmas Song Stream Percentage": 3.0 + }, + { + "Date": "Early December", + "Christmas Song Stream Percentage": 6.0 + }, + { + "Date": "Mid December", + "Christmas Song Stream Percentage": 7.0 + }, + { + "Date": "Late December", + "Christmas Song Stream Percentage": 10.0 + }, + { + "Date": "December 23", + "Christmas Song Stream Percentage": 12.0 + }, + { + "Date": "December 24", + "Christmas Song Stream Percentage": 18.0 + }, + { + "Date": "December 25", + "Christmas Song Stream Percentage": 22.0 + }, + { + "Date": "December 26", + "Christmas Song Stream Percentage": 8.0 + }, + { + "Date": "Late December", + "Christmas Song Stream Percentage": 2.0 + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Month and implied day in 2017", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Christmas Song Stream Percentage", + "importance": "primary", + "description": "Percentage of Spotify streams in the U.S. that were Christmas songs", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "340": { + "metadata": { + "title": "Gap Between Young Republican and Democratic Voters", + "description": "Voting of 18-to-29-year-olds in U.S. midterms from 2000 to 2018 (in percent)", + "main_insight": "Young voter turnout in U.S. midterm elections varies considerably between election cycles. Democratic voters aged 18-29 consistently outnumber Republican voters in the same age group, with the gap widening significantly in 2018." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2018, + "Units": 67.0, + "Party Affiliation": "Democratic" + }, + { + "Year": 2018, + "Units": 32.0, + "Party Affiliation": "Republican" + } + ], + "score": 0.95, + "annotation": "The Units of Democratic and Republican show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Democratic and Republican is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2000, + "Units": 49.0, + "Party Affiliation": "Democratic" + }, + { + "Year": 2000, + "Units": 48.0, + "Party Affiliation": "Republican" + }, + { + "Year": 2002, + "Units": 49.0, + "Party Affiliation": "Democratic" + }, + { + "Year": 2002, + "Units": 47.0, + "Party Affiliation": "Republican" + }, + { + "Year": 2004, + "Units": 55.0, + "Party Affiliation": "Democratic" + }, + { + "Year": 2004, + "Units": 44.0, + "Party Affiliation": "Republican" + }, + { + "Year": 2006, + "Units": 58.0, + "Party Affiliation": "Democratic" + }, + { + "Year": 2006, + "Units": 38.0, + "Party Affiliation": "Republican" + }, + { + "Year": 2008, + "Units": 60.0, + "Party Affiliation": "Democratic" + }, + { + "Year": 2008, + "Units": 33.0, + "Party Affiliation": "Republican" + }, + { + "Year": 2010, + "Units": 55.0, + "Party Affiliation": "Democratic" + }, + { + "Year": 2010, + "Units": 42.0, + "Party Affiliation": "Republican" + }, + { + "Year": 2012, + "Units": 60.0, + "Party Affiliation": "Democratic" + }, + { + "Year": 2012, + "Units": 37.0, + "Party Affiliation": "Republican" + }, + { + "Year": 2014, + "Units": 54.0, + "Party Affiliation": "Democratic" + }, + { + "Year": 2014, + "Units": 43.0, + "Party Affiliation": "Republican" + }, + { + "Year": 2016, + "Units": 55.0, + "Party Affiliation": "Democratic" + }, + { + "Year": 2016, + "Units": 40.0, + "Party Affiliation": "Republican" + }, + { + "Year": 2018, + "Units": 67.0, + "Party Affiliation": "Democratic" + }, + { + "Year": 2018, + "Units": 32.0, + "Party Affiliation": "Republican" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Midterm election year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding party affiliation", + "role": "y" + }, + { + "name": "Party Affiliation", + "importance": "primary", + "description": "The party affiliation of the data point", + "role": "group" + } + ] + } + }, + "2464": { + "metadata": { + "title": "The Fall and Rise of the U.S. Top 1 Percent", + "description": "Share of net wealth held by the top 1 percent of wealthiest people in selected countries", + "main_insight": "The share of net wealth held by the top 1% in the U.S. has significantly increased since the 1980s, surpassing that of the U.K. and France, after a period of decline." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2014, + "Score": 20.0, + "Country": "U.K." + }, + { + "Year": 2014, + "Score": 23.0, + "Country": "France" + } + ], + "score": 0.95, + "annotation": "The Score of U.K. and France show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between U.K. and France is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2014, + "Score": 39.0, + "Country": "U.S." + }, + { + "Year": 2014, + "Score": 23.0, + "Country": "France" + } + ], + "score": 0.73, + "annotation": "The Score of U.S. and France show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between U.S. and France is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2014, + "Score": 39.0, + "Country": "U.S." + }, + { + "Year": 2014, + "Score": 20.0, + "Country": "U.K." + } + ], + "score": 0.69, + "annotation": "The Score of U.S. and U.K. show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between U.S. and U.K. is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 1900, + "Score": 70.0, + "Country": "U.S." + }, + { + "Year": 1900, + "Score": 70.0, + "Country": "U.K." + }, + { + "Year": 1900, + "Score": 60.0, + "Country": "France" + }, + { + "Year": 1910, + "Score": 75.0, + "Country": "U.S." + }, + { + "Year": 1910, + "Score": 70.0, + "Country": "U.K." + }, + { + "Year": 1910, + "Score": 58.0, + "Country": "France" + }, + { + "Year": 1920, + "Score": 35.0, + "Country": "U.S." + }, + { + "Year": 1920, + "Score": 60.0, + "Country": "U.K." + }, + { + "Year": 1920, + "Score": 55.0, + "Country": "France" + }, + { + "Year": 1930, + "Score": 40.0, + "Country": "U.S." + }, + { + "Year": 1930, + "Score": 58.0, + "Country": "U.K." + }, + { + "Year": 1930, + "Score": 50.0, + "Country": "France" + }, + { + "Year": 1940, + "Score": 35.0, + "Country": "U.S." + }, + { + "Year": 1940, + "Score": 55.0, + "Country": "U.K." + }, + { + "Year": 1940, + "Score": 30.0, + "Country": "France" + }, + { + "Year": 1950, + "Score": 30.0, + "Country": "U.S." + }, + { + "Year": 1950, + "Score": 45.0, + "Country": "U.K." + }, + { + "Year": 1950, + "Score": 40.0, + "Country": "France" + }, + { + "Year": 1960, + "Score": 28.0, + "Country": "U.S." + }, + { + "Year": 1960, + "Score": 35.0, + "Country": "U.K." + }, + { + "Year": 1960, + "Score": 30.0, + "Country": "France" + }, + { + "Year": 1970, + "Score": 25.0, + "Country": "U.S." + }, + { + "Year": 1970, + "Score": 30.0, + "Country": "U.K." + }, + { + "Year": 1970, + "Score": 25.0, + "Country": "France" + }, + { + "Year": 1980, + "Score": 20.0, + "Country": "U.S." + }, + { + "Year": 1980, + "Score": 22.0, + "Country": "U.K." + }, + { + "Year": 1980, + "Score": 18.0, + "Country": "France" + }, + { + "Year": 1990, + "Score": 30.0, + "Country": "U.S." + }, + { + "Year": 1990, + "Score": 18.0, + "Country": "U.K." + }, + { + "Year": 1990, + "Score": 18.0, + "Country": "France" + }, + { + "Year": 2000, + "Score": 35.0, + "Country": "U.S." + }, + { + "Year": 2000, + "Score": 20.0, + "Country": "U.K." + }, + { + "Year": 2000, + "Score": 20.0, + "Country": "France" + }, + { + "Year": 2010, + "Score": 37.0, + "Country": "U.S." + }, + { + "Year": 2010, + "Score": 21.0, + "Country": "U.K." + }, + { + "Year": 2010, + "Score": 22.0, + "Country": "France" + }, + { + "Year": 2014, + "Score": 39.0, + "Country": "U.S." + }, + { + "Year": 2014, + "Score": 20.0, + "Country": "U.K." + }, + { + "Year": 2014, + "Score": 23.0, + "Country": "France" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 1900 to 2014", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding country", + "role": "y" + }, + { + "name": "Country", + "importance": "primary", + "description": "The country of the data point", + "role": "group" + } + ] + } + }, + "1943": { + "metadata": { + "title": "Nearly Half of Americans Aren't Prepared for Retirement", + "description": "Percentage that think they will fare well in retirement", + "main_insight": "A significant portion of Americans, especially those aged 18-49, don't believe they will fare well in retirement. The percentage of people not feeling prepared decreases with age." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Age Group": "Age 50+", + "Units": 32.0, + "Importance": "Not very/Not at all" + }, + { + "Age Group": "Age 50+", + "Units": 29.0, + "Importance": "Extremely/Very" + } + ], + "score": 1.0, + "annotation": "The Units of Not very/Not at all and Extremely/Very show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Not very/Not at all and Extremely/Very is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Age Group": "Age 50+", + "Units": 32.0, + "Importance": "Not very/Not at all" + }, + { + "Age Group": "Age 50+", + "Units": 39.0, + "Importance": "Somewhat" + } + ], + "score": 1.0, + "annotation": "The Units of Not very/Not at all and Somewhat show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Not very/Not at all and Somewhat is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Age Group": "Age 50+", + "Units": 39.0, + "Importance": "Somewhat" + }, + { + "Age Group": "Age 50+", + "Units": 29.0, + "Importance": "Extremely/Very" + } + ], + "score": 1.0, + "annotation": "The Units of Somewhat and Extremely/Very show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Somewhat and Extremely/Very is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "Age 50+", + "Units": 39.0, + "Importance": "Somewhat" + } + ], + "score": 0.88, + "annotation": "The Somewhat has the largest proportion in Age 50+.", + "reason": "The Units of Somewhat in Age 50+ accounts for 0.39, larger than all other Importance, of the total of Units of all Importance." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Age Group": "Age 50+", + "Units": 39.0, + "Importance": "Somewhat" + } + ], + "score": 0.88, + "annotation": "The Somewhat shows a sudden jump in Units from All Adults to Age 50+.", + "reason": "The Units for Somewhat increases from 33.0 at All Adults to 39.0 at Age 50+, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Age Group": "All Adults", + "Units": 45.0, + "Importance": "Not very/Not at all" + }, + { + "Age Group": "All Adults", + "Units": 33.0, + "Importance": "Somewhat" + }, + { + "Age Group": "All Adults", + "Units": 21.0, + "Importance": "Extremely/Very" + }, + { + "Age Group": "Age 18-49", + "Units": 56.0, + "Importance": "Not very/Not at all" + }, + { + "Age Group": "Age 18-49", + "Units": 29.0, + "Importance": "Somewhat" + }, + { + "Age Group": "Age 18-49", + "Units": 14.0, + "Importance": "Extremely/Very" + }, + { + "Age Group": "Age 50+", + "Units": 32.0, + "Importance": "Not very/Not at all" + }, + { + "Age Group": "Age 50+", + "Units": 39.0, + "Importance": "Somewhat" + }, + { + "Age Group": "Age 50+", + "Units": 29.0, + "Importance": "Extremely/Very" + } + ], + "columns": [ + { + "name": "Age Group", + "importance": "primary", + "description": "The age groups of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding importance", + "role": "y" + }, + { + "name": "Importance", + "importance": "primary", + "description": "The importance of the data point", + "role": "group" + } + ] + } + }, + "1436": { + "metadata": { + "title": "Hotels are the most popular accommodation type in European countries", + "description": "Type of accommodation used on the last trip in 2021, by country", + "main_insight": "Hotels are the most popular form of accommodation in France, Germany, Spain, and the UK, significantly outpacing short-term rentals, staying with friends/family, and Inns/B&Bs." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Accommodation Type": "Inn/bed & breakfast", + "Ratio": 0.08, + "Country": "Spain" + }, + { + "Accommodation Type": "Inn/bed & breakfast", + "Ratio": 0.17, + "Country": "UK" + } + ], + "score": 0.99, + "annotation": "The Ratio of Spain and UK show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Spain and UK is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Accommodation Type": "Inn/bed & breakfast", + "Ratio": 0.09, + "Country": "France" + }, + { + "Accommodation Type": "Inn/bed & breakfast", + "Ratio": 0.08, + "Country": "Germany" + } + ], + "score": 0.98, + "annotation": "The Ratio of France and Germany show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between France and Germany is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Accommodation Type": "Inn/bed & breakfast", + "Ratio": 0.08, + "Country": "Germany" + }, + { + "Accommodation Type": "Inn/bed & breakfast", + "Ratio": 0.08, + "Country": "Spain" + } + ], + "score": 0.97, + "annotation": "The Ratio of Germany and Spain show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Germany and Spain is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Accommodation Type": "Inn/bed & breakfast", + "Ratio": 0.08, + "Country": "Germany" + }, + { + "Accommodation Type": "Inn/bed & breakfast", + "Ratio": 0.17, + "Country": "UK" + } + ], + "score": 0.96, + "annotation": "The Ratio of Germany and UK show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Germany and UK is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Accommodation Type": "Hotel", + "Ratio": 0.53, + "Country": "Spain" + } + ], + "score": 0.94, + "annotation": "The Spain shows a sudden jump in Ratio from Short-term rental to Hotel.", + "reason": "The Ratio for Spain increases from 0.17 at Short-term rental to 0.53 at Hotel, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Accommodation Type": "Hotel", + "Ratio": 0.39, + "Country": "France" + }, + { + "Accommodation Type": "Hotel", + "Ratio": 0.43, + "Country": "Germany" + }, + { + "Accommodation Type": "Hotel", + "Ratio": 0.53, + "Country": "Spain" + }, + { + "Accommodation Type": "Hotel", + "Ratio": 0.44, + "Country": "UK" + }, + { + "Accommodation Type": "Short-term rental", + "Ratio": 0.26, + "Country": "France" + }, + { + "Accommodation Type": "Short-term rental", + "Ratio": 0.23, + "Country": "Germany" + }, + { + "Accommodation Type": "Short-term rental", + "Ratio": 0.17, + "Country": "Spain" + }, + { + "Accommodation Type": "Short-term rental", + "Ratio": 0.22, + "Country": "UK" + }, + { + "Accommodation Type": "Stayed with friends or family", + "Ratio": 0.18, + "Country": "France" + }, + { + "Accommodation Type": "Stayed with friends or family", + "Ratio": 0.14, + "Country": "Germany" + }, + { + "Accommodation Type": "Stayed with friends or family", + "Ratio": 0.13, + "Country": "Spain" + }, + { + "Accommodation Type": "Stayed with friends or family", + "Ratio": 0.16, + "Country": "UK" + }, + { + "Accommodation Type": "Inn/bed & breakfast", + "Ratio": 0.09, + "Country": "France" + }, + { + "Accommodation Type": "Inn/bed & breakfast", + "Ratio": 0.08, + "Country": "Germany" + }, + { + "Accommodation Type": "Inn/bed & breakfast", + "Ratio": 0.08, + "Country": "Spain" + }, + { + "Accommodation Type": "Inn/bed & breakfast", + "Ratio": 0.17, + "Country": "UK" + } + ], + "columns": [ + { + "name": "Accommodation Type", + "importance": "primary", + "description": "Different types of accommodation", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding country", + "role": "y" + }, + { + "name": "Country", + "importance": "primary", + "description": "The country of the data point", + "role": "group" + } + ] + } + }, + "1128": { + "metadata": { + "title": "The Top 5 Worldwide Importers of Champagne", + "description": "Top importers of champagne in 2017, in millions of bottles of 75 cl", + "main_insight": "The United Kingdom and the United States were by far the largest importers of champagne in 2017, importing significantly more than Japan, Germany, and Belgium." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Millions of Bottles": 23.1 + } + ], + "score": 0.86, + "annotation": "The shows a sudden jump in Millions of Bottles from Japan to United States.", + "reason": "The Millions of Bottles for increases from 12.9 at Japan to 23.1 at United States, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United Kingdom", + "Millions of Bottles": 27.8 + } + ], + "score": 0.75, + "annotation": "The has a maximum value at United Kingdom", + "reason": "The [{'Country': 'United Kingdom', 'Millions of Bottles': 27.8}] of has a maximum value of 27.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Belgium", + "Millions of Bottles": 9.1 + } + ], + "score": 0.6, + "annotation": "The has a minimum value at Belgium", + "reason": "The [{'Country': 'Belgium', 'Millions of Bottles': 9.1}] of has a mainimum value of 9.1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "United Kingdom", + "Millions of Bottles": 27.8 + }, + { + "Country": "United States", + "Millions of Bottles": 23.1 + }, + { + "Country": "Japan", + "Millions of Bottles": 12.9 + }, + { + "Country": "Germany", + "Millions of Bottles": 12.3 + }, + { + "Country": "Belgium", + "Millions of Bottles": 9.1 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Importing country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Millions of Bottles", + "importance": "primary", + "description": "Number of 75cl bottles imported (in millions)", + "unit": "Millions of Bottles", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2062": { + "metadata": { + "title": "A Robot May Take Your Job Sooner Than You Think", + "description": "Forecast ratio of human/machine working hours in 2018 and 2022, by task type", + "main_insight": "The chart shows a significant projected increase in the proportion of working hours performed by machines across various task types between 2018 and 2022. The largest increases are in tasks related to information and data processing." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Task Type": "Information and data processing", + "Score": 53.0, + "Source": "Human 2018" + }, + { + "Task Type": "Information and data processing", + "Score": 47.0, + "Source": "Machine 2018" + } + ], + "score": 1.0, + "annotation": "The Score of Human 2018 and Machine 2018 show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Human 2018 and Machine 2018 is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Task Type": "Information and data processing", + "Score": 38.0, + "Source": "Human 2022" + }, + { + "Task Type": "Information and data processing", + "Score": 62.0, + "Source": "Machine 2022" + } + ], + "score": 1.0, + "annotation": "The Score of Human 2022 and Machine 2022 show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Human 2022 and Machine 2022 is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Task Type": "Information and data processing", + "Score": 53.0, + "Source": "Human 2018" + }, + { + "Task Type": "Information and data processing", + "Score": 38.0, + "Source": "Human 2022" + } + ], + "score": 0.97, + "annotation": "The Score of Human 2018 and Human 2022 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Human 2018 and Human 2022 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Task Type": "Information and data processing", + "Score": 53.0, + "Source": "Human 2018" + }, + { + "Task Type": "Information and data processing", + "Score": 62.0, + "Source": "Machine 2022" + } + ], + "score": 0.97, + "annotation": "The Score of Human 2018 and Machine 2022 show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Human 2018 and Machine 2022 is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Task Type": "Information and data processing", + "Score": 47.0, + "Source": "Machine 2018" + }, + { + "Task Type": "Information and data processing", + "Score": 38.0, + "Source": "Human 2022" + } + ], + "score": 0.97, + "annotation": "The Score of Machine 2018 and Human 2022 show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Machine 2018 and Human 2022 is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Task Type": "Reasoning and decision-making", + "Score": 81.0, + "Source": "Human 2018" + }, + { + "Task Type": "Reasoning and decision-making", + "Score": 19.0, + "Source": "Machine 2018" + }, + { + "Task Type": "Reasoning and decision-making", + "Score": 72.0, + "Source": "Human 2022" + }, + { + "Task Type": "Reasoning and decision-making", + "Score": 28.0, + "Source": "Machine 2022" + }, + { + "Task Type": "Reasoning and decision-making", + "Score": 47.0, + "Source": "Growth Rate" + }, + { + "Task Type": "Coordinating, developing, managing and advising", + "Score": 81.0, + "Source": "Human 2018" + }, + { + "Task Type": "Coordinating, developing, managing and advising", + "Score": 19.0, + "Source": "Machine 2018" + }, + { + "Task Type": "Coordinating, developing, managing and advising", + "Score": 71.0, + "Source": "Human 2022" + }, + { + "Task Type": "Coordinating, developing, managing and advising", + "Score": 29.0, + "Source": "Machine 2022" + }, + { + "Task Type": "Coordinating, developing, managing and advising", + "Score": 53.0, + "Source": "Growth Rate" + }, + { + "Task Type": "Communicating and interacting", + "Score": 77.0, + "Source": "Human 2018" + }, + { + "Task Type": "Communicating and interacting", + "Score": 23.0, + "Source": "Machine 2018" + }, + { + "Task Type": "Communicating and interacting", + "Score": 69.0, + "Source": "Human 2022" + }, + { + "Task Type": "Communicating and interacting", + "Score": 31.0, + "Source": "Machine 2022" + }, + { + "Task Type": "Communicating and interacting", + "Score": 35.0, + "Source": "Growth Rate" + }, + { + "Task Type": "Administering", + "Score": 72.0, + "Source": "Human 2018" + }, + { + "Task Type": "Administering", + "Score": 28.0, + "Source": "Machine 2018" + }, + { + "Task Type": "Administering", + "Score": 56.0, + "Source": "Human 2022" + }, + { + "Task Type": "Administering", + "Score": 44.0, + "Source": "Machine 2022" + }, + { + "Task Type": "Administering", + "Score": 57.0, + "Source": "Growth Rate" + }, + { + "Task Type": "Performing physical and manual work activities", + "Score": 69.0, + "Source": "Human 2018" + }, + { + "Task Type": "Performing physical and manual work activities", + "Score": 31.0, + "Source": "Machine 2018" + }, + { + "Task Type": "Performing physical and manual work activities", + "Score": 56.0, + "Source": "Human 2022" + }, + { + "Task Type": "Performing physical and manual work activities", + "Score": 44.0, + "Source": "Machine 2022" + }, + { + "Task Type": "Performing physical and manual work activities", + "Score": 42.0, + "Source": "Growth Rate" + }, + { + "Task Type": "Identifying and evaluating job-relevant information", + "Score": 71.0, + "Source": "Human 2018" + }, + { + "Task Type": "Identifying and evaluating job-relevant information", + "Score": 29.0, + "Source": "Machine 2018" + }, + { + "Task Type": "Identifying and evaluating job-relevant information", + "Score": 54.0, + "Source": "Human 2022" + }, + { + "Task Type": "Identifying and evaluating job-relevant information", + "Score": 46.0, + "Source": "Machine 2022" + }, + { + "Task Type": "Identifying and evaluating job-relevant information", + "Score": 59.0, + "Source": "Growth Rate" + }, + { + "Task Type": "Performing complex and technical activities", + "Score": 66.0, + "Source": "Human 2018" + }, + { + "Task Type": "Performing complex and technical activities", + "Score": 34.0, + "Source": "Machine 2018" + }, + { + "Task Type": "Performing complex and technical activities", + "Score": 54.0, + "Source": "Human 2022" + }, + { + "Task Type": "Performing complex and technical activities", + "Score": 46.0, + "Source": "Machine 2022" + }, + { + "Task Type": "Performing complex and technical activities", + "Score": 35.0, + "Source": "Growth Rate" + }, + { + "Task Type": "Looking for and receiving job-related information", + "Score": 64.0, + "Source": "Human 2018" + }, + { + "Task Type": "Looking for and receiving job-related information", + "Score": 36.0, + "Source": "Machine 2018" + }, + { + "Task Type": "Looking for and receiving job-related information", + "Score": 45.0, + "Source": "Human 2022" + }, + { + "Task Type": "Looking for and receiving job-related information", + "Score": 55.0, + "Source": "Machine 2022" + }, + { + "Task Type": "Looking for and receiving job-related information", + "Score": 53.0, + "Source": "Growth Rate" + }, + { + "Task Type": "Information and data processing", + "Score": 53.0, + "Source": "Human 2018" + }, + { + "Task Type": "Information and data processing", + "Score": 47.0, + "Source": "Machine 2018" + }, + { + "Task Type": "Information and data processing", + "Score": 38.0, + "Source": "Human 2022" + }, + { + "Task Type": "Information and data processing", + "Score": 62.0, + "Source": "Machine 2022" + }, + { + "Task Type": "Information and data processing", + "Score": 32.0, + "Source": "Growth Rate" + } + ], + "columns": [ + { + "name": "Task Type", + "importance": "primary", + "description": "Type of work task", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding source", + "role": "y" + }, + { + "name": "Source", + "importance": "primary", + "description": "The source of the data point", + "role": "group" + } + ] + } + }, + "429": { + "metadata": { + "title": "Can the Wii U Revive the Video Game Industry?", + "description": "Video game hardware sales in the United States (in million U.S. dollars)", + "main_insight": "Video game hardware sales generally declined significantly from 2011 to 2012 across all months, suggesting a challenging market for the Wii U." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "Oct", + "Units": 350.0, + "Sales Metrics": "2011 Sales" + }, + { + "Month": "Oct", + "Units": 220.0, + "Sales Metrics": "2012 Sales" + } + ], + "score": 0.99, + "annotation": "The Units of 2011 Sales and 2012 Sales show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2011 Sales and 2012 Sales is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Dec", + "Units": 1850.0, + "Sales Metrics": "2011 Sales" + } + ], + "score": 0.98, + "annotation": "The 2011 Sales has a maximum value at Dec", + "reason": "The [{'Month': 'Dec', 'Units': 1850.0, 'Sales Metrics': '2011 Sales'}] of 2011 Sales has a maximum value of 1850.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Dec", + "Units": 1330.0, + "Sales Metrics": "2012 Sales" + } + ], + "score": 0.97, + "annotation": "The 2012 Sales has a maximum value at Dec", + "reason": "The [{'Month': 'Dec', 'Units': 1330.0, 'Sales Metrics': '2012 Sales'}] of 2012 Sales has a maximum value of 1330.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Nov", + "Units": -9.0, + "Sales Metrics": "YoY Change" + } + ], + "score": 0.96, + "annotation": "The YoY Change has a maximum value at Nov", + "reason": "The [{'Month': 'Nov', 'Units': -9.0, 'Sales Metrics': 'YoY Change'}] of YoY Change has a maximum value of -9.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Dec", + "Units": 1850.0, + "Sales Metrics": "2011 Sales" + } + ], + "score": 0.83, + "annotation": "The 2011 Sales shows a sudden jump in Units from Nov to Dec.", + "reason": "The Units for 2011 Sales increases from 1050.0 at Nov to 1850.0 at Dec, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Month": "Nov", + "Units": 1050.0, + "Sales Metrics": "2011 Sales" + }, + { + "Month": "Nov", + "Units": 950.0, + "Sales Metrics": "2012 Sales" + }, + { + "Month": "Nov", + "Units": -9.0, + "Sales Metrics": "YoY Change" + }, + { + "Month": "Dec", + "Units": 1850.0, + "Sales Metrics": "2011 Sales" + }, + { + "Month": "Dec", + "Units": 1330.0, + "Sales Metrics": "2012 Sales" + }, + { + "Month": "Dec", + "Units": -28.0, + "Sales Metrics": "YoY Change" + }, + { + "Month": "Jan", + "Units": 450.0, + "Sales Metrics": "2011 Sales" + }, + { + "Month": "Jan", + "Units": 280.0, + "Sales Metrics": "2012 Sales" + }, + { + "Month": "Jan", + "Units": -38.0, + "Sales Metrics": "YoY Change" + }, + { + "Month": "Feb", + "Units": 470.0, + "Sales Metrics": "2011 Sales" + }, + { + "Month": "Feb", + "Units": 380.0, + "Sales Metrics": "2012 Sales" + }, + { + "Month": "Feb", + "Units": -18.0, + "Sales Metrics": "YoY Change" + }, + { + "Month": "Mar", + "Units": 600.0, + "Sales Metrics": "2011 Sales" + }, + { + "Month": "Mar", + "Units": 390.0, + "Sales Metrics": "2012 Sales" + }, + { + "Month": "Mar", + "Units": -35.0, + "Sales Metrics": "YoY Change" + }, + { + "Month": "Apr", + "Units": 350.0, + "Sales Metrics": "2011 Sales" + }, + { + "Month": "Apr", + "Units": 240.0, + "Sales Metrics": "2012 Sales" + }, + { + "Month": "Apr", + "Units": -32.0, + "Sales Metrics": "YoY Change" + }, + { + "Month": "May", + "Units": 310.0, + "Sales Metrics": "2011 Sales" + }, + { + "Month": "May", + "Units": 190.0, + "Sales Metrics": "2012 Sales" + }, + { + "Month": "May", + "Units": -39.0, + "Sales Metrics": "YoY Change" + }, + { + "Month": "Jun", + "Units": 370.0, + "Sales Metrics": "2011 Sales" + }, + { + "Month": "Jun", + "Units": 205.0, + "Sales Metrics": "2012 Sales" + }, + { + "Month": "Jun", + "Units": -45.0, + "Sales Metrics": "YoY Change" + }, + { + "Month": "Jul", + "Units": 250.0, + "Sales Metrics": "2011 Sales" + }, + { + "Month": "Jul", + "Units": 170.0, + "Sales Metrics": "2012 Sales" + }, + { + "Month": "Jul", + "Units": -32.0, + "Sales Metrics": "YoY Change" + }, + { + "Month": "Aug", + "Units": 280.0, + "Sales Metrics": "2011 Sales" + }, + { + "Month": "Aug", + "Units": 170.0, + "Sales Metrics": "2012 Sales" + }, + { + "Month": "Aug", + "Units": -39.0, + "Sales Metrics": "YoY Change" + }, + { + "Month": "Sept", + "Units": 360.0, + "Sales Metrics": "2011 Sales" + }, + { + "Month": "Sept", + "Units": 220.0, + "Sales Metrics": "2012 Sales" + }, + { + "Month": "Sept", + "Units": -39.0, + "Sales Metrics": "YoY Change" + }, + { + "Month": "Oct", + "Units": 350.0, + "Sales Metrics": "2011 Sales" + }, + { + "Month": "Oct", + "Units": 220.0, + "Sales Metrics": "2012 Sales" + }, + { + "Month": "Oct", + "Units": -37.0, + "Sales Metrics": "YoY Change" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sales metrics", + "role": "y" + }, + { + "name": "Sales Metrics", + "importance": "primary", + "description": "The sales metrics of the data point", + "role": "group" + } + ] + } + }, + "165": { + "metadata": { + "title": "When People Get Married Around the World", + "description": "Median first marriage age of men and women in selected countries (2018)", + "main_insight": "The median age of first marriage varies significantly across different countries, with men consistently marrying later than women in all countries shown." + }, + "chart_type": "Dot Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Mali", + "Women": 19 + } + ], + "score": 0.89, + "annotation": "The has a minimum value at Mali", + "reason": "The [{'Country': 'Mali', 'Women': 19}] of has a mainimum value of 19, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Italy", + "Women": 32 + }, + { + "Country": "Germany", + "Women": 32 + }, + { + "Country": "Ireland", + "Women": 32 + } + ], + "score": 0.73, + "annotation": "The has maximum values at Italy, Germany, Ireland", + "reason": "The [{'Country': 'Italy', 'Women': 32}, {'Country': 'Germany', 'Women': 32}, {'Country': 'Ireland', 'Women': 32}] of have maximum values of 32, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Mozambique", + "Women": 20 + }, + { + "Country": "Nepal", + "Women": 21 + }, + { + "Country": "India", + "Women": 22 + }, + { + "Country": "Senegal", + "Women": 23 + }, + { + "Country": "Colombia", + "Women": 24 + }, + { + "Country": "Rwanda", + "Women": 25 + }, + { + "Country": "Morocco", + "Women": 26 + }, + { + "Country": "Albania", + "Women": 27 + }, + { + "Country": "United States", + "Women": 28 + }, + { + "Country": "Japan", + "Women": 29 + }, + { + "Country": "Estonia", + "Women": 30 + }, + { + "Country": "Slovenia", + "Women": 31 + }, + { + "Country": "Italy", + "Women": 32 + } + ], + "score": 0.48, + "annotation": "The exhibits multiple sudden jumps in Women, transitioning from Mali, Mozambique, Nepal, India, Senegal, Colombia, China, Israel, Albania, United States, Japan, South Korea, Slovenia to Mozambique, Nepal, India, Senegal, Colombia, Rwanda, Morocco, Albania, United States, Japan, Estonia, Slovenia, Italy.", + "reason": "Across these points, the Women for increases from 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 at Mali, Mozambique, Nepal, India, Senegal, Colombia, China, Israel, Albania, United States, Japan, South Korea, Slovenia to 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32 at Mozambique, Nepal, India, Senegal, Colombia, Rwanda, Morocco, Albania, United States, Japan, Estonia, Slovenia, Italy, suggesting significant shifts in value." + } + ], + "data": { + "data": [ + { + "Country": "Estonia", + "Women": 30 + }, + { + "Country": "Slovenia", + "Women": 31 + }, + { + "Country": "Italy", + "Women": 32 + }, + { + "Country": "Namibia", + "Women": 30 + }, + { + "Country": "Germany", + "Women": 32 + }, + { + "Country": "South Korea", + "Women": 30 + }, + { + "Country": "Ireland", + "Women": 32 + }, + { + "Country": "Morocco", + "Women": 26 + }, + { + "Country": "Albania", + "Women": 27 + }, + { + "Country": "Japan", + "Women": 29 + }, + { + "Country": "Senegal", + "Women": 23 + }, + { + "Country": "United States", + "Women": 28 + }, + { + "Country": "Israel", + "Women": 26 + }, + { + "Country": "Rwanda", + "Women": 25 + }, + { + "Country": "China", + "Women": 25 + }, + { + "Country": "Colombia", + "Women": 24 + }, + { + "Country": "Mali", + "Women": 19 + }, + { + "Country": "India", + "Women": 22 + }, + { + "Country": "Nepal", + "Women": 21 + }, + { + "Country": "Mozambique", + "Women": 20 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Women", + "importance": "primary", + "description": "Median age of first marriage for women", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1879": { + "metadata": { + "title": "Smartphones Aren't as Ubiquitous as You Think", + "description": "Share of adults in selected countries that report owning the following phone types (in %)", + "main_insight": "Smartphone ownership varies significantly across countries, with Sweden having the highest percentage and Greece the lowest among the countries shown. A considerable portion of adults in many countries still use non-smart mobile phones or no mobile phones at all." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Greece", + "Score": 46.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "Greece", + "Score": 43.0, + "Phone Ownership": "Mobile phone (not smart)" + } + ], + "score": 0.97, + "annotation": "The Score of Smartphone and Mobile phone (not smart) show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Smartphone and Mobile phone (not smart) is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Sweden", + "Score": 2.0, + "Phone Ownership": "No mobile phone" + } + ], + "score": 0.91, + "annotation": "The No mobile phone has the smallest proportion in Sweden.", + "reason": "The Score of No mobile phone in Sweden accounts for 0.020202020202020204, smaller than all other Phone Ownership, of the total of Score of all Phone Ownership." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Canada", + "Score": 14.0, + "Phone Ownership": "No mobile phone" + } + ], + "score": 0.88, + "annotation": "The No mobile phone has a maximum value at Canada", + "reason": "The [{'Country': 'Canada', 'Score': 14.0, 'Phone Ownership': 'No mobile phone'}] of No mobile phone has a maximum value of 14.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Spain", + "Score": 3.0, + "Phone Ownership": "No mobile phone" + } + ], + "score": 0.87, + "annotation": "The No mobile phone has the smallest proportion in Spain.", + "reason": "The Score of No mobile phone in Spain accounts for 0.03, smaller than all other Phone Ownership, of the total of Score of all Phone Ownership." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Greece", + "Score": 46.0, + "Phone Ownership": "Smartphone" + } + ], + "score": 0.87, + "annotation": "The Smartphone has the largest proportion in Greece.", + "reason": "The Score of Smartphone in Greece accounts for 0.46, larger than all other Phone Ownership, of the total of Score of all Phone Ownership." + } + ], + "data": { + "data": [ + { + "Country": "Sweden", + "Score": 80.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "Sweden", + "Score": 17.0, + "Phone Ownership": "Mobile phone (not smart)" + }, + { + "Country": "Sweden", + "Score": 2.0, + "Phone Ownership": "No mobile phone" + }, + { + "Country": "Netherlands", + "Score": 79.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "Netherlands", + "Score": 17.0, + "Phone Ownership": "Mobile phone (not smart)" + }, + { + "Country": "Netherlands", + "Score": 4.0, + "Phone Ownership": "No mobile phone" + }, + { + "Country": "Spain", + "Score": 79.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "Spain", + "Score": 18.0, + "Phone Ownership": "Mobile phone (not smart)" + }, + { + "Country": "Spain", + "Score": 3.0, + "Phone Ownership": "No mobile phone" + }, + { + "Country": "Australia", + "Score": 79.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "Australia", + "Score": 16.0, + "Phone Ownership": "Mobile phone (not smart)" + }, + { + "Country": "Australia", + "Score": 5.0, + "Phone Ownership": "No mobile phone" + }, + { + "Country": "U.S.", + "Score": 77.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "U.S.", + "Score": 18.0, + "Phone Ownership": "Mobile phone (not smart)" + }, + { + "Country": "U.S.", + "Score": 5.0, + "Phone Ownership": "No mobile phone" + }, + { + "Country": "Canada", + "Score": 72.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "Canada", + "Score": 14.0, + "Phone Ownership": "Mobile phone (not smart)" + }, + { + "Country": "Canada", + "Score": 14.0, + "Phone Ownership": "No mobile phone" + }, + { + "Country": "UK", + "Score": 72.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "UK", + "Score": 21.0, + "Phone Ownership": "Mobile phone (not smart)" + }, + { + "Country": "UK", + "Score": 7.0, + "Phone Ownership": "No mobile phone" + }, + { + "Country": "Germany", + "Score": 66.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "Germany", + "Score": 27.0, + "Phone Ownership": "Mobile phone (not smart)" + }, + { + "Country": "Germany", + "Score": 6.0, + "Phone Ownership": "No mobile phone" + }, + { + "Country": "Italy", + "Score": 63.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "Italy", + "Score": 26.0, + "Phone Ownership": "Mobile phone (not smart)" + }, + { + "Country": "Italy", + "Score": 11.0, + "Phone Ownership": "No mobile phone" + }, + { + "Country": "France", + "Score": 58.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "France", + "Score": 32.0, + "Phone Ownership": "Mobile phone (not smart)" + }, + { + "Country": "France", + "Score": 9.0, + "Phone Ownership": "No mobile phone" + }, + { + "Country": "Japan", + "Score": 55.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "Japan", + "Score": 37.0, + "Phone Ownership": "Mobile phone (not smart)" + }, + { + "Country": "Japan", + "Score": 8.0, + "Phone Ownership": "No mobile phone" + }, + { + "Country": "Poland", + "Score": 52.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "Poland", + "Score": 37.0, + "Phone Ownership": "Mobile phone (not smart)" + }, + { + "Country": "Poland", + "Score": 11.0, + "Phone Ownership": "No mobile phone" + }, + { + "Country": "Hungary", + "Score": 49.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "Hungary", + "Score": 41.0, + "Phone Ownership": "Mobile phone (not smart)" + }, + { + "Country": "Hungary", + "Score": 10.0, + "Phone Ownership": "No mobile phone" + }, + { + "Country": "Greece", + "Score": 46.0, + "Phone Ownership": "Smartphone" + }, + { + "Country": "Greece", + "Score": 43.0, + "Phone Ownership": "Mobile phone (not smart)" + }, + { + "Country": "Greece", + "Score": 11.0, + "Phone Ownership": "No mobile phone" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding phone ownership", + "role": "y" + }, + { + "name": "Phone Ownership", + "importance": "primary", + "description": "The phone ownership of the data point", + "role": "group" + } + ] + } + }, + "229": { + "metadata": { + "title": "What Cloud Vendors Do Tech Professionals Use?", + "description": "Share of industry professionals planning to use or using cloud platform services by vendor", + "main_insight": "AWS is the most popular cloud vendor among tech professionals, with the highest current usage rate. Microsoft Azure is second and has similar usage rate plus those that plan to use." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Cloud Vendor": "Microsoft Azure", + "Ratio": 0.52, + "Usage Intent": "Currently Using" + } + ], + "score": 0.87, + "annotation": "The Currently Using shows a sudden jump in Ratio from Google Cloud to Microsoft Azure.", + "reason": "The Ratio for Currently Using increases from 0.19 at Google Cloud to 0.52 at Microsoft Azure, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Cloud Vendor": "AWS (Amazon Web Service)", + "Ratio": 0.61, + "Usage Intent": "Currently Using" + } + ], + "score": 0.84, + "annotation": "The Currently Using has a maximum value at AWS (Amazon Web Service)", + "reason": "The [{'Cloud Vendor': 'AWS (Amazon Web Service)', 'Ratio': 0.61, 'Usage Intent': 'Currently Using'}] of Currently Using has a maximum value of 0.61, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Cloud Vendor": "VMware Cloud on AWS", + "Ratio": 0.13, + "Usage Intent": "Currently Using" + }, + { + "Cloud Vendor": "VMware Cloud on AWS", + "Ratio": 0.13, + "Usage Intent": "Plan to use" + } + ], + "score": 0.8, + "annotation": "The Currently Using, Plan to use all have the largest proportion in VMware Cloud on AWS.", + "reason": "The Ratio of Currently Using, Plan to use in VMware Cloud on AWS all account for 0.5, larger than all other Usage Intent, of the total of Ratio of all Usage Intent." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Cloud Vendor": "Alibaba Cloud", + "Ratio": 0.04, + "Usage Intent": "Currently Using" + }, + { + "Cloud Vendor": "Alibaba Cloud", + "Ratio": 0.04, + "Usage Intent": "Plan to use" + } + ], + "score": 0.8, + "annotation": "The Currently Using, Plan to use all have the largest proportion in Alibaba Cloud.", + "reason": "The Ratio of Currently Using, Plan to use in Alibaba Cloud all account for 0.5, larger than all other Usage Intent, of the total of Ratio of all Usage Intent." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Cloud Vendor": "Google Cloud", + "Ratio": 0.13, + "Usage Intent": "Plan to use" + } + ], + "score": 0.8, + "annotation": "The Plan to use shows a sudden jump in Ratio from Oracle Cloud to Google Cloud.", + "reason": "The Ratio for Plan to use increases from 0.09 at Oracle Cloud to 0.13 at Google Cloud, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Cloud Vendor": "AWS (Amazon Web Service)", + "Ratio": 0.61, + "Usage Intent": "Currently Using" + }, + { + "Cloud Vendor": "AWS (Amazon Web Service)", + "Ratio": 0.06, + "Usage Intent": "Plan to use" + }, + { + "Cloud Vendor": "Microsoft Azure", + "Ratio": 0.52, + "Usage Intent": "Currently Using" + }, + { + "Cloud Vendor": "Microsoft Azure", + "Ratio": 0.09, + "Usage Intent": "Plan to use" + }, + { + "Cloud Vendor": "Google Cloud", + "Ratio": 0.19, + "Usage Intent": "Currently Using" + }, + { + "Cloud Vendor": "Google Cloud", + "Ratio": 0.13, + "Usage Intent": "Plan to use" + }, + { + "Cloud Vendor": "VMware Cloud on AWS", + "Ratio": 0.13, + "Usage Intent": "Currently Using" + }, + { + "Cloud Vendor": "VMware Cloud on AWS", + "Ratio": 0.13, + "Usage Intent": "Plan to use" + }, + { + "Cloud Vendor": "IBM Cloud", + "Ratio": 0.1, + "Usage Intent": "Currently Using" + }, + { + "Cloud Vendor": "IBM Cloud", + "Ratio": 0.07, + "Usage Intent": "Plan to use" + }, + { + "Cloud Vendor": "Oracle Cloud", + "Ratio": 0.07, + "Usage Intent": "Currently Using" + }, + { + "Cloud Vendor": "Oracle Cloud", + "Ratio": 0.09, + "Usage Intent": "Plan to use" + }, + { + "Cloud Vendor": "Alibaba Cloud", + "Ratio": 0.04, + "Usage Intent": "Currently Using" + }, + { + "Cloud Vendor": "Alibaba Cloud", + "Ratio": 0.04, + "Usage Intent": "Plan to use" + } + ], + "columns": [ + { + "name": "Cloud Vendor", + "importance": "primary", + "description": "Name of the cloud service provider", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding usage intent", + "role": "y" + }, + { + "name": "Usage Intent", + "importance": "primary", + "description": "The usage intent of the data point", + "role": "group" + } + ] + } + }, + "1223": { + "metadata": { + "title": "Targeted Ads: Interesting or Intrusive?", + "description": "View of targeted online advertising among Americans, by age group", + "main_insight": "Older Americans are more likely to view targeted ads as an inappropriate use of personal data, while younger age groups are more divided on the issue." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Age Group": "Total", + "Units": 51.0, + "Ad Sentiment": "Targeted ads are an inappropriate use of personal data" + }, + { + "Age Group": "Total", + "Units": 27.0, + "Ad Sentiment": "Targeted ads are a convenient way to see products I'm interested in" + } + ], + "score": 0.95, + "annotation": "The Units of Targeted ads are an inappropriate use of personal data and Targeted ads are a convenient way to see products I'm interested in show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Targeted ads are an inappropriate use of personal data and Targeted ads are a convenient way to see products I'm interested in is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Age Group": "55+", + "Units": 61.0, + "Ad Sentiment": "Targeted ads are an inappropriate use of personal data" + } + ], + "score": 0.82, + "annotation": "The Targeted ads are an inappropriate use of personal data has a maximum value at 55+", + "reason": "The [{'Age Group': '55+', 'Units': 61.0, 'Ad Sentiment': 'Targeted ads are an inappropriate use of personal data'}] of Targeted ads are an inappropriate use of personal data has a maximum value of 61.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Age Group": "18-24", + "Units": 34.0, + "Ad Sentiment": "Targeted ads are an inappropriate use of personal data" + } + ], + "score": 0.81, + "annotation": "The Targeted ads are an inappropriate use of personal data has a minimum value at 18-24", + "reason": "The [{'Age Group': '18-24', 'Units': 34.0, 'Ad Sentiment': 'Targeted ads are an inappropriate use of personal data'}] of Targeted ads are an inappropriate use of personal data has a mainimum value of 34.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Age Group": "55+", + "Units": 18.0, + "Ad Sentiment": "Targeted ads are a convenient way to see products I'm interested in" + } + ], + "score": 0.8, + "annotation": "The Targeted ads are a convenient way to see products I'm interested in has a minimum value at 55+", + "reason": "The [{'Age Group': '55+', 'Units': 18.0, 'Ad Sentiment': \"Targeted ads are a convenient way to see products I'm interested in\"}] of Targeted ads are a convenient way to see products I'm interested in has a mainimum value of 18.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "18-24", + "Units": 41.0, + "Ad Sentiment": "Targeted ads are a convenient way to see products I'm interested in" + } + ], + "score": 0.79, + "annotation": "The Targeted ads are a convenient way to see products I'm interested in accounts for the majority in 18-24.", + "reason": "The Units of Targeted ads are a convenient way to see products I'm interested in in 18-24 accounts for 0.5466666666666666 (more than 50%) of the total of Units of all Ad Sentiment." + } + ], + "data": { + "data": [ + { + "Age Group": "18-24", + "Units": 34.0, + "Ad Sentiment": "Targeted ads are an inappropriate use of personal data" + }, + { + "Age Group": "18-24", + "Units": 41.0, + "Ad Sentiment": "Targeted ads are a convenient way to see products I'm interested in" + }, + { + "Age Group": "25-34", + "Units": 45.0, + "Ad Sentiment": "Targeted ads are an inappropriate use of personal data" + }, + { + "Age Group": "25-34", + "Units": 37.0, + "Ad Sentiment": "Targeted ads are a convenient way to see products I'm interested in" + }, + { + "Age Group": "35-44", + "Units": 48.0, + "Ad Sentiment": "Targeted ads are an inappropriate use of personal data" + }, + { + "Age Group": "35-44", + "Units": 27.0, + "Ad Sentiment": "Targeted ads are a convenient way to see products I'm interested in" + }, + { + "Age Group": "45-54", + "Units": 45.0, + "Ad Sentiment": "Targeted ads are an inappropriate use of personal data" + }, + { + "Age Group": "45-54", + "Units": 31.0, + "Ad Sentiment": "Targeted ads are a convenient way to see products I'm interested in" + }, + { + "Age Group": "55+", + "Units": 61.0, + "Ad Sentiment": "Targeted ads are an inappropriate use of personal data" + }, + { + "Age Group": "55+", + "Units": 18.0, + "Ad Sentiment": "Targeted ads are a convenient way to see products I'm interested in" + }, + { + "Age Group": "Total", + "Units": 51.0, + "Ad Sentiment": "Targeted ads are an inappropriate use of personal data" + }, + { + "Age Group": "Total", + "Units": 27.0, + "Ad Sentiment": "Targeted ads are a convenient way to see products I'm interested in" + } + ], + "columns": [ + { + "name": "Age Group", + "importance": "primary", + "description": "Age ranges of survey respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding ad sentiment", + "role": "y" + }, + { + "name": "Ad Sentiment", + "importance": "primary", + "description": "The ad sentiment of the data point", + "role": "group" + } + ] + } + }, + "1942": { + "metadata": { + "title": "Where Digital Media Is Making Strides", + "description": "Estimated share of digital products in all media revenue (in percent)", + "main_insight": "The share of digital products in all media revenue is projected to increase across all regions between 2023 and 2029, with the U.S. and Asia having the highest projected shares." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Region": "Europe", + "Units": 37.0, + "Year": "2029" + }, + { + "Region": "Europe", + "Units": 30.0, + "Year": "2023" + } + ], + "score": 0.98, + "annotation": "The Units of 2029 and 2023 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2029 and 2023 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "World", + "Units": 41.0, + "Year": "2023" + } + ], + "score": 0.9, + "annotation": "The 2023 shows a sudden jump in Units from Europe to World.", + "reason": "The Units for 2023 increases from 30.0 at Europe to 41.0 at World, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "World", + "Units": 48.0, + "Year": "2029" + } + ], + "score": 0.89, + "annotation": "The 2029 shows a sudden jump in Units from Europe to World.", + "reason": "The Units for 2029 increases from 37.0 at Europe to 48.0 at World, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Asia", + "Units": 53.0, + "Year": "2029" + } + ], + "score": 0.79, + "annotation": "The 2029 accounts for the majority in Asia.", + "reason": "The Units of 2029 in Asia accounts for 0.53 (more than 50%) of the total of Units of all Year." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "World", + "Units": 48.0, + "Year": "2029" + } + ], + "score": 0.79, + "annotation": "The 2029 accounts for the majority in World.", + "reason": "The Units of 2029 in World accounts for 0.5393258426966292 (more than 50%) of the total of Units of all Year." + } + ], + "data": { + "data": [ + { + "Region": "Asia", + "Units": 53.0, + "Year": "2029" + }, + { + "Region": "Asia", + "Units": 47.0, + "Year": "2023" + }, + { + "Region": "U.S.", + "Units": 55.0, + "Year": "2029" + }, + { + "Region": "U.S.", + "Units": 45.0, + "Year": "2023" + }, + { + "Region": "World", + "Units": 48.0, + "Year": "2029" + }, + { + "Region": "World", + "Units": 41.0, + "Year": "2023" + }, + { + "Region": "Europe", + "Units": 37.0, + "Year": "2029" + }, + { + "Region": "Europe", + "Units": 30.0, + "Year": "2023" + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Geographic region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "1726": { + "metadata": { + "title": "Rosé Tinted Glasses", + "description": "Potential profit made from fraudulently selling wine from Spain as 'French'", + "main_insight": "The infographic shows the potential profit gained by mislabeling Spanish rosé as French rosé. There is a significant price difference between Spanish and French rosé, leading to substantial profits when 10 million bottles are fraudulently sold." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Wine Type": "French Rosé (conservative estimate)", + "Bulk price per liter": 0.75 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in Bulk price per liter from Spanish Rosé to French Rosé (conservative estimate).", + "reason": "The Bulk price per liter for increases from 0.34 at Spanish Rosé to 0.75 at French Rosé (conservative estimate), indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Wine Type": "Spanish Rosé", + "Bulk price per liter": 0.34 + } + ], + "score": 0.65, + "annotation": "The has a minimum value at Spanish Rosé", + "reason": "The [{'Wine Type': 'Spanish Rosé', 'Bulk price per liter': 0.34}] of has a mainimum value of 0.34, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Wine Type": "French Rosé (liberal estimate)", + "Bulk price per liter": 0.9 + } + ], + "score": 0.51, + "annotation": "The has a maximum value at French Rosé (liberal estimate)", + "reason": "The [{'Wine Type': 'French Rosé (liberal estimate)', 'Bulk price per liter': 0.9}] of has a maximum value of 0.9, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Wine Type": "Spanish Rosé", + "Bulk price per liter": 0.34 + }, + { + "Wine Type": "French Rosé (conservative estimate)", + "Bulk price per liter": 0.75 + }, + { + "Wine Type": "French Rosé (liberal estimate)", + "Bulk price per liter": 0.9 + } + ], + "columns": [ + { + "name": "Wine Type", + "importance": "primary", + "description": "The type of wine: Spanish Rosé, French Rosé (conservative estimate), or French Rosé (liberal estimate)", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Bulk price per liter", + "importance": "primary", + "description": "The cost of the wine per liter in Euros", + "unit": "€", + "data_type": "number", + "role": "y" + } + ] + } + }, + "436": { + "metadata": { + "title": "U.S. Trade With Cuba is Worth Millions", + "description": "U.S. monthly exports to Cuba (in million U.S. dollars)", + "main_insight": "U.S. exports to Cuba peaked in April 2008 at $83.2 million and have fluctuated since, reaching $29.6 million in February 2019. There have been some periods of volatility but generally show a cyclical pattern." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2000, + "Exports": 0.0 + }, + { + "Year": 2001, + "Exports": 0.0 + }, + { + "Year": 2002, + "Exports": 15.0 + }, + { + "Year": 2003, + "Exports": 25.0 + }, + { + "Year": 2004, + "Exports": 40.0 + }, + { + "Year": 2005, + "Exports": 35.0 + }, + { + "Year": 2006, + "Exports": 30.0 + }, + { + "Year": 2007, + "Exports": 50.0 + }, + { + "Year": 2008, + "Exports": 83.2 + }, + { + "Year": 2009, + "Exports": 45.0 + }, + { + "Year": 2010, + "Exports": 35.0 + }, + { + "Year": 2011, + "Exports": 40.0 + }, + { + "Year": 2012, + "Exports": 50.0 + }, + { + "Year": 2013, + "Exports": 35.0 + }, + { + "Year": 2014, + "Exports": 45.0 + }, + { + "Year": 2015, + "Exports": 15.0 + }, + { + "Year": 2016, + "Exports": 25.0 + }, + { + "Year": 2017, + "Exports": 30.0 + }, + { + "Year": 2018, + "Exports": 25.0 + }, + { + "Year": 2019, + "Exports": 29.6 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of U.S. exports to Cuba", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Exports", + "importance": "primary", + "description": "U.S. monthly exports to Cuba (in million U.S. dollars)", + "unit": "Million U.S. Dollars", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2318": { + "metadata": { + "title": "Russia 2018 an Average World Cup in Terms of Goals", + "description": "Average number of goals per game scored at each FIFA World Cup from 1930 to 2018", + "main_insight": "The 2018 World Cup had an average number of goals per game (2.64) compared to other World Cups, and sits slightly above the 1998-2018 average and significantly below the peak in 1954." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "World Cup Year": 1930, + "Units": 3.88, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1930, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1930, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1934, + "Units": 4.11, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1934, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1934, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1938, + "Units": 4.67, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1938, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1938, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1950, + "Units": 4.0, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1950, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1950, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1954, + "Units": 5.38, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1954, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1954, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1958, + "Units": 3.6, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1958, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1958, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1962, + "Units": 2.78, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1962, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1962, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1966, + "Units": 2.78, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1966, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1966, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1970, + "Units": 2.97, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1970, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1970, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1974, + "Units": 2.55, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1974, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1974, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1978, + "Units": 2.68, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1978, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1978, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1982, + "Units": 2.81, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1982, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1982, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1986, + "Units": 2.54, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1986, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1986, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1990, + "Units": 2.21, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1990, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1990, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1994, + "Units": 2.71, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1994, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1994, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 1998, + "Units": 2.67, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 1998, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 1998, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 2002, + "Units": 2.52, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 2002, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 2002, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 2006, + "Units": 2.3, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 2006, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 2006, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 2010, + "Units": 2.27, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 2010, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 2010, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 2014, + "Units": 2.67, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 2014, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 2014, + "Units": 2.51, + "Statistic": "Average 1998-2018" + }, + { + "World Cup Year": 2018, + "Units": 2.64, + "Statistic": "Goals per game" + }, + { + "World Cup Year": 2018, + "Units": 3.08, + "Statistic": "Average 1930-2018" + }, + { + "World Cup Year": 2018, + "Units": 2.51, + "Statistic": "Average 1998-2018" + } + ], + "columns": [ + { + "name": "World Cup Year", + "importance": "primary", + "description": "Year of the FIFA World Cup", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding statistic", + "role": "y" + }, + { + "name": "Statistic", + "importance": "primary", + "description": "The statistic of the data point", + "role": "group" + } + ] + } + }, + "2326": { + "metadata": { + "title": "Which U.S. Sports Do Other Countries Follow?", + "description": "Share of respondents who said they followed the following U.S. sports leagues", + "main_insight": "The NBA is the most followed U.S. sports league in China, while the NFL is the most popular in the U.S., UK, and Germany among the listed leagues. The popularity of specific U.S. sports leagues varies significantly across different countries." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Sport League": "MLS", + "Score": 22.0, + "Country": "U.S." + }, + { + "Sport League": "MLS", + "Score": 12.0, + "Country": "UK" + } + ], + "score": 0.96, + "annotation": "The Score of U.S. and UK show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between U.S. and UK is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Sport League": "MLS", + "Score": 28.0, + "Country": "China" + } + ], + "score": 0.92, + "annotation": "The China has the largest proportion in MLS.", + "reason": "The Score of China in MLS accounts for 0.32941176470588235, larger than all other Country, of the total of Score of all Country." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "U.S.", + "Sport League": "total", + "Score": 150.0 + } + ], + "score": 0.91, + "annotation": "The total value of U.S. has the largest proportion.", + "reason": "The total value of Score of U.S. accounts for 0.3875968992248062, larger than all other Country, of the total of Score of all Country." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Sport League": "NBA", + "Score": 62.0, + "Country": "China" + } + ], + "score": 0.9, + "annotation": "The China has the largest proportion in NBA.", + "reason": "The Score of China in NBA accounts for 0.43661971830985913, larger than all other Country, of the total of Score of all Country." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Sport League": "NBA", + "Score": 62.0, + "Country": "China" + } + ], + "score": 0.89, + "annotation": "The China shows a sudden jump in Score from MLS to NBA.", + "reason": "The Score for China increases from 28.0 at MLS to 62.0 at NBA, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Sport League": "NFL", + "Score": 55.0, + "Country": "U.S." + }, + { + "Sport League": "NFL", + "Score": 25.0, + "Country": "UK" + }, + { + "Sport League": "NFL", + "Score": 12.0, + "Country": "Germany" + }, + { + "Sport League": "NFL", + "Score": 8.0, + "Country": "China" + }, + { + "Sport League": "NBA", + "Score": 42.0, + "Country": "U.S." + }, + { + "Sport League": "NBA", + "Score": 18.0, + "Country": "UK" + }, + { + "Sport League": "NBA", + "Score": 20.0, + "Country": "Germany" + }, + { + "Sport League": "NBA", + "Score": 62.0, + "Country": "China" + }, + { + "Sport League": "MLB", + "Score": 31.0, + "Country": "U.S." + }, + { + "Sport League": "MLB", + "Score": 12.0, + "Country": "UK" + }, + { + "Sport League": "MLB", + "Score": 6.0, + "Country": "Germany" + }, + { + "Sport League": "MLB", + "Score": 11.0, + "Country": "China" + }, + { + "Sport League": "MLS", + "Score": 22.0, + "Country": "U.S." + }, + { + "Sport League": "MLS", + "Score": 12.0, + "Country": "UK" + }, + { + "Sport League": "MLS", + "Score": 23.0, + "Country": "Germany" + }, + { + "Sport League": "MLS", + "Score": 28.0, + "Country": "China" + } + ], + "columns": [ + { + "name": "Sport League", + "importance": "primary", + "description": "US Sports League", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding country", + "role": "y" + }, + { + "name": "Country", + "importance": "primary", + "description": "The country of the data point", + "role": "group" + } + ] + } + }, + "2176": { + "metadata": { + "title": "Terrorism Cost the EU €180 Billion Between 2004 & 2016", + "description": "Estimated GDP cost of terrorism in the EU from 2004 to 2016*", + "main_insight": "Terrorism cost the EU approximately €180 billion between 2004 and 2016, with the United Kingdom, France, and Spain experiencing the highest economic impact." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United Kingdom", + "GDP Cost": 43.71 + } + ], + "score": 0.98, + "annotation": "The has a maximum value at United Kingdom", + "reason": "The [{'Country': 'United Kingdom', 'GDP Cost': 43.71}] of has a maximum value of 43.71, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Spain", + "GDP Cost": 40.8 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in GDP Cost from Germany to Spain.", + "reason": "The GDP Cost for increases from 19.17 at Germany to 40.8 at Spain, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Latvia", + "GDP Cost": 0 + }, + { + "Country": "Lithuania", + "GDP Cost": 0 + }, + { + "Country": "Luxembourg", + "GDP Cost": 0 + }, + { + "Country": "Slovenia", + "GDP Cost": 0 + } + ], + "score": 0.35, + "annotation": "The has minimum values at Latvia, Lithuania, Luxembourg, Slovenia", + "reason": "The [{'Country': 'Latvia', 'GDP Cost': 0}, {'Country': 'Lithuania', 'GDP Cost': 0}, {'Country': 'Luxembourg', 'GDP Cost': 0}, {'Country': 'Slovenia', 'GDP Cost': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "United Kingdom", + "GDP Cost": 43.71 + }, + { + "Country": "France", + "GDP Cost": 43.01 + }, + { + "Country": "Spain", + "GDP Cost": 40.8 + }, + { + "Country": "Germany", + "GDP Cost": 19.17 + }, + { + "Country": "Greece", + "GDP Cost": 10.4 + }, + { + "Country": "Belgium", + "GDP Cost": 7.83 + }, + { + "Country": "Ireland", + "GDP Cost": 4.34 + }, + { + "Country": "Sweden", + "GDP Cost": 2.87 + }, + { + "Country": "Italy", + "GDP Cost": 2.21 + }, + { + "Country": "Netherlands", + "GDP Cost": 1.7 + }, + { + "Country": "Austria", + "GDP Cost": 0.86 + }, + { + "Country": "Cyprus", + "GDP Cost": 0.86 + }, + { + "Country": "Denmark", + "GDP Cost": 0.68 + }, + { + "Country": "Finland", + "GDP Cost": 0.48 + }, + { + "Country": "Czech Republic", + "GDP Cost": 0.38 + }, + { + "Country": "Bulgaria", + "GDP Cost": 0.22 + }, + { + "Country": "Slovakia", + "GDP Cost": 0.15 + }, + { + "Country": "Hungary", + "GDP Cost": 0.11 + }, + { + "Country": "Croatia", + "GDP Cost": 0.04 + }, + { + "Country": "Estonia", + "GDP Cost": 0.03 + }, + { + "Country": "Malta", + "GDP Cost": 0.03 + }, + { + "Country": "Portugal", + "GDP Cost": 0.03 + }, + { + "Country": "Poland", + "GDP Cost": 0.02 + }, + { + "Country": "Romania", + "GDP Cost": 0.01 + }, + { + "Country": "Latvia", + "GDP Cost": 0 + }, + { + "Country": "Lithuania", + "GDP Cost": 0 + }, + { + "Country": "Luxembourg", + "GDP Cost": 0 + }, + { + "Country": "Slovenia", + "GDP Cost": 0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "EU Member State", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "GDP Cost", + "importance": "primary", + "description": "Estimated GDP cost of terrorism in billions of Euros", + "unit": "Billion Euros", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2374": { + "metadata": { + "title": "America's Post 9/11 Medal Of Honor Recipients", + "description": "Number of Medal of Honor recipients in Afghanistan & Iraq (living and posthumous)", + "main_insight": "The visualization shows the number of Medal of Honor recipients, categorized by military branch and conflict (Afghanistan and Iraq), further distinguishing between living and posthumous awards. The Army has the most recipients in both conflicts, and the majority of awards in Afghanistan were given to Army members." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Branch": "Army", + "Location": "Afghanistan" + }, + { + "Branch": "Navy", + "Location": "Afghanistan" + }, + { + "Branch": "Marine Corps", + "Location": "Afghanistan" + }, + { + "Branch": "Air Force", + "Location": "Afghanistan" + }, + { + "Branch": "Army", + "Location": "Iraq" + }, + { + "Branch": "Marine Corps", + "Location": "Iraq" + }, + { + "Branch": "Navy", + "Location": "Iraq" + } + ], + "columns": [ + { + "name": "Branch", + "importance": "primary", + "description": "Military branch", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Location", + "importance": "primary", + "description": "Location of service (Afghanistan or Iraq)", + "unit": "none", + "data_type": "string", + "role": "y" + } + ] + } + }, + "1562": { + "metadata": { + "title": "Public Debt Edges up in U.S., EU and China", + "description": "Gross public debt (in % of gross domestic product)", + "main_insight": "Public debt as a percentage of GDP is projected to increase in the U.S., China, and the EU between 2017 and 2024. The US has the highest public debt, and is the only one decreasing for a period, before resuming its rise." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Units": 121.0, + "Region": "United States" + }, + { + "Year": 2024, + "Units": 82.7, + "Region": "EU" + } + ], + "score": 0.87, + "annotation": "The Units of United States and EU show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between United States and EU is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Units": 121.0, + "Region": "United States" + }, + { + "Year": 2024, + "Units": 90.1, + "Region": "China" + } + ], + "score": 0.64, + "annotation": "The Units of United States and China show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between United States and China is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Units": 82.7, + "Region": "EU" + }, + { + "Year": 2024, + "Units": 90.1, + "Region": "China" + } + ], + "score": 0.26, + "annotation": "The Units of EU and China show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between EU and China is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2017, + "Units": 105.0, + "Region": "United States" + }, + { + "Year": 2017, + "Units": 83.0, + "Region": "EU" + }, + { + "Year": 2017, + "Units": 51.0, + "Region": "China" + }, + { + "Year": 2018, + "Units": 106.0, + "Region": "United States" + }, + { + "Year": 2018, + "Units": 80.0, + "Region": "EU" + }, + { + "Year": 2018, + "Units": 54.0, + "Region": "China" + }, + { + "Year": 2019, + "Units": 108.0, + "Region": "United States" + }, + { + "Year": 2019, + "Units": 78.0, + "Region": "EU" + }, + { + "Year": 2019, + "Units": 60.0, + "Region": "China" + }, + { + "Year": 2020, + "Units": 134.0, + "Region": "United States" + }, + { + "Year": 2020, + "Units": 97.0, + "Region": "EU" + }, + { + "Year": 2020, + "Units": 69.0, + "Region": "China" + }, + { + "Year": 2021, + "Units": 126.0, + "Region": "United States" + }, + { + "Year": 2021, + "Units": 94.0, + "Region": "EU" + }, + { + "Year": 2021, + "Units": 72.0, + "Region": "China" + }, + { + "Year": 2022, + "Units": 121.0, + "Region": "United States" + }, + { + "Year": 2022, + "Units": 90.0, + "Region": "EU" + }, + { + "Year": 2022, + "Units": 77.0, + "Region": "China" + }, + { + "Year": 2023, + "Units": 120.0, + "Region": "United States" + }, + { + "Year": 2023, + "Units": 84.0, + "Region": "EU" + }, + { + "Year": 2023, + "Units": 83.0, + "Region": "China" + }, + { + "Year": 2024, + "Units": 121.0, + "Region": "United States" + }, + { + "Year": 2024, + "Units": 82.7, + "Region": "EU" + }, + { + "Year": 2024, + "Units": 90.1, + "Region": "China" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding region", + "role": "y" + }, + { + "name": "Region", + "importance": "primary", + "description": "The region of the data point", + "role": "group" + } + ] + } + }, + "2281": { + "metadata": { + "title": "Consumer Trust: Does Company Location Matter?", + "description": "Share of respondents trusting companies headquartered in the following countries (in percent)", + "main_insight": "Consumer trust in companies varies significantly based on the company's headquarters location and the respondent's region. German companies are generally the most trusted, followed by U.S. companies, and then Chinese companies." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Respondents From": "Southeast Asia", + "Germany": 66.0 + } + ], + "score": 0.74, + "annotation": "The has a maximum value at Southeast Asia", + "reason": "The [{'Respondents From': 'Southeast Asia', 'Germany': 66.0}] of has a maximum value of 66.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Respondents From": "All respondents*", + "Germany": 62.0 + }, + { + "Respondents From": "Other Asia", + "Germany": 64.0 + }, + { + "Respondents From": "Southeast Asia", + "Germany": 66.0 + } + ], + "score": 0.68, + "annotation": "The exhibits multiple sudden jumps in Germany, transitioning from Europe, All respondents*, Other Asia to All respondents*, Other Asia, Southeast Asia.", + "reason": "Across these points, the Germany for increases from 60.0, 62.0, 64.0 at Europe, All respondents*, Other Asia to 62.0, 64.0, 66.0 at All respondents*, Other Asia, Southeast Asia, suggesting significant shifts in value." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Respondents From": "Americans", + "Germany": 59.0 + } + ], + "score": 0.65, + "annotation": "The has a minimum value at Americans", + "reason": "The [{'Respondents From': 'Americans', 'Germany': 59.0}] of has a mainimum value of 59.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Respondents From": "All respondents*", + "Germany": 62.0 + }, + { + "Respondents From": "Southeast Asia", + "Germany": 66.0 + }, + { + "Respondents From": "Other Asia", + "Germany": 64.0 + }, + { + "Respondents From": "Europe", + "Germany": 60.0 + }, + { + "Respondents From": "Americans", + "Germany": 59.0 + } + ], + "columns": [ + { + "name": "Respondents From", + "importance": "primary", + "description": "Region of survey respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Germany", + "importance": "primary", + "description": "Percentage of respondents trusting companies headquartered in Germany", + "unit": "percent", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1880": { + "metadata": { + "title": "Olympics Draw Millions, But Rarely Sell Out", + "description": "Number of tickets sold/available for Summer Olympic Games since 1984", + "main_insight": "The Summer Olympics consistently draw millions of spectators, but ticket sales rarely reach full capacity. Atlanta 1996 had the highest number of both available and sold tickets." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Olympics": "Paris 2024", + "Units": 9.0, + "Ticket Status": "Tickets sold" + }, + { + "Olympics": "Paris 2024", + "Units": 10.0, + "Ticket Status": "Tickets available" + } + ], + "score": 0.95, + "annotation": "The Units of Tickets sold and Tickets available show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Tickets sold and Tickets available is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Olympics": "Atlanta 1996", + "Units": 11.0, + "Ticket Status": "Tickets available" + } + ], + "score": 0.86, + "annotation": "The Tickets available has a maximum value at Atlanta 1996", + "reason": "The [{'Olympics': 'Atlanta 1996', 'Units': 11.0, 'Ticket Status': 'Tickets available'}] of Tickets available has a maximum value of 11.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Olympics": "London 2012", + "Units": 8.5, + "Ticket Status": "Tickets available" + } + ], + "score": 0.8, + "annotation": "The Tickets available accounts for the majority in London 2012.", + "reason": "The Units of Tickets available in London 2012 accounts for 0.5089820359281437 (more than 50%) of the total of Units of all Ticket Status." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Olympics": "Beijing 2008", + "Units": 6.8, + "Ticket Status": "Tickets available" + } + ], + "score": 0.8, + "annotation": "The Tickets available accounts for the majority in Beijing 2008.", + "reason": "The Units of Tickets available in Beijing 2008 accounts for 0.5112781954887218 (more than 50%) of the total of Units of all Ticket Status." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Olympics": "Rio de Janeiro 2016", + "Units": 6.8, + "Ticket Status": "Tickets available" + } + ], + "score": 0.79, + "annotation": "The Tickets available accounts for the majority in Rio de Janeiro 2016.", + "reason": "The Units of Tickets available in Rio de Janeiro 2016 accounts for 0.5230769230769231 (more than 50%) of the total of Units of all Ticket Status." + } + ], + "data": { + "data": [ + { + "Olympics": "Los Angeles 1984", + "Units": 5.7, + "Ticket Status": "Tickets sold" + }, + { + "Olympics": "Los Angeles 1984", + "Units": 6.9, + "Ticket Status": "Tickets available" + }, + { + "Olympics": "Seoul 1988", + "Units": 3.3, + "Ticket Status": "Tickets sold" + }, + { + "Olympics": "Seoul 1988", + "Units": 4.4, + "Ticket Status": "Tickets available" + }, + { + "Olympics": "Barcelona 1992", + "Units": 3.0, + "Ticket Status": "Tickets sold" + }, + { + "Olympics": "Barcelona 1992", + "Units": 3.9, + "Ticket Status": "Tickets available" + }, + { + "Olympics": "Atlanta 1996", + "Units": 8.3, + "Ticket Status": "Tickets sold" + }, + { + "Olympics": "Atlanta 1996", + "Units": 11.0, + "Ticket Status": "Tickets available" + }, + { + "Olympics": "Sydney 2000", + "Units": 6.7, + "Ticket Status": "Tickets sold" + }, + { + "Olympics": "Sydney 2000", + "Units": 7.6, + "Ticket Status": "Tickets available" + }, + { + "Olympics": "Athens 2004", + "Units": 3.8, + "Ticket Status": "Tickets sold" + }, + { + "Olympics": "Athens 2004", + "Units": 5.3, + "Ticket Status": "Tickets available" + }, + { + "Olympics": "Beijing 2008", + "Units": 6.5, + "Ticket Status": "Tickets sold" + }, + { + "Olympics": "Beijing 2008", + "Units": 6.8, + "Ticket Status": "Tickets available" + }, + { + "Olympics": "London 2012", + "Units": 8.2, + "Ticket Status": "Tickets sold" + }, + { + "Olympics": "London 2012", + "Units": 8.5, + "Ticket Status": "Tickets available" + }, + { + "Olympics": "Rio de Janeiro 2016", + "Units": 6.2, + "Ticket Status": "Tickets sold" + }, + { + "Olympics": "Rio de Janeiro 2016", + "Units": 6.8, + "Ticket Status": "Tickets available" + }, + { + "Olympics": "Paris 2024", + "Units": 9.0, + "Ticket Status": "Tickets sold" + }, + { + "Olympics": "Paris 2024", + "Units": 10.0, + "Ticket Status": "Tickets available" + } + ], + "columns": [ + { + "name": "Olympics", + "importance": "primary", + "description": "Summer Olympic Games Location and Year", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding ticket status", + "role": "y" + }, + { + "name": "Ticket Status", + "importance": "primary", + "description": "The ticket status of the data point", + "role": "group" + } + ] + } + }, + "752": { + "metadata": { + "title": "Advanced Microchip Production Relies on Taiwan", + "description": "Share of global semiconductor wafer fabrication capacity, by type and location in 2019 (in percent)", + "main_insight": "Taiwan dominates the production of advanced logic chips (<10 nm) with 92% of global capacity. Taiwan also holds significant shares in other categories, making it a critical hub for global semiconductor production." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Microchip Type": "Discrete, Analog & others", + "Score": 27, + "Region": "Taiwan" + }, + { + "Microchip Type": "Discrete, Analog & others", + "Score": 74, + "Region": "Other Logic" + } + ], + "score": 1.0, + "annotation": "The Score of Taiwan and Other Logic show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Taiwan and Other Logic is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Microchip Type": "Discrete, Analog & others", + "Score": 17, + "Region": "China" + }, + { + "Microchip Type": "Discrete, Analog & others", + "Score": 7, + "Region": "Others" + } + ], + "score": 0.99, + "annotation": "The Score of China and Others show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between China and Others is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Microchip Type": "Memory", + "Score": 20, + "Region": "South Korea" + } + ], + "score": 0.97, + "annotation": "The South Korea shows a sudden jump in Score from Other Logic to Memory.", + "reason": "The Score for South Korea increases from 8 at Other Logic to 20 at Memory, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Microchip Type": "Discrete, Analog & others", + "Score": 22, + "Region": "Europe" + } + ], + "score": 0.97, + "annotation": "The Europe shows a sudden jump in Score from Other Logic to Discrete, Analog & others.", + "reason": "The Score for Europe increases from 7 at Other Logic to 22 at Discrete, Analog & others, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Microchip Type": "Discrete, Analog & others", + "Score": 17, + "Region": "China" + }, + { + "Microchip Type": "Other Logic", + "Score": 8, + "Region": "South Korea" + } + ], + "score": 0.97, + "annotation": "The Score of China and South Korea show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between China and South Korea is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Microchip Type": "Memory", + "Score": 44, + "Region": "Taiwan" + }, + { + "Microchip Type": "Memory", + "Score": 56, + "Region": "Other Logic" + }, + { + "Microchip Type": "Memory", + "Score": 5, + "Region": "U.S." + }, + { + "Microchip Type": "Memory", + "Score": 14, + "Region": "China" + }, + { + "Microchip Type": "Memory", + "Score": 20, + "Region": "South Korea" + }, + { + "Microchip Type": "Memory", + "Score": 11, + "Region": "Japan" + }, + { + "Microchip Type": "Memory", + "Score": 4, + "Region": "Others" + }, + { + "Microchip Type": "Memory", + "Score": 33, + "Region": "Share of total capacity" + }, + { + "Microchip Type": "Logic (< 10 nm)", + "Score": 92, + "Region": "Taiwan" + }, + { + "Microchip Type": "Logic (< 10 nm)", + "Score": 8, + "Region": "Other Logic" + }, + { + "Microchip Type": "Logic (< 10 nm)", + "Score": 8, + "Region": "South Korea" + }, + { + "Microchip Type": "Logic (< 10 nm)", + "Score": 2, + "Region": "Share of total capacity" + }, + { + "Microchip Type": "Other Logic", + "Score": 34, + "Region": "Taiwan" + }, + { + "Microchip Type": "Other Logic", + "Score": 65, + "Region": "Other Logic" + }, + { + "Microchip Type": "Other Logic", + "Score": 15, + "Region": "U.S." + }, + { + "Microchip Type": "Other Logic", + "Score": 18, + "Region": "China" + }, + { + "Microchip Type": "Other Logic", + "Score": 8, + "Region": "South Korea" + }, + { + "Microchip Type": "Other Logic", + "Score": 8, + "Region": "Japan" + }, + { + "Microchip Type": "Other Logic", + "Score": 7, + "Region": "Europe" + }, + { + "Microchip Type": "Other Logic", + "Score": 9, + "Region": "Others" + }, + { + "Microchip Type": "Other Logic", + "Score": 39, + "Region": "Share of total capacity" + }, + { + "Microchip Type": "Discrete, Analog & others", + "Score": 27, + "Region": "Taiwan" + }, + { + "Microchip Type": "Discrete, Analog & others", + "Score": 74, + "Region": "Other Logic" + }, + { + "Microchip Type": "Discrete, Analog & others", + "Score": 19, + "Region": "U.S." + }, + { + "Microchip Type": "Discrete, Analog & others", + "Score": 17, + "Region": "China" + }, + { + "Microchip Type": "Discrete, Analog & others", + "Score": 25, + "Region": "Japan" + }, + { + "Microchip Type": "Discrete, Analog & others", + "Score": 22, + "Region": "Europe" + }, + { + "Microchip Type": "Discrete, Analog & others", + "Score": 7, + "Region": "Others" + }, + { + "Microchip Type": "Discrete, Analog & others", + "Score": 26, + "Region": "Share of total capacity" + } + ], + "columns": [ + { + "name": "Microchip Type", + "importance": "primary", + "description": "Category of semiconductor wafer", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding region", + "role": "y" + }, + { + "name": "Region", + "importance": "primary", + "description": "The region of the data point", + "role": "group" + } + ] + } + }, + "1778": { + "metadata": { + "title": "Death From Above", + "description": "U.S. drone strikes outside war zones between January 2009 and December 2015", + "main_insight": "U.S. drone strikes outside of war zones between 2009 and 2015 resulted in a significantly higher number of combatant deaths compared to civilian deaths." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Category": "Values", + "Total number of strikes": 473 + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Type of data presented", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Total number of strikes", + "importance": "tertiary", + "description": "Number of drone strikes", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "522": { + "metadata": { + "title": "Most Britons want the police to be armed", + "description": "% of people in the UK who support/oppose arming the police with guns in 2017", + "main_insight": "A large majority of Britons (72%) supported arming the police with guns in 2017, while only 20% opposed." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Response": "Support", + "Percentage": 72.0 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in Percentage from Oppose to Support.", + "reason": "The Percentage for increases from 20.0 at Oppose to 72.0 at Support, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Response": "Support", + "Percentage": 72.0 + } + ], + "score": 0.79, + "annotation": "The has a maximum value at Support", + "reason": "The [{'Response': 'Support', 'Percentage': 72.0}] of has a maximum value of 72.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Response": "Don't know", + "Percentage": 3.0 + } + ], + "score": 0.44, + "annotation": "The has a minimum value at Don't know", + "reason": "The [{'Response': \"Don't know\", 'Percentage': 3.0}] of has a mainimum value of 3.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Response": "Support", + "Percentage": 72.0 + }, + { + "Response": "Oppose", + "Percentage": 20.0 + }, + { + "Response": "Neither", + "Percentage": 5.0 + }, + { + "Response": "Don't know", + "Percentage": 3.0 + } + ], + "columns": [ + { + "name": "Response", + "importance": "primary", + "description": "Categories of responses to the survey question", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents in each category", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "571": { + "metadata": { + "title": "Great Expectations: If Trump were elected president...", + "description": "How supporters think Trump would perform on selected issues once in office (in %)", + "main_insight": "Trump supporters have high expectations for his performance, particularly on immigration, security from terrorism, and the economy. A majority believe he would make things 'a lot better' in these areas." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Issue": "Foreign policy", + "Score": 4.0, + "Improvement": "A lot worse" + } + ], + "score": 0.97, + "annotation": "The A lot worse shows a sudden jump in Score from Health care to Foreign policy.", + "reason": "The Score for A lot worse increases from 2.0 at Health care to 4.0 at Foreign policy, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Issue": "Foreign policy", + "Score": 9.0, + "Improvement": "A little worse" + }, + { + "Issue": "Foreign policy", + "Score": 4.0, + "Improvement": "A lot worse" + } + ], + "score": 0.96, + "annotation": "The Score of A little worse and A lot worse show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between A little worse and A lot worse is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Issue": "Foreign policy", + "Score": 29.0, + "Improvement": "A lot better" + }, + { + "Issue": "Foreign policy", + "Score": 21.0, + "Improvement": "Not much diff." + } + ], + "score": 0.94, + "annotation": "The Score of A lot better and Not much diff. show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between A lot better and Not much diff. is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Issue": "Foreign policy", + "Score": 36.0, + "Improvement": "A little better" + } + ], + "score": 0.93, + "annotation": "The A little better has the largest proportion in Foreign policy.", + "reason": "The Score of A little better in Foreign policy accounts for 0.36363636363636365, larger than all other Improvement, of the total of Score of all Improvement." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Issue": "Health care", + "Score": 37.0, + "Improvement": "A little better" + } + ], + "score": 0.93, + "annotation": "The A little better has the largest proportion in Health care.", + "reason": "The Score of A little better in Health care accounts for 0.37373737373737376, larger than all other Improvement, of the total of Score of all Improvement." + } + ], + "data": { + "data": [ + { + "Issue": "Immigration", + "Score": 58.0, + "Improvement": "A lot better" + }, + { + "Issue": "Immigration", + "Score": 28.0, + "Improvement": "A little better" + }, + { + "Issue": "Immigration", + "Score": 9.0, + "Improvement": "Not much diff." + }, + { + "Issue": "Immigration", + "Score": 3.0, + "Improvement": "A little worse" + }, + { + "Issue": "Immigration", + "Score": 2.0, + "Improvement": "A lot worse" + }, + { + "Issue": "Security from terrorism", + "Score": 53.0, + "Improvement": "A lot better" + }, + { + "Issue": "Security from terrorism", + "Score": 30.0, + "Improvement": "A little better" + }, + { + "Issue": "Security from terrorism", + "Score": 12.0, + "Improvement": "Not much diff." + }, + { + "Issue": "Security from terrorism", + "Score": 2.0, + "Improvement": "A little worse" + }, + { + "Issue": "Security from terrorism", + "Score": 2.0, + "Improvement": "A lot worse" + }, + { + "Issue": "Economy", + "Score": 47.0, + "Improvement": "A lot better" + }, + { + "Issue": "Economy", + "Score": 36.0, + "Improvement": "A little better" + }, + { + "Issue": "Economy", + "Score": 12.0, + "Improvement": "Not much diff." + }, + { + "Issue": "Economy", + "Score": 2.0, + "Improvement": "A little worse" + }, + { + "Issue": "Economy", + "Score": 2.0, + "Improvement": "A lot worse" + }, + { + "Issue": "Budget deficit", + "Score": 37.0, + "Improvement": "A lot better" + }, + { + "Issue": "Budget deficit", + "Score": 41.0, + "Improvement": "A little better" + }, + { + "Issue": "Budget deficit", + "Score": 16.0, + "Improvement": "Not much diff." + }, + { + "Issue": "Budget deficit", + "Score": 3.0, + "Improvement": "A little worse" + }, + { + "Issue": "Budget deficit", + "Score": 2.0, + "Improvement": "A lot worse" + }, + { + "Issue": "Health care", + "Score": 33.0, + "Improvement": "A lot better" + }, + { + "Issue": "Health care", + "Score": 37.0, + "Improvement": "A little better" + }, + { + "Issue": "Health care", + "Score": 23.0, + "Improvement": "Not much diff." + }, + { + "Issue": "Health care", + "Score": 4.0, + "Improvement": "A little worse" + }, + { + "Issue": "Health care", + "Score": 2.0, + "Improvement": "A lot worse" + }, + { + "Issue": "Foreign policy", + "Score": 29.0, + "Improvement": "A lot better" + }, + { + "Issue": "Foreign policy", + "Score": 36.0, + "Improvement": "A little better" + }, + { + "Issue": "Foreign policy", + "Score": 21.0, + "Improvement": "Not much diff." + }, + { + "Issue": "Foreign policy", + "Score": 9.0, + "Improvement": "A little worse" + }, + { + "Issue": "Foreign policy", + "Score": 4.0, + "Improvement": "A lot worse" + } + ], + "columns": [ + { + "name": "Issue", + "importance": "primary", + "description": "The issue on which Trump's performance is being evaluated", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding improvement", + "role": "y" + }, + { + "name": "Improvement", + "importance": "primary", + "description": "The improvement of the data point", + "role": "group" + } + ] + } + }, + "161": { + "metadata": { + "title": "The Countries Currently Worst Hit By COVID-19 Deaths", + "description": "Countries with the highest rolling 7-day averages for new COVID-19 deaths per million people (as of 2 June)", + "main_insight": "As of June 2nd, Sweden had the highest 7-day average of COVID-19 deaths per million people, followed by the United Kingdom and Peru." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Sweden", + "Deaths per Million": 5.29 + } + ], + "score": 0.87, + "annotation": "The has a maximum value at Sweden", + "reason": "The [{'Country': 'Sweden', 'Deaths per Million': 5.29}] of has a maximum value of 5.29, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Brazil", + "Deaths per Million": 4.34 + } + ], + "score": 0.79, + "annotation": "The shows a sudden jump in Deaths per Million from United States to Brazil.", + "reason": "The Deaths per Million for increases from 2.99 at United States to 4.34 at Brazil, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Belgium", + "Deaths per Million": 2.14 + } + ], + "score": 0.7, + "annotation": "The has a minimum value at Belgium", + "reason": "The [{'Country': 'Belgium', 'Deaths per Million': 2.14}] of has a mainimum value of 2.14, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Sweden", + "Deaths per Million": 5.29 + }, + { + "Country": "United Kingdom", + "Deaths per Million": 4.48 + }, + { + "Country": "Peru", + "Deaths per Million": 4.35 + }, + { + "Country": "Brazil", + "Deaths per Million": 4.34 + }, + { + "Country": "United States", + "Deaths per Million": 2.99 + }, + { + "Country": "Canada", + "Deaths per Million": 2.96 + }, + { + "Country": "Mexico", + "Deaths per Million": 2.81 + }, + { + "Country": "Chile", + "Deaths per Million": 2.63 + }, + { + "Country": "Armenia", + "Deaths per Million": 2.51 + }, + { + "Country": "Belgium", + "Deaths per Million": 2.14 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country Name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Deaths per Million", + "importance": "primary", + "description": "Rolling 7-day average of new COVID-19 deaths per million people", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1985": { + "metadata": { + "title": "Thanksgiving E-Commerce Spending in Perspective", + "description": "Thanksgiving weekend e-commerce sales compared to average daily sales in the U.S.", + "main_insight": "E-commerce spending on Thanksgiving, Black Friday, and Cyber Monday has consistently increased from 2008 to 2015, with Cyber Monday experiencing the highest spending." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2015, + "Units": 2280.0, + "Shopping Events": "Cyber Monday" + }, + { + "Year": 2015, + "Units": 1656.0, + "Shopping Events": "Black Friday" + } + ], + "score": 1.0, + "annotation": "The Units of Cyber Monday and Black Friday show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Cyber Monday and Black Friday is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2015, + "Units": 2280.0, + "Shopping Events": "Cyber Monday" + }, + { + "Year": 2015, + "Units": 1096.0, + "Shopping Events": "Thanksgiving" + } + ], + "score": 0.99, + "annotation": "The Units of Cyber Monday and Thanksgiving show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Cyber Monday and Thanksgiving is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2015, + "Units": 1096.0, + "Shopping Events": "Thanksgiving" + }, + { + "Year": 2015, + "Units": 702.0, + "Shopping Events": "Average Day" + } + ], + "score": 0.99, + "annotation": "The Units of Thanksgiving and Average Day show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Thanksgiving and Average Day is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2015, + "Units": 1656.0, + "Shopping Events": "Black Friday" + }, + { + "Year": 2015, + "Units": 1096.0, + "Shopping Events": "Thanksgiving" + } + ], + "score": 0.99, + "annotation": "The Units of Black Friday and Thanksgiving show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Black Friday and Thanksgiving is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2015, + "Units": 2280.0, + "Shopping Events": "Cyber Monday" + }, + { + "Year": 2015, + "Units": 702.0, + "Shopping Events": "Average Day" + } + ], + "score": 0.98, + "annotation": "The Units of Cyber Monday and Average Day show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Cyber Monday and Average Day is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2008, + "Units": 846.0, + "Shopping Events": "Cyber Monday" + }, + { + "Year": 2008, + "Units": 534.0, + "Shopping Events": "Black Friday" + }, + { + "Year": 2008, + "Units": 318.0, + "Shopping Events": "Thanksgiving" + }, + { + "Year": 2008, + "Units": 336.0, + "Shopping Events": "Average Day" + }, + { + "Year": 2009, + "Units": 887.0, + "Shopping Events": "Cyber Monday" + }, + { + "Year": 2009, + "Units": 595.0, + "Shopping Events": "Black Friday" + }, + { + "Year": 2009, + "Units": 370.0, + "Shopping Events": "Thanksgiving" + }, + { + "Year": 2009, + "Units": 368.0, + "Shopping Events": "Average Day" + }, + { + "Year": 2010, + "Units": 1028.0, + "Shopping Events": "Cyber Monday" + }, + { + "Year": 2010, + "Units": 648.0, + "Shopping Events": "Black Friday" + }, + { + "Year": 2010, + "Units": 479.0, + "Shopping Events": "Thanksgiving" + }, + { + "Year": 2010, + "Units": 406.0, + "Shopping Events": "Average Day" + }, + { + "Year": 2011, + "Units": 1251.0, + "Shopping Events": "Cyber Monday" + }, + { + "Year": 2011, + "Units": 816.0, + "Shopping Events": "Black Friday" + }, + { + "Year": 2011, + "Units": 531.0, + "Shopping Events": "Thanksgiving" + }, + { + "Year": 2011, + "Units": 476.0, + "Shopping Events": "Average Day" + }, + { + "Year": 2012, + "Units": 1465.0, + "Shopping Events": "Cyber Monday" + }, + { + "Year": 2012, + "Units": 1042.0, + "Shopping Events": "Black Friday" + }, + { + "Year": 2012, + "Units": 684.0, + "Shopping Events": "Thanksgiving" + }, + { + "Year": 2012, + "Units": 549.0, + "Shopping Events": "Average Day" + }, + { + "Year": 2013, + "Units": 1735.0, + "Shopping Events": "Cyber Monday" + }, + { + "Year": 2013, + "Units": 1198.0, + "Shopping Events": "Black Friday" + }, + { + "Year": 2013, + "Units": 920.0, + "Shopping Events": "Thanksgiving" + }, + { + "Year": 2013, + "Units": 617.0, + "Shopping Events": "Average Day" + }, + { + "Year": 2014, + "Units": 2038.0, + "Shopping Events": "Cyber Monday" + }, + { + "Year": 2014, + "Units": 1505.0, + "Shopping Events": "Black Friday" + }, + { + "Year": 2014, + "Units": 1007.0, + "Shopping Events": "Thanksgiving" + }, + { + "Year": 2014, + "Units": 633.0, + "Shopping Events": "Average Day" + }, + { + "Year": 2015, + "Units": 2280.0, + "Shopping Events": "Cyber Monday" + }, + { + "Year": 2015, + "Units": 1656.0, + "Shopping Events": "Black Friday" + }, + { + "Year": 2015, + "Units": 1096.0, + "Shopping Events": "Thanksgiving" + }, + { + "Year": 2015, + "Units": 702.0, + "Shopping Events": "Average Day" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of sales data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding shopping events", + "role": "y" + }, + { + "name": "Shopping Events", + "importance": "primary", + "description": "The shopping events of the data point", + "role": "group" + } + ] + } + }, + "2351": { + "metadata": { + "title": "79% of Young Italians Live With Their Parents", + "description": "Proportion of people aged 18-29 living with their parents in selected EU countries*", + "main_insight": "A significantly higher proportion of young adults in Italy live with their parents compared to other selected EU countries, with 79% of 18-29 year olds living at home." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Italy", + "Percentage": 79.0 + } + ], + "score": 0.84, + "annotation": "The has a maximum value at Italy", + "reason": "The [{'Country': 'Italy', 'Percentage': 79.0}] of has a maximum value of 79.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Greece", + "Percentage": 46.0 + } + ], + "score": 0.71, + "annotation": "The shows a sudden jump in Percentage from France to Greece.", + "reason": "The Percentage for increases from 28.0 at France to 46.0 at Greece, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Germany", + "Percentage": 23.0 + } + ], + "score": 0.62, + "annotation": "The has a minimum value at Germany", + "reason": "The [{'Country': 'Germany', 'Percentage': 23.0}] of has a mainimum value of 23.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Italy", + "Percentage": 79.0 + }, + { + "Country": "Poland", + "Percentage": 64.0 + }, + { + "Country": "Spain", + "Percentage": 49.0 + }, + { + "Country": "Greece", + "Percentage": 46.0 + }, + { + "Country": "France", + "Percentage": 28.0 + }, + { + "Country": "UK", + "Percentage": 26.0 + }, + { + "Country": "Germany", + "Percentage": 23.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Selected EU countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Proportion of people aged 18-29 living with their parents", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "263": { + "metadata": { + "title": "Has Pro Cycling Cleaned Up Its Act?", + "description": "Share of Tour de France riders who have violated anti-doping regulations*", + "main_insight": "The percentage of Tour de France riders who have violated anti-doping regulations has significantly decreased from 54.5% in 1998 to 3.4% in 2023." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1998, + "Violations": 54.5 + }, + { + "Year": 1999, + "Violations": 50.0 + }, + { + "Year": 2000, + "Violations": 48.0 + }, + { + "Year": 2001, + "Violations": 43.0 + }, + { + "Year": 2002, + "Violations": 46.0 + }, + { + "Year": 2003, + "Violations": 45.0 + }, + { + "Year": 2004, + "Violations": 40.0 + }, + { + "Year": 2005, + "Violations": 38.0 + }, + { + "Year": 2006, + "Violations": 31.0 + }, + { + "Year": 2007, + "Violations": 26.0 + }, + { + "Year": 2008, + "Violations": 24.0 + }, + { + "Year": 2009, + "Violations": 21.0 + }, + { + "Year": 2010, + "Violations": 25.0 + }, + { + "Year": 2011, + "Violations": 17.0 + }, + { + "Year": 2012, + "Violations": 12.0 + }, + { + "Year": 2013, + "Violations": 12.0 + }, + { + "Year": 2014, + "Violations": 11.0 + }, + { + "Year": 2015, + "Violations": 10.0 + }, + { + "Year": 2016, + "Violations": 9.0 + }, + { + "Year": 2017, + "Violations": 7.0 + }, + { + "Year": 2018, + "Violations": 6.5 + }, + { + "Year": 2019, + "Violations": 5.0 + }, + { + "Year": 2020, + "Violations": 4.5 + }, + { + "Year": 2021, + "Violations": 4.0 + }, + { + "Year": 2022, + "Violations": 3.8 + }, + { + "Year": 2023, + "Violations": 3.4 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of Tour de France", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Violations", + "importance": "primary", + "description": "Percentage of riders who violated anti-doping regulations", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "586": { + "metadata": { + "title": "U.S. Will Trail Other Rich Nations In Life Expectancy By 2030", + "description": "Average life expectancy at birth in selected rich nations by 2030 (in years)", + "main_insight": "The United States is projected to have the lowest life expectancy among selected rich nations by 2030, for both men and women." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "United States", + "Men": 79.51 + } + ], + "score": 0.85, + "annotation": "The has a minimum value at United States", + "reason": "The [{'Country': 'United States', 'Men': 79.51}] of has a mainimum value of 79.51, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "France", + "Men": 81.74 + } + ], + "score": 0.82, + "annotation": "The shows a sudden jump in Men from Singapore to France.", + "reason": "The Men for increases from 79.57 at Singapore to 81.74 at France, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "South Korea", + "Men": 84.07 + } + ], + "score": 0.58, + "annotation": "The has a maximum value at South Korea", + "reason": "The [{'Country': 'South Korea', 'Men': 84.07}] of has a maximum value of 84.07, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "South Korea", + "Men": 84.07 + }, + { + "Country": "France", + "Men": 81.74 + }, + { + "Country": "Japan", + "Men": 82.75 + }, + { + "Country": "Switzerland", + "Men": 83.95 + }, + { + "Country": "Australia", + "Men": 84 + }, + { + "Country": "Canada", + "Men": 83.98 + }, + { + "Country": "Germany", + "Men": 81.96 + }, + { + "Country": "United Kingdom", + "Men": 82.47 + }, + { + "Country": "Singapore", + "Men": 79.57 + }, + { + "Country": "United States", + "Men": 79.51 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Selected rich nations", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Men", + "importance": "primary", + "description": "Average life expectancy at birth for men in 2030", + "unit": "years", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2099": { + "metadata": { + "title": "The Trump-Harris Gender Gap", + "description": "Share of female/male U.S. voters who picked Donald Trump/Kamala Harris for president according to exit polls (in %)", + "main_insight": "Kamala Harris received significantly more votes from women than Donald Trump, particularly among Hispanic and Black women. Men slightly favored Trump over Harris." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Voter Group": "Black women", + "Units": 92.0, + "Candidates": "Kamala Harris" + }, + { + "Voter Group": "Black women", + "Units": 7.0, + "Candidates": "Donald Trump" + } + ], + "score": 1.0, + "annotation": "The Units of Kamala Harris and Donald Trump show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Kamala Harris and Donald Trump is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Voter Group": "Black women", + "Units": 92.0, + "Candidates": "Kamala Harris" + } + ], + "score": 0.9, + "annotation": "The Kamala Harris shows a sudden jump in Units from Hispanic women to Black women.", + "reason": "The Units for Kamala Harris increases from 61.0 at Hispanic women to 92.0 at Black women, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Voter Group": "Hispanic women", + "Units": 37.0, + "Candidates": "Donald Trump" + } + ], + "score": 0.9, + "annotation": "The Donald Trump shows a sudden jump in Units from Black women to Hispanic women.", + "reason": "The Units for Donald Trump increases from 7.0 at Black women to 37.0 at Hispanic women, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Voter Group": "Black women", + "Units": 92.0, + "Candidates": "Kamala Harris" + } + ], + "score": 0.85, + "annotation": "The Kamala Harris has a maximum value at Black women", + "reason": "The [{'Voter Group': 'Black women', 'Units': 92.0, 'Candidates': 'Kamala Harris'}] of Kamala Harris has a maximum value of 92.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Voter Group": "Black women", + "Units": 7.0, + "Candidates": "Donald Trump" + } + ], + "score": 0.85, + "annotation": "The Donald Trump has a minimum value at Black women", + "reason": "The [{'Voter Group': 'Black women', 'Units': 7.0, 'Candidates': 'Donald Trump'}] of Donald Trump has a mainimum value of 7.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Voter Group": "Men", + "Units": 44.0, + "Candidates": "Kamala Harris" + }, + { + "Voter Group": "Men", + "Units": 54.0, + "Candidates": "Donald Trump" + }, + { + "Voter Group": "Women", + "Units": 54.0, + "Candidates": "Kamala Harris" + }, + { + "Voter Group": "Women", + "Units": 44.0, + "Candidates": "Donald Trump" + }, + { + "Voter Group": "White women", + "Units": 47.0, + "Candidates": "Kamala Harris" + }, + { + "Voter Group": "White women", + "Units": 52.0, + "Candidates": "Donald Trump" + }, + { + "Voter Group": "Hispanic women", + "Units": 61.0, + "Candidates": "Kamala Harris" + }, + { + "Voter Group": "Hispanic women", + "Units": 37.0, + "Candidates": "Donald Trump" + }, + { + "Voter Group": "Black women", + "Units": 92.0, + "Candidates": "Kamala Harris" + }, + { + "Voter Group": "Black women", + "Units": 7.0, + "Candidates": "Donald Trump" + } + ], + "columns": [ + { + "name": "Voter Group", + "importance": "primary", + "description": "Demographic group of voters", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding candidates", + "role": "y" + }, + { + "name": "Candidates", + "importance": "primary", + "description": "The candidates of the data point", + "role": "group" + } + ] + } + }, + "600": { + "metadata": { + "title": "Majority of Americans Accept Risks of Reopening Businesses", + "description": "Percentage of U.S adults who agree/disagree on allowing businesses to open even if COVID-19 isn't fully contained", + "main_insight": "A majority of American adults (53%) either strongly or somewhat agree with allowing businesses to reopen even if COVID-19 is not fully contained, while 43% disagree." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Response": "Strongly agree", + "Percentage": 19.0 + } + ], + "score": 0.84, + "annotation": "The shows a sudden jump in Percentage from Don't know to Strongly agree.", + "reason": "The Percentage for increases from 3.0 at Don't know to 19.0 at Strongly agree, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Response": "Don't know", + "Percentage": 3.0 + } + ], + "score": 0.8, + "annotation": "The has a minimum value at Don't know", + "reason": "The [{'Response': \"Don't know\", 'Percentage': 3.0}] of has a mainimum value of 3.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Response": "Somewhat agree", + "Percentage": 34.0 + } + ], + "score": 0.72, + "annotation": "The has a maximum value at Somewhat agree", + "reason": "The [{'Response': 'Somewhat agree', 'Percentage': 34.0}] of has a maximum value of 34.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Response": "Strongly agree", + "Percentage": 19.0 + }, + { + "Response": "Somewhat agree", + "Percentage": 34.0 + }, + { + "Response": "Don't know", + "Percentage": 3.0 + }, + { + "Response": "Somewhat disagree", + "Percentage": 24.0 + }, + { + "Response": "Strongly disagree", + "Percentage": 19.0 + } + ], + "columns": [ + { + "name": "Response", + "importance": "primary", + "description": "Level of agreement or disagreement", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of U.S. adults holding that opinion", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "951": { + "metadata": { + "title": "U.S. Ad Revenue Likely to Plummet", + "description": "Percentage growth forecasts in ad revenue for each form of U.S. media", + "main_insight": "U.S. ad revenue growth forecasts significantly decreased across all media types in March 2020 compared to pre-epidemic forecasts, indicating a substantial negative impact." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Media Type": "Print", + "Units": -17.0, + "Timeframe": "Pre-epidemic" + }, + { + "Media Type": "Print", + "Units": -25.4, + "Timeframe": "March 2020" + } + ], + "score": 0.98, + "annotation": "The Units of Pre-epidemic and March 2020 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Pre-epidemic and March 2020 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Media Type": "Print", + "Units": -17.0, + "Timeframe": "Pre-epidemic" + } + ], + "score": 0.91, + "annotation": "The Pre-epidemic has a minimum value at Print", + "reason": "The [{'Media Type': 'Print', 'Units': -17.0, 'Timeframe': 'Pre-epidemic'}] of Pre-epidemic has a mainimum value of -17.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Media Type": "Print", + "Units": -25.4, + "Timeframe": "March 2020" + } + ], + "score": 0.87, + "annotation": "The March 2020 has a minimum value at Print", + "reason": "The [{'Media Type': 'Print', 'Units': -25.4, 'Timeframe': 'March 2020'}] of March 2020 has a mainimum value of -25.4, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Media Type": "Radio", + "Units": -2.3, + "Timeframe": "Pre-epidemic" + } + ], + "score": 0.79, + "annotation": "The Pre-epidemic shows a sudden jump in Units from Print to Radio.", + "reason": "The Units for Pre-epidemic increases from -17.0 at Print to -2.3 at Radio, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Media Type": "Radio", + "Units": -14.1, + "Timeframe": "March 2020" + } + ], + "score": 0.72, + "annotation": "The March 2020 shows a sudden jump in Units from Print to Radio.", + "reason": "The Units for March 2020 increases from -25.4 at Print to -14.1 at Radio, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Media Type": "All media", + "Units": 6.6, + "Timeframe": "Pre-epidemic" + }, + { + "Media Type": "All media", + "Units": -2.8, + "Timeframe": "March 2020" + }, + { + "Media Type": "Digital search", + "Units": 11.6, + "Timeframe": "Pre-epidemic" + }, + { + "Media Type": "Digital search", + "Units": 4.5, + "Timeframe": "March 2020" + }, + { + "Media Type": "Digital video", + "Units": 14.2, + "Timeframe": "Pre-epidemic" + }, + { + "Media Type": "Digital video", + "Units": 8.3, + "Timeframe": "March 2020" + }, + { + "Media Type": "Social media", + "Units": 17.2, + "Timeframe": "Pre-epidemic" + }, + { + "Media Type": "Social media", + "Units": 8.7, + "Timeframe": "March 2020" + }, + { + "Media Type": "National TV", + "Units": -0.4, + "Timeframe": "Pre-epidemic" + }, + { + "Media Type": "National TV", + "Units": -12.7, + "Timeframe": "March 2020" + }, + { + "Media Type": "Local TV", + "Units": 12.8, + "Timeframe": "Pre-epidemic" + }, + { + "Media Type": "Local TV", + "Units": 0.9, + "Timeframe": "March 2020" + }, + { + "Media Type": "Radio", + "Units": -2.3, + "Timeframe": "Pre-epidemic" + }, + { + "Media Type": "Radio", + "Units": -14.1, + "Timeframe": "March 2020" + }, + { + "Media Type": "Print", + "Units": -17.0, + "Timeframe": "Pre-epidemic" + }, + { + "Media Type": "Print", + "Units": -25.4, + "Timeframe": "March 2020" + } + ], + "columns": [ + { + "name": "Media Type", + "importance": "primary", + "description": "Different forms of U.S. media", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding timeframe", + "role": "y" + }, + { + "name": "Timeframe", + "importance": "primary", + "description": "The timeframe of the data point", + "role": "group" + } + ] + } + }, + "1853": { + "metadata": { + "title": "How Much Do Countries Spend on Education?", + "description": "Education spending as a share of GDP in selected countries (2018)", + "main_insight": "Norway spends the highest percentage of its GDP on education among the selected countries, at 6.7%, while Russia spends the lowest at 3.4%. Most countries spend more on primary, secondary, and post-secondary non-tertiary education than on tertiary education." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Russia", + "Magnitude": 3.4, + "Education Level": "Total Education Spending" + }, + { + "Country": "Russia", + "Magnitude": 2.4, + "Education Level": "Primary, secondary and post-secondary non-tertiary" + } + ], + "score": 0.92, + "annotation": "The Magnitude of Total Education Spending and Primary, secondary and post-secondary non-tertiary show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Magnitude between Total Education Spending and Primary, secondary and post-secondary non-tertiary is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United States", + "Magnitude": 2.5, + "Education Level": "Tertiary" + } + ], + "score": 0.88, + "annotation": "The Tertiary has a maximum value at United States", + "reason": "The [{'Country': 'United States', 'Magnitude': 2.5, 'Education Level': 'Tertiary'}] of Tertiary has a maximum value of 2.5, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Norway", + "Magnitude": 4.7, + "Education Level": "Primary, secondary and post-secondary non-tertiary" + } + ], + "score": 0.87, + "annotation": "The Primary, secondary and post-secondary non-tertiary has a maximum value at Norway", + "reason": "The [{'Country': 'Norway', 'Magnitude': 4.7, 'Education Level': 'Primary, secondary and post-secondary non-tertiary'}] of Primary, secondary and post-secondary non-tertiary has a maximum value of 4.7, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Norway", + "Magnitude": 6.7, + "Education Level": "Total Education Spending" + } + ], + "score": 0.86, + "annotation": "The Total Education Spending has the largest proportion in Norway.", + "reason": "The Magnitude of Total Education Spending in Norway accounts for 0.5, larger than all other Education Level, of the total of Magnitude of all Education Level." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "New Zealand", + "Magnitude": 6.2, + "Education Level": "Total Education Spending" + } + ], + "score": 0.86, + "annotation": "The Total Education Spending accounts for the majority in New Zealand.", + "reason": "The Magnitude of Total Education Spending in New Zealand accounts for 0.5000000000000001 (more than 50%) of the total of Magnitude of all Education Level." + } + ], + "data": { + "data": [ + { + "Country": "Norway", + "Magnitude": 6.7, + "Education Level": "Total Education Spending" + }, + { + "Country": "Norway", + "Magnitude": 4.7, + "Education Level": "Primary, secondary and post-secondary non-tertiary" + }, + { + "Country": "Norway", + "Magnitude": 2.0, + "Education Level": "Tertiary" + }, + { + "Country": "New Zealand", + "Magnitude": 6.2, + "Education Level": "Total Education Spending" + }, + { + "Country": "New Zealand", + "Magnitude": 4.5, + "Education Level": "Primary, secondary and post-secondary non-tertiary" + }, + { + "Country": "New Zealand", + "Magnitude": 1.7, + "Education Level": "Tertiary" + }, + { + "Country": "United Kingdom", + "Magnitude": 6.1, + "Education Level": "Total Education Spending" + }, + { + "Country": "United Kingdom", + "Magnitude": 4.1, + "Education Level": "Primary, secondary and post-secondary non-tertiary" + }, + { + "Country": "United Kingdom", + "Magnitude": 2.0, + "Education Level": "Tertiary" + }, + { + "Country": "United States", + "Magnitude": 6.0, + "Education Level": "Total Education Spending" + }, + { + "Country": "United States", + "Magnitude": 3.5, + "Education Level": "Primary, secondary and post-secondary non-tertiary" + }, + { + "Country": "United States", + "Magnitude": 2.5, + "Education Level": "Tertiary" + }, + { + "Country": "Canada", + "Magnitude": 5.8, + "Education Level": "Total Education Spending" + }, + { + "Country": "Canada", + "Magnitude": 3.5, + "Education Level": "Primary, secondary and post-secondary non-tertiary" + }, + { + "Country": "Canada", + "Magnitude": 2.3, + "Education Level": "Tertiary" + }, + { + "Country": "France", + "Magnitude": 5.2, + "Education Level": "Total Education Spending" + }, + { + "Country": "France", + "Magnitude": 3.7, + "Education Level": "Primary, secondary and post-secondary non-tertiary" + }, + { + "Country": "France", + "Magnitude": 1.5, + "Education Level": "Tertiary" + }, + { + "Country": "OECD average", + "Magnitude": 4.8, + "Education Level": "Total Education Spending" + }, + { + "Country": "OECD average", + "Magnitude": 3.4, + "Education Level": "Primary, secondary and post-secondary non-tertiary" + }, + { + "Country": "OECD average", + "Magnitude": 1.4, + "Education Level": "Tertiary" + }, + { + "Country": "Spain", + "Magnitude": 4.3, + "Education Level": "Total Education Spending" + }, + { + "Country": "Spain", + "Magnitude": 3.0, + "Education Level": "Primary, secondary and post-secondary non-tertiary" + }, + { + "Country": "Spain", + "Magnitude": 1.3, + "Education Level": "Tertiary" + }, + { + "Country": "Germany", + "Magnitude": 4.3, + "Education Level": "Total Education Spending" + }, + { + "Country": "Germany", + "Magnitude": 3.0, + "Education Level": "Primary, secondary and post-secondary non-tertiary" + }, + { + "Country": "Germany", + "Magnitude": 1.3, + "Education Level": "Tertiary" + }, + { + "Country": "Italy", + "Magnitude": 4.1, + "Education Level": "Total Education Spending" + }, + { + "Country": "Italy", + "Magnitude": 3.2, + "Education Level": "Primary, secondary and post-secondary non-tertiary" + }, + { + "Country": "Italy", + "Magnitude": 0.9, + "Education Level": "Tertiary" + }, + { + "Country": "Japan", + "Magnitude": 4.0, + "Education Level": "Total Education Spending" + }, + { + "Country": "Japan", + "Magnitude": 2.6, + "Education Level": "Primary, secondary and post-secondary non-tertiary" + }, + { + "Country": "Japan", + "Magnitude": 1.4, + "Education Level": "Tertiary" + }, + { + "Country": "Russia", + "Magnitude": 3.4, + "Education Level": "Total Education Spending" + }, + { + "Country": "Russia", + "Magnitude": 2.4, + "Education Level": "Primary, secondary and post-secondary non-tertiary" + }, + { + "Country": "Russia", + "Magnitude": 1.0, + "Education Level": "Tertiary" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Name of the country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Magnitude", + "importance": "primary", + "description": "Magnitude for the corresponding education level", + "role": "y" + }, + { + "name": "Education Level", + "importance": "primary", + "description": "The education level of the data point", + "role": "group" + } + ] + } + }, + "1189": { + "metadata": { + "title": "World's Approval of U.S. Leadership Drops", + "description": "Median approval rating of U.S. leadership worldwide (in %)", + "main_insight": "Global approval of U.S. leadership has declined significantly in 2017, falling below disapproval ratings for the first time since 2007. Approval has declined sharply, while disapproval ratings rose, between 2016 and 2017." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2017, + "Units": 30.0, + "Sentiment": "Approve" + }, + { + "Year": 2017, + "Units": 43.0, + "Sentiment": "Disapprove" + } + ], + "score": 0.86, + "annotation": "The Units of Approve and Disapprove show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Approve and Disapprove is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2007, + "Units": 38.0, + "Sentiment": "Approve" + }, + { + "Year": 2007, + "Units": 33.0, + "Sentiment": "Disapprove" + }, + { + "Year": 2008, + "Units": 34.0, + "Sentiment": "Approve" + }, + { + "Year": 2008, + "Units": 34.0, + "Sentiment": "Disapprove" + }, + { + "Year": 2009, + "Units": 49.0, + "Sentiment": "Approve" + }, + { + "Year": 2009, + "Units": 22.0, + "Sentiment": "Disapprove" + }, + { + "Year": 2010, + "Units": 47.0, + "Sentiment": "Approve" + }, + { + "Year": 2010, + "Units": 22.0, + "Sentiment": "Disapprove" + }, + { + "Year": 2011, + "Units": 46.0, + "Sentiment": "Approve" + }, + { + "Year": 2011, + "Units": 23.0, + "Sentiment": "Disapprove" + }, + { + "Year": 2012, + "Units": 41.0, + "Sentiment": "Approve" + }, + { + "Year": 2012, + "Units": 24.0, + "Sentiment": "Disapprove" + }, + { + "Year": 2013, + "Units": 46.0, + "Sentiment": "Approve" + }, + { + "Year": 2013, + "Units": 25.0, + "Sentiment": "Disapprove" + }, + { + "Year": 2014, + "Units": 45.0, + "Sentiment": "Approve" + }, + { + "Year": 2014, + "Units": 28.0, + "Sentiment": "Disapprove" + }, + { + "Year": 2015, + "Units": 45.0, + "Sentiment": "Approve" + }, + { + "Year": 2015, + "Units": 28.0, + "Sentiment": "Disapprove" + }, + { + "Year": 2016, + "Units": 48.0, + "Sentiment": "Approve" + }, + { + "Year": 2016, + "Units": 30.0, + "Sentiment": "Disapprove" + }, + { + "Year": 2017, + "Units": 30.0, + "Sentiment": "Approve" + }, + { + "Year": 2017, + "Units": 43.0, + "Sentiment": "Disapprove" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "809": { + "metadata": { + "title": "Landing on the Moon", + "description": "Successful Moon lander and impactor missions, by country", + "main_insight": "The United States has the most successful lunar landing and impactor missions, followed by the USSR/Russia and China. The chart distinguishes between lander and impactor missions." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Japan", + "Units": 1, + "Mission Type": "Lander" + }, + { + "Country": "Japan", + "Units": 1, + "Mission Type": "Total" + } + ], + "score": 0.99, + "annotation": "The Units of Lander and Total show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Lander and Total is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Units": 4, + "Mission Type": "Impactor" + } + ], + "score": 0.97, + "annotation": "The Impactor shows a sudden jump in Units from India to United States.", + "reason": "The Units for Impactor increases from 1 at India to 4 at United States, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "China", + "Units": 3, + "Mission Type": "Lander" + }, + { + "Country": "China", + "Units": 3, + "Mission Type": "Total" + } + ], + "score": 0.88, + "annotation": "The Lander, Total all have the largest proportion in China.", + "reason": "The Units of Lander, Total in China all account for 0.42857142857142855, larger than all other Mission Type, of the total of Units of all Mission Type." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "United States", + "Units": 15, + "Mission Type": "Total" + } + ], + "score": 0.86, + "annotation": "The Total has the largest proportion in United States.", + "reason": "The Units of Total in United States accounts for 0.5, larger than all other Mission Type, of the total of Units of all Mission Type." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "USSR", + "Units": 8, + "Mission Type": "Total" + } + ], + "score": 0.86, + "annotation": "The Total has the largest proportion in USSR.", + "reason": "The Units of Total in USSR accounts for 0.5, larger than all other Mission Type, of the total of Units of all Mission Type." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Units": 11, + "Mission Type": "Lander" + }, + { + "Country": "United States", + "Units": 4, + "Mission Type": "Impactor" + }, + { + "Country": "United States", + "Units": 15, + "Mission Type": "Total" + }, + { + "Country": "USSR", + "Units": 7, + "Mission Type": "Lander" + }, + { + "Country": "USSR", + "Units": 1, + "Mission Type": "Impactor" + }, + { + "Country": "USSR", + "Units": 8, + "Mission Type": "Total" + }, + { + "Country": "China", + "Units": 3, + "Mission Type": "Lander" + }, + { + "Country": "China", + "Units": 3, + "Mission Type": "Total" + }, + { + "Country": "India", + "Units": 1, + "Mission Type": "Lander" + }, + { + "Country": "India", + "Units": 1, + "Mission Type": "Impactor" + }, + { + "Country": "India", + "Units": 2, + "Mission Type": "Total" + }, + { + "Country": "Japan", + "Units": 1, + "Mission Type": "Lander" + }, + { + "Country": "Japan", + "Units": 1, + "Mission Type": "Total" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country that launched the mission", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding mission type", + "role": "y" + }, + { + "name": "Mission Type", + "importance": "primary", + "description": "The mission type of the data point", + "role": "group" + } + ] + } + }, + "2042": { + "metadata": { + "title": "The World's Biggest Producers of Dates", + "description": "Total production of dates by the top 8 producing countries in 2022 (in million tonnes)", + "main_insight": "Egypt is the world's largest producer of dates, producing 1.73 million tonnes in 2022, followed by Saudi Arabia at 1.61 million tonnes." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Egypt", + "Production (Million Tonnes)": 1.73 + } + ], + "score": 0.79, + "annotation": "The has a maximum value at Egypt", + "reason": "The [{'Country': 'Egypt', 'Production (Million Tonnes)': 1.73}] of has a maximum value of 1.73, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "United Arab Emirates", + "Production (Million Tonnes)": 0.4 + } + ], + "score": 0.67, + "annotation": "The has a minimum value at United Arab Emirates", + "reason": "The [{'Country': 'United Arab Emirates', 'Production (Million Tonnes)': 0.4}] of has a mainimum value of 0.4, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Saudi Arabia", + "Production (Million Tonnes)": 1.61 + } + ], + "score": 0.66, + "annotation": "The shows a sudden jump in Production (Million Tonnes) from Algeria to Saudi Arabia.", + "reason": "The Production (Million Tonnes) for increases from 1.25 at Algeria to 1.61 at Saudi Arabia, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Egypt", + "Production (Million Tonnes)": 1.73 + }, + { + "Country": "Saudi Arabia", + "Production (Million Tonnes)": 1.61 + }, + { + "Country": "Algeria", + "Production (Million Tonnes)": 1.25 + }, + { + "Country": "Iran", + "Production (Million Tonnes)": 1.03 + }, + { + "Country": "Pakistan", + "Production (Million Tonnes)": 0.73 + }, + { + "Country": "Iraq", + "Production (Million Tonnes)": 0.72 + }, + { + "Country": "Sudan", + "Production (Million Tonnes)": 0.44 + }, + { + "Country": "United Arab Emirates", + "Production (Million Tonnes)": 0.4 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country producing dates", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Production (Million Tonnes)", + "importance": "primary", + "description": "Total date production in million tonnes", + "unit": "Million Tonnes", + "data_type": "number", + "role": "y" + } + ] + } + }, + "909": { + "metadata": { + "title": "Where are people the happiest/unhappiest about their bodies?", + "description": "\"How happy are you regarding your own weight and body image?\" (%)", + "main_insight": "Indonesia reports the highest percentage of people happy with their weight and body image, while Hong Kong reports the lowest, with the highest percentage being unhappy." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Hong Kong", + "Score": 49.0, + "Sentiment": "Happy" + }, + { + "Country": "Hong Kong", + "Score": 50.0, + "Sentiment": "Unhappy" + } + ], + "score": 0.98, + "annotation": "The Score of Happy and Unhappy show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Happy and Unhappy is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Hong Kong", + "Score": 50.0, + "Sentiment": "Unhappy" + } + ], + "score": 0.91, + "annotation": "The Unhappy has a maximum value at Hong Kong", + "reason": "The [{'Country': 'Hong Kong', 'Score': 50.0, 'Sentiment': 'Unhappy'}] of Unhappy has a maximum value of 50.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Hong Kong", + "Score": 49.0, + "Sentiment": "Happy" + } + ], + "score": 0.87, + "annotation": "The Happy has a minimum value at Hong Kong", + "reason": "The [{'Country': 'Hong Kong', 'Score': 49.0, 'Sentiment': 'Happy'}] of Happy has a mainimum value of 49.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Indonesia", + "Score": 78.0, + "Sentiment": "Happy" + } + ], + "score": 0.83, + "annotation": "The Happy has a maximum value at Indonesia", + "reason": "The [{'Country': 'Indonesia', 'Score': 78.0, 'Sentiment': 'Happy'}] of Happy has a maximum value of 78.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Hong Kong", + "Score": 50.0, + "Sentiment": "Unhappy" + } + ], + "score": 0.8, + "annotation": "The Unhappy shows a sudden jump in Score from United States to Hong Kong.", + "reason": "The Score for Unhappy increases from 37.0 at United States to 50.0 at Hong Kong, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Indonesia", + "Score": 78.0, + "Sentiment": "Happy" + }, + { + "Country": "Indonesia", + "Score": 21.0, + "Sentiment": "Unhappy" + }, + { + "Country": "Saudi Arabia", + "Score": 72.0, + "Sentiment": "Happy" + }, + { + "Country": "Saudi Arabia", + "Score": 25.0, + "Sentiment": "Unhappy" + }, + { + "Country": "Qatar", + "Score": 70.0, + "Sentiment": "Happy" + }, + { + "Country": "Qatar", + "Score": 27.0, + "Sentiment": "Unhappy" + }, + { + "Country": "Egypt", + "Score": 68.0, + "Sentiment": "Happy" + }, + { + "Country": "Egypt", + "Score": 30.0, + "Sentiment": "Unhappy" + }, + { + "Country": "Australia", + "Score": 63.0, + "Sentiment": "Happy" + }, + { + "Country": "Australia", + "Score": 36.0, + "Sentiment": "Unhappy" + }, + { + "Country": "United Kingdom", + "Score": 61.0, + "Sentiment": "Happy" + }, + { + "Country": "United Kingdom", + "Score": 37.0, + "Sentiment": "Unhappy" + }, + { + "Country": "Germany", + "Score": 60.0, + "Sentiment": "Happy" + }, + { + "Country": "Germany", + "Score": 37.0, + "Sentiment": "Unhappy" + }, + { + "Country": "United States", + "Score": 57.0, + "Sentiment": "Happy" + }, + { + "Country": "United States", + "Score": 37.0, + "Sentiment": "Unhappy" + }, + { + "Country": "Hong Kong", + "Score": 49.0, + "Sentiment": "Happy" + }, + { + "Country": "Hong Kong", + "Score": 50.0, + "Sentiment": "Unhappy" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "32": { + "metadata": { + "title": "Which Countries Invest The Most In Education?", + "description": "Expenditure on education as a % of total public expenditure in selected countries*", + "main_insight": "New Zealand and Mexico invest the most in education as a percentage of their total public expenditure, at 21.6% and 20.5% respectively. Italy invests the least among the listed countries, at 8.6%." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "New Zealand", + "Education Expenditure (% of total public expenditure)": 21.6 + } + ], + "score": 0.85, + "annotation": "The has a maximum value at New Zealand", + "reason": "The [{'Country': 'New Zealand', 'Education Expenditure (% of total public expenditure)': 21.6}] of has a maximum value of 21.6, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Italy", + "Education Expenditure (% of total public expenditure)": 8.6 + } + ], + "score": 0.7, + "annotation": "The has a minimum value at Italy", + "reason": "The [{'Country': 'Italy', 'Education Expenditure (% of total public expenditure)': 8.6}] of has a mainimum value of 8.6, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Brazil", + "Education Expenditure (% of total public expenditure)": 19.2 + } + ], + "score": 0.61, + "annotation": "The shows a sudden jump in Education Expenditure (% of total public expenditure) from South Korea to Brazil.", + "reason": "The Education Expenditure (% of total public expenditure) for increases from 16.5 at South Korea to 19.2 at Brazil, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "New Zealand", + "Education Expenditure (% of total public expenditure)": 21.6 + }, + { + "Country": "Mexico", + "Education Expenditure (% of total public expenditure)": 20.5 + }, + { + "Country": "Brazil", + "Education Expenditure (% of total public expenditure)": 19.2 + }, + { + "Country": "South Korea", + "Education Expenditure (% of total public expenditure)": 16.5 + }, + { + "Country": "Switzerland", + "Education Expenditure (% of total public expenditure)": 15.7 + }, + { + "Country": "United States", + "Education Expenditure (% of total public expenditure)": 13.6 + }, + { + "Country": "United Kingdom", + "Education Expenditure (% of total public expenditure)": 12.2 + }, + { + "Country": "Germany", + "Education Expenditure (% of total public expenditure)": 11.0 + }, + { + "Country": "Russia", + "Education Expenditure (% of total public expenditure)": 10.9 + }, + { + "Country": "France", + "Education Expenditure (% of total public expenditure)": 10.2 + }, + { + "Country": "Japan", + "Education Expenditure (% of total public expenditure)": 9.1 + }, + { + "Country": "Italy", + "Education Expenditure (% of total public expenditure)": 8.6 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Name of the country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Education Expenditure (% of total public expenditure)", + "importance": "primary", + "description": "Expenditure on education as a percentage of the country's total public expenditure", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "666": { + "metadata": { + "title": "'The Oscar Effect' May Be Overrated", + "description": "How would a movie winning an Oscar affect how likely you are to see it?", + "main_insight": "The majority of U.S. residents surveyed (68%) stated that a movie winning an Oscar would have no effect on their likelihood to see it, suggesting the 'Oscar Effect' may be overrated." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Response": "No effect", + "Percentage": 68.0 + } + ], + "score": 0.94, + "annotation": "The shows a sudden jump in Percentage from More likely to see it to No effect.", + "reason": "The Percentage for increases from 20.0 at More likely to see it to 68.0 at No effect, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Response": "No effect", + "Percentage": 68.0 + } + ], + "score": 0.79, + "annotation": "The has a maximum value at No effect", + "reason": "The [{'Response': 'No effect', 'Percentage': 68.0}] of has a maximum value of 68.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Response": "Less likely to see it", + "Percentage": 6.0 + }, + { + "Response": "Not sure", + "Percentage": 6.0 + } + ], + "score": 0.42, + "annotation": "The has minimum values at Less likely to see it, Not sure", + "reason": "The [{'Response': 'Less likely to see it', 'Percentage': 6.0}, {'Response': 'Not sure', 'Percentage': 6.0}] of have minimum values of 6.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Response": "More likely to see it", + "Percentage": 20.0 + }, + { + "Response": "No effect", + "Percentage": 68.0 + }, + { + "Response": "Less likely to see it", + "Percentage": 6.0 + }, + { + "Response": "Not sure", + "Percentage": 6.0 + } + ], + "columns": [ + { + "name": "Response", + "importance": "primary", + "description": "Survey response categories", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents selecting each response", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2209": { + "metadata": { + "title": "The most frequent causes of selfie-fatalities", + "description": "Reported selfie-related deaths by cause since 2014", + "main_insight": "Falls from heights and drowning are the two most frequent causes of selfie-related deaths since 2014, significantly outpacing other causes." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Cause of Death": "Fall from heights", + "Number of Deaths": 16 + } + ], + "score": 0.85, + "annotation": "The has a maximum value at Fall from heights", + "reason": "The [{'Cause of Death': 'Fall from heights', 'Number of Deaths': 16}] of has a maximum value of 16, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Cause of Death": "Drowning", + "Number of Deaths": 14 + } + ], + "score": 0.77, + "annotation": "The shows a sudden jump in Number of Deaths from Train to Drowning.", + "reason": "The Number of Deaths for increases from 8 at Train to 14 at Drowning, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Cause of Death": "Animal", + "Number of Deaths": 1 + } + ], + "score": 0.53, + "annotation": "The has a minimum value at Animal", + "reason": "The [{'Cause of Death': 'Animal', 'Number of Deaths': 1}] of has a mainimum value of 1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Cause of Death": "Fall from heights", + "Number of Deaths": 16 + }, + { + "Cause of Death": "Drowning", + "Number of Deaths": 14 + }, + { + "Cause of Death": "Train", + "Number of Deaths": 8 + }, + { + "Cause of Death": "Gunshot", + "Number of Deaths": 4 + }, + { + "Cause of Death": "Grenade", + "Number of Deaths": 2 + }, + { + "Cause of Death": "Plane crash", + "Number of Deaths": 2 + }, + { + "Cause of Death": "Car crash", + "Number of Deaths": 2 + }, + { + "Cause of Death": "Animal", + "Number of Deaths": 1 + } + ], + "columns": [ + { + "name": "Cause of Death", + "importance": "primary", + "description": "The cause of selfie-related death", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Deaths", + "importance": "primary", + "description": "Number of reported deaths for each cause", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1168": { + "metadata": { + "title": "Voters Still Distrust Both Presidential Candidates", + "description": "% of voters saying the following characteristics apply to Clinton & Trump (Sept 2016)", + "main_insight": "The chart compares voter perceptions of Hillary Clinton and Donald Trump across various characteristics in September 2016. While Clinton was seen as more experienced, Trump was perceived as healthier." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Characteristic": "Is healthy enough to be president", + "Units": 77.0, + "Affiliation": "Applies to Trump" + } + ], + "score": 0.95, + "annotation": "The Applies to Trump has a maximum value at Is healthy enough to be president", + "reason": "The [{'Characteristic': 'Is healthy enough to be president', 'Units': 77.0, 'Affiliation': 'Applies to Trump'}] of Applies to Trump has a maximum value of 77.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Characteristic": "Is honest and trustworthy", + "Units": 33.0, + "Affiliation": "Applies to Clinton" + } + ], + "score": 0.9, + "annotation": "The Applies to Clinton has a minimum value at Is honest and trustworthy", + "reason": "The [{'Characteristic': 'Is honest and trustworthy', 'Units': 33.0, 'Affiliation': 'Applies to Clinton'}] of Applies to Clinton has a mainimum value of 33.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Characteristic": "Has the experience it takes to be president", + "Units": 69.0, + "Affiliation": "Applies to Clinton" + } + ], + "score": 0.84, + "annotation": "The Applies to Clinton has a maximum value at Has the experience it takes to be president", + "reason": "The [{'Characteristic': 'Has the experience it takes to be president', 'Units': 69.0, 'Affiliation': 'Applies to Clinton'}] of Applies to Clinton has a maximum value of 69.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Characteristic": "Can implement the changes this country needs", + "Units": 41.0, + "Affiliation": "Applies to Clinton" + }, + { + "Characteristic": "Can implement the changes this country needs", + "Units": 41.0, + "Affiliation": "Applies to Trump" + } + ], + "score": 0.8, + "annotation": "The Applies to Clinton, Applies to Trump all have the largest proportion in Can implement the changes this country needs.", + "reason": "The Units of Applies to Clinton, Applies to Trump in Can implement the changes this country needs all account for 0.5, larger than all other Affiliation, of the total of Units of all Affiliation." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Characteristic": "Is a strong and decisive leader", + "Units": 57.0, + "Affiliation": "Applies to Trump" + } + ], + "score": 0.8, + "annotation": "The Applies to Trump accounts for the majority in Is a strong and decisive leader.", + "reason": "The Units of Applies to Trump in Is a strong and decisive leader accounts for 0.504424778761062 (more than 50%) of the total of Units of all Affiliation." + } + ], + "data": { + "data": [ + { + "Characteristic": "Has the experience it takes to be president", + "Units": 69.0, + "Affiliation": "Applies to Clinton" + }, + { + "Characteristic": "Has the experience it takes to be president", + "Units": 29.0, + "Affiliation": "Applies to Trump" + }, + { + "Characteristic": "Can get things done", + "Units": 60.0, + "Affiliation": "Applies to Clinton" + }, + { + "Characteristic": "Can get things done", + "Units": 56.0, + "Affiliation": "Applies to Trump" + }, + { + "Characteristic": "Is healthy enough to be president", + "Units": 60.0, + "Affiliation": "Applies to Clinton" + }, + { + "Characteristic": "Is healthy enough to be president", + "Units": 77.0, + "Affiliation": "Applies to Trump" + }, + { + "Characteristic": "Is a strong and decisive leader", + "Units": 56.0, + "Affiliation": "Applies to Clinton" + }, + { + "Characteristic": "Is a strong and decisive leader", + "Units": 57.0, + "Affiliation": "Applies to Trump" + }, + { + "Characteristic": "Would display good judgment in a crisis", + "Units": 54.0, + "Affiliation": "Applies to Clinton" + }, + { + "Characteristic": "Would display good judgment in a crisis", + "Units": 39.0, + "Affiliation": "Applies to Trump" + }, + { + "Characteristic": "Can manage the government effectively", + "Units": 54.0, + "Affiliation": "Applies to Clinton" + }, + { + "Characteristic": "Can manage the government effectively", + "Units": 41.0, + "Affiliation": "Applies to Trump" + }, + { + "Characteristic": "Is likable", + "Units": 50.0, + "Affiliation": "Applies to Clinton" + }, + { + "Characteristic": "Is likable", + "Units": 38.0, + "Affiliation": "Applies to Trump" + }, + { + "Characteristic": "Cares about the needs of people like you", + "Units": 48.0, + "Affiliation": "Applies to Clinton" + }, + { + "Characteristic": "Cares about the needs of people like you", + "Units": 40.0, + "Affiliation": "Applies to Trump" + }, + { + "Characteristic": "Can implement the changes this country needs", + "Units": 41.0, + "Affiliation": "Applies to Clinton" + }, + { + "Characteristic": "Can implement the changes this country needs", + "Units": 41.0, + "Affiliation": "Applies to Trump" + }, + { + "Characteristic": "Is honest and trustworthy", + "Units": 33.0, + "Affiliation": "Applies to Clinton" + }, + { + "Characteristic": "Is honest and trustworthy", + "Units": 35.0, + "Affiliation": "Applies to Trump" + } + ], + "columns": [ + { + "name": "Characteristic", + "importance": "primary", + "description": "The characteristic being evaluated", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding affiliation", + "role": "y" + }, + { + "name": "Affiliation", + "importance": "primary", + "description": "The affiliation of the data point", + "role": "group" + } + ] + } + }, + "1134": { + "metadata": { + "title": "Big Sam in bad company", + "description": "Full-time England managers with the shortest time in charge", + "main_insight": "Sam Allardyce had the shortest tenure as England manager, lasting only 67 days and managing just one game." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Manager": "Sam Allardyce", + "Time in Charge": 67.0 + } + ], + "score": 0.85, + "annotation": "The has a minimum value at Sam Allardyce", + "reason": "The [{'Manager': 'Sam Allardyce', 'Time in Charge': 67.0}] of has a mainimum value of 67.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Manager": "Kevin Keegan", + "Time in Charge": 565.0 + } + ], + "score": 0.82, + "annotation": "The shows a sudden jump in Time in Charge from Sam Allardyce to Kevin Keegan.", + "reason": "The Time in Charge for increases from 67.0 at Sam Allardyce to 565.0 at Kevin Keegan, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Manager": "Don Revie", + "Time in Charge": 1112.0 + } + ], + "score": 0.64, + "annotation": "The has a maximum value at Don Revie", + "reason": "The [{'Manager': 'Don Revie', 'Time in Charge': 1112.0}] of has a maximum value of 1112.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Manager": "Sam Allardyce", + "Time in Charge": 67.0 + }, + { + "Manager": "Steve McClaren", + "Time in Charge": 566.0 + }, + { + "Manager": "Kevin Keegan", + "Time in Charge": 565.0 + }, + { + "Manager": "Terry Venables", + "Time in Charge": 879.0 + }, + { + "Manager": "Glenn Hoddle", + "Time in Charge": 1005.0 + }, + { + "Manager": "Don Revie", + "Time in Charge": 1112.0 + } + ], + "columns": [ + { + "name": "Manager", + "importance": "primary", + "description": "Name of the England manager", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Time in Charge", + "importance": "primary", + "description": "Duration of the manager's tenure", + "unit": "days", + "data_type": "number", + "role": "y" + } + ] + } + }, + "849": { + "metadata": { + "title": "How Does ISIS Fund Itself?", + "description": "Estimated monthly and yearly revenue streams of the so-called Islamic State", + "main_insight": "Oil is the largest source of funding for ISIS, with an estimated $40 million in monthly revenue and $500 million in annual revenue. Iraqi & Syrian farmland constitutes the second largest annual revenue stream." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Revenue Stream": "Oil", + "Monthly Revenue (Millions)": 40.0 + } + ], + "score": 0.96, + "annotation": "The shows a sudden jump in Monthly Revenue (Millions) from Theft to Oil.", + "reason": "The Monthly Revenue (Millions) for increases from 3.7 at Theft to 40.0 at Oil, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Revenue Stream": "Oil", + "Monthly Revenue (Millions)": 40.0 + } + ], + "score": 0.92, + "annotation": "The has a maximum value at Oil", + "reason": "The [{'Revenue Stream': 'Oil', 'Monthly Revenue (Millions)': 40.0}] of has a maximum value of 40.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Revenue Stream": "Iraqi & Syrian farmland", + "Monthly Revenue (Millions)": 0 + }, + { + "Revenue Stream": "Kidnap & ransom", + "Monthly Revenue (Millions)": 0 + }, + { + "Revenue Stream": "Foreign donations", + "Monthly Revenue (Millions)": 0 + } + ], + "score": 0.34, + "annotation": "The has minimum values at Iraqi & Syrian farmland, Kidnap & ransom, Foreign donations", + "reason": "The [{'Revenue Stream': 'Iraqi & Syrian farmland', 'Monthly Revenue (Millions)': 0}, {'Revenue Stream': 'Kidnap & ransom', 'Monthly Revenue (Millions)': 0}, {'Revenue Stream': 'Foreign donations', 'Monthly Revenue (Millions)': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Revenue Stream": "Oil", + "Monthly Revenue (Millions)": 40.0 + }, + { + "Revenue Stream": "Theft", + "Monthly Revenue (Millions)": 3.7 + }, + { + "Revenue Stream": "Taxation", + "Monthly Revenue (Millions)": 2.0 + }, + { + "Revenue Stream": "Iraqi & Syrian farmland", + "Monthly Revenue (Millions)": 0 + }, + { + "Revenue Stream": "Kidnap & ransom", + "Monthly Revenue (Millions)": 0 + }, + { + "Revenue Stream": "Foreign donations", + "Monthly Revenue (Millions)": 0 + } + ], + "columns": [ + { + "name": "Revenue Stream", + "importance": "primary", + "description": "Source of revenue", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Monthly Revenue (Millions)", + "importance": "primary", + "description": "Estimated monthly revenue in millions of US dollars", + "unit": "Millions USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1665": { + "metadata": { + "title": "What The Saudi-Led Coalition Is Striking In Yemen", + "description": "Selected targets of air raids in Yemen (from 26 Mar 2015-25 Mar 2018)", + "main_insight": "Military sites were the most frequent target of Saudi-led coalition air raids in Yemen between March 2015 and March 2018, followed by ground forces and residential areas." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Target Category": "Military sites", + "Number of Strikes": 3396 + } + ], + "score": 0.97, + "annotation": "The has a maximum value at Military sites", + "reason": "The [{'Target Category': 'Military sites', 'Number of Strikes': 3396}] of has a maximum value of 3396, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Target Category": "Military sites", + "Number of Strikes": 3396 + } + ], + "score": 0.79, + "annotation": "The shows a sudden jump in Number of Strikes from Ground forces to Military sites.", + "reason": "The Number of Strikes for increases from 1968 at Ground forces to 3396 at Military sites, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Target Category": "Historical buildings", + "Number of Strikes": 43 + } + ], + "score": 0.46, + "annotation": "The has a minimum value at Historical buildings", + "reason": "The [{'Target Category': 'Historical buildings', 'Number of Strikes': 43}] of has a mainimum value of 43, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Target Category": "Military sites", + "Number of Strikes": 3396 + }, + { + "Target Category": "Ground forces", + "Number of Strikes": 1968 + }, + { + "Target Category": "Residential areas", + "Number of Strikes": 1543 + }, + { + "Target Category": "Transportation", + "Number of Strikes": 671 + }, + { + "Target Category": "Political figures/buildings", + "Number of Strikes": 565 + }, + { + "Target Category": "Farms", + "Number of Strikes": 456 + }, + { + "Target Category": "Schools/universities/education", + "Number of Strikes": 266 + }, + { + "Target Category": "Market places", + "Number of Strikes": 195 + }, + { + "Target Category": "Water & electricity", + "Number of Strikes": 110 + }, + { + "Target Category": "Medical facilities", + "Number of Strikes": 70 + }, + { + "Target Category": "Mosques", + "Number of Strikes": 45 + }, + { + "Target Category": "Historical buildings", + "Number of Strikes": 43 + } + ], + "columns": [ + { + "name": "Target Category", + "importance": "primary", + "description": "Categories of targets struck by air raids", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Strikes", + "importance": "primary", + "description": "Number of air raids targeting each category", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "305": { + "metadata": { + "title": "Most Cyber Attacks Are An Inside Job", + "description": "Cyber attacks by origin of attacker (2015)", + "main_insight": "In 2015, the majority of cyber attacks (60%) originated from insiders, with a significant portion (44.5%) being attributed to malicious insiders." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Origin of Attacker": "Outsiders", + "Percentage of Attacks": 40.0 + } + ], + "score": 0.86, + "annotation": "The shows a sudden jump in Percentage of Attacks from Inadvertent actors to Outsiders.", + "reason": "The Percentage of Attacks for increases from 15.5 at Inadvertent actors to 40.0 at Outsiders, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Origin of Attacker": "Inadvertent actors", + "Percentage of Attacks": 15.5 + } + ], + "score": 0.74, + "annotation": "The has a minimum value at Inadvertent actors", + "reason": "The [{'Origin of Attacker': 'Inadvertent actors', 'Percentage of Attacks': 15.5}] of has a mainimum value of 15.5, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Origin of Attacker": "Insiders", + "Percentage of Attacks": 60.0 + } + ], + "score": 0.64, + "annotation": "The has a maximum value at Insiders", + "reason": "The [{'Origin of Attacker': 'Insiders', 'Percentage of Attacks': 60.0}] of has a maximum value of 60.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Origin of Attacker": "Outsiders", + "Percentage of Attacks": 40.0 + }, + { + "Origin of Attacker": "Insiders", + "Percentage of Attacks": 60.0 + }, + { + "Origin of Attacker": "Inadvertent actors", + "Percentage of Attacks": 15.5 + }, + { + "Origin of Attacker": "Malicious insiders", + "Percentage of Attacks": 44.5 + } + ], + "columns": [ + { + "name": "Origin of Attacker", + "importance": "primary", + "description": "Category of attacker", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Attacks", + "importance": "primary", + "description": "Percentage of total cyber attacks attributed to each attacker category", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2442": { + "metadata": { + "title": "Everybody's Talking About Ice Bucketing", + "description": "ALS Ice Bucket Challenge by the numbers", + "main_insight": "The ALS Ice Bucket Challenge in 2014 generated significantly more donations and social media engagement compared to the previous year." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Metric": "Donations 2014*", + "Value": 11400000.0 + } + ], + "score": 0.89, + "annotation": "The shows a sudden jump in Value from Mentioned on Twitter to Donations 2014*.", + "reason": "The Value for increases from 2200000.0 at Mentioned on Twitter to 11400000.0 at Donations 2014*, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Metric": "People commenting, posting or liking Ice Bucket Challenge on Facebook", + "Value": 15000000.0 + } + ], + "score": 0.82, + "annotation": "The has a maximum value at People commenting, posting or liking Ice Bucket Challenge on Facebook", + "reason": "The [{'Metric': 'People commenting, posting or liking Ice Bucket Challenge on Facebook', 'Value': 15000000.0}] of has a maximum value of 15000000.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Metric": "Number of Americans with amyotrophic lateral sclerosis", + "Value": 30000.0 + } + ], + "score": 0.51, + "annotation": "The has a minimum value at Number of Americans with amyotrophic lateral sclerosis", + "reason": "The [{'Metric': 'Number of Americans with amyotrophic lateral sclerosis', 'Value': 30000.0}] of has a mainimum value of 30000.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Metric": "Mentioned on Twitter", + "Value": 2200000.0 + }, + { + "Metric": "Videos posted on Facebook", + "Value": 1200000.0 + }, + { + "Metric": "People commenting, posting or liking Ice Bucket Challenge on Facebook", + "Value": 15000000.0 + }, + { + "Metric": "Number of Americans with amyotrophic lateral sclerosis", + "Value": 30000.0 + }, + { + "Metric": "Donations 2013*", + "Value": 1700000.0 + }, + { + "Metric": "Donations 2014*", + "Value": 11400000.0 + } + ], + "columns": [ + { + "name": "Metric", + "importance": "primary", + "description": "Type of metric related to the Ice Bucket Challenge", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "The quantified value for each metric", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "868": { + "metadata": { + "title": "Top Brands See Facebook Engagement Plummeting", + "description": "Average engagement per post for the most engaging brands on Facebook", + "main_insight": "Facebook engagement per post decreased significantly for top brands between May 2013 and May 2014." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Brand": "Disney", + "Dollars": -77.0, + "Metric": "Percent Change" + } + ], + "score": 0.89, + "annotation": "The Percent Change has a minimum value at Disney", + "reason": "The [{'Brand': 'Disney', 'Dollars': -77.0, 'Metric': 'Percent Change'}] of Percent Change has a mainimum value of -77.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Brand": "Intel", + "Dollars": 5500.0, + "Metric": "May 2014" + } + ], + "score": 0.86, + "annotation": "The May 2014 has a maximum value at Intel", + "reason": "The [{'Brand': 'Intel', 'Dollars': 5500.0, 'Metric': 'May 2014'}] of May 2014 has a maximum value of 5500.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Brand": "BMW", + "Dollars": 4000.0, + "Metric": "May 2014" + } + ], + "score": 0.77, + "annotation": "The May 2014 shows a sudden jump in Dollars from Disney to BMW.", + "reason": "The Dollars for May 2014 increases from 2000.0 at Disney to 4000.0 at BMW, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Brand": "Ferrari", + "Dollars": 1000.0, + "Metric": "May 2013" + }, + { + "Brand": "Ferrari", + "Dollars": 550.0, + "Metric": "May 2014" + } + ], + "score": 0.76, + "annotation": "The Dollars of May 2013 and May 2014 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between May 2013 and May 2014 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Brand": "Mercedes-Benz", + "Dollars": -61.0, + "Metric": "Percent Change" + } + ], + "score": 0.73, + "annotation": "The Percent Change shows a sudden jump in Dollars from Disney to Mercedes-Benz.", + "reason": "The Dollars for Percent Change increases from -77.0 at Disney to -61.0 at Mercedes-Benz, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Brand": "Disney", + "Dollars": 9000.0, + "Metric": "May 2013" + }, + { + "Brand": "Disney", + "Dollars": 2000.0, + "Metric": "May 2014" + }, + { + "Brand": "Disney", + "Dollars": -77.0, + "Metric": "Percent Change" + }, + { + "Brand": "BMW", + "Dollars": 8500.0, + "Metric": "May 2013" + }, + { + "Brand": "BMW", + "Dollars": 4000.0, + "Metric": "May 2014" + }, + { + "Brand": "BMW", + "Dollars": -53.0, + "Metric": "Percent Change" + }, + { + "Brand": "Intel", + "Dollars": 8000.0, + "Metric": "May 2013" + }, + { + "Brand": "Intel", + "Dollars": 5500.0, + "Metric": "May 2014" + }, + { + "Brand": "Intel", + "Dollars": -31.0, + "Metric": "Percent Change" + }, + { + "Brand": "Starbucks", + "Dollars": 6000.0, + "Metric": "May 2013" + }, + { + "Brand": "Starbucks", + "Dollars": 4000.0, + "Metric": "May 2014" + }, + { + "Brand": "Starbucks", + "Dollars": -33.0, + "Metric": "Percent Change" + }, + { + "Brand": "Mercedes-Benz", + "Dollars": 3500.0, + "Metric": "May 2013" + }, + { + "Brand": "Mercedes-Benz", + "Dollars": 1350.0, + "Metric": "May 2014" + }, + { + "Brand": "Mercedes-Benz", + "Dollars": -61.0, + "Metric": "Percent Change" + }, + { + "Brand": "Tiffany & Co.", + "Dollars": 2500.0, + "Metric": "May 2013" + }, + { + "Brand": "Tiffany & Co.", + "Dollars": 1650.0, + "Metric": "May 2014" + }, + { + "Brand": "Tiffany & Co.", + "Dollars": -34.0, + "Metric": "Percent Change" + }, + { + "Brand": "Harley-Davidson", + "Dollars": 1800.0, + "Metric": "May 2013" + }, + { + "Brand": "Harley-Davidson", + "Dollars": 950.0, + "Metric": "May 2014" + }, + { + "Brand": "Harley-Davidson", + "Dollars": -48.0, + "Metric": "Percent Change" + }, + { + "Brand": "Ferrari", + "Dollars": 1000.0, + "Metric": "May 2013" + }, + { + "Brand": "Ferrari", + "Dollars": 550.0, + "Metric": "May 2014" + }, + { + "Brand": "Ferrari", + "Dollars": -45.0, + "Metric": "Percent Change" + } + ], + "columns": [ + { + "name": "Brand", + "importance": "primary", + "description": "The brand name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding metric", + "role": "y" + }, + { + "name": "Metric", + "importance": "primary", + "description": "The metric of the data point", + "role": "group" + } + ] + } + }, + "283": { + "metadata": { + "title": "Where the Global Teacher Shortage Is Hitting Hardest", + "description": "Estimated number of school teacher hires needed until 2030, by world region (in millions)", + "main_insight": "Subsaharan Africa faces the largest teacher shortage, needing 15 million new and replacement teachers by 2030, which is significantly higher than any other region. New hires represent a large share, 178% of the region's 2022 teacher numbers, indicating very rapid growth needed." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "World Region": "Southeast Asia", + "Units": 0.5, + "Teacher Metrics": "New teachers" + } + ], + "score": 0.97, + "annotation": "The New teachers has the smallest proportion in Southeast Asia.", + "reason": "The Units of New teachers in Southeast Asia accounts for 0.006993006993006993, smaller than all other Teacher Metrics, of the total of Units of all Teacher Metrics." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "World Region": "Europe & North America", + "Units": 0.3, + "Teacher Metrics": "New teachers" + } + ], + "score": 0.97, + "annotation": "The New teachers has the smallest proportion in Europe & North America.", + "reason": "The Units of New teachers in Europe & North America accounts for 0.007009345794392523, smaller than all other Teacher Metrics, of the total of Units of all Teacher Metrics." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "World Region": "Latin America & the Caribbean", + "Units": 0.4, + "Teacher Metrics": "New teachers" + } + ], + "score": 0.96, + "annotation": "The New teachers has the smallest proportion in Latin America & the Caribbean.", + "reason": "The Units of New teachers in Latin America & the Caribbean accounts for 0.008298755186721992, smaller than all other Teacher Metrics, of the total of Units of all Teacher Metrics." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "World Region": "Subsaharan Africa", + "Units": 178.0, + "Teacher Metrics": "New hires needed as share of 2022 teacher numbers" + } + ], + "score": 0.94, + "annotation": "The New hires needed as share of 2022 teacher numbers has a maximum value at Subsaharan Africa", + "reason": "The [{'World Region': 'Subsaharan Africa', 'Units': 178.0, 'Teacher Metrics': 'New hires needed as share of 2022 teacher numbers'}] of New hires needed as share of 2022 teacher numbers has a maximum value of 178.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "World Region": "Subsaharan Africa", + "Units": 9.4, + "Teacher Metrics": "New teachers" + } + ], + "score": 0.92, + "annotation": "The New teachers has a maximum value at Subsaharan Africa", + "reason": "The [{'World Region': 'Subsaharan Africa', 'Units': 9.4, 'Teacher Metrics': 'New teachers'}] of New teachers has a maximum value of 9.4, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "World Region": "Subsaharan Africa", + "Units": 5.6, + "Teacher Metrics": "Replacement teachers" + }, + { + "World Region": "Subsaharan Africa", + "Units": 9.4, + "Teacher Metrics": "New teachers" + }, + { + "World Region": "Subsaharan Africa", + "Units": 178.0, + "Teacher Metrics": "New hires needed as share of 2022 teacher numbers" + }, + { + "World Region": "Southern Asia", + "Units": 3.1, + "Teacher Metrics": "Replacement teachers" + }, + { + "World Region": "Southern Asia", + "Units": 4.7, + "Teacher Metrics": "New teachers" + }, + { + "World Region": "Southern Asia", + "Units": 52.0, + "Teacher Metrics": "New hires needed as share of 2022 teacher numbers" + }, + { + "World Region": "Europe & North America", + "Units": 4.5, + "Teacher Metrics": "Replacement teachers" + }, + { + "World Region": "Europe & North America", + "Units": 0.3, + "Teacher Metrics": "New teachers" + }, + { + "World Region": "Europe & North America", + "Units": 38.0, + "Teacher Metrics": "New hires needed as share of 2022 teacher numbers" + }, + { + "World Region": "Southeast Asia", + "Units": 4.0, + "Teacher Metrics": "Replacement teachers" + }, + { + "World Region": "Southeast Asia", + "Units": 0.5, + "Teacher Metrics": "New teachers" + }, + { + "World Region": "Southeast Asia", + "Units": 67.0, + "Teacher Metrics": "New hires needed as share of 2022 teacher numbers" + }, + { + "World Region": "North Africa & West Asia", + "Units": 2.5, + "Teacher Metrics": "Replacement teachers" + }, + { + "World Region": "North Africa & West Asia", + "Units": 1.8, + "Teacher Metrics": "New teachers" + }, + { + "World Region": "North Africa & West Asia", + "Units": 69.0, + "Teacher Metrics": "New hires needed as share of 2022 teacher numbers" + }, + { + "World Region": "Eastern Asia", + "Units": 2.3, + "Teacher Metrics": "Replacement teachers" + }, + { + "World Region": "Eastern Asia", + "Units": 1.0, + "Teacher Metrics": "New teachers" + }, + { + "World Region": "Eastern Asia", + "Units": 21.0, + "Teacher Metrics": "New hires needed as share of 2022 teacher numbers" + }, + { + "World Region": "Latin America & the Caribbean", + "Units": 2.8, + "Teacher Metrics": "Replacement teachers" + }, + { + "World Region": "Latin America & the Caribbean", + "Units": 0.4, + "Teacher Metrics": "New teachers" + }, + { + "World Region": "Latin America & the Caribbean", + "Units": 45.0, + "Teacher Metrics": "New hires needed as share of 2022 teacher numbers" + } + ], + "columns": [ + { + "name": "World Region", + "importance": "primary", + "description": "Regions of the world", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding teacher metrics", + "role": "y" + }, + { + "name": "Teacher Metrics", + "importance": "primary", + "description": "The teacher metrics of the data point", + "role": "group" + } + ] + } + }, + "658": { + "metadata": { + "title": "Tropical Cyclones Intensify Faster", + "description": "Number of Atlantic tropical cyclones undergoing (extreme) rapid intensification, by year", + "main_insight": "The number of Atlantic tropical cyclones undergoing rapid and extreme rapid intensification has generally increased over the observed period, with notable peaks in recent years." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1980, + "Count": 6, + "Intensification": "Rapid intensification" + }, + { + "Year": 1981, + "Count": 2, + "Intensification": "Rapid intensification" + }, + { + "Year": 1983, + "Count": 3, + "Intensification": "Rapid intensification" + }, + { + "Year": 1984, + "Count": 3, + "Intensification": "Rapid intensification" + }, + { + "Year": 1985, + "Count": 2, + "Intensification": "Rapid intensification" + }, + { + "Year": 1985, + "Count": 1, + "Intensification": "Extreme rapid intensification" + }, + { + "Year": 1986, + "Count": 3, + "Intensification": "Rapid intensification" + }, + { + "Year": 1987, + "Count": 1, + "Intensification": "Rapid intensification" + }, + { + "Year": 1988, + "Count": 4, + "Intensification": "Rapid intensification" + }, + { + "Year": 1989, + "Count": 2, + "Intensification": "Rapid intensification" + }, + { + "Year": 1990, + "Count": 3, + "Intensification": "Rapid intensification" + }, + { + "Year": 1990, + "Count": 1, + "Intensification": "Extreme rapid intensification" + }, + { + "Year": 1991, + "Count": 4, + "Intensification": "Rapid intensification" + }, + { + "Year": 1992, + "Count": 1, + "Intensification": "Rapid intensification" + }, + { + "Year": 1993, + "Count": 1, + "Intensification": "Rapid intensification" + }, + { + "Year": 1994, + "Count": 1, + "Intensification": "Rapid intensification" + }, + { + "Year": 1995, + "Count": 9, + "Intensification": "Rapid intensification" + }, + { + "Year": 1996, + "Count": 5, + "Intensification": "Rapid intensification" + }, + { + "Year": 1997, + "Count": 1, + "Intensification": "Rapid intensification" + }, + { + "Year": 1998, + "Count": 4, + "Intensification": "Rapid intensification" + }, + { + "Year": 1998, + "Count": 1, + "Intensification": "Extreme rapid intensification" + }, + { + "Year": 1999, + "Count": 6, + "Intensification": "Rapid intensification" + }, + { + "Year": 2000, + "Count": 4, + "Intensification": "Rapid intensification" + }, + { + "Year": 2000, + "Count": 2, + "Intensification": "Extreme rapid intensification" + }, + { + "Year": 2001, + "Count": 6, + "Intensification": "Rapid intensification" + }, + { + "Year": 2002, + "Count": 3, + "Intensification": "Rapid intensification" + }, + { + "Year": 2003, + "Count": 6, + "Intensification": "Rapid intensification" + }, + { + "Year": 2003, + "Count": 1, + "Intensification": "Extreme rapid intensification" + }, + { + "Year": 2004, + "Count": 6, + "Intensification": "Rapid intensification" + }, + { + "Year": 2005, + "Count": 7, + "Intensification": "Rapid intensification" + }, + { + "Year": 2006, + "Count": 3, + "Intensification": "Rapid intensification" + }, + { + "Year": 2007, + "Count": 5, + "Intensification": "Rapid intensification" + }, + { + "Year": 2008, + "Count": 6, + "Intensification": "Rapid intensification" + }, + { + "Year": 2009, + "Count": 2, + "Intensification": "Rapid intensification" + }, + { + "Year": 2010, + "Count": 8, + "Intensification": "Rapid intensification" + }, + { + "Year": 2011, + "Count": 4, + "Intensification": "Rapid intensification" + }, + { + "Year": 2012, + "Count": 3, + "Intensification": "Rapid intensification" + }, + { + "Year": 2013, + "Count": 1, + "Intensification": "Rapid intensification" + }, + { + "Year": 2014, + "Count": 2, + "Intensification": "Rapid intensification" + }, + { + "Year": 2015, + "Count": 5, + "Intensification": "Rapid intensification" + }, + { + "Year": 2015, + "Count": 1, + "Intensification": "Extreme rapid intensification" + }, + { + "Year": 2016, + "Count": 8, + "Intensification": "Rapid intensification" + }, + { + "Year": 2017, + "Count": 7, + "Intensification": "Rapid intensification" + }, + { + "Year": 2018, + "Count": 5, + "Intensification": "Rapid intensification" + }, + { + "Year": 2019, + "Count": 6, + "Intensification": "Rapid intensification" + }, + { + "Year": 2020, + "Count": 7, + "Intensification": "Rapid intensification" + }, + { + "Year": 2020, + "Count": 1, + "Intensification": "Extreme rapid intensification" + }, + { + "Year": 2021, + "Count": 7, + "Intensification": "Rapid intensification" + }, + { + "Year": 2022, + "Count": 3, + "Intensification": "Rapid intensification" + }, + { + "Year": 2023, + "Count": 4, + "Intensification": "Rapid intensification" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of observation", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Count", + "importance": "primary", + "description": "Count for the corresponding intensification", + "role": "y" + }, + { + "name": "Intensification", + "importance": "primary", + "description": "The intensification of the data point", + "role": "group" + } + ] + } + }, + "1309": { + "metadata": { + "title": "Which Sports Do Brits Follow?", + "description": "Share of UK sports fans who say they follow competitions/leagues/teams in the following professional sports", + "main_insight": "Football is by far the most followed sport in the UK, with 80% of UK sports fans saying they follow it. Other popular sports include Boxing, Cricket, Rugby and Tennis, while sports like Golf, American Football and Basketball are much less followed." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Sport": "Football", + "Fan Share": 80.0 + } + ], + "score": 0.98, + "annotation": "The has a maximum value at Football", + "reason": "The [{'Sport': 'Football', 'Fan Share': 80.0}] of has a maximum value of 80.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Sport": "Football", + "Fan Share": 80.0 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in Fan Share from Boxing to Football.", + "reason": "The Fan Share for increases from 29.0 at Boxing to 80.0 at Football, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Sport": "American Football", + "Fan Share": 13.0 + }, + { + "Sport": "Basketball", + "Fan Share": 13.0 + } + ], + "score": 0.43, + "annotation": "The has minimum values at American Football, Basketball", + "reason": "The [{'Sport': 'American Football', 'Fan Share': 13.0}, {'Sport': 'Basketball', 'Fan Share': 13.0}] of have minimum values of 13.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Sport": "Football", + "Fan Share": 80.0 + }, + { + "Sport": "Boxing", + "Fan Share": 29.0 + }, + { + "Sport": "Cricket", + "Fan Share": 26.0 + }, + { + "Sport": "Rugby", + "Fan Share": 25.0 + }, + { + "Sport": "Tennis", + "Fan Share": 25.0 + }, + { + "Sport": "Motorsports", + "Fan Share": 21.0 + }, + { + "Sport": "Golf", + "Fan Share": 16.0 + }, + { + "Sport": "Athletics (Track & Field)", + "Fan Share": 14.0 + }, + { + "Sport": "American Football", + "Fan Share": 13.0 + }, + { + "Sport": "Basketball", + "Fan Share": 13.0 + } + ], + "columns": [ + { + "name": "Sport", + "importance": "primary", + "description": "Name of the professional sport", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Fan Share", + "importance": "primary", + "description": "Percentage of UK sports fans who follow the sport", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "79": { + "metadata": { + "title": "2020 Election Spending Highest Ever Recorded", + "description": "Total spending on the U.S. congressional and presidential elections (in billion U.S. dollars)", + "main_insight": "Election spending in 2020 reached a record high of $10.9 billion, significantly exceeding spending in previous election years. Both Democrats and Republicans greatly increased their spending over time." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 10.9, + "Spending": "Total Spending" + }, + { + "Year": 2020, + "Units": 7.1, + "Spending": "Spending by Democrats" + } + ], + "score": 0.99, + "annotation": "The Units of Total Spending and Spending by Democrats show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Spending and Spending by Democrats is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 10.9, + "Spending": "Total Spending" + }, + { + "Year": 2020, + "Units": 3.8, + "Spending": "Spending by Republicans" + } + ], + "score": 0.91, + "annotation": "The Units of Total Spending and Spending by Republicans show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Spending and Spending by Republicans is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 7.1, + "Spending": "Spending by Democrats" + }, + { + "Year": 2020, + "Units": 3.8, + "Spending": "Spending by Republicans" + } + ], + "score": 0.83, + "annotation": "The Units of Spending by Democrats and Spending by Republicans show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Spending by Democrats and Spending by Republicans is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2000, + "Units": 4.1, + "Spending": "Total Spending" + }, + { + "Year": 2000, + "Units": 2.1, + "Spending": "Spending by Democrats" + }, + { + "Year": 2000, + "Units": 2.0, + "Spending": "Spending by Republicans" + }, + { + "Year": 2004, + "Units": 5.5, + "Spending": "Total Spending" + }, + { + "Year": 2004, + "Units": 2.8, + "Spending": "Spending by Democrats" + }, + { + "Year": 2004, + "Units": 2.7, + "Spending": "Spending by Republicans" + }, + { + "Year": 2008, + "Units": 6.4, + "Spending": "Total Spending" + }, + { + "Year": 2008, + "Units": 3.6, + "Spending": "Spending by Democrats" + }, + { + "Year": 2008, + "Units": 2.8, + "Spending": "Spending by Republicans" + }, + { + "Year": 2012, + "Units": 7.1, + "Spending": "Total Spending" + }, + { + "Year": 2012, + "Units": 3.8, + "Spending": "Spending by Democrats" + }, + { + "Year": 2012, + "Units": 3.3, + "Spending": "Spending by Republicans" + }, + { + "Year": 2016, + "Units": 7.2, + "Spending": "Total Spending" + }, + { + "Year": 2016, + "Units": 3.7, + "Spending": "Spending by Democrats" + }, + { + "Year": 2016, + "Units": 3.5, + "Spending": "Spending by Republicans" + }, + { + "Year": 2020, + "Units": 10.9, + "Spending": "Total Spending" + }, + { + "Year": 2020, + "Units": 7.1, + "Spending": "Spending by Democrats" + }, + { + "Year": 2020, + "Units": 3.8, + "Spending": "Spending by Republicans" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Election year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding spending", + "role": "y" + }, + { + "name": "Spending", + "importance": "primary", + "description": "The spending of the data point", + "role": "group" + } + ] + } + }, + "76": { + "metadata": { + "title": "U.S. Bankruptcies at 10-Year High As Pandemic Takes Its Toll", + "description": "Announced bankruptcies in the United States, by year*", + "main_insight": "U.S. bankruptcies in 2020 (through August 9) have already surpassed the total number of bankruptcies in any year since 2010, highlighting the economic impact of the pandemic." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 424, + "Bankruptcy Period": "Through August 9" + }, + { + "Year": 2020, + "Units": 424, + "Bankruptcy Period": "Total Bankruptcies" + } + ], + "score": 0.77, + "annotation": "The Units of Through August 9 and Total Bankruptcies show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Through August 9 and Total Bankruptcies is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2010, + "Units": 546, + "Bankruptcy Period": "Through August 9" + }, + { + "Year": 2010, + "Units": 305, + "Bankruptcy Period": "Rest of the year" + }, + { + "Year": 2010, + "Units": 851, + "Bankruptcy Period": "Total Bankruptcies" + }, + { + "Year": 2011, + "Units": 383, + "Bankruptcy Period": "Through August 9" + }, + { + "Year": 2011, + "Units": 257, + "Bankruptcy Period": "Rest of the year" + }, + { + "Year": 2011, + "Units": 640, + "Bankruptcy Period": "Total Bankruptcies" + }, + { + "Year": 2012, + "Units": 387, + "Bankruptcy Period": "Through August 9" + }, + { + "Year": 2012, + "Units": 219, + "Bankruptcy Period": "Rest of the year" + }, + { + "Year": 2012, + "Units": 606, + "Bankruptcy Period": "Total Bankruptcies" + }, + { + "Year": 2013, + "Units": 360, + "Bankruptcy Period": "Through August 9" + }, + { + "Year": 2013, + "Units": 178, + "Bankruptcy Period": "Rest of the year" + }, + { + "Year": 2013, + "Units": 538, + "Bankruptcy Period": "Total Bankruptcies" + }, + { + "Year": 2014, + "Units": 278, + "Bankruptcy Period": "Through August 9" + }, + { + "Year": 2014, + "Units": 178, + "Bankruptcy Period": "Rest of the year" + }, + { + "Year": 2014, + "Units": 456, + "Bankruptcy Period": "Total Bankruptcies" + }, + { + "Year": 2015, + "Units": 306, + "Bankruptcy Period": "Through August 9" + }, + { + "Year": 2015, + "Units": 225, + "Bankruptcy Period": "Rest of the year" + }, + { + "Year": 2015, + "Units": 531, + "Bankruptcy Period": "Total Bankruptcies" + }, + { + "Year": 2016, + "Units": 371, + "Bankruptcy Period": "Through August 9" + }, + { + "Year": 2016, + "Units": 184, + "Bankruptcy Period": "Rest of the year" + }, + { + "Year": 2016, + "Units": 555, + "Bankruptcy Period": "Total Bankruptcies" + }, + { + "Year": 2017, + "Units": 316, + "Bankruptcy Period": "Through August 9" + }, + { + "Year": 2017, + "Units": 180, + "Bankruptcy Period": "Rest of the year" + }, + { + "Year": 2017, + "Units": 496, + "Bankruptcy Period": "Total Bankruptcies" + }, + { + "Year": 2018, + "Units": 328, + "Bankruptcy Period": "Through August 9" + }, + { + "Year": 2018, + "Units": 148, + "Bankruptcy Period": "Rest of the year" + }, + { + "Year": 2018, + "Units": 476, + "Bankruptcy Period": "Total Bankruptcies" + }, + { + "Year": 2019, + "Units": 347, + "Bankruptcy Period": "Through August 9" + }, + { + "Year": 2019, + "Units": 169, + "Bankruptcy Period": "Rest of the year" + }, + { + "Year": 2019, + "Units": 516, + "Bankruptcy Period": "Total Bankruptcies" + }, + { + "Year": 2020, + "Units": 424, + "Bankruptcy Period": "Through August 9" + }, + { + "Year": 2020, + "Units": 424, + "Bankruptcy Period": "Total Bankruptcies" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of bankruptcy announcement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding bankruptcy period", + "role": "y" + }, + { + "name": "Bankruptcy Period", + "importance": "primary", + "description": "The bankruptcy period of the data point", + "role": "group" + } + ] + } + }, + "2013": { + "metadata": { + "title": "Twice the Time - Same Number of Apps", + "description": "Average monthly app usage by smartphone users in the United States", + "main_insight": "Smartphone users in the US doubled their time spent on apps between Q4 2011 and Q4 2015, while the number of apps used per month remained relatively constant." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Quarter": "Q4 2013", + "Time Spent Per Month (hh:mm)": 30.25 + }, + { + "Quarter": "Q4 2014", + "Time Spent Per Month (hh:mm)": 37.47 + } + ], + "score": 0.71, + "annotation": "The exhibits multiple sudden jumps in Time Spent Per Month (hh:mm), transitioning from Q4 2012, Q4 2013 to Q4 2013, Q4 2014.", + "reason": "Across these points, the Time Spent Per Month (hh:mm) for increases from 23.03, 30.25 at Q4 2012, Q4 2013 to 30.25, 37.47 at Q4 2013, Q4 2014, suggesting significant shifts in value." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Quarter": "Q4 2011", + "Time Spent Per Month (hh:mm)": 18.3 + } + ], + "score": 0.7, + "annotation": "The has a minimum value at Q4 2011", + "reason": "The [{'Quarter': 'Q4 2011', 'Time Spent Per Month (hh:mm)': 18.3}] of has a mainimum value of 18.3, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Quarter": "Q4 2015", + "Time Spent Per Month (hh:mm)": 40.85 + } + ], + "score": 0.67, + "annotation": "The has a maximum value at Q4 2015", + "reason": "The [{'Quarter': 'Q4 2015', 'Time Spent Per Month (hh:mm)': 40.85}] of has a maximum value of 40.85, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Quarter": "Q4 2011", + "Time Spent Per Month (hh:mm)": 18.3 + }, + { + "Quarter": "Q4 2012", + "Time Spent Per Month (hh:mm)": 23.03 + }, + { + "Quarter": "Q4 2013", + "Time Spent Per Month (hh:mm)": 30.25 + }, + { + "Quarter": "Q4 2014", + "Time Spent Per Month (hh:mm)": 37.47 + }, + { + "Quarter": "Q4 2015", + "Time Spent Per Month (hh:mm)": 40.85 + } + ], + "columns": [ + { + "name": "Quarter", + "importance": "primary", + "description": "The quarter and year of the measurement.", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Time Spent Per Month (hh:mm)", + "importance": "primary", + "description": "Average time spent per month using apps.", + "unit": "hh:mm", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1018": { + "metadata": { + "title": "Darknet Bitcoin Use Is Persistent Despite Busts", + "description": "Estimated amount of Bitcoin flowing to darknet markets (in million U.S. dollars)", + "main_insight": "Bitcoin flow to darknet markets increased steadily from 2011 to 2017, peaking in 2017 despite the closure of AlphaBay and Hansa Market. It shows resilience and continued usage even with major disruptions." + }, + "chart_type": "Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2011, + "Bitcoin Flow (Million USD)": 5.0 + }, + { + "Year": 2012, + "Bitcoin Flow (Million USD)": 57.0 + }, + { + "Year": 2013, + "Bitcoin Flow (Million USD)": 196.0 + }, + { + "Year": 2014, + "Bitcoin Flow (Million USD)": 254.0 + }, + { + "Year": 2015, + "Bitcoin Flow (Million USD)": 357.0 + }, + { + "Year": 2016, + "Bitcoin Flow (Million USD)": 566.0 + }, + { + "Year": 2017, + "Bitcoin Flow (Million USD)": 707.0 + }, + { + "Year": 2018, + "Bitcoin Flow (Million USD)": 603.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of Bitcoin transaction", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Bitcoin Flow (Million USD)", + "importance": "primary", + "description": "Estimated amount of Bitcoin flowing to darknet markets in million U.S. dollars", + "unit": "Million USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1415": { + "metadata": { + "title": "The CHP Now Rules 6 of Turkey's 10 Biggest Cities", + "description": null, + "main_insight": "After the 2019 local elections, the CHP (Republican People's Party) gained control of the mayor's office in 6 of Turkey's 10 largest cities, including Istanbul and Ankara, which were previously held by the AKP (Justice and Development Party)." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Istanbul", + "Inhabitants (millions)": 15.1 + } + ], + "score": 0.98, + "annotation": "The has a maximum value at Istanbul", + "reason": "The [{'City': 'Istanbul', 'Inhabitants (millions)': 15.1}] of has a maximum value of 15.1, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Istanbul", + "Inhabitants (millions)": 15.1 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in Inhabitants (millions) from Ankara to Istanbul.", + "reason": "The Inhabitants (millions) for increases from 5.5 at Ankara to 15.1 at Istanbul, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "Mersin", + "Inhabitants (millions)": 1.8 + } + ], + "score": 0.38, + "annotation": "The has a minimum value at Mersin", + "reason": "The [{'City': 'Mersin', 'Inhabitants (millions)': 1.8}] of has a mainimum value of 1.8, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "City": "Istanbul", + "Inhabitants (millions)": 15.1 + }, + { + "City": "Ankara", + "Inhabitants (millions)": 5.5 + }, + { + "City": "Izmir", + "Inhabitants (millions)": 4.3 + }, + { + "City": "Bursa", + "Inhabitants (millions)": 3.0 + }, + { + "City": "Antalya", + "Inhabitants (millions)": 2.4 + }, + { + "City": "Adana", + "Inhabitants (millions)": 2.2 + }, + { + "City": "Konya", + "Inhabitants (millions)": 2.2 + }, + { + "City": "Gaziantep", + "Inhabitants (millions)": 2.0 + }, + { + "City": "Şanlıurfa", + "Inhabitants (millions)": 2.0 + }, + { + "City": "Mersin", + "Inhabitants (millions)": 1.8 + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "Name of the city", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Inhabitants (millions)", + "importance": "secondary", + "description": "Population of the city in millions", + "unit": "millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2439": { + "metadata": { + "title": "Where Are The World's Dollar Millionaires?", + "description": "Distribution of individuals with a fortune of 1 million U.S. dollars or more (2016)", + "main_insight": "The United States has the highest percentage of the world's dollar millionaires, accounting for 41% of the total. Japan is a distant second with 12%." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country/Region": "United States", + "Percentage of Millionaires": 41.0 + } + ], + "score": 0.99, + "annotation": "The has a maximum value at United States", + "reason": "The [{'Country/Region': 'United States', 'Percentage of Millionaires': 41.0}] of has a maximum value of 41.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country/Region": "United States", + "Percentage of Millionaires": 41.0 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in Percentage of Millionaires from Japan to United States.", + "reason": "The Percentage of Millionaires for increases from 12.0 at Japan to 41.0 at United States, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country/Region": "Spain", + "Percentage of Millionaires": 1.0 + }, + { + "Country/Region": "Rest of the world", + "Percentage of Millionaires": 1.0 + } + ], + "score": 0.4, + "annotation": "The has minimum values at Spain, Rest of the world", + "reason": "The [{'Country/Region': 'Spain', 'Percentage of Millionaires': 1.0}, {'Country/Region': 'Rest of the world', 'Percentage of Millionaires': 1.0}] of have minimum values of 1.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country/Region": "United States", + "Percentage of Millionaires": 41.0 + }, + { + "Country/Region": "Japan", + "Percentage of Millionaires": 12.0 + }, + { + "Country/Region": "UK", + "Percentage of Millionaires": 9.0 + }, + { + "Country/Region": "France", + "Percentage of Millionaires": 7.0 + }, + { + "Country/Region": "Germany", + "Percentage of Millionaires": 5.0 + }, + { + "Country/Region": "China", + "Percentage of Millionaires": 5.0 + }, + { + "Country/Region": "Italy", + "Percentage of Millionaires": 5.0 + }, + { + "Country/Region": "Canada", + "Percentage of Millionaires": 3.0 + }, + { + "Country/Region": "Australia", + "Percentage of Millionaires": 3.0 + }, + { + "Country/Region": "Switzerland", + "Percentage of Millionaires": 3.0 + }, + { + "Country/Region": "Korea", + "Percentage of Millionaires": 2.0 + }, + { + "Country/Region": "Taiwan", + "Percentage of Millionaires": 2.0 + }, + { + "Country/Region": "Spain", + "Percentage of Millionaires": 1.0 + }, + { + "Country/Region": "Rest of the world", + "Percentage of Millionaires": 1.0 + } + ], + "columns": [ + { + "name": "Country/Region", + "importance": "primary", + "description": "Country or region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Millionaires", + "importance": "primary", + "description": "Percentage of global millionaires residing in that country/region", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1865": { + "metadata": { + "title": "Google's Smartphone Loyalty Problem", + "description": "\"How likely are you to change your smartphone brand at the next possible occasion?\" (by currently used brand)", + "main_insight": "Google smartphone users are significantly more likely to switch brands compared to Apple and Samsung users, indicating a lower brand loyalty." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Brand": "Google", + "Units": 57.0, + "Likelihood": "(Very) Likely" + } + ], + "score": 0.97, + "annotation": "The (Very) Likely shows a sudden jump in Units from SAMSUNG to Google.", + "reason": "The Units for (Very) Likely increases from 34.0 at SAMSUNG to 57.0 at Google, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Brand": "Google", + "Units": 25.0, + "Likelihood": "(Very) Unlikely" + }, + { + "Brand": "Google", + "Units": 57.0, + "Likelihood": "(Very) Likely" + } + ], + "score": 0.97, + "annotation": "The Units of (Very) Unlikely and (Very) Likely show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between (Very) Unlikely and (Very) Likely is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Brand": "SAMSUNG", + "Units": 44.0, + "Likelihood": "(Very) Unlikely" + } + ], + "score": 0.93, + "annotation": "The (Very) Unlikely shows a sudden jump in Units from Google to SAMSUNG.", + "reason": "The Units for (Very) Unlikely increases from 25.0 at Google to 44.0 at SAMSUNG, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Likelihood": "(Very) Likely", + "Brand": "total", + "Units": 125.0 + } + ], + "score": 0.8, + "annotation": "The total value of (Very) Likely accounts for the majority in all Likelihood.", + "reason": "The total value of Units of (Very) Likely accounts for 0.5122950819672131 (more than 50%) of the total of Units of all Likelihood." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Brand": "SAMSUNG", + "Units": 44.0, + "Likelihood": "(Very) Unlikely" + } + ], + "score": 0.78, + "annotation": "The (Very) Unlikely accounts for the majority in SAMSUNG.", + "reason": "The Units of (Very) Unlikely in SAMSUNG accounts for 0.5641025641025641 (more than 50%) of the total of Units of all Likelihood." + } + ], + "data": { + "data": [ + { + "Brand": "Apple", + "Units": 50.0, + "Likelihood": "(Very) Unlikely" + }, + { + "Brand": "Apple", + "Units": 34.0, + "Likelihood": "(Very) Likely" + }, + { + "Brand": "SAMSUNG", + "Units": 44.0, + "Likelihood": "(Very) Unlikely" + }, + { + "Brand": "SAMSUNG", + "Units": 34.0, + "Likelihood": "(Very) Likely" + }, + { + "Brand": "Google", + "Units": 25.0, + "Likelihood": "(Very) Unlikely" + }, + { + "Brand": "Google", + "Units": 57.0, + "Likelihood": "(Very) Likely" + } + ], + "columns": [ + { + "name": "Brand", + "importance": "primary", + "description": "Smartphone brand", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding likelihood", + "role": "y" + }, + { + "name": "Likelihood", + "importance": "primary", + "description": "The likelihood of the data point", + "role": "group" + } + ] + } + }, + "2196": { + "metadata": { + "title": "The Top Performing Places for Education", + "description": "Mean average of PISA 2022 maths, science and reading scores", + "main_insight": "Singapore has the highest overall PISA 2022 score, significantly outperforming other top-performing places like Macau, Taiwan, Japan, South Korea, and Hong Kong in math, science, and reading." + }, + "chart_type": "Dot Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Singapore", + "Overall 2022 PISA score": 562 + } + ], + "score": 0.9, + "annotation": "The has a maximum value at Singapore", + "reason": "The [{'Country': 'Singapore', 'Overall 2022 PISA score': 562}] of has a maximum value of 562, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Canada", + "Overall 2022 PISA score": 509 + } + ], + "score": 0.76, + "annotation": "The has a minimum value at Canada", + "reason": "The [{'Country': 'Canada', 'Overall 2022 PISA score': 509}] of has a mainimum value of 509, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Singapore", + "Overall 2022 PISA score": 562 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Overall 2022 PISA score from Macau to Singapore.", + "reason": "The Overall 2022 PISA score for increases from 542 at Macau to 562 at Singapore, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Singapore", + "Overall 2022 PISA score": 562 + }, + { + "Country": "Macau", + "Overall 2022 PISA score": 542 + }, + { + "Country": "Taiwan", + "Overall 2022 PISA score": 534 + }, + { + "Country": "Japan", + "Overall 2022 PISA score": 534 + }, + { + "Country": "South Korea", + "Overall 2022 PISA score": 525 + }, + { + "Country": "Hong Kong", + "Overall 2022 PISA score": 528 + }, + { + "Country": "Estonia", + "Overall 2022 PISA score": 517 + }, + { + "Country": "Canada", + "Overall 2022 PISA score": 509 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country or region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Overall 2022 PISA score", + "importance": "primary", + "description": "Mean average of PISA 2022 scores", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2084": { + "metadata": { + "title": "Americans Split on 'the Russia Story'", + "description": "Two questions regarding American public opinion on Russian interference in the 2016 US Presidential Election.", + "main_insight": "Americans are divided on whether Trump's campaign team worked with Russia, with equal percentages saying yes and no. A majority believe Trump should resign if collusion is proven." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [], + "data": { + "data": [ + { + "Question 1": "Yes", + "Score": 42.0, + "Survey Items": "Response 1 (%)" + }, + { + "Question 1": "Yes", + "Score": "Continue", + "Survey Items": "Question 2" + }, + { + "Question 1": "Yes", + "Score": 36.0, + "Survey Items": "Response 2 (%)" + }, + { + "Question 1": "No", + "Score": 42.0, + "Survey Items": "Response 1 (%)" + }, + { + "Question 1": "No", + "Score": "Resign", + "Survey Items": "Question 2" + }, + { + "Question 1": "No", + "Score": 54.0, + "Survey Items": "Response 2 (%)" + }, + { + "Question 1": "Not sure", + "Score": 17.0, + "Survey Items": "Response 1 (%)" + }, + { + "Question 1": "Not sure", + "Score": "Not sure", + "Survey Items": "Question 2" + }, + { + "Question 1": "Not sure", + "Score": 9.0, + "Survey Items": "Response 2 (%)" + } + ], + "columns": [ + { + "name": "Question 1", + "importance": "primary", + "description": "Do you think that members of Donald Trump's campaign team worked in association with Russia to help Trump win the election for President, or not?", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding survey items", + "role": "y" + }, + { + "name": "Survey Items", + "importance": "primary", + "description": "The survey items of the data point", + "role": "group" + } + ] + } + }, + "2500": { + "metadata": { + "title": "The Countries With the Best Work-Life Balance", + "description": "OECD countries ranked highest for the quality of their work-life balance in 2020 (10=best balance)", + "main_insight": "Italy had the highest work-life balance score among OECD countries in 2020, with a score of 9.4. The top 8 countries all scored 8.0 or higher." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Italy", + "Work-Life Balance Score": 9.4 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at Italy", + "reason": "The [{'Country': 'Italy', 'Work-Life Balance Score': 9.4}] of has a maximum value of 9.4, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Italy", + "Work-Life Balance Score": 9.4 + } + ], + "score": 0.87, + "annotation": "The shows a sudden jump in Work-Life Balance Score from Denmark to Italy.", + "reason": "The Work-Life Balance Score for increases from 8.6 at Denmark to 9.4 at Italy, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Germany", + "Work-Life Balance Score": 8.0 + } + ], + "score": 0.58, + "annotation": "The has a minimum value at Germany", + "reason": "The [{'Country': 'Germany', 'Work-Life Balance Score': 8.0}] of has a mainimum value of 8.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Italy", + "Work-Life Balance Score": 9.4 + }, + { + "Country": "Denmark", + "Work-Life Balance Score": 8.6 + }, + { + "Country": "Norway", + "Work-Life Balance Score": 8.5 + }, + { + "Country": "Spain", + "Work-Life Balance Score": 8.4 + }, + { + "Country": "Netherlands", + "Work-Life Balance Score": 8.3 + }, + { + "Country": "France", + "Work-Life Balance Score": 8.1 + }, + { + "Country": "Sweden", + "Work-Life Balance Score": 8.1 + }, + { + "Country": "Germany", + "Work-Life Balance Score": 8.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "OECD country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Work-Life Balance Score", + "importance": "primary", + "description": "Work-life balance score in 2020 (10 = best balance)", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "16": { + "metadata": { + "title": "Pandemic causes huge increase in UK borrowing", + "description": "UK public sector net borrowing (in billion GBP)", + "main_insight": "UK public sector net borrowing increased dramatically in 2020-21 due to the pandemic, reaching 393.5 billion GBP, significantly higher than the previous year and projected future borrowing." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Fiscal Year": "2020-21", + "Borrowing (Billion GBP)": 393.5 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at 2020-21", + "reason": "The [{'Fiscal Year': '2020-21', 'Borrowing (Billion GBP)': 393.5}] of has a maximum value of 393.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Fiscal Year": "2020-21", + "Borrowing (Billion GBP)": 393.5 + } + ], + "score": 0.91, + "annotation": "The shows a sudden jump in Borrowing (Billion GBP) from 2021-22 to 2020-21.", + "reason": "The Borrowing (Billion GBP) for increases from 164.2 at 2021-22 to 393.5 at 2020-21, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Fiscal Year": "2019-20", + "Borrowing (Billion GBP)": 56.1 + } + ], + "score": 0.49, + "annotation": "The has a minimum value at 2019-20", + "reason": "The [{'Fiscal Year': '2019-20', 'Borrowing (Billion GBP)': 56.1}] of has a mainimum value of 56.1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Fiscal Year": "2019-20", + "Borrowing (Billion GBP)": 56.1 + }, + { + "Fiscal Year": "2020-21", + "Borrowing (Billion GBP)": 393.5 + }, + { + "Fiscal Year": "2021-22", + "Borrowing (Billion GBP)": 164.2 + }, + { + "Fiscal Year": "2022-23", + "Borrowing (Billion GBP)": 104.6 + }, + { + "Fiscal Year": "2023-24", + "Borrowing (Billion GBP)": 100.4 + }, + { + "Fiscal Year": "2024-25", + "Borrowing (Billion GBP)": 99.6 + }, + { + "Fiscal Year": "2025-26", + "Borrowing (Billion GBP)": 101.8 + } + ], + "columns": [ + { + "name": "Fiscal Year", + "importance": "primary", + "description": "Fiscal year of borrowing", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Borrowing (Billion GBP)", + "importance": "primary", + "description": "UK public sector net borrowing in billion GBP", + "unit": "Billion GBP", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1113": { + "metadata": { + "title": "The Most Loved Digital Audio Streaming Platforms", + "description": "Share of respondents who have paid for audio downloads or streaming services from the following platforms", + "main_insight": "Spotify and Amazon Music are the leading digital audio streaming platforms in both the UK and the US, with Spotify holding a larger share in the UK and the two being equally popular in the US." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Platform": "Spotify", + "UK": 0.56 + } + ], + "score": 0.75, + "annotation": "The has a maximum value at Spotify", + "reason": "The [{'Platform': 'Spotify', 'UK': 0.56}] of has a maximum value of 0.56, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Platform": "Pandora", + "UK": 0 + } + ], + "score": 0.75, + "annotation": "The has a minimum value at Pandora", + "reason": "The [{'Platform': 'Pandora', 'UK': 0}] of has a mainimum value of 0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Platform": "Amazon Music", + "UK": 0.46 + } + ], + "score": 0.71, + "annotation": "The shows a sudden jump in UK from Apple Music to Amazon Music.", + "reason": "The UK for increases from 0.28 at Apple Music to 0.46 at Amazon Music, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Platform": "Spotify", + "UK": 0.56 + }, + { + "Platform": "Amazon Music", + "UK": 0.46 + }, + { + "Platform": "Apple Music", + "UK": 0.28 + }, + { + "Platform": "YouTube Music", + "UK": 0.26 + }, + { + "Platform": "SoundCloud", + "UK": 0.12 + }, + { + "Platform": "Pandora", + "UK": 0 + } + ], + "columns": [ + { + "name": "Platform", + "importance": "primary", + "description": "Name of the digital audio streaming platform", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "UK", + "importance": "primary", + "description": "Percentage of respondents in the United Kingdom who paid for services from the platform", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1241": { + "metadata": { + "title": "Hotels faring significantly better than STRs on the Japanese travel accommodation market", + "description": "Comparison of Short-term rentals (STRs) and Hotels performance based on survey responses.", + "main_insight": "Hotels in Japan consistently outperform short-term rentals across various performance indicators, including general development, average daily rate, occupancy rate, and economic outlook." + }, + "chart_type": "Dot Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Question Category": "Access to financing and capital Not difficult (at all)", + "Hotels": 39.0 + } + ], + "score": 0.86, + "annotation": "The has a minimum value at Access to financing and capital Not difficult (at all)", + "reason": "The [{'Question Category': 'Access to financing and capital Not difficult (at all)', 'Hotels': 39.0}] of has a mainimum value of 39.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Question Category": "Current economic situation (Very) good", + "Hotels": 49.0 + } + ], + "score": 0.82, + "annotation": "The shows a sudden jump in Hotels from Access to financing and capital Not difficult (at all) to Current economic situation (Very) good.", + "reason": "The Hotels for increases from 39.0 at Access to financing and capital Not difficult (at all) to 49.0 at Current economic situation (Very) good, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Question Category": "General development in the past 6 months (Very) good", + "Hotels": 60.0 + } + ], + "score": 0.69, + "annotation": "The has a maximum value at General development in the past 6 months (Very) good", + "reason": "The [{'Question Category': 'General development in the past 6 months (Very) good', 'Hotels': 60.0}] of has a maximum value of 60.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Question Category": "General development in the past 6 months (Very) good", + "Hotels": 60.0 + }, + { + "Question 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"description": "Average monthly operating profit/loss per subscriber", + "main_insight": "Netflix consistently makes a profit per subscriber in the United States, but incurs a loss per international subscriber, although the international losses have been decreasing." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Quarter": "Q1 2014", + "United States": 1.9 + } + ], + "score": 0.8, + "annotation": "The has a minimum value at Q1 2014", + "reason": "The [{'Quarter': 'Q1 2014', 'United States': 1.9}] of has a mainimum value of 1.9, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Quarter": "Q1 2016", + "United States": 3.0 + } + ], + "score": 0.69, + "annotation": "The has a maximum value at Q1 2016", + "reason": "The [{'Quarter': 'Q1 2016', 'United States': 3.0}] of has a maximum value of 3.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Quarter": "Q2 2015", + "United States": 2.8 + } + ], + "score": 0.67, + "annotation": "The shows a sudden jump in United States from Q1 2015 to Q2 2015.", + "reason": "The United States for increases from 2.5 at Q1 2015 to 2.8 at Q2 2015, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Quarter": "Q1 2014", + "United States": 1.9 + }, + { + "Quarter": "Q2 2014", + "United States": 2.0 + }, + { + "Quarter": "Q3 2014", + "United States": 2.3 + }, + { + "Quarter": "Q4 2014", + "United States": 2.1 + }, + { + "Quarter": "Q1 2015", + "United States": 2.5 + }, + { + "Quarter": "Q2 2015", + "United States": 2.8 + }, + { + "Quarter": "Q3 2015", + "United States": 2.9 + }, + { + "Quarter": "Q4 2015", + "United States": 2.8 + }, + { + "Quarter": "Q1 2016", + "United States": 3.0 + }, + { + "Quarter": "Q2 2016", + "United States": 2.8 + } + ], + "columns": [ + { + "name": "Quarter", + "importance": "primary", + "description": "Quarter of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "United States", + "importance": "primary", + "description": "Average monthly operating profit/loss per subscriber in the United States", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1692": { + "metadata": { + "title": "Alcohol & Sport: A Match Made In Heaven?", + "description": "% of Americans consuming alcohol while watching the following sports on television/live", + "main_insight": "A significant percentage of Americans consume alcohol while watching sports, both on television and live. American Football has the highest percentage of viewers consuming alcohol, while Tennis has the lowest." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Sport": "Tennis", + "Score": 67.0, + "Media Type": "Television" + } + ], + "score": 0.9, + "annotation": "The Television has a minimum value at Tennis", + "reason": "The [{'Sport': 'Tennis', 'Score': 67.0, 'Media Type': 'Television'}] of Television has a mainimum value of 67.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Sport": "American Football", + "Score": 84.0, + "Media Type": "Television" + } + ], + "score": 0.88, + "annotation": "The Television has a maximum value at American Football", + "reason": "The [{'Sport': 'American Football', 'Score': 84.0, 'Media Type': 'Television'}] of Television has a maximum value of 84.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Sport": "Tennis", + "Score": 67.0, + "Media Type": "Television" + }, + { + "Sport": "Tennis", + "Score": 75.0, + "Media Type": "Live" + } + ], + "score": 0.88, + "annotation": "The Score of Television and Live show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Television and Live is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Sport": "American Football", + "Score": 83.0, + "Media Type": "Live" + } + ], + "score": 0.81, + "annotation": "The Live has a maximum value at American Football", + "reason": "The [{'Sport': 'American Football', 'Score': 83.0, 'Media Type': 'Live'}] of Live has a maximum value of 83.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Sport": "American Football", + "Score": 84.0, + "Media Type": "Television" + } + ], + "score": 0.8, + "annotation": "The Television accounts for the majority in American Football.", + "reason": "The Score of Television in American Football accounts for 0.5029940119760479 (more than 50%) of the total of Score of all Media Type." + } + ], + "data": { + "data": [ + { + "Sport": "American Football", + "Score": 84.0, + "Media Type": "Television" + }, + { + "Sport": "American Football", + "Score": 83.0, + "Media Type": "Live" + }, + { + "Sport": "Boxing", + "Score": 80.0, + "Media Type": "Television" + }, + { + "Sport": "Boxing", + "Score": 81.0, + "Media Type": "Live" + }, + { + "Sport": "Hockey", + "Score": 78.0, + "Media Type": "Television" + }, + { + "Sport": "Hockey", + "Score": 79.0, + "Media Type": "Live" + }, + { + "Sport": "Basketball", + "Score": 78.0, + "Media Type": "Television" + }, + { + "Sport": "Basketball", + "Score": 80.0, + "Media Type": "Live" + }, + { + "Sport": "Baseball", + "Score": 77.0, + "Media Type": "Television" + }, + { + "Sport": "Baseball", + "Score": 81.0, + "Media Type": "Live" + }, + { + "Sport": "Car Racing", + "Score": 76.0, + "Media Type": "Television" + }, + { + "Sport": "Car Racing", + "Score": 81.0, + "Media Type": "Live" + }, + { + "Sport": "Horse Racing", + "Score": 75.0, + "Media Type": "Television" + }, + { + "Sport": "Horse Racing", + "Score": 80.0, + "Media Type": "Live" + }, + { + "Sport": "Soccer", + "Score": 74.0, + "Media Type": "Television" + }, + { + "Sport": "Soccer", + "Score": 76.0, + "Media Type": "Live" + }, + { + "Sport": "Beach Volleyball", + "Score": 73.0, + "Media Type": "Television" + }, + { + "Sport": "Beach Volleyball", + "Score": 76.0, + "Media Type": "Live" + }, + { + "Sport": "Golf", + "Score": 71.0, + "Media Type": "Television" + }, + { + "Sport": "Golf", + "Score": 76.0, + "Media Type": "Live" + }, + { + "Sport": "Tennis", + "Score": 67.0, + "Media Type": "Television" + }, + { + "Sport": "Tennis", + "Score": 75.0, + "Media Type": "Live" + } + ], + "columns": [ + { + "name": "Sport", + "importance": "primary", + "description": "Name of the sport", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding media type", + "role": "y" + }, + { + "name": "Media Type", + "importance": "primary", + "description": "The media type of the data point", + "role": "group" + } + ] + } + }, + "1973": { + "metadata": { + "title": "Beer Falls Out of Favor in the U.S.", + "description": "Percent change in U.S. alcohol consumption, by type", + "main_insight": "Beer consumption has been declining or showing minimal growth, while spirits consumption has consistently increased, and wine consumption has also mostly increased." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2019, + "Ratio": 14.4, + "Beverage Type": "Spirits" + }, + { + "Year": 2019, + "Ratio": 6.9, + "Beverage Type": "Wine" + } + ], + "score": 1.0, + "annotation": "The Ratio of Spirits and Wine show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Spirits and Wine is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2019, + "Ratio": -3.7, + "Beverage Type": "Beer" + }, + { + "Year": 2019, + "Ratio": 6.9, + "Beverage Type": "Wine" + } + ], + "score": 0.97, + "annotation": "The Ratio of Beer and Wine show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Beer and Wine is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2019, + "Ratio": -3.7, + "Beverage Type": "Beer" + }, + { + "Year": 2019, + "Ratio": 14.4, + "Beverage Type": "Spirits" + } + ], + "score": 0.97, + "annotation": "The Ratio of Beer and Spirits show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Beer and Spirits is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2015, + "Ratio": 0.3, + "Beverage Type": "Beer" + }, + { + "Year": 2015, + "Ratio": 4.6, + "Beverage Type": "Spirits" + }, + { + "Year": 2015, + "Ratio": 2.1, + "Beverage Type": "Wine" + }, + { + "Year": 2016, + "Ratio": 0.1, + "Beverage Type": "Beer" + }, + { + "Year": 2016, + "Ratio": 7.4, + "Beverage Type": "Spirits" + }, + { + "Year": 2016, + "Ratio": 3.5, + "Beverage Type": "Wine" + }, + { + "Year": 2017, + "Ratio": -1.0, + "Beverage Type": "Beer" + }, + { + "Year": 2017, + "Ratio": 9.8, + "Beverage Type": "Spirits" + }, + { + "Year": 2017, + "Ratio": 4.5, + "Beverage Type": "Wine" + }, + { + "Year": 2018, + "Ratio": -2.4, + "Beverage Type": "Beer" + }, + { + "Year": 2018, + "Ratio": 11.9, + "Beverage Type": "Spirits" + }, + { + "Year": 2018, + "Ratio": 5.5, + "Beverage Type": "Wine" + }, + { + "Year": 2019, + "Ratio": -3.7, + "Beverage Type": "Beer" + }, + { + "Year": 2019, + "Ratio": 14.4, + "Beverage Type": "Spirits" + }, + { + "Year": 2019, + "Ratio": 6.9, + "Beverage Type": "Wine" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding beverage type", + "role": "y" + }, + { + "name": "Beverage Type", + "importance": "primary", + "description": "The beverage type of the data point", + "role": "group" + } + ] + } + }, + "94": { + "metadata": { + "title": "Americans Spent Nearly $150 Billion On Illegal Drugs In 2016", + "description": "Estimated retail expenditure on illicit drugs", + "main_insight": "Spending on Marijuana has significantly increased since 2006, becoming the most consumed illegal drug in terms of expenditure by 2016. Cocaine expenditure dramatically decreased between 2006 and 2016." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2016, + "Units": 52.0, + "Drug Type": "Marijuana" + }, + { + "Year": 2016, + "Units": 43.0, + "Drug Type": "Heroin" + } + ], + "score": 0.98, + "annotation": "The Units of Marijuana and Heroin show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Marijuana and Heroin is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2016, + "Units": 24.0, + "Drug Type": "Cocaine" + }, + { + "Year": 2016, + "Units": 43.0, + "Drug Type": "Heroin" + } + ], + "score": 0.91, + "annotation": "The Units of Cocaine and Heroin show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Cocaine and Heroin is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2016, + "Units": 52.0, + "Drug Type": "Marijuana" + }, + { + "Year": 2016, + "Units": 24.0, + "Drug Type": "Cocaine" + } + ], + "score": 0.91, + "annotation": "The Units of Marijuana and Cocaine show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Marijuana and Cocaine is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2016, + "Units": 27.0, + "Drug Type": "Methamphetamine" + }, + { + "Year": 2016, + "Units": 146.0, + "Drug Type": "Total" + } + ], + "score": 0.89, + "annotation": "The Units of Methamphetamine and Total show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Methamphetamine and Total is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2016, + "Units": 52.0, + "Drug Type": "Marijuana" + }, + { + "Year": 2016, + "Units": 27.0, + "Drug Type": "Methamphetamine" + } + ], + "score": 0.74, + "annotation": "The Units of Marijuana and Methamphetamine show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Marijuana and Methamphetamine is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2006, + "Units": 34.0, + "Drug Type": "Marijuana" + }, + { + "Year": 2006, + "Units": 58.0, + "Drug Type": "Cocaine" + }, + { + "Year": 2006, + "Units": 28.0, + "Drug Type": "Heroin" + }, + { + "Year": 2006, + "Units": 22.0, + "Drug Type": "Methamphetamine" + }, + { + "Year": 2006, + "Units": 145.0, + "Drug Type": "Total" + }, + { + "Year": 2007, + "Units": 34.0, + "Drug Type": "Marijuana" + }, + { + "Year": 2007, + "Units": 52.0, + "Drug Type": "Cocaine" + }, + { + "Year": 2007, + "Units": 29.0, + "Drug Type": "Heroin" + }, + { + "Year": 2007, + "Units": 20.0, + "Drug Type": "Methamphetamine" + }, + { + "Year": 2007, + "Units": 128.0, + "Drug Type": "Total" + }, + { + "Year": 2008, + "Units": 36.0, + "Drug Type": "Marijuana" + }, + { + "Year": 2008, + "Units": 40.0, + "Drug Type": "Cocaine" + }, + { + "Year": 2008, + "Units": 31.0, + "Drug Type": "Heroin" + }, + { + "Year": 2008, + "Units": 18.0, + "Drug Type": "Methamphetamine" + }, + { + "Year": 2008, + "Units": 122.0, + "Drug Type": "Total" + }, + { + "Year": 2009, + "Units": 38.0, + "Drug Type": "Marijuana" + }, + { + "Year": 2009, + "Units": 38.0, + "Drug Type": "Cocaine" + }, + { + "Year": 2009, + "Units": 33.0, + "Drug Type": "Heroin" + }, + { + "Year": 2009, + "Units": 17.0, + "Drug Type": "Methamphetamine" + }, + { + "Year": 2009, + "Units": 126.0, + "Drug Type": "Total" + }, + { + "Year": 2010, + "Units": 40.0, + "Drug Type": "Marijuana" + }, + { + "Year": 2010, + "Units": 35.0, + "Drug Type": "Cocaine" + }, + { + "Year": 2010, + "Units": 35.0, + "Drug Type": "Heroin" + }, + { + "Year": 2010, + "Units": 18.0, + "Drug Type": "Methamphetamine" + }, + { + "Year": 2010, + "Units": 124.0, + "Drug Type": "Total" + }, + { + "Year": 2011, + "Units": 42.0, + "Drug Type": "Marijuana" + }, + { + "Year": 2011, + "Units": 33.0, + "Drug Type": "Cocaine" + }, + { + "Year": 2011, + "Units": 35.0, + "Drug Type": "Heroin" + }, + { + "Year": 2011, + "Units": 18.0, + "Drug Type": "Methamphetamine" + }, + { + "Year": 2011, + "Units": 121.0, + "Drug Type": "Total" + }, + { + "Year": 2012, + "Units": 44.0, + "Drug Type": "Marijuana" + }, + { + "Year": 2012, + "Units": 31.0, + "Drug Type": "Cocaine" + }, + { + "Year": 2012, + "Units": 37.0, + "Drug Type": "Heroin" + }, + { + "Year": 2012, + "Units": 20.0, + "Drug Type": "Methamphetamine" + }, + { + "Year": 2012, + "Units": 123.0, + "Drug Type": "Total" + }, + { + "Year": 2013, + "Units": 46.0, + "Drug Type": "Marijuana" + }, + { + "Year": 2013, + "Units": 29.0, + "Drug Type": "Cocaine" + }, + { + "Year": 2013, + "Units": 41.0, + "Drug Type": "Heroin" + }, + { + "Year": 2013, + "Units": 22.0, + "Drug Type": "Methamphetamine" + }, + { + "Year": 2013, + "Units": 129.0, + "Drug Type": "Total" + }, + { + "Year": 2014, + "Units": 48.0, + "Drug Type": "Marijuana" + }, + { + "Year": 2014, + "Units": 27.0, + "Drug Type": "Cocaine" + }, + { + "Year": 2014, + "Units": 44.0, + "Drug Type": "Heroin" + }, + { + "Year": 2014, + "Units": 25.0, + "Drug Type": "Methamphetamine" + }, + { + "Year": 2014, + "Units": 143.0, + "Drug Type": "Total" + }, + { + "Year": 2015, + "Units": 50.0, + "Drug Type": "Marijuana" + }, + { + "Year": 2015, + "Units": 26.0, + "Drug Type": "Cocaine" + }, + { + "Year": 2015, + "Units": 44.0, + "Drug Type": "Heroin" + }, + { + "Year": 2015, + "Units": 26.0, + "Drug Type": "Methamphetamine" + }, + { + "Year": 2015, + "Units": 145.0, + "Drug Type": "Total" + }, + { + "Year": 2016, + "Units": 52.0, + "Drug Type": "Marijuana" + }, + { + "Year": 2016, + "Units": 24.0, + "Drug Type": "Cocaine" + }, + { + "Year": 2016, + "Units": 43.0, + "Drug Type": "Heroin" + }, + { + "Year": 2016, + "Units": 27.0, + "Drug Type": "Methamphetamine" + }, + { + "Year": 2016, + "Units": 146.0, + "Drug Type": "Total" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of expenditure", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding drug type", + "role": "y" + }, + { + "name": "Drug Type", + "importance": "primary", + "description": "The drug type of the data point", + "role": "group" + } + ] + } + }, + "1798": { + "metadata": { + "title": "Where Global Poverty Is Rampant", + "description": "Share of the population in severe multidimensional poverty*", + "main_insight": "Niger and South Sudan have the highest shares of their populations living in severe multidimensional poverty, at 74.8% and 74.3% respectively. All countries listed are in Africa." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Burundi", + "Poverty Rate": 45.3 + } + ], + "score": 0.81, + "annotation": "The has a minimum value at Burundi", + "reason": "The [{'Country': 'Burundi', 'Poverty Rate': 45.3}] of has a mainimum value of 45.3, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Niger", + "Poverty Rate": 74.8 + } + ], + "score": 0.79, + "annotation": "The has a maximum value at Niger", + "reason": "The [{'Country': 'Niger', 'Poverty Rate': 74.8}] of has a maximum value of 74.8, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "South Sudan", + "Poverty Rate": 74.3 + } + ], + "score": 0.67, + "annotation": "The shows a sudden jump in Poverty Rate from Chad to South Sudan.", + "reason": "The Poverty Rate for increases from 66.1 at Chad to 74.3 at South Sudan, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Niger", + "Poverty Rate": 74.8 + }, + { + "Country": "South Sudan", + "Poverty Rate": 74.3 + }, + { + "Country": "Chad", + "Poverty Rate": 66.1 + }, + { + "Country": "Burkina Faso", + "Poverty Rate": 64.8 + }, + { + "Country": "Ethiopia", + "Poverty Rate": 61.5 + }, + { + "Country": "Madagascar", + "Poverty Rate": 57.1 + }, + { + "Country": "Mali", + "Poverty Rate": 56.6 + }, + { + "Country": "Central African Republic", + "Poverty Rate": 54.7 + }, + { + "Country": "Mozambique", + "Poverty Rate": 49.1 + }, + { + "Country": "Burundi", + "Poverty Rate": 45.3 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country Name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Poverty Rate", + "importance": "primary", + "description": "Share of the population in severe multidimensional poverty", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1186": { + "metadata": { + "title": "Will 2023 Bring China's Tourism Recovery?", + "description": "Outbound tourism expenditure in the global top 5 markets (in billion U.S. dollars)", + "main_insight": "China's outbound tourism expenditure significantly decreased from 2019 to 2020 and has not yet recovered to pre-pandemic levels by 2022. The expenditure of other countries like the US, Germany, UK and France, decreased during 2020 but saw recovery or stayed somewhat stable." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 115.0, + "Countries": "U.S." + }, + { + "Year": 2022, + "Units": 65.0, + "Countries": "UK" + } + ], + "score": 0.97, + "annotation": "The Units of U.S. and UK show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between U.S. and UK is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 100.0, + "Countries": "Germany" + }, + { + "Year": 2022, + "Units": 50.0, + "Countries": "France" + } + ], + "score": 0.97, + "annotation": "The Units of Germany and France show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Germany and France is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 65.0, + "Countries": "UK" + }, + { + "Year": 2022, + "Units": 50.0, + "Countries": "France" + } + ], + "score": 0.93, + "annotation": "The Units of UK and France show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between UK and France is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 100.0, + "Countries": "Germany" + }, + { + "Year": 2022, + "Units": 65.0, + "Countries": "UK" + } + ], + "score": 0.93, + "annotation": "The Units of Germany and UK show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Germany and UK is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 115.0, + "Countries": "U.S." + }, + { + "Year": 2022, + "Units": 50.0, + "Countries": "France" + } + ], + "score": 0.91, + "annotation": "The Units of U.S. and France show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between U.S. and France is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2018, + "Units": 275.0, + "Countries": "China" + }, + { + "Year": 2018, + "Units": 145.0, + "Countries": "U.S." + }, + { + "Year": 2018, + "Units": 95.0, + "Countries": "Germany" + }, + { + "Year": 2018, + "Units": 75.0, + "Countries": "UK" + }, + { + "Year": 2018, + "Units": 48.0, + "Countries": "France" + }, + { + "Year": 2019, + "Units": 250.0, + "Countries": "China" + }, + { + "Year": 2019, + "Units": 150.0, + "Countries": "U.S." + }, + { + "Year": 2019, + "Units": 85.0, + "Countries": "Germany" + }, + { + "Year": 2019, + "Units": 70.0, + "Countries": "UK" + }, + { + "Year": 2019, + "Units": 50.0, + "Countries": "France" + }, + { + "Year": 2020, + "Units": 130.0, + "Countries": "China" + }, + { + "Year": 2020, + "Units": 60.0, + "Countries": "U.S." + }, + { + "Year": 2020, + "Units": 35.0, + "Countries": "Germany" + }, + { + "Year": 2020, + "Units": 28.0, + "Countries": "UK" + }, + { + "Year": 2020, + "Units": 25.0, + "Countries": "France" + }, + { + "Year": 2021, + "Units": 105.0, + "Countries": "China" + }, + { + "Year": 2021, + "Units": 68.0, + "Countries": "U.S." + }, + { + "Year": 2021, + "Units": 55.0, + "Countries": "Germany" + }, + { + "Year": 2021, + "Units": 30.0, + "Countries": "UK" + }, + { + "Year": 2021, + "Units": 35.0, + "Countries": "France" + }, + { + "Year": 2022, + "Units": 115.0, + "Countries": "China" + }, + { + "Year": 2022, + "Units": 115.0, + "Countries": "U.S." + }, + { + "Year": 2022, + "Units": 100.0, + "Countries": "Germany" + }, + { + "Year": 2022, + "Units": 65.0, + "Countries": "UK" + }, + { + "Year": 2022, + "Units": 50.0, + "Countries": "France" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding countries", + "role": "y" + }, + { + "name": "Countries", + "importance": "primary", + "description": "The countries of the data point", + "role": "group" + } + ] + } + }, + "1740": { + "metadata": { + "title": "Denver Nuggets Win Their First NBA Championship", + "description": "Number of NBA championships, by franchise (as of June 13, 2023)", + "main_insight": "The Boston Celtics and Minneapolis/Los Angeles Lakers are tied for the most NBA championships with 17 wins each. The Denver Nuggets won their first championship in 2023." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Franchise": "Boston Celtics", + "Championships": 17 + }, + { + "Franchise": "Minneapolis/Los Angeles Lakers", + "Championships": 17 + } + ], + "score": 0.92, + "annotation": "The has maximum values at Boston Celtics, Minneapolis/Los Angeles Lakers", + "reason": "The [{'Franchise': 'Boston Celtics', 'Championships': 17}, {'Franchise': 'Minneapolis/Los Angeles Lakers', 'Championships': 17}] of have maximum values of 17, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Franchise": "Boston Celtics", + "Championships": 17 + } + ], + "score": 0.89, + "annotation": "The shows a sudden jump in Championships from Philadelphia/San Francisco/Golden State Warriors to Boston Celtics.", + "reason": "The Championships for increases from 7 at Philadelphia/San Francisco/Golden State Warriors to 17 at Boston Celtics, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Franchise": "Denver Nuggets", + "Championships": 1 + } + ], + "score": 0.52, + "annotation": "The has a minimum value at Denver Nuggets", + "reason": "The [{'Franchise': 'Denver Nuggets', 'Championships': 1}] of has a mainimum value of 1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Franchise": "Boston Celtics", + "Championships": 17 + }, + { + "Franchise": "Minneapolis/Los Angeles Lakers", + "Championships": 17 + }, + { + "Franchise": "Philadelphia/San Francisco/Golden State Warriors", + "Championships": 7 + }, + { + "Franchise": "Chicago Bulls", + "Championships": 6 + }, + { + "Franchise": "San Antonio Spurs", + "Championships": 5 + }, + { + "Franchise": "Syracuse Nationals/Philadelphia 76ers", + "Championships": 3 + }, + { + "Franchise": "Fort Wayne/Detroit Pistons", + "Championships": 3 + }, + { + "Franchise": "Miami Heat", + "Championships": 3 + }, + { + "Franchise": "Houston Rockets", + "Championships": 2 + }, + { + "Franchise": "New York Knicks", + "Championships": 2 + }, + { + "Franchise": "Milwaukee Bucks", + "Championships": 2 + }, + { + "Franchise": "Denver Nuggets", + "Championships": 1 + } + ], + "columns": [ + { + "name": "Franchise", + "importance": "primary", + "description": "NBA Franchise", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Championships", + "importance": "primary", + "description": "Number of NBA Championships won", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1578": { + "metadata": { + "title": "U.S. Confidence in Military Hits Lowest Point in 20 Years", + "description": "Share of respondents who say they have quite a lot/a great deal of confidence in the military", + "main_insight": "Public confidence in the U.S. military has declined significantly, reaching a 20-year low in 2023. The data shows a fluctuating but overall downward trend since a peak in the early 1990s." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1975, + "Confidence Level": 58.0 + }, + { + "Year": 1981, + "Confidence Level": 50.0 + }, + { + "Year": 1988, + "Confidence Level": 61.0 + }, + { + "Year": 1990, + "Confidence Level": 69.0 + }, + { + "Year": 1991, + "Confidence Level": 85.0 + }, + { + "Year": 1992, + "Confidence Level": 68.0 + }, + { + "Year": 1993, + "Confidence Level": 63.0 + }, + { + "Year": 1995, + "Confidence Level": 68.0 + }, + { + "Year": 1997, + "Confidence Level": 64.0 + }, + { + "Year": 1998, + "Confidence Level": 67.0 + }, + { + "Year": 1999, + "Confidence Level": 68.0 + }, + { + "Year": 2000, + "Confidence Level": 80.0 + }, + { + "Year": 2001, + "Confidence Level": 82.0 + }, + { + "Year": 2002, + "Confidence Level": 79.0 + }, + { + "Year": 2003, + "Confidence Level": 69.0 + }, + { + "Year": 2004, + "Confidence Level": 82.0 + }, + { + "Year": 2005, + "Confidence Level": 72.0 + }, + { + "Year": 2006, + "Confidence Level": 73.0 + }, + { + "Year": 2007, + "Confidence Level": 69.0 + }, + { + "Year": 2008, + "Confidence Level": 71.0 + }, + { + "Year": 2009, + "Confidence Level": 82.0 + }, + { + "Year": 2010, + "Confidence Level": 78.0 + }, + { + "Year": 2011, + "Confidence Level": 75.0 + }, + { + "Year": 2012, + "Confidence Level": 75.0 + }, + { + "Year": 2013, + "Confidence Level": 78.0 + }, + { + "Year": 2014, + "Confidence Level": 73.0 + }, + { + "Year": 2015, + "Confidence Level": 72.0 + }, + { + "Year": 2016, + "Confidence Level": 72.0 + }, + { + "Year": 2017, + "Confidence Level": 74.0 + }, + { + "Year": 2018, + "Confidence Level": 74.0 + }, + { + "Year": 2019, + "Confidence Level": 73.0 + }, + { + "Year": 2020, + "Confidence Level": 69.0 + }, + { + "Year": 2021, + "Confidence Level": 69.0 + }, + { + "Year": 2022, + "Confidence Level": 64.0 + }, + { + "Year": 2023, + "Confidence Level": 60.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Confidence Level", + "importance": "primary", + "description": "Percentage of respondents with a great deal/quite a lot of confidence", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "630": { + "metadata": { + "title": "Russian Population has Stagnated in Last Decades", + "description": "Population and relative labor force of Russia between 1990-2018 (in millions)", + "main_insight": "Russia's population and labor force have both declined slightly between 1990 and 2018, indicating stagnation in demographic growth." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2018, + "Units": 144.0, + "Population Group": "Total population" + }, + { + "Year": 2018, + "Units": 73.5, + "Population Group": "Labor force" + } + ], + "score": 0.03, + "annotation": "The Units of Total population and Labor force show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total population and Labor force is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 1990, + "Units": 148.0, + "Population Group": "Total population" + }, + { + "Year": 1990, + "Units": 76.0, + "Population Group": "Labor force" + }, + { + "Year": 1994, + "Units": 148.5, + "Population Group": "Total population" + }, + { + "Year": 1994, + "Units": 74.0, + "Population Group": "Labor force" + }, + { + "Year": 1998, + "Units": 147.0, + "Population Group": "Total population" + }, + { + "Year": 1998, + "Units": 73.0, + "Population Group": "Labor force" + }, + { + "Year": 2002, + "Units": 145.0, + "Population Group": "Total population" + }, + { + "Year": 2002, + "Units": 71.0, + "Population Group": "Labor force" + }, + { + "Year": 2006, + "Units": 143.0, + "Population Group": "Total population" + }, + { + "Year": 2006, + "Units": 74.0, + "Population Group": "Labor force" + }, + { + "Year": 2010, + "Units": 143.0, + "Population Group": "Total population" + }, + { + "Year": 2010, + "Units": 76.0, + "Population Group": "Labor force" + }, + { + "Year": 2014, + "Units": 144.0, + "Population Group": "Total population" + }, + { + "Year": 2014, + "Units": 75.0, + "Population Group": "Labor force" + }, + { + "Year": 2018, + "Units": 144.0, + "Population Group": "Total population" + }, + { + "Year": 2018, + "Units": 73.5, + "Population Group": "Labor force" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 1990 to 2018", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding population group", + "role": "y" + }, + { + "name": "Population Group", + "importance": "primary", + "description": "The population group of the data point", + "role": "group" + } + ] + } + }, + "1222": { + "metadata": { + "title": "How Was 2024? Not as Bad as the Last Few Years", + "description": "Share of respondents who agree that the given year was a bad year for their country/them and their family", + "main_insight": "Fewer respondents in 2024 thought it was a bad year for their country or for them and their family compared to the peak in 2020, indicating a perceived improvement in conditions." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Units": 65.0, + "Sentiment": "A bad year for my country" + }, + { + "Year": 2024, + "Units": 51.0, + "Sentiment": "A bad year for me and my family" + } + ], + "score": 0.98, + "annotation": "The Units of A bad year for my country and A bad year for me and my family show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between A bad year for my country and A bad year for me and my family is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2018, + "Units": 63.0, + "Sentiment": "A bad year for my country" + }, + { + "Year": 2018, + "Units": 46.0, + "Sentiment": "A bad year for me and my family" + }, + { + "Year": 2019, + "Units": 64.0, + "Sentiment": "A bad year for my country" + }, + { + "Year": 2019, + "Units": 47.0, + "Sentiment": "A bad year for me and my family" + }, + { + "Year": 2020, + "Units": 91.0, + "Sentiment": "A bad year for my country" + }, + { + "Year": 2020, + "Units": 67.0, + "Sentiment": "A bad year for me and my family" + }, + { + "Year": 2021, + "Units": 77.0, + "Sentiment": "A bad year for my country" + }, + { + "Year": 2021, + "Units": 58.0, + "Sentiment": "A bad year for me and my family" + }, + { + "Year": 2022, + "Units": 78.0, + "Sentiment": "A bad year for my country" + }, + { + "Year": 2022, + "Units": 57.0, + "Sentiment": "A bad year for me and my family" + }, + { + "Year": 2023, + "Units": 73.0, + "Sentiment": "A bad year for my 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countries, with a large majority of the expenditure going towards prescription medicines." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "US", + "Units": 1220.0, + "Medicine Type": "Prescription Medicines" + } + ], + "score": 0.93, + "annotation": "The Prescription Medicines has a maximum value at US", + "reason": "The [{'Country': 'US', 'Units': 1220.0, 'Medicine Type': 'Prescription Medicines'}] of Prescription Medicines has a maximum value of 1220.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "CR", + "Units": 263.0, + "Medicine Type": "Prescription Medicines" + }, + { + "Country": "CR", + "Units": 41.0, + "Medicine Type": "Over-the-counter Medicines" + } + ], + "score": 0.93, + "annotation": "The Units of Prescription Medicines and Over-the-counter Medicines show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Prescription Medicines and Over-the-counter Medicines is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "US", + "Units": 144.0, + "Medicine Type": "Over-the-counter Medicines" + } + ], + "score": 0.9, + "annotation": "The Over-the-counter Medicines has a maximum value at US", + "reason": "The [{'Country': 'US', 'Units': 144.0, 'Medicine Type': 'Over-the-counter Medicines'}] of Over-the-counter Medicines has a maximum value of 144.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "CR", + "Units": 41.0, + "Medicine Type": "Over-the-counter Medicines" + } + ], + "score": 0.79, + "annotation": "The Over-the-counter Medicines has a minimum value at CR", + "reason": "The [{'Country': 'CR', 'Units': 41.0, 'Medicine Type': 'Over-the-counter Medicines'}] of Over-the-counter Medicines has a mainimum value of 41.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "US", + "Units": 1220.0, + "Medicine Type": "Prescription Medicines" + } + ], + "score": 0.78, + "annotation": "The Prescription Medicines shows a sudden jump in Units from DE to US.", + "reason": "The Units for Prescription Medicines increases from 828.0 at DE to 1220.0 at US, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "US", + "Units": 1220.0, + "Medicine Type": "Prescription Medicines" + }, + { + "Country": "US", + "Units": 144.0, + "Medicine Type": "Over-the-counter Medicines" + }, + { + "Country": "DE", + "Units": 828.0, + "Medicine Type": "Prescription Medicines" + }, + { + "Country": "DE", + "Units": 122.0, + "Medicine Type": "Over-the-counter Medicines" + }, + { + "Country": "CA", + "Units": 767.0, + "Medicine Type": "Prescription Medicines" + }, + { + "Country": "CA", + "Units": 87.0, + "Medicine Type": "Over-the-counter Medicines" + }, + { + "Country": "JP", + "Units": 752.0, + "Medicine Type": "Prescription Medicines" + }, + { + "Country": "JP", + "Units": 102.0, + "Medicine Type": "Over-the-counter Medicines" + }, + { + "Country": "AU", + "Units": 585.0, + "Medicine Type": "Prescription Medicines" + }, + { + "Country": "AU", + "Units": 98.0, + "Medicine Type": "Over-the-counter Medicines" + }, + { + "Country": "FR", + "Units": 584.0, + "Medicine Type": "Prescription Medicines" + }, + { + "Country": "FR", + "Units": 63.0, + "Medicine Type": "Over-the-counter Medicines" + }, + { + "Country": "ES", + "Units": 460.0, + "Medicine Type": "Prescription Medicines" + }, + { + "Country": "ES", + "Units": 74.0, + "Medicine Type": "Over-the-counter Medicines" + }, + { + "Country": "GB", + "Units": 418.0, + "Medicine Type": "Prescription Medicines" + }, + { + "Country": "GB", + "Units": 68.0, + "Medicine Type": "Over-the-counter Medicines" + }, + { + "Country": "NO", + "Units": 368.0, + "Medicine Type": "Prescription Medicines" + }, + { + "Country": "NO", + "Units": 65.0, + "Medicine Type": "Over-the-counter Medicines" + }, + { + "Country": "CR", + "Units": 263.0, + "Medicine Type": "Prescription Medicines" + }, + { + "Country": "CR", + "Units": 41.0, + "Medicine Type": "Over-the-counter Medicines" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Selected OECD countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding medicine type", + "role": "y" + }, + { + "name": "Medicine Type", + "importance": "primary", + "description": "The medicine type of the data point", + "role": "group" + } + ] + } + }, + "2459": { + "metadata": { + "title": "Tesla Hasn't Conquered the World", + "description": "Tesla's market share of total BEV unit sales in 2021, by region", + "main_insight": "Tesla has a dominant market share in North America and Australia & Oceania, but a relatively small share in Europe, Asia, and South America." + }, + "chart_type": "Multiple Pie Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Region": "South America", + "Percentage": 3.3, + "Market Share": "Tesla" + }, + { + "Region": "South America", + "Percentage": 96.7, + "Market Share": "Other" + } + ], + "score": 1.0, + "annotation": "The Percentage of Tesla and Other show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Percentage between Tesla and Other is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Australia & Oceania", + "Percentage": 65.8, + "Market Share": "Tesla" + } + ], + "score": 0.94, + "annotation": "The Tesla shows a sudden jump in Percentage from Europe to Australia & Oceania.", + "reason": "The Percentage for Tesla increases from 13.2 at Europe to 65.8 at Australia & Oceania, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Europe", + "Percentage": 86.8, + "Market Share": "Other" + } + ], + "score": 0.94, + "annotation": "The Other shows a sudden jump in Percentage from Australia & Oceania to Europe.", + "reason": "The Percentage for Other increases from 34.2 at Australia & Oceania to 86.8 at Europe, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Region": "South America", + "Percentage": 3.3, + "Market Share": "Tesla" + } + ], + "score": 0.86, + "annotation": "The Tesla has the smallest proportion in South America.", + "reason": "The Percentage of Tesla in South America accounts for 0.033, smaller than all other Market Share, of the total of Percentage of all Market Share." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Australia & Oceania", + "Percentage": 65.8, + "Market Share": "Tesla" + } + ], + "score": 0.75, + "annotation": "The Tesla accounts for the majority in Australia & Oceania.", + "reason": "The Percentage of Tesla in Australia & Oceania accounts for 0.6579999999999999 (more than 50%) of the total of Percentage of all Market Share." + } + ], + "data": { + "data": [ + { + "Region": "North America", + "Percentage": 70.6, + "Market Share": "Tesla" + }, + { + "Region": "North America", + "Percentage": 29.4, + "Market Share": "Other" + }, + { + "Region": "Australia & Oceania", + "Percentage": 65.8, + "Market Share": "Tesla" + }, + { + "Region": "Australia & Oceania", + "Percentage": 34.2, + "Market Share": "Other" + }, + { + "Region": "Europe", + "Percentage": 13.2, + "Market Share": "Tesla" + }, + { + "Region": "Europe", + "Percentage": 86.8, + "Market Share": "Other" + }, + { + "Region": "Asia", + "Percentage": 12.4, + "Market Share": "Tesla" + }, + { + "Region": "Asia", + "Percentage": 87.6, + "Market Share": "Other" + }, + { + "Region": "South America", + "Percentage": 3.3, + "Market Share": "Tesla" + }, + { + "Region": "South America", + "Percentage": 96.7, + "Market Share": "Other" + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Geographic region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage for the corresponding market share", + "role": "y" + }, + { + "name": "Market Share", + "importance": "primary", + "description": "The market share of the data point", + "role": "group" + } + ] + } + }, + "77": { + "metadata": { + "title": "Perceived Rise in Prescription Drug Prices Since 2017", + "description": "Percentage of people who think prescription drug prices have changed since 2017", + "main_insight": "A significant majority of US adults, and an even larger percentage across all political affiliations, believe prescription drug prices have increased since 2017." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Perception of Price Change": "Same", + "Score": 20.0, + "Political Affiliation": "Independents" + }, + { + "Perception of Price Change": "Same", + "Score": 13.0, + "Political Affiliation": "Democrats" + } + ], + "score": 1.0, + "annotation": "The Score of Independents and Democrats show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Independents and Democrats is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Perception of Price Change": "Same", + "Score": 25.0, + "Political Affiliation": "U.S. Adults" + }, + { + "Perception of Price Change": "Same", + "Score": 20.0, + "Political Affiliation": "Independents" + } + ], + "score": 0.99, + "annotation": "The Score of U.S. Adults and Independents show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between U.S. Adults and Independents is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Perception of Price Change": "Same", + "Score": 25.0, + "Political Affiliation": "U.S. Adults" + }, + { + "Perception of Price Change": "Same", + "Score": 13.0, + "Political Affiliation": "Democrats" + } + ], + "score": 0.98, + "annotation": "The Score of U.S. Adults and Democrats show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between U.S. Adults and Democrats is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Perception of Price Change": "Same", + "Score": 42.0, + "Political Affiliation": "Republicans" + } + ], + "score": 0.96, + "annotation": "The Republicans shows a sudden jump in Score from Decrease to Same.", + "reason": "The Score for Republicans increases from 8.0 at Decrease to 42.0 at Same, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Perception of Price Change": "Increase", + "Score": 81.0, + "Political Affiliation": "Democrats" + } + ], + "score": 0.96, + "annotation": "The Democrats shows a sudden jump in Score from Same to Increase.", + "reason": "The Score for Democrats increases from 13.0 at Same to 81.0 at Increase, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Perception of Price Change": "Increase", + "Score": 66.0, + "Political Affiliation": "U.S. Adults" + }, + { + "Perception of Price Change": "Increase", + "Score": 47.0, + "Political Affiliation": "Republicans" + }, + { + "Perception of Price Change": "Increase", + "Score": 70.0, + "Political Affiliation": "Independents" + }, + { + "Perception of Price Change": "Increase", + "Score": 81.0, + "Political Affiliation": "Democrats" + }, + { + "Perception of Price Change": "Decrease", + "Score": 7.0, + "Political Affiliation": "U.S. Adults" + }, + { + "Perception of Price Change": "Decrease", + "Score": 8.0, + "Political Affiliation": "Republicans" + }, + { + "Perception of Price Change": "Decrease", + "Score": 8.0, + "Political Affiliation": "Independents" + }, + { + "Perception of Price Change": "Decrease", + "Score": 3.0, + "Political Affiliation": "Democrats" + }, + { + "Perception of Price Change": "Same", + "Score": 25.0, + "Political Affiliation": "U.S. Adults" + }, + { + "Perception of Price Change": "Same", + "Score": 42.0, + "Political Affiliation": "Republicans" + }, + { + "Perception of Price Change": "Same", + "Score": 20.0, + "Political Affiliation": "Independents" + }, + { + "Perception of Price Change": "Same", + "Score": 13.0, + "Political Affiliation": "Democrats" + } + ], + "columns": [ + { + "name": "Perception of Price Change", + "importance": "primary", + "description": "Categories of perceived change in prescription drug prices", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding political affiliation", + "role": "y" + }, + { + "name": "Political Affiliation", + "importance": "primary", + "description": "The political affiliation of the data point", + "role": "group" + } + ] + } + }, + "2367": { + "metadata": { + "title": "How Mobile Are Social Networks?", + "description": "% of time spent on social networks in the United States, by platform*", + "main_insight": "Most social networks are used predominantly on mobile devices, with LinkedIn being the only platform with a higher percentage of desktop usage in December 2013." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Social Network": "LinkedIn", + "Score": 26.0, + "Device Type": "Mobile" + }, + { + "Social Network": "LinkedIn", + "Score": 74.0, + "Device Type": "Desktop" + } + ], + "score": 1.0, + "annotation": "The Score of Mobile and Desktop show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Mobile and Desktop is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Social Network": "Instagram", + "Score": 2.0, + "Device Type": "Desktop" + } + ], + "score": 0.91, + "annotation": "The Desktop has the smallest proportion in Instagram.", + "reason": "The Score of Desktop in Instagram accounts for 0.02, smaller than all other Device Type, of the total of Score of all Device Type." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Social Network": "LinkedIn", + "Score": 26.0, + "Device Type": "Mobile" + } + ], + "score": 0.81, + "annotation": "The Mobile has a minimum value at LinkedIn", + "reason": "The [{'Social Network': 'LinkedIn', 'Score': 26.0, 'Device Type': 'Mobile'}] of Mobile has a mainimum value of 26.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Social Network": "LinkedIn", + "Score": 74.0, + "Device Type": "Desktop" + } + ], + "score": 0.81, + "annotation": "The Desktop has a maximum value at LinkedIn", + "reason": "The [{'Social Network': 'LinkedIn', 'Score': 74.0, 'Device Type': 'Desktop'}] of Desktop has a maximum value of 74.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Social Network": "Tumblr", + "Score": 54.0, + "Device Type": "Desktop" + } + ], + "score": 0.79, + "annotation": "The Desktop accounts for the majority in Tumblr.", + "reason": "The Score of Desktop in Tumblr accounts for 0.54 (more than 50%) of the total of Score of all Device Type." + } + ], + "data": { + "data": [ + { + "Social Network": "Instagram", + "Score": 98.0, + "Device Type": "Mobile" + }, + { + "Social Network": "Instagram", + "Score": 2.0, + "Device Type": "Desktop" + }, + { + "Social Network": "Pinterest", + "Score": 92.0, + "Device Type": "Mobile" + }, + { + "Social Network": "Pinterest", + "Score": 8.0, + "Device Type": "Desktop" + }, + { + "Social Network": "Twitter", + "Score": 86.0, + "Device Type": "Mobile" + }, + { + "Social Network": "Twitter", + "Score": 14.0, + "Device Type": "Desktop" + }, + { + "Social Network": "Facebook", + "Score": 68.0, + "Device Type": "Mobile" + }, + { + "Social Network": "Facebook", + "Score": 32.0, + "Device Type": "Desktop" + }, + { + "Social Network": "Tumblr", + "Score": 46.0, + "Device Type": "Mobile" + }, + { + "Social Network": "Tumblr", + "Score": 54.0, + "Device Type": "Desktop" + }, + { + "Social Network": "LinkedIn", + "Score": 26.0, + "Device Type": "Mobile" + }, + { + "Social Network": "LinkedIn", + "Score": 74.0, + "Device Type": "Desktop" + } + ], + "columns": [ + { + "name": "Social Network", + "importance": "primary", + "description": "Social media platform", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding device type", + "role": "y" + }, + { + "name": "Device Type", + "importance": "primary", + "description": "The device type of the data point", + "role": "group" + } + ] + } + }, + "1315": { + "metadata": { + "title": "Where a World Cup Spot Is Like Gold Dust", + "description": "Number of teams at the qualifying stage playing for one allocated berth at the FIFA World Cup 2022, by region", + "main_insight": "The visualization shows the number of teams competing for a single World Cup spot in each region, highlighting the intense competition, especially in Africa and North/Central America & Caribbean." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Region": "South America (CONMEBOL)", + "Teams per Spot": 2 + } + ], + "score": 0.79, + "annotation": "The has a minimum value at South America (CONMEBOL)", + "reason": "The [{'Region': 'South America (CONMEBOL)', 'Teams per Spot': 2}] of has a mainimum value of 2, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Oceania (OFC)", + "Teams per Spot": 7 + } + ], + "score": 0.72, + "annotation": "The shows a sudden jump in Teams per Spot from Europe (UEFA) to Oceania (OFC).", + "reason": "The Teams per Spot for increases from 4 at Europe (UEFA) to 7 at Oceania (OFC), indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "Africa (CAF)", + "Teams per Spot": 11 + } + ], + "score": 0.71, + "annotation": "The has a maximum value at Africa (CAF)", + "reason": "The [{'Region': 'Africa (CAF)', 'Teams per Spot': 11}] of has a maximum value of 11, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Region": "South America (CONMEBOL)", + "Teams per Spot": 2 + }, + { + "Region": "Europe (UEFA)", + "Teams per Spot": 4 + }, + { + "Region": "Oceania (OFC)", + "Teams per Spot": 7 + }, + { + "Region": "Asia (AFC)", + "Teams per Spot": 8 + }, + { + "Region": "North/Central America & Caribbean (CONCACAF)", + "Teams per Spot": 9 + }, + { + "Region": "Africa (CAF)", + "Teams per Spot": 11 + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "FIFA World Cup region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Teams per Spot", + "importance": "primary", + "description": "Number of teams competing for one allocated berth", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2314": { + "metadata": { + "title": "iOS 12 Adoption Trails Previous Versions", + "description": "Worldwide adoption rate of iOS versions on the first 10 days after their respective releases", + "main_insight": "iOS 12 has the slowest adoption rate among all iOS versions in the first 10 days of its release." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Days After Release": "10", + "Score": 36.1, + "iOS Versions": "iOS 10 (2016)" + }, + { + "Days After Release": "10", + "Score": 28.6, + "iOS Versions": "iOS 11 (2017)" + } + ], + "score": 1.0, + "annotation": "The Score of iOS 10 (2016) and iOS 11 (2017) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between iOS 10 (2016) and iOS 11 (2017) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Days After Release": "10", + "Score": 20.7, + "iOS Versions": "iOS 12 (2018)" + }, + { + "Days After Release": "10", + "Score": 37.5, + "iOS Versions": "iOS 8 (2014)" + } + ], + "score": 1.0, + "annotation": "The Score of iOS 12 (2018) and iOS 8 (2014) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between iOS 12 (2018) and iOS 8 (2014) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Days After Release": "10", + "Score": 37.5, + "iOS Versions": "iOS 8 (2014)" + }, + { + "Days After Release": "10", + "Score": 28.6, + "iOS Versions": "iOS 11 (2017)" + } + ], + "score": 1.0, + "annotation": "The Score of iOS 8 (2014) and iOS 11 (2017) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between iOS 8 (2014) and iOS 11 (2017) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Days After Release": "10", + "Score": 20.7, + "iOS Versions": "iOS 12 (2018)" + }, + { + "Days After Release": "10", + "Score": 28.6, + "iOS Versions": "iOS 11 (2017)" + } + ], + "score": 1.0, + "annotation": "The Score of iOS 12 (2018) and iOS 11 (2017) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between iOS 12 (2018) and iOS 11 (2017) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Days After Release": "10", + "Score": 37.5, + "iOS Versions": "iOS 8 (2014)" + }, + { + "Days After Release": "10", + "Score": 36.1, + "iOS Versions": "iOS 10 (2016)" + } + ], + "score": 1.0, + "annotation": "The Score of iOS 8 (2014) and iOS 10 (2016) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between iOS 8 (2014) and iOS 10 (2016) is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Days After Release": "1", + "Score": 5.0, + "iOS Versions": "iOS 12 (2018)" + }, + { + "Days After Release": "1", + "Score": 8.0, + "iOS Versions": "iOS 9 (2015)" + }, + { + "Days After Release": "1", + "Score": 6.0, + "iOS Versions": "iOS 8 (2014)" + }, + { + "Days After Release": "1", + "Score": 7.0, + "iOS Versions": "iOS 10 (2016)" + }, + { + "Days After Release": "1", + "Score": 7.0, + "iOS Versions": "iOS 11 (2017)" + }, + { + "Days After Release": "1", + "Score": 15.0, + "iOS Versions": "iOS 7 (2013)" + }, + { + "Days After Release": "2", + "Score": 8.0, + "iOS Versions": "iOS 12 (2018)" + }, + { + "Days After Release": "2", + "Score": 15.0, + "iOS Versions": "iOS 9 (2015)" + }, + { + "Days After Release": "2", + "Score": 13.0, + "iOS Versions": "iOS 8 (2014)" + }, + { + "Days After Release": "2", + "Score": 14.0, + "iOS Versions": "iOS 10 (2016)" + }, + { + "Days After Release": "2", + "Score": 12.0, + "iOS Versions": "iOS 11 (2017)" + }, + { + "Days After Release": "2", + "Score": 35.0, + "iOS Versions": "iOS 7 (2013)" + }, + { + "Days After Release": "3", + "Score": 11.0, + "iOS Versions": "iOS 12 (2018)" + }, + { + "Days After Release": "3", + "Score": 21.0, + "iOS Versions": "iOS 9 (2015)" + }, + { + "Days After Release": "3", + "Score": 18.0, + "iOS Versions": "iOS 8 (2014)" + }, + { + "Days After Release": "3", + "Score": 19.0, + "iOS Versions": "iOS 10 (2016)" + }, + { + "Days After Release": "3", + "Score": 16.0, + "iOS Versions": "iOS 11 (2017)" + }, + { + "Days After Release": "3", + "Score": 45.0, + "iOS Versions": "iOS 7 (2013)" + }, + { + "Days After Release": "4", + "Score": 13.0, + "iOS Versions": "iOS 12 (2018)" + }, + { + "Days After Release": "4", + "Score": 26.0, + "iOS Versions": "iOS 9 (2015)" + }, + { + "Days After Release": "4", + "Score": 22.0, + "iOS Versions": "iOS 8 (2014)" + }, + { + "Days After Release": "4", + "Score": 23.0, + "iOS Versions": "iOS 10 (2016)" + }, + { + "Days After Release": "4", + "Score": 19.0, + "iOS Versions": "iOS 11 (2017)" + }, + { + "Days After Release": "4", + "Score": 52.0, + "iOS Versions": "iOS 7 (2013)" + }, + { + "Days After Release": "5", + "Score": 15.0, + "iOS Versions": "iOS 12 (2018)" + }, + { + "Days After Release": "5", + "Score": 30.0, + "iOS Versions": "iOS 9 (2015)" + }, + { + "Days After Release": "5", + "Score": 25.0, + "iOS Versions": "iOS 8 (2014)" + }, + { + "Days After Release": "5", + "Score": 26.0, + "iOS Versions": "iOS 10 (2016)" + }, + { + "Days After Release": "5", + "Score": 21.0, + "iOS Versions": "iOS 11 (2017)" + }, + { + "Days After Release": "5", + "Score": 57.0, + "iOS Versions": "iOS 7 (2013)" + }, + { + "Days After Release": "6", + "Score": 16.0, + "iOS Versions": "iOS 12 (2018)" + }, + { + "Days After Release": "6", + "Score": 33.0, + "iOS Versions": "iOS 9 (2015)" + }, + { + "Days After Release": "6", + "Score": 28.0, + "iOS Versions": "iOS 8 (2014)" + }, + { + "Days After Release": "6", + "Score": 29.0, + "iOS Versions": "iOS 10 (2016)" + }, + { + "Days After Release": "6", + "Score": 23.0, + "iOS Versions": "iOS 11 (2017)" + }, + { + "Days After Release": "6", + "Score": 59.0, + "iOS Versions": "iOS 7 (2013)" + }, + { + "Days After Release": "7", + "Score": 17.0, + "iOS Versions": "iOS 12 (2018)" + }, + { + "Days After Release": "7", + "Score": 37.0, + "iOS Versions": "iOS 9 (2015)" + }, + { + "Days After Release": "7", + "Score": 30.0, + "iOS Versions": "iOS 8 (2014)" + }, + { + "Days After Release": "7", + "Score": 31.0, + "iOS Versions": "iOS 10 (2016)" + }, + { + "Days After Release": "7", + "Score": 25.0, + "iOS Versions": "iOS 11 (2017)" + }, + { + "Days After Release": "7", + "Score": 61.0, + "iOS Versions": "iOS 7 (2013)" + }, + { + "Days After Release": "8", + "Score": 18.0, + "iOS Versions": "iOS 12 (2018)" + }, + { + "Days After Release": "8", + "Score": 40.0, + "iOS Versions": "iOS 9 (2015)" + }, + { + "Days After Release": "8", + "Score": 32.0, + "iOS Versions": "iOS 8 (2014)" + }, + { + "Days After Release": "8", + "Score": 33.0, + "iOS Versions": "iOS 10 (2016)" + }, + { + "Days After Release": "8", + "Score": 26.0, + "iOS Versions": "iOS 11 (2017)" + }, + { + "Days After Release": "8", + "Score": 62.0, + "iOS Versions": "iOS 7 (2013)" + }, + { + "Days After Release": "9", + "Score": 19.0, + "iOS Versions": "iOS 12 (2018)" + }, + { + "Days After Release": "9", + "Score": 44.0, + "iOS Versions": "iOS 9 (2015)" + }, + { + "Days After Release": "9", + "Score": 34.0, + "iOS Versions": "iOS 8 (2014)" + }, + { + "Days After Release": "9", + "Score": 35.0, + "iOS Versions": "iOS 10 (2016)" + }, + { + "Days After Release": "9", + "Score": 27.0, + "iOS Versions": "iOS 11 (2017)" + }, + { + "Days After Release": "9", + "Score": 63.0, + "iOS Versions": "iOS 7 (2013)" + }, + { + "Days After Release": "10", + "Score": 20.7, + "iOS Versions": "iOS 12 (2018)" + }, + { + "Days After Release": "10", + "Score": 48.1, + "iOS Versions": "iOS 9 (2015)" + }, + { + "Days After Release": "10", + "Score": 37.5, + "iOS Versions": "iOS 8 (2014)" + }, + { + "Days After Release": "10", + "Score": 36.1, + "iOS Versions": "iOS 10 (2016)" + }, + { + "Days After Release": "10", + "Score": 28.6, + "iOS Versions": "iOS 11 (2017)" + }, + { + "Days After Release": "10", + "Score": 63.4, + "iOS Versions": "iOS 7 (2013)" + } + ], + "columns": [ + { + "name": "Days After Release", + "importance": "primary", + "description": "Number of days after the release of each iOS version", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding ios versions", + "role": "y" + }, + { + "name": "iOS Versions", + "importance": "primary", + "description": "The ios versions of the data point", + "role": "group" + } + ] + } + }, + "1724": { + "metadata": { + "title": "Moscow Commuters Spend 127 Hours Stuck in Traffic Each Year", + "description": "Annual hourly delays per commuter in Europe's most congested cities", + "main_insight": "Moscow commuters experience the highest annual traffic delays among major European cities, spending 127 hours stuck in traffic." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Moscow", + "Annual Hours Delayed": 127.0 + } + ], + "score": 0.92, + "annotation": "The has a maximum value at Moscow", + "reason": "The [{'City': 'Moscow', 'Annual Hours Delayed': 127.0}] of has a maximum value of 127.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "Palermo", + "Annual Hours Delayed": 90.0 + }, + { + "City": "Lyon", + "Annual Hours Delayed": 90.0 + } + ], + "score": 0.65, + "annotation": "The has minimum values at Palermo, Lyon", + "reason": "The [{'City': 'Palermo', 'Annual Hours Delayed': 90.0}, {'City': 'Lyon', 'Annual Hours Delayed': 90.0}] of have minimum values of 90.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Moscow", + "Annual Hours Delayed": 127.0 + } + ], + "score": 0.64, + "annotation": "The shows a sudden jump in Annual Hours Delayed from Istanbul to Moscow.", + "reason": "The Annual Hours Delayed for increases from 118.0 at Istanbul to 127.0 at Moscow, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "City": "Moscow", + "Annual Hours Delayed": 127.0 + }, + { + "City": "Istanbul", + "Annual Hours Delayed": 118.0 + }, + { + "City": "Warsaw", + "Annual Hours Delayed": 110.0 + }, + { + "City": "Brussels", + "Annual Hours Delayed": 103.0 + }, + { + "City": "Stockholm", + "Annual Hours Delayed": 102.0 + }, + { + "City": "Marseille", + "Annual Hours Delayed": 99.0 + }, + { + "City": "Rome", + "Annual Hours Delayed": 97.0 + }, + { + "City": "Paris", + "Annual Hours Delayed": 97.0 + }, + { + "City": "Palermo", + "Annual Hours Delayed": 90.0 + }, + { + "City": "Lyon", + "Annual Hours Delayed": 90.0 + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "Name of the European City", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Annual Hours Delayed", + "importance": "primary", + "description": "Annual hours of delay per commuter", + "unit": "hours", + "data_type": "number", + "role": "y" + } + ] + } + }, + "338": { + "metadata": { + "title": "Rise and Shine On Black Friday 2017", + "description": "Peak shopping hours for Americans to shop in stores on Black Friday", + "main_insight": "Almost half of the respondents won't shop on Black Friday, and among those that do, Early Morning (5-8 AM) is the most popular time." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Time of Day": "Won't shop on Black Friday", + "Percentage of Shoppers": 47.0 + } + ], + "score": 0.93, + "annotation": "The has a maximum value at Won't shop on Black Friday", + "reason": "The [{'Time of Day': \"Won't shop on Black Friday\", 'Percentage of Shoppers': 47.0}] of has a maximum value of 47.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time of Day": "Won't shop on Black Friday", + "Percentage of Shoppers": 47.0 + } + ], + "score": 0.87, + "annotation": "The shows a sudden jump in Percentage of Shoppers from Early Morning (5-8 AM) to Won't shop on Black Friday.", + "reason": "The Percentage of Shoppers for increases from 21.0 at Early Morning (5-8 AM) to 47.0 at Won't shop on Black Friday, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Time of Day": "Night (9 PM-4 AM)", + "Percentage of Shoppers": 2.0 + } + ], + "score": 0.48, + "annotation": "The has a minimum value at Night (9 PM-4 AM)", + "reason": "The [{'Time of Day': 'Night (9 PM-4 AM)', 'Percentage of Shoppers': 2.0}] of has a mainimum value of 2.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Time of Day": "Won't shop on Black Friday", + "Percentage of Shoppers": 47.0 + }, + { + "Time of Day": "Early Morning (5-8 AM)", + "Percentage of Shoppers": 21.0 + }, + { + "Time of Day": "Late Morning (11-12 PM)", + "Percentage of Shoppers": 13.0 + }, + { + "Time of Day": "Early Afternoon (1-3 PM)", + "Percentage of Shoppers": 8.0 + }, + { + "Time of Day": "Late Afternoon (4-5 PM)", + "Percentage of Shoppers": 4.0 + }, + { + "Time of Day": "Early Evening (5-7 PM)", + "Percentage of Shoppers": 4.0 + }, + { + "Time of Day": "Night (9 PM-4 AM)", + "Percentage of Shoppers": 2.0 + } + ], + "columns": [ + { + "name": "Time of Day", + "importance": "primary", + "description": "Time slot during Black Friday", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Shoppers", + "importance": "primary", + "description": "Percentage of respondents who shop during that time slot", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2093": { + "metadata": { + "title": "Quarter of U.S. Cats Pick Their Owners", + "description": "Channels of pet acquisition in 2022 according to U.S. pet owners (in percent)", + "main_insight": "In 2022, a significant proportion of cats (24%) were acquired from the street/as strays, highlighting a different acquisition pattern compared to dogs and other pets. Pet shops are a much more common source for 'Other pets' compared to Dogs and Cats." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Pet Type": "Other pets", + "Units": 7.0, + "Source": "From the street/stray animal" + }, + { + "Pet Type": "Other pets", + "Units": 19.0, + "Source": "Others" + } + ], + "score": 1.0, + "annotation": "The Units of From the street/stray animal and Others show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between From the street/stray animal and Others is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Pet Type": "Other pets", + "Units": 16.0, + "Source": "Friends/acquaintances" + }, + { + "Pet Type": "Other pets", + "Units": 48.0, + "Source": "Pet Shop" + } + ], + "score": 1.0, + "annotation": "The Units of Friends/acquaintances and Pet Shop show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Friends/acquaintances and Pet Shop is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Pet Type": "Other pets", + "Units": 16.0, + "Source": "Friends/acquaintances" + }, + { + "Pet Type": "Other pets", + "Units": 5.0, + "Source": "Animal Shelter" + } + ], + "score": 0.98, + "annotation": "The Units of Friends/acquaintances and Animal Shelter show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Friends/acquaintances and Animal Shelter is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Pet Type": "Other pets", + "Units": 5.0, + "Source": "Animal Shelter" + }, + { + "Pet Type": "Other pets", + "Units": 48.0, + "Source": "Pet Shop" + } + ], + "score": 0.97, + "annotation": "The Units of Animal Shelter and Pet Shop show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Animal Shelter and Pet Shop is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Pet Type": "Other pets", + "Units": 48.0, + "Source": "Pet Shop" + } + ], + "score": 0.97, + "annotation": "The Pet Shop shows a sudden jump in Units from Dogs to Other pets.", + "reason": "The Units for Pet Shop increases from 12.0 at Dogs to 48.0 at Other pets, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Pet Type": "Dogs", + "Units": 5.0, + "Source": "From the street/stray animal" + }, + { + "Pet Type": "Dogs", + "Units": 24.0, + "Source": "Friends/acquaintances" + }, + { + "Pet Type": "Dogs", + "Units": 21.0, + "Source": "Breeder" + }, + { + "Pet Type": "Dogs", + "Units": 19.0, + "Source": "Animal Shelter" + }, + { + "Pet Type": "Dogs", + "Units": 12.0, + "Source": "Pet Shop" + }, + { + "Pet Type": "Dogs", + "Units": 20.0, + "Source": "Others" + }, + { + "Pet Type": "Cats", + "Units": 24.0, + "Source": "From the street/stray animal" + }, + { + "Pet Type": "Cats", + "Units": 25.0, + "Source": "Friends/acquaintances" + }, + { + "Pet Type": "Cats", + "Units": 4.0, + "Source": "Breeder" + }, + { + "Pet Type": "Cats", + "Units": 25.0, + "Source": "Animal Shelter" + }, + { + "Pet Type": "Cats", + "Units": 10.0, + "Source": "Pet Shop" + }, + { + "Pet Type": "Cats", + "Units": 13.0, + "Source": "Others" + }, + { + "Pet Type": "Other pets", + "Units": 7.0, + "Source": "From the street/stray animal" + }, + { + "Pet Type": "Other pets", + "Units": 16.0, + "Source": "Friends/acquaintances" + }, + { + "Pet Type": "Other pets", + "Units": 5.0, + "Source": "Breeder" + }, + { + "Pet Type": "Other pets", + "Units": 5.0, + "Source": "Animal Shelter" + }, + { + "Pet Type": "Other pets", + "Units": 48.0, + "Source": "Pet Shop" + }, + { + "Pet Type": "Other pets", + "Units": 19.0, + "Source": "Others" + } + ], + "columns": [ + { + "name": "Pet Type", + "importance": "primary", + "description": "Category of pet (Dogs, Cats, Other pets)", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding source", + "role": "y" + }, + { + "name": "Source", + "importance": "primary", + "description": "The source of the data point", + "role": "group" + } + ] + } + }, + "1445": { + "metadata": { + "title": "End of XP Is Bandage for Ailing PC Market", + "description": "Global PC shipments by vendor (in million units)", + "main_insight": "While most PC vendors saw growth in shipments between Q1 2013 and Q1 2014, Acer experienced a significant decline. Overall, the total PC market shipments slightly decreased during this period." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Vendor": "ASUS", + "Units": 5.31, + "Shipments": "Q1'14" + }, + { + "Vendor": "ASUS", + "Units": 5.06, + "Shipments": "Q1'13" + } + ], + "score": 0.98, + "annotation": "The Units of Q1'14 and Q1'13 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Q1'14 and Q1'13 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Vendor": "Acer", + "Units": -14.8, + "Shipments": "Growth" + } + ], + "score": 0.86, + "annotation": "The Growth has a minimum value at Acer", + "reason": "The [{'Vendor': 'Acer', 'Units': -14.8, 'Shipments': 'Growth'}] of Growth has a mainimum value of -14.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Vendor": "ASUS", + "Units": 5.06, + "Shipments": "Q1'13" + } + ], + "score": 0.7, + "annotation": "The Q1'13 has a minimum value at ASUS", + "reason": "The [{'Vendor': 'ASUS', 'Units': 5.06, 'Shipments': \"Q1'13\"}] of Q1'13 has a mainimum value of 5.06, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Vendor": "ASUS", + "Units": 5.31, + "Shipments": "Q1'14" + }, + { + "Vendor": "ASUS", + "Units": 4.8, + "Shipments": "Growth" + } + ], + "score": 0.63, + "annotation": "The Units of Q1'14 and Growth show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Q1'14 and Growth is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Vendor": "ASUS", + "Units": 5.31, + "Shipments": "Q1'14" + } + ], + "score": 0.63, + "annotation": "The Q1'14 has a minimum value at ASUS", + "reason": "The [{'Vendor': 'ASUS', 'Units': 5.31, 'Shipments': \"Q1'14\"}] of Q1'14 has a mainimum value of 5.31, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Vendor": "Lenovo", + "Units": 12.91, + "Shipments": "Q1'14" + }, + { + "Vendor": "Lenovo", + "Units": 11.64, + "Shipments": "Q1'13" + }, + { + "Vendor": "Lenovo", + "Units": 10.9, + "Shipments": "Growth" + }, + { + "Vendor": "Lenovo", + "Units": 77.9, + "Shipments": "Total Shipments Q1'13" + }, + { + "Vendor": "Lenovo", + "Units": 76.6, + "Shipments": "Total Shipments Q1'14" + }, + { + "Vendor": "Lenovo", + "Units": -1.7, + "Shipments": "Total Shipments Growth" + }, + { + "Vendor": "HP", + "Units": 12.25, + "Shipments": "Q1'14" + }, + { + "Vendor": "HP", + "Units": 11.77, + "Shipments": "Q1'13" + }, + { + "Vendor": "HP", + "Units": 4.1, + "Shipments": "Growth" + }, + { + "Vendor": "HP", + "Units": 77.9, + "Shipments": "Total Shipments Q1'13" + }, + { + "Vendor": "HP", + "Units": 76.6, + "Shipments": "Total Shipments Q1'14" + }, + { + "Vendor": "HP", + "Units": -1.7, + "Shipments": "Total Shipments Growth" + }, + { + "Vendor": "Dell", + "Units": 9.54, + "Shipments": "Q1'14" + }, + { + "Vendor": "Dell", + "Units": 8.76, + "Shipments": "Q1'13" + }, + { + "Vendor": "Dell", + "Units": 9.0, + "Shipments": "Growth" + }, + { + "Vendor": "Dell", + "Units": 77.9, + "Shipments": "Total Shipments Q1'13" + }, + { + "Vendor": "Dell", + "Units": 76.6, + "Shipments": "Total Shipments Q1'14" + }, + { + "Vendor": "Dell", + "Units": -1.7, + "Shipments": "Total Shipments Growth" + }, + { + "Vendor": "Acer", + "Units": 5.56, + "Shipments": "Q1'14" + }, + { + "Vendor": "Acer", + "Units": 6.53, + "Shipments": "Q1'13" + }, + { + "Vendor": "Acer", + "Units": -14.8, + "Shipments": "Growth" + }, + { + "Vendor": "Acer", + "Units": 77.9, + "Shipments": "Total Shipments Q1'13" + }, + { + "Vendor": "Acer", + "Units": 76.6, + "Shipments": "Total Shipments Q1'14" + }, + { + "Vendor": "Acer", + "Units": -1.7, + "Shipments": "Total Shipments Growth" + }, + { + "Vendor": "ASUS", + "Units": 5.31, + "Shipments": "Q1'14" + }, + { + "Vendor": "ASUS", + "Units": 5.06, + "Shipments": "Q1'13" + }, + { + "Vendor": "ASUS", + "Units": 4.8, + "Shipments": "Growth" + }, + { + "Vendor": "ASUS", + "Units": 77.9, + "Shipments": "Total Shipments Q1'13" + }, + { + "Vendor": "ASUS", + "Units": 76.6, + "Shipments": "Total Shipments Q1'14" + }, + { + "Vendor": "ASUS", + "Units": -1.7, + "Shipments": "Total Shipments Growth" + } + ], + "columns": [ + { + "name": "Vendor", + "importance": "primary", + "description": "PC Vendor", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding shipments", + "role": "y" + }, + { + "name": "Shipments", + "importance": "primary", + "description": "The shipments of the data point", + "role": "group" + } + ] + } + }, + "888": { + "metadata": { + "title": "Sharp Racial Divide Over Robert E. Lee Statue", + "description": "Share approving/disapproving of the decision to remove General Robert E. Lee statue", + "main_insight": "There is a significant difference in opinion between Black and White respondents regarding the removal of the Robert E. Lee statue, with Black respondents more likely to approve and White respondents more likely to disapprove." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Opinion": "Strongly disapprove", + "Units": 40.0, + "Race": "Whites" + } + ], + "score": 0.93, + "annotation": "The Whites shows a sudden jump in Units from No opinion to Strongly disapprove.", + "reason": "The Units for Whites increases from 17.0 at No opinion to 40.0 at Strongly disapprove, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Opinion": "Strongly disapprove", + "Units": 40.0, + "Race": "Whites" + } + ], + "score": 0.86, + "annotation": "The Whites has a maximum value at Strongly disapprove", + "reason": "The [{'Opinion': 'Strongly disapprove', 'Units': 40.0, 'Race': 'Whites'}] of Whites has a maximum value of 40.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Opinion": "Strongly approve", + "Units": 33.0, + "Race": "Blacks" + } + ], + "score": 0.82, + "annotation": "The Blacks shows a sudden jump in Units from Somewhat approve to Strongly approve.", + "reason": "The Units for Blacks increases from 16.0 at Somewhat approve to 33.0 at Strongly approve, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Race": "Blacks", + "Opinion": "total", + "Units": 100.0 + } + ], + "score": 0.8, + "annotation": "The total value of Blacks accounts for the majority in all Race.", + "reason": "The total value of Units of Blacks accounts for 0.5025125628140703 (more than 50%) of the total of Units of all Race." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Opinion": "Somewhat approve", + "Units": 16.0, + "Race": "Blacks" + } + ], + "score": 0.76, + "annotation": "The Blacks accounts for the majority in Somewhat approve.", + "reason": "The Units of Blacks in Somewhat approve accounts for 0.64 (more than 50%) of the total of Units of all Race." + } + ], + "data": { + "data": [ + { + "Opinion": "Strongly approve", + "Units": 33.0, + "Race": "Blacks" + }, + { + "Opinion": "Strongly approve", + "Units": 16.0, + "Race": "Whites" + }, + { + "Opinion": "Somewhat approve", + "Units": 16.0, + "Race": "Blacks" + }, + { + "Opinion": "Somewhat approve", + "Units": 9.0, + "Race": "Whites" + }, + { + "Opinion": "Somewhat disapprove", + "Units": 7.0, + "Race": "Blacks" + }, + { + "Opinion": "Somewhat disapprove", + "Units": 17.0, + "Race": "Whites" + }, + { + "Opinion": "Strongly disapprove", + "Units": 4.0, + "Race": "Blacks" + }, + { + "Opinion": "Strongly disapprove", + "Units": 40.0, + "Race": "Whites" + }, + { + "Opinion": "No opinion", + "Units": 40.0, + "Race": "Blacks" + }, + { + "Opinion": "No opinion", + "Units": 17.0, + "Race": "Whites" + } + ], + "columns": [ + { + "name": "Opinion", + "importance": "primary", + "description": "Level of approval or disapproval", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding race", + "role": "y" + }, + { + "name": "Race", + "importance": "primary", + "description": "The race of the data point", + "role": "group" + } + ] + } + }, + "1576": { + "metadata": { + "title": "Australia's Growing Government Debt", + "description": "Gross government debt in Australia 2003-2020 (in billion AUS$)", + "main_insight": "Australia's gross government debt has significantly increased from 2003 to 2020, with a notable acceleration around 2008 and continuing to rise sharply." + }, + "chart_type": "Spline Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2003, + "Gross Debt (billion AUS$)": 55.6 + }, + { + "Year": 2004, + "Gross Debt (billion AUS$)": 55.6 + }, + { + "Year": 2005, + "Gross Debt (billion AUS$)": 55.6 + }, + { + "Year": 2006, + "Gross Debt (billion AUS$)": 55.6 + }, + { + "Year": 2007, + "Gross Debt (billion AUS$)": 55.6 + }, + { + "Year": 2008, + "Gross Debt (billion AUS$)": 55.6 + }, + { + "Year": 2009, + "Gross Debt (billion AUS$)": 128.7 + }, + { + "Year": 2010, + "Gross Debt (billion AUS$)": 178.7 + }, + { + "Year": 2011, + "Gross Debt (billion AUS$)": 216.6 + }, + { + "Year": 2012, + "Gross Debt (billion AUS$)": 257.4 + }, + { + "Year": 2013, + "Gross Debt (billion AUS$)": 299.7 + }, + { + "Year": 2014, + "Gross Debt (billion AUS$)": 352.7 + }, + { + "Year": 2015, + "Gross Debt (billion AUS$)": 421.2 + }, + { + "Year": 2016, + "Gross Debt (billion AUS$)": 474.8 + }, + { + "Year": 2017, + "Gross Debt (billion AUS$)": 523.0 + }, + { + "Year": 2018, + "Gross Debt (billion AUS$)": 534.2 + }, + { + "Year": 2019, + "Gross Debt (billion AUS$)": 547.9 + }, + { + "Year": 2020, + "Gross Debt (billion AUS$)": 571.4 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of reported debt, from 2003 to 2020", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Gross Debt (billion AUS$)", + "importance": "primary", + "description": "Gross government debt in billions of Australian dollars", + "unit": "billion AUS$", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1118": { + "metadata": { + "title": "Sustainable Tourism: Collective Responsibility of Providers, Travelers, and Governments", + "description": "Who do you think has the most potential for counteracting the environmental impacts of travel and tourism?", + "main_insight": "Travel service providers are seen as having the most potential for counteracting the environmental impacts of travel and tourism, followed closely by travelers and governments." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Entity": "Major travel sites/apps", + "Percentage": 0.28 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Percentage from NGOs and non-profit organizations to Major travel sites/apps.", + "reason": "The Percentage for increases from 0.13 at NGOs and non-profit organizations to 0.28 at Major travel sites/apps, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Entity": "NGOs and non-profit organizations", + "Percentage": 0.13 + } + ], + "score": 0.83, + "annotation": "The has a minimum value at NGOs and non-profit organizations", + "reason": "The [{'Entity': 'NGOs and non-profit organizations', 'Percentage': 0.13}] of has a mainimum value of 0.13, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Entity": "Travel service providers", + "Percentage": 0.43 + } + ], + "score": 0.53, + "annotation": "The has a maximum value at Travel service providers", + "reason": "The [{'Entity': 'Travel service providers', 'Percentage': 0.43}] of has a maximum value of 0.43, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Entity": "Travel service providers", + "Percentage": 0.43 + }, + { + "Entity": "Travelers", + "Percentage": 0.4 + }, + { + "Entity": "Governments", + "Percentage": 0.38 + }, + { + "Entity": "Major travel sites/apps", + "Percentage": 0.28 + }, + { + "Entity": "NGOs and non-profit organizations", + "Percentage": 0.13 + } + ], + "columns": [ + { + "name": "Entity", + "importance": "primary", + "description": "The entity being asked about", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents who believe the entity has the most potential", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1408": { + "metadata": { + "title": "Women Work More Than Men", + "description": "Average total hours of work done per day (paid and unpaid), by gender and region", + "main_insight": "Women consistently work more total hours (paid and unpaid) than men across all regions. The difference is largely due to women performing a significantly larger share of unpaid work." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Middle East & North Africa", + "Score": 6.0, + "Work Hours": "Total Hours (Women)" + } + ], + "score": 0.96, + "annotation": "The Total Hours (Women) has the largest proportion in Middle East & North Africa.", + "reason": "The Score of Total Hours (Women) in Middle East & North Africa accounts for 0.2586206896551724, larger than all other Work Hours, of the total of Score of all Work Hours." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Latin America", + "Score": 8.3, + "Work Hours": "Total Hours (Women)" + } + ], + "score": 0.96, + "annotation": "The Total Hours (Women) has the largest proportion in Latin America.", + "reason": "The Score of Total Hours (Women) in Latin America accounts for 0.259375, larger than all other Work Hours, of the total of Score of all Work Hours." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Developed countries", + "Score": 7.2, + "Work Hours": "Total Hours (Women)" + } + ], + "score": 0.96, + "annotation": "The Total Hours (Women) has the largest proportion in Developed countries.", + "reason": "The Score of Total Hours (Women) in Developed countries accounts for 0.26277372262773724, larger than all other Work Hours, of the total of Score of all Work Hours." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Work Hours": "Total Hours (Women)", + "Region": "total", + "Score": 51.50000000000001 + } + ], + "score": 0.96, + "annotation": "The total value of Total Hours (Women) has the largest proportion.", + "reason": "The total value of Score of Total Hours (Women) accounts for 0.26491769547325106, larger than all other Work Hours, of the total of Score of all Work Hours." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "East Asia & the Pacific", + "Score": 7.7, + "Work Hours": "Total Hours (Women)" + } + ], + "score": 0.96, + "annotation": "The Total Hours (Women) has the largest proportion in East Asia & the Pacific.", + "reason": "The Score of Total Hours (Women) in East Asia & the Pacific accounts for 0.2655172413793104, larger than all other Work Hours, of the total of Score of all Work Hours." + } + ], + "data": { + "data": [ + { + "Region": "Latin America", + "Score": 8.3, + "Work Hours": "Total Hours (Women)" + }, + { + "Region": "Latin America", + "Score": 5.0, + "Work Hours": "Unpaid Hours (Women)" + }, + { + "Region": "Latin America", + "Score": 3.3, + "Work Hours": "Paid Hours (Women)" + }, + { + "Region": "Latin America", + "Score": 7.7, + "Work Hours": "Total Hours (Men)" + }, + { + "Region": "Latin America", + "Score": 2.0, + "Work Hours": "Unpaid Hours (Men)" + }, + { + "Region": "Latin America", + "Score": 5.7, + "Work Hours": "Paid Hours (Men)" + }, + { + "Region": "South Asia", + "Score": 7.8, + "Work Hours": "Total Hours (Women)" + }, + { + "Region": "South Asia", + "Score": 5.1, + "Work Hours": "Unpaid Hours (Women)" + }, + { + "Region": "South Asia", + "Score": 2.7, + "Work Hours": "Paid Hours (Women)" + }, + { + "Region": "South Asia", + "Score": 6.8, + "Work Hours": "Total Hours (Men)" + }, + { + "Region": "South Asia", + "Score": 1.2, + "Work Hours": "Unpaid Hours (Men)" + }, + { + "Region": "South Asia", + "Score": 5.6, + "Work Hours": "Paid Hours (Men)" + }, + { + "Region": "East Asia & the Pacific", + "Score": 7.7, + "Work Hours": "Total Hours (Women)" + }, + { + "Region": "East Asia & the Pacific", + "Score": 4.1, + "Work Hours": "Unpaid Hours (Women)" + }, + { + "Region": "East Asia & the Pacific", + "Score": 3.6, + "Work Hours": "Paid Hours (Women)" + }, + { + "Region": "East Asia & the Pacific", + "Score": 6.8, + "Work Hours": "Total Hours (Men)" + }, + { + "Region": "East Asia & the Pacific", + "Score": 1.6, + "Work Hours": "Unpaid Hours (Men)" + }, + { + "Region": "East Asia & the Pacific", + "Score": 5.2, + "Work Hours": "Paid Hours (Men)" + }, + { + "Region": "Central/ Eastern Europe & Central Asia", + "Score": 7.4, + "Work Hours": "Total Hours (Women)" + }, + { + "Region": "Central/ Eastern Europe & Central Asia", + "Score": 4.3, + "Work Hours": "Unpaid Hours (Women)" + }, + { + "Region": "Central/ Eastern Europe & Central Asia", + "Score": 3.1, + "Work Hours": "Paid Hours (Women)" + }, + { + "Region": "Central/ Eastern Europe & Central Asia", + "Score": 6.2, + "Work Hours": "Total Hours (Men)" + }, + { + "Region": "Central/ Eastern Europe & Central Asia", + "Score": 1.8, + "Work Hours": "Unpaid Hours (Men)" + }, + { + "Region": "Central/ Eastern Europe & Central Asia", + "Score": 4.4, + "Work Hours": "Paid Hours (Men)" + }, + { + "Region": "Developed countries", + "Score": 7.2, + "Work Hours": "Total Hours (Women)" + }, + { + "Region": "Developed countries", + "Score": 4.1, + "Work Hours": "Unpaid Hours (Women)" + }, + { + "Region": "Developed countries", + "Score": 3.1, + "Work Hours": "Paid Hours (Women)" + }, + { + "Region": "Developed countries", + "Score": 6.5, + "Work Hours": "Total Hours (Men)" + }, + { + "Region": "Developed countries", + "Score": 2.3, + "Work Hours": "Unpaid Hours (Men)" + }, + { + "Region": "Developed countries", + "Score": 4.2, + "Work Hours": "Paid Hours (Men)" + }, + { + "Region": "Sub-Saharan Africa", + "Score": 7.1, + "Work Hours": "Total Hours (Women)" + }, + { + "Region": "Sub-Saharan Africa", + "Score": 4.2, + "Work Hours": "Unpaid Hours (Women)" + }, + { + "Region": "Sub-Saharan Africa", + "Score": 2.9, + "Work Hours": "Paid Hours (Women)" + }, + { + "Region": "Sub-Saharan Africa", + "Score": 6.1, + "Work Hours": "Total Hours (Men)" + }, + { + "Region": "Sub-Saharan Africa", + "Score": 1.8, + "Work Hours": "Unpaid Hours (Men)" + }, + { + "Region": "Sub-Saharan Africa", + "Score": 4.3, + "Work Hours": "Paid Hours (Men)" + }, + { + "Region": "Middle East & North Africa", + "Score": 6.0, + "Work Hours": "Total Hours (Women)" + }, + { + "Region": "Middle East & North Africa", + "Score": 4.2, + "Work Hours": "Unpaid Hours (Women)" + }, + { + "Region": "Middle East & North Africa", + "Score": 1.8, + "Work Hours": "Paid Hours (Women)" + }, + { + "Region": "Middle East & North Africa", + "Score": 5.6, + "Work Hours": "Total Hours (Men)" + }, + { + "Region": "Middle East & North Africa", + "Score": 1.1, + "Work Hours": "Unpaid Hours (Men)" + }, + { + "Region": "Middle East & North Africa", + "Score": 4.5, + "Work Hours": "Paid Hours (Men)" + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Geographic region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding work hours", + "role": "y" + }, + { + "name": "Work Hours", + "importance": "primary", + "description": "The work hours of the data point", + "role": "group" + } + ] + } + }, + "1214": { + "metadata": { + "title": "Youth Unemployment High in South Asia", + "description": "Share of labor force aged 15-24 years old who are unemployed, by year (in percent)", + "main_insight": "Youth unemployment varies significantly across South Asian countries. Sri Lanka had the highest youth unemployment in 2023, while Pakistan had the lowest." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Score": 15.7, + "Region": "Bangladesh" + }, + { + "Year": 2023, + "Score": 9.7, + "Region": "Pakistan" + } + ], + "score": 0.82, + "annotation": "The Score of Bangladesh and Pakistan show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Bangladesh and Pakistan is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Score": 20.4, + "Region": "Nepal" + }, + { + "Year": 2023, + "Score": 15.8, + "Region": "India" + } + ], + "score": 0.77, + "annotation": "The Score of Nepal and India show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Nepal and India is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2023, + "Score": 25.3, + "Region": "Sri Lanka" + }, + { + "Year": 2023, + "Score": 15.8, + "Region": "India" + } + ], + "score": 0.58, + "annotation": "The Score of Sri Lanka and India show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Sri Lanka and India is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Score": 15.8, + "Region": "India" + }, + { + "Year": 2023, + "Score": 9.7, + "Region": "Pakistan" + } + ], + "score": 0.48, + "annotation": "The Score of India and Pakistan show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between India and Pakistan is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Score": 20.4, + "Region": "Nepal" + }, + { + "Year": 2023, + "Score": 15.7, + "Region": "Bangladesh" + } + ], + "score": 0.46, + "annotation": "The Score of Nepal and Bangladesh show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Nepal and Bangladesh is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2003, + "Score": 28.0, + "Region": "Sri Lanka" + }, + { + "Year": 2003, + "Score": 20.0, + "Region": "Nepal" + }, + { + "Year": 2003, + "Score": 14.0, + "Region": "India" + }, + { + "Year": 2003, + "Score": 6.0, + "Region": "Bangladesh" + }, + { + "Year": 2003, + "Score": 1.0, + "Region": "Pakistan" + }, + { + "Year": 2005, + "Score": 28.0, + "Region": "Sri Lanka" + }, + { + "Year": 2005, + "Score": 19.0, + "Region": "Nepal" + }, + { + "Year": 2005, + "Score": 15.0, + "Region": "India" + }, + { + "Year": 2005, + "Score": 7.0, + "Region": "Bangladesh" + }, + { + "Year": 2005, + "Score": 1.0, + "Region": "Pakistan" + }, + { + "Year": 2010, + "Score": 20.0, + "Region": "Sri Lanka" + }, + { + "Year": 2010, + "Score": 19.0, + "Region": "Nepal" + }, + { + "Year": 2010, + "Score": 19.0, + "Region": "India" + }, + { + "Year": 2010, + "Score": 8.0, + "Region": "Bangladesh" + }, + { + "Year": 2010, + "Score": 8.0, + "Region": "Pakistan" + }, + { + "Year": 2015, + "Score": 18.0, + "Region": "Sri Lanka" + }, + { + "Year": 2015, + "Score": 21.0, + "Region": "Nepal" + }, + { + "Year": 2015, + "Score": 21.0, + "Region": "India" + }, + { + "Year": 2015, + "Score": 9.0, + "Region": "Bangladesh" + }, + { + "Year": 2015, + "Score": 4.0, + "Region": "Pakistan" + }, + { + "Year": 2020, + "Score": 25.0, + "Region": "Sri Lanka" + }, + { + "Year": 2020, + "Score": 24.0, + "Region": "Nepal" + }, + { + "Year": 2020, + "Score": 24.0, + "Region": "India" + }, + { + "Year": 2020, + "Score": 12.0, + "Region": "Bangladesh" + }, + { + "Year": 2020, + "Score": 8.0, + "Region": "Pakistan" + }, + { + "Year": 2023, + "Score": 25.3, + "Region": "Sri Lanka" + }, + { + "Year": 2023, + "Score": 20.4, + "Region": "Nepal" + }, + { + "Year": 2023, + "Score": 15.8, + "Region": "India" + }, + { + "Year": 2023, + "Score": 15.7, + "Region": "Bangladesh" + }, + { + "Year": 2023, + "Score": 9.7, + "Region": "Pakistan" + }, + { + "Year": 2023, + "Score": 13.8, + "Region": "World youth unemployment 2023" + }, + { + "Year": 2023, + "Score": 14.1, + "Region": "Low and middle income youth unemployment 2023" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding region", + "role": "y" + }, + { + "name": "Region", + "importance": "primary", + "description": "The region of the data point", + "role": "group" + } + ] + } + }, + "905": { + "metadata": { + "title": "Some Cities Suffer Enormous Water Losses Every Year", + "description": "% share of water loss in selected cities in 2012", + "main_insight": "In 2012, Mexico City had the highest water loss percentage among the selected cities at 44%, while Amsterdam had the lowest at 4%." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Mexico City", + "Water Loss (%)": 44.0 + } + ], + "score": 0.84, + "annotation": "The has a maximum value at Mexico City", + "reason": "The [{'City': 'Mexico City', 'Water Loss (%)': 44.0}] of has a maximum value of 44.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "Amsterdam", + "Water Loss (%)": 4.0 + } + ], + "score": 0.74, + "annotation": "The has a minimum value at Amsterdam", + "reason": "The [{'City': 'Amsterdam', 'Water Loss (%)': 4.0}] of has a mainimum value of 4.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Hong Kong", + "Water Loss (%)": 18.0 + } + ], + "score": 0.67, + "annotation": "The shows a sudden jump in Water Loss (%) from Cologne to Hong Kong.", + "reason": "The Water Loss (%) for increases from 7.0 at Cologne to 18.0 at Hong Kong, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "City": "Mexico City", + "Water Loss (%)": 44.0 + }, + { + "City": "Naples", + "Water Loss (%)": 37.0 + }, + { + "City": "Montreal", + "Water Loss (%)": 33.0 + }, + { + "City": "Rome", + "Water Loss (%)": 26.0 + }, + { + "City": "Edinburgh", + "Water Loss (%)": 24.0 + }, + { + "City": "Stockholm", + "Water Loss (%)": 19.0 + }, + { + "City": "Hong Kong", + "Water Loss (%)": 18.0 + }, + { + "City": "New York", + "Water Loss (%)": 7.0 + }, + { + "City": "Cologne", + "Water Loss (%)": 7.0 + }, + { + "City": "Amsterdam", + "Water Loss (%)": 4.0 + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "Name of the city", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Water Loss (%)", + "importance": "primary", + "description": "Percentage of water loss", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "194": { + "metadata": { + "title": "Prepaid Plans Are Rising Faster than Others for AT&T", + "description": "Number of worldwide AT&T wireless subscribers by subscription type (in thousands)", + "main_insight": "AT&T's prepaid subscribers have increased at a faster rate than postpaid or reseller subscribers from 2009 to 2018, although postpaid remains the largest segment." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2018, + "Dollars": 78000.0, + "Customer Segment": "Postpaid" + }, + { + "Year": 2018, + "Dollars": 19000.0, + "Customer Segment": "Prepaid" + } + ], + "score": 0.95, + "annotation": "The Dollars of Postpaid and Prepaid show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between Postpaid and Prepaid is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2018, + "Dollars": 78000.0, + "Customer Segment": "Postpaid" + }, + { + "Year": 2018, + "Dollars": 8000.0, + "Customer Segment": "Reseller" + } + ], + "score": 0.36, + "annotation": "The Dollars of Postpaid and Reseller show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between Postpaid and Reseller is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2018, + "Dollars": 19000.0, + "Customer Segment": "Prepaid" + }, + { + "Year": 2018, + "Dollars": 8000.0, + "Customer Segment": "Reseller" + } + ], + "score": 0.14, + "annotation": "The Dollars of Prepaid and Reseller show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between Prepaid and Reseller is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2009, + "Dollars": 65000.0, + "Customer Segment": "Postpaid" + }, + { + "Year": 2009, + "Dollars": 7500.0, + "Customer Segment": "Prepaid" + }, + { + "Year": 2009, + "Dollars": 10000.0, + "Customer Segment": "Reseller" + }, + { + "Year": 2010, + "Dollars": 68000.0, + "Customer Segment": "Postpaid" + }, + { + "Year": 2010, + "Dollars": 8000.0, + "Customer Segment": "Prepaid" + }, + { + "Year": 2010, + "Dollars": 12500.0, + "Customer Segment": "Reseller" + }, + { + "Year": 2011, + "Dollars": 69000.0, + "Customer Segment": "Postpaid" + }, + { + "Year": 2011, + "Dollars": 9000.0, + "Customer Segment": "Prepaid" + }, + { + "Year": 2011, + "Dollars": 13000.0, + "Customer Segment": "Reseller" + }, + { + "Year": 2012, + "Dollars": 70000.0, + "Customer Segment": "Postpaid" + }, + { + "Year": 2012, + "Dollars": 10000.0, + "Customer Segment": "Prepaid" + }, + { + "Year": 2012, + "Dollars": 14000.0, + "Customer Segment": "Reseller" + }, + { + "Year": 2013, + "Dollars": 73000.0, + "Customer Segment": "Postpaid" + }, + { + "Year": 2013, + "Dollars": 11000.0, + "Customer Segment": "Prepaid" + }, + { + "Year": 2013, + "Dollars": 14000.0, + "Customer Segment": "Reseller" + }, + { + "Year": 2014, + "Dollars": 75000.0, + "Customer Segment": "Postpaid" + }, + { + "Year": 2014, + "Dollars": 13500.0, + "Customer Segment": "Prepaid" + }, + { + "Year": 2014, + "Dollars": 15500.0, + "Customer Segment": "Reseller" + }, + { + "Year": 2015, + "Dollars": 77000.0, + "Customer Segment": "Postpaid" + }, + { + "Year": 2015, + "Dollars": 15000.0, + "Customer Segment": "Prepaid" + }, + { + "Year": 2015, + "Dollars": 16000.0, + "Customer Segment": "Reseller" + }, + { + "Year": 2016, + "Dollars": 78000.0, + "Customer Segment": "Postpaid" + }, + { + "Year": 2016, + "Dollars": 16500.0, + "Customer Segment": "Prepaid" + }, + { + "Year": 2016, + "Dollars": 16500.0, + "Customer Segment": "Reseller" + }, + { + "Year": 2017, + "Dollars": 77000.0, + "Customer Segment": "Postpaid" + }, + { + "Year": 2017, + "Dollars": 18000.0, + "Customer Segment": "Prepaid" + }, + { + "Year": 2017, + "Dollars": 15000.0, + "Customer Segment": "Reseller" + }, + { + "Year": 2018, + "Dollars": 78000.0, + "Customer Segment": "Postpaid" + }, + { + "Year": 2018, + "Dollars": 19000.0, + "Customer Segment": "Prepaid" + }, + { + "Year": 2018, + "Dollars": 8000.0, + "Customer Segment": "Reseller" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of subscriber count", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding customer segment", + "role": "y" + }, + { + "name": "Customer Segment", + "importance": "primary", + "description": "The customer segment of the data point", + "role": "group" + } + ] + } + }, + "690": { + "metadata": { + "title": "The Countries Importing The Most Drones", + "description": "% of UAVs imported by countries from 2010 to 2014*", + "main_insight": "The United Kingdom imported the largest percentage of UAVs between 2010 and 2014, accounting for 33.9% of the total. India was a distant second with 13.2%." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United Kingdom", + "Percentage of UAVs Imported": 33.9 + } + ], + "score": 0.97, + "annotation": "The has a maximum value at United Kingdom", + "reason": "The [{'Country': 'United Kingdom', 'Percentage of UAVs Imported': 33.9}] of has a maximum value of 33.9, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United Kingdom", + "Percentage of UAVs Imported": 33.9 + } + ], + "score": 0.91, + "annotation": "The shows a sudden jump in Percentage of UAVs Imported from India to United Kingdom.", + "reason": "The Percentage of UAVs Imported for increases from 13.2 at India to 33.9 at United Kingdom, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Brazil", + "Percentage of UAVs Imported": 2.9 + } + ], + "score": 0.46, + "annotation": "The has a minimum value at Brazil", + "reason": "The [{'Country': 'Brazil', 'Percentage of UAVs Imported': 2.9}] of has a mainimum value of 2.9, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "United Kingdom", + "Percentage of UAVs Imported": 33.9 + }, + { + "Country": "India", + "Percentage of UAVs Imported": 13.2 + }, + { + "Country": "Italy", + "Percentage of UAVs Imported": 9.8 + }, + { + "Country": "Azerbaijan", + "Percentage of UAVs Imported": 7.8 + }, + { + "Country": "Germany", + "Percentage of UAVs Imported": 7.3 + }, + { + "Country": "Turkey", + "Percentage of UAVs Imported": 6.2 + }, + { + "Country": "France", + "Percentage of UAVs Imported": 3.7 + }, + { + "Country": "Singapore", + "Percentage of UAVs Imported": 3.2 + }, + { + "Country": "Brazil", + "Percentage of UAVs Imported": 2.9 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Importing country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of UAVs Imported", + "importance": "primary", + "description": "Percentage of total UAVs imported by each country between 2010 and 2014", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2048": { + "metadata": { + "title": "Are You Not Entertained?", + "description": "Estimated global revenue from video games, books, filmed entertainment and recorded music in 2021", + "main_insight": "Video games generated the highest revenue among the listed entertainment categories in 2021, significantly exceeding the revenue from books, filmed entertainment, and recorded music." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Entertainment Category": "Filmed entertainment (home and theatrical)*", + "Revenue (Billions)": 99.7 + } + ], + "score": 0.8, + "annotation": "The shows a sudden jump in Revenue (Billions) from Recorded music (physical, digital and others) to Filmed entertainment (home and theatrical)*.", + "reason": "The Revenue (Billions) for increases from 25.9 at Recorded music (physical, digital and others) to 99.7 at Filmed entertainment (home and theatrical)*, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Entertainment Category": "Recorded music (physical, digital and others)", + "Revenue (Billions)": 25.9 + } + ], + "score": 0.7, + "annotation": "The has a minimum value at Recorded music (physical, digital and others)", + "reason": "The [{'Entertainment Category': 'Recorded music (physical, digital and others)', 'Revenue (Billions)': 25.9}] of has a mainimum value of 25.9, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Entertainment Category": "Video games (mobile, console and PC)", + "Revenue (Billions)": 192.7 + } + ], + "score": 0.69, + "annotation": "The has a maximum value at Video games (mobile, console and PC)", + "reason": "The [{'Entertainment Category': 'Video games (mobile, console and PC)', 'Revenue (Billions)': 192.7}] of has a maximum value of 192.7, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Entertainment Category": "Video games (mobile, console and PC)", + "Revenue (Billions)": 192.7 + }, + { + "Entertainment Category": "Books (print and e-books)", + "Revenue (Billions)": 120.1 + }, + { + "Entertainment Category": "Filmed entertainment (home and theatrical)*", + "Revenue (Billions)": 99.7 + }, + { + "Entertainment Category": "Recorded music (physical, digital and others)", + "Revenue (Billions)": 25.9 + } + ], + "columns": [ + { + "name": "Entertainment Category", + "importance": "primary", + "description": "Type of entertainment", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Revenue (Billions)", + "importance": "primary", + "description": "Global revenue in billions of US dollars", + "unit": "Billions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1684": { + "metadata": { + "title": "Nuclear weapons in 1945 and 2020 in comparison", + "description": "Estimated yield of nuclear weapons in kilotons, TBT equivalent", + "main_insight": "The explosive yield of nuclear weapons has significantly increased from 1945 to 2020. The most powerful weapon in 2020 had a significantly greater kiloton yield than those used in 1945." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1945, + "Weapon": "\"Little Boy\"" + }, + { + "Year": 1945, + "Weapon": "\"Fat Man\"" + }, + { + "Year": 2020, + "Weapon": "Trident (SLBM)" + }, + { + "Year": 2020, + "Weapon": "SS (ICBM)" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of weapon", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Weapon", + "importance": "primary", + "description": "Name and type of weapon", + "unit": "none", + "data_type": "string", + "role": "y" + } + ] + } + }, + "958": { + "metadata": { + "title": "Quarter of Older Americans Live Alone", + "description": "Percentage of people 60 years and older in each household type", + "main_insight": "A significantly smaller percentage of older adults in North America live in extended family households compared to other regions, while a quarter live alone." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Region": "North America", + "Units": 26.0, + "Group Size": "Solo" + }, + { + "Region": "North America", + "Units": 7.0, + "Group Size": "Extended" + } + ], + "score": 0.94, + "annotation": "The Units of Solo and Extended show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Solo and Extended is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "North America", + "Units": 26.0, + "Group Size": "Solo" + } + ], + "score": 0.9, + "annotation": "The Solo shows a sudden jump in Units from Latin America to North America.", + "reason": "The Units for Solo increases from 12.0 at Latin America to 26.0 at North America, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Middle East", + "Units": 39.0, + "Group Size": "Extended" + } + ], + "score": 0.84, + "annotation": "The Extended shows a sudden jump in Units from Europe to Middle East.", + "reason": "The Units for Extended increases from 16.0 at Europe to 39.0 at Middle East, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Region": "North America", + "Units": 7.0, + "Group Size": "Extended" + } + ], + "score": 0.79, + "annotation": "The Extended has a minimum value at North America", + "reason": "The [{'Region': 'North America', 'Units': 7.0, 'Group Size': 'Extended'}] of Extended has a mainimum value of 7.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Europe", + "Units": 28.0, + "Group Size": "Solo" + } + ], + "score": 0.76, + "annotation": "The Solo accounts for the majority in Europe.", + "reason": "The Units of Solo in Europe accounts for 0.6363636363636364 (more than 50%) of the total of Units of all Group Size." + } + ], + "data": { + "data": [ + { + "Region": "Sub-Saharan Africa", + "Units": 6.0, + "Group Size": "Solo" + }, + { + "Region": "Sub-Saharan Africa", + "Units": 51.0, + "Group Size": "Extended" + }, + { + "Region": "Asia-Pacific", + "Units": 11.0, + "Group Size": "Solo" + }, + { + "Region": "Asia-Pacific", + "Units": 50.0, + "Group Size": "Extended" + }, + { + "Region": "Latin America", + "Units": 12.0, + "Group Size": "Solo" + }, + { + "Region": "Latin America", + "Units": 41.0, + "Group Size": "Extended" + }, + { + "Region": "Middle East", + "Units": 9.0, + "Group Size": "Solo" + }, + { + "Region": "Middle East", + "Units": 39.0, + "Group Size": "Extended" + }, + { + "Region": "Europe", + "Units": 28.0, + "Group Size": "Solo" + }, + { + "Region": "Europe", + "Units": 16.0, + "Group Size": "Extended" + }, + { + "Region": "North America", + "Units": 26.0, + "Group Size": "Solo" + }, + { + "Region": "North America", + "Units": 7.0, + "Group Size": "Extended" + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Geographic region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding group size", + "role": "y" + }, + { + "name": "Group Size", + "importance": "primary", + "description": "The group size of the data point", + "role": "group" + } + ] + } + }, + "1369": { + "metadata": { + "title": "Majority of Americans Blame Hamas for Current Violence", + "description": "\"Who is most responsible for the current violence in Gaza/Israel?\"", + "main_insight": "A plurality of Americans (40%) blame Hamas for the current violence in Gaza/Israel, with significant variations across political affiliations." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Group": "Independents", + "Score": 42.0, + "Sentiment": "Hamas" + }, + { + "Group": "Independents", + "Score": 20.0, + "Sentiment": "Israel" + } + ], + "score": 0.98, + "annotation": "The Score of Hamas and Israel show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Hamas and Israel is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Group": "Independents", + "Score": 20.0, + "Sentiment": "Israel" + }, + { + "Group": "Independents", + "Score": 13.0, + "Sentiment": "Both" + } + ], + "score": 0.98, + "annotation": "The Score of Israel and Both show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Israel and Both is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Group": "Independents", + "Score": 42.0, + "Sentiment": "Hamas" + }, + { + "Group": "Independents", + "Score": 13.0, + "Sentiment": "Both" + } + ], + "score": 0.97, + "annotation": "The Score of Hamas and Both show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Hamas and Both is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Group": "Democrats", + "Score": 29.0, + "Sentiment": "Hamas" + } + ], + "score": 0.93, + "annotation": "The Hamas has the largest proportion in Democrats.", + "reason": "The Score of Hamas in Democrats accounts for 0.29, larger than all other Sentiment, of the total of Score of all Sentiment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Group": "Total", + "Score": 40.0, + "Sentiment": "Hamas" + } + ], + "score": 0.91, + "annotation": "The Hamas has the largest proportion in Total.", + "reason": "The Score of Hamas in Total accounts for 0.39603960396039606, larger than all other Sentiment, of the total of Score of all Sentiment." + } + ], + "data": { + "data": [ + { + "Group": "Total", + "Score": 40.0, + "Sentiment": "Hamas" + }, + { + "Group": "Total", + "Score": 19.0, + "Sentiment": "Israel" + }, + { + "Group": "Total", + "Score": 14.0, + "Sentiment": "Both" + }, + { + "Group": "Total", + "Score": 28.0, + "Sentiment": "Don't know" + }, + { + "Group": "Republicans", + "Score": 60.0, + "Sentiment": "Hamas" + }, + { + "Group": "Republicans", + "Score": 13.0, + "Sentiment": "Israel" + }, + { + "Group": "Republicans", + "Score": 10.0, + "Sentiment": "Both" + }, + { + "Group": "Republicans", + "Score": 17.0, + "Sentiment": "Don't know" + }, + { + "Group": "Democrats", + "Score": 29.0, + "Sentiment": "Hamas" + }, + { + "Group": "Democrats", + "Score": 26.0, + "Sentiment": "Israel" + }, + { + "Group": "Democrats", + "Score": 18.0, + "Sentiment": "Both" + }, + { + "Group": "Democrats", + "Score": 27.0, + "Sentiment": "Don't know" + }, + { + "Group": "Independents", + "Score": 42.0, + "Sentiment": "Hamas" + }, + { + "Group": "Independents", + "Score": 20.0, + "Sentiment": "Israel" + }, + { + "Group": "Independents", + "Score": 13.0, + "Sentiment": "Both" + }, + { + "Group": "Independents", + "Score": 24.0, + "Sentiment": "Don't know" + } + ], + "columns": [ + { + "name": "Group", + "importance": "primary", + "description": "The group responding to the survey question.", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "1585": { + "metadata": { + "title": "Media side of ad campaigns grows more important", + "description": "Sales contribution of advertising elements", + "main_insight": "The contribution of Media to advertising sales significantly increased from 2006 to 2017, while the contribution of Creative decreased." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2017, + "Units": 36.0, + "Team": "Media" + }, + { + "Year": 2017, + "Units": 49.0, + "Team": "Creative" + } + ], + "score": 1.0, + "annotation": "The Units of Media and Creative show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Media and Creative is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2017, + "Units": 36.0, + "Team": "Media" + }, + { + "Year": 2017, + "Units": 15.0, + "Team": "Other (Brand)" + } + ], + "score": 1.0, + "annotation": "The Units of Media and Other (Brand) show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Media and Other (Brand) is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2017, + "Units": 49.0, + "Team": "Creative" + }, + { + "Year": 2017, + "Units": 15.0, + "Team": "Other (Brand)" + } + ], + "score": 1.0, + "annotation": "The Units of Creative and Other (Brand) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Creative and Other (Brand) is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2006, + "Units": 15.0, + "Team": "Media" + }, + { + "Year": 2006, + "Units": 65.0, + "Team": "Creative" + }, + { + "Year": 2006, + "Units": 20.0, + "Team": "Other (Brand)" + }, + { + "Year": 2017, + "Units": 36.0, + "Team": "Media" + }, + { + "Year": 2017, + "Units": 49.0, + "Team": "Creative" + }, + { + "Year": 2017, + "Units": 15.0, + "Team": "Other (Brand)" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding team", + "role": "y" + }, + { + "name": "Team", + "importance": "primary", + "description": "The team of the data point", + "role": "group" + } + ] + } + }, + "1597": { + "metadata": { + "title": "Generational Differences in UK LGBTQI+ Identification", + "description": "Share of UK adults who described their sexuality as follows, by generation (in %)", + "main_insight": "The percentage of UK adults identifying as Heterosexual decreases across generations, from Baby Boomers to Gen Z. Conversely, the percentage of individuals identifying with LGBTQI+ labels increases across the same generations." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Generation": "Gen Z", + "Score": 84.0, + "Sexualities": "Heterosexual" + }, + { + "Generation": "Gen Z", + "Score": 1.0, + "Sexualities": "Homosexual" + } + ], + "score": 0.8, + "annotation": "The Score of Heterosexual and Homosexual show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Heterosexual and Homosexual is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Generation": "Baby Boomers", + "Score": 96.0, + "Sexualities": "Heterosexual" + }, + { + "Generation": "Baby Boomers", + "Score": 3.0, + "Sexualities": "Homosexual" + }, + { + "Generation": "Generation X", + "Score": 95.0, + "Sexualities": "Heterosexual" + }, + { + "Generation": "Generation X", + "Score": 3.0, + "Sexualities": "Homosexual" + }, + { + "Generation": "Generation X", + "Score": 2.0, + "Sexualities": "Bisexual" + }, + { + "Generation": "Millennials", + "Score": 92.0, + "Sexualities": "Heterosexual" + }, + { + "Generation": "Millennials", + "Score": 1.0, + "Sexualities": "Homosexual" + }, + { + "Generation": "Millennials", + "Score": 3.0, + "Sexualities": "Bisexual" + }, + { + "Generation": "Millennials", + "Score": 4.0, + "Sexualities": "Asexual" + }, + { + "Generation": "Gen Z", + "Score": 84.0, + "Sexualities": "Heterosexual" + }, + { + "Generation": "Gen Z", + "Score": 1.0, + "Sexualities": "Homosexual" + }, + { + "Generation": "Gen Z", + "Score": 3.0, + "Sexualities": "Bisexual" + }, + { + "Generation": "Gen Z", + "Score": 1.0, + "Sexualities": "Pansexual" + }, + { + "Generation": "Gen Z", + "Score": 9.0, + "Sexualities": "Asexual" + }, + { + "Generation": "Gen Z", + "Score": 1.0, + "Sexualities": "Queer" + } + ], + "columns": [ + { + "name": "Generation", + "importance": "primary", + "description": "The generational cohort of the respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding sexualities", + "role": "y" + }, + { + "name": "Sexualities", + "importance": "primary", + "description": "The sexualities of the data point", + "role": "group" + } + ] + } + }, + "220": { + "metadata": { + "title": "Are Americans Falling Out of Love With the Cinema?", + "description": "Estimated number of tickets sold at the North American box office since 1995", + "main_insight": "Cinema ticket sales in North America have generally declined since 2002, with a dramatic drop in 2020." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Year": "2020", + "Tickets Sold": 224.0 + } + ], + "score": 1.0, + "annotation": "The has a minimum value at 2020", + "reason": "The [{'Year': '2020', 'Tickets Sold': 224.0}] of has a mainimum value of 224.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Year": "1995", + "Tickets Sold": 1220.0 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in Tickets Sold from 2020 to 1995.", + "reason": "The Tickets Sold for increases from 224.0 at 2020 to 1220.0 at 1995, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Year": "2002", + "Tickets Sold": 1576.0 + } + ], + "score": 0.61, + "annotation": "The has a maximum value at 2002", + "reason": "The [{'Year': '2002', 'Tickets Sold': 1576.0}] of has a maximum value of 1576.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Year": "1995", + "Tickets Sold": 1220.0 + }, + { + "Year": "1996", + "Tickets Sold": 1340.0 + }, + { + "Year": "1997", + "Tickets Sold": 1390.0 + }, + { + "Year": "1998", + "Tickets Sold": 1470.0 + }, + { + "Year": "1999", + "Tickets Sold": 1440.0 + }, + { + "Year": "2000", + "Tickets Sold": 1410.0 + }, + { + "Year": "2001", + "Tickets Sold": 1480.0 + }, + { + "Year": "2002", + "Tickets Sold": 1576.0 + }, + { + "Year": "2003", + "Tickets Sold": 1550.0 + }, + { + "Year": "2004", + "Tickets Sold": 1490.0 + }, + { + "Year": "2005", + "Tickets Sold": 1380.0 + }, + { + "Year": "2006", + "Tickets Sold": 1400.0 + }, + { + "Year": "2007", + "Tickets Sold": 1400.0 + }, + { + "Year": "2008", + "Tickets Sold": 1340.0 + }, + { + "Year": "2009", + "Tickets Sold": 1410.0 + }, + { + "Year": "2010", + "Tickets Sold": 1330.0 + }, + { + "Year": "2011", + "Tickets Sold": 1280.0 + }, + { + "Year": "2012", + "Tickets Sold": 1380.0 + }, + { + "Year": "2013", + "Tickets Sold": 1340.0 + }, + { + "Year": "2014", + "Tickets Sold": 1260.0 + }, + { + "Year": "2015", + "Tickets Sold": 1330.0 + }, + { + "Year": "2016", + "Tickets Sold": 1310.0 + }, + { + "Year": "2017", + "Tickets Sold": 1230.0 + }, + { + "Year": "2018", + "Tickets Sold": 1310.0 + }, + { + "Year": "2019", + "Tickets Sold": 1229.0 + }, + { + "Year": "2020", + "Tickets Sold": 224.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of ticket sales", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Tickets Sold", + "importance": "primary", + "description": "Estimated number of tickets sold (in millions)", + "unit": "Millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1900": { + "metadata": { + "title": "Amazon's Online Sales Dwarf the Competition", + "description": "The 10 largest U.S. online retailers in 2013 (based on worldwide online sales)", + "main_insight": "Amazon's online sales in 2013 were significantly larger than its competitors, exceeding the second-largest retailer (Apple) by over $49 billion." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Rank": 1, + "Company": "Amazon" + }, + { + "Rank": 2, + "Company": "Apple" + }, + { + "Rank": 3, + "Company": "Staples" + }, + { + "Rank": 4, + "Company": "Walmart" + }, + { + "Rank": 5, + "Company": "Sears" + }, + { + "Rank": 6, + "Company": "Liberty Interactive" + }, + { + "Rank": 7, + "Company": "Netflix" + }, + { + "Rank": 8, + "Company": "Macy's Inc" + }, + { + "Rank": 9, + "Company": "Office Depot" + }, + { + "Rank": 10, + "Company": "Dell" + } + ], + "columns": [ + { + "name": "Rank", + "importance": "primary", + "description": "Ranking of the online retailer", + "unit": "none", + "data_type": "number", + "role": "x" + }, + { + "name": "Company", + "importance": "primary", + "description": "Name of the online retailer", + "unit": "none", + "data_type": "string", + "role": "y" + } + ] + } + }, + "462": { + "metadata": { + "title": "Twitter's Top 5 Markets Account for 50% of Active Users", + "description": "Regional breakdown of Twitter's active tweeting users (as of October 2013)", + "main_insight": "The United States accounts for the largest share of Twitter's active users at 24.3%, followed by Japan at 9.3%. The top 5 countries combined account for 50% of active users." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Other", + "Percentage of Active Users": 26.0 + } + ], + "score": 0.96, + "annotation": "The has a maximum value at Other", + "reason": "The [{'Country': 'Other', 'Percentage of Active Users': 26.0}] of has a maximum value of 26.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Percentage of Active Users": 24.3 + } + ], + "score": 0.89, + "annotation": "The shows a sudden jump in Percentage of Active Users from Japan to United States.", + "reason": "The Percentage of Active Users for increases from 9.3 at Japan to 24.3 at United States, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Colombia", + "Percentage of Active Users": 1.9 + } + ], + "score": 0.43, + "annotation": "The has a minimum value at Colombia", + "reason": "The [{'Country': 'Colombia', 'Percentage of Active Users': 1.9}] of has a mainimum value of 1.9, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Percentage of Active Users": 24.3 + }, + { + "Country": "Japan", + "Percentage of Active Users": 9.3 + }, + { + "Country": "Indonesia", + "Percentage of Active Users": 6.5 + }, + { + "Country": "UK", + "Percentage of Active Users": 5.6 + }, + { + "Country": "Brazil", + "Percentage of Active Users": 4.3 + }, + { + "Country": "Spain", + "Percentage of Active Users": 4.3 + }, + { + "Country": "Saudi Arabia", + "Percentage of Active Users": 4.1 + }, + { + "Country": "Turkey", + "Percentage of Active Users": 3.0 + }, + { + "Country": "Mexico", + "Percentage of Active Users": 3.0 + }, + { + "Country": "Russia", + "Percentage of Active Users": 2.6 + }, + { + "Country": "Argentina", + "Percentage of Active Users": 2.6 + }, + { + "Country": "France", + "Percentage of Active Users": 2.1 + }, + { + "Country": "Colombia", + "Percentage of Active Users": 1.9 + }, + { + "Country": "Other", + "Percentage of Active Users": 26.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country or category", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Active Users", + "importance": "primary", + "description": "Percentage of Twitter's active tweeting users in each country/category", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "204": { + "metadata": { + "title": "Americans Are Divided About 2020 Election Security", + "description": "% confident the U.S. can defend against foreign government interference in the 2020 election", + "main_insight": "There is a significant partisan divide in confidence regarding election security, with Republicans much more confident than Democrats. Overall, Americans are nearly evenly split on the issue." + }, + "chart_type": "Multiple Pie Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Group": "Republicans", + "Units": 75.0, + "Confidence Level": "Very/somewhat confident" + }, + { + "Group": "Republicans", + "Units": 26.0, + "Confidence Level": "Not so/not at all confident" + } + ], + "score": 1.0, + "annotation": "The Units of Very/somewhat confident and Not so/not at all confident show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Very/somewhat confident and Not so/not at all confident is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Group": "Republicans", + "Units": 75.0, + "Confidence Level": "Very/somewhat confident" + } + ], + "score": 0.89, + "annotation": "The Very/somewhat confident shows a sudden jump in Units from Total to Republicans.", + "reason": "The Units for Very/somewhat confident increases from 51.0 at Total to 75.0 at Republicans, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Group": "Total", + "Units": 49.0, + "Confidence Level": "Not so/not at all confident" + } + ], + "score": 0.89, + "annotation": "The Not so/not at all confident shows a sudden jump in Units from Republicans to Total.", + "reason": "The Units for Not so/not at all confident increases from 26.0 at Republicans to 49.0 at Total, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Group": "Total", + "Units": 51.0, + "Confidence Level": "Very/somewhat confident" + } + ], + "score": 0.8, + "annotation": "The Very/somewhat confident accounts for the majority in Total.", + "reason": "The Units of Very/somewhat confident in Total accounts for 0.51 (more than 50%) of the total of Units of all Confidence Level." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Confidence Level": "Very/somewhat confident", + "Group": "total", + "Units": 163.0 + } + ], + "score": 0.79, + "annotation": "The total value of Very/somewhat confident accounts for the majority in all Confidence Level.", + "reason": "The total value of Units of Very/somewhat confident accounts for 0.5415282392026578 (more than 50%) of the total of Units of all Confidence Level." + } + ], + "data": { + "data": [ + { + "Group": "Total", + "Units": 51.0, + "Confidence Level": "Very/somewhat confident" + }, + { + "Group": "Total", + "Units": 49.0, + "Confidence Level": "Not so/not at all confident" + }, + { + "Group": "Democrats", + "Units": 37.0, + "Confidence Level": "Very/somewhat confident" + }, + { + "Group": "Democrats", + "Units": 63.0, + "Confidence Level": "Not so/not at all confident" + }, + { + "Group": "Republicans", + "Units": 75.0, + "Confidence Level": "Very/somewhat confident" + }, + { + "Group": "Republicans", + "Units": 26.0, + "Confidence Level": "Not so/not at all confident" + } + ], + "columns": [ + { + "name": "Group", + "importance": "primary", + "description": "Political affiliation or total population", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding confidence level", + "role": "y" + }, + { + "name": "Confidence Level", + "importance": "primary", + "description": "The confidence level of the data point", + "role": "group" + } + ] + } + }, + "935": { + "metadata": { + "title": "Who's Going on Package Holidays?", + "description": "Share of respondents in selected countries who have booked a package vacation in the last 12 months", + "main_insight": "China and Mexico had the highest percentage of respondents who booked a package vacation in the last 12 months, at 27% and 25% respectively." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "China", + "Package Vacation Booked": 27.0 + } + ], + "score": 0.77, + "annotation": "The has a maximum value at China", + "reason": "The [{'Country': 'China', 'Package Vacation Booked': 27.0}] of has a maximum value of 27.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "UK", + "Package Vacation Booked": 23.0 + } + ], + "score": 0.73, + "annotation": "The shows a sudden jump in Package Vacation Booked from Germany to UK.", + "reason": "The Package Vacation Booked for increases from 17.0 at Germany to 23.0 at UK, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "France", + "Package Vacation Booked": 10.0 + }, + { + "Country": "U.S.", + "Package Vacation Booked": 10.0 + } + ], + "score": 0.66, + "annotation": "The has minimum values at France, U.S.", + "reason": "The [{'Country': 'France', 'Package Vacation Booked': 10.0}, {'Country': 'U.S.', 'Package Vacation Booked': 10.0}] of have minimum values of 10.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "China", + "Package Vacation Booked": 27.0 + }, + { + "Country": "Mexico", + "Package Vacation Booked": 25.0 + }, + { + "Country": "UK", + "Package Vacation Booked": 23.0 + }, + { + "Country": "Germany", + "Package Vacation Booked": 17.0 + }, + { + "Country": "Austria", + "Package Vacation Booked": 16.0 + }, + { + "Country": "Spain", + "Package Vacation Booked": 13.0 + }, + { + "Country": "France", + "Package Vacation Booked": 10.0 + }, + { + "Country": "U.S.", + "Package Vacation Booked": 10.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Package Vacation Booked", + "importance": "primary", + "description": "Percentage of respondents who booked a package vacation in the last 12 months", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1765": { + "metadata": { + "title": "Restaurant Industry Slow to Recover", + "description": "Number of new eating and drinking jobs created in the U.S. relative to the previous month", + "main_insight": "The restaurant industry experienced a significant decline in job creation in April, followed by a recovery that has slowed down by September." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Apr", + "Job_Change": -5500000.0 + } + ], + "score": 0.97, + "annotation": "The has a minimum value at Apr", + "reason": "The [{'Month': 'Apr', 'Job_Change': -5500000.0}] of has a mainimum value of -5500000.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Mar", + "Job_Change": -1000000.0 + } + ], + "score": 0.9, + "annotation": "The shows a sudden jump in Job_Change from Apr to Mar.", + "reason": "The Job_Change for increases from -5500000.0 at Apr to -1000000.0 at Mar, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Jun", + "Job_Change": 1500000.0 + } + ], + "score": 0.53, + "annotation": "The has a maximum value at Jun", + "reason": "The [{'Month': 'Jun', 'Job_Change': 1500000.0}] of has a maximum value of 1500000.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Month": "Jan", + "Job_Change": 0.0 + }, + { + "Month": "Feb", + "Job_Change": 0.0 + }, + { + "Month": "Mar", + "Job_Change": -1000000.0 + }, + { + "Month": "Apr", + "Job_Change": -5500000.0 + }, + { + "Month": "May", + "Job_Change": 1200000.0 + }, + { + "Month": "Jun", + "Job_Change": 1500000.0 + }, + { + "Month": "Jul", + "Job_Change": 600000.0 + }, + { + "Month": "Aug", + "Job_Change": 300000.0 + }, + { + "Month": "Sep", + "Job_Change": 200300.0 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year 2020", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Job_Change", + "importance": "primary", + "description": "Number of new jobs created relative to the previous month", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2003": { + "metadata": { + "title": "Tablet's Reign is Being Chipped Away by Laptops", + "description": "Shipments forecast of tablets, laptops, & desktop PCs worldwide (in million units)*", + "main_insight": "Tablet shipments are forecast to decline significantly from 2014 to 2022, while laptop shipments are projected to increase and surpass tablet shipments." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Score": 165.0, + "Device Type": "Laptops" + }, + { + "Year": 2022, + "Score": 84.5, + "Device Type": "Desktop PCs" + } + ], + "score": 0.93, + "annotation": "The Score of Laptops and Desktop PCs show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Laptops and Desktop PCs is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2022, + "Score": 134.1, + "Device Type": "Tablets" + }, + { + "Year": 2022, + "Score": 84.5, + "Device Type": "Desktop PCs" + } + ], + "score": 0.51, + "annotation": "The Score of Tablets and Desktop PCs show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Tablets and Desktop PCs is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2022, + "Score": 134.1, + "Device Type": "Tablets" + }, + { + "Year": 2022, + "Score": 165.0, + "Device Type": "Laptops" + } + ], + "score": 0.26, + "annotation": "The Score of Tablets and Laptops show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Tablets and Laptops is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2010, + "Score": 19.0, + "Device Type": "Tablets" + }, + { + "Year": 2010, + "Score": 201.0, + "Device Type": "Laptops" + }, + { + "Year": 2010, + "Score": 157.0, + "Device Type": "Desktop PCs" + }, + { + "Year": 2011, + "Score": 68.7, + "Device Type": "Tablets" + }, + { + "Year": 2011, + "Score": 207.8, + "Device Type": "Laptops" + }, + { + "Year": 2011, + "Score": 154.2, + "Device Type": "Desktop PCs" + }, + { + "Year": 2012, + "Score": 122.3, + "Device Type": "Tablets" + }, + { + "Year": 2012, + "Score": 211.9, + "Device Type": "Laptops" + }, + { + "Year": 2012, + "Score": 148.7, + "Device Type": "Desktop PCs" + }, + { + "Year": 2013, + "Score": 195.4, + "Device Type": "Tablets" + }, + { + "Year": 2013, + "Score": 205.0, + "Device Type": "Laptops" + }, + { + "Year": 2013, + "Score": 137.1, + "Device Type": "Desktop PCs" + }, + { + "Year": 2014, + "Score": 229.6, + "Device Type": "Tablets" + }, + { + "Year": 2014, + "Score": 200.7, + "Device Type": "Laptops" + }, + { + "Year": 2014, + "Score": 127.5, + "Device Type": "Desktop PCs" + }, + { + "Year": 2015, + "Score": 219.5, + "Device Type": "Tablets" + }, + { + "Year": 2015, + "Score": 183.0, + "Device Type": "Laptops" + }, + { + "Year": 2015, + "Score": 116.8, + "Device Type": "Desktop PCs" + }, + { + "Year": 2016, + "Score": 204.3, + "Device Type": "Tablets" + }, + { + "Year": 2016, + "Score": 179.7, + "Device Type": "Laptops" + }, + { + "Year": 2016, + "Score": 105.4, + "Device Type": "Desktop PCs" + }, + { + "Year": 2017, + "Score": 183.5, + "Device Type": "Tablets" + }, + { + "Year": 2017, + "Score": 183.2, + "Device Type": "Laptops" + }, + { + "Year": 2017, + "Score": 100.1, + "Device Type": "Desktop PCs" + }, + { + "Year": 2018, + "Score": 150.3, + "Device Type": "Tablets" + }, + { + "Year": 2018, + "Score": 162.3, + "Device Type": "Laptops" + }, + { + "Year": 2018, + "Score": 94.4, + "Device Type": "Desktop PCs" + }, + { + "Year": 2022, + "Score": 134.1, + "Device Type": "Tablets" + }, + { + "Year": 2022, + "Score": 165.0, + "Device Type": "Laptops" + }, + { + "Year": 2022, + "Score": 84.5, + "Device Type": "Desktop PCs" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of shipment forecast", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding device type", + "role": "y" + }, + { + "name": "Device Type", + "importance": "primary", + "description": "The device type of the data point", + "role": "group" + } + ] + } + }, + "2192": { + "metadata": { + "title": "Travelers' Biggest Complaints About Air Travel", + "description": "% of travelers who like these things the least when they fly", + "main_insight": "Uncomfortable seats/limited legroom is the biggest complaint among air travelers, followed by added fees and unpredictable flight delays/cancellations." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Complaint": "Loud/crying children", + "Percentage": 50.0 + } + ], + "score": 0.8, + "annotation": "The has a minimum value at Loud/crying children", + "reason": "The [{'Complaint': 'Loud/crying children', 'Percentage': 50.0}] of has a mainimum value of 50.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Complaint": "Cost of airline airfare", + "Percentage": 62.0 + } + ], + "score": 0.8, + "annotation": "The shows a sudden jump in Percentage from Loud/crying children to Cost of airline airfare.", + "reason": "The Percentage for increases from 50.0 at Loud/crying children to 62.0 at Cost of airline airfare, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Complaint": "Uncomfortable seats/limited legroom", + "Percentage": 77.0 + } + ], + "score": 0.66, + "annotation": "The has a maximum value at Uncomfortable seats/limited legroom", + "reason": "The [{'Complaint': 'Uncomfortable seats/limited legroom', 'Percentage': 77.0}] of has a maximum value of 77.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Complaint": "Uncomfortable seats/limited legroom", + "Percentage": 77.0 + }, + { + "Complaint": "Added fees (baggage, seat fees, etc.)", + "Percentage": 71.0 + }, + { + "Complaint": "Unpredictable flight delays/cancellations", + "Percentage": 67.0 + }, + { + "Complaint": "Cost of airline airfare", + "Percentage": 62.0 + }, + { + "Complaint": "Loud/crying children", + "Percentage": 50.0 + } + ], + "columns": [ + { + "name": "Complaint", + "importance": "primary", + "description": "Type of air travel complaint", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of travelers who dislike this aspect of air travel", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1138": { + "metadata": { + "title": "Bye Bye Birdie", + "description": "Estimated population of house sparrows in the EU", + "main_insight": "The estimated population of house sparrows in the EU has significantly decreased from 508.6 million in 1982 to 261.3 million in 2017." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1982, + "Estimated Population (millions)": 508.6 + }, + { + "Year": 2017, + "Estimated Population (millions)": 261.3 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of observation", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Estimated Population (millions)", + "importance": "primary", + "description": "Estimated population of house sparrows in the EU", + "unit": "millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1793": { + "metadata": { + "title": "The Foreign-Born Congress", + "description": "Members of the 118th U.S. Congress who are immigrants, by birthplace", + "main_insight": "Mexico is the most common birthplace of foreign-born members of the 118th U.S. Congress, with four members. South Korea and India each have three." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Birthplace": "Mexico", + "Number of Members": 4 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at Mexico", + "reason": "The [{'Birthplace': 'Mexico', 'Number of Members': 4}] of has a maximum value of 4, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Birthplace": "Cuba", + "Number of Members": 2 + }, + { + "Birthplace": "South Korea", + "Number of Members": 3 + }, + { + "Birthplace": "Mexico", + "Number of Members": 4 + } + ], + "score": 0.72, + "annotation": "The exhibits multiple sudden jumps in Number of Members, transitioning from Ukraine, Cuba, India to Cuba, South Korea, Mexico.", + "reason": "Across these points, the Number of Members for increases from 1, 2, 3 at Ukraine, Cuba, India to 2, 3, 4 at Cuba, South Korea, Mexico, suggesting significant shifts in value." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Birthplace": "Taiwan", + "Number of Members": 1 + }, + { + "Birthplace": "Japan", + "Number of Members": 1 + }, + { + "Birthplace": "Thailand", + "Number of Members": 1 + }, + { + "Birthplace": "Somalia", + "Number of Members": 1 + }, + { + "Birthplace": "Dominican Republic", + "Number of Members": 1 + }, + { + "Birthplace": "Guatemala", + "Number of Members": 1 + }, + { + "Birthplace": "Peru", + "Number of Members": 1 + }, + { + "Birthplace": "Ukraine", + "Number of Members": 1 + } + ], + "score": 0.41, + "annotation": "The has minimum values at Taiwan, Japan, Thailand, Somalia, Dominican Republic, Guatemala, Peru, Ukraine", + "reason": "The [{'Birthplace': 'Taiwan', 'Number of Members': 1}, {'Birthplace': 'Japan', 'Number of Members': 1}, {'Birthplace': 'Thailand', 'Number of Members': 1}, {'Birthplace': 'Somalia', 'Number of Members': 1}, {'Birthplace': 'Dominican Republic', 'Number of Members': 1}, {'Birthplace': 'Guatemala', 'Number of Members': 1}, {'Birthplace': 'Peru', 'Number of Members': 1}, {'Birthplace': 'Ukraine', 'Number of Members': 1}] of have minimum values of 1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Birthplace": "Mexico", + "Number of Members": 4 + }, + { + "Birthplace": "South Korea", + "Number of Members": 3 + }, + { + "Birthplace": "India", + "Number of Members": 3 + }, + { + "Birthplace": "Cuba", + "Number of Members": 2 + }, + { + "Birthplace": "Taiwan", + "Number of Members": 1 + }, + { + "Birthplace": "Japan", + "Number of Members": 1 + }, + { + "Birthplace": "Thailand", + "Number of Members": 1 + }, + { + "Birthplace": "Somalia", + "Number of Members": 1 + }, + { + "Birthplace": "Dominican Republic", + "Number of Members": 1 + }, + { + "Birthplace": "Guatemala", + "Number of Members": 1 + }, + { + "Birthplace": "Peru", + "Number of Members": 1 + }, + { + "Birthplace": "Ukraine", + "Number of Members": 1 + } + ], + "columns": [ + { + "name": "Birthplace", + "importance": "primary", + "description": "Country of origin of the immigrant members of Congress", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Members", + "importance": "primary", + "description": "Number of members in the 118th U.S. Congress born in that country", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "587": { + "metadata": { + "title": "How The UK's EU Parliament Seats Will Be Distributed", + "description": "Distribution of UK European Parliament seats by Member State", + "main_insight": "27 of the UK's 73 EU Parliament seats will be distributed among 14 Member States, with France and Spain receiving the most (5 each) and the rest getting between 1 and 3. The remaining 46 seats will be placed in reserve." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Member State": "France", + "Seats": 5 + }, + { + "Member State": "Spain", + "Seats": 5 + } + ], + "score": 0.93, + "annotation": "The has maximum values at France, Spain", + "reason": "The [{'Member State': 'France', 'Seats': 5}, {'Member State': 'Spain', 'Seats': 5}] of have maximum values of 5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Member State": "France", + "Seats": 5 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Seats from Netherlands to France.", + "reason": "The Seats for increases from 3 at Netherlands to 5 at France, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Member State": "Austria", + "Seats": 1 + }, + { + "Member State": "Denmark", + "Seats": 1 + }, + { + "Member State": "Estonia", + "Seats": 1 + }, + { + "Member State": "Finland", + "Seats": 1 + }, + { + "Member State": "Hungary", + "Seats": 1 + }, + { + "Member State": "Poland", + "Seats": 1 + }, + { + "Member State": "Romania", + "Seats": 1 + }, + { + "Member State": "Slovakia", + "Seats": 1 + }, + { + "Member State": "Sweden", + "Seats": 1 + } + ], + "score": 0.41, + "annotation": "The has minimum values at Austria, Denmark, Estonia, Finland, Hungary, Poland, Romania, Slovakia, Sweden", + "reason": "The [{'Member State': 'Austria', 'Seats': 1}, {'Member State': 'Denmark', 'Seats': 1}, {'Member State': 'Estonia', 'Seats': 1}, {'Member State': 'Finland', 'Seats': 1}, {'Member State': 'Hungary', 'Seats': 1}, {'Member State': 'Poland', 'Seats': 1}, {'Member State': 'Romania', 'Seats': 1}, {'Member State': 'Slovakia', 'Seats': 1}, {'Member State': 'Sweden', 'Seats': 1}] of have minimum values of 1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Member State": "France", + "Seats": 5 + }, + { + "Member State": "Spain", + "Seats": 5 + }, + { + "Member State": "Italy", + "Seats": 3 + }, + { + "Member State": "Netherlands", + "Seats": 3 + }, + { + "Member State": "Ireland", + "Seats": 2 + }, + { + "Member State": "Austria", + "Seats": 1 + }, + { + "Member State": "Denmark", + "Seats": 1 + }, + { + "Member State": "Estonia", + "Seats": 1 + }, + { + "Member State": "Finland", + "Seats": 1 + }, + { + "Member State": "Hungary", + "Seats": 1 + }, + { + "Member State": "Poland", + "Seats": 1 + }, + { + "Member State": "Romania", + "Seats": 1 + }, + { + "Member State": "Slovakia", + "Seats": 1 + }, + { + "Member State": "Sweden", + "Seats": 1 + } + ], + "columns": [ + { + "name": "Member State", + "importance": "primary", + "description": "Country receiving UK's EU Parliament seats", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Seats", + "importance": "primary", + "description": "Number of seats received by each country", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "508": { + "metadata": { + "title": "Instagram's Referral Traffic Has Sky-Rocketed", + "description": "Year-on-year growth of referral traffic from leading social networks to U.S. websites", + "main_insight": "Instagram's referral traffic to U.S. websites grew dramatically (114%) year-over-year, significantly outpacing other major social networks." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Social Network": "Instagram", + "Instagram": 1.14 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Instagram from Twitter to Instagram.", + "reason": "The Instagram for increases from 0 at Twitter to 1.14 at Instagram, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Social Network": "Instagram", + "Instagram": 1.14 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at Instagram", + "reason": "The [{'Social Network': 'Instagram', 'Instagram': 1.14}] of has a maximum value of 1.14, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Social Network": "Facebook", + "Instagram": 0 + }, + { + "Social Network": "Pinterest", + "Instagram": 0 + }, + { + "Social Network": "YouTube", + "Instagram": 0 + }, + { + "Social Network": "Twitter", + "Instagram": 0 + } + ], + "score": 0.33, + "annotation": "The has minimum values at Facebook, Pinterest, YouTube, Twitter", + "reason": "The [{'Social Network': 'Facebook', 'Instagram': 0}, {'Social Network': 'Pinterest', 'Instagram': 0}, {'Social Network': 'YouTube', 'Instagram': 0}, {'Social Network': 'Twitter', 'Instagram': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Social Network": "Instagram", + "Instagram": 1.14 + }, + { + "Social Network": "Facebook", + "Instagram": 0 + }, + { + "Social Network": "Pinterest", + "Instagram": 0 + }, + { + "Social Network": "YouTube", + "Instagram": 0 + }, + { + "Social Network": "Twitter", + "Instagram": 0 + } + ], + "columns": [ + { + "name": "Social Network", + "importance": "primary", + "description": "The social media platform", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Instagram", + "importance": "primary", + "description": "Year-on-year growth of referral traffic for Instagram", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1915": { + "metadata": { + "title": "Church vs. Pub on Christmas Day", + "description": "Share of respondents saying they plan to go to church/the pub on Christmas day in 2024", + "main_insight": "In all three countries surveyed, a greater percentage of respondents plan to go to church than to the pub on Christmas Day. The US has the largest difference between church and pub attendance." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United Kingdom", + "Church": 16.0 + } + ], + "score": 0.87, + "annotation": "The shows a sudden jump in Church from Germany to United Kingdom.", + "reason": "The Church for increases from 12.0 at Germany to 16.0 at United Kingdom, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Germany", + "Church": 12.0 + } + ], + "score": 0.62, + "annotation": "The has a minimum value at Germany", + "reason": "The [{'Country': 'Germany', 'Church': 12.0}] of has a mainimum value of 12.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United States", + "Church": 19.0 + } + ], + "score": 0.57, + "annotation": "The has a maximum value at United States", + "reason": "The [{'Country': 'United States', 'Church': 19.0}] of has a maximum value of 19.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Church": 19.0 + }, + { + "Country": "United Kingdom", + "Church": 16.0 + }, + { + "Country": "Germany", + "Church": 12.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Church", + "importance": "primary", + "description": "Percentage of respondents planning to go to church", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1429": { + "metadata": { + "title": "Who's Only Looking Busy at Work?", + "description": "Share of respondents from selected countries reporting the following shares of productive and performative work", + "main_insight": "India has the highest percentage of respondents reporting performative work (appearing busy), while the United States and South Korea have the lowest. Across all countries, a larger percentage of respondents report engaging in productive work than performative work." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "South Korea", + "Units": 72.0, + "Work Type": "Productive work" + }, + { + "Country": "South Korea", + "Units": 28.0, + "Work Type": "Performative work" + } + ], + "score": 1.0, + "annotation": "The Units of Productive work and Performative work show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Productive work and Performative work is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "India", + "Units": 57.0, + "Work Type": "Productive work" + } + ], + "score": 0.9, + "annotation": "The Productive work has a minimum value at India", + "reason": "The [{'Country': 'India', 'Units': 57.0, 'Work Type': 'Productive work'}] of Productive work has a mainimum value of 57.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "India", + "Units": 43.0, + "Work Type": "Performative work" + } + ], + "score": 0.9, + "annotation": "The Performative work has a maximum value at India", + "reason": "The [{'Country': 'India', 'Units': 43.0, 'Work Type': 'Performative work'}] of Performative work has a maximum value of 43.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "India", + "Units": 57.0, + "Work Type": "Productive work" + } + ], + "score": 0.78, + "annotation": "The Productive work accounts for the majority in India.", + "reason": "The Units of Productive work in India accounts for 0.57 (more than 50%) of the total of Units of all Work Type." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Japan", + "Units": 63.0, + "Work Type": "Productive work" + } + ], + "score": 0.77, + "annotation": "The Productive work shows a sudden jump in Units from India to Japan.", + "reason": "The Units for Productive work increases from 57.0 at India to 63.0 at Japan, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "India", + "Units": 57.0, + "Work Type": "Productive work" + }, + { + "Country": "India", + "Units": 43.0, + "Work Type": "Performative work" + }, + { + "Country": "Japan", + "Units": 63.0, + "Work Type": "Productive work" + }, + { + "Country": "Japan", + "Units": 37.0, + "Work Type": "Performative work" + }, + { + "Country": "Singapore", + "Units": 64.0, + "Work Type": "Productive work" + }, + { + "Country": "Singapore", + "Units": 36.0, + "Work Type": "Performative work" + }, + { + "Country": "France", + "Units": 69.0, + "Work Type": "Productive work" + }, + { + "Country": "France", + "Units": 31.0, + "Work Type": "Performative work" + }, + { + "Country": "United Kingdom", + "Units": 70.0, + "Work Type": "Productive work" + }, + { + "Country": "United Kingdom", + "Units": 30.0, + "Work Type": "Performative work" + }, + { + "Country": "Germany", + "Units": 71.0, + "Work Type": "Productive work" + }, + { + "Country": "Germany", + "Units": 29.0, + "Work Type": "Performative work" + }, + { + "Country": "United States", + "Units": 72.0, + "Work Type": "Productive work" + }, + { + "Country": "United States", + "Units": 28.0, + "Work Type": "Performative work" + }, + { + "Country": "South Korea", + "Units": 72.0, + "Work Type": "Productive work" + }, + { + "Country": "South Korea", + "Units": 28.0, + "Work Type": "Performative work" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding work type", + "role": "y" + }, + { + "name": "Work Type", + "importance": "primary", + "description": "The work type of the data point", + "role": "group" + } + ] + } + }, + "110": { + "metadata": { + "title": "Instagram's Biggest Names of 2019", + "description": "People with the most Instagram followers worldwide as of January 2019", + "main_insight": "Cristiano Ronaldo had the most Instagram followers in January 2019, with 152.62 million followers. Selena Gomez and Ariana Grande were close behind with 144.75m and 143.54m followers respectively, all significantly higher than others" + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Instagram User": "Cristiano Ronaldo", + "Followers (Millions)": 152.62 + } + ], + "score": 0.83, + "annotation": "The has a maximum value at Cristiano Ronaldo", + "reason": "The [{'Instagram User': 'Cristiano Ronaldo', 'Followers (Millions)': 152.62}] of has a maximum value of 152.62, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Instagram User": "Neymar", + "Followers (Millions)": 110.17 + } + ], + "score": 0.76, + "annotation": "The has a minimum value at Neymar", + "reason": "The [{'Instagram User': 'Neymar', 'Followers (Millions)': 110.17}] of has a mainimum value of 110.17, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Instagram User": "Ariana Grande", + "Followers (Millions)": 143.54 + } + ], + "score": 0.71, + "annotation": "The shows a sudden jump in Followers (Millions) from Dwayne Johnson (The Rock) to Ariana Grande.", + "reason": "The Followers (Millions) for increases from 129.72 at Dwayne Johnson (The Rock) to 143.54 at Ariana Grande, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Instagram User": "Cristiano Ronaldo", + "Followers (Millions)": 152.62 + }, + { + "Instagram User": "Selena Gomez", + "Followers (Millions)": 144.75 + }, + { + "Instagram User": "Ariana Grande", + "Followers (Millions)": 143.54 + }, + { + "Instagram User": "Dwayne Johnson (The Rock)", + "Followers (Millions)": 129.72 + }, + { + "Instagram User": "Kim Kardashian", + "Followers (Millions)": 125.74 + }, + { + "Instagram User": "Kylie Jenner", + "Followers (Millions)": 125.06 + }, + { + "Instagram User": "Beyoncé", + "Followers (Millions)": 123.05 + }, + { + "Instagram User": "Taylor Swift", + "Followers (Millions)": 114.06 + }, + { + "Instagram User": "Neymar", + "Followers (Millions)": 110.17 + } + ], + "columns": [ + { + "name": "Instagram User", + "importance": "primary", + "description": "Name of the Instagram user", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Followers (Millions)", + "importance": "primary", + "description": "Number of followers in millions", + "unit": "Millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1284": { + "metadata": { + "title": "Fed Projections Suggest Three Rate Cuts in 2024", + "description": "FOMC members' projections for the appropriate target level of the federal funds rate at the end of the specified year*", + "main_insight": "FOMC members project a median federal funds rate suggesting three rate cuts in 2024, with a gradual decrease projected through 2026 and into the longer run." + }, + "chart_type": "Dot Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2024, + "FOMC Member Projections": [ + 5.4, + 5.4, + 5.1, + 5.1, + 5.1, + 4.9, + 4.9, + 4.9, + 4.9, + 4.9, + 4.9, + 4.6, + 4.6, + 4.6, + 4.6, + 4.6, + 4.4, + 4.4, + 4.1 + ] + }, + { + "Year": 2025, + "FOMC Member Projections": [ + 5.4, + 4.9, + 4.6, + 4.4, + 4.1, + 4.1, + 3.9, + 3.9, + 3.9, + 3.9, + 3.6, + 3.6, + 3.6, + 3.4, + 3.4, + 3.1, + 3.1, + 2.9, + 2.6 + ] + }, + { + "Year": 2026, + "FOMC Member Projections": [ + 4.6, + 4.4, + 3.9, + 3.6, + 3.4, + 3.4, + 3.1, + 3.1, + 3.1, + 3.1, + 2.9, + 2.9, + 2.9, + 2.6, + 2.6, + 2.6, + 2.4, + 2.1, + 1.9 + ] + }, + { + "Year": 0, + "FOMC Member Projections": [ + 3.6, + 3.4, + 2.9, + 2.9, + 2.6, + 2.6, + 2.6, + 2.6, + 2.6, + 2.6, + 2.6, + 2.6, + 2.6, + 2.6, + 2.4, + 2.4, + 2.4, + 2.1, + 2.0 + ] + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Time periods for projections", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "FOMC Member Projections", + "importance": "primary", + "description": "Individual FOMC member projections of federal funds rate", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1835": { + "metadata": { + "title": "Ahead of the Game: Nike Rules the Sneaker World", + "description": "Worldwide footwear sales of selected sports brands in 2022", + "main_insight": "Nike significantly leads the sneaker market with worldwide footwear sales of $29.1 billion in 2022, more than double that of its closest competitor, Adidas." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Brand": "Nike", + "Footwear Sales (2022)": 29.1 + } + ], + "score": 0.93, + "annotation": "The has a maximum value at Nike", + "reason": "The [{'Brand': 'Nike', 'Footwear Sales (2022)': 29.1}] of has a maximum value of 29.1, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Brand": "Nike", + "Footwear Sales (2022)": 29.1 + } + ], + "score": 0.87, + "annotation": "The shows a sudden jump in Footwear Sales (2022) from Adidas to Nike.", + "reason": "The Footwear Sales (2022) for increases from 13.1 at Adidas to 29.1 at Nike, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Brand": "Under Armour", + "Footwear Sales (2022)": 1.4 + } + ], + "score": 0.47, + "annotation": "The has a minimum value at Under Armour", + "reason": "The [{'Brand': 'Under Armour', 'Footwear Sales (2022)': 1.4}] of has a mainimum value of 1.4, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Brand": "Nike", + "Footwear Sales (2022)": 29.1 + }, + { + "Brand": "Adidas", + "Footwear Sales (2022)": 13.1 + }, + { + "Brand": "Skechers", + "Footwear Sales (2022)": 7.4 + }, + { + "Brand": "Puma", + "Footwear Sales (2022)": 4.5 + }, + { + "Brand": "Asics", + "Footwear Sales (2022)": 3.0 + }, + { + "Brand": "Converse", + "Footwear Sales (2022)": 2.1 + }, + { + "Brand": "Under Armour", + "Footwear Sales (2022)": 1.4 + } + ], + "columns": [ + { + "name": "Brand", + "importance": "primary", + "description": "Sports brand names", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Footwear Sales (2022)", + "importance": "primary", + "description": "Worldwide footwear sales in billions of US dollars for 2022", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "460": { + "metadata": { + "title": "How Are U.S. Employers Keeping Their Workers Safe?", + "description": "\"To what extent is your employer taking these steps to avoid spreading COVID-19\" (in %)", + "main_insight": "The infographic shows the percentage of U.S. employers implementing various safety measures to prevent the spread of COVID-19. A majority of employers are adopting cleaning practices and providing PPE, at least sometimes, while enforcing distance and screening for symptoms are less consistent." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Safety Measure": "Screening employees for symptoms of fever or cough", + "Units": 41.0, + "Frequency": "Always" + } + ], + "score": 0.88, + "annotation": "The Always has the largest proportion in Screening employees for symptoms of fever or cough.", + "reason": "The Units of Always in Screening employees for symptoms of fever or cough accounts for 0.41, larger than all other Frequency, of the total of Units of all Frequency." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Safety Measure": "Enforcing a distance of at least 6 feet", + "Units": 54.0, + "Frequency": "Always" + } + ], + "score": 0.85, + "annotation": "The Always accounts for the majority in Enforcing a distance of at least 6 feet.", + "reason": "The Units of Always in Enforcing a distance of at least 6 feet accounts for 0.54 (more than 50%) of the total of Units of all Frequency." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Frequency": "Always", + "Safety Measure": "total", + "Units": 222.0 + } + ], + "score": 0.84, + "annotation": "The total value of Always accounts for the majority in all Frequency.", + "reason": "The total value of Units of Always accounts for 0.555 (more than 50%) of the total of Units of all Frequency." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Safety Measure": "Providing personal protective equipment", + "Units": 58.0, + "Frequency": "Always" + } + ], + "score": 0.84, + "annotation": "The Always accounts for the majority in Providing personal protective equipment.", + "reason": "The Units of Always in Providing personal protective equipment accounts for 0.58 (more than 50%) of the total of Units of all Frequency." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Safety Measure": "Adopting new/more frequent cleaning practices", + "Units": 69.0, + "Frequency": "Always" + } + ], + "score": 0.81, + "annotation": "The Always accounts for the majority in Adopting new/more frequent cleaning practices.", + "reason": "The Units of Always in Adopting new/more frequent cleaning practices accounts for 0.69 (more than 50%) of the total of Units of all Frequency." + } + ], + "data": { + "data": [ + { + "Safety Measure": "Adopting new/more frequent cleaning practices", + "Units": 69.0, + "Frequency": "Always" + }, + { + "Safety Measure": "Adopting new/more frequent cleaning practices", + "Units": 25.0, + "Frequency": "Sometimes" + }, + { + "Safety Measure": "Adopting new/more frequent cleaning practices", + "Units": 6.0, + "Frequency": "Never" + }, + { + "Safety Measure": "Providing 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"Frequency": "Never" + } + ], + "columns": [ + { + "name": "Safety Measure", + "importance": "primary", + "description": "Different safety measures taken by employers", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding frequency", + "role": "y" + }, + { + "name": "Frequency", + "importance": "primary", + "description": "The frequency of the data point", + "role": "group" + } + ] + } + }, + "61": { + "metadata": { + "title": "Boris Johnson's popularity has grown despite Brexit chaos", + "description": "\"Do you have a favourable or unfavourable opinion of the following...?\" (net favourabilty*)", + "main_insight": "Boris Johnson's net favorability rating has significantly improved from July to September 2019, while Jeremy Corbyn's rating has remained largely unchanged and deeply negative." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "July 17", + "Units": -27.0, + "Politicians": "Boris Johnson" + } + ], + "score": 0.98, + "annotation": "The Boris Johnson has a minimum value at July 17", + "reason": "The [{'Month': 'July 17', 'Units': -27.0, 'Politicians': 'Boris Johnson'}] of Boris Johnson has a mainimum value of -27.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "July 29", + "Units": -54.0, + "Politicians": "Jeremy Corbyn" + } + ], + "score": 0.96, + "annotation": "The Jeremy Corbyn has a minimum value at July 29", + "reason": "The [{'Month': 'July 29', 'Units': -54.0, 'Politicians': 'Jeremy Corbyn'}] of Jeremy Corbyn has a mainimum value of -54.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "July 31", + "Units": -45.0, + "Politicians": "Jeremy Corbyn" + } + ], + "score": 0.91, + "annotation": "The Jeremy 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sharp change." + } + ], + "data": { + "data": [ + { + "Month": "July 17", + "Units": -27.0, + "Politicians": "Boris Johnson" + }, + { + "Month": "July 17", + "Units": -48.0, + "Politicians": "Jeremy Corbyn" + }, + { + "Month": "July 22", + "Units": -23.0, + "Politicians": "Boris Johnson" + }, + { + "Month": "July 22", + "Units": -50.0, + "Politicians": "Jeremy Corbyn" + }, + { + "Month": "July 29", + "Units": -18.0, + "Politicians": "Boris Johnson" + }, + { + "Month": "July 29", + "Units": -54.0, + "Politicians": "Jeremy Corbyn" + }, + { + "Month": "July 31", + "Units": -18.0, + "Politicians": "Boris Johnson" + }, + { + "Month": "July 31", + "Units": -45.0, + "Politicians": "Jeremy Corbyn" + }, + { + "Month": "August 5", + "Units": -17.0, + "Politicians": "Boris Johnson" + }, + { + "Month": "August 5", + "Units": -48.0, + "Politicians": "Jeremy Corbyn" + }, + { + "Month": "August 14", + "Units": -18.0, + "Politicians": "Boris Johnson" + }, + { + "Month": "August 14", + "Units": -48.0, + "Politicians": "Jeremy Corbyn" + }, + { + "Month": "August 19", + "Units": -18.0, + "Politicians": "Boris Johnson" + }, + { + "Month": "August 19", + "Units": -50.0, + "Politicians": "Jeremy Corbyn" + }, + { + "Month": "August 26", + "Units": -17.0, + "Politicians": "Boris Johnson" + }, + { + "Month": "August 26", + "Units": -49.0, + "Politicians": "Jeremy Corbyn" + }, + { + "Month": "August 28", + "Units": -17.0, + "Politicians": "Boris Johnson" + }, + { + "Month": "August 28", + "Units": -49.0, + "Politicians": "Jeremy Corbyn" + }, + { + "Month": "September 4", + "Units": -17.0, + "Politicians": "Boris Johnson" + }, + { + "Month": "September 4", + "Units": -49.0, + "Politicians": "Jeremy Corbyn" + }, + { + "Month": "September 9", + "Units": -17.0, + "Politicians": "Boris Johnson" + }, + { + "Month": "September 9", + "Units": -49.0, + "Politicians": "Jeremy Corbyn" + }, + { + "Month": "September 17", + "Units": -16.0, + "Politicians": "Boris Johnson" + }, + { + "Month": "September 17", + "Units": -49.0, + "Politicians": "Jeremy Corbyn" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Time period", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding politicians", + "role": "y" + }, + { + "name": "Politicians", + "importance": "primary", + "description": "The politicians of the data point", + "role": "group" + } + ] + } + }, + "62": { + "metadata": { + "title": "Online Banking Rivals Physical Branch Banking", + "description": "Channels used for investment transactions in the last three months (in %)", + "main_insight": "Online and physical bank branches are the most popular channels for investment transactions, with usage varying significantly across different regions." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "North America", + "Score": 55.0, + "Channel": "Online" + } + ], + "score": 0.96, + "annotation": "The Online has the largest proportion in North America.", + "reason": "The Score of Online in North America accounts for 0.2534562211981567, larger than all other Channel, of the total of Score of all Channel." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Middle East/Africa", + "Score": 63.0, + "Channel": "Online" + } + ], + "score": 0.95, + "annotation": "The Online has the largest proportion in Middle East/Africa.", + "reason": "The Score of Online in Middle East/Africa accounts for 0.2863636363636364, larger than all other Channel, of the total of Score of all Channel." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Channel": "Physical Bank", + "Region": "total", + "Score": 329.0 + } + ], + "score": 0.95, + "annotation": "The total value of Physical Bank has the largest proportion.", + "reason": "The total value of Score of Physical Bank accounts for 0.2990909090909091, larger than all other Channel, of the total of Score of all Channel." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Asia-Pacific", + "Score": 77.0, + "Channel": "Physical Bank" + } + ], + "score": 0.95, + "annotation": "The Physical Bank has the largest proportion in Asia-Pacific.", + "reason": "The Score of Physical Bank in Asia-Pacific accounts for 0.30078125, larger than all other Channel, of the total of Score of all Channel." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Region": "Europe", + "Score": 60.0, + "Channel": "Online" + } + ], + "score": 0.95, + "annotation": "The Online has the largest proportion in Europe.", + "reason": "The Score of Online in Europe accounts for 0.32786885245901637, larger than all other Channel, of the total of Score of all Channel." + } + ], + "data": { + "data": [ + { + "Region": "Asia-Pacific", + "Score": 74.0, + "Channel": "Online" + }, + { + "Region": "Asia-Pacific", + "Score": 77.0, + "Channel": "Physical Bank" + }, + { + "Region": "Asia-Pacific", + "Score": 36.0, + "Channel": "Mobile Phone" + }, + { + "Region": "Asia-Pacific", + "Score": 30.0, + "Channel": "Phone (call center)" + }, + { + "Region": "Asia-Pacific", + "Score": 21.0, + "Channel": "Online Investment Broker" + }, + { + "Region": "Asia-Pacific", + "Score": 18.0, + "Channel": "Financial Planner" + }, + { + "Region": "Europe", + "Score": 60.0, + "Channel": "Online" + }, + { + "Region": "Europe", + "Score": 59.0, + "Channel": "Physical Bank" + }, + { + "Region": "Europe", + "Score": 17.0, + "Channel": "Mobile Phone" + }, + { + "Region": "Europe", + "Score": 19.0, + "Channel": "Phone (call center)" + }, + { + "Region": "Europe", + "Score": 14.0, + "Channel": "Online Investment Broker" + }, + { + "Region": "Europe", + "Score": 14.0, + "Channel": "Financial Planner" + }, + { + "Region": "Latin America", + "Score": 58.0, + "Channel": "Online" + }, + { + "Region": "Latin America", + "Score": 85.0, + "Channel": "Physical Bank" + }, + { + "Region": "Latin America", + "Score": 24.0, + "Channel": "Mobile Phone" + }, + { + "Region": "Latin America", + "Score": 31.0, + "Channel": "Phone (call center)" + }, + { + "Region": "Latin America", + "Score": 11.0, + "Channel": "Online Investment Broker" + }, + { + "Region": "Latin America", + "Score": 15.0, + "Channel": "Financial Planner" + }, + { + "Region": "Middle East/Africa", + "Score": 63.0, + "Channel": "Online" + }, + { + "Region": "Middle East/Africa", + "Score": 55.0, + "Channel": "Physical Bank" + }, + { + "Region": "Middle East/Africa", + "Score": 34.0, + "Channel": "Mobile Phone" + }, + { + "Region": "Middle East/Africa", + "Score": 28.0, + "Channel": "Phone (call center)" + }, + { + "Region": "Middle East/Africa", + "Score": 17.0, + "Channel": "Online Investment Broker" + }, + { + "Region": "Middle East/Africa", + "Score": 23.0, + "Channel": "Financial Planner" + }, + { + "Region": "North America", + "Score": 55.0, + "Channel": "Online" + }, + { + "Region": "North America", + "Score": 53.0, + "Channel": "Physical Bank" + }, + { + "Region": "North America", + "Score": 21.0, + "Channel": "Mobile Phone" + }, + { + "Region": "North America", + "Score": 30.0, + "Channel": "Phone (call center)" + }, + { + "Region": "North America", + "Score": 27.0, + "Channel": "Online Investment Broker" + }, + { + "Region": "North America", + "Score": 31.0, + "Channel": "Financial Planner" + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Geographic region of survey respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding channel", + "role": "y" + }, + { + "name": "Channel", + "importance": "primary", + "description": "The channel of the data point", + "role": "group" + } + ] + } + }, + "549": { + "metadata": { + "title": "A PS4 Costs 4x as Much in Brazil as in the U.S.", + "description": "Suggested retail price* for a PlayStation 4 in selected countries in 2014 (in U.S. dollars)", + "main_insight": "The suggested retail price of a PlayStation 4 in Brazil was significantly higher than in other countries, costing more than four times the price in the United States." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Argentina", + "Price (USD)": 1380.72 + } + ], + "score": 0.88, + "annotation": "The shows a sudden jump in Price (USD) from United Kingdom to Argentina.", + "reason": "The Price (USD) for increases from 580.94 at United Kingdom to 1380.72 at Argentina, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Brazil", + "Price (USD)": 1716.71 + } + ], + "score": 0.75, + "annotation": "The has a maximum value at Brazil", + "reason": "The [{'Country': 'Brazil', 'Price (USD)': 1716.71}] of has a maximum value of 1716.71, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Japan", + "Price (USD)": 391.09 + } + ], + "score": 0.51, + "annotation": "The has a minimum value at Japan", + "reason": "The [{'Country': 'Japan', 'Price (USD)': 391.09}] of has a mainimum value of 391.09, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Brazil", + "Price (USD)": 1716.71 + }, + { + "Country": "Argentina", + "Price (USD)": 1380.72 + }, + { + "Country": "United Kingdom", + "Price (USD)": 580.94 + }, + { + "Country": "United States", + "Price (USD)": 399.99 + }, + { + "Country": "Japan", + "Price (USD)": 391.09 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where the PlayStation 4 was sold", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Price (USD)", + "importance": "primary", + "description": "Suggested retail price of a PlayStation 4 in U.S. dollars", + "unit": "USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2457": { + "metadata": { + "title": "Record-breaking Heat in Europe", + "description": "New temperature records compared to former all-time records in European cities/regions/countries (in °C/°F)", + "main_insight": "Several European cities and regions experienced record-breaking temperatures in July 2022, surpassing previous all-time highs." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Location": "Biarritz", + "Value": 2003, + "Record Temperatures": "Former Record Year" + }, + { + "Location": "Biarritz", + "Value": "Jul 18, 2022", + "Record Temperatures": "New Record Date" + }, + { + "Location": "Biarritz", + "Value": 40.6, + "Record Temperatures": "Former Record (°C)" + }, + { + "Location": "Biarritz", + "Value": 105.1, + "Record Temperatures": "Former Record (°F)" + }, + { + "Location": "Biarritz", + "Value": 42.9, + "Record Temperatures": "New Record (°C)" + }, + { + "Location": "Biarritz", + "Value": 109.2, + "Record Temperatures": "New Record (°F)" + }, + { + "Location": "Nantes", + "Value": 1949, + "Record Temperatures": "Former Record Year" + }, + { + "Location": "Nantes", + "Value": "Jul 18, 2022", + "Record Temperatures": "New Record Date" + }, + { + "Location": "Nantes", + "Value": 40.3, + "Record Temperatures": "Former Record (°C)" + }, + { + "Location": "Nantes", + "Value": 104.5, + "Record Temperatures": "Former Record (°F)" + }, + { + "Location": "Nantes", + "Value": 42.0, + "Record Temperatures": "New Record (°C)" + }, + { + "Location": "Nantes", + "Value": 107.6, + "Record Temperatures": "New Record (°F)" + }, + { + "Location": "UK", + "Value": 2019, + "Record Temperatures": "Former Record Year" + }, + { + "Location": "UK", + "Value": "Jul 19, 2022", + "Record Temperatures": "New Record Date" + }, + { + "Location": "UK", + "Value": 38.7, + "Record Temperatures": "Former Record (°C)" + }, + { + "Location": "UK", + "Value": 101.7, + "Record Temperatures": "Former Record (°F)" + }, + { + "Location": "UK", + "Value": 40.2, + "Record Temperatures": "New Record (°C)" + }, + { + "Location": "UK", + "Value": 104.4, + "Record Temperatures": "New Record (°F)" + }, + { + "Location": "Brest", + "Value": 2003, + "Record Temperatures": "Former Record Year" + }, + { + "Location": "Brest", + "Value": "Jul 18, 2022", + "Record Temperatures": "New Record Date" + }, + { + "Location": "Brest", + "Value": 35.1, + "Record Temperatures": "Former Record (°C)" + }, + { + "Location": "Brest", + "Value": 95.2, + "Record Temperatures": "Former Record (°F)" + }, + { + "Location": "Brest", + "Value": 39.3, + "Record Temperatures": "New Record (°C)" + }, + { + "Location": "Brest", + "Value": 102.7, + "Record Temperatures": "New Record (°F)" + }, + { + "Location": "Wales", + "Value": 1990, + "Record Temperatures": "Former Record Year" + }, + { + "Location": "Wales", + "Value": "Jul 18, 2022", + "Record Temperatures": "New Record Date" + }, + { + "Location": "Wales", + "Value": 35.2, + "Record Temperatures": "Former Record (°C)" + }, + { + "Location": "Wales", + "Value": 95.4, + "Record Temperatures": "Former Record (°F)" + }, + { + "Location": "Wales", + "Value": 37.1, + "Record Temperatures": "New Record (°C)" + }, + { + "Location": "Wales", + "Value": 98.8, + "Record Temperatures": "New Record (°F)" + }, + { + "Location": "Scotland", + "Value": 2003, + "Record Temperatures": "Former Record Year" + }, + { + "Location": "Scotland", + "Value": "Jul 19, 2022", + "Record Temperatures": "New Record Date" + }, + { + "Location": "Scotland", + "Value": 32.9, + "Record Temperatures": "Former Record (°C)" + }, + { + "Location": "Scotland", + "Value": 91.2, + "Record Temperatures": "Former Record (°F)" + }, + { + "Location": "Scotland", + "Value": 34.8, + "Record Temperatures": "New Record (°C)" + }, + { + "Location": "Scotland", + "Value": 94.6, + "Record Temperatures": "New Record (°F)" + }, + { + "Location": "Dublin", + "Value": 2006, + "Record Temperatures": "Former Record Year" + }, + { + "Location": "Dublin", + "Value": "Jul 18, 2022", + "Record Temperatures": "New Record Date" + }, + { + "Location": "Dublin", + "Value": 31.0, + "Record Temperatures": "Former Record (°C)" + }, + { + "Location": "Dublin", + "Value": 87.8, + "Record Temperatures": "Former Record (°F)" + }, + { + "Location": "Dublin", + "Value": 33.0, + "Record Temperatures": "New Record (°C)" + }, + { + "Location": "Dublin", + "Value": 91.4, + "Record Temperatures": "New Record (°F)" + } + ], + "columns": [ + { + "name": "Location", + "importance": "primary", + "description": "City/region/country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding record temperatures", + "role": "y" + }, + { + "name": "Record Temperatures", + "importance": "primary", + "description": "The record temperatures of the data point", + "role": "group" + } + ] + } + }, + "43": { + "metadata": { + "title": "The Rise of the American 1-Person Household", + "description": "American households by type 1970-2012 (in %)", + "main_insight": "The percentage of 1-person households in America has significantly increased from 1970 to 2012, while the percentage of married couples has decreased." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2012, + "Units": 28.0, + "Household Composition": "1-Person Households" + }, + { + "Year": 2012, + "Units": 49.0, + "Household Composition": "Married Couples" + } + ], + "score": 0.99, + "annotation": "The Units of 1-Person Households and Married Couples show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between 1-Person Households and Married Couples is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2012, + "Units": 49.0, + "Household Composition": "Married Couples" + }, + { + "Year": 2012, + "Units": 24.0, + "Household Composition": "Other Multi-Person Households" + } + ], + "score": 0.99, + "annotation": "The Units of Married Couples and Other Multi-Person Households show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Married Couples and Other Multi-Person Households is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2012, + "Units": 28.0, + "Household Composition": "1-Person Households" + }, + { + "Year": 2012, + "Units": 24.0, + "Household Composition": "Other Multi-Person Households" + } + ], + "score": 0.97, + "annotation": "The Units of 1-Person Households and Other Multi-Person Households show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 1-Person Households and Other Multi-Person Households is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 1970, + "Units": 17.0, + "Household Composition": "1-Person Households" + }, + { + "Year": 1970, + "Units": 71.0, + "Household Composition": "Married Couples" + }, + { + "Year": 1970, + "Units": 12.0, + "Household Composition": "Other Multi-Person Households" + }, + { + "Year": 1980, + "Units": 23.0, + "Household Composition": "1-Person Households" + }, + { + "Year": 1980, + "Units": 61.0, + "Household Composition": "Married Couples" + }, + { + "Year": 1980, + "Units": 17.0, + "Household Composition": "Other Multi-Person Households" + }, + { + "Year": 1990, + "Units": 25.0, + "Household Composition": "1-Person Households" + }, + { + "Year": 1990, + "Units": 56.0, + "Household Composition": "Married Couples" + }, + { + "Year": 1990, + "Units": 19.0, + "Household Composition": "Other Multi-Person Households" + }, + { + "Year": 1995, + "Units": 25.0, + "Household Composition": "1-Person Households" + }, + { + "Year": 1995, + "Units": 54.0, + "Household Composition": "Married Couples" + }, + { + "Year": 1995, + "Units": 21.0, + "Household Composition": "Other Multi-Person Households" + }, + { + "Year": 2000, + "Units": 26.0, + "Household Composition": "1-Person Households" + }, + { + "Year": 2000, + "Units": 53.0, + "Household Composition": "Married Couples" + }, + { + "Year": 2000, + "Units": 22.0, + "Household Composition": "Other Multi-Person Households" + }, + { + "Year": 2005, + "Units": 27.0, + "Household Composition": "1-Person Households" + }, + { + "Year": 2005, + "Units": 51.0, + "Household Composition": "Married Couples" + }, + { + "Year": 2005, + "Units": 22.0, + "Household Composition": "Other Multi-Person Households" + }, + { + "Year": 2010, + "Units": 27.0, + "Household Composition": "1-Person Households" + }, + { + "Year": 2010, + "Units": 50.0, + "Household Composition": "Married Couples" + }, + { + "Year": 2010, + "Units": 24.0, + "Household Composition": "Other Multi-Person Households" + }, + { + "Year": 2012, + "Units": 28.0, + "Household Composition": "1-Person Households" + }, + { + "Year": 2012, + "Units": 49.0, + "Household Composition": "Married Couples" + }, + { + "Year": 2012, + "Units": 24.0, + "Household Composition": "Other Multi-Person Households" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding household composition", + "role": "y" + }, + { + "name": "Household Composition", + "importance": "primary", + "description": "The household composition of the data point", + "role": "group" + } + ] + } + }, + "671": { + "metadata": { + "title": "The Snowballing Costs Of Sochi's Olympic Venues", + "description": "Original estimated and actual estimated costs of 2014 Winter Olympic venues", + "main_insight": "The actual costs of the 2014 Sochi Winter Olympic venues significantly exceeded the original estimated costs, with the largest overruns seen in the Laura Biathlon & Ski Complex and Rosa Khutor Alpine Centre." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Venue": "Complex Mountain Carousel", + "Dollars": 2450.0, + "Cost Estimate": "Actual Estimated Cost" + } + ], + "score": 0.92, + "annotation": "The Actual Estimated Cost shows a sudden jump in Dollars from Fisht Olympic Stadium to Complex Mountain Carousel.", + "reason": "The Dollars for Actual Estimated Cost increases from 519.0 at Fisht Olympic Stadium to 2450.0 at Complex Mountain Carousel, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Venue": "Laura Biathlon & Ski Complex", + "Dollars": 2700.0, + "Cost Estimate": "Actual Estimated Cost" + } + ], + "score": 0.81, + "annotation": "The Actual Estimated Cost has a maximum value at Laura Biathlon & Ski Complex", + "reason": "The [{'Venue': 'Laura Biathlon & Ski Complex', 'Dollars': 2700.0, 'Cost Estimate': 'Actual Estimated Cost'}] of Actual Estimated Cost has a maximum value of 2700.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Venue": "Ice Cube Curling Centre", + "Dollars": 30.0, + "Cost Estimate": "Actual Estimated Cost" + } + ], + "score": 0.5, + "annotation": "The Actual Estimated Cost has a minimum value at Ice Cube Curling Centre", + "reason": "The [{'Venue': 'Ice Cube Curling Centre', 'Dollars': 30.0, 'Cost Estimate': 'Actual Estimated Cost'}] of Actual Estimated Cost has a mainimum value of 30.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Venue": "Laura Biathlon & Ski Complex", + "Dollars": 2700.0, + "Cost Estimate": "Actual Estimated Cost" + }, + { + "Venue": "Rosa Khutor Alpine Centre", + "Dollars": 2600.0, + "Cost Estimate": "Actual Estimated Cost" + }, + { + "Venue": "Complex Mountain Carousel", + "Dollars": 2450.0, + "Cost Estimate": "Actual Estimated Cost" + }, + { + "Venue": "Fisht Olympic Stadium", + "Dollars": 519.0, + "Cost Estimate": "Actual Estimated Cost" + }, + { + "Venue": "Bolshoy Ice Dome", + "Dollars": 302.0, + "Cost Estimate": "Actual Estimated Cost" + }, + { + "Venue": "Iceberg Skating Palace", + "Dollars": 272.0, + "Cost Estimate": "Actual Estimated Cost" + }, + { + "Venue": "Sanki Sliding Centre", + "Dollars": 240.0, + "Cost Estimate": "Actual Estimated Cost" + }, + { + "Venue": "Adler Arena", + "Dollars": 226.0, + "Cost Estimate": "Actual Estimated Cost" + }, + { + "Venue": "Shayba Arena", + "Dollars": 104.0, + "Cost Estimate": "Actual Estimated Cost" + }, + { + "Venue": "Ice Cube Curling Centre", + "Dollars": 30.0, + "Cost Estimate": "Actual Estimated Cost" + } + ], + "columns": [ + { + "name": "Venue", + "importance": "primary", + "description": "Name of the Olympic venue", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding cost estimate", + "role": "y" + }, + { + "name": "Cost Estimate", + "importance": "primary", + "description": "The cost estimate of the data point", + "role": "group" + } + ] + } + }, + "68": { + "metadata": { + "title": "Decline of Poverty Has Slowed", + "description": "Share of population living in poverty worldwide and in South Asia, by the following poverty lines (in %)", + "main_insight": "The decline in poverty, both globally and in South Asia, has slowed down in recent years, across all poverty lines." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 0, + "Score": 50.0, + "Poverty Rate": "World $6.85 per day" + }, + { + "Year": 0, + "Score": 7.6, + "Poverty Rate": "South Asia $2.15 per day" + } + ], + "score": 1.0, + "annotation": "The Score of World $6.85 per day and South Asia $2.15 per day show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between World $6.85 per day and South Asia $2.15 per day is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 0, + "Score": 75.6, + "Poverty Rate": "South Asia $6.85 per day" + }, + { + "Year": 0, + "Score": 7.6, + "Poverty Rate": "South Asia $2.15 per day" + } + ], + "score": 1.0, + "annotation": "The Score of South Asia $6.85 per day and South Asia $2.15 per day show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between South Asia $6.85 per day and South Asia $2.15 per day is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 0, + "Score": 50.0, + "Poverty Rate": "World $6.85 per day" + }, + { + "Year": 0, + "Score": 75.6, + "Poverty Rate": "South Asia $6.85 per day" + } + ], + "score": 1.0, + "annotation": "The Score of World $6.85 per day and South Asia $6.85 per day show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between World $6.85 per day and South Asia $6.85 per day is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 0, + "Score": 75.6, + "Poverty Rate": "South Asia $6.85 per day" + }, + { + "Year": 0, + "Score": 8.5, + "Poverty Rate": "World $2.15 per day" + } + ], + "score": 1.0, + "annotation": "The Score of South Asia $6.85 per day and World $2.15 per day show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between South Asia $6.85 per day and World $2.15 per day is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 0, + "Score": 8.5, + "Poverty Rate": "World $2.15 per day" + }, + { + "Year": 0, + "Score": 7.6, + "Poverty Rate": "South Asia $2.15 per day" + } + ], + "score": 1.0, + "annotation": "The Score of World $2.15 per day and South Asia $2.15 per day show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between World $2.15 per day and South Asia $2.15 per day is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 1990, + "Score": 69.0, + "Poverty Rate": "World $6.85 per day" + }, + { + "Year": 1990, + "Score": 94.0, + "Poverty Rate": "South Asia $6.85 per day" + }, + { + "Year": 1990, + "Score": 55.0, + "Poverty Rate": "World $3.65 per day" + }, + { + "Year": 1990, + "Score": 83.0, + "Poverty Rate": "South Asia $3.65 per day" + }, + { + "Year": 1990, + "Score": 36.0, + "Poverty Rate": "World $2.15 per day" + }, + { + "Year": 1990, + "Score": 53.0, + "Poverty Rate": "South Asia $2.15 per day" + }, + { + "Year": 1995, + "Score": 68.0, + "Poverty Rate": "World $6.85 per day" + }, + { + "Year": 1995, + "Score": 92.0, + "Poverty Rate": "South Asia $6.85 per day" + }, + { + "Year": 1995, + "Score": 53.0, + "Poverty Rate": "World $3.65 per day" + }, + { + "Year": 1995, + "Score": 81.0, + "Poverty Rate": "South Asia $3.65 per day" + }, + { + "Year": 1995, + "Score": 33.0, + "Poverty Rate": "World $2.15 per day" + }, + { + "Year": 1995, + "Score": 49.0, + "Poverty Rate": "South Asia $2.15 per day" + }, + { + "Year": 2000, + "Score": 66.0, + "Poverty Rate": "World $6.85 per day" + }, + { + "Year": 2000, + "Score": 90.0, + "Poverty Rate": "South Asia $6.85 per day" + }, + { + "Year": 2000, + "Score": 51.0, + "Poverty Rate": "World $3.65 per day" + }, + { + "Year": 2000, + "Score": 78.0, + "Poverty Rate": "South Asia $3.65 per day" + }, + { + "Year": 2000, + "Score": 29.0, + "Poverty Rate": "World $2.15 per day" + }, + { + "Year": 2000, + "Score": 44.0, + "Poverty Rate": "South Asia $2.15 per day" + }, + { + "Year": 2005, + "Score": 63.0, + "Poverty Rate": "World $6.85 per day" + }, + { + "Year": 2005, + "Score": 88.0, + "Poverty Rate": "South Asia $6.85 per day" + }, + { + "Year": 2005, + "Score": 47.0, + "Poverty Rate": "World $3.65 per day" + }, + { + "Year": 2005, + "Score": 74.0, + "Poverty Rate": "South Asia $3.65 per day" + }, + { + "Year": 2005, + "Score": 25.0, + "Poverty Rate": "World $2.15 per day" + }, + { + "Year": 2005, + "Score": 38.0, + "Poverty Rate": "South Asia $2.15 per day" + }, + { + "Year": 2010, + "Score": 59.0, + "Poverty Rate": "World $6.85 per day" + }, + { + "Year": 2010, + "Score": 84.0, + "Poverty Rate": "South Asia $6.85 per day" + }, + { + "Year": 2010, + "Score": 41.0, + "Poverty Rate": "World $3.65 per day" + }, + { + "Year": 2010, + "Score": 66.0, + "Poverty Rate": "South Asia $3.65 per day" + }, + { + "Year": 2010, + "Score": 19.0, + "Poverty Rate": "World $2.15 per day" + }, + { + "Year": 2010, + "Score": 29.0, + "Poverty Rate": "South Asia $2.15 per day" + }, + { + "Year": 2015, + "Score": 55.0, + "Poverty Rate": "World $6.85 per day" + }, + { + "Year": 2015, + "Score": 80.0, + "Poverty Rate": "South Asia $6.85 per day" + }, + { + "Year": 2015, + "Score": 35.0, + "Poverty Rate": "World $3.65 per day" + }, + { + "Year": 2015, + "Score": 55.0, + "Poverty Rate": "South Asia $3.65 per day" + }, + { + "Year": 2015, + "Score": 13.0, + "Poverty Rate": "World $2.15 per day" + }, + { + "Year": 2015, + "Score": 19.0, + "Poverty Rate": "South Asia $2.15 per day" + }, + { + "Year": 2020, + "Score": 52.0, + "Poverty Rate": "World $6.85 per day" + }, + { + "Year": 2020, + "Score": 78.0, + "Poverty Rate": "South Asia $6.85 per day" + }, + { + "Year": 2020, + "Score": 34.0, + "Poverty Rate": "World $3.65 per day" + }, + { + "Year": 2020, + "Score": 47.0, + "Poverty Rate": "South Asia $3.65 per day" + }, + { + "Year": 2020, + "Score": 10.0, + "Poverty Rate": "World $2.15 per day" + }, + { + "Year": 2020, + "Score": 11.0, + "Poverty Rate": "South Asia $2.15 per day" + }, + { + "Year": 0, + "Score": 51.0, + "Poverty Rate": "World $6.85 per day" + }, + { + "Year": 0, + "Score": 75.6, + "Poverty Rate": "South Asia $6.85 per day" + }, + { + "Year": 0, + "Score": 34.3, + "Poverty Rate": "World $3.65 per day" + }, + { + "Year": 0, + "Score": 43.6, + "Poverty Rate": "South Asia $3.65 per day" + }, + { + "Year": 0, + "Score": 8.5, + "Poverty Rate": "World $2.15 per day" + }, + { + "Year": 0, + "Score": 7.6, + "Poverty Rate": "South Asia $2.15 per day" + }, + { + "Year": 0, + "Score": 50.0, + "Poverty Rate": "World $6.85 per day" + }, + { + "Year": 0, + "Score": 75.6, + "Poverty Rate": "South Asia $6.85 per day" + }, + { + "Year": 0, + "Score": 33.0, + "Poverty Rate": "World $3.65 per day" + }, + { + "Year": 0, + "Score": 42.9, + "Poverty Rate": "South Asia $3.65 per day" + }, + { + "Year": 0, + "Score": 8.5, + "Poverty Rate": "World $2.15 per day" + }, + { + "Year": 0, + "Score": 7.6, + "Poverty Rate": "South Asia $2.15 per day" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the poverty measurement", + "unit": "none", + "data_type": "time", + "discarded_count": 2, + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding poverty rate", + "role": "y" + }, + { + "name": "Poverty Rate", + "importance": "primary", + "description": "The poverty rate of the data point", + "role": "group" + } + ] + } + }, + "1290": { + "metadata": { + "title": "Mobile Devices - The 21st Century Nanny", + "description": "% of U.S. parents of children under 18 who have used the following devices to keep their child occupied", + "main_insight": "A significant percentage of US parents use mobile devices, particularly smartphones or tablets, to keep their children occupied. Over 60% use a combination of smartphones or tablets, indicating their prevalent role in childcare." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Device Type": "Tablet", + "Percentage of Parents": 44.0 + } + ], + "score": 0.84, + "annotation": "The shows a sudden jump in Percentage of Parents from Never used a device to keep child entertained to Tablet.", + "reason": "The Percentage of Parents for increases from 20.0 at Never used a device to keep child entertained to 44.0 at Tablet, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Device Type": "Smartphone or tablet", + "Percentage of Parents": 61.0 + } + ], + "score": 0.82, + "annotation": "The has a maximum value at Smartphone or tablet", + "reason": "The [{'Device Type': 'Smartphone or tablet', 'Percentage of Parents': 61.0}] of has a maximum value of 61.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Device Type": "Any other mobile devices", + "Percentage of Parents": 14.0 + } + ], + "score": 0.57, + "annotation": "The has a minimum value at Any other mobile devices", + "reason": "The [{'Device Type': 'Any other mobile devices', 'Percentage of Parents': 14.0}] of has a mainimum value of 14.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Device Type": "Smartphone or tablet", + "Percentage of Parents": 61.0 + }, + { + "Device Type": "Smartphone", + "Percentage of Parents": 47.0 + }, + { + "Device Type": "Tablet", + "Percentage of Parents": 44.0 + }, + { + "Device Type": "Mobile phone", + "Percentage of Parents": 18.0 + }, + { + "Device Type": "eReader", + "Percentage of Parents": 17.0 + }, + { + "Device Type": "Any other mobile devices", + "Percentage of Parents": 14.0 + }, + { + "Device Type": "Never used a device to keep child entertained", + "Percentage of Parents": 20.0 + } + ], + "columns": [ + { + "name": "Device Type", + "importance": "primary", + "description": "Type of mobile device used", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Parents", + "importance": "primary", + "description": "Percentage of parents who used the device", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2344": { + "metadata": { + "title": "VPN Use Surges in Ukraine and Russia", + "description": "Increase in the search volume for VPNs during global events in 2022*", + "main_insight": "The search volume for VPNs increased significantly in Russia and Ukraine during the Russian invasion in February-March 2022, with increases of 668% and 609%, respectively." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Kazakhstan", + "VPN_Search_Increase": 3405.0 + } + ], + "score": 0.95, + "annotation": "The shows a sudden jump in VPN_Search_Increase from Russia to Kazakhstan.", + "reason": "The VPN_Search_Increase for increases from 668.0 at Russia to 3405.0 at Kazakhstan, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Kazakhstan", + "VPN_Search_Increase": 3405.0 + } + ], + "score": 0.87, + "annotation": "The has a maximum value at Kazakhstan", + "reason": "The [{'Country': 'Kazakhstan', 'VPN_Search_Increase': 3405.0}] of has a maximum value of 3405.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Cambodia", + "VPN_Search_Increase": 47.0 + } + ], + "score": 0.43, + "annotation": "The has a minimum value at Cambodia", + "reason": "The [{'Country': 'Cambodia', 'VPN_Search_Increase': 47.0}] of has a mainimum value of 47.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Russia", + "VPN_Search_Increase": 668.0 + }, + { + "Country": "Ukraine", + "VPN_Search_Increase": 609.0 + }, + { + "Country": "Kazakhstan", + "VPN_Search_Increase": 3405.0 + }, + { + "Country": "Myanmar", + "VPN_Search_Increase": 53.0 + }, + { + "Country": "Cambodia", + "VPN_Search_Increase": 47.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where VPN search volume was measured", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "VPN_Search_Increase", + "importance": "primary", + "description": "Percentage increase in VPN search volume compared to the daily average of the previous 30 days", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1776": { + "metadata": { + "title": "The Largest Online Gambling Markets", + "description": "Largest online gambling markets in 2021, by gross gaming revenue (in billion U.S. dollars)", + "main_insight": "The United Kingdom and the United States were the two largest online gambling markets in 2021, with gross gaming revenues of 12.5 and 11.0 billion U.S. dollars, respectively." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United Kingdom", + "Gross Gaming Revenue (Billion USD)": 12.5 + } + ], + "score": 0.86, + "annotation": "The has a maximum value at United Kingdom", + "reason": "The [{'Country': 'United Kingdom', 'Gross Gaming Revenue (Billion USD)': 12.5}] of has a maximum value of 12.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Gross Gaming Revenue (Billion USD)": 11.0 + } + ], + "score": 0.79, + "annotation": "The shows a sudden jump in Gross Gaming Revenue (Billion USD) from Australia to United States.", + "reason": "The Gross Gaming Revenue (Billion USD) for increases from 6.5 at Australia to 11.0 at United States, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Sweden", + "Gross Gaming Revenue (Billion USD)": 2.1 + } + ], + "score": 0.58, + "annotation": "The has a minimum value at Sweden", + "reason": "The [{'Country': 'Sweden', 'Gross Gaming Revenue (Billion USD)': 2.1}] of has a mainimum value of 2.1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "United Kingdom", + "Gross Gaming Revenue (Billion USD)": 12.5 + }, + { + "Country": "United States", + "Gross Gaming Revenue (Billion USD)": 11.0 + }, + { + "Country": "Australia", + "Gross Gaming Revenue (Billion USD)": 6.5 + }, + { + "Country": "Italy", + "Gross Gaming Revenue (Billion USD)": 4.5 + }, + { + "Country": "France", + "Gross Gaming Revenue (Billion USD)": 3.8 + }, + { + "Country": "Germany", + "Gross Gaming Revenue (Billion USD)": 3.6 + }, + { + "Country": "Canada", + "Gross Gaming Revenue (Billion USD)": 2.6 + }, + { + "Country": "Sweden", + "Gross Gaming Revenue (Billion USD)": 2.1 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country with online gambling market", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Gross Gaming Revenue (Billion USD)", + "importance": "primary", + "description": "Gross gaming revenue in billion U.S. dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1450": { + "metadata": { + "title": "Developing Countries Outpace Developed for Business Trust", + "description": "Trust in business and trust in companies headquartered in selected countries (in percent)", + "main_insight": "Developing countries like India, China, and Saudi Arabia show significantly higher levels of trust in business compared to developed countries like the US, Germany, Japan, the UK and South Korea." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "SA", + "Trust in business": 78.0 + } + ], + "score": 0.87, + "annotation": "The shows a sudden jump in Trust in business from CA to SA.", + "reason": "The Trust in business for increases from 57.0 at CA to 78.0 at SA, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "IN", + "Trust in business": 82.0 + } + ], + "score": 0.81, + "annotation": "The has a maximum value at IN", + "reason": "The [{'Country': 'IN', 'Trust in business': 82.0}] of has a maximum value of 82.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "KR", + "Trust in business": 45.0 + } + ], + "score": 0.6, + "annotation": "The has a minimum value at KR", + "reason": "The [{'Country': 'KR', 'Trust in business': 45.0}] of has a mainimum value of 45.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "IN", + "Trust in business": 82.0 + }, + { + "Country": "CN", + "Trust in business": 81.0 + }, + { + "Country": "SA", + "Trust in business": 78.0 + }, + { + "Country": "CA", + "Trust in business": 57.0 + }, + { + "Country": "FR", + "Trust in business": 53.0 + }, + { + "Country": "US", + "Trust in business": 53.0 + }, + { + "Country": "DE", + "Trust in business": 50.0 + }, + { + "Country": "JP", + "Trust in business": 50.0 + }, + { + "Country": "GB", + "Trust in business": 48.0 + }, + { + "Country": "KR", + "Trust in business": 45.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Selected countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Trust in business", + "importance": "primary", + "description": "Percentage of respondents who trust in business (60-100 = Trust, 50-59 = Neutral, 1-49 = Distrust)", + "unit": "percent", + "data_type": "number", + "role": "y" + } + ] + } + }, + "715": { + "metadata": { + "title": "Offshore Wind Energy to Double", + "description": "Total estimated electrical capacity of offshore wind energy, 2020-2025 (in gigawatts)", + "main_insight": "The total estimated electrical capacity of offshore wind energy is projected to more than double between 2020 and 2025, growing from 25.0 GW to 61.0 GW." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2020, + "Offshore Wind Energy Capacity (GW)": 25.0 + }, + { + "Year": 2021, + "Offshore Wind Energy Capacity (GW)": 28.7 + }, + { + "Year": 2022, + "Offshore Wind Energy Capacity (GW)": 34.2 + }, + { + "Year": 2023, + "Offshore Wind Energy Capacity (GW)": 40.1 + }, + { + "Year": 2024, + "Offshore Wind Energy Capacity (GW)": 47.3 + }, + { + "Year": 2025, + "Offshore Wind Energy Capacity (GW)": 61.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Offshore Wind Energy Capacity (GW)", + "importance": "primary", + "description": "Total estimated electrical capacity of offshore wind energy in gigawatts", + "unit": "GW", + "data_type": "number", + "role": "y" + } + ] + } + }, + "197": { + "metadata": { + "title": "Has Obesity Peaked?", + "description": "Age-adjusted prevalence of obesity among U.S. adults (ages 20 and over)", + "main_insight": "The prevalence of obesity among U.S. adults has steadily increased from 30.5% in 1999-2000 to 40.3% in 2021-2023, showing a significant rise over the past two decades." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Survey Period": "2017-2020", + "Obesity Prevalence": 41.9 + } + ], + "score": 0.85, + "annotation": "The has a maximum value at 2017-2020", + "reason": "The [{'Survey Period': '2017-2020', 'Obesity Prevalence': 41.9}] of has a maximum value of 41.9, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Survey Period": "1999-2000", + "Obesity Prevalence": 30.5 + } + ], + "score": 0.78, + "annotation": "The has a minimum value at 1999-2000", + "reason": "The [{'Survey Period': '1999-2000', 'Obesity Prevalence': 30.5}] of has a mainimum value of 30.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Survey Period": "2013-2014", + "Obesity Prevalence": 37.7 + } + ], + "score": 0.58, + "annotation": "The shows a sudden jump in Obesity Prevalence from 2009-2010 to 2013-2014.", + "reason": "The Obesity Prevalence for increases from 35.6 at 2009-2010 to 37.7 at 2013-2014, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Survey Period": "1999-2000", + "Obesity Prevalence": 30.5 + }, + { + "Survey Period": "2001-2002", + "Obesity Prevalence": 31.3 + }, + { + "Survey Period": "2003-2004", + "Obesity Prevalence": 32.9 + }, + { + "Survey Period": "2005-2006", + "Obesity Prevalence": 34.8 + }, + { + "Survey Period": "2007-2008", + "Obesity Prevalence": 34.4 + }, + { + "Survey Period": "2009-2010", + "Obesity Prevalence": 35.6 + }, + { + "Survey Period": "2011-2012", + "Obesity Prevalence": 34.8 + }, + { + "Survey Period": "2013-2014", + "Obesity Prevalence": 37.7 + }, + { + "Survey Period": "2015-2016", + "Obesity Prevalence": 39.6 + }, + { + "Survey Period": "2017-2020", + "Obesity Prevalence": 41.9 + }, + { + "Survey Period": "2021-2023", + "Obesity Prevalence": 40.3 + } + ], + "columns": [ + { + "name": "Survey Period", + "importance": "primary", + "description": "Two-year survey periods", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Obesity Prevalence", + "importance": "primary", + "description": "Percentage of U.S. adults (20+) with obesity", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2249": { + "metadata": { + "title": "Apple's Record-Breaking Quarter", + "description": "Apple's segment revenue in Q1 2018 (ended Dec. 30, 2017) compared to previous records", + "main_insight": "Apple achieved a record-breaking total revenue of $88.3 billion in Q1 2018, surpassing the previous record of $78.4 billion. iPhone sales were the largest contributor to this record." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Segment": "iPhone", + "Percentage": 61.6, + "Revenue Context": "Q1 2018 Revenue" + }, + { + "Segment": "iPhone", + "Percentage": 54.4, + "Revenue Context": "Previous Record Revenue" + }, + { + "Segment": "iPhone", + "Percentage": "Q1 '17", + "Revenue Context": "Previous Record Quarter" + }, + { + "Segment": "iPad", + "Percentage": 5.9, + "Revenue Context": "Q1 2018 Revenue" + }, + { + "Segment": "iPad", + "Percentage": 11.5, + "Revenue Context": "Previous Record Revenue" + }, + { + "Segment": "iPad", + "Percentage": "Q1 '14", + "Revenue Context": "Previous Record Quarter" + }, + { + "Segment": "Mac", + "Percentage": 6.9, + "Revenue Context": "Q1 2018 Revenue" + }, + { + "Segment": "Mac", + "Percentage": 7.2, + "Revenue Context": "Previous Record Revenue" + }, + { + "Segment": "Mac", + "Percentage": "Q1 '17", + "Revenue Context": "Previous Record Quarter" + }, + { + "Segment": "Services", + "Percentage": 8.5, + "Revenue Context": "Q1 2018 Revenue" + }, + { + "Segment": "Services", + "Percentage": 8.5, + "Revenue Context": "Previous Record Revenue" + }, + { + "Segment": "Services", + "Percentage": "Q1 '17", + "Revenue Context": "Previous Record Quarter" + }, + { + "Segment": "Other products", + "Percentage": 5.5, + "Revenue Context": "Q1 2018 Revenue" + }, + { + "Segment": "Other products", + "Percentage": 4.4, + "Revenue Context": "Previous Record Revenue" + }, + { + "Segment": "Other products", + "Percentage": "Q1 '16", + "Revenue Context": "Previous Record Quarter" + }, + { + "Segment": "Total Revenue", + "Percentage": 88.3, + "Revenue Context": "Q1 2018 Revenue" + }, + { + "Segment": "Total Revenue", + "Percentage": 78.4, + "Revenue Context": "Previous Record Revenue" + }, + { + "Segment": "Total Revenue", + "Percentage": "Q1 2017", + "Revenue Context": "Previous Record Quarter" + } + ], + "columns": [ + { + "name": "Segment", + "importance": "primary", + "description": "Apple's product/service segments", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage for the corresponding revenue context", + "role": "y" + }, + { + "name": "Revenue Context", + "importance": "primary", + "description": "The revenue context of the data point", + "role": "group" + } + ] + } + }, + "837": { + "metadata": { + "title": "Where Sexual Harassment Is Seen As The Biggest Issue", + "description": "Share agreeing that sexual harassment is the biggest issue facing women", + "main_insight": "There is a significant variation across countries in the perception of sexual harassment as the biggest issue facing women, with a general tendency for women to perceive it as a bigger issue than men do." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Russia", + "Units": 7.0, + "Gender": "Women" + }, + { + "Country": "Russia", + "Units": 7.0, + "Gender": "Men" + } + ], + "score": 0.96, + "annotation": "The Units of Women and Men show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Women and Men is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Russia", + "Units": 7.0, + "Gender": "Men" + } + ], + "score": 0.95, + "annotation": "The Men has a minimum value at Russia", + "reason": "The [{'Country': 'Russia', 'Units': 7.0, 'Gender': 'Men'}] of Men has a mainimum value of 7.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Russia", + "Units": 7.0, + "Gender": "Women" + } + ], + "score": 0.91, + "annotation": "The Women has a minimum value at Russia", + "reason": "The [{'Country': 'Russia', 'Units': 7.0, 'Gender': 'Women'}] of Women has a mainimum value of 7.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Mexico", + "Units": 52.0, + "Gender": "Women" + } + ], + "score": 0.84, + "annotation": "The Women has a maximum value at Mexico", + "reason": "The [{'Country': 'Mexico', 'Units': 52.0, 'Gender': 'Women'}] of Women has a maximum value of 52.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Turkey", + "Units": 50.0, + "Gender": "Men" + } + ], + "score": 0.82, + "annotation": "The Men has a maximum value at Turkey", + "reason": "The [{'Country': 'Turkey', 'Units': 50.0, 'Gender': 'Men'}] of Men has a maximum value of 50.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Turkey", + "Units": 51.0, + "Gender": "Women" + }, + { + "Country": "Turkey", + "Units": 50.0, + "Gender": "Men" + }, + { + "Country": "India", + "Units": 47.0, + "Gender": "Women" + }, + { + "Country": "India", + "Units": 47.0, + "Gender": "Men" + }, + { + "Country": "Mexico", + "Units": 52.0, + "Gender": "Women" + }, + { + "Country": "Mexico", + "Units": 44.0, + "Gender": "Men" + }, + { + "Country": "Brazil", + "Units": 37.0, + "Gender": "Women" + }, + { + "Country": "Brazil", + "Units": 39.0, + "Gender": "Men" + }, + { + "Country": "Spain", + "Units": 38.0, + "Gender": "Women" + }, + { + "Country": "Spain", + "Units": 39.0, + "Gender": "Men" + }, + { + "Country": "France", + "Units": 35.0, + "Gender": "Women" + }, + { + "Country": "France", + "Units": 39.0, + "Gender": "Men" + }, + { + "Country": "United States", + "Units": 35.0, + "Gender": "Women" + }, + { + "Country": "United States", + "Units": 38.0, + "Gender": "Men" + }, + { + "Country": "Italy", + "Units": 32.0, + "Gender": "Women" + }, + { + "Country": "Italy", + "Units": 30.0, + "Gender": "Men" + }, + { + "Country": "Australia", + "Units": 22.0, + "Gender": "Women" + }, + { + "Country": "Australia", + "Units": 29.0, + "Gender": "Men" + }, + { + "Country": "China", + "Units": 27.0, + "Gender": "Women" + }, + { + "Country": "China", + "Units": 28.0, + "Gender": "Men" + }, + { + "Country": "South Korea", + "Units": 28.0, + "Gender": "Women" + }, + { + "Country": "South Korea", + "Units": 28.0, + "Gender": "Men" + }, + { + "Country": "Germany", + "Units": 19.0, + "Gender": "Women" + }, + { + "Country": "Germany", + "Units": 24.0, + "Gender": "Men" + }, + { + "Country": "United Kingdom", + "Units": 24.0, + "Gender": "Women" + }, + { + "Country": "United Kingdom", + "Units": 23.0, + "Gender": "Men" + }, + { + "Country": "Japan", + "Units": 18.0, + "Gender": "Women" + }, + { + "Country": "Japan", + "Units": 22.0, + "Gender": "Men" + }, + { + "Country": "Russia", + "Units": 7.0, + "Gender": "Women" + }, + { + "Country": "Russia", + "Units": 7.0, + "Gender": "Men" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where the survey was conducted", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding gender", + "role": "y" + }, + { + "name": "Gender", + "importance": "primary", + "description": "The gender of the data point", + "role": "group" + } + ] + } + }, + "804": { + "metadata": { + "title": "VAR: What is it Good For?", + "description": "Football fans' view on whether the \"Video Assistant Referee\" should be kept or abandoned (in %)", + "main_insight": "A majority of football fans in Germany, France, Spain, and Italy believe VAR should be kept, while the UK has the lowest support, but still more in favor of keeping it than abandoning it." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Italy", + "Units": 71.0, + "Action Type": "Keep" + }, + { + "Country": "Italy", + "Units": 5.0, + "Action Type": "Abandon" + } + ], + "score": 0.96, + "annotation": "The Units of Keep and Abandon show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Keep and Abandon is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Germany", + "Units": 63.0, + "Action Type": "Keep" + } + ], + "score": 0.92, + "annotation": "The Keep shows a sudden jump in Units from UK to Germany.", + "reason": "The Units for Keep increases from 43.0 at UK to 63.0 at Germany, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "UK", + "Units": 43.0, + "Action Type": "Keep" + } + ], + "score": 0.86, + "annotation": "The Keep has a minimum value at UK", + "reason": "The [{'Country': 'UK', 'Units': 43.0, 'Action Type': 'Keep'}] of Keep has a mainimum value of 43.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "UK", + "Units": 20.0, + "Action Type": "Abandon" + } + ], + "score": 0.82, + "annotation": "The Abandon has a maximum value at UK", + "reason": "The [{'Country': 'UK', 'Units': 20.0, 'Action Type': 'Abandon'}] of Abandon has a maximum value of 20.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "UK", + "Units": 20.0, + "Action Type": "Abandon" + } + ], + "score": 0.81, + "annotation": "The Abandon shows a sudden jump in Units from Germany to UK.", + "reason": "The Units for Abandon increases from 13.0 at Germany to 20.0 at UK, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "UK", + "Units": 43.0, + "Action Type": "Keep" + }, + { + "Country": "UK", + "Units": 20.0, + "Action Type": "Abandon" + }, + { + "Country": "Germany", + "Units": 63.0, + "Action Type": "Keep" + }, + { + "Country": "Germany", + "Units": 13.0, + "Action Type": "Abandon" + }, + { + "Country": "France", + "Units": 65.0, + "Action Type": "Keep" + }, + { + "Country": "France", + "Units": 8.0, + "Action Type": "Abandon" + }, + { + "Country": "Spain", + "Units": 69.0, + "Action Type": "Keep" + }, + { + "Country": "Spain", + "Units": 8.0, + "Action Type": "Abandon" + }, + { + "Country": "Italy", + "Units": 71.0, + "Action Type": "Keep" + }, + { + "Country": "Italy", + "Units": 5.0, + "Action Type": "Abandon" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding action type", + "role": "y" + }, + { + "name": "Action Type", + "importance": "primary", + "description": "The action type of the data point", + "role": "group" + } + ] + } + }, + "302": { + "metadata": { + "title": "Everyone Lies on Their Resume, Right?", + "description": "Percentage of U.S. adults who lied on their resumes and were caught or not", + "main_insight": "The vast majority of U.S. adults who lied on their resumes were not found out. A small percentage faced negative consequences." + }, + "chart_type": "Donut Chart", + "datafacts": [], + "data": { + "data": [ + { + "Category": "Outcome", + "No one found out": 79.0 + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Categories of outcomes for people who lied on their resume", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "No one found out", + "importance": "primary", + "description": "Percentage of people who lied and were not caught", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2143": { + "metadata": { + "title": "Public opinion on France's 'yellow vests' movement", + "description": "\"What is your attitude towards the 'yellow vests' movement?\"", + "main_insight": "Support and sympathy for the 'yellow vests' movement remained relatively high, but generally decreased over the observed period, while indifference, opposition, and hostility increased slightly." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Date": "Nov 20-21", + "Units": 42.0, + "Sentiment": "Support it" + } + ], + "score": 0.92, + "annotation": "The Support it has the largest proportion in Nov 20-21.", + "reason": "The Units of Support it in Nov 20-21 accounts for 0.42, larger than all other Sentiment, of the total of Units of all Sentiment." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Date": "Dec 3-4", + "Units": 26.0, + "Sentiment": "Sympathise with it" + }, + { + "Date": "Dec 3-4", + "Units": 8.0, + "Sentiment": "Indifferent to it" + } + ], + "score": 0.92, + "annotation": "The Units of Sympathise with it and Indifferent to it show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Sympathise with it and Indifferent to it is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Date": "Dec 3-4", + "Units": 46.0, + "Sentiment": "Support it" + } + ], + "score": 0.92, + "annotation": "The Support it has the largest proportion in Dec 3-4.", + "reason": "The Units of Support it in Dec 3-4 accounts for 0.46, larger than all other Sentiment, of the total of Units of all Sentiment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Sentiment": "Support it", + "Date": "total", + "Units": 234.0 + } + ], + "score": 0.91, + "annotation": "The total value of Support it has the largest proportion.", + "reason": "The total value of Units of Support it accounts for 0.468, larger than all other Sentiment, of the total of Units of all Sentiment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Date": "Nov 13-14", + "Units": 47.0, + "Sentiment": "Support it" + } + ], + "score": 0.91, + "annotation": "The Support it has the largest proportion in Nov 13-14.", + "reason": "The Units of Support it in Nov 13-14 accounts for 0.47, larger than all other Sentiment, of the total of Units of all Sentiment." + } + ], + "data": { + "data": [ + { + "Date": "Nov 6-7", + "Units": 51.0, + "Sentiment": "Support it" + }, + { + "Date": "Nov 6-7", + "Units": 20.0, + "Sentiment": "Sympathise with it" + }, + { + "Date": "Nov 6-7", + "Units": 18.0, + "Sentiment": "Indifferent to it" + }, + { + "Date": "Nov 6-7", + "Units": 6.0, + "Sentiment": "Opposed to it" + }, + { + "Date": "Nov 6-7", + "Units": 5.0, + "Sentiment": "Hostile to it" + }, + { + "Date": "Nov 13-14", + "Units": 47.0, + "Sentiment": "Support it" + }, + { + "Date": "Nov 13-14", + "Units": 22.0, + "Sentiment": "Sympathise with it" + }, + { + "Date": "Nov 13-14", + "Units": 16.0, + "Sentiment": "Indifferent to it" + }, + { + "Date": "Nov 13-14", + "Units": 9.0, + "Sentiment": "Opposed to it" + }, + { + "Date": "Nov 13-14", + "Units": 6.0, + "Sentiment": "Hostile to it" + }, + { + "Date": "Nov 20-21", + "Units": 42.0, + "Sentiment": "Support it" + }, + { + "Date": "Nov 20-21", + "Units": 24.0, + "Sentiment": "Sympathise with it" + }, + { + "Date": "Nov 20-21", + "Units": 12.0, + "Sentiment": "Indifferent to it" + }, + { + "Date": "Nov 20-21", + "Units": 14.0, + "Sentiment": "Opposed to it" + }, + { + "Date": "Nov 20-21", + "Units": 8.0, + "Sentiment": "Hostile to it" + }, + { + "Date": "Nov 27-28", + "Units": 48.0, + "Sentiment": "Support it" + }, + { + "Date": "Nov 27-28", + "Units": 23.0, + "Sentiment": "Sympathise with it" + }, + { + "Date": "Nov 27-28", + "Units": 10.0, + "Sentiment": "Indifferent to it" + }, + { + "Date": "Nov 27-28", + "Units": 11.0, + "Sentiment": "Opposed to it" + }, + { + "Date": "Nov 27-28", + "Units": 8.0, + "Sentiment": "Hostile to it" + }, + { + "Date": "Dec 3-4", + "Units": 46.0, + "Sentiment": "Support it" + }, + { + "Date": "Dec 3-4", + "Units": 26.0, + "Sentiment": "Sympathise with it" + }, + { + "Date": "Dec 3-4", + "Units": 8.0, + "Sentiment": "Indifferent to it" + }, + { + "Date": "Dec 3-4", + "Units": 13.0, + "Sentiment": "Opposed to it" + }, + { + "Date": "Dec 3-4", + "Units": 7.0, + "Sentiment": "Hostile to it" + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Survey date ranges", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "799": { + "metadata": { + "title": "The Scale Of The Pandora Papers Leak", + "description": "Size of selected major data leaks (in terabytes & gigabytes)", + "main_insight": "The Pandora Papers (2021) represents the largest data leak in terms of size (2.94 TB), closely followed by the Panama Papers (2016) at 2.60 TB. Other leaks are significantly smaller." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Leak Name": "Pandora Papers (2021)", + "Size": 2.94 + } + ], + "score": 0.84, + "annotation": "The has a maximum value at Pandora Papers (2021)", + "reason": "The [{'Leak Name': 'Pandora Papers (2021)', 'Size': 2.94}] of has a maximum value of 2.94, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Leak Name": "Panama Papers (2016)", + "Size": 2.6 + } + ], + "score": 0.77, + "annotation": "The shows a sudden jump in Size from Paradise Papers (2017) to Panama Papers (2016).", + "reason": "The Size for increases from 1.4 at Paradise Papers (2017) to 2.6 at Panama Papers (2016), indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Leak Name": "Wikileaks' 'cablegate' (2010)", + "Size": 0.0017 + } + ], + "score": 0.46, + "annotation": "The has a minimum value at Wikileaks' 'cablegate' (2010)", + "reason": "The [{'Leak Name': \"Wikileaks' 'cablegate' (2010)\", 'Size': 0.0017}] of has a mainimum value of 0.0017, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Leak Name": "Pandora Papers (2021)", + "Size": 2.94 + }, + { + "Leak Name": "Panama Papers (2016)", + "Size": 2.6 + }, + { + "Leak Name": "Paradise Papers (2017)", + "Size": 1.4 + }, + { + "Leak Name": "Offshore leaks (2013)", + "Size": 0.26 + }, + { + "Leak Name": "Bahamas leaks (2016)", + "Size": 0.038 + }, + { + "Leak Name": "Luxembourg leaks (2014)", + "Size": 0.0044 + }, + { + "Leak Name": "Swiss leaks (2015)", + "Size": 0.0033 + }, + { + "Leak Name": "Wikileaks' 'cablegate' (2010)", + "Size": 0.0017 + } + ], + "columns": [ + { + "name": "Leak Name", + "importance": "primary", + "description": "Name and year of the data leak", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Size", + "importance": "primary", + "description": "Size of the data leak in TB or GB", + "unit": "TB", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1516": { + "metadata": { + "title": "Tencent's Impressive Line Of Messaging + Social Platforms", + "description": "Monthly active users of Tencent's messaging apps and social networks", + "main_insight": "WeChat/Weixin experienced significant growth in monthly active users between Q4 2012 and Q2 2014, while QQ and Qzone maintained relatively stable user numbers." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Quarter": "Q4 2012", + "Units": 600.0, + "Social Platforms": "QZone" + } + ], + "score": 0.92, + "annotation": "The QZone has a minimum value at Q4 2012", + "reason": "The [{'Quarter': 'Q4 2012', 'Units': 600.0, 'Social Platforms': 'QZone'}] of QZone has a mainimum value of 600.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Quarter": "Q1 2013", + "Units": 630.0, + "Social Platforms": "QZone" + } + ], + "score": 0.91, + "annotation": "The QZone shows a sudden jump in Units from Q4 2012 to Q1 2013.", + "reason": "The Units for QZone increases from 600.0 at Q4 2012 to 630.0 at Q1 2013, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q2 2014", + "Units": 829.0, + "Social Platforms": "QQ" + } + ], + "score": 0.87, + "annotation": "The QQ has the largest proportion in Q2 2014.", + "reason": "The Units of QQ in Q2 2014 accounts for 0.43357740585774057, larger than all other Social Platforms, of the total of Units of all Social Platforms." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q1 2014", + "Units": 840.0, + "Social Platforms": "QQ" + } + ], + "score": 0.87, + "annotation": "The QQ has the largest proportion in Q1 2014.", + "reason": "The Units of QQ in Q1 2014 accounts for 0.448, larger than all other Social Platforms, of the total of Units of all Social Platforms." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q4 2013", + "Units": 810.0, + "Social Platforms": "QQ" + } + ], + "score": 0.87, + "annotation": "The QQ has the largest proportion in Q4 2013.", + "reason": "The Units of QQ in Q4 2013 accounts for 0.45125348189415043, larger than all other Social Platforms, of the total of Units of all Social Platforms." + } + ], + "data": { + "data": [ + { + "Quarter": "Q4 2012", + "Units": 190.0, + "Social Platforms": "WeChat/Weixin" + }, + { + "Quarter": "Q4 2012", + "Units": 810.0, + "Social Platforms": "QQ" + }, + { + "Quarter": "Q4 2012", + "Units": 600.0, + "Social Platforms": "QZone" + }, + { + "Quarter": "Q1 2013", + "Units": 230.0, + "Social Platforms": "WeChat/Weixin" + }, + { + "Quarter": "Q1 2013", + "Units": 840.0, + "Social Platforms": "QQ" + }, + { + "Quarter": "Q1 2013", + "Units": 630.0, + "Social Platforms": "QZone" + }, + { + "Quarter": "Q2 2013", + "Units": 260.0, + "Social Platforms": "WeChat/Weixin" + }, + { + "Quarter": "Q2 2013", + "Units": 830.0, + "Social Platforms": "QQ" + }, + { + "Quarter": "Q2 2013", + "Units": 630.0, + "Social Platforms": "QZone" + }, + { + "Quarter": "Q3 2013", + "Units": 310.0, + "Social Platforms": "WeChat/Weixin" + }, + { + "Quarter": "Q3 2013", + "Units": 820.0, + "Social Platforms": "QQ" + }, + { + "Quarter": "Q3 2013", + "Units": 630.0, + "Social Platforms": "QZone" + }, + { + "Quarter": "Q4 2013", + "Units": 355.0, + "Social Platforms": "WeChat/Weixin" + }, + { + "Quarter": "Q4 2013", + "Units": 810.0, + "Social Platforms": "QQ" + }, + { + "Quarter": "Q4 2013", + "Units": 630.0, + "Social Platforms": "QZone" + }, + { + "Quarter": "Q1 2014", + "Units": 395.0, + "Social Platforms": "WeChat/Weixin" + }, + { + "Quarter": "Q1 2014", + "Units": 840.0, + "Social Platforms": "QQ" + }, + { + "Quarter": "Q1 2014", + "Units": 640.0, + "Social Platforms": "QZone" + }, + { + "Quarter": "Q2 2014", + "Units": 438.0, + "Social Platforms": "WeChat/Weixin" + }, + { + "Quarter": "Q2 2014", + "Units": 829.0, + "Social Platforms": "QQ" + }, + { + "Quarter": "Q2 2014", + "Units": 645.0, + "Social Platforms": "QZone" + } + ], + "columns": [ + { + "name": "Quarter", + "importance": "primary", + "description": "Quarter of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding social platforms", + "role": "y" + }, + { + "name": "Social Platforms", + "importance": "primary", + "description": "The social platforms of the data point", + "role": "group" + } + ] + } + }, + "85": { + "metadata": { + "title": "IPhone Estimates Q2 2012", + "description": "iPhone sales in million units", + "main_insight": "iPhone sales estimates for Q2 2012 range from 27 to 38.5 million units, significantly higher than previous quarters. There's a clear upward trend in iPhone sales from Q2 2010 to Q2 2012." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Quarter": "Q2 2012", + "iPhone Sales": 0 + } + ], + "score": 0.83, + "annotation": "The has a minimum value at Q2 2012", + "reason": "The [{'Quarter': 'Q2 2012', 'iPhone Sales': 0}] of has a mainimum value of 0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Quarter": "Q4 2011", + "iPhone Sales": 37.04 + } + ], + "score": 0.83, + "annotation": "The has a maximum value at Q4 2011", + "reason": "The [{'Quarter': 'Q4 2011', 'iPhone Sales': 37.04}] of has a maximum value of 37.04, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Quarter": "Q1 2012", + "iPhone Sales": 35.06 + } + ], + "score": 0.77, + "annotation": "The shows a sudden jump in iPhone Sales from Q2 2011 to Q1 2012.", + "reason": "The iPhone Sales for increases from 20.34 at Q2 2011 to 35.06 at Q1 2012, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Quarter": "Q2 2010", + "iPhone Sales": 8.4 + }, + { + "Quarter": "Q3 2010", + "iPhone Sales": 14.1 + }, + { + "Quarter": "Q4 2010", + "iPhone Sales": 16.24 + }, + { + "Quarter": "Q1 2011", + "iPhone Sales": 18.65 + }, + { + "Quarter": "Q2 2011", + "iPhone Sales": 20.34 + }, + { + "Quarter": "Q3 2011", + "iPhone Sales": 17.07 + }, + { + "Quarter": "Q4 2011", + "iPhone Sales": 37.04 + }, + { + "Quarter": "Q1 2012", + "iPhone Sales": 35.06 + }, + { + "Quarter": "Q2 2012", + "iPhone Sales": 0 + } + ], + "columns": [ + { + "name": "Quarter", + "importance": "primary", + "description": "Represents the quarter of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "iPhone Sales", + "importance": "primary", + "description": "Represents iPhone sales in million units", + "unit": "Million Units", + "data_type": "number", + "role": "y" + } + ] + } + }, + "195": { + "metadata": { + "title": "Hanging the Department of State Out to Dry?", + "description": "U.S. State Department budget outlays since 1962 (in % of federal budget)", + "main_insight": "The U.S. State Department budget as a percentage of the total federal budget has significantly decreased since 2014, reaching a low of 0.37% in 2022, down from a peak of 0.78% in 2014, indicating a relative decline in funding for the State Department." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1962, + "State Department Outlays (% of Federal Budget)": 0.43 + }, + { + "Year": 1963, + "State Department Outlays (% of Federal Budget)": 0.51 + }, + { + "Year": 1964, + "State Department Outlays (% of Federal Budget)": 0.4 + }, + { + "Year": 1965, + "State Department Outlays (% of Federal Budget)": 0.45 + }, + { + "Year": 1966, + "State Department Outlays (% of Federal Budget)": 0.48 + }, + { + "Year": 1967, + "State Department Outlays (% of Federal Budget)": 0.46 + }, + { + "Year": 1968, + "State Department Outlays (% of Federal Budget)": 0.4 + }, + { + "Year": 1969, + "State Department Outlays (% of Federal Budget)": 0.35 + }, + { + "Year": 1970, + "State Department Outlays (% of Federal Budget)": 0.34 + }, + { + "Year": 1971, + "State Department Outlays (% of Federal Budget)": 0.33 + }, + { + "Year": 1972, + "State Department Outlays (% of Federal Budget)": 0.32 + }, + { + "Year": 1973, + "State Department Outlays (% of Federal Budget)": 0.33 + }, + { + "Year": 1974, + "State Department Outlays (% of Federal Budget)": 0.33 + }, + { + "Year": 1975, + "State Department Outlays (% of Federal Budget)": 0.35 + }, + { + "Year": 1976, + "State Department Outlays (% of Federal Budget)": 0.33 + }, + { + "Year": 1977, + "State Department Outlays (% of Federal Budget)": 0.37 + }, + { + "Year": 1978, + "State Department Outlays (% of Federal Budget)": 0.32 + }, + { + "Year": 1979, + "State Department Outlays (% of Federal Budget)": 0.36 + }, + { + "Year": 1980, + "State Department Outlays (% of Federal Budget)": 0.4 + }, + { + "Year": 1981, + "State Department Outlays (% of Federal Budget)": 0.38 + }, + { + "Year": 1982, + "State Department Outlays (% of Federal Budget)": 0.35 + }, + { + "Year": 1983, + "State Department Outlays (% of Federal Budget)": 0.35 + }, + { + "Year": 1984, + "State Department Outlays (% of Federal Budget)": 0.37 + }, + { + "Year": 1985, + "State Department Outlays (% of Federal Budget)": 0.4 + }, + { + "Year": 1986, + "State Department Outlays (% of Federal Budget)": 0.43 + }, + { + "Year": 1987, + "State Department Outlays (% of Federal Budget)": 0.41 + }, + { + "Year": 1988, + "State Department Outlays (% of Federal Budget)": 0.39 + }, + { + "Year": 1989, + "State Department Outlays (% of Federal Budget)": 0.41 + }, + { + "Year": 1990, + "State Department Outlays (% of Federal Budget)": 0.41 + }, + { + "Year": 1991, + "State Department Outlays (% of Federal Budget)": 0.4 + }, + { + "Year": 1992, + "State Department Outlays (% of Federal Budget)": 0.4 + }, + { + "Year": 1993, + "State Department Outlays (% of Federal Budget)": 0.38 + }, + { + "Year": 1994, + "State Department Outlays (% of Federal Budget)": 0.36 + }, + { + "Year": 1995, + "State Department Outlays (% of Federal Budget)": 0.34 + }, + { + "Year": 1996, + "State Department Outlays (% of Federal Budget)": 0.34 + }, + { + "Year": 1997, + "State Department Outlays (% of Federal Budget)": 0.32 + }, + { + "Year": 1998, + "State Department Outlays (% of Federal Budget)": 0.34 + }, + { + "Year": 1999, + "State Department Outlays (% of Federal Budget)": 0.36 + }, + { + "Year": 2000, + "State Department Outlays (% of Federal Budget)": 0.38 + }, + { + "Year": 2001, + "State Department Outlays (% of Federal Budget)": 0.41 + }, + { + "Year": 2002, + "State Department Outlays (% of Federal Budget)": 0.44 + }, + { + "Year": 2003, + "State Department Outlays (% of Federal Budget)": 0.47 + }, + { + "Year": 2004, + "State Department Outlays (% of Federal Budget)": 0.51 + }, + { + "Year": 2005, + "State Department Outlays (% of Federal Budget)": 0.52 + }, + { + "Year": 2006, + "State Department Outlays (% of Federal Budget)": 0.53 + }, + { + "Year": 2007, + "State Department Outlays (% of Federal Budget)": 0.54 + }, + { + "Year": 2008, + "State Department Outlays (% of Federal Budget)": 0.6 + }, + { + "Year": 2009, + "State Department Outlays (% of Federal Budget)": 0.7 + }, + { + "Year": 2010, + "State Department Outlays (% of Federal Budget)": 0.75 + }, + { + "Year": 2011, + "State Department Outlays (% of Federal Budget)": 0.75 + }, + { + "Year": 2012, + "State Department Outlays (% of Federal Budget)": 0.74 + }, + { + "Year": 2013, + "State Department Outlays (% of Federal Budget)": 0.72 + }, + { + "Year": 2014, + "State Department Outlays (% of Federal Budget)": 0.78 + }, + { + "Year": 2015, + "State Department Outlays (% of Federal Budget)": 0.73 + }, + { + "Year": 2016, + "State Department Outlays (% of Federal Budget)": 0.74 + }, + { + "Year": 2017, + "State Department Outlays (% of Federal Budget)": 0.66 + }, + { + "Year": 2018, + "State Department Outlays (% of Federal Budget)": 0.55 + }, + { + "Year": 2019, + "State Department Outlays (% of Federal Budget)": 0.44 + }, + { + "Year": 2020, + "State Department Outlays (% of Federal Budget)": 0.41 + }, + { + "Year": 2021, + "State Department Outlays (% of Federal Budget)": 0.38 + }, + { + "Year": 2022, + "State Department Outlays (% of Federal Budget)": 0.37 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of budget outlay", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "State Department Outlays (% of Federal Budget)", + "importance": "primary", + "description": "U.S. State Department budget outlays as percentage of the total federal budget", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1163": { + "metadata": { + "title": "Americans' Trust in Congress Is Waning", + "description": "How has your level of trust in these institutions changed over the past few years?", + "main_insight": "A large majority of Americans (72%) have less trust in Congress. Across various institutions, trust levels have decreased, with Congress exhibiting the highest level of distrust." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Institution": "Small business", + "Score": 53.0, + "Trust Levels": "Same amount of trust" + }, + { + "Institution": "Small business", + "Score": 9.0, + "Trust Levels": "Less trust" + } + ], + "score": 0.97, + "annotation": "The Score of Same amount of trust and Less trust show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Same amount of trust and Less trust is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Institution": "Small business", + "Score": 31.0, + "Trust Levels": "More trust" + }, + { + "Institution": "Small business", + "Score": 9.0, + "Trust Levels": "Less trust" + } + ], + "score": 0.95, + "annotation": "The Score of More trust and Less trust show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between More trust and Less trust is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Trust Levels": "Less trust", + "Institution": "total", + "Score": 342.0 + } + ], + "score": 0.9, + "annotation": "The total value of Less trust has the largest proportion.", + "reason": "The total value of Score of Less trust accounts for 0.42696629213483145, larger than all other Trust Levels, of the total of Score of all Trust Levels." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Institution": "Religious institutions", + "Score": 43.0, + "Trust Levels": "Same amount of trust" + } + ], + "score": 0.9, + "annotation": "The Same amount of trust has the largest proportion in Religious institutions.", + "reason": "The Score of Same amount of trust in Religious institutions accounts for 0.43, larger than all other Trust Levels, of the total of Score of all Trust Levels." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Institution": "The US military", + "Score": 47.0, + "Trust Levels": "Same amount of trust" + } + ], + "score": 0.89, + "annotation": "The Same amount of trust has the largest proportion in The US military.", + "reason": "The Score of Same amount of trust in The US military accounts for 0.47, larger than all other Trust Levels, of the total of Score of all Trust Levels." + } + ], + "data": { + "data": [ + { + "Institution": "Congress", + "Score": 6.0, + "Trust Levels": "More trust" + }, + { + "Institution": "Congress", + "Score": 16.0, + "Trust Levels": "Same amount of trust" + }, + { + "Institution": "Congress", + "Score": 72.0, + "Trust Levels": "Less trust" + }, + { + "Institution": "Congress", + "Score": 6.0, + "Trust Levels": "Not sure" + }, + { + "Institution": "Mortgage lenders", + "Score": 6.0, + "Trust Levels": "More trust" + }, + { + "Institution": "Mortgage lenders", + "Score": 29.0, + "Trust Levels": "Same amount of trust" + }, + { + "Institution": "Mortgage lenders", + "Score": 57.0, + "Trust Levels": "Less trust" + }, + { + "Institution": "Mortgage lenders", + "Score": 8.0, + "Trust Levels": "Not sure" + }, + { + "Institution": "The White House", + "Score": 13.0, + "Trust Levels": "More trust" + }, + { + "Institution": "The White House", + "Score": 28.0, + "Trust Levels": "Same amount of trust" + }, + { + "Institution": "The White House", + "Score": 53.0, + "Trust Levels": "Less trust" + }, + { + "Institution": "The White House", + "Score": 6.0, + "Trust Levels": "Not sure" + }, + { + "Institution": "Major corporations", + "Score": 8.0, + "Trust Levels": "More trust" + }, + { + "Institution": "Major corporations", + "Score": 35.0, + "Trust Levels": "Same amount of trust" + }, + { + "Institution": "Major corporations", + "Score": 51.0, + "Trust Levels": "Less trust" + }, + { + "Institution": "Major corporations", + "Score": 7.0, + "Trust Levels": "Not sure" + }, + { + "Institution": "Banks", + "Score": 9.0, + "Trust Levels": "More trust" + }, + { + "Institution": "Banks", + "Score": 35.0, + "Trust Levels": "Same amount of trust" + }, + { + "Institution": "Banks", + "Score": 50.0, + "Trust Levels": "Less trust" + }, + { + "Institution": "Banks", + "Score": 6.0, + "Trust Levels": "Not sure" + }, + { + "Institution": "Religious institutions", + "Score": 16.0, + "Trust Levels": "More trust" + }, + { + "Institution": "Religious institutions", + "Score": 43.0, + "Trust Levels": "Same amount of trust" + }, + { + "Institution": "Religious institutions", + "Score": 34.0, + "Trust Levels": "Less trust" + }, + { + "Institution": "Religious institutions", + "Score": 7.0, + "Trust Levels": "Not sure" + }, + { + "Institution": "The US military", + "Score": 31.0, + "Trust Levels": "More trust" + }, + { + "Institution": "The US military", + "Score": 47.0, + "Trust Levels": "Same amount of trust" + }, + { + "Institution": "The US military", + "Score": 16.0, + "Trust Levels": "Less trust" + }, + { + "Institution": "The US military", + "Score": 6.0, + "Trust Levels": "Not sure" + }, + { + "Institution": "Small business", + "Score": 31.0, + "Trust Levels": "More trust" + }, + { + "Institution": "Small business", + "Score": 53.0, + "Trust Levels": "Same amount of trust" + }, + { + "Institution": "Small business", + "Score": 9.0, + "Trust Levels": "Less trust" + }, + { + "Institution": "Small business", + "Score": 7.0, + "Trust Levels": "Not sure" + } + ], + "columns": [ + { + "name": "Institution", + "importance": "primary", + "description": "The institution being evaluated", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding trust levels", + "role": "y" + }, + { + "name": "Trust Levels", + "importance": "primary", + "description": "The trust levels of the data point", + "role": "group" + } + ] + } + }, + "329": { + "metadata": { + "title": "Fans Bet on Chiefs for Super Bowl LV", + "description": "Percentage of U.S. adults who expect either team to win Super Bowl LV", + "main_insight": "More US adults expected the Kansas City Chiefs to win Super Bowl LV than the Tampa Bay Buccaneers, though a significant portion had no opinion." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Team": "Don't know/No opinion", + "Percentage": 35.0 + } + ], + "score": 0.9, + "annotation": "The shows a sudden jump in Percentage from Tampa Bay Buccaneers to Don't know/No opinion.", + "reason": "The Percentage for increases from 24.0 at Tampa Bay Buccaneers to 35.0 at Don't know/No opinion, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Team": "Tampa Bay Buccaneers", + "Percentage": 24.0 + } + ], + "score": 0.64, + "annotation": "The has a minimum value at Tampa Bay Buccaneers", + "reason": "The [{'Team': 'Tampa Bay Buccaneers', 'Percentage': 24.0}] of has a mainimum value of 24.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Team": "Kansas City Chiefs", + "Percentage": 41.0 + } + ], + "score": 0.54, + "annotation": "The has a maximum value at Kansas City Chiefs", + "reason": "The [{'Team': 'Kansas City Chiefs', 'Percentage': 41.0}] of has a maximum value of 41.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Team": "Kansas City Chiefs", + "Percentage": 41.0 + }, + { + "Team": "Tampa Bay Buccaneers", + "Percentage": 24.0 + }, + { + "Team": "Don't know/No opinion", + "Percentage": 35.0 + } + ], + "columns": [ + { + "name": "Team", + "importance": "primary", + "description": "Super Bowl LV Team or No Opinion", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of U.S. adults expecting the team to win", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1402": { + "metadata": { + "title": "The Varying State of Retirement Ages", + "description": "Average effective labor market exit age of selected countries in 2022, by gender (in years)", + "main_insight": "The average effective retirement age varies significantly between countries and genders. In most of the displayed countries, men tend to retire later than women." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "DE", + "Score": 64.4, + "Gender Group": "Men" + }, + { + "Country": "DE", + "Score": 64.4, + "Gender Group": "Women" + }, + { + "Country": "DE", + "Score": 64.4, + "Gender Group": "OECD Average Men" + } + ], + "score": 0.94, + "annotation": "The Men, Women, OECD Average Men all have the largest proportion in DE.", + "reason": "The Score of Men, Women, OECD Average Men in DE all account for 0.25126804525946156, larger than all other Gender Group, of the total of Score of all Gender Group." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "US", + "Score": 65.0, + "Gender Group": "Men" + }, + { + "Country": "US", + "Score": 65.0, + "Gender Group": "Women" + } + ], + "score": 0.94, + "annotation": "The Men, Women all have the largest proportion in US.", + "reason": "The Score of Men, Women in US all account for 0.2524271844660194, larger than all other Gender Group, of the total of Score of all Gender Group." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "GB", + "Score": 64.4, + "Gender Group": "OECD Average Men" + } + ], + "score": 0.94, + "annotation": "The OECD Average Men has the largest proportion in GB.", + "reason": "The Score of OECD Average Men in GB accounts for 0.2526480972930561, larger than all other Gender Group, of the total of Score of all Gender Group." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Gender Group": "Men", + "Country": "total", + "Score": 718.8 + } + ], + "score": 0.94, + "annotation": "The total value of Men has the largest proportion.", + "reason": "The total value of Score of Men accounts for 0.2551741275870637, larger than all other Gender Group, of the total of Score of all Gender Group." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "IT", + "Score": 64.4, + "Gender Group": "OECD Average Men" + } + ], + "score": 0.94, + "annotation": "The OECD Average Men has the largest proportion in IT.", + "reason": "The Score of OECD Average Men in IT accounts for 0.2561654733492442, larger than all other Gender Group, of the total of Score of all Gender Group." + } + ], + "data": { + "data": [ + { + "Country": "ID", + "Score": 70.4, + "Gender Group": "Men" + }, + { + "Country": "ID", + "Score": 68.9, + "Gender Group": "Women" + }, + { + "Country": "ID", + "Score": 64.4, + "Gender Group": "OECD Average Men" + }, + { + "Country": "ID", + "Score": 63.1, + "Gender Group": "OECD Average Women" + }, + { + "Country": "IS", + "Score": 67.0, + "Gender Group": "Men" + }, + { + "Country": "IS", + "Score": 69.2, + "Gender Group": "Women" + }, + { + "Country": "IS", + "Score": 64.4, + "Gender Group": "OECD Average Men" + }, + { + "Country": "IS", + "Score": 63.1, + "Gender Group": "OECD Average Women" + }, + { + "Country": "JP", + "Score": 68.7, + "Gender Group": "Men" + }, + { + "Country": "JP", + "Score": 65.8, + "Gender Group": "Women" + }, + { + "Country": "JP", + "Score": 64.4, + "Gender Group": "OECD Average Men" + }, + { + "Country": "JP", + "Score": 63.1, + "Gender Group": "OECD Average Women" + }, + { + "Country": "CO", + "Score": 68.6, + "Gender Group": "Men" + }, + { + "Country": "CO", + "Score": 60.7, + "Gender Group": "Women" + }, + { + "Country": "CO", + "Score": 64.4, + "Gender Group": "OECD Average Men" + }, + { + "Country": "CO", + "Score": 63.1, + "Gender Group": "OECD Average Women" + }, + { + "Country": "IN", + "Score": 67.4, + "Gender Group": "Men" + }, + { + "Country": "IN", + "Score": 57.7, + "Gender Group": "Women" + }, + { + "Country": "IN", + "Score": 64.4, + "Gender Group": "OECD Average Men" + }, + { + "Country": "IN", + "Score": 63.1, + "Gender Group": "OECD Average Women" + }, + { + "Country": "US", + "Score": 65.0, + "Gender Group": "Men" + }, + { + "Country": "US", + "Score": 65.0, + "Gender Group": "Women" + }, + { + "Country": "US", + "Score": 64.4, + "Gender Group": "OECD Average Men" + }, + { + "Country": "US", + "Score": 63.1, + "Gender Group": "OECD Average Women" + }, + { + "Country": "DE", + "Score": 64.4, + "Gender Group": "Men" + }, + { + "Country": "DE", + "Score": 64.4, + "Gender Group": "Women" + }, + { + "Country": "DE", + "Score": 64.4, + "Gender Group": "OECD Average Men" + }, + { + "Country": "DE", + "Score": 63.1, + "Gender Group": "OECD Average Women" + }, + { + "Country": "GB", + "Score": 63.7, + "Gender Group": "Men" + }, + { + "Country": "GB", + "Score": 63.7, + "Gender Group": "Women" + }, + { + "Country": "GB", + "Score": 64.4, + "Gender Group": "OECD Average Men" + }, + { + "Country": "GB", + "Score": 63.1, + "Gender Group": "OECD Average Women" + }, + { + "Country": "IT", + "Score": 62.9, + "Gender Group": "Men" + }, + { + "Country": "IT", + "Score": 61.0, + "Gender Group": "Women" + }, + { + "Country": "IT", + "Score": 64.4, + "Gender Group": "OECD Average Men" + }, + { + "Country": "IT", + "Score": 63.1, + "Gender Group": "OECD Average Women" + }, + { + "Country": "FR", + "Score": 60.4, + "Gender Group": "Men" + }, + { + "Country": "FR", + "Score": 60.9, + "Gender Group": "Women" + }, + { + "Country": "FR", + "Score": 64.4, + "Gender Group": "OECD Average Men" + }, + { + "Country": "FR", + "Score": 63.1, + "Gender Group": "OECD Average Women" + }, + { + "Country": "ZA", + "Score": 60.3, + "Gender Group": "Men" + }, + { + "Country": "ZA", + "Score": 58.3, + "Gender Group": "Women" + }, + { + "Country": "ZA", + "Score": 64.4, + "Gender Group": "OECD Average Men" + }, + { + "Country": "ZA", + "Score": 63.1, + "Gender Group": "OECD Average Women" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country code", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding gender group", + "role": "y" + }, + { + "name": "Gender Group", + "importance": "primary", + "description": "The gender group of the data point", + "role": "group" + } + ] + } + }, + "2525": { + "metadata": { + "title": "6 in 10 Female Homicides Occur Within the Private Sphere", + "description": "Estimated share of female homicide victims killed by intimate partners or family members in 2023", + "main_insight": "Globally, 60% of female homicide victims are killed by intimate partners or family members, with significant regional variations. Oceania has the highest percentage at 80%, while the Americas has the lowest at 47%." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Africa", + "Points": 74, + "Victim Group": "Female Homicide Victims Killed by Intimate Partners or Family" + } + ], + "score": 0.79, + "annotation": "The Female Homicide Victims Killed by Intimate Partners or Family shows a sudden jump in Points from Global to Africa.", + "reason": "The Points for Female Homicide Victims Killed by Intimate Partners or Family increases from 60 at Global to 74 at Africa, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "Oceania", + "Points": 80, + "Victim Group": "Female Homicide Victims Killed by Intimate Partners or Family" + } + ], + "score": 0.78, + "annotation": "The Female Homicide Victims Killed by Intimate Partners or Family has a maximum value at Oceania", + "reason": "The [{'Region': 'Oceania', 'Points': 80, 'Victim Group': 'Female Homicide Victims Killed by Intimate Partners or Family'}] of Female Homicide Victims Killed by Intimate Partners or Family has a maximum value of 80, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Region": "Americas", + "Points": 47, + "Victim Group": "Female Homicide Victims Killed by Intimate Partners or Family" + } + ], + "score": 0.67, + "annotation": "The Female Homicide Victims Killed by Intimate Partners or Family has a minimum value at Americas", + "reason": "The [{'Region': 'Americas', 'Points': 47, 'Victim Group': 'Female Homicide Victims Killed by Intimate Partners or Family'}] of Female Homicide Victims Killed by Intimate Partners or Family has a mainimum value of 47, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Region": "Global", + "Points": 60, + "Victim Group": "Female Homicide Victims Killed by Intimate Partners or Family" + }, + { + "Region": "Oceania", + "Points": 80, + "Victim Group": "Female Homicide Victims Killed by Intimate Partners or Family" + }, + { + "Region": "Africa", + "Points": 74, + "Victim Group": "Female Homicide Victims Killed by Intimate Partners or Family" + }, + { + "Region": "Asia", + "Points": 56, + "Victim Group": "Female Homicide Victims Killed by Intimate Partners or Family" + }, + { + "Region": "Europe", + "Points": 53, + "Victim Group": "Female Homicide Victims Killed by Intimate Partners or Family" + }, + { + "Region": "Americas", + "Points": 47, + "Victim Group": "Female Homicide Victims Killed by Intimate Partners or Family" + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Geographic region or global average", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding victim group", + "role": "y" + }, + { + "name": "Victim Group", + "importance": "primary", + "description": "The victim group of the data point", + "role": "group" + } + ] + } + }, + "2271": { + "metadata": { + "title": "UK election: the great media coverage disparity", + "description": "Appearances of politicians in national news coverage between 7 Nov and 4 Dec 2019*", + "main_insight": "Jeremy Corbyn (Labour) and Boris Johnson (Conservative) received the most media coverage, significantly more than any other political figures." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Party Source": "DUP", + "Values": 5, + "Attributes": "Appearances" + }, + { + "Party Source": "DUP", + "Values": 0.2, + "Attributes": "Percentage" + } + ], + "score": 1.0, + "annotation": "The Values of Appearances and Percentage show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Values between Appearances and Percentage is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Party Source": "Labour source", + "Values": 1205, + "Attributes": "Appearances" + }, + { + "Party Source": "Labour source", + "Values": 42.7, + "Attributes": "Percentage" + }, + { + "Party Source": "Labour source", + "Values": "Jeremy Corbyn", + "Attributes": "Politician" + }, + { + "Party Source": "Conservative source", + "Values": 1096, + "Attributes": "Appearances" + }, + { + "Party Source": "Conservative source", + "Values": 38.8, + "Attributes": "Percentage" + }, + { + "Party Source": "Conservative source", + "Values": "Boris Johnson", + "Attributes": "Politician" + }, + { + "Party Source": "Lib Dem source", + "Values": 210, + "Attributes": "Appearances" + }, + { + "Party Source": "Lib Dem source", + "Values": 7.4, + "Attributes": "Percentage" + }, + { + "Party Source": "Lib Dem source", + "Values": "Jo Swinson", + "Attributes": "Politician" + }, + { + "Party Source": "Brexit party source", + "Values": 145, + "Attributes": "Appearances" + }, + { + "Party Source": "Brexit party source", + "Values": 5.1, + "Attributes": "Percentage" + }, + { + "Party Source": "Brexit party source", + "Values": "Nigel Farage", + "Attributes": "Politician" + }, + { + "Party Source": "SNP source", + "Values": 81, + "Attributes": "Appearances" + }, + { + "Party Source": "SNP source", + "Values": 2.9, + "Attributes": "Percentage" + }, + { + "Party Source": "SNP source", + "Values": "Nicola Sturgeon", + "Attributes": "Politician" + }, + { + "Party Source": "Green source", + "Values": 35, + "Attributes": "Appearances" + }, + { + "Party Source": "Green source", + "Values": 1.3, + "Attributes": "Percentage" + }, + { + "Party Source": "Green source", + "Values": "Green leader", + "Attributes": "Politician" + }, + { + "Party Source": "All other parties", + "Values": 31, + "Attributes": "Appearances" + }, + { + "Party Source": "All other parties", + "Values": 1.1, + "Attributes": "Percentage" + }, + { + "Party Source": "Plaid Cymru source", + "Values": 16, + "Attributes": "Appearances" + }, + { + "Party Source": "Plaid Cymru source", + "Values": 0.6, + "Attributes": "Percentage" + }, + { + "Party Source": "Plaid Cymru source", + "Values": "Adam Price", + "Attributes": "Politician" + }, + { + "Party Source": "DUP", + "Values": 5, + "Attributes": "Appearances" + }, + { + "Party Source": "DUP", + "Values": 0.2, + "Attributes": "Percentage" + } + ], + "columns": [ + { + "name": "Party Source", + "importance": "primary", + "description": "Political party affiliation", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Values", + "importance": "primary", + "description": "Values for the corresponding attributes", + "role": "y" + }, + { + "name": "Attributes", + "importance": "primary", + "description": "The attributes of the data point", + "role": "group" + } + ] + } + }, + "1254": { + "metadata": { + "title": "Travel is coming back: How do European hoteliers feel about it?", + "description": "Survey of European hoteliers' sentiment about the travel industry.", + "main_insight": "European hoteliers are largely positive about the past, present, and future state of the travel industry, with the highest positivity regarding the general development in the last 6 months." + }, + "chart_type": "Multiple Semi Circle Donut Chart", + "datafacts": [], + "data": { + "data": [ + { + "Timeframe": "Past", + "Score": 0.7, + "Sentiment Type": "Positive Sentiment" + }, + { + "Timeframe": "Past", + "Score": 0.07, + "Sentiment Type": "Negative Sentiment" + }, + { + "Timeframe": "Past", + "Score": "General development in the last 6 months", + "Sentiment Type": "Question" + }, + { + "Timeframe": "Present", + "Score": 0.57, + "Sentiment Type": "Positive Sentiment" + }, + { + "Timeframe": "Present", + "Score": 0.07, + "Sentiment Type": "Negative Sentiment" + }, + { + "Timeframe": "Present", + "Score": "Current economic situation", + "Sentiment Type": "Question" + }, + { + "Timeframe": "Future", + "Score": 0.38, + "Sentiment Type": "Positive Sentiment" + }, + { + "Timeframe": "Future", + "Score": 0.17, + "Sentiment Type": "Negative Sentiment" + }, + { + "Timeframe": "Future", + "Score": "Expected economic situation in the next 6 months", + "Sentiment Type": "Question" + } + ], + "columns": [ + { + "name": "Timeframe", + "importance": "primary", + "description": "The period the survey question refers to (Past, Present, Future)", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding sentiment type", + "role": "y" + }, + { + "name": "Sentiment Type", + "importance": "primary", + "description": "The sentiment type of the data point", + "role": "group" + } + ] + } + }, + "985": { + "metadata": { + "title": "Panda Populations in the Wild Rebound", + "description": "Number of Giant Pandas living in the wild and in captivity", + "main_insight": "The population of Giant Pandas in the wild has increased significantly between 1974-1977 and 2011-2014. The population in captivity is much smaller but has also grown." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time Period": "2011-2014", + "Units": 376, + "Population Status": "Population in captivity" + } + ], + "score": 0.97, + "annotation": "The Population in captivity shows a sudden jump in Units from 1999-2003 to 2011-2014.", + "reason": "The Units for Population in captivity increases from 164 at 1999-2003 to 376 at 2011-2014, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Time Period": "1974-1977", + "Units": 2459, + "Population Status": "Wild population" + } + ], + "score": 0.8, + "annotation": "The Wild population shows a sudden jump in Units from 2011-2014 to 1974-1977.", + "reason": "The Units for Wild population increases from 1864 at 2011-2014 to 2459 at 1974-1977, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Time Period": "1999-2003", + "Units": 164, + "Population Status": "Population in captivity" + } + ], + "score": 0.76, + "annotation": "The Population in captivity has the smallest proportion in 1974-1977.", + "reason": "The Units of Population in captivity in 1974-1977 accounts for 0.06252382767823103, smaller than all other Population Status, of the total of Units of all Population Status." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Population Status": "Wild population", + "Time Period": "total", + "Units": 7033 + } + ], + "score": 0.74, + "annotation": "The total value of Wild population has the smallest proportion.", + "reason": "The total value of Units of Wild population accounts for 0.07130595536775386, smaller than all other Population Status, of the total of Units of all Population Status." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Time Period": "1985-1988", + "Units": 1114, + "Population Status": "Wild population" + } + ], + "score": 0.73, + "annotation": "The Wild population accounts for the majority in 1985-1988.", + "reason": "The Units of Wild population in 1985-1988 accounts for 0.7476510067114094 (more than 50%) of the total of Units of all Population Status." + } + ], + "data": { + "data": [ + { + "Time Period": "1974-1977", + "Units": 2459, + "Population Status": "Wild population" + }, + { + "Time Period": "1985-1988", + "Units": 1114, + "Population Status": "Wild population" + }, + { + "Time Period": "1999-2003", + "Units": 1596, + "Population Status": "Wild population" + }, + { + "Time Period": "1999-2003", + "Units": 164, + "Population Status": "Population in captivity" + }, + { + "Time Period": "2011-2014", + "Units": 1864, + "Population Status": "Wild population" + }, + { + "Time Period": "2011-2014", + "Units": 376, + "Population Status": "Population in captivity" + } + ], + "columns": [ + { + "name": "Time Period", + "importance": "primary", + "description": "Represents ranges of years", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding population status", + "role": "y" + }, + { + "name": "Population Status", + "importance": "primary", + "description": "The population status of the data point", + "role": "group" + } + ] + } + }, + "480": { + "metadata": { + "title": "Remain Camp Gaining Ground", + "description": "British residents in favour of remaining in or leaving the EU", + "main_insight": "Support for remaining in the EU has steadily increased throughout 2017, surpassing the percentage of people who want to leave by December 2017. The percentage of people who don't know or have no answer decreased." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Nov '16", + "Score": 14.0, + "Referendum Votes": "Don't know/No answer" + } + ], + "score": 0.97, + "annotation": "The Don't know/No answer has a maximum value at Nov '16", + "reason": "The [{'Month': \"Nov '16\", 'Score': 14.0, 'Referendum Votes': \"Don't know/No answer\"}] of Don't know/No answer has a maximum value of 14.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Dec '17", + "Score": 51.0, + "Referendum Votes": "Remain" + } + ], + "score": 0.96, + "annotation": "The Remain has a maximum value at Dec '17", + "reason": "The [{'Month': \"Dec '17\", 'Score': 51.0, 'Referendum Votes': 'Remain'}] of Remain has a maximum value of 51.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Dec '17", + "Score": 41.0, + "Referendum Votes": "Leave" + } + ], + "score": 0.93, + "annotation": "The Leave has a minimum value at Dec '17", + "reason": "The [{'Month': \"Dec '17\", 'Score': 41.0, 'Referendum Votes': 'Leave'}] of Leave has a mainimum value of 41.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Nov '16", + "Score": 43.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Nov '16", + "Score": 43.0, + "Referendum Votes": "Leave" + } + ], + "score": 0.87, + "annotation": "The Remain, Leave all have the largest proportion in Nov '16.", + "reason": "The Score of Remain, Leave in Nov '16 all account for 0.43, larger than all other Referendum Votes, of the total of Score of all Referendum Votes." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Dec '16", + "Score": 45.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Dec '16", + "Score": 45.0, + "Referendum Votes": "Leave" + } + ], + "score": 0.87, + "annotation": "The Remain, Leave all have the largest proportion in Dec '16.", + "reason": "The Score of Remain, Leave in Dec '16 all account for 0.45, larger than all other Referendum Votes, of the total of Score of all Referendum Votes." + } + ], + "data": { + "data": [ + { + "Month": "Nov '16", + "Score": 43.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Nov '16", + "Score": 43.0, + "Referendum Votes": "Leave" + }, + { + "Month": "Nov '16", + "Score": 14.0, + "Referendum Votes": "Don't know/No answer" + }, + { + "Month": "Dec '16", + "Score": 45.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Dec '16", + "Score": 45.0, + "Referendum Votes": "Leave" + }, + { + "Month": "Dec '16", + "Score": 10.0, + "Referendum Votes": "Don't know/No answer" + }, + { + "Month": "Jan '17", + "Score": 43.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Jan '17", + "Score": 45.0, + "Referendum Votes": "Leave" + }, + { + "Month": "Jan '17", + "Score": 12.0, + "Referendum Votes": "Don't know/No answer" + }, + { + "Month": "Feb '17", + "Score": 44.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Feb '17", + "Score": 46.0, + "Referendum Votes": "Leave" + }, + { + "Month": "Feb '17", + "Score": 10.0, + "Referendum Votes": "Don't know/No answer" + }, + { + "Month": "Mar '17", + "Score": 44.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Mar '17", + "Score": 45.0, + "Referendum Votes": "Leave" + }, + { + "Month": "Mar '17", + "Score": 11.0, + "Referendum Votes": "Don't know/No answer" + }, + { + "Month": "Apr '17", + "Score": 44.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Apr '17", + "Score": 45.0, + "Referendum Votes": "Leave" + }, + { + "Month": "Apr '17", + "Score": 11.0, + "Referendum Votes": "Don't know/No answer" + }, + { + "Month": "May '17", + "Score": 45.0, + "Referendum Votes": "Remain" + }, + { + "Month": "May '17", + "Score": 45.0, + "Referendum Votes": "Leave" + }, + { + "Month": "May '17", + "Score": 10.0, + "Referendum Votes": "Don't know/No answer" + }, + { + "Month": "Jun '17", + "Score": 46.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Jun '17", + "Score": 44.0, + "Referendum Votes": "Leave" + }, + { + "Month": "Jun '17", + "Score": 10.0, + "Referendum Votes": "Don't know/No answer" + }, + { + "Month": "Jul '17", + "Score": 46.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Jul '17", + "Score": 44.0, + "Referendum Votes": "Leave" + }, + { + "Month": "Jul '17", + "Score": 10.0, + "Referendum Votes": "Don't know/No answer" + }, + { + "Month": "Aug '17", + "Score": 47.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Aug '17", + "Score": 43.0, + "Referendum Votes": "Leave" + }, + { + "Month": "Aug '17", + "Score": 10.0, + "Referendum Votes": "Don't know/No answer" + }, + { + "Month": "Sep '17", + "Score": 46.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Sep '17", + "Score": 44.0, + "Referendum Votes": "Leave" + }, + { + "Month": "Sep '17", + "Score": 10.0, + "Referendum Votes": "Don't know/No answer" + }, + { + "Month": "Oct '17", + "Score": 47.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Oct '17", + "Score": 43.0, + "Referendum Votes": "Leave" + }, + { + "Month": "Oct '17", + "Score": 10.0, + "Referendum Votes": "Don't know/No answer" + }, + { + "Month": "Nov '17", + "Score": 49.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Nov '17", + "Score": 42.0, + "Referendum Votes": "Leave" + }, + { + "Month": "Nov '17", + "Score": 9.0, + "Referendum Votes": "Don't know/No answer" + }, + { + "Month": "Dec '17", + "Score": 51.0, + "Referendum Votes": "Remain" + }, + { + "Month": "Dec '17", + "Score": 41.0, + "Referendum Votes": "Leave" + }, + { + "Month": "Dec '17", + "Score": 8.0, + "Referendum Votes": "Don't know/No answer" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the poll", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding referendum votes", + "role": "y" + }, + { + "name": "Referendum Votes", + "importance": "primary", + "description": "The referendum votes of the data point", + "role": "group" + } + ] + } + }, + "2156": { + "metadata": { + "title": "Some Europeans Have Never Been Outside The EU", + "description": "Share of Europeans who have travelled at least once outside the EU (2014)", + "main_insight": "There is a wide variation in the percentage of Europeans who have traveled outside the EU, with Sweden having the highest percentage (85%) and Romania the lowest (15%)." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Sweden", + "Travel_Outside_EU": 85.0 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at Sweden", + "reason": "The [{'Country': 'Sweden', 'Travel_Outside_EU': 85.0}] of has a maximum value of 85.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Romania", + "Travel_Outside_EU": 15.0 + } + ], + "score": 0.86, + "annotation": "The has a minimum value at Romania", + "reason": "The [{'Country': 'Romania', 'Travel_Outside_EU': 15.0}] of has a mainimum value of 15.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Sweden", + "Travel_Outside_EU": 85.0 + } + ], + "score": 0.54, + "annotation": "The shows a sudden jump in Travel_Outside_EU from Denmark to Sweden.", + "reason": "The Travel_Outside_EU for increases from 75.0 at Denmark to 85.0 at Sweden, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Sweden", + "Travel_Outside_EU": 85.0 + }, + { + "Country": "Denmark", + "Travel_Outside_EU": 75.0 + }, + { + "Country": "Austria", + "Travel_Outside_EU": 70.0 + }, + { + "Country": "Finland", + "Travel_Outside_EU": 68.0 + }, + { + "Country": "Ireland", + "Travel_Outside_EU": 62.0 + }, + { + "Country": "United Kingdom", + "Travel_Outside_EU": 60.0 + }, + { + "Country": "Luxembourg", + "Travel_Outside_EU": 58.0 + }, + { + "Country": "Croatia", + "Travel_Outside_EU": 57.0 + }, + { + "Country": "Slovenia", + "Travel_Outside_EU": 56.0 + }, + { + "Country": "Germany", + "Travel_Outside_EU": 56.0 + }, + { + "Country": "Netherlands", + "Travel_Outside_EU": 56.0 + }, + { + "Country": "Estonia", + "Travel_Outside_EU": 51.0 + }, + { + "Country": "Belgium", + "Travel_Outside_EU": 49.0 + }, + { + "Country": "Czech Republic", + "Travel_Outside_EU": 49.0 + }, + { + "Country": "Cyprus", + "Travel_Outside_EU": 47.0 + }, + { + "Country": "European Union", + "Travel_Outside_EU": 44.0 + }, + { + "Country": "France", + "Travel_Outside_EU": 44.0 + }, + { + "Country": "Slovakia", + "Travel_Outside_EU": 44.0 + }, + { + "Country": "Latvia", + "Travel_Outside_EU": 40.0 + }, + { + "Country": "Italy", + "Travel_Outside_EU": 37.0 + }, + { + "Country": "Malta", + "Travel_Outside_EU": 37.0 + }, + { + "Country": "Spain", + "Travel_Outside_EU": 29.0 + }, + { + "Country": "Bulgaria", + "Travel_Outside_EU": 27.0 + }, + { + "Country": "Greece", + "Travel_Outside_EU": 26.0 + }, + { + "Country": "Lithuania", + "Travel_Outside_EU": 25.0 + }, + { + "Country": "Poland", + "Travel_Outside_EU": 25.0 + }, + { + "Country": "Hungary", + "Travel_Outside_EU": 19.0 + }, + { + "Country": "Portugal", + "Travel_Outside_EU": 18.0 + }, + { + "Country": "Romania", + "Travel_Outside_EU": 15.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "EU Country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Travel_Outside_EU", + "importance": "primary", + "description": "Percentage of citizens who have traveled outside the EU", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "543": { + "metadata": { + "title": "Where Make-Up Routines Take the Longest", + "description": "Time spent on make-up routines in selected countries (in percent)", + "main_insight": "China has the highest percentage of people who spend less than 15 minutes on their make-up routine, while France has the highest percentage spending more than 60 minutes." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "China", + "Units": 75.0, + "Timeframe": "< 15 minutes" + }, + { + "Country": "China", + "Units": 16.0, + "Timeframe": "15 - 30 minutes" + } + ], + "score": 1.0, + "annotation": "The Units of < 15 minutes and 15 - 30 minutes show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between < 15 minutes and 15 - 30 minutes is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "U.S.", + "Units": 3.0, + "Timeframe": "46 - 59 minutes" + } + ], + "score": 0.97, + "annotation": "The 46 - 59 minutes shows a sudden jump in Units from China to U.S..", + "reason": "The Units for 46 - 59 minutes increases from 2.0 at China to 3.0 at U.S., indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "France", + "Units": 2.0, + "Timeframe": "> 60 minutes" + } + ], + "score": 0.97, + "annotation": "The > 60 minutes shows a sudden jump in Units from China to France.", + "reason": "The Units for > 60 minutes increases from 1.0 at China to 2.0 at France, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "UK", + "Units": 1.0, + "Timeframe": "> 60 minutes" + } + ], + "score": 0.95, + "annotation": "The > 60 minutes has the smallest proportion in UK.", + "reason": "The Units of > 60 minutes in UK accounts for 0.01, smaller than all other Timeframe, of the total of Units of all Timeframe." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Russia", + "Units": 1.0, + "Timeframe": "> 60 minutes" + } + ], + "score": 0.95, + "annotation": "The > 60 minutes has the smallest proportion in Russia.", + "reason": "The Units of > 60 minutes in Russia accounts for 0.01, smaller than all other Timeframe, of the total of Units of all Timeframe." + } + ], + "data": { + "data": [ + { + "Country": "France", + "Units": 68.0, + "Timeframe": "< 15 minutes" + }, + { + "Country": "France", + "Units": 23.0, + "Timeframe": "15 - 30 minutes" + }, + { + "Country": "France", + "Units": 5.0, + "Timeframe": "31 - 45 minutes" + }, + { + "Country": "France", + "Units": 2.0, + "Timeframe": "46 - 59 minutes" + }, + { + "Country": "France", + "Units": 2.0, + "Timeframe": "> 60 minutes" + }, + { + "Country": "Germany", + "Units": 60.0, + "Timeframe": "< 15 minutes" + }, + { + "Country": "Germany", + "Units": 29.0, + "Timeframe": "15 - 30 minutes" + }, + { + "Country": "Germany", + "Units": 7.0, + "Timeframe": "31 - 45 minutes" + }, + { + "Country": "Germany", + "Units": 2.0, + "Timeframe": "46 - 59 minutes" + }, + { + "Country": "Germany", + "Units": 1.0, + "Timeframe": "> 60 minutes" + }, + { + "Country": "UK", + "Units": 56.0, + "Timeframe": "< 15 minutes" + }, + { + "Country": "UK", + "Units": 33.0, + "Timeframe": "15 - 30 minutes" + }, + { + "Country": "UK", + "Units": 8.0, + "Timeframe": "31 - 45 minutes" + }, + { + "Country": "UK", + "Units": 2.0, + "Timeframe": "46 - 59 minutes" + }, + { + "Country": "UK", + "Units": 1.0, + "Timeframe": "> 60 minutes" + }, + { + "Country": "U.S.", + "Units": 55.0, + "Timeframe": "< 15 minutes" + }, + { + "Country": "U.S.", + "Units": 32.0, + "Timeframe": "15 - 30 minutes" + }, + { + "Country": "U.S.", + "Units": 8.0, + "Timeframe": "31 - 45 minutes" + }, + { + "Country": "U.S.", + "Units": 3.0, + "Timeframe": "46 - 59 minutes" + }, + { + "Country": "U.S.", + "Units": 2.0, + "Timeframe": "> 60 minutes" + }, + { + "Country": "South Korea", + "Units": 52.0, + "Timeframe": "< 15 minutes" + }, + { + "Country": "South Korea", + "Units": 36.0, + "Timeframe": "15 - 30 minutes" + }, + { + "Country": "South Korea", + "Units": 8.0, + "Timeframe": "31 - 45 minutes" + }, + { + "Country": "South Korea", + "Units": 2.0, + "Timeframe": "46 - 59 minutes" + }, + { + "Country": "South Korea", + "Units": 1.0, + "Timeframe": "> 60 minutes" + }, + { + "Country": "Brazil", + "Units": 45.0, + "Timeframe": "< 15 minutes" + }, + { + "Country": "Brazil", + "Units": 41.0, + "Timeframe": "15 - 30 minutes" + }, + { + "Country": "Brazil", + "Units": 9.0, + "Timeframe": "31 - 45 minutes" + }, + { + "Country": "Brazil", + "Units": 3.0, + "Timeframe": "46 - 59 minutes" + }, + { + "Country": "Brazil", + "Units": 2.0, + "Timeframe": "> 60 minutes" + }, + { + "Country": "Russia", + "Units": 42.0, + "Timeframe": "< 15 minutes" + }, + { + "Country": "Russia", + "Units": 44.0, + "Timeframe": "15 - 30 minutes" + }, + { + "Country": "Russia", + "Units": 10.0, + "Timeframe": "31 - 45 minutes" + }, + { + "Country": "Russia", + "Units": 3.0, + "Timeframe": "46 - 59 minutes" + }, + { + "Country": "Russia", + "Units": 1.0, + "Timeframe": "> 60 minutes" + }, + { + "Country": "China", + "Units": 75.0, + "Timeframe": "< 15 minutes" + }, + { + "Country": "China", + "Units": 16.0, + "Timeframe": "15 - 30 minutes" + }, + { + "Country": "China", + "Units": 6.0, + "Timeframe": "31 - 45 minutes" + }, + { + "Country": "China", + "Units": 2.0, + "Timeframe": "46 - 59 minutes" + }, + { + "Country": "China", + "Units": 1.0, + "Timeframe": "> 60 minutes" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where the survey was conducted", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding timeframe", + "role": "y" + }, + { + "name": "Timeframe", + "importance": "primary", + "description": "The timeframe of the data point", + "role": "group" + } + ] + } + }, + "839": { + "metadata": { + "title": "The 'Boris bounce'", + "description": "Voting intention in the United Kingdom in July 2019 (selected parties)", + "main_insight": "The Conservative party saw a significant increase in voting intention between July 3rd and July 24th, 2019, while the Brexit Party experienced a notable decrease." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Party": "SNP", + "Units": 5.0, + "Metric": "3 July" + }, + { + "Party": "SNP", + "Units": 5.0, + "Metric": "24 July" + } + ], + "score": 0.9, + "annotation": "The Units of 3 July and 24 July show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 3 July and 24 July is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Party": "Labour", + "Units": 28.0, + "Metric": "24 July" + } + ], + "score": 0.82, + "annotation": "The 24 July shows a sudden jump in Units from Lib Dem to Labour.", + "reason": "The Units for 24 July increases from 16.0 at Lib Dem to 28.0 at Labour, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Party": "SNP", + "Units": 5.0, + "Metric": "3 July" + } + ], + "score": 0.75, + "annotation": "The 3 July has a minimum value at SNP", + "reason": "The [{'Party': 'SNP', 'Units': 5.0, 'Metric': '3 July'}] of 3 July has a mainimum value of 5.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Party": "Brexit Party", + "Units": -7.0, + "Metric": "Change" + } + ], + "score": 0.74, + "annotation": "The Change has a minimum value at Brexit Party", + "reason": "The [{'Party': 'Brexit Party', 'Units': -7.0, 'Metric': 'Change'}] of Change has a mainimum value of -7.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Party": "Lib Dem", + "Units": 15.0, + "Metric": "3 July" + }, + { + "Party": "Brexit Party", + "Units": 22.0, + "Metric": "3 July" + } + ], + "score": 0.73, + "annotation": "The 3 July exhibits multiple sudden jumps in Units, transitioning from Green, Lib Dem to Lib Dem, Brexit Party.", + "reason": "Across these points, the Units for 3 July increases from 8.0, 15.0 at Green, Lib Dem to 15.0, 22.0 at Lib Dem, Brexit Party, suggesting significant shifts in value." + } + ], + "data": { + "data": [ + { + "Party": "Conservative", + "Units": 23.0, + "Metric": "3 July" + }, + { + "Party": "Conservative", + "Units": 30.0, + "Metric": "24 July" + }, + { + "Party": "Conservative", + "Units": 7.0, + "Metric": "Change" + }, + { + "Party": "Labour", + "Units": 25.0, + "Metric": "3 July" + }, + { + "Party": "Labour", + "Units": 28.0, + "Metric": "24 July" + }, + { + "Party": "Labour", + "Units": 3.0, + "Metric": "Change" + }, + { + "Party": "Lib Dem", + "Units": 15.0, + "Metric": "3 July" + }, + { + "Party": "Lib Dem", + "Units": 16.0, + "Metric": "24 July" + }, + { + "Party": "Lib Dem", + "Units": 1.0, + "Metric": "Change" + }, + { + "Party": "Green", + "Units": 8.0, + "Metric": "3 July" + }, + { + "Party": "Green", + "Units": 5.0, + "Metric": "24 July" + }, + { + "Party": "Green", + "Units": -3.0, + "Metric": "Change" + }, + { + "Party": "Brexit Party", + "Units": 22.0, + "Metric": "3 July" + }, + { + "Party": "Brexit Party", + "Units": 15.0, + "Metric": "24 July" + }, + { + "Party": "Brexit Party", + "Units": -7.0, + "Metric": "Change" + }, + { + "Party": "SNP", + "Units": 5.0, + "Metric": "3 July" + }, + { + "Party": "SNP", + "Units": 5.0, + "Metric": "24 July" + } + ], + "columns": [ + { + "name": "Party", + "importance": "primary", + "description": "Political party", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding metric", + "role": "y" + }, + { + "name": "Metric", + "importance": "primary", + "description": "The metric of the data point", + "role": "group" + } + ] + } + }, + "2364": { + "metadata": { + "title": "Should Society Accept Homosexuality?", + "description": "Surveys conducted in Spring 2013. Around 1,000 respondents per country, only yes and no responses taken into account.", + "main_insight": "There is a wide range of opinions on whether society should accept homosexuality, with significantly higher acceptance rates in countries like Germany, France, and the UK compared to countries like Russia and Turkey." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Turkey", + "Score": 9.0, + "Response": "Yes" + }, + { + "Country": "Turkey", + "Score": 78.0, + "Response": "No" + } + ], + "score": 1.0, + "annotation": "The Score of Yes and No show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Yes and No is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Turkey", + "Score": 9.0, + "Response": "Yes" + } + ], + "score": 0.83, + "annotation": "The Yes has a minimum value at Turkey", + "reason": "The [{'Country': 'Turkey', 'Score': 9.0, 'Response': 'Yes'}] of Yes has a mainimum value of 9.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Turkey", + "Score": 78.0, + "Response": "No" + } + ], + "score": 0.82, + "annotation": "The No has a maximum value at Turkey", + "reason": "The [{'Country': 'Turkey', 'Score': 78.0, 'Response': 'No'}] of No has a maximum value of 78.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Poland", + "Score": 46.0, + "Response": "No" + } + ], + "score": 0.79, + "annotation": "The No accounts for the majority in Poland.", + "reason": "The Score of No in Poland accounts for 0.5227272727272727 (more than 50%) of the total of Score of all Response." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Response": "Yes", + "Country": "total", + "Score": 512.0 + } + ], + "score": 0.78, + "annotation": "The total value of Yes accounts for the majority in all Response.", + "reason": "The total value of Score of Yes accounts for 0.5499462943071965 (more than 50%) of the total of Score of all Response." + } + ], + "data": { + "data": [ + { + "Country": "Germany", + "Score": 87.0, + "Response": "Yes" + }, + { + "Country": "Germany", + "Score": 11.0, + "Response": "No" + }, + { + "Country": "France", + "Score": 77.0, + "Response": "Yes" + }, + { + "Country": "France", + "Score": 22.0, + "Response": "No" + }, + { + "Country": "UK", + "Score": 76.0, + "Response": "Yes" + }, + { + "Country": "UK", + "Score": 18.0, + "Response": "No" + }, + { + "Country": "Brazil", + "Score": 60.0, + "Response": "Yes" + }, + { + "Country": "Brazil", + "Score": 36.0, + "Response": "No" + }, + { + "Country": "United States", + "Score": 60.0, + "Response": "Yes" + }, + { + "Country": "United States", + "Score": 33.0, + "Response": "No" + }, + { + "Country": "Greece", + "Score": 53.0, + "Response": "Yes" + }, + { + "Country": "Greece", + "Score": 40.0, + "Response": "No" + }, + { + "Country": "Poland", + "Score": 42.0, + "Response": "Yes" + }, + { + "Country": "Poland", + "Score": 46.0, + "Response": "No" + }, + { + "Country": "South Africa", + "Score": 32.0, + "Response": "Yes" + }, + { + "Country": "South Africa", + "Score": 61.0, + "Response": "No" + }, + { + "Country": "Russia", + "Score": 16.0, + "Response": "Yes" + }, + { + "Country": "Russia", + "Score": 74.0, + "Response": "No" + }, + { + "Country": "Turkey", + "Score": 9.0, + "Response": "Yes" + }, + { + "Country": "Turkey", + "Score": 78.0, + "Response": "No" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where the survey was conducted", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding response", + "role": "y" + }, + { + "name": "Response", + "importance": "primary", + "description": "The response of the data point", + "role": "group" + } + ] + } + }, + "1463": { + "metadata": { + "title": "One Third of Americans Believe in Aliens", + "description": "Survey responses from U.S. adults on the existence of alien spacecraft among UFO sightings (2019)", + "main_insight": "One-third of Americans believe that some UFOs are alien spacecraft, while the majority believe sightings can be explained by human activity or natural phenomena." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Response": "All sightings can be explained by human activity/natural phenomenon", + "Percentage": 60.0 + } + ], + "score": 0.84, + "annotation": "The shows a sudden jump in Percentage from Some UFOs have been alien spacecraft to All sightings can be explained by human activity/natural phenomenon.", + "reason": "The Percentage for increases from 33.0 at Some UFOs have been alien spacecraft to 60.0 at All sightings can be explained by human activity/natural phenomenon, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Response": "All sightings can be explained by human activity/natural phenomenon", + "Percentage": 60.0 + } + ], + "score": 0.6, + "annotation": "The has a maximum value at All sightings can be explained by human activity/natural phenomenon", + "reason": "The [{'Response': 'All sightings can be explained by human activity/natural phenomenon', 'Percentage': 60.0}] of has a maximum value of 60.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Response": "No opinion", + "Percentage": 7.0 + } + ], + "score": 0.6, + "annotation": "The has a minimum value at No opinion", + "reason": "The [{'Response': 'No opinion', 'Percentage': 7.0}] of has a mainimum value of 7.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Response": "Some UFOs have been alien spacecraft", + "Percentage": 33.0 + }, + { + "Response": "All sightings can be explained by human activity/natural phenomenon", + "Percentage": 60.0 + }, + { + "Response": "No opinion", + "Percentage": 7.0 + } + ], + "columns": [ + { + "name": "Response", + "importance": "primary", + "description": "Survey response categories", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents for each category", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2438": { + "metadata": { + "title": "Most Girls Get Unsolicited Messages on Social Media", + "description": "Share of adolescent female users who have been contacted by strangers in ways that made them uncomfortable", + "main_insight": "A significant percentage of adolescent girls receive unsolicited messages on social media platforms, with Instagram and Snapchat having the highest percentages." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Social Media Platform": "TikTok", + "Unsolicited Contact Percentage": 0.46 + } + ], + "score": 0.87, + "annotation": "The shows a sudden jump in Unsolicited Contact Percentage from YouTube to TikTok.", + "reason": "The Unsolicited Contact Percentage for increases from 0.3 at YouTube to 0.46 at TikTok, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Social Media Platform": "YouTube", + "Unsolicited Contact Percentage": 0.3 + } + ], + "score": 0.83, + "annotation": "The has a minimum value at YouTube", + "reason": "The [{'Social Media Platform': 'YouTube', 'Unsolicited Contact Percentage': 0.3}] of has a mainimum value of 0.3, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Social Media Platform": "Instagram", + "Unsolicited Contact Percentage": 0.58 + } + ], + "score": 0.55, + "annotation": "The has a maximum value at Instagram", + "reason": "The [{'Social Media Platform': 'Instagram', 'Unsolicited Contact Percentage': 0.58}] of has a maximum value of 0.58, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Social Media Platform": "Instagram", + "Unsolicited Contact Percentage": 0.58 + }, + { + "Social Media Platform": "Snapchat", + "Unsolicited Contact Percentage": 0.57 + }, + { + "Social Media Platform": "Messaging apps", + "Unsolicited Contact Percentage": 0.48 + }, + { + "Social Media Platform": "TikTok", + "Unsolicited Contact Percentage": 0.46 + }, + { + "Social Media Platform": "YouTube", + "Unsolicited Contact Percentage": 0.3 + } + ], + "columns": [ + { + "name": "Social Media Platform", + "importance": "primary", + "description": "The social media or messaging platform", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Unsolicited Contact Percentage", + "importance": "primary", + "description": "Percentage of adolescent female users (11-15 y/o) who received uncomfortable contact from strangers", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "231": { + "metadata": { + "title": "Where Does Iran Ship its Crude to?", + "description": "Iranian crude oil exports by destination (1,000 barrels per day)", + "main_insight": "Iran's crude oil exports increased significantly between 2015 and 2016, particularly to Europe, after international sanctions were lifted. Asia Pacific remains the largest importer of Iranian crude oil." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2017, + "Units": 1417.9, + "Region": "Asia Pacific" + }, + { + "Year": 2017, + "Units": 674.3, + "Region": "Europe" + } + ], + "score": 0.43, + "annotation": "The Units of Asia Pacific and Europe show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Asia Pacific and Europe is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2012, + "Units": 1863.3, + "Region": "Asia Pacific" + }, + { + "Year": 2012, + "Units": 238.7, + "Region": "Europe" + }, + { + "Year": 2013, + "Units": 1069.7, + "Region": "Asia Pacific" + }, + { + "Year": 2013, + "Units": 114.3, + "Region": "Europe" + }, + { + "Year": 2013, + "Units": 31.4, + "Region": "Africa" + }, + { + "Year": 2014, + "Units": 1021.6, + "Region": "Asia Pacific" + }, + { + "Year": 2014, + "Units": 58.1, + "Region": "Europe" + }, + { + "Year": 2014, + "Units": 29.5, + "Region": "Africa" + }, + { + "Year": 2015, + "Units": 1013.9, + "Region": "Asia Pacific" + }, + { + "Year": 2015, + "Units": 37.9, + "Region": "Europe" + }, + { + "Year": 2015, + "Units": 29.3, + "Region": "Africa" + }, + { + "Year": 2016, + "Units": 1311.8, + "Region": "Asia Pacific" + }, + { + "Year": 2016, + "Units": 578.4, + "Region": "Europe" + }, + { + "Year": 2016, + "Units": 31.5, + "Region": "Africa" + }, + { + "Year": 2017, + "Units": 1417.9, + "Region": "Asia Pacific" + }, + { + "Year": 2017, + "Units": 674.3, + "Region": "Europe" + }, + { + "Year": 2017, + "Units": 32.8, + "Region": "Africa" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of crude oil export", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding region", + "role": "y" + }, + { + "name": "Region", + "importance": "primary", + "description": "The region of the data point", + "role": "group" + } + ] + } + }, + "1602": { + "metadata": { + "title": "Cybercrime: Critical Infrastructure Is Top Target", + "description": "Number of worldwide political cyber attacks aimed at the following sectors, by reported period", + "main_insight": "Critical infrastructure is the most targeted sector for cyberattacks, with a significantly higher number of attacks in 2023 compared to the other sectors. Although, there are far fewer attacks in 2024 YTD across all sectors." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Sector": "Other", + "Units": 63, + "Year": "2023" + }, + { + "Sector": "Other", + "Units": 10, + "Year": "2024 YTD" + } + ], + "score": 0.98, + "annotation": "The Units of 2023 and 2024 YTD show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2023 and 2024 YTD is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Sector": "State institutions/political systems", + "Units": 82, + "Year": "2024 YTD" + } + ], + "score": 0.93, + "annotation": "The 2024 YTD shows a sudden jump in Units from Education to State institutions/political systems.", + "reason": "The Units for 2024 YTD increases from 19 at Education to 82 at State institutions/political systems, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Sector": "State institutions/political systems", + "Units": 376, + "Year": "2023" + } + ], + "score": 0.86, + "annotation": "The 2023 shows a sudden jump in Units from Corporate targets to State institutions/political systems.", + "reason": "The Units for 2023 increases from 113 at Corporate targets to 376 at State institutions/political systems, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Sector": "Critical infrastructure", + "Units": 500, + "Year": "2023" + } + ], + "score": 0.82, + "annotation": "The 2023 has a maximum value at Critical infrastructure", + "reason": "The [{'Sector': 'Critical infrastructure', 'Units': 500, 'Year': '2023'}] of 2023 has a maximum value of 500, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Sector": "Critical infrastructure", + "Units": 89, + "Year": "2024 YTD" + } + ], + "score": 0.76, + "annotation": "The 2024 YTD has a maximum value at Critical infrastructure", + "reason": "The [{'Sector': 'Critical infrastructure', 'Units': 89, 'Year': '2024 YTD'}] of 2024 YTD has a maximum value of 89, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Sector": "Critical infrastructure", + "Units": 500, + "Year": "2023" + }, + { + "Sector": "Critical infrastructure", + "Units": 89, + "Year": "2024 YTD" + }, + { + "Sector": "State institutions/political systems", + "Units": 376, + "Year": "2023" + }, + { + "Sector": "State institutions/political systems", + "Units": 82, + "Year": "2024 YTD" + }, + { + "Sector": "Education", + "Units": 83, + "Year": "2023" + }, + { + "Sector": "Education", + "Units": 19, + "Year": "2024 YTD" + }, + { + "Sector": "Corporate targets", + "Units": 113, + "Year": "2023" + }, + { + "Sector": "Corporate targets", + "Units": 10, + "Year": "2024 YTD" + }, + { + "Sector": "Media", + "Units": 29, + "Year": "2023" + }, + { + "Sector": "Media", + "Units": 5, + "Year": "2024 YTD" + }, + { + "Sector": "Other", + "Units": 63, + "Year": "2023" + }, + { + "Sector": "Other", + "Units": 10, + "Year": "2024 YTD" + } + ], + "columns": [ + { + "name": "Sector", + "importance": "primary", + "description": "Target sector of cyber attacks", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "1586": { + "metadata": { + "title": "NBA Fans Pick Jordan Over James in GOAT Debate", + "description": "% of respondents picking Michael Jordan/LeBron James in the following categories", + "main_insight": "In a poll of 600 NBA fans, Michael Jordan was favored over LeBron James in most categories related to the 'Greatest of All Time' debate." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Category": "Better defensive player", + "Score": 59.0, + "Greatest Players": "Michael Jordan" + }, + { + "Category": "Better defensive player", + "Score": 41.0, + "Greatest Players": "LeBron James" + } + ], + "score": 1.0, + "annotation": "The Score of Michael Jordan and LeBron James show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Michael Jordan and LeBron James is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Category": "Better defensive player", + "Score": 59.0, + "Greatest Players": "Michael Jordan" + } + ], + "score": 0.79, + "annotation": "The Michael Jordan has a minimum value at Better defensive player", + "reason": "The [{'Category': 'Better defensive player', 'Score': 59.0, 'Greatest Players': 'Michael Jordan'}] of Michael Jordan has a mainimum value of 59.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Better defensive player", + "Score": 41.0, + "Greatest Players": "LeBron James" + } + ], + "score": 0.79, + "annotation": "The LeBron James has a maximum value at Better defensive player", + "reason": "The [{'Category': 'Better defensive player', 'Score': 41.0, 'Greatest Players': 'LeBron James'}] of LeBron James has a maximum value of 41.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Category": "Better defensive player", + "Score": 59.0, + "Greatest Players": "Michael Jordan" + } + ], + "score": 0.77, + "annotation": "The Michael Jordan accounts for the majority in Better defensive player.", + "reason": "The Score of Michael Jordan in Better defensive player accounts for 0.59 (more than 50%) of the total of Score of all Greatest Players." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Pick for game-winning shot", + "Score": 76.0, + "Greatest Players": "Michael Jordan" + } + ], + "score": 0.76, + "annotation": "The Michael Jordan has a maximum value at Pick for game-winning shot", + "reason": "The [{'Category': 'Pick for game-winning shot', 'Score': 76.0, 'Greatest Players': 'Michael Jordan'}] of Michael Jordan has a maximum value of 76.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Category": "Pick for game-winning shot", + "Score": 76.0, + "Greatest Players": "Michael Jordan" + }, + { + "Category": "Pick for game-winning shot", + "Score": 24.0, + "Greatest Players": "LeBron James" + }, + { + "Category": "Spectacular to watch", + "Score": 74.0, + "Greatest Players": "Michael Jordan" + }, + { + "Category": "Spectacular to watch", + "Score": 26.0, + "Greatest Players": "LeBron James" + }, + { + "Category": "Better overall player", + "Score": 73.0, + "Greatest Players": "Michael Jordan" + }, + { + "Category": "Better overall player", + "Score": 27.0, + "Greatest Players": "LeBron James" + }, + { + "Category": "Win one-on-one to 21", + "Score": 67.0, + "Greatest Players": "Michael Jordan" + }, + { + "Category": "Win one-on-one to 21", + "Score": 33.0, + "Greatest Players": "LeBron James" + }, + { + "Category": "Better offensive player", + "Score": 65.0, + "Greatest Players": "Michael Jordan" + }, + { + "Category": "Better offensive player", + "Score": 35.0, + "Greatest Players": "LeBron James" + }, + { + "Category": "Have a drink with", + "Score": 65.0, + "Greatest Players": "Michael Jordan" + }, + { + "Category": "Have a drink with", + "Score": 35.0, + "Greatest Players": "LeBron James" + }, + { + "Category": "Choose as teammate", + "Score": 63.0, + "Greatest Players": "Michael Jordan" + }, + { + "Category": "Choose as teammate", + "Score": 37.0, + "Greatest Players": "LeBron James" + }, + { + "Category": "Better defensive player", + "Score": 59.0, + "Greatest Players": "Michael Jordan" + }, + { + "Category": "Better defensive player", + "Score": 41.0, + "Greatest Players": "LeBron James" + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "The category being polled", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding greatest players", + "role": "y" + }, + { + "name": "Greatest Players", + "importance": "primary", + "description": "The greatest players of the data point", + "role": "group" + } + ] + } + }, + "347": { + "metadata": { + "title": "Bankruptcy Surging Among Older Americans", + "description": "Percentage change in bankruptcy filings from 1991-2016 by age group", + "main_insight": "Bankruptcy filings have significantly increased among older Americans (55+) between 1991 and 2016, while decreasing for younger age groups." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Age Group": "65-74", + "Percentage Change": 2.04 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at 65-74", + "reason": "The [{'Age Group': '65-74', 'Percentage Change': 2.04}] of has a maximum value of 2.04, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Age Group": "65-74", + "Percentage Change": 2.04 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Percentage Change from 55-64 to 65-74.", + "reason": "The Percentage Change for increases from 0.66 at 55-64 to 2.04 at 65-74, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Age Group": "18-24", + "Percentage Change": -0.77 + } + ], + "score": 0.53, + "annotation": "The has a minimum value at 18-24", + "reason": "The [{'Age Group': '18-24', 'Percentage Change': -0.77}] of has a mainimum value of -0.77, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Age Group": "18-24", + "Percentage Change": -0.77 + }, + { + "Age Group": "25-34", + "Percentage Change": -0.64 + }, + { + "Age Group": "35-44", + "Percentage Change": -0.4 + }, + { + "Age Group": "45-54", + "Percentage Change": -0.02 + }, + { + "Age Group": "55-64", + "Percentage Change": 0.66 + }, + { + "Age Group": "65-74", + "Percentage Change": 2.04 + } + ], + "columns": [ + { + "name": "Age Group", + "importance": "primary", + "description": "Age ranges of individuals", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage Change", + "importance": "primary", + "description": "Percentage change in bankruptcy filings from 1991-2016", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1990": { + "metadata": { + "title": "Where Renewable Energy Is Growing (And Where It Is Stalling)", + "description": "Total renewable energy capacity per region 2013-2021 (in megawatts)", + "main_insight": "Global renewable energy capacity has significantly increased from 2013 to 2021, with China experiencing the largest growth, followed by Other Asia & Oceania, and Europe." + }, + "chart_type": "Stacked Area Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Dollars": 1230000, + "Region": "China" + }, + { + "Year": 2021, + "Dollars": 290000, + "Region": "South/Central American & Caribbean" + } + ], + "score": 1.0, + "annotation": "The Dollars of China and South/Central American & Caribbean show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between China and South/Central American & Caribbean is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Dollars": 650000, + "Region": "Other Asia & Oceania" + }, + { + "Year": 2021, + "Dollars": 670000, + "Region": "Europe" + } + ], + "score": 1.0, + "annotation": "The Dollars of Other Asia & Oceania and Europe show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between Other Asia & Oceania and Europe is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Dollars": 670000, + "Region": "Europe" + }, + { + "Year": 2021, + "Dollars": 470000, + "Region": "North America" + } + ], + "score": 1.0, + "annotation": "The Dollars of Europe and North America show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between Europe and North America is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Dollars": 670000, + "Region": "Europe" + }, + { + "Year": 2021, + "Dollars": 110000, + "Region": "Middle East & Africa" + } + ], + "score": 0.99, + "annotation": "The Dollars of Europe and Middle East & Africa show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between Europe and Middle East & Africa is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2021, + "Dollars": 1230000, + "Region": "China" + }, + { + "Year": 2021, + "Dollars": 470000, + "Region": "North America" + } + ], + "score": 0.99, + "annotation": "The Dollars of China and North America show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between China and North America is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2013, + "Dollars": 380000, + "Region": "China" + }, + { + "Year": 2013, + "Dollars": 280000, + "Region": "Other Asia & Oceania" + }, + { + "Year": 2013, + "Dollars": 420000, + "Region": "Europe" + }, + { + "Year": 2013, + "Dollars": 260000, + "Region": "North America" + }, + { + "Year": 2013, + "Dollars": 20000, + "Region": "Middle East & Africa" + }, + { + "Year": 2013, + "Dollars": 180000, + "Region": "South/Central American & Caribbean" + }, + { + "Year": 2014, + "Dollars": 440000, + "Region": "China" + }, + { + "Year": 2014, + "Dollars": 300000, + "Region": "Other Asia & Oceania" + }, + { + "Year": 2014, + "Dollars": 450000, + "Region": "Europe" + }, + { + "Year": 2014, + "Dollars": 280000, + "Region": "North America" + }, + { + "Year": 2014, + "Dollars": 30000, + "Region": "Middle East & Africa" + }, + { + "Year": 2014, + "Dollars": 190000, + "Region": "South/Central American & Caribbean" + }, + { + "Year": 2015, + "Dollars": 510000, + "Region": "China" + }, + { + "Year": 2015, + "Dollars": 340000, + "Region": "Other Asia & Oceania" + }, + { + "Year": 2015, + "Dollars": 470000, + "Region": "Europe" + }, + { + "Year": 2015, + "Dollars": 310000, + "Region": "North America" + }, + { + "Year": 2015, + "Dollars": 40000, + "Region": "Middle East & Africa" + }, + { + "Year": 2015, + "Dollars": 200000, + "Region": "South/Central American & Caribbean" + }, + { + "Year": 2016, + "Dollars": 600000, + "Region": "China" + }, + { + "Year": 2016, + "Dollars": 390000, + "Region": "Other Asia & Oceania" + }, + { + "Year": 2016, + "Dollars": 490000, + "Region": "Europe" + }, + { + "Year": 2016, + "Dollars": 330000, + "Region": "North America" + }, + { + "Year": 2016, + "Dollars": 40000, + "Region": "Middle East & Africa" + }, + { + "Year": 2016, + "Dollars": 210000, + "Region": "South/Central American & Caribbean" + }, + { + "Year": 2017, + "Dollars": 690000, + "Region": "China" + }, + { + "Year": 2017, + "Dollars": 430000, + "Region": "Other Asia & Oceania" + }, + { + "Year": 2017, + "Dollars": 520000, + "Region": "Europe" + }, + { + "Year": 2017, + "Dollars": 350000, + "Region": "North America" + }, + { + "Year": 2017, + "Dollars": 50000, + "Region": "Middle East & Africa" + }, + { + "Year": 2017, + "Dollars": 220000, + "Region": "South/Central American & Caribbean" + }, + { + "Year": 2018, + "Dollars": 780000, + "Region": "China" + }, + { + "Year": 2018, + "Dollars": 480000, + "Region": "Other Asia & Oceania" + }, + { + "Year": 2018, + "Dollars": 550000, + "Region": "Europe" + }, + { + "Year": 2018, + "Dollars": 370000, + "Region": "North America" + }, + { + "Year": 2018, + "Dollars": 60000, + "Region": "Middle East & Africa" + }, + { + "Year": 2018, + "Dollars": 230000, + "Region": "South/Central American & Caribbean" + }, + { + "Year": 2019, + "Dollars": 840000, + "Region": "China" + }, + { + "Year": 2019, + "Dollars": 530000, + "Region": "Other Asia & Oceania" + }, + { + "Year": 2019, + "Dollars": 580000, + "Region": "Europe" + }, + { + "Year": 2019, + "Dollars": 390000, + "Region": "North America" + }, + { + "Year": 2019, + "Dollars": 80000, + "Region": "Middle East & Africa" + }, + { + "Year": 2019, + "Dollars": 240000, + "Region": "South/Central American & Caribbean" + }, + { + "Year": 2020, + "Dollars": 940000, + "Region": "China" + }, + { + "Year": 2020, + "Dollars": 600000, + "Region": "Other Asia & Oceania" + }, + { + "Year": 2020, + "Dollars": 620000, + "Region": "Europe" + }, + { + "Year": 2020, + "Dollars": 420000, + "Region": "North America" + }, + { + "Year": 2020, + "Dollars": 90000, + "Region": "Middle East & Africa" + }, + { + "Year": 2020, + "Dollars": 250000, + "Region": "South/Central American & Caribbean" + }, + { + "Year": 2021, + "Dollars": 1230000, + "Region": "China" + }, + { + "Year": 2021, + "Dollars": 650000, + "Region": "Other Asia & Oceania" + }, + { + "Year": 2021, + "Dollars": 670000, + "Region": "Europe" + }, + { + "Year": 2021, + "Dollars": 470000, + "Region": "North America" + }, + { + "Year": 2021, + "Dollars": 110000, + "Region": "Middle East & Africa" + }, + { + "Year": 2021, + "Dollars": 290000, + "Region": "South/Central American & Caribbean" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 2013-2021", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding region", + "role": "y" + }, + { + "name": "Region", + "importance": "primary", + "description": "The region of the data point", + "role": "group" + } + ] + } + }, + "1998": { + "metadata": { + "title": "U.S. Oil Production Heading for New Highs", + "description": "Weekly U.S. field production of crude oil (thousand barrels per day)", + "main_insight": "U.S. oil production has significantly increased since the early 2010s, reaching levels near those seen in the early 1980s, almost reaching new highs." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1983, + "Oil Production": 8634.0 + }, + { + "Year": 1990, + "Oil Production": 7500.0 + }, + { + "Year": 2000, + "Oil Production": 5800.0 + }, + { + "Year": 2010, + "Oil Production": 5500.0 + }, + { + "Year": 2017, + "Oil Production": 9789.0 + }, + { + "Year": 2017, + "Oil Production": 9782.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of oil production", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Oil Production", + "importance": "primary", + "description": "Weekly U.S. field production of crude oil in thousand barrels per day", + "unit": "thousand barrels per day", + "data_type": "number", + "role": "y" + } + ] + } + }, + "267": { + "metadata": { + "title": "U.S. Video Game Sales Shine Amid Pandemic", + "description": "Video game industry sales in the United States*", + "main_insight": "Video game sales in the U.S. significantly increased in 2020, during the pandemic, compared to 2018 and 2019, across all months shown." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "June", + "Ratio": 0.89, + "Year": "2019" + }, + { + "Month": "June", + "Ratio": 1.04, + "Year": "2018" + } + ], + "score": 0.96, + "annotation": "The Ratio of 2019 and 2018 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between 2019 and 2018 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "June", + "Ratio": 1.18, + "Year": "2020" + } + ], + "score": 0.89, + "annotation": "The 2020 has the largest proportion in June.", + "reason": "The Ratio of 2020 in June accounts for 0.37942122186495175, larger than all other Year, of the total of Ratio of all Year." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "March", + "Ratio": 1.6, + "Year": "2020" + } + ], + "score": 0.88, + "annotation": "The 2020 has the largest proportion in March.", + "reason": "The Ratio of 2020 in March accounts for 0.3931203931203931, larger than all other Year, of the total of Ratio of all Year." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "2020", + "Month": "total", + "Ratio": 5.23 + } + ], + "score": 0.88, + "annotation": "The total value of 2020 has the largest proportion.", + "reason": "The total value of Ratio of 2020 accounts for 0.4147501982553529, larger than all other Year, of the total of Ratio of all Year." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "May", + "Ratio": 0.98, + "Year": "2020" + } + ], + "score": 0.88, + "annotation": "The 2020 has the largest proportion in May.", + "reason": "The Ratio of 2020 in May accounts for 0.4242424242424242, larger than all other Year, of the total of Ratio of all Year." + } + ], + "data": { + "data": [ + { + "Month": "March", + "Ratio": 1.6, + "Year": "2020" + }, + { + "Month": "March", + "Ratio": 1.16, + "Year": "2019" + }, + { + "Month": "March", + "Ratio": 1.31, + "Year": "2018" + }, + { + "Month": "April", + "Ratio": 1.47, + "Year": "2020" + }, + { + "Month": "April", + "Ratio": 0.84, + "Year": "2019" + }, + { + "Month": "April", + "Ratio": 0.81, + "Year": "2018" + }, + { + "Month": "May", + "Ratio": 0.98, + "Year": "2020" + }, + { + "Month": "May", + "Ratio": 0.62, + "Year": "2019" + }, + { + "Month": "May", + "Ratio": 0.71, + "Year": "2018" + }, + { + "Month": "June", + "Ratio": 1.18, + "Year": "2020" + }, + { + "Month": "June", + "Ratio": 0.89, + "Year": "2019" + }, + { + "Month": "June", + "Ratio": 1.04, + "Year": "2018" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "556": { + "metadata": { + "title": "IMPACT OF BRANDING, LOYALTY PROGRAMS, AND HOTEL STARS ON TRAVELERS' CHOICES", + "description": "Factors seen as important when selecting a travel destination, by country of origin", + "main_insight": "Travelers from different countries prioritize different factors when selecting travel destinations, with hotel star ratings, branding, and loyalty programs all being considered. Generally, the importance of all three factors decreases in the West when contrasted with China and the UAE" + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Factor": "Loyalty program", + "China": 0.9 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in China from Hotel star ratings to Loyalty program.", + "reason": "The China for increases from 0.89 at Hotel star ratings to 0.9 at Loyalty program, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Factor": "Hotel star ratings", + "China": 0.89 + } + ], + "score": 0.67, + "annotation": "The has a minimum value at Hotel star ratings", + "reason": "The [{'Factor': 'Hotel star ratings', 'China': 0.89}] of has a mainimum value of 0.89, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Factor": "Loyalty program", + "China": 0.9 + }, + { + "Factor": "Hotel branding", + "China": 0.9 + } + ], + "score": 0.39, + "annotation": "The has maximum values at Loyalty program, Hotel branding", + "reason": "The [{'Factor': 'Loyalty program', 'China': 0.9}, {'Factor': 'Hotel branding', 'China': 0.9}] of have maximum values of 0.9, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Factor": "Loyalty program", + "China": 0.9 + }, + { + "Factor": "Hotel branding", + "China": 0.9 + }, + { + "Factor": "Hotel star ratings", + "China": 0.89 + } + ], + "columns": [ + { + "name": "Factor", + "importance": "primary", + "description": "Factor considered important when selecting a travel destination", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "China", + "importance": "primary", + "description": "Percentage of respondents from China who consider the factor important", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "525": { + "metadata": { + "title": "British public critical of half-mast Saudi flag", + "description": "Should the government have flown the Saudi flag at half-mast?", + "main_insight": "A plurality of the British public (48%) believe the government should not have flown the Saudi flag at half-mast, while 29% believe they should have and 23% don't know." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Response": "Should not have", + "Percentage": 48.0 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in Percentage from Should have to Should not have.", + "reason": "The Percentage for increases from 29.0 at Should have to 48.0 at Should not have, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Response": "Should not have", + "Percentage": 48.0 + } + ], + "score": 0.66, + "annotation": "The has a maximum value at Should not have", + "reason": "The [{'Response': 'Should not have', 'Percentage': 48.0}] of has a maximum value of 48.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Response": "Don't know", + "Percentage": 23.0 + } + ], + "score": 0.5, + "annotation": "The has a minimum value at Don't know", + "reason": "The [{'Response': \"Don't know\", 'Percentage': 23.0}] of has a mainimum value of 23.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Response": "Should not have", + "Percentage": 48.0 + }, + { + "Response": "Should have", + "Percentage": 29.0 + }, + { + "Response": "Don't know", + "Percentage": 23.0 + } + ], + "columns": [ + { + "name": "Response", + "importance": "primary", + "description": "The response to the question", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents who selected the given response", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "903": { + "metadata": { + "title": "Congress's Alma Mater", + "description": "Colleges/universities where most members of the 117th U.S. Congress received their first degree", + "main_insight": "Harvard University is the most common alma mater for members of the 117th U.S. Congress, with a significantly higher number of Representatives than Senators." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "University": "University of California", + "Score": 17, + "Legislators": "Representatives" + } + ], + "score": 0.94, + "annotation": "The Representatives has a maximum value at University of California", + "reason": "The [{'University': 'University of California', 'Score': 17, 'Legislators': 'Representatives'}] of Representatives has a maximum value of 17, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "University": "Mizzou (University of Missouri)", + "Score": 5, + "Legislators": "Representatives" + } + ], + "score": 0.78, + "annotation": "The Representatives accounts for the majority in Mizzou (University of Missouri).", + "reason": "The Score of Representatives in Mizzou (University of Missouri) accounts for 0.5555555555555556 (more than 50%) of the total of Score of all Legislators." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "University": "Princeton University", + "Score": 1, + "Legislators": "Senators" + }, + { + "University": "University of Maryland", + "Score": 1, + "Legislators": "Senators" + } + ], + "score": 0.77, + "annotation": "The Senators has minimum values at Princeton University, University of Maryland", + "reason": "The [{'University': 'Princeton University', 'Score': 1, 'Legislators': 'Senators'}, {'University': 'University of Maryland', 'Score': 1, 'Legislators': 'Senators'}] of Senators have minimum values of 1, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "University": "Yale University", + "Score": 3, + "Legislators": "Senators" + }, + { + "University": "Harvard University", + "Score": 6, + "Legislators": "Senators" + } + ], + "score": 0.77, + "annotation": "The Senators exhibits multiple sudden jumps in Score, transitioning from University of Maryland, Dartmouth to Yale University, Harvard University.", + "reason": "Across these points, the Score for Senators increases from 1, 4 at University of Maryland, Dartmouth to 3, 6 at Yale University, Harvard University, suggesting significant shifts in value." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "University": "The California State University", + "Score": 7, + "Legislators": "Representatives" + } + ], + "score": 0.76, + "annotation": "The Representatives accounts for the majority in The California State University.", + "reason": "The Score of Representatives in The California State University accounts for 0.6363636363636364 (more than 50%) of the total of Score of all Legislators." + } + ], + "data": { + "data": [ + { + "University": "Harvard University", + "Score": 14, + "Legislators": "Representatives" + }, + { + "University": "Harvard University", + "Score": 6, + "Legislators": "Senators" + }, + { + "University": "University of California", + "Score": 17, + "Legislators": "Representatives" + }, + { + "University": "Stanford University", + "Score": 8, + "Legislators": "Representatives" + }, + { + "University": "Stanford University", + "Score": 6, + "Legislators": "Senators" + }, + { + "University": "Georgetown University", + "Score": 6, + "Legislators": "Representatives" + }, + { + "University": "Georgetown University", + "Score": 4, + "Legislators": "Senators" + }, + { + "University": "University of Wisconsin", + "Score": 10, + "Legislators": "Representatives" + }, + { + "University": "Yale University", + "Score": 5, + "Legislators": "Representatives" + }, + { + "University": "Yale University", + "Score": 3, + "Legislators": "Senators" + }, + { + "University": "Mizzou (University of Missouri)", + "Score": 5, + "Legislators": "Representatives" + }, + { + "University": "Mizzou (University of Missouri)", + "Score": 3, + "Legislators": "Senators" + }, + { + "University": "The California State University", + "Score": 7, + "Legislators": "Representatives" + }, + { + "University": "Princeton University", + "Score": 6, + "Legislators": "Representatives" + }, + { + "University": "Princeton University", + "Score": 1, + "Legislators": "Senators" + }, + { + "University": "BYU (Brigham Young University)", + "Score": 2, + "Legislators": "Representatives" + }, + { + "University": "BYU (Brigham Young University)", + "Score": 4, + "Legislators": "Senators" + }, + { + "University": "Dartmouth", + "Score": 2, + "Legislators": "Representatives" + }, + { + "University": "Dartmouth", + "Score": 4, + "Legislators": "Senators" + }, + { + "University": "University of Maryland", + "Score": 5, + "Legislators": "Representatives" + }, + { + "University": "University of Maryland", + "Score": 1, + "Legislators": "Senators" + } + ], + "columns": [ + { + "name": "University", + "importance": "primary", + "description": "Name of the college or university", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding legislators", + "role": "y" + }, + { + "name": "Legislators", + "importance": "primary", + "description": "The legislators of the data point", + "role": "group" + } + ] + } + }, + "1545": { + "metadata": { + "title": "Over 85's with high-level care needs to double in 20 years", + "description": "Projected number of people aged 85 and over with high-dependency care needs in the UK", + "main_insight": "The number of people aged 85 and over with high-dependency care needs in the UK is projected to nearly double between 2015 and 2035, increasing from 233,000 to 446,000." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2015, + "People over 85 with high-level care needs": 233 + }, + { + "Year": 2025, + "People over 85 with high-level care needs": 309 + }, + { + "Year": 2035, + "People over 85 with high-level care needs": 446 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of projection", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "People over 85 with high-level care needs", + "importance": "primary", + "description": "Projected number of people aged 85 and over with high-dependency care needs", + "unit": "thousands", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1027": { + "metadata": { + "title": "Russia & Iran Are Facebook's Top Sources Of Disinformation", + "description": "Coordinated Inauthentic Behavior networks removed by Facebook by country of origin (2017-2020)", + "main_insight": "Russia and Iran are the top sources of disinformation on Facebook, with 27 and 23 networks removed, respectively, between 2017 and 2020. Other countries like Myanmar, the United States, and Ukraine had significantly fewer networks removed." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Iran", + "Networks Removed": 23 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in Networks Removed from United States to Iran.", + "reason": "The Networks Removed for increases from 9 at United States to 23 at Iran, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Russia", + "Networks Removed": 27 + } + ], + "score": 0.73, + "annotation": "The has a maximum value at Russia", + "reason": "The [{'Country': 'Russia', 'Networks Removed': 27}] of has a maximum value of 27, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Ukraine", + "Networks Removed": 8 + } + ], + "score": 0.5, + "annotation": "The has a minimum value at Ukraine", + "reason": "The [{'Country': 'Ukraine', 'Networks Removed': 8}] of has a mainimum value of 8, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Russia", + "Networks Removed": 27 + }, + { + "Country": "Iran", + "Networks Removed": 23 + }, + { + "Country": "Myanmar", + "Networks Removed": 9 + }, + { + "Country": "United States", + "Networks Removed": 9 + }, + { + "Country": "Ukraine", + "Networks Removed": 8 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of origin of the disinformation network", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Networks Removed", + "importance": "primary", + "description": "Number of Coordinated Inauthentic Behavior networks removed by Facebook", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "664": { + "metadata": { + "title": "Where Food Preparation Leads To Food Poisoning", + "description": "Number of illnesses from foodborne disease outbreaks in selected U.S. locations. Reported number of cases in 2013", + "main_insight": "Sit-down restaurants and catering/banquet facilities are the locations with the highest number of food poisoning illnesses, significantly exceeding other locations." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Location": "Sit-down restaurant", + "Illnesses": 4494 + } + ], + "score": 0.98, + "annotation": "The has a maximum value at Sit-down restaurant", + "reason": "The [{'Location': 'Sit-down restaurant', 'Illnesses': 4494}] of has a maximum value of 4494, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Location": "Sit-down restaurant", + "Illnesses": 4494 + } + ], + "score": 0.81, + "annotation": "The shows a sudden jump in Illnesses from Catering/banquet facility to Sit-down restaurant.", + "reason": "The Illnesses for increases from 2435 at Catering/banquet facility to 4494 at Sit-down restaurant, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Location": "Picnic", + "Illnesses": 9 + } + ], + "score": 0.41, + "annotation": "The has a minimum value at Picnic", + "reason": "The [{'Location': 'Picnic', 'Illnesses': 9}] of has a mainimum value of 9, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Location": "Sit-down restaurant", + "Illnesses": 4494 + }, + { + "Location": "Catering/banquet facility", + "Illnesses": 2435 + }, + { + "Location": "Private home", + "Illnesses": 1078 + }, + { + "Location": "Fast-food", + "Illnesses": 611 + }, + { + "Location": "School", + "Illnesses": 362 + }, + { + "Location": "Place of worship", + "Illnesses": 361 + }, + { + "Location": "Prison", + "Illnesses": 311 + }, + { + "Location": "Nursing home", + "Illnesses": 223 + }, + { + "Location": "Workplace cafeteria", + "Illnesses": 116 + }, + { + "Location": "Grocery store", + "Illnesses": 86 + }, + { + "Location": "Hospital", + "Illnesses": 33 + }, + { + "Location": "Picnic", + "Illnesses": 9 + } + ], + "columns": [ + { + "name": "Location", + "importance": "primary", + "description": "Location of food preparation", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Illnesses", + "importance": "primary", + "description": "Number of illnesses", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1860": { + "metadata": { + "title": "Rent or Own? Location is Everything", + "description": "Share of survey respondents saying they rent or own their current accommodation", + "main_insight": "The proportion of renters versus homeowners varies significantly across different countries. Switzerland and Germany have the highest percentage of renters, while Russia and China have the highest percentage of homeowners." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Russia", + "Units": 11.0, + "Housing Status": "Rent" + }, + { + "Country": "Russia", + "Units": 87.0, + "Housing Status": "Own" + } + ], + "score": 1.0, + "annotation": "The Units of Rent and Own show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Rent and Own is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Russia", + "Units": 87.0, + "Housing Status": "Own" + } + ], + "score": 0.82, + "annotation": "The Own has a maximum value at Russia", + "reason": "The [{'Country': 'Russia', 'Units': 87.0, 'Housing Status': 'Own'}] of Own has a maximum value of 87.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Switzerland", + "Units": 68.0, + "Housing Status": "Rent" + } + ], + "score": 0.81, + "annotation": "The Rent has a maximum value at Switzerland", + "reason": "The [{'Country': 'Switzerland', 'Units': 68.0, 'Housing Status': 'Rent'}] of Rent has a maximum value of 68.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Switzerland", + "Units": 31.0, + "Housing Status": "Own" + } + ], + "score": 0.81, + "annotation": "The Own has a minimum value at Switzerland", + "reason": "The [{'Country': 'Switzerland', 'Units': 31.0, 'Housing Status': 'Own'}] of Own has a mainimum value of 31.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Russia", + "Units": 11.0, + "Housing Status": "Rent" + } + ], + "score": 0.8, + 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sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Source": "Trump*", + "Net Worth Valuation": 10.0 + } + ], + "score": 0.8, + "annotation": "The has a maximum value at Trump*", + "reason": "The [{'Source': 'Trump*', 'Net Worth Valuation': 10.0}] of has a maximum value of 10.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Source": "Bloomberg", + "Net Worth Valuation": 3.0 + } + ], + "score": 0.43, + "annotation": "The has a minimum value at Bloomberg", + "reason": "The [{'Source': 'Bloomberg', 'Net Worth Valuation': 3.0}] of has a mainimum value of 3.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Source": "Bloomberg", + "Net Worth Valuation": 3.0 + }, + { + "Source": "Forbes", + "Net Worth Valuation": 3.7 + }, + { + "Source": "FORTUNE", + "Net Worth Valuation": 3.9 + }, + { + "Source": "Trump*", + "Net Worth Valuation": 10.0 + } + ], + "columns": [ + { + "name": "Source", + "importance": "primary", + "description": "Source of net worth valuation", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Net Worth Valuation", + "importance": "primary", + "description": "Estimated net worth in billions of US dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1375": { + "metadata": { + "title": "Most Voters Already Expected President Trump Scandals", + "description": "Share of registered voters very/somewhat concerned about the following in October 2016*", + "main_insight": "In October 2016, a majority of registered voters were concerned about potential scandals involving President Trump, with the highest concern related to inappropriate comments or statements." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Potential_Scandal": "Inappropriate comments or 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elections, showing that Trump won in 2016 with fewer votes than Romney in 2012, even though Clinton also received fewer votes than Obama in the previous two elections." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2016, + "Dollars": 59814018.0, + "Candidates": "Clinton" + }, + { + "Year": 2016, + "Dollars": 59611678.0, + "Candidates": "Trump" + }, + { + "Year": 2012, + "Dollars": 65915795.0, + "Candidates": "Obama (2012)" + }, + { + "Year": 2012, + "Dollars": 60933504.0, + "Candidates": "Romney" + }, + { + "Year": 2008, + "Dollars": 69498516.0, + "Candidates": "Obama (2008)" + }, + { + "Year": 2008, + "Dollars": 59984323.0, + "Candidates": "McCain" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "US Presidential Election Year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the 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"title": "Majority of U.S. Adults Say Trump Abused His Power", + "description": "\"Has President Donald Trump...\"", + "main_insight": "A majority of surveyed U.S. adults believed President Trump abused his power, while smaller percentages believed he obstructed Congress or committed bribery." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Question": "Committed bribery?", + "Points": 37.0, + "Response": "No" + } + ], + "score": 0.93, + "annotation": "The No shows a sudden jump in Points from Obstructed Congress? to Committed bribery?.", + "reason": "The Points for No increases from 34.0 at Obstructed Congress? to 37.0 at Committed bribery?, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Question": "Abused his power?", + "Points": 53.0, + "Response": "Yes" + } + ], + "score": 0.89, + "annotation": "The Yes shows 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"data_points": [ + { + "Carrier": "Sprint", + "Units": 12.0, + "Time Period": "Q4 2017" + }, + { + "Carrier": "Sprint", + "Units": 87.74, + "Time Period": "4G Availability" + } + ], + "score": 0.98, + "annotation": "The Units of Q4 2017 and 4G Availability show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Q4 2017 and 4G Availability is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Carrier": "T-Mobile", + "Units": 93.67, + "Time Period": "4G Availability" + } + ], + "score": 0.96, + "annotation": "The 4G Availability shows a sudden jump in Units from AT&T to T-Mobile.", + "reason": "The Units for 4G Availability increases from 88.43 at AT&T to 93.67 at T-Mobile, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Carrier": "Sprint", + "Units": 9.8, + "Time Period": "Q2 2017" + }, + { + "Carrier": "Sprint", + "Units": 12.0, + "Time Period": "Q4 2017" + } + ], + "score": 0.96, + "annotation": "The Units of Q2 2017 and Q4 2017 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Q2 2017 and Q4 2017 is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Carrier": "T-Mobile", + "Units": 16.7, + "Time Period": "Q4 2016" + }, + { + "Carrier": "T-Mobile", + "Units": 17.5, + "Time Period": "Q2 2017" + }, + { + "Carrier": "T-Mobile", + "Units": 19.4, + "Time Period": "Q4 2017" + }, + { + "Carrier": "T-Mobile", + "Units": 21.6, + "Time Period": "Q2 2018" + }, + { + "Carrier": "T-Mobile", + "Units": 93.67, + "Time Period": "4G Availability" + }, + { + "Carrier": "Verizon", + "Units": 16.9, + "Time Period": "Q4 2016" + }, + { + "Carrier": "Verizon", + "Units": 14.9, + "Time Period": "Q2 2017" + }, + { + "Carrier": "Verizon", + "Units": 17.8, + "Time Period": "Q4 2017" + }, + { + "Carrier": "Verizon", + "Units": 20.6, + "Time Period": "Q2 2018" + }, + { + "Carrier": "Verizon", + "Units": 93.67, + "Time Period": "4G Availability" + }, + { + "Carrier": "AT&T", + "Units": 13.9, + "Time Period": "Q4 2016" + }, + { + "Carrier": "AT&T", + "Units": 12.9, + "Time Period": "Q2 2017" + }, + { + "Carrier": "AT&T", + "Units": 13.3, + "Time Period": "Q4 2017" + }, + { + "Carrier": "AT&T", + "Units": 15.1, + "Time Period": "Q2 2018" + }, + { + "Carrier": "AT&T", + "Units": 88.43, + "Time Period": "4G Availability" + }, + { + "Carrier": "Sprint", + "Units": 9.0, + "Time Period": "Q4 2016" + }, + { + "Carrier": "Sprint", + "Units": 9.8, + "Time Period": "Q2 2017" + }, + { + "Carrier": "Sprint", + "Units": 12.0, + "Time Period": "Q4 2017" + }, + { + "Carrier": "Sprint", + "Units": 14.5, + "Time Period": "Q2 2018" + }, + { + "Carrier": "Sprint", + "Units": 87.74, + "Time Period": "4G Availability" + } + ], + "columns": [ + { + "name": "Carrier", + "importance": "primary", + "description": "Mobile network provider", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding time period", + "role": "y" + }, + { + "name": "Time Period", + "importance": "primary", + "description": "The time period of the data point", + "role": "group" + } + ] + } + }, + "227": { + "metadata": { + "title": "Nearly 1,300 Migrants Cross English Channel in a Day", + "description": "Estimated number of migrants arriving in England via small boat channel crossings", + "main_insight": "The number of migrants crossing the English Channel by small boat reached a peak of 1,295 on August 22, 2022, significantly higher than any other day shown in the data from April to August 2022." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "2022-08-22", + "Migrants": 1295 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Migrants from 2022-04-14 to 2022-08-22.", + "reason": "The Migrants for increases from 562 at 2022-04-14 to 1295 at 2022-08-22, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "2022-08-22", + "Migrants": 1295 + } + ], + "score": 0.45, + "annotation": "The has a maximum value at 2022-08-22", + "reason": "The [{'Date': '2022-08-22', 'Migrants': 1295}] of has a maximum value of 1295, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "2022-04-14", + "Migrants": 562 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at 2022-04-14", + "reason": "The [{'Date': '2022-04-14', 'Migrants': 562}] of has a mainimum value of 562, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Date": "2022-04-14", + "Migrants": 562 + }, + { + "Date": "2022-08-22", + "Migrants": 1295 + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Date of migrant crossings", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Migrants", + "importance": "primary", + "description": "Estimated number of migrants", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "661": { + "metadata": { + "title": "Turning The Other Cheek", + "description": "Buttock lift procedures on men in the U.S.", + "main_insight": "The number of buttock lift procedures on men in the U.S. increased significantly from 48 in 2000 to 357 in 2020." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2000, + "Buttock Lifts": 48 + }, + { + "Year": 2020, + "Buttock Lifts": 357 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Buttock Lifts", + "importance": "primary", + "description": "Number of buttock lift procedures on men in the U.S.", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2255": { + "metadata": { + "title": "The World Is Not Enough", + "description": "Number of earths/its resources needed if the world's population lived like the following countries", + "main_insight": "The infographic shows the number of Earths required to sustain the global population if everyone lived like the citizens of various countries, highlighting significant differences in resource consumption." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Indonesia", + "Earths Needed": 1.1 + } + ], + "score": 0.76, + "annotation": "The has a minimum value at Indonesia", + "reason": "The [{'Country': 'Indonesia', 'Earths Needed': 1.1}] of has a mainimum value of 1.1, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "USA", + "Earths Needed": 4.9 + } + ], + "score": 0.75, + "annotation": "The has a maximum value at USA", + "reason": "The [{'Country': 'USA', 'Earths Needed': 4.9}] of has a maximum value of 4.9, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "South Korea", + "Earths Needed": 3.9 + }, + { + "Country": "Denmark", + "Earths Needed": 4.8 + } + ], + "score": 0.63, + "annotation": "The exhibits multiple sudden jumps in Earths Needed, transitioning from Germany, South Korea to South Korea, Denmark.", + "reason": "Across these points, the Earths Needed for increases from 3.0, 3.9 at Germany, South Korea to 3.9, 4.8 at South Korea, Denmark, suggesting significant shifts in value." + } + ], + "data": { + "data": [ + { + "Country": "USA", + "Earths Needed": 4.9 + }, + { + "Country": "Denmark", + "Earths Needed": 4.8 + }, + { + "Country": "South Korea", + "Earths Needed": 3.9 + }, + { + "Country": "Germany", + "Earths Needed": 3.0 + }, + { + "Country": "UK", + "Earths Needed": 2.4 + }, + { + "Country": "China", + "Earths Needed": 2.4 + }, + { + "Country": "Brazil", + "Earths Needed": 1.7 + }, + { + "Country": "Indonesia", + "Earths Needed": 1.1 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country Name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Earths Needed", + "importance": "primary", + "description": "Number of Earths needed if the world's population lived like this country", + "unit": "Earths", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1962": { + "metadata": { + "title": "Electric Vehicle Market to Hit Ludicrous Mode", + "description": "Projected number of electric vehicles driving globally up to 2030", + "main_insight": "The number of electric vehicles globally is projected to grow significantly, increasing from 11.3 million in 2020 to 144.3 million by 2030." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2020, + "Number of Electric Vehicles": 11.3 + }, + { + "Year": 2025, + "Number of Electric Vehicles": 51.7 + }, + { + "Year": 2030, + "Number of Electric Vehicles": 144.3 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the projected number of electric vehicles", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Number of Electric Vehicles", + "importance": "primary", + "description": "Projected number of electric vehicles (in millions)", + "unit": "Millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2533": { + "metadata": { + "title": "Major American Cities See Sharp Spike In Murders", + "description": "Number of homicides in selected U.S. cities in 2019 and 2020 (up to June 30)", + "main_insight": "Major American cities experienced a significant increase in homicides from 2019 to 2020 (up to June 30). Chicago saw the largest increase in absolute numbers." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "City": "Boston", + "Units": 32, + "Year": "YTD 2020" + }, + { + "City": "Boston", + "Units": 23, + "Year": "YTD 2019" + } + ], + "score": 0.98, + "annotation": "The Units of YTD 2020 and YTD 2019 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between YTD 2020 and YTD 2019 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Chicago", + "Units": 433, + "Year": "YTD 2020" + } + ], + "score": 0.92, + "annotation": "The YTD 2020 has a maximum value at Chicago", + "reason": "The [{'City': 'Chicago', 'Units': 433, 'Year': 'YTD 2020'}] of YTD 2020 has a maximum value of 433, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Chicago", + "Units": 284, + "Year": "YTD 2019" + } + ], + "score": 0.88, + "annotation": "The YTD 2019 has a maximum value at Chicago", + "reason": "The [{'City': 'Chicago', 'Units': 284, 'Year': 'YTD 2019'}] of YTD 2019 has a maximum value of 284, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "Chicago", + "Units": 433, + "Year": "YTD 2020" + } + ], + "score": 0.81, + "annotation": "The YTD 2020 shows a sudden jump in Units from Philadelphia to Chicago.", + "reason": "The Units for YTD 2020 increases from 247 at Philadelphia to 433 at Chicago, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "City": "Los Angeles", + "Units": 157, + "Year": "YTD 2020" + } + ], + "score": 0.8, + "annotation": "The YTD 2020 accounts for the majority in Los Angeles.", + "reason": "The Units of YTD 2020 in Los Angeles accounts for 0.5147540983606558 (more than 50%) of the total of Units of all Year." + } + ], + "data": { + "data": [ + { + "City": "Chicago", + "Units": 433, + "Year": "YTD 2020" + }, + { + "City": "Chicago", + "Units": 284, + "Year": "YTD 2019" + }, + { + "City": "Philadelphia", + "Units": 247, + "Year": "YTD 2020" + }, + { + "City": "Philadelphia", + "Units": 187, + "Year": "YTD 2019" + }, + { + "City": "New York", + "Units": 227, + "Year": "YTD 2020" + }, + { + "City": "New York", + "Units": 176, + "Year": "YTD 2019" + }, + { + "City": "Los Angeles", + "Units": 157, + "Year": "YTD 2020" + }, + { + "City": "Los Angeles", + "Units": 148, + "Year": "YTD 2019" + }, + { + "City": "Washington D.C.", + "Units": 112, + "Year": "YTD 2020" + }, + { + "City": "Washington D.C.", + "Units": 96, + "Year": "YTD 2019" + }, + { + "City": "New Orleans", + "Units": 102, + "Year": "YTD 2020" + }, + { + "City": "New Orleans", + "Units": 69, + "Year": "YTD 2019" + }, + { + "City": "Atlanta", + "Units": 75, + "Year": "YTD 2020" + }, + { + "City": "Atlanta", + "Units": 56, + "Year": "YTD 2019" + }, + { + "City": "Boston", + "Units": 32, + "Year": "YTD 2020" + }, + { + "City": "Boston", + "Units": 23, + "Year": "YTD 2019" + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "Name of U.S. City", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "731": { + "metadata": { + "title": "The Discounters Vying for World Domination", + "description": "Share of respondents from selected countries saying they regularly shopped at Aldi/Lidl (in percent)", + "main_insight": "Aldi and Lidl have varying levels of market penetration across different countries, with both discounters being most popular in Germany. Lidl generally has a higher market share than Aldi in most of the surveyed European countries, while Aldi is significantly more popular in Austria and the U.S." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Austria", + "Score": 68.0, + "Retailer": "Aldi" + } + ], + "score": 0.94, + "annotation": "The Aldi has a maximum value at Austria", + "reason": "The [{'Country': 'Austria', 'Score': 68.0, 'Retailer': 'Aldi'}] of Aldi has a maximum value of 68.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "China", + "Score": 2.0, + "Retailer": "Lidl" + } + ], + "score": 0.88, + "annotation": "The Lidl has a minimum value at China", + "reason": "The [{'Country': 'China', 'Score': 2.0, 'Retailer': 'Lidl'}] of Lidl has a mainimum value of 2.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Germany", + "Score": 50.0, + "Retailer": "Aldi" + }, + { + "Country": "Germany", + "Score": 50.0, + "Retailer": "Lidl" + } + ], + "score": 0.8, + "annotation": "The Aldi, Lidl all have the largest proportion in Germany.", + "reason": "The Score of Aldi, Lidl in Germany all account for 0.5, larger than all other Retailer, of the total of Score of all Retailer." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Switzerland", + "Score": 43.0, + "Retailer": "Lidl" + } + ], + "score": 0.79, + "annotation": "The Lidl accounts for the majority in Switzerland.", + "reason": "The Score of Lidl in Switzerland accounts for 0.5375 (more than 50%) of the total of Score of all Retailer." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Retailer": "Lidl", + "Country": "total", + "Score": 495.0 + } + ], + "score": 0.78, + "annotation": "The total value of Lidl accounts for the majority in all Retailer.", + "reason": "The total value of Score of Lidl accounts for 0.5702764976958525 (more than 50%) of the total of Score of all Retailer." + } + ], + "data": { + "data": [ + { + "Country": "Finland", + "Score": 5.0, + "Retailer": "Aldi" + }, + { + "Country": "Finland", + "Score": 61.0, + "Retailer": "Lidl" + }, + { + "Country": "Italy", + "Score": 9.0, + "Retailer": "Aldi" + }, + { + "Country": "Italy", + "Score": 45.0, + "Retailer": "Lidl" + }, + { + "Country": "Poland", + "Score": 15.0, + "Retailer": "Aldi" + }, + { + "Country": "Poland", + "Score": 64.0, + "Retailer": "Lidl" + }, + { + "Country": "France", + "Score": 21.0, + "Retailer": "Aldi" + }, + { + "Country": "France", + "Score": 45.0, + "Retailer": "Lidl" + }, + { + "Country": "Spain", + "Score": 22.0, + "Retailer": "Aldi" + }, + { + "Country": "Spain", + "Score": 38.0, + "Retailer": "Lidl" + }, + { + "Country": "Netherlands", + "Score": 31.0, + "Retailer": "Aldi" + }, + { + "Country": "Netherlands", + "Score": 51.0, + "Retailer": "Lidl" + }, + { + "Country": "Switzerland", + "Score": 37.0, + "Retailer": "Aldi" + }, + { + "Country": "Switzerland", + "Score": 43.0, + "Retailer": "Lidl" + }, + { + "Country": "Australia", + "Score": 39.0, + "Retailer": "Aldi" + }, + { + "Country": "Australia", + "Score": 12.0, + "Retailer": "Lidl" + }, + { + "Country": "UK", + "Score": 40.0, + "Retailer": "Aldi" + }, + { + "Country": "UK", + "Score": 30.0, + "Retailer": "Lidl" + }, + { + "Country": "Germany", + "Score": 50.0, + "Retailer": "Aldi" + }, + { + "Country": "Germany", + "Score": 50.0, + "Retailer": "Lidl" + }, + { + "Country": "Austria", + "Score": 68.0, + "Retailer": "Aldi" + }, + { + "Country": "Austria", + "Score": 49.0, + "Retailer": "Lidl" + }, + { + "Country": "U.S.", + "Score": 30.0, + "Retailer": "Aldi" + }, + { + "Country": "U.S.", + "Score": 5.0, + "Retailer": "Lidl" + }, + { + "Country": "China", + "Score": 6.0, + "Retailer": "Aldi" + }, + { + "Country": "China", + "Score": 2.0, + "Retailer": "Lidl" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding retailer", + "role": "y" + }, + { + "name": "Retailer", + "importance": "primary", + "description": "The retailer of the data point", + "role": "group" + } + ] + } + }, + "1630": { + "metadata": { + "title": "Tourism Suffered Massive Losses In 2020", + "description": "Travel and tourism's contribution to and share of national GDP", + "main_insight": "The contribution of travel and tourism to GDP decreased significantly in 2020 compared to 2019 for all listed countries, due to the COVID-19 pandemic." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "United States", + "Units": 5.3, + "GDP": "2020 % of GDP" + } + ], + "score": 0.99, + "annotation": "The 2020 % of GDP has the smallest proportion in United States.", + "reason": "The Units of 2020 % of GDP in United States accounts for 0.0017741773507849896, smaller than all other GDP, of the total of Units of all GDP." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "China", + "Units": 4.5, + "GDP": "2020 % of GDP" + } + ], + "score": 0.99, + "annotation": "The 2020 % of GDP has the smallest proportion in China.", + "reason": "The Units of 2020 % of GDP in China accounts for 0.0019157903699604068, smaller than all other GDP, of the total of Units of all GDP." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "GDP": "2019 GDP Contribution (billion USD)", + "Country": "total", + "Units": 5308.0 + } + ], + "score": 0.98, + "annotation": "The total value of 2019 GDP Contribution (billion USD) has the smallest proportion.", + "reason": "The total value of Units of 2019 GDP Contribution (billion USD) accounts for 0.005005349698080259, smaller than all other GDP, of the total of Units of all GDP." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "India", + "Units": 191.3, + "GDP": "2019 GDP Contribution (billion USD)" + }, + { + "Country": "India", + "Units": 121.9, + "GDP": "2020 GDP Contribution (billion USD)" + } + ], + "score": 0.97, + "annotation": "The Units of 2019 GDP Contribution (billion USD) and 2020 GDP Contribution (billion USD) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2019 GDP Contribution (billion USD) and 2020 GDP Contribution (billion USD) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Japan", + "Units": 4.7, + "GDP": "2020 % of GDP" + } + ], + "score": 0.96, + "annotation": "The 2020 % of GDP has the smallest proportion in Japan.", + "reason": "The Units of 2020 % of GDP in Japan accounts for 0.007584314991124738, smaller than all other GDP, of the total of Units of all GDP." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Units": 1869.7, + "GDP": "2019 GDP Contribution (billion USD)" + }, + { + "Country": "United States", + "Units": 1103.7, + "GDP": "2020 GDP Contribution (billion USD)" + }, + { + "Country": "United States", + "Units": 8.6, + "GDP": "2019 % of GDP" + }, + { + "Country": "United States", + "Units": 5.3, + "GDP": "2020 % of GDP" + }, + { + "Country": "China", + "Units": 1665.6, + "GDP": "2019 GDP Contribution (billion USD)" + }, + { + "Country": "China", + "Units": 667.2, + "GDP": "2020 GDP Contribution (billion USD)" + }, + { + "Country": "China", + "Units": 11.6, + "GDP": "2019 % of GDP" + }, + { + "Country": "China", + "Units": 4.5, + "GDP": "2020 % of GDP" + }, + { + "Country": "Germany", + "Units": 393.1, + "GDP": "2019 GDP Contribution (billion USD)" + }, + { + "Country": "Germany", + "Units": 208.8, + "GDP": "2020 GDP Contribution (billion USD)" + }, + { + "Country": "Germany", + "Units": 9.8, + "GDP": "2019 % of GDP" + }, + { + "Country": "Germany", + "Units": 5.5, + "GDP": "2020 % of GDP" + }, + { + "Country": "Japan", + "Units": 373.0, + "GDP": "2019 GDP Contribution (billion USD)" + }, + { + "Country": "Japan", + "Units": 234.9, + "GDP": "2020 GDP Contribution (billion USD)" + }, + { + "Country": "Japan", + "Units": 7.1, + "GDP": "2019 % of GDP" + }, + { + "Country": "Japan", + "Units": 4.7, + "GDP": "2020 % of GDP" + }, + { + "Country": "United Kingdom", + "Units": 305.0, + "GDP": "2019 GDP Contribution (billion USD)" + }, + { + "Country": "United Kingdom", + "Units": 115.0, + "GDP": "2020 GDP Contribution (billion USD)" + }, + { + "Country": "United Kingdom", + "Units": 10.1, + "GDP": "2019 % of GDP" + }, + { + "Country": "United Kingdom", + "Units": 4.2, + "GDP": "2020 % of GDP" + }, + { + "Country": "Italy", + "Units": 269.8, + "GDP": "2019 GDP Contribution (billion USD)" + }, + { + "Country": "Italy", + "Units": 132.2, + "GDP": "2020 GDP Contribution (billion USD)" + }, + { + "Country": "Italy", + "Units": 13.1, + "GDP": "2019 % of GDP" + }, + { + "Country": "Italy", + "Units": 7.0, + "GDP": "2020 % of GDP" + }, + { + "Country": "France", + "Units": 240.5, + "GDP": "2019 GDP Contribution (billion USD)" + }, + { + "Country": "France", + "Units": 123.2, + "GDP": "2020 GDP Contribution (billion USD)" + }, + { + "Country": "France", + "Units": 8.5, + "GDP": "2019 % of GDP" + }, + { + "Country": "France", + "Units": 4.8, + "GDP": "2020 % of GDP" + }, + { + "Country": "India", + "Units": 191.3, + "GDP": "2019 GDP Contribution (billion USD)" + }, + { + "Country": "India", + "Units": 121.9, + "GDP": "2020 GDP Contribution (billion USD)" + }, + { + "Country": "India", + "Units": 6.9, + "GDP": "2019 % of GDP" + }, + { + "Country": "India", + "Units": 4.7, + "GDP": "2020 % of GDP" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country Name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding gdp", + "role": "y" + }, + { + "name": "GDP", + "importance": "primary", + "description": "The gdp of the data point", + "role": "group" + } + ] + } + }, + "638": { + "metadata": { + "title": "Social Distancing Is the Order of the Day", + "description": "Worldwide search interest for the term \"social distancing\" on Google Search", + "main_insight": "Search interest in \"social distancing\" on Google Search saw a dramatic increase in early March 2020, reaching peak popularity by March 13th." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "March 13", + "Search Interest": 100 + } + ], + "score": 0.99, + "annotation": "The has a maximum value at March 13", + "reason": "The [{'Date': 'March 13', 'Search Interest': 100}] of has a maximum value of 100, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "March 13", + "Search Interest": 100 + } + ], + "score": 0.85, + "annotation": "The shows a sudden jump in Search Interest from March 11 to March 13.", + "reason": "The Search Interest for increases from 48 at March 11 to 100 at March 13, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "February 16", + "Search Interest": 1 + }, + { + "Date": "February 18", + "Search Interest": 1 + }, + { + "Date": "February 20", + "Search Interest": 1 + }, + { + "Date": "February 22", + "Search Interest": 1 + } + ], + "score": 0.34, + "annotation": "The has minimum values at February 16, February 18, February 20, February 22", + "reason": "The [{'Date': 'February 16', 'Search Interest': 1}, {'Date': 'February 18', 'Search Interest': 1}, {'Date': 'February 20', 'Search Interest': 1}, {'Date': 'February 22', 'Search Interest': 1}] of have minimum values of 1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Date": "February 16", + "Search Interest": 1 + }, + { + "Date": "February 18", + "Search Interest": 1 + }, + { + "Date": "February 20", + "Search Interest": 1 + }, + { + "Date": "February 22", + "Search Interest": 1 + }, + { + "Date": "February 24", + "Search Interest": 2 + }, + { + "Date": "February 26", + "Search Interest": 2 + }, + { + "Date": "February 28", + "Search Interest": 2 + }, + { + "Date": "March 1", + "Search Interest": 3 + }, + { + "Date": "March 3", + "Search Interest": 3 + }, + { + "Date": "March 5", + "Search Interest": 5 + }, + { + "Date": "March 7", + "Search Interest": 9 + }, + { + "Date": "March 9", + "Search Interest": 21 + }, + { + "Date": "March 11", + "Search Interest": 48 + }, + { + "Date": "March 13", + "Search Interest": 100 + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Date in February and March 2020", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Search Interest", + "importance": "primary", + "description": "Relative search interest for \"social distancing\", where 100 is peak popularity", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1257": { + "metadata": { + "title": "The Targets Of Erdogan's Purge", + "description": "Estimated number of people purged after Turkish coup attempt", + "main_insight": "The infographic shows the number of people affected by Erdogan's purge across various sectors, with the largest impact on teachers and education ministry officials." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Target Group": "Teachers", + "Number Affected": 21000.0 + } + ], + "score": 0.95, + "annotation": "The has a maximum value at Teachers", + "reason": "The [{'Target Group': 'Teachers', 'Number Affected': 21000.0}] of has a maximum value of 21000.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Target Group": "Education Ministry Officials", + "Number Affected": 15200.0 + } + ], + "score": 0.73, + "annotation": "The shows a sudden jump in Number Affected from Police to Education Ministry Officials.", + "reason": "The Number Affected for increases from 8000.0 at Police to 15200.0 at Education Ministry Officials, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Target Group": "Intelligence Officials", + "Number Affected": 100.0 + } + ], + "score": 0.48, + "annotation": "The has a minimum value at Intelligence Officials", + "reason": "The [{'Target Group': 'Intelligence Officials', 'Number Affected': 100.0}] of has a mainimum value of 100.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Target Group": "Teachers", + "Number Affected": 21000.0 + }, + { + "Target Group": "Education Ministry Officials", + "Number Affected": 15200.0 + }, + { + "Target Group": "Police", + "Number Affected": 8000.0 + }, + { + "Target Group": "Soldiers", + "Number Affected": 7500.0 + }, + { + "Target Group": "Judiciary Members", + "Number Affected": 3000.0 + }, + { + "Target Group": "University Deans", + "Number Affected": 1577.0 + }, + { + "Target Group": "Finance Ministry Staff", + "Number Affected": 1500.0 + }, + { + "Target Group": "Clerics, Preachers, and Religious Teachers", + "Number Affected": 492.0 + }, + { + "Target Group": "Social Policy Ministry Staff", + "Number Affected": 393.0 + }, + { + "Target Group": "Prime Minister's Office Staff", + "Number Affected": 257.0 + }, + { + "Target Group": "Intelligence Officials", + "Number Affected": 100.0 + } + ], + "columns": [ + { + "name": "Target Group", + "importance": "primary", + "description": "Different sectors or groups of people affected by the purge", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number Affected", + "importance": "primary", + "description": "Estimated number of people purged in each group", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1805": { + "metadata": { + "title": "The World's Largest Smartphone Markets", + "description": "Estimated number of smartphone users in 2021*", + "main_insight": "China has by far the largest number of smartphone users, almost double that of India, which is the second-largest market. The United States is third." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "China", + "Smartphone Users": 954.0 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at China", + "reason": "The [{'Country': 'China', 'Smartphone Users': 954.0}] of has a maximum value of 954.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "China", + "Smartphone Users": 954.0 + } + ], + "score": 0.85, + "annotation": "The shows a sudden jump in Smartphone Users from India to China.", + "reason": "The Smartphone Users for increases from 493.0 at India to 954.0 at China, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Mexico", + "Smartphone Users": 75.0 + } + ], + "score": 0.44, + "annotation": "The has a minimum value at Mexico", + "reason": "The [{'Country': 'Mexico', 'Smartphone Users': 75.0}] of has a mainimum value of 75.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "China", + "Smartphone Users": 954.0 + }, + { + "Country": "India", + "Smartphone Users": 493.0 + }, + { + "Country": "United States", + "Smartphone Users": 274.0 + }, + { + "Country": "Indonesia", + "Smartphone Users": 170.0 + }, + { + "Country": "Brazil", + "Smartphone Users": 119.0 + }, + { + "Country": "Russia", + "Smartphone Users": 102.0 + }, + { + "Country": "Japan", + "Smartphone Users": 83.0 + }, + { + "Country": "Mexico", + "Smartphone Users": 75.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Smartphone Users", + "importance": "primary", + "description": "Number of smartphone users in millions", + "unit": "millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2356": { + "metadata": { + "title": "Drink Local or Global? How About Both!", + "description": "Share of beer drinkers in selected countries who prefer domestic/imported beer", + "main_insight": "Beer drinkers in Germany and Mexico show a strong preference for domestic beer, while those in the United States and the United Kingdom are more likely to indicate they like both domestic and imported beer equally." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Units": 14.0, + "Origin": "Imported" + } + ], + "score": 0.95, + "annotation": "The Imported shows a sudden jump in Units from Mexico to United States.", + "reason": "The Units for Imported increases from 5.0 at Mexico to 14.0 at United States, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "United Kingdom", + "Units": 16.0, + "Origin": "Domestic" + }, + { + "Country": "United Kingdom", + "Units": 16.0, + "Origin": "Imported" + } + ], + "score": 0.92, + "annotation": "The Units of Domestic and Imported show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Domestic and Imported is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Units": 39.0, + "Origin": "Both equally" + } + ], + "score": 0.89, + "annotation": "The Both equally shows a sudden jump in Units from Germany to United States.", + "reason": "The Units for Both equally increases from 26.0 at Germany to 39.0 at United States, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Origin": "Both equally", + "Country": "total", + "Units": 153.0 + } + ], + "score": 0.87, + "annotation": "The total value of Both equally has the largest proportion.", + "reason": "The total value of Units of Both equally accounts for 0.4608433734939759, larger than all other Origin, of the total of Units of all Origin." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "United States", + "Units": 39.0, + "Origin": "Both equally" + } + ], + "score": 0.86, + "annotation": "The Both equally has the largest proportion in United States.", + "reason": "The Units of Both equally in United States accounts for 0.46987951807228917, larger than all other Origin, of the total of Units of all Origin." + } + ], + "data": { + "data": [ + { + "Country": "Germany", + "Units": 52.0, + "Origin": "Domestic" + }, + { + "Country": "Germany", + "Units": 26.0, + "Origin": "Both equally" + }, + { + "Country": "Germany", + "Units": 5.0, + "Origin": "Imported" + }, + { + "Country": "Mexico", + "Units": 41.0, + "Origin": "Domestic" + }, + { + "Country": "Mexico", + "Units": 47.0, + "Origin": "Both equally" + }, + { + "Country": "Mexico", + "Units": 5.0, + "Origin": "Imported" + }, + { + "Country": "United States", + "Units": 30.0, + "Origin": "Domestic" + }, + { + "Country": "United States", + "Units": 39.0, + "Origin": "Both equally" + }, + { + "Country": "United States", + "Units": 14.0, + "Origin": "Imported" + }, + { + "Country": "United Kingdom", + "Units": 16.0, + "Origin": "Domestic" + }, + { + "Country": "United Kingdom", + "Units": 41.0, + "Origin": "Both equally" + }, + { + "Country": "United Kingdom", + "Units": 16.0, + "Origin": "Imported" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of beer drinkers", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding origin", + "role": "y" + }, + { + "name": "Origin", + "importance": "primary", + "description": "The origin of the data point", + "role": "group" + } + ] + } + }, + "760": { + "metadata": { + "title": "Spotify Keeps Apple Music at Arm's Length", + "description": "Number of paid Spotify and Apple Music subscribers worldwide", + "main_insight": "Spotify maintains a significantly larger number of paid subscribers than Apple Music, and the gap has been widening from 2015 to 2020." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2020, + "Units": 130.0, + "Music Service": "Spotify" + }, + { + "Year": 2020, + "Units": 72.0, + "Music Service": "Apple Music" + } + ], + "score": 0.99, + "annotation": "The Units of Spotify and Apple Music show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Spotify and Apple Music is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2015, + "Units": 20.0, + "Music Service": "Spotify" + }, + { + "Year": 2015, + "Units": 10.0, + "Music Service": "Apple Music" + }, + { + "Year": 2016, + "Units": 40.0, + "Music Service": "Spotify" + }, + { + "Year": 2016, + "Units": 20.0, + "Music Service": "Apple Music" + }, + { + "Year": 2017, + "Units": 60.0, + "Music Service": "Spotify" + }, + { + "Year": 2017, + "Units": 30.0, + "Music Service": "Apple Music" + }, + { + "Year": 2018, + "Units": 85.0, + "Music Service": "Spotify" + }, + { + "Year": 2018, + "Units": 40.0, + "Music Service": "Apple Music" + }, + { + "Year": 2019, + "Units": 110.0, + "Music Service": "Spotify" + }, + { + "Year": 2019, + "Units": 60.0, + "Music Service": "Apple Music" + }, + { + "Year": 2020, + "Units": 130.0, + "Music Service": "Spotify" + }, + { + "Year": 2020, + "Units": 72.0, + "Music Service": "Apple Music" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding music service", + "role": "y" + }, + { + "name": "Music Service", + "importance": "primary", + "description": "The music service of the data point", + "role": "group" + } + ] + } + }, + "1758": { + "metadata": { + "title": "PC Shipments Drop Below 250 Million in 2023", + "description": "Estimated worldwide PC shipments per year (in million units)", + "main_insight": "Worldwide PC shipments have significantly decreased in 2023, falling below 250 million units, a stark contrast to the higher numbers seen in previous years, with largest dip between 2021 and 2023." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2008, + "PC Shipments (Millions)": 291.0 + }, + { + "Year": 2009, + "PC Shipments (Millions)": 308.0 + }, + { + "Year": 2010, + "PC Shipments (Millions)": 351.0 + }, + { + "Year": 2011, + "PC Shipments (Millions)": 365.0 + }, + { + "Year": 2012, + "PC Shipments (Millions)": 351.0 + }, + { + "Year": 2013, + "PC Shipments (Millions)": 316.0 + }, + { + "Year": 2014, + "PC Shipments (Millions)": 314.0 + }, + { + "Year": 2015, + "PC Shipments (Millions)": 288.0 + }, + { + "Year": 2016, + "PC Shipments (Millions)": 270.0 + }, + { + "Year": 2017, + "PC Shipments (Millions)": 263.0 + }, + { + "Year": 2018, + "PC Shipments (Millions)": 260.0 + }, + { + "Year": 2019, + "PC Shipments (Millions)": 263.0 + }, + { + "Year": 2020, + "PC Shipments (Millions)": 309.0 + }, + { + "Year": 2021, + "PC Shipments (Millions)": 342.0 + }, + { + "Year": 2022, + "PC Shipments (Millions)": 286.0 + }, + { + "Year": 2023, + "PC Shipments (Millions)": 242.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of PC shipment data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "PC Shipments (Millions)", + "importance": "primary", + "description": "Estimated worldwide PC shipments in millions of units", + "unit": "Millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "553": { + "metadata": { + "title": "The Employment Gap for People With Disabilities", + "description": "Comparison of the employment rate of people with disabilities with that of people without disabilities (in %)*", + "main_insight": "Across various countries, the employment rate for people with disabilities is significantly lower than for people without disabilities, highlighting a substantial employment gap." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "GB", + "Points": 78.0, + "Employment Group": "Employment for people without disabilites" + } + ], + "score": 0.83, + "annotation": "The Employment for people without disabilites has a maximum value at GB", + "reason": "The [{'Country': 'GB', 'Points': 78.0, 'Employment Group': 'Employment for people without disabilites'}] of Employment for people without disabilites has a maximum value of 78.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "ES", + "Points": 62.0, + "Employment Group": "Employment for people without disabilites" + } + ], + "score": 0.82, + "annotation": "The Employment for people without disabilites has a minimum value at ES", + "reason": "The [{'Country': 'ES', 'Points': 62.0, 'Employment Group': 'Employment for people without disabilites'}] of Employment for people without disabilites has a mainimum value of 62.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "CH", + "Points": 58.0, + "Employment Group": "Employment rate for people with disabilites" + } + ], + "score": 0.79, + "annotation": "The Employment rate for people with disabilites has a maximum value at CH", + "reason": "The [{'Country': 'CH', 'Points': 58.0, 'Employment Group': 'Employment rate for people with disabilites'}] of Employment rate for people with disabilites has a maximum value of 58.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "CH", + "Points": 74.0, + "Employment Group": "Employment for people without disabilites" + } + ], + "score": 0.78, + "annotation": "The Employment for people without disabilites accounts for the majority in CH.", + "reason": "The Points of Employment for people without disabilites in CH accounts for 0.5606060606060606 (more than 50%) of the total of Points of all Employment Group." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "CA", + "Points": 72.0, + "Employment Group": "Employment for people without disabilites" + } + ], + "score": 0.78, + "annotation": "The Employment for people without disabilites accounts for the majority in CA.", + "reason": "The Points of Employment for people without disabilites in CA accounts for 0.5714285714285714 (more than 50%) of the total of Points of all Employment Group." + } + ], + "data": { + "data": [ + { + "Country": "CH", + "Points": 74.0, + "Employment Group": "Employment for people without disabilites" + }, + { + "Country": "CH", + "Points": 58.0, + "Employment Group": "Employment rate for people with disabilites" + }, + { + "Country": "CA", + "Points": 72.0, + "Employment Group": "Employment for people without disabilites" + }, + { + "Country": "CA", + "Points": 54.0, + "Employment Group": "Employment rate for people with disabilites" + }, + { + "Country": "GB", + "Points": 78.0, + "Employment Group": "Employment for people without disabilites" + }, + { + "Country": "GB", + "Points": 51.0, + "Employment Group": "Employment rate for people with disabilites" + }, + { + "Country": "FR", + "Points": 65.0, + "Employment Group": "Employment for people without disabilites" + }, + { + "Country": "FR", + "Points": 45.0, + "Employment Group": "Employment rate for people with disabilites" + }, + { + "Country": "OCDE", + "Points": 70.0, + "Employment Group": "Employment for people without disabilites" + }, + { + "Country": "OCDE", + "Points": 44.0, + "Employment Group": "Employment rate for people with disabilites" + }, + { + "Country": "DE", + "Points": 72.0, + "Employment Group": "Employment for people without disabilites" + }, + { + "Country": "DE", + "Points": 42.0, + "Employment Group": "Employment rate for people with disabilites" + }, + { + "Country": "BE", + "Points": 65.0, + "Employment Group": "Employment for people without disabilites" + }, + { + "Country": "BE", + "Points": 33.0, + "Employment Group": "Employment rate for people with disabilites" + }, + { + "Country": "US", + "Points": 71.0, + "Employment Group": "Employment for people without disabilites" + }, + { + "Country": "US", + "Points": 32.0, + "Employment Group": "Employment rate for people with disabilites" + }, + { + "Country": "ES", + "Points": 62.0, + "Employment Group": "Employment for people without disabilites" + }, + { + "Country": "ES", + "Points": 29.0, + "Employment Group": "Employment rate for people with disabilites" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country or organization", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding employment group", + "role": "y" + }, + { + "name": "Employment Group", + "importance": "primary", + "description": "The employment group of the data point", + "role": "group" + } + ] + } + }, + "2284": { + "metadata": { + "title": "75% Of Mobile Apps Want Access To User Data", + "description": "Percentage of apps seeking permission to access the following data", + "main_insight": "A large percentage of mobile apps request access to user data, with location being the most commonly requested data point at 32%." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Data Type": "Location", + "Percentage of Apps": 32.0 + } + ], + "score": 0.95, + "annotation": "The has a maximum value at Location", + "reason": "The [{'Data Type': 'Location', 'Percentage of Apps': 32.0}] of has a maximum value of 32.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Data Type": "Location", + "Percentage of Apps": 32.0 + } + ], + "score": 0.85, + "annotation": "The shows a sudden jump in Percentage of Apps from Device ID to Location.", + "reason": "The Percentage of Apps for increases from 16.0 at Device ID to 32.0 at Location, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Data Type": "Calendar", + "Percentage of Apps": 2.0 + } + ], + "score": 0.6, + "annotation": "The has a minimum value at Calendar", + "reason": "The [{'Data Type': 'Calendar', 'Percentage of Apps': 2.0}] of has a mainimum value of 2.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Data Type": "Location", + "Percentage of Apps": 32.0 + }, + { + "Data Type": "Device ID", + "Percentage of Apps": 16.0 + }, + { + "Data Type": "Other user accounts", + "Percentage of Apps": 15.0 + }, + { + "Data Type": "Camera", + "Percentage of Apps": 10.0 + }, + { + "Data Type": "Contacts", + "Percentage of Apps": 9.0 + }, + { + "Data Type": "Call log", + "Percentage of Apps": 7.0 + }, + { + "Data Type": "Microphone", + "Percentage of Apps": 5.0 + }, + { + "Data Type": "Messages", + "Percentage of Apps": 4.0 + }, + { + "Data Type": "Calendar", + "Percentage of Apps": 2.0 + } + ], + "columns": [ + { + "name": "Data Type", + "importance": "primary", + "description": "Type of user data that apps request access to", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Apps", + "importance": "primary", + "description": "Percentage of apps seeking permission to access the corresponding data type", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1282": { + "metadata": { + "title": "Tesla's Market Cap Drop Is Bigger Than the Legacy Car Industry", + "description": "Tesla's market capitalization compared to the combined market cap of major car manufacturers", + "main_insight": "Tesla's market capitalization decreased significantly between January 3, 2022 and December 20, 2022. The drop in market cap over this period was larger than the entire combined market cap of several major car manufacturers on December 20, 2022." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "January 3, 2022", + "Tesla Market Cap": 1239.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Tesla Market Cap from December 20, 2022 to January 3, 2022.", + "reason": "The Tesla Market Cap for increases from 0 at December 20, 2022 to 1239.0 at January 3, 2022, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "January 3, 2022", + "Tesla Market Cap": 1239.0 + } + ], + "score": 0.45, + "annotation": "The has a maximum value at January 3, 2022", + "reason": "The [{'Date': 'January 3, 2022', 'Tesla Market Cap': 1239.0}] of has a maximum value of 1239.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "December 20, 2022", + "Tesla Market Cap": 0 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at December 20, 2022", + "reason": "The [{'Date': 'December 20, 2022', 'Tesla Market Cap': 0}] of has a mainimum value of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Date": "January 3, 2022", + "Tesla Market Cap": 1239.0 + }, + { + "Date": "December 20, 2022", + "Tesla Market Cap": 0 + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Date of market capitalization measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Tesla Market Cap", + "importance": "primary", + "description": "Market capitalization of Tesla in billions of dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "842": { + "metadata": { + "title": "The World's Megaprojects", + "description": "Estimated cost of selected megaprojects currently under construction globally", + "main_insight": "The TENT-T Core Rail Network and Neom City are the most expensive megaprojects currently under construction, costing $600 billion and $500 billion respectively." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Project Name": "TENT-T Core Rail Network", + "Estimated Cost (USD)": 600.0 + } + ], + "score": 0.93, + "annotation": "The has a maximum value at TENT-T Core Rail Network", + "reason": "The [{'Project Name': 'TENT-T Core Rail Network', 'Estimated Cost (USD)': 600.0}] of has a maximum value of 600.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Project Name": "Neom City", + "Estimated Cost (USD)": 500.0 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Estimated Cost (USD) from Gulf Railway to Neom City.", + "reason": "The Estimated Cost (USD) for increases from 250.0 at Gulf Railway to 500.0 at Neom City, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Project Name": "King Abdullah Economic City", + "Estimated Cost (USD)": 100.0 + }, + { + "Project Name": "Delhi-Mumbai Industrial Corridor", + "Estimated Cost (USD)": 100.0 + }, + { + "Project Name": "California High-Speed Rail", + "Estimated Cost (USD)": 100.0 + }, + { + "Project Name": "Forest City", + "Estimated Cost (USD)": 100.0 + }, + { + "Project Name": "Microsoft/Open AI data center & supercomputer", + "Estimated Cost (USD)": 100.0 + } + ], + "score": 0.43, + "annotation": "The has minimum values at King Abdullah Economic City, Delhi-Mumbai Industrial Corridor, California High-Speed Rail, Forest City, Microsoft/Open AI data center & supercomputer", + "reason": "The [{'Project Name': 'King Abdullah Economic City', 'Estimated Cost (USD)': 100.0}, {'Project Name': 'Delhi-Mumbai Industrial Corridor', 'Estimated Cost (USD)': 100.0}, {'Project Name': 'California High-Speed Rail', 'Estimated Cost (USD)': 100.0}, {'Project Name': 'Forest City', 'Estimated Cost (USD)': 100.0}, {'Project Name': 'Microsoft/Open AI data center & supercomputer', 'Estimated Cost (USD)': 100.0}] of have minimum values of 100.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Project Name": "TENT-T Core Rail Network", + "Estimated Cost (USD)": 600.0 + }, + { + "Project Name": "Neom City", + "Estimated Cost (USD)": 500.0 + }, + { + "Project Name": "Gulf Railway", + "Estimated Cost (USD)": 250.0 + }, + { + "Project Name": "International Space Station", + "Estimated Cost (USD)": 230.0 + }, + { + "Project Name": "Silk City", + "Estimated Cost (USD)": 132.0 + }, + { + "Project Name": "King Abdullah Economic City", + "Estimated Cost (USD)": 100.0 + }, + { + "Project Name": "Delhi-Mumbai Industrial Corridor", + "Estimated Cost (USD)": 100.0 + }, + { + "Project Name": "California High-Speed Rail", + "Estimated Cost (USD)": 100.0 + }, + { + "Project Name": "Forest City", + "Estimated Cost (USD)": 100.0 + }, + { + "Project Name": "Microsoft/Open AI data center & supercomputer", + "Estimated Cost (USD)": 100.0 + } + ], + "columns": [ + { + "name": "Project Name", + "importance": "primary", + "description": "Name of the megaproject", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Estimated Cost (USD)", + "importance": "primary", + "description": "Estimated cost of the project in billions of US dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1201": { + "metadata": { + "title": "Americans Have Mixed Feelings About the Election Outcome", + "description": "% of U.S. adults expressing positive/negative emotions when thinking about ... being (re-)elected into office in November", + "main_insight": "Americans had mixed feelings about the election outcome, with Joe Biden eliciting the most positive emotions and Donald Trump the most negative." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Candidate": "Kamala Harris", + "Units": 48.0, + "Sentiment": "Positive" + }, + { + "Candidate": "Kamala Harris", + "Units": 42.0, + "Sentiment": "Negative" + } + ], + "score": 0.93, + "annotation": "The Units of Positive and Negative show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Positive and Negative is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Candidate": "Kamala Harris", + "Units": 48.0, + "Sentiment": "Positive" + } + ], + "score": 0.91, + "annotation": "The Positive shows a sudden jump in Units from Donald Trump to Kamala Harris.", + "reason": "The Units for Positive increases from 37.0 at Donald Trump to 48.0 at Kamala Harris, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Candidate": "Mike Pence", + "Units": 51.0, + "Sentiment": "Negative" + } + ], + "score": 0.91, + "annotation": "The Negative shows a sudden jump in Units from Joe Biden to Mike Pence.", + "reason": "The Units for Negative increases from 43.0 at Joe Biden to 51.0 at Mike Pence, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Sentiment": "Negative", + "Candidate": "total", + "Units": 190.0 + } + ], + "score": 0.86, + "annotation": "The total value of Negative has the largest proportion.", + "reason": "The total value of Units of Negative accounts for 0.47619047619047616, larger than all other Sentiment, of the total of Units of all Sentiment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Candidate": "Kamala Harris", + "Units": 48.0, + "Sentiment": "Positive" + } + ], + "score": 0.86, + "annotation": "The Positive has the largest proportion in Kamala Harris.", + "reason": "The Units of Positive in Kamala Harris accounts for 0.48, larger than all other Sentiment, of the total of Units of all Sentiment." + } + ], + "data": { + "data": [ + { + "Candidate": "Donald Trump", + "Units": 37.0, + "Sentiment": "Positive" + }, + { + "Candidate": "Donald Trump", + "Units": 54.0, + "Sentiment": "Negative" + }, + { + "Candidate": "Donald Trump", + "Units": 9.0, + "Sentiment": "No emotion" + }, + { + "Candidate": "Joe Biden", + "Units": 51.0, + "Sentiment": "Positive" + }, + { + "Candidate": "Joe Biden", + "Units": 43.0, + "Sentiment": "Negative" + }, + { + "Candidate": "Joe Biden", + "Units": 5.0, + "Sentiment": "No emotion" + }, + { + "Candidate": "Mike Pence", + "Units": 35.0, + "Sentiment": "Positive" + }, + { + "Candidate": "Mike Pence", + "Units": 51.0, + "Sentiment": "Negative" + }, + { + "Candidate": "Mike Pence", + "Units": 14.0, + "Sentiment": "No emotion" + }, + { + "Candidate": "Kamala Harris", + "Units": 48.0, + "Sentiment": "Positive" + }, + { + "Candidate": "Kamala Harris", + "Units": 42.0, + "Sentiment": "Negative" + }, + { + "Candidate": "Kamala Harris", + "Units": 10.0, + "Sentiment": "No emotion" + } + ], + "columns": [ + { + "name": "Candidate", + "importance": "primary", + "description": "Name of the candidate", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "526": { + "metadata": { + "title": "UK Military Deaths in Iraq", + "description": "Cause of death of UK service personnel killed in Iraq from 2003 to 2009", + "main_insight": "Roadside bombs (IEDs) were the leading cause of death for UK service personnel in Iraq between 2003 and 2009, followed by shootings." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Cause of Death": "Roadside bomb (IED)", + "Number of Deaths": 47 + } + ], + "score": 0.96, + "annotation": "The has a maximum value at Roadside bomb (IED)", + "reason": "The [{'Cause of Death': 'Roadside bomb (IED)', 'Number of Deaths': 47}] of has a maximum value of 47, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Cause of Death": "Roadside bomb (IED)", + "Number of Deaths": 47 + } + ], + "score": 0.77, + "annotation": "The shows a sudden jump in Number of Deaths from Shot to Roadside bomb (IED).", + "reason": "The Number of Deaths for increases from 29 at Shot to 47 at Roadside bomb (IED), indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Cause of Death": "Suicide bomb attack", + "Number of Deaths": 3 + } + ], + "score": 0.62, + "annotation": "The has a minimum value at Suicide bomb attack", + "reason": "The [{'Cause of Death': 'Suicide bomb attack', 'Number of Deaths': 3}] of has a mainimum value of 3, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Cause of Death": "Roadside bomb (IED)", + "Number of Deaths": 47 + }, + { + "Cause of Death": "Shot", + "Number of Deaths": 29 + }, + { + "Cause of Death": "Road traffic accident", + "Number of Deaths": 20 + }, + { + "Cause of Death": "Air crash", + "Number of Deaths": 19 + }, + { + "Cause of Death": "Ambush", + "Number of Deaths": 15 + }, + { + "Cause of Death": "Ground-to-air fire", + "Number of Deaths": 15 + }, + { + "Cause of Death": "Rocket/Mortar attack", + "Number of Deaths": 10 + }, + { + "Cause of Death": "Friendly fire", + "Number of Deaths": 8 + }, + { + "Cause of Death": "Suicide", + "Number of Deaths": 6 + }, + { + "Cause of Death": "Suicide bomb attack", + "Number of Deaths": 3 + }, + { + "Cause of Death": "Other", + "Number of Deaths": 7 + } + ], + "columns": [ + { + "name": "Cause of Death", + "importance": "primary", + "description": "Different causes of death for UK service personnel", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Deaths", + "importance": "primary", + "description": "The number of deaths associated with each cause", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1659": { + "metadata": { + "title": "Where the Uranium comes from", + "description": "Ranking of countries with the largest production volume of uranium in 2016 (in kilotons)", + "main_insight": "Kazakhstan was the world's leading producer of uranium in 2016, accounting for 39.4% of global production, followed by Canada and Australia." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Kazakhstan", + "Kilotons (kt)": 24.6 + } + ], + "score": 0.96, + "annotation": "The has a maximum value at Kazakhstan", + "reason": "The [{'Country': 'Kazakhstan', 'Kilotons (kt)': 24.6}] of has a maximum value of 24.6, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Kazakhstan", + "Kilotons (kt)": 24.6 + } + ], + "score": 0.8, + "annotation": "The shows a sudden jump in Kilotons (kt) from Canada to Kazakhstan.", + "reason": "The Kilotons (kt) for increases from 14.0 at Canada to 24.6 at Kazakhstan, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Ukraine", + "Kilotons (kt)": 1.0 + } + ], + "score": 0.44, + "annotation": "The has a minimum value at Ukraine", + "reason": "The [{'Country': 'Ukraine', 'Kilotons (kt)': 1.0}] of has a mainimum value of 1.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Kazakhstan", + "Kilotons (kt)": 24.6 + }, + { + "Country": "Canada", + "Kilotons (kt)": 14.0 + }, + { + "Country": "Australia", + "Kilotons (kt)": 6.3 + }, + { + "Country": "Namibia", + "Kilotons (kt)": 3.7 + }, + { + "Country": "Niger", + "Kilotons (kt)": 3.5 + }, + { + "Country": "Russia", + "Kilotons (kt)": 3.0 + }, + { + "Country": "Uzbekistan", + "Kilotons (kt)": 2.4 + }, + { + "Country": "China", + "Kilotons (kt)": 1.6 + }, + { + "Country": "United States", + "Kilotons (kt)": 1.1 + }, + { + "Country": "Ukraine", + "Kilotons (kt)": 1.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country producing uranium", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Kilotons (kt)", + "importance": "primary", + "description": "Uranium production volume in kilotons", + "unit": "kt", + "data_type": "number", + "role": "y" + } + ] + } + }, + "783": { + "metadata": { + "title": "Who Are America's Newspapers Endorsing For President?", + "description": "2020 general election editorial endorsements by major U.S. newspapers", + "main_insight": "Major U.S. newspapers overwhelmingly endorsed Joe Biden for president in the 2020 election, both in terms of the number of endorsements and total circulation." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Candidate": "Joe Biden", + "Endorsements": 47 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Endorsements from Donald Trump to Joe Biden.", + "reason": "The Endorsements for increases from 7 at Donald Trump to 47 at Joe Biden, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Candidate": "Joe Biden", + "Endorsements": 47 + } + ], + "score": 0.45, + "annotation": "The has a maximum value at Joe Biden", + "reason": "The [{'Candidate': 'Joe Biden', 'Endorsements': 47}] of has a maximum value of 47, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Candidate": "Donald Trump", + "Endorsements": 7 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at Donald Trump", + "reason": "The [{'Candidate': 'Donald Trump', 'Endorsements': 7}] of has a mainimum value of 7, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Candidate": "Joe Biden", + "Endorsements": 47 + }, + { + "Candidate": "Donald Trump", + "Endorsements": 7 + } + ], + "columns": [ + { + "name": "Candidate", + "importance": "primary", + "description": "Presidential candidate", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Endorsements", + "importance": "primary", + "description": "Number of newspaper endorsements", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "648": { + "metadata": { + "title": "Microsoft Teams Powers Past Slack", + "description": "Daily active users of Slack and Microsoft Teams worldwide", + "main_insight": "Microsoft Teams' daily active users surpassed Slack's in 2018 and continued to grow rapidly, reaching 20 million by November 2019, while Slack had 12 million users by September 2019." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2014, + "Ratio": 0.5, + "Communication Platform": "Slack" + }, + { + "Year": 2015, + "Ratio": 2.0, + "Communication Platform": "Slack" + }, + { + "Year": 2016, + "Ratio": 3.5, + "Communication Platform": "Slack" + }, + { + "Year": 2017, + "Ratio": 1.0, + "Communication Platform": "Microsoft Teams" + }, + { + "Year": 2017, + "Ratio": 5.0, + "Communication Platform": "Slack" + }, + { + "Year": 2018, + "Ratio": 3.0, + "Communication Platform": "Microsoft Teams" + }, + { + "Year": 2018, + "Ratio": 7.0, + "Communication Platform": "Slack" + }, + { + "Year": 2019, + "Ratio": 20.0, + "Communication Platform": "Microsoft Teams" + }, + { + "Year": 2019, + "Ratio": 12.0, + "Communication Platform": "Slack" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding communication platform", + "role": "y" + }, + { + "name": "Communication Platform", + "importance": "primary", + "description": "The communication platform of the data point", + "role": "group" + } + ] + } + }, + "274": { + "metadata": { + "title": "The Rise (and Fall) of Hip-Hop's Fortunes", + "description": "Net worth of the world's wealthiest rappers (in million U.S. dollars)", + "main_insight": "Jay-Z's net worth has consistently increased, reaching $1.5 billion by 2022, while Kanye West's net worth experienced a significant decline from $1.3 billion in 2020 to $0.4 billion in 2022." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 1500.0, + "Net Worth": "Jay-Z" + }, + { + "Year": 2022, + "Units": 1000.0, + "Net Worth": "Sean \"Diddy\" Combs" + } + ], + "score": 0.8, + "annotation": "The Units of Jay-Z and Sean \"Diddy\" Combs show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Jay-Z and Sean \"Diddy\" Combs is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 1000.0, + "Net Worth": "Sean \"Diddy\" Combs" + }, + { + "Year": 2022, + "Units": 500.0, + "Net Worth": "Dr. Dre" + } + ], + "score": 0.65, + "annotation": "The Units of Sean \"Diddy\" Combs and Dr. Dre show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Sean \"Diddy\" Combs and Dr. Dre is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2022, + "Units": 1500.0, + "Net Worth": "Jay-Z" + }, + { + "Year": 2022, + "Units": 500.0, + "Net Worth": "Dr. Dre" + } + ], + "score": 0.29, + "annotation": "The Units of Jay-Z and Dr. Dre show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Jay-Z and Dr. Dre is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2011, + "Units": 450.0, + "Net Worth": "Jay-Z" + }, + { + "Year": 2011, + "Units": 500.0, + "Net Worth": "Sean \"Diddy\" Combs" + }, + { + "Year": 2011, + "Units": 125.0, + "Net Worth": "Dr. Dre" + }, + { + "Year": 2012, + "Units": 475.0, + "Net Worth": "Jay-Z" + }, + { + "Year": 2012, + "Units": 550.0, + "Net Worth": "Sean \"Diddy\" Combs" + }, + { + "Year": 2012, + "Units": 270.0, + "Net Worth": "Dr. Dre" + }, + { + "Year": 2013, + "Units": 500.0, + "Net Worth": "Jay-Z" + }, + { + "Year": 2013, + "Units": 580.0, + "Net Worth": "Sean \"Diddy\" Combs" + }, + { + "Year": 2013, + "Units": 350.0, + "Net Worth": "Dr. Dre" + }, + { + "Year": 2014, + "Units": 520.0, + "Net Worth": "Jay-Z" + }, + { + "Year": 2014, + "Units": 700.0, + "Net Worth": "Sean \"Diddy\" Combs" + }, + { + "Year": 2014, + "Units": 550.0, + "Net Worth": "Dr. Dre" + }, + { + "Year": 2015, + "Units": 550.0, + "Net Worth": "Jay-Z" + }, + { + "Year": 2015, + "Units": 735.0, + "Net Worth": "Sean \"Diddy\" Combs" + }, + { + "Year": 2015, + "Units": 700.0, + "Net Worth": "Dr. Dre" + }, + { + "Year": 2016, + "Units": 610.0, + "Net Worth": "Jay-Z" + }, + { + "Year": 2016, + "Units": 750.0, + "Net Worth": "Sean \"Diddy\" Combs" + }, + { + "Year": 2016, + "Units": 710.0, + "Net Worth": "Dr. Dre" + }, + { + "Year": 2017, + "Units": 810.0, + "Net Worth": "Jay-Z" + }, + { + "Year": 2017, + "Units": 820.0, + "Net Worth": "Sean \"Diddy\" Combs" + }, + { + "Year": 2017, + "Units": 740.0, + "Net Worth": "Dr. Dre" + }, + { + "Year": 2018, + "Units": 900.0, + "Net Worth": "Jay-Z" + }, + { + "Year": 2018, + "Units": 825.0, + "Net Worth": "Sean \"Diddy\" Combs" + }, + { + "Year": 2018, + "Units": 770.0, + "Net Worth": "Dr. Dre" + }, + { + "Year": 2019, + "Units": 1000.0, + "Net Worth": "Jay-Z" + }, + { + "Year": 2019, + "Units": 240.0, + "Net Worth": "Kanye West" + }, + { + "Year": 2019, + "Units": 740.0, + "Net Worth": "Sean \"Diddy\" Combs" + }, + { + "Year": 2019, + "Units": 800.0, + "Net Worth": "Dr. Dre" + }, + { + "Year": 2020, + "Units": 1000.0, + "Net Worth": "Jay-Z" + }, + { + "Year": 2020, + "Units": 1300.0, + "Net Worth": "Kanye West" + }, + { + "Year": 2020, + "Units": 700.0, + "Net Worth": "Sean \"Diddy\" Combs" + }, + { + "Year": 2020, + "Units": 500.0, + "Net Worth": "Dr. Dre" + }, + { + "Year": 2021, + "Units": 1300.0, + "Net Worth": "Jay-Z" + }, + { + "Year": 2021, + "Units": 1000.0, + "Net Worth": "Kanye West" + }, + { + "Year": 2021, + "Units": 800.0, + "Net Worth": "Sean \"Diddy\" Combs" + }, + { + "Year": 2021, + "Units": 500.0, + "Net Worth": "Dr. Dre" + }, + { + "Year": 2022, + "Units": 1500.0, + "Net Worth": "Jay-Z" + }, + { + "Year": 2022, + "Units": 400.0, + "Net Worth": "Kanye West" + }, + { + "Year": 2022, + "Units": 1000.0, + "Net Worth": "Sean \"Diddy\" Combs" + }, + { + "Year": 2022, + "Units": 500.0, + "Net Worth": "Dr. Dre" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year from 2011 to 2022", + "unit": "none", + "data_type": "time", + "discarded_count": 0, + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding net worth", + "role": "y" + }, + { + "name": "Net Worth", + "importance": "primary", + "description": "The net worth of the data point", + "role": "group" + } + ] + } + }, + "171": { + "metadata": { + "title": "Nearly 3 in 10 PCs Still Run Windows XP", + "description": "Global market share of operating systems in March 2014", + "main_insight": "In March 2014, Windows XP still held a significant market share (27.7%), despite being an older operating system, second only to Windows 7 (48.8%)." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Operating System": "Windows 7", + "Market Share": 48.8 + } + ], + "score": 0.82, + "annotation": "The has a maximum value at Windows 7", + "reason": "The [{'Operating System': 'Windows 7', 'Market Share': 48.8}] of has a maximum value of 48.8, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Operating System": "Windows 7", + "Market Share": 48.8 + } + ], + "score": 0.81, + "annotation": "The shows a sudden jump in Market Share from Windows XP to Windows 7.", + "reason": "The Market Share for increases from 27.7 at Windows XP to 48.8 at Windows 7, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Operating System": "OSX", + "Market Share": 3.8 + } + ], + "score": 0.54, + "annotation": "The has a minimum value at OSX", + "reason": "The [{'Operating System': 'OSX', 'Market Share': 3.8}] of has a mainimum value of 3.8, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Operating System": "Windows 7", + "Market Share": 48.8 + }, + { + "Operating System": "Windows XP", + "Market Share": 27.7 + }, + { + "Operating System": "Windows 8", + "Market Share": 11.3 + }, + { + "Operating System": "OSX", + "Market Share": 3.8 + }, + { + "Operating System": "Others", + "Market Share": 8.5 + } + ], + "columns": [ + { + "name": "Operating System", + "importance": "primary", + "description": "Name of the operating system", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Market Share", + "importance": "primary", + "description": "Percentage of global market share", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2155": { + "metadata": { + "title": "There were over a million casualties at the Somme", + "description": "Estimated number of casualties by country at the Battle of the Somme", + "main_insight": "The Battle of the Somme resulted in over a million casualties, with Germany, the United Kingdom, and France suffering the highest losses." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Germany", + "Casualties": 465000.0 + } + ], + "score": 0.86, + "annotation": "The shows a sudden jump in Casualties from France to Germany.", + "reason": "The Casualties for increases from 204253.0 at France to 465000.0 at Germany, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Germany", + "Casualties": 465000.0 + }, + { + "Country": "United Kingdom", + "Casualties": 0 + }, + { + "Country": "France", + "Casualties": 204253.0 + }, + { + "Country": "Canada", + "Casualties": 24029.0 + }, + { + "Country": "Australia", + "Casualties": 23000.0 + }, + { + "Country": "New Zealand", + "Casualties": 7408.0 + }, + { + "Country": "South Africa", + "Casualties": 0 + }, + { + "Country": "Newfoundland", + "Casualties": 0 + }, + { + "Country": 0, + "Casualties": 0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country involved in the Battle of the Somme", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Casualties", + "importance": "primary", + "description": "Estimated number of casualties (dead and wounded)", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1059": { + "metadata": { + "title": "Does Nintendo Have an Awareness Problem?", + "description": "Percentage of consumers in the U.S. (13+) who have heard of these video game systems", + "main_insight": "Nintendo Switch has significantly lower awareness among both the general population and gamers compared to Sony PlayStation 4 and Microsoft Xbox One." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Video Game System": "None of the above", + "Units": 18.0, + "Audience": "General Population" + }, + { + "Video Game System": "None of the above", + "Units": 9.0, + "Audience": "Gamers" + } + ], + "score": 0.98, + "annotation": "The Units of General Population and Gamers show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between General Population and Gamers is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Video Game System": "Microsoft Xbox One (S)", + "Units": 61.0, + "Audience": "General Population" + } + ], + "score": 0.91, + "annotation": "The General Population shows a sudden jump in Units from Nintendo Switch to Microsoft Xbox One (S).", + "reason": "The Units for General Population increases from 22.0 at Nintendo Switch to 61.0 at Microsoft Xbox One (S), indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Video Game System": "Microsoft Xbox One (S)", + "Units": 72.0, + "Audience": "Gamers" + } + ], + "score": 0.89, + "annotation": "The Gamers shows a sudden jump in Units from Nintendo Switch to Microsoft Xbox One (S).", + "reason": "The Units for Gamers increases from 29.0 at Nintendo Switch to 72.0 at Microsoft Xbox One (S), indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Video Game System": "Sony PlayStation 4", + "Units": 77.0, + "Audience": "Gamers" + } + ], + "score": 0.79, + "annotation": "The Gamers accounts for the majority in Sony PlayStation 4.", + "reason": "The Units of Gamers in Sony PlayStation 4 accounts for 0.5273972602739726 (more than 50%) of the total of Units of all Audience." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Audience": "Gamers", + "Video Game System": "total", + "Units": 228.0 + } + ], + "score": 0.79, + "annotation": "The total value of Gamers accounts for the majority in all Audience.", + "reason": "The total value of Units of Gamers accounts for 0.5302325581395348 (more than 50%) of the total of Units of all Audience." + } + ], + "data": { + "data": [ + { + "Video Game System": "Sony PlayStation 4", + "Units": 69.0, + "Audience": "General Population" + }, + { + "Video Game System": "Sony PlayStation 4", + "Units": 77.0, + "Audience": "Gamers" + }, + { + "Video Game System": "Microsoft Xbox One (S)", + "Units": 61.0, + "Audience": "General Population" + }, + { + "Video Game System": "Microsoft Xbox One (S)", + "Units": 72.0, + "Audience": "Gamers" + }, + { + "Video Game System": "Nintendo Switch", + "Units": 22.0, + "Audience": "General Population" + }, + { + "Video Game System": "Nintendo Switch", + "Units": 29.0, + "Audience": "Gamers" + }, + { + "Video Game System": "Sony PlayStation 4 Pro", + "Units": 21.0, + "Audience": "General Population" + }, + { + "Video Game System": "Sony PlayStation 4 Pro", + "Units": 27.0, + "Audience": "Gamers" + }, + { + "Video Game System": "Microsoft Xbox \"Scorpio\"", + "Units": 11.0, + "Audience": "General Population" + }, + { + "Video Game System": "Microsoft Xbox \"Scorpio\"", + "Units": 14.0, + "Audience": "Gamers" + }, + { + "Video Game System": "None of the above", + "Units": 18.0, + "Audience": "General Population" + }, + { + "Video Game System": "None of the above", + "Units": 9.0, + "Audience": "Gamers" + } + ], + "columns": [ + { + "name": "Video Game System", + "importance": "primary", + "description": "Name of the video game console", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding audience", + "role": "y" + }, + { + "name": "Audience", + "importance": "primary", + "description": "The audience of the data point", + "role": "group" + } + ] + } + }, + "1517": { + "metadata": { + "title": "Japanese Consumer Prices on the Increase", + "description": "Consumer price index and year-on-year price index increase for selected goods (2009-2019)", + "main_insight": "Japanese consumer prices have been steadily increasing since 2013, reaching 101.9 in January 2019. The price index is modeled on a 2015 base." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2009, + "Consumer Price Index": 97.0 + }, + { + "Year": 2010, + "Consumer Price Index": 96.0 + }, + { + "Year": 2011, + "Consumer Price Index": 96.0 + }, + { + "Year": 2012, + "Consumer Price Index": 95.0 + }, + { + "Year": 2013, + "Consumer Price Index": 97.0 + }, + { + "Year": 2014, + "Consumer Price Index": 100.0 + }, + { + "Year": 2015, + "Consumer Price Index": 100.0 + }, + { + "Year": 2016, + "Consumer Price Index": 100.0 + }, + { + "Year": 2017, + "Consumer Price Index": 101.0 + }, + { + "Year": 2018, + "Consumer Price Index": 101.5 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the consumer price index", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Consumer Price Index", + "importance": "primary", + "description": "Consumer price index, modeled on 2015 base", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "813": { + "metadata": { + "title": "Which countries host and send the most migrants?", + "description": "Number of migrants resident in/sent by country as of 2015 (million)", + "main_insight": "The United States hosts significantly more migrants than any other country, while India is the top migrant-sending country." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United States", + "Migrants Hosted": 46.6 + } + ], + "score": 1.0, + "annotation": "The has a maximum value at United States", + "reason": "The [{'Country': 'United States', 'Migrants Hosted': 46.6}] of has a maximum value of 46.6, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Migrants Hosted": 46.6 + } + ], + "score": 0.93, + "annotation": "The shows a sudden jump in Migrants Hosted from Germany to United States.", + "reason": "The Migrants Hosted for increases from 12.0 at Germany to 46.6 at United States, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "India", + "Migrants Hosted": 0 + }, + { + "Country": "Mexico", + "Migrants Hosted": 0 + }, + { + "Country": "China", + "Migrants Hosted": 0 + }, + { + "Country": "Bangladesh", + "Migrants Hosted": 0 + }, + { + "Country": "Pakistan", + "Migrants Hosted": 0 + }, + { + "Country": "Ukraine", + "Migrants Hosted": 0 + }, + { + "Country": "Philippines", + "Migrants Hosted": 0 + }, + { + "Country": "Syria", + "Migrants Hosted": 0 + } + ], + "score": 0.42, + "annotation": "The has minimum values at India, Mexico, China, Bangladesh, Pakistan, Ukraine, Philippines, Syria", + "reason": "The [{'Country': 'India', 'Migrants Hosted': 0}, {'Country': 'Mexico', 'Migrants Hosted': 0}, {'Country': 'China', 'Migrants Hosted': 0}, {'Country': 'Bangladesh', 'Migrants Hosted': 0}, {'Country': 'Pakistan', 'Migrants Hosted': 0}, {'Country': 'Ukraine', 'Migrants Hosted': 0}, {'Country': 'Philippines', 'Migrants Hosted': 0}, {'Country': 'Syria', 'Migrants Hosted': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Migrants Hosted": 46.6 + }, + { + "Country": "Germany", + "Migrants Hosted": 12.0 + }, + { + "Country": "Russia", + "Migrants Hosted": 11.6 + }, + { + "Country": "Saudi Arabia", + "Migrants Hosted": 10.2 + }, + { + "Country": "United Kingdom", + "Migrants Hosted": 8.5 + }, + { + "Country": "United Arab Emirates", + "Migrants Hosted": 8.1 + }, + { + "Country": "Canada", + "Migrants Hosted": 7.8 + }, + { + "Country": "France", + "Migrants Hosted": 7.8 + }, + { + "Country": "Australia", + "Migrants Hosted": 6.8 + }, + { + "Country": "Spain", + "Migrants Hosted": 5.9 + }, + { + "Country": "India", + "Migrants Hosted": 0 + }, + { + "Country": "Mexico", + "Migrants Hosted": 0 + }, + { + "Country": "China", + "Migrants Hosted": 0 + }, + { + "Country": "Bangladesh", + "Migrants Hosted": 0 + }, + { + "Country": "Pakistan", + "Migrants Hosted": 0 + }, + { + "Country": "Ukraine", + "Migrants Hosted": 0 + }, + { + "Country": "Philippines", + "Migrants Hosted": 0 + }, + { + "Country": "Syria", + "Migrants Hosted": 0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Migrants Hosted", + "importance": "primary", + "description": "Number of migrants hosted by the country (in millions)", + "unit": "million", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1367": { + "metadata": { + "title": "Most Americans Support Airstrikes in Iraq", + "description": "Views on airstrikes against IS militants in Iraq", + "main_insight": "A majority of Americans, across all political affiliations, approve of airstrikes against IS militants in Iraq, with Republicans showing the highest approval rate." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Political Affiliation": "Independents", + "Units": 50.0, + "Opinion": "Approve" + }, + { + "Political Affiliation": "Independents", + "Units": 37.0, + "Opinion": "Disapprove" + } + ], + "score": 0.99, + "annotation": "The Units of Approve and Disapprove show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Approve and Disapprove is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Political Affiliation": "Republicans", + "Units": 71.0, + "Opinion": "Approve" + } + ], + "score": 0.94, + "annotation": "The Approve shows a sudden jump in Units from Democrats to Republicans.", + "reason": "The Units for Approve increases from 54.0 at Democrats to 71.0 at Republicans, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Political Affiliation": "Total", + "Units": 31.0, + "Opinion": "Disapprove" + } + ], + "score": 0.93, + "annotation": "The Disapprove shows a sudden jump in Units from Republicans to Total.", + "reason": "The Units for Disapprove increases from 14.0 at Republicans to 31.0 at Total, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Political Affiliation": "Independents", + "Units": 50.0, + "Opinion": "Approve" + } + ], + "score": 0.86, + "annotation": "The Approve has the largest proportion in Independents.", + "reason": "The Units of Approve in Independents accounts for 0.5, larger than all other Opinion, of the total of Units of all Opinion." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Political Affiliation": "Total", + "Units": 54.0, + "Opinion": "Approve" + } + ], + "score": 0.85, + "annotation": "The Approve accounts for the majority in Total.", + "reason": "The Units of Approve in Total accounts for 0.54 (more than 50%) of the total of Units of all Opinion." + } + ], + "data": { + "data": [ + { + "Political Affiliation": "Total", + "Units": 54.0, + "Opinion": "Approve" + }, + { + "Political Affiliation": "Total", + "Units": 31.0, + "Opinion": "Disapprove" + }, + { + "Political Affiliation": "Total", + "Units": 15.0, + "Opinion": "Don't Know" + }, + { + "Political Affiliation": "Republicans", + "Units": 71.0, + "Opinion": "Approve" + }, + { + "Political Affiliation": "Republicans", + "Units": 14.0, + "Opinion": "Disapprove" + }, + { + "Political Affiliation": "Republicans", + "Units": 15.0, + "Opinion": "Don't Know" + }, + { + "Political Affiliation": "Democrats", + "Units": 54.0, + "Opinion": "Approve" + }, + { + "Political Affiliation": "Democrats", + "Units": 35.0, + "Opinion": "Disapprove" + }, + { + "Political Affiliation": "Democrats", + "Units": 11.0, + "Opinion": "Don't Know" + }, + { + "Political Affiliation": "Independents", + "Units": 50.0, + "Opinion": "Approve" + }, + { + "Political Affiliation": "Independents", + "Units": 37.0, + "Opinion": "Disapprove" + }, + { + "Political Affiliation": "Independents", + "Units": 13.0, + "Opinion": "Don't Know" + } + ], + "columns": [ + { + "name": "Political Affiliation", + "importance": "primary", + "description": "Political affiliation or group", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding opinion", + "role": "y" + }, + { + "name": "Opinion", + "importance": "primary", + "description": "The opinion of the data point", + "role": "group" + } + ] + } + }, + "478": { + "metadata": { + "title": "Banksy's Art Still Breaks the Bank", + "description": "Most expensive Banksy artworks sold at auction (in million U.S. dollars)", + "main_insight": "Banksy's artwork 'Love is in the Bin' sold for the highest price at auction, reaching $25.4 million in October 2021, followed by 'Game Changer' at $23.1 million." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Artwork Name": "Love is in the Bin", + "Price (Million USD)": 25.4 + } + ], + "score": 0.85, + "annotation": "The has a maximum value at Love is in the Bin", + "reason": "The [{'Artwork Name': 'Love is in the Bin', 'Price (Million USD)': 25.4}] of has a maximum value of 25.4, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Artwork Name": "Game Changer", + "Price (Million USD)": 23.1 + } + ], + "score": 0.82, + "annotation": "The shows a sudden jump in Price (Million USD) from Devolved Parliament to Game Changer.", + "reason": "The Price (Million USD) for increases from 13.7 at Devolved Parliament to 23.1 at Game Changer, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Artwork Name": "Sale Ends Today", + "Price (Million USD)": 5.9 + } + ], + "score": 0.61, + "annotation": "The has a minimum value at Sale Ends Today", + "reason": "The [{'Artwork Name': 'Sale Ends Today', 'Price (Million USD)': 5.9}] of has a mainimum value of 5.9, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Artwork Name": "Love is in the Bin", + "Price (Million USD)": 25.4 + }, + { + "Artwork Name": "Game Changer", + "Price (Million USD)": 23.1 + }, + { + "Artwork Name": "Devolved Parliament", + "Price (Million USD)": 13.7 + }, + { + "Artwork Name": "Love is in the Air", + "Price (Million USD)": 12.9 + }, + { + "Artwork Name": "Show Me The Monet", + "Price (Million USD)": 10.5 + }, + { + "Artwork Name": "Forgive Us Our Trespassing", + "Price (Million USD)": 8.8 + }, + { + "Artwork Name": "Subject to Availability", + "Price (Million USD)": 6.3 + }, + { + "Artwork Name": "Sale Ends Today", + "Price (Million USD)": 5.9 + } + ], + "columns": [ + { + "name": "Artwork Name", + "importance": "primary", + "description": "Name of the Banksy artwork", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Price (Million USD)", + "importance": "primary", + "description": "Price of the artwork in million U.S. dollars", + "unit": "Million USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "219": { + "metadata": { + "title": "Half of Indian Startups In Serious Danger Due to COVID-19", + "description": "Self-declared cash reserves of Indian startups and SMEs (as of June 2020)", + "main_insight": "A significant portion of Indian startups and SMEs were facing financial difficulties in June 2020, with 38% reporting being out of funds and another 12% having less than one month of cash reserves." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Cash Reserve Status": "1-3 months", + "Percentage of Startups": 30.0 + } + ], + "score": 0.78, + "annotation": "The shows a sudden jump in Percentage of Startups from 3-6 months to 1-3 months.", + "reason": "The Percentage of Startups for increases from 16.0 at 3-6 months to 30.0 at 1-3 months, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Cash Reserve Status": "Out of funds", + "Percentage of Startups": 38.0 + } + ], + "score": 0.73, + "annotation": "The has a maximum value at Out of funds", + "reason": "The [{'Cash Reserve Status': 'Out of funds', 'Percentage of Startups': 38.0}] of has a maximum value of 38.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Cash Reserve Status": "Shut down the business", + "Percentage of Startups": 4.0 + } + ], + "score": 0.67, + "annotation": "The has a minimum value at Shut down the business", + "reason": "The [{'Cash Reserve Status': 'Shut down the business', 'Percentage of Startups': 4.0}] of has a mainimum value of 4.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Cash Reserve Status": "Out of funds", + "Percentage of Startups": 38.0 + }, + { + "Cash Reserve Status": "Less than one month", + "Percentage of Startups": 12.0 + }, + { + "Cash Reserve Status": "1-3 months", + "Percentage of Startups": 30.0 + }, + { + "Cash Reserve Status": "3-6 months", + "Percentage of Startups": 16.0 + }, + { + "Cash Reserve Status": "Shut down the business", + "Percentage of Startups": 4.0 + } + ], + "columns": [ + { + "name": "Cash Reserve Status", + "importance": "primary", + "description": "Categories of cash reserve status", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage of Startups", + "importance": "primary", + "description": "Percentage of startups falling into each cash reserve category", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "200": { + "metadata": { + "title": "How Do Kraft Heinz And Unilever Stack Up?", + "description": "Key figures for Kraft Heinz and Unilever", + "main_insight": "Unilever significantly outperforms Kraft Heinz in net profit, brand value, and number of employees, highlighting Unilever's larger scale and financial performance." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Metric": "Employees", + "Kraft Heinz": 42000.0 + } + ], + "score": 0.94, + "annotation": "The shows a sudden jump in Kraft Heinz from Brand value 2015 (million U.S. dollars) to Employees.", + "reason": "The Kraft Heinz for increases from 9977.0 at Brand value 2015 (million U.S. dollars) to 42000.0 at Employees, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Metric": "Employees", + "Kraft Heinz": 42000.0 + } + ], + "score": 0.66, + "annotation": "The has a maximum value at Employees", + "reason": "The [{'Metric': 'Employees', 'Kraft Heinz': 42000.0}] of has a maximum value of 42000.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Metric": "Net profit 2015 (million U.S. dollars)", + "Kraft Heinz": 634.0 + } + ], + "score": 0.49, + "annotation": "The has a minimum value at Net profit 2015 (million U.S. dollars)", + "reason": "The [{'Metric': 'Net profit 2015 (million U.S. dollars)', 'Kraft Heinz': 634.0}] of has a mainimum value of 634.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Metric": "Net profit 2015 (million U.S. dollars)", + "Kraft Heinz": 634.0 + }, + { + "Metric": "Brand value 2015 (million U.S. dollars)", + "Kraft Heinz": 9977.0 + }, + { + "Metric": "Employees", + "Kraft Heinz": 42000.0 + } + ], + "columns": [ + { + "name": "Metric", + "importance": "primary", + "description": "The category being compared", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Kraft Heinz", + "importance": "primary", + "description": "Value for Kraft Heinz", + "unit": "Million U.S. dollars", + "data_type": "number", + "role": "y" + } + ] + } + }, + "557": { + "metadata": { + "title": "The Fight to Tame the Olympic Budget Beast", + "description": "Cost of hosting the Olympic Games since 1992 (in billion U.S. dollars)", + "main_insight": "The cost of hosting the Olympic Games has consistently exceeded initial estimates since 1992, with significant overruns and, in the case of Tokyo 2021, additional postponement costs." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "City": "Barcelona 1992", + "Units": 2.6, + "Cost Components": "Initial estimate" + }, + { + "City": "Barcelona 1992", + "Units": 6.9, + "Cost Components": "Overrun" + }, + { + "City": "Atlanta 1996", + "Units": 1.6, + "Cost Components": "Initial estimate" + }, + { + "City": "Atlanta 1996", + "Units": 2.5, + "Cost Components": "Overrun" + }, + { + "City": "Sydney 2000", + "Units": 2.1, + "Cost Components": "Initial estimate" + }, + { + "City": "Sydney 2000", + "Units": 3.1, + "Cost Components": "Overrun" + }, + { + "City": "Athens 2004", + "Units": 1.6, + "Cost Components": "Initial estimate" + }, + { + "City": "Athens 2004", + "Units": 2.9, + "Cost Components": "Overrun" + }, + { + "City": "Beijing 2008", + "Units": 6.8, + "Cost Components": "Initial estimate" + }, + { + "City": "London 2012", + "Units": 6.5, + "Cost Components": "Initial estimate" + }, + { + "City": "London 2012", + "Units": 8.2, + "Cost Components": "Overrun" + }, + { + "City": "Rio 2016", + "Units": 3.0, + "Cost Components": "Initial estimate" + }, + { + "City": "Rio 2016", + "Units": 1.6, + "Cost Components": "Overrun" + }, + { + "City": "Tokyo 2021", + "Units": 7.3, + "Cost Components": "Initial estimate" + }, + { + "City": "Tokyo 2021", + "Units": 5.5, + "Cost Components": "Overrun" + }, + { + "City": "Tokyo 2021", + "Units": 2.8, + "Cost Components": "Postponement cost" + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "The city and year of the Olympic Games", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding cost components", + "role": "y" + }, + { + "name": "Cost Components", + "importance": "primary", + "description": "The cost components of the data point", + "role": "group" + } + ] + } + }, + "1135": { + "metadata": { + "title": "Where Public Transit Is Punctual", + "description": "Percent of trains that run on-time in selected city in 2017", + "main_insight": "Hong Kong and Berlin have the highest percentage of on-time trains at 99%, significantly outperforming New York City and Mexico City." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "London", + "On-time Percentage": 97.0 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in On-time Percentage from Mexico City to London.", + "reason": "The On-time Percentage for increases from 71.0 at Mexico City to 97.0 at London, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "New York City", + "On-time Percentage": 63.0 + } + ], + "score": 0.81, + "annotation": "The has a minimum value at New York City", + "reason": "The [{'City': 'New York City', 'On-time Percentage': 63.0}] of has a mainimum value of 63.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "Hong Kong", + "On-time Percentage": 99.0 + }, + { + "City": "Berlin", + "On-time Percentage": 99.0 + } + ], + "score": 0.44, + "annotation": "The has maximum values at Hong Kong, Berlin", + "reason": "The [{'City': 'Hong Kong', 'On-time Percentage': 99.0}, {'City': 'Berlin', 'On-time Percentage': 99.0}] of have maximum values of 99.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "City": "Hong Kong", + "On-time Percentage": 99.0 + }, + { + "City": "Berlin", + "On-time Percentage": 99.0 + }, + { + "City": "Paris", + "On-time Percentage": 98.0 + }, + { + "City": "London", + "On-time Percentage": 97.0 + }, + { + "City": "Mexico City", + "On-time Percentage": 71.0 + }, + { + "City": "New York City", + "On-time Percentage": 63.0 + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "Selected cities", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "On-time Percentage", + "importance": "primary", + "description": "Percentage of trains that run on time", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1979": { + "metadata": { + "title": "76 Percent More Mobile Advertising Revenue", + "description": "Facebook revenue by segment (in million U.S. dollars)", + "main_insight": "Facebook's revenue, particularly mobile advertising revenue, has significantly increased from Q2 2010 to Q2 2013." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Quarter": "Q1 2012", + "Units": 186.0, + "Revenue Streams": "Payments" + } + ], + "score": 0.93, + "annotation": "The Payments has a maximum value at Q1 2012", + "reason": "The [{'Quarter': 'Q1 2012', 'Units': 186.0, 'Revenue Streams': 'Payments'}] of Payments has a maximum value of 186.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Quarter": "Q2 2013", + "Units": 1813.0, + "Revenue Streams": "Total Revenue" + }, + { + "Quarter": "Q2 2013", + "Units": 1038.0, + "Revenue Streams": "Advertising" + } + ], + "score": 0.92, + "annotation": "The Units of Total Revenue and Advertising show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Revenue and Advertising is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q2 2011", + "Units": 818.0, + "Revenue Streams": "Total Revenue" + } + ], + "score": 0.92, + "annotation": "The Total Revenue has the largest proportion in Q2 2011.", + "reason": "The Units of Total Revenue in Q2 2011 accounts for 0.3568935427574171, larger than all other Revenue Streams, of the total of Units of all Revenue Streams." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q1 2011", + "Units": 731.0, + "Revenue Streams": "Total Revenue" + } + ], + "score": 0.91, + "annotation": "The Total Revenue has the largest proportion in Q1 2011.", + "reason": "The Units of Total Revenue in Q1 2011 accounts for 0.3979314099074578, larger than all other Revenue Streams, of the total of Units of all Revenue Streams." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Quarter": "Q4 2010", + "Units": 668.0, + "Revenue Streams": "Total Revenue" + } + ], + "score": 0.91, + "annotation": "The Total Revenue has the largest proportion in Q4 2010.", + "reason": "The Units of Total Revenue in Q4 2010 accounts for 0.40682095006090135, larger than all other Revenue Streams, of the total of Units of all Revenue Streams." + } + ], + "data": { + "data": [ + { + "Quarter": "Q2 2010", + "Units": 487.0, + "Revenue Streams": "Total Revenue" + }, + { + "Quarter": "Q2 2010", + "Units": 417.0, + "Revenue Streams": "Advertising" + }, + { + "Quarter": "Q2 2010", + "Units": 70.0, + "Revenue Streams": "Payments" + }, + { + "Quarter": "Q3 2010", + "Units": 514.0, + "Revenue Streams": "Total Revenue" + }, + { + "Quarter": "Q3 2010", + "Units": 437.0, + "Revenue Streams": "Advertising" + }, + { + "Quarter": "Q3 2010", + "Units": 77.0, + "Revenue Streams": "Payments" + }, + { + "Quarter": "Q4 2010", + "Units": 668.0, + "Revenue Streams": "Total Revenue" + }, + { + "Quarter": "Q4 2010", + "Units": 558.0, + "Revenue Streams": "Advertising" + }, + { + "Quarter": "Q4 2010", + "Units": 110.0, + "Revenue Streams": "Payments" + }, + { + "Quarter": "Q1 2011", + "Units": 731.0, + "Revenue Streams": "Total Revenue" + }, + { + "Quarter": "Q1 2011", + "Units": 618.0, + "Revenue Streams": "Advertising" + }, + { + "Quarter": "Q1 2011", + "Units": 113.0, + "Revenue Streams": "Payments" + }, + { + "Quarter": "Q2 2011", + "Units": 818.0, + "Revenue Streams": "Total Revenue" + }, + { + "Quarter": "Q2 2011", + "Units": 702.0, + "Revenue Streams": "Advertising" + }, + { + "Quarter": "Q2 2011", + "Units": 116.0, + "Revenue Streams": "Payments" + }, + { + "Quarter": "Q3 2011", + "Units": 954.0, + "Revenue Streams": "Total Revenue" + }, + { + "Quarter": "Q3 2011", + "Units": 848.0, + "Revenue Streams": "Advertising" + }, + { + "Quarter": "Q3 2011", + "Units": 106.0, + "Revenue Streams": "Payments" + }, + { + "Quarter": "Q4 2011", + "Units": 1058.0, + "Revenue Streams": "Total Revenue" + }, + { + "Quarter": "Q4 2011", + "Units": 928.0, + "Revenue Streams": "Advertising" + }, + { + "Quarter": "Q4 2011", + "Units": 130.0, + "Revenue Streams": "Payments" + }, + { + "Quarter": "Q1 2012", + "Units": 1058.0, + "Revenue Streams": "Total Revenue" + }, + { + "Quarter": "Q1 2012", + "Units": 872.0, + "Revenue Streams": "Advertising" + }, + { + "Quarter": "Q1 2012", + "Units": 186.0, + "Revenue Streams": "Payments" + }, + { + "Quarter": "Q2 2012", + "Units": 1184.0, + "Revenue Streams": "Total Revenue" + }, + { + "Quarter": "Q2 2012", + "Units": 59.0, + "Revenue Streams": "Mobile Advertising" + }, + { + "Quarter": "Q2 2012", + "Units": 992.0, + "Revenue Streams": "Advertising" + }, + { + "Quarter": "Q2 2012", + "Units": 133.0, + "Revenue Streams": "Payments" + }, + { + "Quarter": "Q3 2012", + "Units": 1262.0, + "Revenue Streams": "Total Revenue" + }, + { + "Quarter": "Q3 2012", + "Units": 152.0, + "Revenue Streams": "Mobile Advertising" + }, + { + "Quarter": "Q3 2012", + "Units": 953.0, + "Revenue Streams": "Advertising" + }, + { + "Quarter": "Q3 2012", + "Units": 157.0, + "Revenue Streams": "Payments" + }, + { + "Quarter": "Q4 2012", + "Units": 1585.0, + "Revenue Streams": "Total Revenue" + }, + { + "Quarter": "Q4 2012", + "Units": 306.0, + "Revenue Streams": "Mobile Advertising" + }, + { + "Quarter": "Q4 2012", + "Units": 1135.0, + "Revenue Streams": "Advertising" + }, + { + "Quarter": "Q4 2012", + "Units": 144.0, + "Revenue Streams": "Payments" + }, + { + "Quarter": "Q1 2013", + "Units": 1458.0, + "Revenue Streams": "Total Revenue" + }, + { + "Quarter": "Q1 2013", + "Units": 375.0, + "Revenue Streams": "Mobile Advertising" + }, + { + "Quarter": "Q1 2013", + "Units": 957.0, + "Revenue Streams": "Advertising" + }, + { + "Quarter": "Q1 2013", + "Units": 126.0, + "Revenue Streams": "Payments" + }, + { + "Quarter": "Q2 2013", + "Units": 1813.0, + "Revenue Streams": "Total Revenue" + }, + { + "Quarter": "Q2 2013", + "Units": 656.0, + "Revenue Streams": "Mobile Advertising" + }, + { + "Quarter": "Q2 2013", + "Units": 1038.0, + "Revenue Streams": "Advertising" + }, + { + "Quarter": "Q2 2013", + "Units": 119.0, + "Revenue Streams": "Payments" + } + ], + "columns": [ + { + "name": "Quarter", + "importance": "primary", + "description": "Quarter of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding revenue streams", + "role": "y" + }, + { + "name": "Revenue Streams", + "importance": "primary", + "description": "The revenue streams of the data point", + "role": "group" + } + ] + } + }, + "259": { + "metadata": { + "title": "The EU's Greatest Achievements According To Europeans", + "description": "% of the public in the EU/UK thinking these are the EU's greatest achievements (2017)", + "main_insight": "A significant percentage of respondents, particularly in the UK, believe the EU has no great achievements. Among those who identified achievements, peace on the European continent and freedom to live & work across the EU were most commonly cited." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Achievement": "None", + "Total": 17.0 + } + ], + "score": 0.84, + "annotation": "The has a maximum value at None", + "reason": "The [{'Achievement': 'None', 'Total': 17.0}] of has a maximum value of 17.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Achievement": "The European Union single market", + "Total": 8.0 + } + ], + "score": 0.72, + "annotation": "The shows a sudden jump in Total from Democratic values to The European Union single market.", + "reason": "The Total for increases from 3.0 at Democratic values to 8.0 at The European Union single market, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Achievement": "Protection of workers' rights", + "Total": 2.0 + }, + { + "Achievement": "Higher standards for consumers", + "Total": 2.0 + }, + { + "Achievement": "Protection of the environment", + "Total": 2.0 + } + ], + "score": 0.56, + "annotation": "The has minimum values at Protection of workers' rights, Higher standards for consumers, Protection of the environment", + "reason": "The [{'Achievement': \"Protection of workers' rights\", 'Total': 2.0}, {'Achievement': 'Higher standards for consumers', 'Total': 2.0}, {'Achievement': 'Protection of the environment', 'Total': 2.0}] of have minimum values of 2.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Achievement": "None", + "Total": 17.0 + }, + { + "Achievement": "Peace on the European continent", + "Total": 14.0 + }, + { + "Achievement": "Freedom to live & work across the European Union", + "Total": 14.0 + }, + { + "Achievement": "The European Union single market", + "Total": 8.0 + }, + { + "Achievement": "Protection of workers' rights", + "Total": 2.0 + }, + { + "Achievement": "A European identity", + "Total": 3.0 + }, + { + "Achievement": "Removing borders between states", + "Total": 15.0 + }, + { + "Achievement": "Influence of Europe in the world", + "Total": 3.0 + }, + { + "Achievement": "Support for deprived regions", + "Total": 3.0 + }, + { + "Achievement": "Higher standards for consumers", + "Total": 2.0 + }, + { + "Achievement": "Democratic values", + "Total": 3.0 + }, + { + "Achievement": "Protection of the environment", + "Total": 2.0 + }, + { + "Achievement": "The Euro currency", + "Total": 11.0 + } + ], + "columns": [ + { + "name": "Achievement", + "importance": "primary", + "description": "Potential achievements of the EU", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Total", + "importance": "primary", + "description": "Percentage of respondents in the EU/UK who selected this achievement", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "151": { + "metadata": { + "title": "Major Projects That Went Catastrophically Over-Budget", + "description": "Selected over-budget construction projects worldwide (billion U.S. dollars)*", + "main_insight": "The International Space Station and the Sochi Olympics are the two projects with the largest budget overruns, significantly exceeding their initial planned costs." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Project": "Brazil World Cup", + "Score": 14.0, + "Budget Status": "Current/Final" + }, + { + "Project": "Brazil World Cup", + "Score": 2.5, + "Budget Status": "Over Budget" + } + ], + "score": 0.98, + "annotation": "The Score of Current/Final and Over Budget show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Current/Final and Over Budget is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Project": "International Space Station", + "Score": 68.25, + "Budget Status": "Over Budget" + } + ], + "score": 0.98, + "annotation": "The Over Budget has a maximum value at International Space Station", + "reason": "The [{'Project': 'International Space Station', 'Score': 68.25, 'Budget Status': 'Over Budget'}] of Over Budget has a maximum value of 68.25, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Project": "International Space Station", + "Score": 83.85, + "Budget Status": "Current/Final" + } + ], + "score": 0.97, + "annotation": "The Current/Final has a maximum value at International Space Station", + "reason": "The [{'Project': 'International Space Station', 'Score': 83.85, 'Budget Status': 'Current/Final'}] of Current/Final has a maximum value of 83.85, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Project": "International Space Station", + "Score": 83.85, + "Budget Status": "Current/Final" + } + ], + "score": 0.86, + "annotation": "The Current/Final has the largest proportion in International Space Station.", + "reason": "The Score of Current/Final in International Space Station accounts for 0.5, larger than all other Budget Status, of the total of Score of all Budget Status." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Project": "Sochi Olympics", + "Score": 50.7, + "Budget Status": "Current/Final" + } + ], + "score": 0.86, + "annotation": "The Current/Final has the largest proportion in Sochi Olympics.", + "reason": "The Score of Current/Final in Sochi Olympics accounts for 0.5, larger than all other Budget Status, of the total of Score of all Budget Status." + } + ], + "data": { + "data": [ + { + "Project": "International Space Station", + "Score": 83.85, + "Budget Status": "Current/Final" + }, + { + "Project": "International Space Station", + "Score": 15.6, + "Budget Status": "Planned" + }, + { + "Project": "International Space Station", + "Score": 68.25, + "Budget Status": "Over Budget" + }, + { + "Project": "Sochi Olympics", + "Score": 50.7, + "Budget Status": "Current/Final" + }, + { + "Project": "Sochi Olympics", + "Score": 11.7, + "Budget Status": "Planned" + }, + { + "Project": "Sochi Olympics", + "Score": 39.0, + "Budget Status": "Over Budget" + }, + { + "Project": "The Channel Tunnel", + "Score": 26.6, + "Budget Status": "Current/Final" + }, + { + "Project": "The Channel Tunnel", + "Score": 5.5, + "Budget Status": "Planned" + }, + { + "Project": "The Channel Tunnel", + "Score": 21.1, + "Budget Status": "Over Budget" + }, + { + "Project": "Three Gorges Dam", + "Score": 31.78, + "Budget Status": "Current/Final" + }, + { + "Project": "Three Gorges Dam", + "Score": 15.6, + "Budget Status": "Planned" + }, + { + "Project": "Three Gorges Dam", + "Score": 16.18, + "Budget Status": "Over Budget" + }, + { + "Project": "Boston's Big Dig", + "Score": 21.55, + "Budget Status": "Current/Final" + }, + { + "Project": "Boston's Big Dig", + "Score": 8.1, + "Budget Status": "Planned" + }, + { + "Project": "Boston's Big Dig", + "Score": 13.45, + "Budget Status": "Over Budget" + }, + { + "Project": "London Olympics", + "Score": 14.79, + "Budget Status": "Current/Final" + }, + { + "Project": "London Olympics", + "Score": 2.88, + "Budget Status": "Planned" + }, + { + "Project": "London Olympics", + "Score": 11.91, + "Budget Status": "Over Budget" + }, + { + "Project": "Athens Olympics", + "Score": 9.7, + "Budget Status": "Current/Final" + }, + { + "Project": "Athens Olympics", + "Score": 2.71, + "Budget Status": "Planned" + }, + { + "Project": "Athens Olympics", + "Score": 6.99, + "Budget Status": "Over Budget" + }, + { + "Project": "Jubilee Line Extension", + "Score": 5.86, + "Budget Status": "Current/Final" + }, + { + "Project": "Jubilee Line Extension", + "Score": 1.75, + "Budget Status": "Planned" + }, + { + "Project": "Jubilee Line Extension", + "Score": 4.11, + "Budget Status": "Over Budget" + }, + { + "Project": "Hubble Space Telescope", + "Score": 5.4, + "Budget Status": "Current/Final" + }, + { + "Project": "Hubble Space Telescope", + "Score": 1.6, + "Budget Status": "Planned" + }, + { + "Project": "Hubble Space Telescope", + "Score": 3.8, + "Budget Status": "Over Budget" + }, + { + "Project": "Denver International Airport", + "Score": 6.0, + "Budget Status": "Current/Final" + }, + { + "Project": "Denver International Airport", + "Score": 2.9, + "Budget Status": "Planned" + }, + { + "Project": "Denver International Airport", + "Score": 3.1, + "Budget Status": "Over Budget" + }, + { + "Project": "Montreal Olympic Stadium", + "Score": 3.97, + "Budget Status": "Current/Final" + }, + { + "Project": "Montreal Olympic Stadium", + "Score": 1.01, + "Budget Status": "Planned" + }, + { + "Project": "Montreal Olympic Stadium", + "Score": 2.96, + "Budget Status": "Over Budget" + }, + { + "Project": "Brazil World Cup", + "Score": 14.0, + "Budget Status": "Current/Final" + }, + { + "Project": "Brazil World Cup", + "Score": 11.5, + "Budget Status": "Planned" + }, + { + "Project": "Brazil World Cup", + "Score": 2.5, + "Budget Status": "Over Budget" + } + ], + "columns": [ + { + "name": "Project", + "importance": "primary", + "description": "Name of the construction project", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding budget status", + "role": "y" + }, + { + "name": "Budget Status", + "importance": "primary", + "description": "The budget status of the data point", + "role": "group" + } + ] + } + }, + "1258": { + "metadata": { + "title": "Which Sports Do Americans Follow?", + "description": "Share of U.S. sports fans saying they follow these professional sports", + "main_insight": "Football is by far the most followed sport in the US, with 74.5% of sports fans saying they follow it. Basketball and Baseball are the next most popular, followed by a significant drop to other sports." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Sport": "Football", + "Score": 74.5, + "Share Metric": "Fan_Share" + } + ], + "score": 0.92, + "annotation": "The Fan_Share has a maximum value at Football", + "reason": "The [{'Sport': 'Football', 'Score': 74.5, 'Share Metric': 'Fan_Share'}] of Fan_Share has a maximum value of 74.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Sport": "Baseball", + "Score": 50.5, + "Share Metric": "Fan_Share" + } + ], + "score": 0.8, + "annotation": "The Fan_Share shows a sudden jump in Score from Boxing to Baseball.", + "reason": "The Score for Fan_Share increases from 23.4 at Boxing to 50.5 at Baseball, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Sport": "Motorsports", + "Score": 14.6, + "Share Metric": "Fan_Share" + } + ], + "score": 0.5, + "annotation": "The Fan_Share has a minimum value at Motorsports", + "reason": "The [{'Sport': 'Motorsports', 'Score': 14.6, 'Share Metric': 'Fan_Share'}] of Fan_Share has a mainimum value of 14.6, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Sport": "Football", + "Score": 74.5, + "Share Metric": "Fan_Share" + }, + { + "Sport": "Basketball", + "Score": 56.6, + "Share Metric": "Fan_Share" + }, + { + "Sport": "Baseball", + "Score": 50.5, + "Share Metric": "Fan_Share" + }, + { + "Sport": "Boxing", + "Score": 23.4, + "Share Metric": "Fan_Share" + }, + { + "Sport": "Hockey", + "Score": 22.1, + "Share Metric": "Fan_Share" + }, + { + "Sport": "Soccer", + "Score": 21.6, + "Share Metric": "Fan_Share" + }, + { + "Sport": "Golf", + "Score": 19.7, + "Share Metric": "Fan_Share" + }, + { + "Sport": "MMA", + "Score": 16.7, + "Share Metric": "Fan_Share" + }, + { + "Sport": "Tennis", + "Score": 15.5, + "Share Metric": "Fan_Share" + }, + { + "Sport": "Motorsports", + "Score": 14.6, + "Share Metric": "Fan_Share" + } + ], + "columns": [ + { + "name": "Sport", + "importance": "primary", + "description": "Name of the professional sport", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding share metric", + "role": "y" + }, + { + "name": "Share Metric", + "importance": "primary", + "description": "The share metric of the data point", + "role": "group" + } + ] + } + }, + "1513": { + "metadata": { + "title": "Emerging Market Slump Prevents PC Industry Growth", + "description": "Year-over-year change of global PC shipments*", + "main_insight": "Global PC shipments experienced a significant decline from positive growth in 2010 to near-zero or negative growth between 2012 and 2014, ending with a -0.5% change in Q3 2014." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Quarter": "Q1 2010", + "YoY Change": 0.35 + } + ], + "score": 0.99, + "annotation": "The has a maximum value at Q1 2010", + "reason": "The [{'Quarter': 'Q1 2010', 'YoY Change': 0.35}] of has a maximum value of 0.35, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Quarter": "Q2 2010", + "YoY Change": 0.22 + } + ], + "score": 0.69, + "annotation": "The shows a sudden jump in YoY Change from Q3 2010 to Q2 2010.", + "reason": "The YoY Change for increases from 0.08 at Q3 2010 to 0.22 at Q2 2010, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Quarter": "Q1 2013", + "YoY Change": -0.12 + } + ], + "score": 0.66, + "annotation": "The has a minimum value at Q1 2013", + "reason": "The [{'Quarter': 'Q1 2013', 'YoY Change': -0.12}] of has a mainimum value of -0.12, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Quarter": "Q1 2010", + "YoY Change": 0.35 + }, + { + "Quarter": "Q2 2010", + "YoY Change": 0.22 + }, + { + "Quarter": "Q3 2010", + "YoY Change": 0.08 + }, + { + "Quarter": "Q4 2010", + "YoY Change": 0.03 + }, + { + "Quarter": "Q1 2011", + "YoY Change": 0.03 + }, + { + "Quarter": "Q2 2011", + "YoY Change": 0.05 + }, + { + "Quarter": "Q3 2011", + "YoY Change": 0.05 + }, + { + "Quarter": "Q4 2011", + "YoY Change": -0.04 + }, + { + "Quarter": "Q1 2012", + "YoY Change": -0.03 + }, + { + "Quarter": "Q2 2012", + "YoY Change": -0.03 + }, + { + "Quarter": "Q3 2012", + "YoY Change": -0.08 + }, + { + "Quarter": "Q4 2012", + "YoY Change": -0.07 + }, + { + "Quarter": "Q1 2013", + "YoY Change": -0.12 + }, + { + "Quarter": "Q2 2013", + "YoY Change": -0.1 + }, + { + "Quarter": "Q3 2013", + "YoY Change": -0.08 + }, + { + "Quarter": "Q4 2013", + "YoY Change": -0.05 + }, + { + "Quarter": "Q1 2014", + "YoY Change": -0.03 + }, + { + "Quarter": "Q2 2014", + "YoY Change": -0.02 + }, + { + "Quarter": "Q3 2014", + "YoY Change": -0.005 + } + ], + "columns": [ + { + "name": "Quarter", + "importance": "primary", + "description": "Represents the quarter and year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "YoY Change", + "importance": "primary", + "description": "Year-over-year change in global PC shipments", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1298": { + "metadata": { + "title": "Netflix is Almost as Popular as Cable Among Young Adults", + "description": "% of American adults who subscribe to the following Pay-TV services, by age (as of October 2013)", + "main_insight": "Netflix subscriptions among young adults (18-36) are almost as high as cable TV subscriptions, while older age groups still prefer cable TV." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Pay-TV Service": "Hulu Plus", + "Score": 3.0, + "Age Group": "49-67" + }, + { + "Pay-TV Service": "Hulu Plus", + "Score": 1.0, + "Age Group": "68+" + } + ], + "score": 0.99, + "annotation": "The Score of 49-67 and 68+ show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between 49-67 and 68+ is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Pay-TV Service": "Hulu Plus", + "Score": 5.0, + "Age Group": "37-48" + }, + { + "Pay-TV Service": "Hulu Plus", + "Score": 3.0, + "Age Group": "49-67" + } + ], + "score": 0.96, + "annotation": "The Score of 37-48 and 49-67 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between 37-48 and 49-67 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "18-36", + "Pay-TV Service": "total", + "Score": 130.0 + } + ], + "score": 0.94, + "annotation": "The total value of 18-36 has the largest proportion.", + "reason": "The total value of Score of 18-36 accounts for 0.26859504132231404, larger than all other Age Group, of the total of Score of all Age Group." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Pay-TV Service": "Cable TV", + "Score": 63.0, + "Age Group": "68+" + } + ], + "score": 0.93, + "annotation": "The 68+ has the largest proportion in Cable TV.", + "reason": "The Score of 68+ in Cable TV accounts for 0.2971698113207547, larger than all other Age Group, of the total of Score of all Age Group." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Pay-TV Service": "Satellite TV", + "Score": 30.0, + "Age Group": "37-48" + } + ], + "score": 0.93, + "annotation": "The 37-48 has the largest proportion in Satellite TV.", + "reason": "The Score of 37-48 in Satellite TV accounts for 0.30303030303030304, larger than all other Age Group, of the total of Score of all Age Group." + } + ], + "data": { + "data": [ + { + "Pay-TV Service": "Cable TV", + "Score": 46.0, + "Age Group": "18-36" + }, + { + "Pay-TV Service": "Cable TV", + "Score": 48.0, + "Age Group": "37-48" + }, + { + "Pay-TV Service": "Cable TV", + "Score": 55.0, + "Age Group": "49-67" + }, + { + "Pay-TV Service": "Cable TV", + "Score": 63.0, + "Age Group": "68+" + }, + { + "Pay-TV Service": "Netflix", + "Score": 43.0, + "Age Group": "18-36" + }, + { + "Pay-TV Service": "Netflix", + "Score": 31.0, + "Age Group": "37-48" + }, + { + "Pay-TV Service": "Netflix", + "Score": 21.0, + "Age Group": "49-67" + }, + { + "Pay-TV Service": "Netflix", + "Score": 13.0, + "Age Group": "68+" + }, + { + "Pay-TV Service": "Satellite TV", + "Score": 16.0, + "Age Group": "18-36" + }, + { + "Pay-TV Service": "Satellite TV", + "Score": 30.0, + "Age Group": "37-48" + }, + { + "Pay-TV Service": "Satellite TV", + "Score": 28.0, + "Age Group": "49-67" + }, + { + "Pay-TV Service": "Satellite TV", + "Score": 25.0, + "Age Group": "68+" + }, + { + "Pay-TV Service": "Amazon Prime", + "Score": 17.0, + "Age Group": "18-36" + }, + { + "Pay-TV Service": "Amazon Prime", + "Score": 15.0, + "Age Group": "37-48" + }, + { + "Pay-TV Service": "Amazon Prime", + "Score": 10.0, + "Age Group": "49-67" + }, + { + "Pay-TV Service": "Amazon Prime", + "Score": 6.0, + "Age Group": "68+" + }, + { + "Pay-TV Service": "Hulu Plus", + "Score": 8.0, + "Age Group": "18-36" + }, + { + "Pay-TV Service": "Hulu Plus", + "Score": 5.0, + "Age Group": "37-48" + }, + { + "Pay-TV Service": "Hulu Plus", + "Score": 3.0, + "Age Group": "49-67" + }, + { + "Pay-TV Service": "Hulu Plus", + "Score": 1.0, + "Age Group": "68+" + } + ], + "columns": [ + { + "name": "Pay-TV Service", + "importance": "primary", + "description": "The name of the subscription service", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding age group", + "role": "y" + }, + { + "name": "Age Group", + "importance": "primary", + "description": "The age group of the data point", + "role": "group" + } + ] + } + }, + "2200": { + "metadata": { + "title": "China Tops Global Diplomacy Index", + "description": "Countries with the most diplomatic posts abroad (2023)", + "main_insight": "China has the highest number of diplomatic posts abroad in 2023, slightly surpassing the United States." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "China", + "Count": 1, + "Post Types": "Others" + } + ], + "score": 0.99, + "annotation": "The Others has the smallest proportion in China.", + "reason": "The Count of Others in China accounts for 0.0018248175182481751, smaller than all other Post Types, of the total of Count of all Post Types." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Germany", + "Count": 1, + "Post Types": "Others" + } + ], + "score": 0.99, + "annotation": "The Others has the smallest proportion in France.", + "reason": "The Count of Others in France accounts for 0.0019723865877712033, smaller than all other Post Types, of the total of Count of all Post Types." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Turkey", + "Count": 1, + "Post Types": "Others" + } + ], + "score": 0.99, + "annotation": "The Others has the smallest proportion in Turkey.", + "reason": "The Count of Others in Turkey accounts for 0.001984126984126984, smaller than all other Post Types, of the total of Count of all Post Types." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Post Types": "Total Posts", + "Country": "total", + "Count": 2574 + } + ], + "score": 0.96, + "annotation": "The total value of Total Posts has the smallest proportion.", + "reason": "The total value of Count of Total Posts accounts for 0.009115593483320403, smaller than all other Post Types, of the total of Count of all Post Types." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "United States", + "Count": 10, + "Post Types": "Permanent missions" + }, + { + "Country": "United States", + "Count": 10, + "Post Types": "Others" + } + ], + "score": 0.92, + "annotation": "The Permanent missions, Others all have the smallest proportion in United States.", + "reason": "The Count of Permanent missions, Others in United States all account for 0.01845018450184502, smaller than all other Post Types, of the total of Count of all Post Types." + } + ], + "data": { + "data": [ + { + "Country": "China", + "Count": 274, + "Post Types": "Total Posts" + }, + { + "Country": "China", + "Count": 169, + "Post Types": "Embassies" + }, + { + "Country": "China", + "Count": 96, + "Post Types": "Consulates" + }, + { + "Country": "China", + "Count": 8, + "Post Types": "Permanent missions" + }, + { + "Country": "China", + "Count": 1, + "Post Types": "Others" + }, + { + "Country": "United States", + "Count": 271, + "Post Types": "Total Posts" + }, + { + "Country": "United States", + "Count": 163, + "Post Types": "Embassies" + }, + { + "Country": "United States", + "Count": 88, + "Post Types": "Consulates" + }, + { + "Country": "United States", + "Count": 10, + "Post Types": "Permanent missions" + }, + { + "Country": "United States", + "Count": 10, + "Post Types": "Others" + }, + { + "Country": "Turkey", + "Count": 252, + "Post Types": "Total Posts" + }, + { + "Country": "Turkey", + "Count": 145, + "Post Types": "Embassies" + }, + { + "Country": "Turkey", + "Count": 94, + "Post Types": "Consulates" + }, + { + "Country": "Turkey", + "Count": 12, + "Post Types": "Permanent missions" + }, + { + "Country": "Turkey", + "Count": 1, + "Post Types": "Others" + }, + { + "Country": "Japan", + "Count": 251, + "Post Types": "Total Posts" + }, + { + "Country": "Japan", + "Count": 151, + "Post Types": "Embassies" + }, + { + "Country": "Japan", + "Count": 67, + "Post Types": "Consulates" + }, + { + "Country": "Japan", + "Count": 10, + "Post Types": "Permanent missions" + }, + { + "Country": "Japan", + "Count": 23, + "Post Types": "Others" + }, + { + "Country": "France", + "Count": 249, + "Post Types": "Total Posts" + }, + { + "Country": "France", + "Count": 151, + "Post Types": "Embassies" + }, + { + "Country": "France", + "Count": 89, + "Post Types": "Consulates" + }, + { + "Country": "France", + "Count": 17, + "Post Types": "Permanent missions" + }, + { + "Country": "Russia", + "Count": 230, + "Post Types": "Total Posts" + }, + { + "Country": "Russia", + "Count": 144, + "Post Types": "Embassies" + }, + { + "Country": "Russia", + "Count": 75, + "Post Types": "Consulates" + }, + { + "Country": "Russia", + "Count": 11, + "Post Types": "Permanent missions" + }, + { + "Country": "United Kingdom", + "Count": 225, + "Post Types": "Total Posts" + }, + { + "Country": "United Kingdom", + "Count": 145, + "Post Types": "Embassies" + }, + { + "Country": "United Kingdom", + "Count": 66, + "Post Types": "Consulates" + }, + { + "Country": "United Kingdom", + "Count": 14, + "Post Types": "Permanent missions" + }, + { + "Country": "Germany", + "Count": 217, + "Post Types": "Total Posts" + }, + { + "Country": "Germany", + "Count": 149, + "Post Types": "Embassies" + }, + { + "Country": "Germany", + "Count": 55, + "Post Types": "Consulates" + }, + { + "Country": "Germany", + "Count": 12, + "Post Types": "Permanent missions" + }, + { + "Country": "Germany", + "Count": 1, + "Post Types": "Others" + }, + { + "Country": "Italy", + "Count": 206, + "Post Types": "Total Posts" + }, + { + "Country": "Italy", + "Count": 123, + "Post Types": "Embassies" + }, + { + "Country": "Italy", + "Count": 74, + "Post Types": "Consulates" + }, + { + "Country": "Italy", + "Count": 9, + "Post Types": "Permanent missions" + }, + { + "Country": "Brazil", + "Count": 205, + "Post Types": "Total Posts" + }, + { + "Country": "Brazil", + "Count": 133, + "Post Types": "Embassies" + }, + { + "Country": "Brazil", + "Count": 61, + "Post Types": "Consulates" + }, + { + "Country": "Brazil", + "Count": 11, + "Post Types": "Permanent missions" + }, + { + "Country": "India", + "Count": 194, + "Post Types": "Total Posts" + }, + { + "Country": "India", + "Count": 124, + "Post Types": "Embassies" + }, + { + "Country": "India", + "Count": 54, + "Post Types": "Consulates" + }, + { + "Country": "India", + "Count": 5, + "Post Types": "Permanent missions" + }, + { + "Country": "India", + "Count": 11, + "Post Types": "Others" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Count", + "importance": "primary", + "description": "Count for the corresponding post types", + "role": "y" + }, + { + "name": "Post Types", + "importance": "primary", + "description": "The post types of the data point", + "role": "group" + } + ] + } + }, + "965": { + "metadata": { + "title": "Benzema Triumph Marks (Likely) End of the Messi-Ronaldo Era", + "description": "Ballon d'Or winners since 2008*", + "main_insight": "The infographic highlights the dominance of Lionel Messi and Cristiano Ronaldo in the Ballon d'Or awards since 2008, with K. Benzema's 2022 win potentially signaling the end of their era. Lionel Messi has the most wins." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2022, + "Winner": "K. Benzema" + }, + { + "Year": 2021, + "Winner": "L. Messi" + }, + { + "Year": 2019, + "Winner": "L. Messi" + }, + { + "Year": 2018, + "Winner": "L. Modric" + }, + { + "Year": 2017, + "Winner": "C. Ronaldo" + }, + { + "Year": 2016, + "Winner": "C. Ronaldo" + }, + { + "Year": 2015, + "Winner": "L. Messi" + }, + { + "Year": 2014, + "Winner": "C. Ronaldo" + }, + { + "Year": 2013, + "Winner": "C. Ronaldo" + }, + { + "Year": 2012, + "Winner": "L. Messi" + }, + { + "Year": 2011, + "Winner": "L. Messi" + }, + { + "Year": 2010, + "Winner": "L. Messi" + }, + { + "Year": 2009, + "Winner": "L. Messi" + }, + { + "Year": 2008, + "Winner": "C. 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ExxonMobil is projected to spend the most, at $167 billion, significantly more than any other company." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Company": "EOG Resouces", + "Units": 2.0, + "Resource Type": "Gas Fields" + } + ], + "score": 0.94, + "annotation": "The Gas Fields has the smallest proportion in EOG Resouces.", + "reason": "The Units of Gas Fields in EOG Resouces accounts for 0.0136986301369863, smaller than all other Resource Type, of the total of Units of all Resource Type." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Company": "PetroChina", + "Units": 70.0, + "Resource Type": "Total Expenditure" + }, + { + "Company": "PetroChina", + "Units": 17.0, + "Resource Type": "Gas Fields" + } + ], + "score": 0.92, + "annotation": "The Units of Total Expenditure and Gas Fields show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Expenditure and Gas Fields is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Company": "Gazprom", + "Units": 28.0, + "Resource Type": "Oil Fields" + } + ], + "score": 0.9, + "annotation": "The Oil Fields has a minimum value at Gazprom", + "reason": "The [{'Company': 'Gazprom', 'Units': 28.0, 'Resource Type': 'Oil Fields'}] of Oil Fields has a mainimum value of 28.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Company": "ExxonMobil", + "Units": 167.0, + "Resource Type": "Total Expenditure" + } + ], + "score": 0.89, + "annotation": "The Total Expenditure has a maximum value at ExxonMobil", + "reason": "The [{'Company': 'ExxonMobil', 'Units': 167.0, 'Resource Type': 'Total Expenditure'}] of Total Expenditure has a maximum value of 167.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Company": "ExxonMobil", + "Units": 84.0, + "Resource Type": "Oil Fields" + } + ], + "score": 0.87, + "annotation": "The Oil Fields has a maximum value at ExxonMobil", + "reason": "The [{'Company': 'ExxonMobil', 'Units': 84.0, 'Resource Type': 'Oil Fields'}] of Oil Fields has a maximum value of 84.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Company": "ExxonMobil", + "Units": 167.0, + "Resource Type": "Total Expenditure" + }, + { + "Company": "ExxonMobil", + "Units": 84.0, + "Resource Type": "Oil Fields" + }, + { + "Company": "ExxonMobil", + "Units": 83.0, + "Resource Type": "Gas Fields" + }, + { + "Company": "Shell", + "Units": 149.0, + "Resource Type": "Total Expenditure" + }, + { + "Company": "Shell", + "Units": 50.0, + "Resource Type": "Oil Fields" + }, + { + "Company": "Shell", + "Units": 99.0, + "Resource Type": "Gas Fields" + }, + { + "Company": "Gazprom", + "Units": 132.0, + "Resource Type": "Total Expenditure" + }, + { + "Company": "Gazprom", + "Units": 28.0, + "Resource Type": "Oil Fields" + }, + { + "Company": "Gazprom", + "Units": 104.0, + "Resource Type": "Gas Fields" + }, + { + "Company": "Chevron", + "Units": 94.0, + "Resource Type": "Total Expenditure" + }, + { + "Company": "Chevron", + "Units": 69.0, + "Resource Type": "Oil Fields" + }, + { + "Company": "Chevron", + "Units": 25.0, + "Resource Type": "Gas Fields" + }, + { + "Company": "Total", + "Units": 81.0, + "Resource Type": "Total Expenditure" + }, + { + "Company": "Total", + "Units": 49.0, + "Resource Type": "Oil Fields" + }, + { + "Company": "Total", + "Units": 32.0, + "Resource Type": "Gas Fields" + }, + { + "Company": "Pemex", + "Units": 76.0, + "Resource Type": "Total Expenditure" + }, + { + "Company": "Pemex", + "Units": 53.0, + "Resource Type": "Oil Fields" + }, + { + "Company": "Pemex", + "Units": 23.0, + "Resource Type": "Gas Fields" + }, + { + "Company": "Anadarko", + "Units": 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[ + { + "name": "Company", + "importance": "primary", + "description": "Name of the company", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding resource type", + "role": "y" + }, + { + "name": "Resource Type", + "importance": "primary", + "description": "The resource type of the data point", + "role": "group" + } + ] + } + }, + "591": { + "metadata": { + "title": "Southern Europe skeptical of EU's economic policy", + "description": "% of Europeans who disapprove/approve of the EU's handling of economic issues", + "main_insight": "A majority of respondents in Southern European countries disapprove of the EU's handling of economic issues, with Greece showing the highest disapproval rate. Other European nations show more mixed or positive opinions." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Poland", + "Score": 33.0, + "Approval Rating": "Disapprove" + }, + { + "Country": "Poland", + "Score": 47.0, + "Approval Rating": "Approve" + } + ], + "score": 0.97, + "annotation": "The Score of Disapprove and Approve show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Disapprove and Approve is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Greece", + "Score": 6.0, + "Approval Rating": "Approve" + } + ], + "score": 0.93, + "annotation": "The Approve has a minimum value at Greece", + "reason": "The [{'Country': 'Greece', 'Score': 6.0, 'Approval Rating': 'Approve'}] of Approve has a mainimum value of 6.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Greece", + "Score": 92.0, + "Approval Rating": "Disapprove" + } + ], + "score": 0.92, + "annotation": "The Disapprove has a maximum value at Greece", + "reason": "The [{'Country': 'Greece', 'Score': 92.0, 'Approval Rating': 'Disapprove'}] of Disapprove has a maximum value of 92.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Netherlands", + "Score": 49.0, + "Approval Rating": "Disapprove" + } + ], + "score": 0.79, + "annotation": "The Disapprove accounts for the majority in Netherlands.", + "reason": "The Score of Disapprove in Netherlands accounts for 0.5384615384615384 (more than 50%) of the total of Score of all Approval Rating." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Germany", + "Score": 47.0, + "Approval Rating": "Approve" + } + ], + "score": 0.78, + "annotation": "The Approve accounts for the majority in Germany.", + "reason": "The Score of Approve in Germany accounts for 0.5529411764705883 (more than 50%) of the total of Score of all Approval Rating." + } + ], + "data": { + "data": [ + { + "Country": "Greece", + "Score": 92.0, + "Approval Rating": "Disapprove" + }, + { + "Country": "Greece", + "Score": 6.0, + "Approval Rating": "Approve" + }, + { + "Country": "Italy", + "Score": 68.0, + "Approval Rating": "Disapprove" + }, + { + "Country": "Italy", + "Score": 22.0, + "Approval Rating": "Approve" + }, + { + "Country": "France", + "Score": 66.0, + "Approval Rating": "Disapprove" + }, + { + "Country": "France", + "Score": 27.0, + "Approval Rating": "Approve" + }, + { + "Country": "Spain", + "Score": 65.0, + "Approval Rating": "Disapprove" + }, + { + "Country": "Spain", + "Score": 28.0, + "Approval Rating": "Approve" + }, + { + "Country": "Sweden", + "Score": 59.0, + "Approval Rating": "Disapprove" + }, + { + "Country": "Sweden", + "Score": 34.0, + "Approval Rating": "Approve" + }, + { + "Country": "UK", + "Score": 55.0, + "Approval Rating": "Disapprove" + }, + { + "Country": "UK", + "Score": 28.0, + "Approval Rating": "Approve" + }, + { + "Country": "Netherlands", + "Score": 49.0, + "Approval Rating": "Disapprove" + }, + { + "Country": "Netherlands", + "Score": 42.0, + "Approval Rating": "Approve" + }, + { + "Country": "Hungary", + "Score": 48.0, + "Approval Rating": "Disapprove" + }, + { + "Country": "Hungary", + "Score": 38.0, + "Approval Rating": "Approve" + }, + { + "Country": "Germany", + "Score": 38.0, + "Approval Rating": "Disapprove" + }, + { + "Country": "Germany", + "Score": 47.0, + "Approval Rating": "Approve" + }, + { + "Country": "Poland", + "Score": 33.0, + "Approval Rating": "Disapprove" + }, + { + "Country": "Poland", + "Score": 47.0, + "Approval Rating": "Approve" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "European country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding approval rating", + "role": "y" + }, + { + "name": "Approval Rating", + "importance": "primary", + "description": "The approval rating of the data point", + "role": "group" + } + ] + } + }, + "1526": { + "metadata": { + "title": "US & UK The Least Concerned About Climate Change", + "description": "% of people who think climate change is 'not a serious problem'", + "main_insight": "The United States and the United Kingdom have the highest percentage of people who believe climate change is not a serious problem, significantly higher than other countries surveyed." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United States", + "Percentage": 32.0 + } + ], + "score": 0.92, + "annotation": "The has a maximum value at United States", + "reason": "The [{'Country': 'United States', 'Percentage': 32.0}] of has a maximum value of 32.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Malaysia", + "Percentage": 2.0 + } + ], + "score": 0.67, + "annotation": "The has a minimum value at Malaysia", + "reason": "The [{'Country': 'Malaysia', 'Percentage': 2.0}] of has a mainimum value of 2.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Percentage": 32.0 + } + ], + "score": 0.6, + "annotation": "The shows a sudden jump in Percentage from United Kingdom to United States.", + "reason": "The Percentage for increases from 26.0 at United Kingdom to 32.0 at United States, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Percentage": 32.0 + }, + { + "Country": "United Kingdom", + "Percentage": 26.0 + }, + { + "Country": "Finland", + "Percentage": 23.0 + }, + { + "Country": "Norway", + "Percentage": 22.0 + }, + { + "Country": "Sweden", + "Percentage": 18.0 + }, + { + "Country": "Denmark", + "Percentage": 14.0 + }, + { + "Country": "Germany", + "Percentage": 11.0 + }, + { + "Country": "Australia", + "Percentage": 11.0 + }, + { + "Country": "France", + "Percentage": 10.0 + }, + { + "Country": "Thailand", + "Percentage": 6.0 + }, + { + "Country": "Hong Kong", + "Percentage": 5.0 + }, + { + "Country": "Singapore", + "Percentage": 5.0 + }, + { + "Country": "China", + "Percentage": 4.0 + }, + { + "Country": "Indonesia", + "Percentage": 3.0 + }, + { + "Country": "Malaysia", + "Percentage": 2.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "The country the data refers to", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of people in the given country who consider climate change to not be a serious concern.", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "111": { + "metadata": { + "title": "How do Britons feel about Boris Johnson becoming PM?", + "description": "\"Which of the following best reflects your reaction to Boris Johnson becoming leader?\"", + "main_insight": "A significant portion of Britons (37%) were dismayed by Boris Johnson becoming PM, while a smaller, but still notable, percentage were pleased (18%) or delighted (10%)." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Response": "Dismayed", + "Percentage": 37.0 + } + ], + "score": 0.9, + "annotation": "The shows a sudden jump in Percentage from Pleased to Dismayed.", + "reason": "The Percentage for increases from 18.0 at Pleased to 37.0 at Dismayed, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Response": "Dismayed", + "Percentage": 37.0 + } + ], + "score": 0.9, + "annotation": "The has a maximum value at Dismayed", + "reason": "The [{'Response': 'Dismayed', 'Percentage': 37.0}] of has a maximum value of 37.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Response": "Don't know", + "Percentage": 8.0 + } + ], + "score": 0.5, + "annotation": "The has a minimum value at Don't know", + "reason": "The [{'Response': \"Don't know\", 'Percentage': 8.0}] of has a mainimum value of 8.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Response": "Dismayed", + "Percentage": 37.0 + }, + { + "Response": "Pleased", + "Percentage": 18.0 + }, + { + "Response": "Don't really mind either way", + "Percentage": 17.0 + }, + { + "Response": "Delighted", + "Percentage": 10.0 + }, + { + "Response": "Disappointed", + "Percentage": 10.0 + }, + { + "Response": "Don't know", + "Percentage": 8.0 + } + ], + "columns": [ + { + "name": "Response", + "importance": "primary", + "description": "The different reactions of Britons to Boris Johnson becoming PM", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "The percentage of Britons who selected each response", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2044": { + "metadata": { + "title": "Teacher Vaccinations Before Schools Reopen", + "description": "Percentage of U.S. voters who say schools should wait to reopen until all teachers are vaccinated", + "main_insight": "There is a significant difference between Democratic and Republican voters on whether schools should wait to reopen until all teachers are vaccinated. A large majority of Democrats (74%) believe schools should wait, while a majority of Republicans (55%) do not." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Political Party": "Democrat", + "Yes": 74.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Yes from Republican to Democrat.", + "reason": "The Yes for increases from 33.0 at Republican to 74.0 at Democrat, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Political Party": "Democrat", + "Yes": 74.0 + } + ], + "score": 0.45, + "annotation": "The has a maximum value at Democrat", + "reason": "The [{'Political Party': 'Democrat', 'Yes': 74.0}] of has a maximum value of 74.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Political Party": "Republican", + "Yes": 33.0 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at Republican", + "reason": "The [{'Political Party': 'Republican', 'Yes': 33.0}] of has a mainimum value of 33.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Political Party": "Democrat", + "Yes": 74.0 + }, + { + "Political Party": "Republican", + "Yes": 33.0 + } + ], + "columns": [ + { + "name": "Political Party", + "importance": "primary", + "description": "Political affiliation of survey respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Yes", + "importance": "primary", + "description": "Percentage of voters who say schools should wait to reopen until all teachers are vaccinated", + "unit": "percent", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1470": { + "metadata": { + "title": "Canada Day: Some Fun Facts", + "description": "How Canadians plan to celebrate Canada Day and interesting facts to mark Canada Day", + "main_insight": "The infographic presents various statistics related to Canada Day, including how Canadians plan to celebrate and other interesting facts about Canadian culture." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Celebration Activity": "Cover everything in maple syrup", + "Percentage": 3.0 + } + ], + "score": 0.77, + "annotation": "The has a minimum value at Cover everything in maple syrup", + "reason": "The [{'Celebration Activity': 'Cover everything in maple syrup', 'Percentage': 3.0}] of has a mainimum value of 3.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Celebration Activity": "Attend a fireworks show", + "Percentage": 40.0 + } + ], + "score": 0.72, + "annotation": "The has a maximum value at Attend a fireworks show", + "reason": "The [{'Celebration Activity': 'Attend a fireworks show', 'Percentage': 40.0}] of has a maximum value of 40.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Celebration Activity": "Nothing", + "Percentage": 29.0 + } + ], + "score": 0.69, + "annotation": "The shows a sudden jump in Percentage from Attend an official celebration to Nothing.", + "reason": "The Percentage for increases from 18.0 at Attend an official celebration to 29.0 at Nothing, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Celebration Activity": "Attend a fireworks show", + "Percentage": 40.0 + }, + { + "Celebration Activity": "Gather with friends to celebrate", + "Percentage": 32.0 + }, + { + "Celebration Activity": "Attend an official celebration", + "Percentage": 18.0 + }, + { + "Celebration Activity": "Other", + "Percentage": 12.0 + }, + { + "Celebration Activity": "Cover everything in maple syrup", + "Percentage": 3.0 + }, + { + "Celebration Activity": "Nothing", + "Percentage": 29.0 + } + ], + "columns": [ + { + "name": "Celebration Activity", + "importance": "primary", + "description": "Different ways Canadians plan to celebrate Canada Day", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of Canadians who plan to celebrate in the specified way", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "463": { + "metadata": { + "title": "The Most Annoying Airplane Passengers", + "description": "% of respondents saying they are most annoyed by the following types of passengers", + "main_insight": "Seat kickers/bumpers/grabbers are considered the most annoying type of airplane passenger, cited by 51% of respondents. Aromatic passengers and inattentive parents follow as the next most annoying." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Passenger Type": "The Seat Kicker/Bumper/Grabber", + "Percentage": 51.0 + } + ], + "score": 0.85, + "annotation": "The has a maximum value at The Seat Kicker/Bumper/Grabber", + "reason": "The [{'Passenger Type': 'The Seat Kicker/Bumper/Grabber', 'Percentage': 51.0}] of has a maximum value of 51.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Passenger Type": "Audio Insensitive", + "Percentage": 29.0 + } + ], + "score": 0.67, + "annotation": "The shows a sudden jump in Percentage from Queue Jumper to Audio Insensitive.", + "reason": "The Percentage for increases from 18.0 at Queue Jumper to 29.0 at Audio Insensitive, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Passenger Type": "Airplane Mode Violator", + "Percentage": 11.0 + } + ], + "score": 0.63, + "annotation": "The has a minimum value at Airplane Mode Violator", + "reason": "The [{'Passenger Type': 'Airplane Mode Violator', 'Percentage': 11.0}] of has a mainimum value of 11.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Passenger Type": "The Seat Kicker/Bumper/Grabber", + "Percentage": 51.0 + }, + { + "Passenger Type": "The Aromatic Passenger", + "Percentage": 43.0 + }, + { + "Passenger Type": "Inattentive Parents", + "Percentage": 39.0 + }, + { + "Passenger Type": "Personal Space Violators", + "Percentage": 34.0 + }, + { + "Passenger Type": "Audio Insensitive", + "Percentage": 29.0 + }, + { + "Passenger Type": "Queue Jumper", + "Percentage": 18.0 + }, + { + "Passenger Type": "Pungent Foodies", + "Percentage": 14.0 + }, + { + "Passenger Type": "Baggage Mishandler", + "Percentage": 13.0 + }, + { + "Passenger Type": "Armrest Hog", + "Percentage": 13.0 + }, + { + "Passenger Type": "Airplane Mode Violator", + "Percentage": 11.0 + } + ], + "columns": [ + { + "name": "Passenger Type", + "importance": "primary", + "description": "Types of annoying passengers on airplanes", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents who find the passenger type most annoying", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1411": { + "metadata": { + "title": "How Covid-19 Affected America's Rush Hour In 2020", + "description": "Average change in urban congestion levels in 2020 compared to 2019*", + "main_insight": "Urban congestion levels in the US significantly decreased in 2020 compared to 2019 due to Covid-19, with the largest decreases observed during the initial months of the pandemic (March-May). Both AM and PM rush hours were impacted, though PM rush hour generally saw a larger decrease." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Feb", + "Ratio": -5.0, + "Traffic": "PM Rush Hour" + } + ], + "score": 0.94, + "annotation": "The PM Rush Hour has a maximum value at Feb", + "reason": "The [{'Month': 'Feb', 'Ratio': -5.0, 'Traffic': 'PM Rush Hour'}] of PM Rush Hour has a maximum value of -5.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "Jan", + "Ratio": -1.0, + "Traffic": "AM Rush Hour" + } + ], + "score": 0.92, + "annotation": "The AM Rush Hour has a maximum value at Jan", + "reason": "The [{'Month': 'Jan', 'Ratio': -1.0, 'Traffic': 'AM Rush Hour'}] of AM Rush Hour has a maximum value of -1.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Apr", + "Ratio": -73.0, + "Traffic": "PM Rush Hour" + } + ], + "score": 0.85, + "annotation": "The PM Rush Hour has a minimum value at Apr", + "reason": "The [{'Month': 'Apr', 'Ratio': -73.0, 'Traffic': 'PM Rush Hour'}] of PM Rush Hour has a mainimum value of -73.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Feb", + "Ratio": -4.0, + "Traffic": "AM Rush Hour" + } + ], + "score": 0.77, + "annotation": "The AM Rush Hour shows a sudden jump in Ratio from Mar to Feb.", + "reason": "The Ratio for AM Rush Hour increases from -31.0 at Mar to -4.0 at Feb, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Apr", + "Ratio": -69.0, + "Traffic": "AM Rush Hour" + } + ], + "score": 0.75, + "annotation": "The AM Rush Hour has a minimum value at Apr", + "reason": "The [{'Month': 'Apr', 'Ratio': -69.0, 'Traffic': 'AM Rush Hour'}] of AM Rush Hour has a mainimum value of -69.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Month": "Jan", + "Ratio": -1.0, + "Traffic": "AM Rush Hour" + }, + { + "Month": "Feb", + "Ratio": -4.0, + "Traffic": "AM Rush Hour" + }, + { + "Month": "Feb", + "Ratio": -5.0, + "Traffic": "PM Rush Hour" + }, + { + "Month": "Mar", + "Ratio": -31.0, + "Traffic": "AM Rush Hour" + }, + { + "Month": "Mar", + "Ratio": -29.0, + "Traffic": "PM Rush Hour" + }, + { + "Month": "Apr", + "Ratio": -69.0, + "Traffic": "AM Rush Hour" + }, + { + "Month": "Apr", + "Ratio": -73.0, + "Traffic": "PM Rush Hour" + }, + { + "Month": "May", + "Ratio": -59.0, + "Traffic": "AM Rush Hour" + }, + { + "Month": "May", + "Ratio": -64.0, + "Traffic": "PM Rush Hour" + }, + { + "Month": "Jun", + "Ratio": -46.0, + "Traffic": "AM Rush Hour" + }, + { + "Month": "Jun", + "Ratio": -53.0, + "Traffic": "PM Rush Hour" + }, + { + "Month": "Jul", + "Ratio": -46.0, + "Traffic": "AM Rush Hour" + }, + { + "Month": "Jul", + "Ratio": -47.0, + "Traffic": "PM Rush Hour" + }, + { + "Month": "Aug", + "Ratio": -46.0, + "Traffic": "AM Rush Hour" + }, + { + "Month": "Aug", + "Ratio": -46.0, + "Traffic": "PM Rush Hour" + }, + { + "Month": "Sep", + "Ratio": -48.0, + "Traffic": "AM Rush Hour" + }, + { + "Month": "Sep", + "Ratio": -41.0, + "Traffic": "PM Rush Hour" + }, + { + "Month": "Oct", + "Ratio": -49.0, + "Traffic": "AM Rush Hour" + }, + { + "Month": "Oct", + "Ratio": -42.0, + "Traffic": "PM Rush Hour" + }, + { + "Month": "Nov", + "Ratio": -49.0, + "Traffic": "AM Rush Hour" + }, + { + "Month": "Nov", + "Ratio": -37.0, + "Traffic": "PM Rush Hour" + }, + { + "Month": "Dec", + "Ratio": -52.0, + "Traffic": "AM Rush Hour" + }, + { + "Month": "Dec", + "Ratio": -42.0, + "Traffic": "PM Rush Hour" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year 2020", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding traffic", + "role": "y" + }, + { + "name": "Traffic", + "importance": "primary", + "description": "The traffic of the data point", + "role": "group" + } + ] + } + }, + "279": { + "metadata": { + "title": "Will Climate Change End The Winter Games?", + "description": "Share of Olympic Winter Games locations by reliability to ensure safe and fair snow sport conditions in February (in %)", + "main_insight": "The percentage of Winter Games locations considered reliable is projected to decrease significantly by 2071-2100, particularly under high emissions scenarios. The percentage of unreliable locations increases substantially in the same period." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Time Period": "2071-2100 high emissions*", + "Score": 5.0, + "Reliability": "Reliable" + }, + { + "Time Period": "2071-2100 high emissions*", + "Score": 67.0, + "Reliability": "Unreliable" + } + ], + "score": 0.98, + "annotation": "The Score of Reliable and Unreliable show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Reliable and Unreliable is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Reliability": "Unreliable", + "Time Period": "total", + "Score": 182.0 + } + ], + "score": 0.89, + "annotation": "The total value of Unreliable has the largest proportion.", + "reason": "The total value of Score of Unreliable accounts for 0.36327345309381237, larger than all other Reliability, of the total of Score of all Reliability." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Time Period": "2071-2100 low emissions*", + "Score": 38.0, + "Reliability": "Reliable" + } + ], + "score": 0.89, + "annotation": "The Reliable has the largest proportion in 2071-2100 low emissions*.", + "reason": "The Score of Reliable in 2071-2100 low emissions* accounts for 0.38, larger than all other Reliability, of the total of Score of all Reliability." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Time Period": "1981-2010", + "Score": 43.0, + "Reliability": "Reliable" + } + ], + "score": 0.87, + "annotation": "The Reliable has the largest proportion in 1981-2010.", + "reason": "The Score of Reliable in 1981-2010 accounts for 0.43, larger than all other Reliability, of the total of Score of all Reliability." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Time Period": "2041-2070 low emissions*", + "Score": 43.0, + "Reliability": "Reliable" + } + ], + "score": 0.87, + "annotation": "The Reliable has the largest proportion in 2041-2070 low emissions*.", + "reason": "The Score of Reliable in 2041-2070 low emissions* accounts for 0.43, larger than all other Reliability, of the total of Score of all Reliability." + } + ], + "data": { + "data": [ + { + "Time Period": "1981-2010", + "Score": 43.0, + "Reliability": "Reliable" + }, + { + "Time Period": "1981-2010", + "Score": 38.0, + "Reliability": "Marginally reliable" + }, + { + "Time Period": "1981-2010", + "Score": 19.0, + "Reliability": "Unreliable" + }, + { + "Time Period": "2041-2070 low emissions*", + "Score": 43.0, + "Reliability": "Reliable" + }, + { + "Time Period": "2041-2070 low emissions*", + "Score": 33.0, + "Reliability": "Marginally reliable" + }, + { + "Time Period": "2041-2070 low emissions*", + "Score": 24.0, + "Reliability": "Unreliable" + }, + { + "Time Period": "2041-2070 high emissions*", + "Score": 19.0, + "Reliability": "Reliable" + }, + { + "Time Period": "2041-2070 high emissions*", + "Score": 38.0, + "Reliability": "Marginally reliable" + }, + { + "Time Period": "2041-2070 high emissions*", + "Score": 43.0, + "Reliability": "Unreliable" + }, + { + "Time Period": "2071-2100 low emissions*", + "Score": 38.0, + "Reliability": "Reliable" + }, + { + "Time Period": "2071-2100 low emissions*", + "Score": 33.0, + "Reliability": "Marginally reliable" + }, + { + "Time Period": "2071-2100 low emissions*", + "Score": 29.0, + "Reliability": "Unreliable" + }, + { + "Time Period": "2071-2100 high emissions*", + "Score": 5.0, + "Reliability": "Reliable" + }, + { + "Time Period": "2071-2100 high emissions*", + "Score": 29.0, + "Reliability": "Marginally reliable" + }, + { + "Time Period": "2071-2100 high emissions*", + "Score": 67.0, + "Reliability": "Unreliable" + } + ], + "columns": [ + { + "name": "Time Period", + "importance": "primary", + "description": "Time period and emissions scenario", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding reliability", + "role": "y" + }, + { + "name": "Reliability", + "importance": "primary", + "description": "The reliability of the data point", + "role": "group" + } + ] + } + }, + "1031": { + "metadata": { + "title": "South Korea Leads the World in Fiber Adoption", + "description": "% of fiber connections in total fixed broadband subscriptions in selected countries (Dec. 2022)", + "main_insight": "South Korea has the highest percentage of fiber connections among the selected countries, significantly exceeding the OECD average." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "BE", + "Fiber Connection Percentage": 5.0 + } + ], + "score": 0.72, + "annotation": "The has a minimum value at BE", + "reason": "The [{'Country': 'BE', 'Fiber Connection Percentage': 5.0}] of has a mainimum value of 5.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "SE", + "Fiber Connection Percentage": 81.2 + } + ], + "score": 0.69, + "annotation": "The shows a sudden jump in Fiber Connection Percentage from FR to SE.", + "reason": "The Fiber Connection Percentage for increases from 56.7 at FR to 81.2 at SE, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "KR", + "Fiber Connection Percentage": 88.0 + } + ], + "score": 0.67, + "annotation": "The has a maximum value at KR", + "reason": "The [{'Country': 'KR', 'Fiber Connection Percentage': 88.0}] of has a maximum value of 88.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "KR", + "Fiber Connection Percentage": 88.0 + }, + { + "Country": "JP", + "Fiber Connection Percentage": 84.8 + }, + { + "Country": "ES", + "Fiber Connection Percentage": 83.1 + }, + { + "Country": "SE", + "Fiber Connection Percentage": 81.2 + }, + { + "Country": "FR", + "Fiber Connection Percentage": 56.7 + }, + { + "Country": "OECD average", + "Fiber Connection Percentage": 37.7 + }, + { + "Country": "US", + "Fiber Connection Percentage": 20.4 + }, + { + "Country": "IT", + "Fiber Connection Percentage": 18.7 + }, + { + "Country": "DE", + "Fiber Connection Percentage": 9.2 + }, + { + "Country": "BE", + "Fiber Connection Percentage": 5.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country or region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Fiber Connection Percentage", + "importance": "primary", + "description": "Percentage of fiber connections in total fixed broadband subscriptions", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2279": { + "metadata": { + "title": "Environmental Standards: The Transatlantic Divide", + "description": "% of Americans/Germans who trust the following American/European standards", + "main_insight": "Germans overwhelmingly trust European standards more than American standards across various sectors, while Americans show greater trust in American standards, though with less pronounced differences than their German counterparts." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Standard": "Environmental safety", + "Score": 2.0, + "Trust Origin": "Germany_Trust_American" + } + ], + "score": 0.95, + "annotation": "The Germany_Trust_American has the smallest proportion in Environmental safety.", + "reason": "The Score of Germany_Trust_American in Environmental safety accounts for 0.010810810810810811, smaller than all other Trust Origin, of the total of Score of all Trust Origin." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Standard": "Food safety", + "Score": 2.0, + "Trust Origin": "Germany_Trust_American" + } + ], + "score": 0.95, + "annotation": "The Germany_Trust_American has the smallest proportion in Food safety.", + "reason": "The Score of Germany_Trust_American in Food safety accounts for 0.010810810810810811, smaller than all other Trust Origin, of the total of Score of all Trust Origin." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Trust Origin": "Germany_Trust_European", + "Standard": "total", + "Score": 366.0 + } + ], + "score": 0.93, + "annotation": "The total value of Germany_Trust_European has the smallest proportion.", + "reason": "The total value of Score of Germany_Trust_European accounts for 0.015299026425591099, smaller than all other Trust Origin, of the total of Score of all Trust Origin." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Standard": "Data privacy", + "Score": 3.0, + "Trust Origin": "Germany_Trust_American" + } + ], + "score": 0.92, + "annotation": "The Germany_Trust_American has the smallest proportion in Data privacy.", + "reason": "The Score of Germany_Trust_American in Data privacy accounts for 0.018072289156626505, smaller than all other Trust Origin, of the total of Score of all Trust Origin." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Standard": "Auto safety", + "Score": 4.0, + "Trust Origin": "Germany_Trust_American" + } + ], + "score": 0.9, + "annotation": "The Germany_Trust_American has the smallest proportion in Auto safety.", + "reason": "The Score of Germany_Trust_American in Auto safety accounts for 0.02185792349726776, smaller than all other Trust Origin, of the total of Score of all Trust Origin." + } + ], + "data": { + "data": [ + { + "Standard": "Auto safety", + "Score": 33.0, + "Trust Origin": "US_Trust_European" + }, + { + "Standard": "Auto safety", + "Score": 91.0, + "Trust Origin": "Germany_Trust_European" + }, + { + "Standard": "Auto safety", + "Score": 55.0, + "Trust Origin": "US_Trust_American" + }, + { + "Standard": "Auto safety", + "Score": 4.0, + "Trust Origin": "Germany_Trust_American" + }, + { + "Standard": "Data privacy", + "Score": 29.0, + "Trust Origin": "US_Trust_European" + }, + { + "Standard": "Data privacy", + "Score": 85.0, + "Trust Origin": "Germany_Trust_European" + }, + { + "Standard": "Data privacy", + "Score": 49.0, + "Trust Origin": "US_Trust_American" + }, + { + "Standard": "Data privacy", + "Score": 3.0, + "Trust Origin": "Germany_Trust_American" + }, + { + "Standard": "Environmental safety", + "Score": 27.0, + "Trust Origin": "US_Trust_European" + }, + { + "Standard": "Environmental safety", + "Score": 96.0, + "Trust Origin": "Germany_Trust_European" + }, + { + "Standard": "Environmental safety", + "Score": 60.0, + "Trust Origin": "US_Trust_American" + }, + { + "Standard": "Environmental safety", + "Score": 2.0, + "Trust Origin": "Germany_Trust_American" + }, + { + "Standard": "Food safety", + "Score": 22.0, + "Trust Origin": "US_Trust_European" + }, + { + "Standard": "Food safety", + "Score": 94.0, + "Trust Origin": "Germany_Trust_European" + }, + { + "Standard": "Food safety", + "Score": 67.0, + "Trust Origin": "US_Trust_American" + }, + { + "Standard": "Food safety", + "Score": 2.0, + "Trust Origin": "Germany_Trust_American" + } + ], + "columns": [ + { + "name": "Standard", + "importance": "primary", + "description": "The sector or area of standards being compared", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding trust origin", + "role": "y" + }, + { + "name": "Trust Origin", + "importance": "primary", + "description": "The trust origin of the data point", + "role": "group" + } + ] + } + }, + "764": { + "metadata": { + "title": "Freshly Crowned European Champions Sport No. 1 Brand in Football", + "description": "Brand value of the most valuable football clubs in 2013 (in million U.S. dollars)", + "main_insight": "Bayern Munich had the highest brand value among football clubs in 2013, at 668 million U.S. dollars, followed closely by Manchester United at 650 million U.S. dollars." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Football Club": "Bayern Munich", + "Brand Value": 668.0 + } + ], + "score": 0.85, + "annotation": "The has a maximum value at Bayern Munich", + "reason": "The [{'Football Club': 'Bayern Munich', 'Brand Value': 668.0}] of has a maximum value of 668.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Football Club": "Manchester United", + "Brand Value": 650.0 + } + ], + "score": 0.74, + "annotation": "The shows a sudden jump in Brand Value from Real Madrid to Manchester United.", + "reason": "The Brand Value for increases from 482.0 at Real Madrid to 650.0 at Manchester United, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Football Club": "Borussia Dortmund", + "Brand Value": 202.0 + } + ], + "score": 0.63, + "annotation": "The has a minimum value at Borussia Dortmund", + "reason": "The [{'Football Club': 'Borussia Dortmund', 'Brand Value': 202.0}] of has a mainimum value of 202.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Football Club": "Bayern Munich", + "Brand Value": 668.0 + }, + { + "Football Club": "Manchester United", + "Brand Value": 650.0 + }, + { + "Football Club": "Real Madrid", + "Brand Value": 482.0 + }, + { + "Football Club": "FC Barcelona", + "Brand Value": 444.0 + }, + { + "Football Club": "Chelsea FC", + "Brand Value": 325.0 + }, + { + "Football Club": "Arsenal FC", + "Brand Value": 319.0 + }, + { + "Football Club": "Liverpool FC", + "Brand Value": 280.0 + }, + { + "Football Club": "Manchester City", + "Brand Value": 257.0 + }, + { + "Football Club": "AC Milan", + "Brand Value": 204.0 + }, + { + "Football Club": "Borussia Dortmund", + "Brand Value": 202.0 + } + ], + "columns": [ + { + "name": "Football Club", + "importance": "primary", + "description": "Name of the football club", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Brand Value", + "importance": "primary", + "description": "Brand value in million U.S. dollars", + "unit": "Million U.S. dollars", + "data_type": "number", + "role": "y" + } + ] + } + }, + "222": { + "metadata": { + "title": "COVID-19 Had an Unprecedented Impact on Tourism Revenues", + "description": "International tourist receipts in Europe, 2000-2022 (in billion euros)", + "main_insight": "International tourist receipts in Europe saw a dramatic drop in 2020 due to the COVID-19 pandemic, significantly lower than previous dips caused by the SARS epidemic and the Global Financial Crisis." + }, + "chart_type": "Line Graph", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2000, + "Receipts (Billion Euros)": 223.0 + }, + { + "Year": 2003, + "Receipts (Billion Euros)": 222.0 + }, + { + "Year": 2009, + "Receipts (Billion Euros)": 251.0 + }, + { + "Year": 2020, + "Receipts (Billion Euros)": 159.0 + }, + { + "Year": 2022, + "Receipts (Billion Euros)": 240.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of recorded tourist receipts", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Receipts (Billion Euros)", + "importance": "primary", + "description": "International tourist receipts in Europe", + "unit": "Billion Euros", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2167": { + "metadata": { + "title": "Who's Most Vulnerable to COVID-19's Impact on Tourism?", + "description": "Total contribution of travel and tourism to GDP of the world's largest economies in 2019*", + "main_insight": "Mexico and Spain are the most vulnerable large economies to COVID-19's impact on tourism, as travel and tourism contributed the highest percentage to their GDP in 2019." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Mexico", + "Tourism Contribution to GDP": 15.5 + } + ], + "score": 0.91, + "annotation": "The has a maximum value at Mexico", + "reason": "The [{'Country': 'Mexico', 'Tourism Contribution to GDP': 15.5}] of has a maximum value of 15.5, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "South Korea", + "Tourism Contribution to GDP": 4.2 + } + ], + "score": 0.8, + "annotation": "The has a minimum value at South Korea", + "reason": "The [{'Country': 'South Korea', 'Tourism Contribution to GDP': 4.2}] of has a mainimum value of 4.2, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Australia", + "Tourism Contribution to GDP": 10.8 + } + ], + "score": 0.55, + "annotation": "The shows a sudden jump in Tourism Contribution to GDP from Germany to Australia.", + "reason": "The Tourism Contribution to GDP for increases from 9.1 at Germany to 10.8 at Australia, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Mexico", + "Tourism Contribution to GDP": 15.5 + }, + { + "Country": "Spain", + "Tourism Contribution to GDP": 14.3 + }, + { + "Country": "Italy", + "Tourism Contribution to GDP": 13.0 + }, + { + "Country": "China", + "Tourism Contribution to GDP": 11.3 + }, + { + "Country": "Australia", + "Tourism Contribution to GDP": 10.8 + }, + { + "Country": "Germany", + "Tourism Contribution to GDP": 9.1 + }, + { + "Country": "United Kingdom", + "Tourism Contribution to GDP": 9.0 + }, + { + "Country": "United States", + "Tourism Contribution to GDP": 8.6 + }, + { + "Country": "France", + "Tourism Contribution to GDP": 8.5 + }, + { + "Country": "Brazil", + "Tourism Contribution to GDP": 7.7 + }, + { + "Country": "Japan", + "Tourism Contribution to GDP": 7.0 + }, + { + "Country": "India", + "Tourism Contribution to GDP": 6.8 + }, + { + "Country": "Canada", + "Tourism Contribution to GDP": 6.3 + }, + { + "Country": "Russia", + "Tourism Contribution to GDP": 5.0 + }, + { + "Country": "South Korea", + "Tourism Contribution to GDP": 4.2 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Tourism Contribution to GDP", + "importance": "primary", + "description": "Percentage of GDP contributed by travel and tourism", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1802": { + "metadata": { + "title": "Indian National Congress Gains Ground in State Elections", + "description": "Share of votes by major political party in recent state elections across India 2018", + "main_insight": "The Indian National Congress and BJP received the largest share of votes in most states, though their performance varied significantly across different regions." + }, + "chart_type": "Multiple Semi Circle Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "State": "Mizoram", + "Value": 30.3, + "Parties": "Congress" + }, + { + "State": "Mizoram", + "Value": 8.1, + "Parties": "BSP" + } + ], + "score": 0.66, + "annotation": "The Value of Congress and BSP show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Value between Congress and BSP is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "State": "Mizoram", + "Value": 30.3, + "Parties": "Congress" + }, + { + "State": "Mizoram", + "Value": 23.9, + "Parties": "Others" + } + ], + "score": 0.45, + "annotation": "The Value of Congress and Others show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Value between Congress and Others is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "State": "Mizoram", + "Value": 8.1, + "Parties": "BSP" + }, + { + "State": "Mizoram", + "Value": 23.9, + "Parties": "Others" + } + ], + "score": 0.17, + "annotation": "The Value of BSP and Others show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Value between BSP and Others is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "State": "Madhya Pradesh", + "Value": 41.5, + "Parties": "Congress" + }, + { + "State": "Madhya Pradesh", + "Value": 41.6, + "Parties": "BJP" + }, + { + "State": "Madhya Pradesh", + "Value": 5.1, + "Parties": "BSP" + }, + { + "State": "Madhya Pradesh", + "Value": 11.8, + "Parties": "Others" + }, + { + "State": "Rajasthan", + "Value": 39.3, + "Parties": "Congress" + }, + { + "State": "Rajasthan", + "Value": 40.4, + "Parties": "BJP" + }, + { + "State": "Rajasthan", + "Value": 4.0, + "Parties": "BSP" + }, + { + "State": "Rajasthan", + "Value": 16.3, + "Parties": "Others" + }, + { + "State": "Chhatisgarh", + "Value": 33.6, + "Parties": "Congress" + }, + { + "State": "Chhatisgarh", + "Value": 43.9, + "Parties": "BJP" + }, + { + "State": "Chhatisgarh", + "Value": 12.1, + "Parties": "BSP" + }, + { + "State": "Chhatisgarh", + "Value": 10.4, + "Parties": "Others" + }, + { + "State": "Telegana", + "Value": 33.1, + "Parties": "Congress" + }, + { + "State": "Telegana", + "Value": 47.4, + "Parties": "TRS" + }, + { + "State": "Telegana", + "Value": 7.1, + "Parties": "BSP" + }, + { + "State": "Telegana", + "Value": 12.4, + "Parties": "Others" + }, + { + "State": "Mizoram", + "Value": 30.3, + "Parties": "Congress" + }, + { + "State": "Mizoram", + "Value": 37.8, + "Parties": "MNF" + }, + { + "State": "Mizoram", + "Value": 8.1, + "Parties": "BSP" + }, + { + "State": "Mizoram", + "Value": 23.9, + "Parties": "Others" + } + ], + "columns": [ + { + "name": "State", + "importance": "primary", + "description": "State in which the election was held", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding parties", + "role": "y" + }, + { + "name": "Parties", + "importance": "primary", + "description": "The parties of the data point", + "role": "group" + } + ] + } + }, + "2290": { + "metadata": { + "title": "The Rising Power of the Dollar", + "description": "Change in the value of the U.S. dollar against other currencies from 8/3/2021 to 7/29/2022", + "main_insight": "The US dollar has significantly increased in value against the Euro, Pound Sterling, Renminbi (yuan), and Yen between August 2021 and July 2022, with the most dramatic increase against the Yen." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "Jul 2022", + "Ratio": 24.0, + "Currencies": "Yen" + }, + { + "Month": "Jul 2022", + "Ratio": 18.0, + "Currencies": "Pound sterling" + } + ], + "score": 0.97, + "annotation": "The Ratio of Yen and Pound sterling show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Yen and Pound sterling is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "Jul 2022", + "Ratio": 24.0, + "Currencies": "Yen" + }, + { + "Month": "Jul 2022", + "Ratio": 16.0, + "Currencies": "Euro" + } + ], + "score": 0.97, + "annotation": "The Ratio of Yen and Euro show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Yen and Euro is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "Jul 2022", + "Ratio": 18.0, + "Currencies": "Pound sterling" + }, + { + "Month": "Jul 2022", + "Ratio": 16.0, + "Currencies": "Euro" + } + ], + "score": 0.97, + "annotation": "The Ratio of Pound sterling and Euro show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Pound sterling and Euro is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "Jul 2022", + "Ratio": 24.0, + "Currencies": "Yen" + }, + { + "Month": "Jul 2022", + "Ratio": 6.0, + "Currencies": "Renminbi (yuan)" + } + ], + "score": 0.96, + "annotation": "The Ratio of Yen and Renminbi (yuan) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Yen and Renminbi (yuan) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "Jul 2022", + "Ratio": 16.0, + "Currencies": "Euro" + }, + { + "Month": "Jul 2022", + "Ratio": 6.0, + "Currencies": "Renminbi (yuan)" + } + ], + "score": 0.95, + "annotation": "The Ratio of Euro and Renminbi (yuan) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Euro and Renminbi (yuan) is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Month": "Sep 2021", + "Ratio": 2.0, + "Currencies": "Yen" + }, + { + "Month": "Sep 2021", + "Ratio": 1.0, + "Currencies": "Pound sterling" + }, + { + "Month": "Sep 2021", + "Ratio": 2.0, + "Currencies": "Euro" + }, + { + "Month": "Sep 2021", + "Ratio": 1.0, + "Currencies": "Renminbi (yuan)" + }, + { + "Month": "Oct 2021", + "Ratio": 3.0, + "Currencies": "Yen" + }, + { + "Month": "Oct 2021", + "Ratio": 1.0, + "Currencies": "Pound sterling" + }, + { + "Month": "Oct 2021", + "Ratio": 2.0, + "Currencies": "Euro" + }, + { + "Month": "Oct 2021", + "Ratio": 1.0, + "Currencies": "Renminbi (yuan)" + }, + { + "Month": "Nov 2021", + "Ratio": 4.0, + "Currencies": "Yen" + }, + { + "Month": "Nov 2021", + "Ratio": 3.0, + "Currencies": "Pound sterling" + }, + { + "Month": "Nov 2021", + "Ratio": 5.0, + "Currencies": "Euro" + }, + { + "Month": "Nov 2021", + "Ratio": 1.0, + "Currencies": "Renminbi (yuan)" + }, + { + "Month": "Dec 2021", + "Ratio": 4.0, + "Currencies": "Yen" + }, + { + "Month": "Dec 2021", + "Ratio": 2.0, + "Currencies": "Pound sterling" + }, + { + "Month": "Dec 2021", + "Ratio": 5.0, + "Currencies": "Euro" + }, + { + "Month": "Dec 2021", + "Ratio": 2.0, + "Currencies": "Renminbi (yuan)" + }, + { + "Month": "Jan 2022", + "Ratio": 5.0, + "Currencies": "Yen" + }, + { + "Month": "Jan 2022", + "Ratio": 4.0, + "Currencies": "Pound sterling" + }, + { + "Month": "Jan 2022", + "Ratio": 5.0, + "Currencies": "Euro" + }, + { + "Month": "Jan 2022", + "Ratio": 2.0, + "Currencies": "Renminbi (yuan)" + }, + { + "Month": "Feb 2022", + "Ratio": 6.0, + "Currencies": "Yen" + }, + { + "Month": "Feb 2022", + "Ratio": 5.0, + "Currencies": "Pound sterling" + }, + { + "Month": "Feb 2022", + "Ratio": 8.0, + "Currencies": "Euro" + }, + { + "Month": "Feb 2022", + "Ratio": 2.0, + "Currencies": "Renminbi (yuan)" + }, + { + "Month": "Mar 2022", + "Ratio": 10.0, + "Currencies": "Yen" + }, + { + "Month": "Mar 2022", + "Ratio": 5.0, + "Currencies": "Pound sterling" + }, + { + "Month": "Mar 2022", + "Ratio": 7.0, + "Currencies": "Euro" + }, + { + "Month": "Mar 2022", + "Ratio": 2.0, + "Currencies": "Renminbi (yuan)" + }, + { + "Month": "Apr 2022", + "Ratio": 17.0, + "Currencies": "Yen" + }, + { + "Month": "Apr 2022", + "Ratio": 9.0, + "Currencies": "Pound sterling" + }, + { + "Month": "Apr 2022", + "Ratio": 12.0, + "Currencies": "Euro" + }, + { + "Month": "Apr 2022", + "Ratio": 5.0, + "Currencies": "Renminbi (yuan)" + }, + { + "Month": "May 2022", + "Ratio": 17.0, + "Currencies": "Yen" + }, + { + "Month": "May 2022", + "Ratio": 10.0, + "Currencies": "Pound sterling" + }, + { + "Month": "May 2022", + "Ratio": 12.0, + "Currencies": "Euro" + }, + { + "Month": "May 2022", + "Ratio": 6.0, + "Currencies": "Renminbi (yuan)" + }, + { + "Month": "Jun 2022", + "Ratio": 23.0, + "Currencies": "Yen" + }, + { + "Month": "Jun 2022", + "Ratio": 14.0, + "Currencies": "Pound sterling" + }, + { + "Month": "Jun 2022", + "Ratio": 14.0, + "Currencies": "Euro" + }, + { + "Month": "Jun 2022", + "Ratio": 6.0, + "Currencies": "Renminbi (yuan)" + }, + { + "Month": "Jul 2022", + "Ratio": 24.0, + "Currencies": "Yen" + }, + { + "Month": "Jul 2022", + "Ratio": 18.0, + "Currencies": "Pound sterling" + }, + { + "Month": "Jul 2022", + "Ratio": 16.0, + "Currencies": "Euro" + }, + { + "Month": "Jul 2022", + "Ratio": 6.0, + "Currencies": "Renminbi (yuan)" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month and Year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding currencies", + "role": "y" + }, + { + "name": "Currencies", + "importance": "primary", + "description": "The currencies of the data point", + "role": "group" + } + ] + } + }, + "635": { + "metadata": { + "title": "U.S. Households Are Becoming Smarter", + "description": "Estimated number of U.S. households with smart home applications, by type", + "main_insight": "The number of U.S. households with smart home applications is projected to increase significantly between 2020 and 2024 across all application types, with Control and Connectivity applications showing the largest number of households." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Smart Home Application Type": "Smart Appliances", + "Units": 15.9, + "Year": "2020" + }, + { + "Smart Home Application Type": "Smart Appliances", + "Units": 27.9, + "Year": "2024" + } + ], + "score": 0.98, + "annotation": "The Units of 2020 and 2024 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2020 and 2024 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Smart Home Application Type": "Control and Connectivity", + "Units": 67.2, + "Year": "2024" + } + ], + "score": 0.85, + "annotation": "The 2024 has a maximum value at Control and Connectivity", + "reason": "The [{'Smart Home Application Type': 'Control and Connectivity', 'Units': 67.2, 'Year': '2024'}] of 2024 has a maximum value of 67.2, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Smart Home Application Type": "Control and Connectivity", + "Units": 40.5, + "Year": "2020" + } + ], + "score": 0.84, + "annotation": "The 2020 has a maximum value at Control and Connectivity", + "reason": "The [{'Smart Home Application Type': 'Control and Connectivity', 'Units': 40.5, 'Year': '2020'}] of 2020 has a maximum value of 40.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Smart Home Application Type": "Control and Connectivity", + "Units": 67.2, + "Year": "2024" + } + ], + "score": 0.82, + "annotation": "The 2024 shows a sudden jump in Units from Comfort and Lighting to Control and Connectivity.", + "reason": "The Units for 2024 increases from 48.5 at Comfort and Lighting to 67.2 at Control and Connectivity, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Smart Home Application Type": "Control and Connectivity", + "Units": 40.5, + "Year": "2020" + } + ], + "score": 0.79, + "annotation": "The 2020 shows a sudden jump in Units from Home Entertainment to Control and Connectivity.", + "reason": "The Units for 2020 increases from 29.9 at Home Entertainment to 40.5 at Control and Connectivity, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Smart Home Application Type": "Control and Connectivity", + "Units": 40.5, + "Year": "2020" + }, + { + "Smart Home Application Type": "Control and Connectivity", + "Units": 67.2, + "Year": "2024" + }, + { + "Smart Home Application Type": "Home Entertainment", + "Units": 29.9, + "Year": "2020" + }, + { + "Smart Home Application Type": "Home Entertainment", + "Units": 46.8, + "Year": "2024" + }, + { + "Smart Home Application Type": "Comfort and Lighting", + "Units": 28.4, + "Year": "2020" + }, + { + "Smart Home Application Type": "Comfort and Lighting", + "Units": 48.5, + "Year": "2024" + }, + { + "Smart Home Application Type": "Security", + "Units": 23.9, + "Year": "2020" + }, + { + "Smart Home Application Type": "Security", + "Units": 41.1, + "Year": "2024" + }, + { + "Smart Home Application Type": "Energy Management", + "Units": 23.5, + "Year": "2020" + }, + { + "Smart Home Application Type": "Energy Management", + "Units": 45.1, + "Year": "2024" + }, + { + "Smart Home Application Type": "Smart Appliances", + "Units": 15.9, + "Year": "2020" + }, + { + "Smart Home Application Type": "Smart Appliances", + "Units": 27.9, + "Year": "2024" + } + ], + "columns": [ + { + "name": "Smart Home Application Type", + "importance": "primary", + "description": "Category of smart home application", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "246": { + "metadata": { + "title": "Young Adults Support Turkey Day Store Openings", + "description": "Share of adults who support retailers keeping stores open on Thanksgiving", + "main_insight": "Younger generations, particularly Gen Z, show significantly higher support for stores being open on Thanksgiving compared to older generations like Boomers." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Generation": "Gen Z (18-21)", + "Support for Thanksgiving Day Openings": 0.53 + } + ], + "score": 0.81, + "annotation": "The shows a sudden jump in Support for Thanksgiving Day Openings from Millennials (22-37) to Gen Z (18-21).", + "reason": "The Support for Thanksgiving Day Openings for increases from 0.39 at Millennials (22-37) to 0.53 at Gen Z (18-21), indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Generation": "Gen Z (18-21)", + "Support for Thanksgiving Day Openings": 0.53 + } + ], + "score": 0.73, + "annotation": "The has a maximum value at Gen Z (18-21)", + "reason": "The [{'Generation': 'Gen Z (18-21)', 'Support for Thanksgiving Day Openings': 0.53}] of has a maximum value of 0.53, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Generation": "Boomers (54-72)", + "Support for Thanksgiving Day Openings": 0.23 + } + ], + "score": 0.61, + "annotation": "The has a minimum value at Boomers (54-72)", + "reason": "The [{'Generation': 'Boomers (54-72)', 'Support for Thanksgiving Day Openings': 0.23}] of has a mainimum value of 0.23, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Generation": "Gen Z (18-21)", + "Support for Thanksgiving Day Openings": 0.53 + }, + { + "Generation": "Millennials (22-37)", + "Support for Thanksgiving Day Openings": 0.39 + }, + { + "Generation": "Gen X (38-53)", + "Support for Thanksgiving Day Openings": 0.3 + }, + { + "Generation": "Boomers (54-72)", + "Support for Thanksgiving Day Openings": 0.23 + } + ], + "columns": [ + { + "name": "Generation", + "importance": "primary", + "description": "Demographic cohort", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Support for Thanksgiving Day Openings", + "importance": "primary", + "description": "Percentage of each generation that supports stores opening on Thanksgiving", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1033": { + "metadata": { + "title": "The World's Top 10 Gaming Markets", + "description": "Estimated revenue of video games per country (in billion U.S. dollars)", + "main_insight": "China and the U.S. are by far the largest gaming markets, with China projected to surpass the US significantly by 2025. The gaming market is growing globally." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Mexico", + "Value": 2.3, + "Revenue": "2021 Revenue (Billion USD)" + }, + { + "Country": "Mexico", + "Value": 3.4, + "Revenue": "2025 Revenue (Billion USD)" + } + ], + "score": 1.0, + "annotation": "The Value of 2021 Revenue (Billion USD) and 2025 Revenue (Billion USD) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Value between 2021 Revenue (Billion USD) and 2025 Revenue (Billion USD) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "China", + "Value": 71.2, + "Revenue": "2025 Revenue (Billion USD)" + } + ], + "score": 0.96, + "annotation": "The 2025 Revenue (Billion USD) has a maximum value at China", + "reason": "The [{'Country': 'China', 'Value': 71.2, 'Revenue': '2025 Revenue (Billion USD)'}] of 2025 Revenue (Billion USD) has a maximum value of 71.2, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "China", + "Value": 49.3, + "Revenue": "2021 Revenue (Billion USD)" + } + ], + "score": 0.96, + "annotation": "The 2021 Revenue (Billion USD) has a maximum value at China", + "reason": "The [{'Country': 'China', 'Value': 49.3, 'Revenue': '2021 Revenue (Billion USD)'}] of 2021 Revenue (Billion USD) has a maximum value of 49.3, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "China", + "Value": 71.2, + "Revenue": "2025 Revenue (Billion USD)" + } + ], + "score": 0.78, + "annotation": "The 2025 Revenue (Billion USD) shows a sudden jump in Value from U.S to China.", + "reason": "The Value for 2025 Revenue (Billion USD) increases from 42.5 at U.S to 71.2 at China, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Germany", + "Value": 3.5, + "Revenue": "2025 Revenue (Billion USD)" + } + ], + "score": 0.78, + "annotation": "The 2025 Revenue (Billion USD) accounts for the majority in Germany.", + "reason": "The Value of 2025 Revenue (Billion USD) in Germany accounts for 0.564516129032258 (more than 50%) of the total of Value of all Revenue." + } + ], + "data": { + "data": [ + { + "Country": "China", + "Value": 49.3, + "Revenue": "2021 Revenue (Billion USD)" + }, + { + "Country": "China", + "Value": 71.2, + "Revenue": "2025 Revenue (Billion USD)" + }, + { + "Country": "U.S", + "Value": 30.4, + "Revenue": "2021 Revenue (Billion USD)" + }, + { + "Country": "U.S", + "Value": 42.5, + "Revenue": "2025 Revenue (Billion USD)" + }, + { + "Country": "Japan", + "Value": 18.2, + "Revenue": "2021 Revenue (Billion USD)" + }, + { + "Country": "Japan", + "Value": 24.0, + "Revenue": "2025 Revenue (Billion USD)" + }, + { + "Country": "South Korea", + "Value": 6.2, + "Revenue": "2021 Revenue (Billion USD)" + }, + { + "Country": "South Korea", + "Value": 8.7, + "Revenue": "2025 Revenue (Billion USD)" + }, + { + "Country": "UK", + "Value": 6.1, + "Revenue": "2021 Revenue (Billion USD)" + }, + { + "Country": "UK", + "Value": 8.6, + "Revenue": "2025 Revenue (Billion USD)" + }, + { + "Country": "India", + "Value": 5.2, + "Revenue": "2021 Revenue (Billion USD)" + }, + { + "Country": "India", + "Value": 8.6, + "Revenue": "2025 Revenue (Billion USD)" + }, + { + "Country": "France", + "Value": 2.7, + "Revenue": "2021 Revenue (Billion USD)" + }, + { + "Country": "France", + "Value": 3.8, + "Revenue": "2025 Revenue (Billion USD)" + }, + { + "Country": "Russia", + "Value": 2.7, + "Revenue": "2021 Revenue (Billion USD)" + }, + { + "Country": "Russia", + "Value": 3.6, + "Revenue": "2025 Revenue (Billion USD)" + }, + { + "Country": "Germany", + "Value": 2.7, + "Revenue": "2021 Revenue (Billion USD)" + }, + { + "Country": "Germany", + "Value": 3.5, + "Revenue": "2025 Revenue (Billion USD)" + }, + { + "Country": "Mexico", + "Value": 2.3, + "Revenue": "2021 Revenue (Billion USD)" + }, + { + "Country": "Mexico", + "Value": 3.4, + "Revenue": "2025 Revenue (Billion USD)" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Top 10 countries by gaming market revenue", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding revenue", + "role": "y" + }, + { + "name": "Revenue", + "importance": "primary", + "description": "The revenue of the data point", + "role": "group" + } + ] + } + }, + "1629": { + "metadata": { + "title": "Canada's Best Employers 2016", + "description": "Top Canadian employers as rated by 8,000 employees on a scale from 0-10*", + "main_insight": "BC Hydro is the highest-rated employer in Canada for 2016, with a score of 9.45, closely followed by Vancity at 9.41." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Employer": "BC Hydro", + "Rating": 9.45 + } + ], + "score": 0.83, + "annotation": "The has a maximum value at BC Hydro", + "reason": "The [{'Employer': 'BC Hydro', 'Rating': 9.45}] of has a maximum value of 9.45, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Employer": "Université Laval", + "Rating": 9.21 + } + ], + "score": 0.82, + "annotation": "The has a minimum value at Université Laval", + "reason": "The [{'Employer': 'Université Laval', 'Rating': 9.21}] of has a mainimum value of 9.21, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Employer": "Google", + "Rating": 9.25 + }, + { + "Employer": "Ubisoft", + "Rating": 9.37 + }, + { + "Employer": "BC Hydro", + "Rating": 9.45 + } + ], + "score": 0.57, + "annotation": "The exhibits multiple sudden jumps in Rating, transitioning from Université Laval, Costco, Vancity to Google, Ubisoft, BC Hydro.", + "reason": "Across these points, the Rating for increases from 9.21, 9.33, 9.41 at Université Laval, Costco, Vancity to 9.25, 9.37, 9.45 at Google, Ubisoft, BC Hydro, suggesting significant shifts in value." + } + ], + "data": { + "data": [ + { + "Employer": "BC Hydro", + "Rating": 9.45 + }, + { + "Employer": "Vancity", + "Rating": 9.41 + }, + { + "Employer": "ABB", + "Rating": 9.39 + }, + { + "Employer": "Ubisoft", + "Rating": 9.37 + }, + { + "Employer": "Costco", + "Rating": 9.33 + }, + { + "Employer": "University of Guelph", + "Rating": 9.3 + }, + { + "Employer": "Metrolinx", + "Rating": 9.29 + }, + { + "Employer": "Toronto Hydro", + "Rating": 9.28 + }, + { + "Employer": "Google", + "Rating": 9.25 + }, + { + "Employer": "Université Laval", + "Rating": 9.21 + } + ], + "columns": [ + { + "name": "Employer", + "importance": "primary", + "description": "Name of the employer", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Rating", + "importance": "primary", + "description": "Employer rating on a scale of 0-10", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "199": { + "metadata": { + "title": "How 2020 Thanksgiving Passenger Numbers Compare", + "description": "Total traveler throughput at U.S. airports in 2020 compared to the same period in 2019", + "main_insight": "Thanksgiving air travel in 2020 was significantly lower than in 2019, with passenger numbers peaking at just over 1 million compared to over 2.6 million in 2019." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Day": 28, + "Dollars": 2600000, + "Year": 2019 + }, + { + "Day": 28, + "Dollars": 950000, + "Year": 2020 + } + ], + "score": 0.81, + "annotation": "The Dollars of 2019 and 2020 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between 2019 and 2020 is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Day": 18, + "Dollars": 2000000, + "Year": 2019 + }, + { + "Day": 18, + "Dollars": 650000, + "Year": 2020 + }, + { + "Day": 19, + "Dollars": 2300000, + "Year": 2019 + }, + { + "Day": 19, + "Dollars": 800000, + "Year": 2020 + }, + { + "Day": 20, + "Dollars": 2500000, + "Year": 2019 + }, + { + "Day": 20, + "Dollars": 950000, + "Year": 2020 + }, + { + "Day": 21, + "Dollars": 2200000, + "Year": 2019 + }, + { + "Day": 21, + "Dollars": 1000000, + "Year": 2020 + }, + { + "Day": 22, + "Dollars": 2300000, + "Year": 2019 + }, + { + "Day": 22, + "Dollars": 1000000, + "Year": 2020 + }, + { + "Day": 23, + "Dollars": 2300000, + "Year": 2019 + }, + { + "Day": 23, + "Dollars": 900000, + "Year": 2020 + }, + { + "Day": 24, + "Dollars": 2400000, + "Year": 2019 + }, + { + "Day": 24, + "Dollars": 900000, + "Year": 2020 + }, + { + "Day": 25, + "Dollars": 2624250, + "Year": 2019 + }, + { + "Day": 25, + "Dollars": 1000000, + "Year": 2020 + }, + { + "Day": 26, + "Dollars": 1750000, + "Year": 2019 + }, + { + "Day": 26, + "Dollars": 600000, + "Year": 2020 + }, + { + "Day": 27, + "Dollars": 2000000, + "Year": 2019 + }, + { + "Day": 27, + "Dollars": 800000, + "Year": 2020 + }, + { + "Day": 28, + "Dollars": 2600000, + "Year": 2019 + }, + { + "Day": 28, + "Dollars": 950000, + "Year": 2020 + } + ], + "columns": [ + { + "name": "Day", + "importance": "primary", + "description": "Day in November", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "122": { + "metadata": { + "title": "Should I Stay or Should I Go?", + "description": "% of respondents likely to avoid the following for a while even after COVID-19 restrictions are lifted", + "main_insight": "A significant percentage of US adults surveyed in May 2020 planned to avoid public places like cinemas, pubs, and concerts even after COVID-19 restrictions were lifted, with over half indicating continued avoidance of these venues." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Activity": "Supermarkets", + "Avoidance Percentage": 17.0 + } + ], + "score": 0.87, + "annotation": "The has a minimum value at Supermarkets", + "reason": "The [{'Activity': 'Supermarkets', 'Avoidance Percentage': 17.0}] of has a mainimum value of 17.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Activity": "Hairdressers/beauty salons", + "Avoidance Percentage": 30.0 + } + ], + "score": 0.7, + "annotation": "The shows a sudden jump in Avoidance Percentage from Not planning to avoid anything to Hairdressers/beauty salons.", + "reason": "The Avoidance Percentage for increases from 18.0 at Not planning to avoid anything to 30.0 at Hairdressers/beauty salons, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Activity": "Cinema/theater", + "Avoidance Percentage": 55.0 + }, + { + "Activity": "Pubs/bars/clubs", + "Avoidance Percentage": 55.0 + } + ], + "score": 0.57, + "annotation": "The has maximum values at Cinema/theater, Pubs/bars/clubs", + "reason": "The [{'Activity': 'Cinema/theater', 'Avoidance Percentage': 55.0}, {'Activity': 'Pubs/bars/clubs', 'Avoidance Percentage': 55.0}] of have maximum values of 55.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Activity": "Cinema/theater", + "Avoidance Percentage": 55.0 + }, + { + "Activity": "Pubs/bars/clubs", + "Avoidance Percentage": 55.0 + }, + { + "Activity": "Concerts", + "Avoidance Percentage": 53.0 + }, + { + "Activity": "Sporting events", + "Avoidance Percentage": 53.0 + }, + { + "Activity": "Gym/sports centers", + "Avoidance Percentage": 49.0 + }, + { + "Activity": "Restaurants/cafés", + "Avoidance Percentage": 46.0 + }, + { + "Activity": "Large retail stores/malls", + "Avoidance Percentage": 45.0 + }, + { + "Activity": "Museums/galleries", + "Avoidance Percentage": 37.0 + }, + { + "Activity": "Hairdressers/beauty salons", + "Avoidance Percentage": 30.0 + }, + { + "Activity": "Not planning to avoid anything", + "Avoidance Percentage": 18.0 + }, + { + "Activity": "Supermarkets", + "Avoidance Percentage": 17.0 + } + ], + "columns": [ + { + "name": "Activity", + "importance": "primary", + "description": "List of activities or places", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Avoidance Percentage", + "importance": "primary", + "description": "Percentage of respondents who plan to avoid the activity", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2217": { + "metadata": { + "title": "Trump Is Losing Approval Over Coronavirus Outbreak", + "description": "Percentage of U.S. registered voters who approve of how President Trump is handling the coronavirus outbreak", + "main_insight": "President Trump's net approval rating regarding his handling of the coronavirus outbreak decreased significantly from February 7th to March 3rd." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Date Range": "Feb. 28-March 3", + "Score": 12.0, + "Approval Rating": "Net Approval" + }, + { + "Date Range": "Feb. 28-March 3", + "Score": 12.0, + "Approval Rating": "Somewhat Disapprove" + } + ], + "score": 1.0, + "annotation": "The Score of Net Approval and Somewhat Disapprove show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Net Approval and Somewhat Disapprove is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Date Range": "Feb. 28-March 3", + "Score": 12.0, + "Approval Rating": "Net Approval" + }, + { + "Date Range": "Feb. 28-March 3", + "Score": 25.0, + "Approval Rating": "Strongly Disapprove" + } + ], + "score": 1.0, + "annotation": "The Score of Net Approval and Strongly Disapprove show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Net Approval and Strongly Disapprove is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Date Range": "Feb. 28-March 3", + "Score": 12.0, + "Approval Rating": "Somewhat Disapprove" + }, + { + "Date Range": "Feb. 28-March 3", + "Score": 25.0, + "Approval Rating": "Strongly Disapprove" + } + ], + "score": 1.0, + "annotation": "The Score of Somewhat Disapprove and Strongly Disapprove show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Somewhat Disapprove and Strongly Disapprove is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Date Range": "Feb. 28-March 3", + "Score": 14.0, + "Approval Rating": "Don't know/No opinion" + }, + { + "Date Range": "Feb. 28-March 3", + "Score": 25.0, + "Approval Rating": "Strongly Disapprove" + } + ], + "score": 0.99, + "annotation": "The Score of Don't know/No opinion and Strongly Disapprove show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Don't know/No opinion and Strongly Disapprove is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Date Range": "Feb. 28-March 3", + "Score": 12.0, + "Approval Rating": "Net Approval" + }, + { + "Date Range": "Feb. 28-March 3", + "Score": 14.0, + "Approval Rating": "Don't know/No opinion" + } + ], + "score": 0.99, + "annotation": "The Score of Net Approval and Don't know/No opinion show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Net Approval and Don't know/No opinion is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Date Range": "Feb. 7 to 9", + "Score": 40.0, + "Approval Rating": "Net Approval" + }, + { + "Date Range": "Feb. 7 to 9", + "Score": 34.0, + "Approval Rating": "Strongly Approve" + }, + { + "Date Range": "Feb. 7 to 9", + "Score": 27.0, + "Approval Rating": "Somewhat Approve" + }, + { + "Date Range": "Feb. 7 to 9", + "Score": 18.0, + "Approval Rating": "Don't know/No opinion" + }, + { + "Date Range": "Feb. 7 to 9", + "Score": 7.0, + "Approval Rating": "Somewhat Disapprove" + }, + { + "Date Range": "Feb. 7 to 9", + "Score": 14.0, + "Approval Rating": "Strongly Disapprove" + }, + { + "Date Range": "Feb. 24 to 26", + "Score": 29.0, + "Approval Rating": "Net Approval" + }, + { + "Date Range": "Feb. 24 to 26", + "Score": 29.0, + "Approval Rating": "Strongly Approve" + }, + { + "Date Range": "Feb. 24 to 26", + "Score": 27.0, + "Approval Rating": "Somewhat Approve" + }, + { + "Date Range": "Feb. 24 to 26", + "Score": 17.0, + "Approval Rating": "Don't know/No opinion" + }, + { + "Date Range": "Feb. 24 to 26", + "Score": 9.0, + "Approval Rating": "Somewhat Disapprove" + }, + { + "Date Range": "Feb. 24 to 26", + "Score": 18.0, + "Approval Rating": "Strongly Disapprove" + }, + { + "Date Range": "Feb. 28-March 3", + "Score": 12.0, + "Approval Rating": "Net Approval" + }, + { + "Date Range": "Feb. 28-March 3", + "Score": 27.0, + "Approval Rating": "Strongly Approve" + }, + { + "Date Range": "Feb. 28-March 3", + "Score": 22.0, + "Approval Rating": "Somewhat Approve" + }, + { + "Date Range": "Feb. 28-March 3", + "Score": 14.0, + "Approval Rating": "Don't know/No opinion" + }, + { + "Date Range": "Feb. 28-March 3", + "Score": 12.0, + "Approval Rating": "Somewhat Disapprove" + }, + { + "Date Range": "Feb. 28-March 3", + "Score": 25.0, + "Approval Rating": "Strongly Disapprove" + } + ], + "columns": [ + { + "name": "Date Range", + "importance": "primary", + "description": "Date ranges of the polls", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding approval rating", + "role": "y" + }, + { + "name": "Approval Rating", + "importance": "primary", + "description": "The approval rating of the data point", + "role": "group" + } + ] + } + }, + "1165": { + "metadata": { + "title": "Soldiers of the German Armed Forces on Deployment", + "description": "Number of Bundeswehr soldiers participating in international operations", + "main_insight": "The largest contingent of German Bundeswehr soldiers is deployed in Resolute Support (Afghanistan, Uzbekistan), followed by KFOR (Kosovo) and Anti-ISIS Operations (Middle East). Deployments in other locations involve significantly fewer personnel." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Operation": "Resolute Support (Afghanistan, Uzbekistan)", + "Soldiers": 841 + } + ], + "score": 0.97, + "annotation": "The has a maximum value at Resolute Support (Afghanistan, Uzbekistan)", + "reason": "The [{'Operation': 'Resolute Support (Afghanistan, Uzbekistan)', 'Soldiers': 841}] of has a maximum value of 841, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Operation": "KFOR (Kosovo)", + "Soldiers": 657 + } + ], + "score": 0.66, + "annotation": "The shows a sudden jump in Soldiers from Anti-ISIS operations (Middle East) to KFOR (Kosovo).", + "reason": "The Soldiers for increases from 437 at Anti-ISIS operations (Middle East) to 657 at KFOR (Kosovo), indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Operation": "UNAMA (Afghanistan)", + "Soldiers": 2 + } + ], + "score": 0.47, + "annotation": "The has a minimum value at UNAMA (Afghanistan)", + "reason": "The [{'Operation': 'UNAMA (Afghanistan)', 'Soldiers': 2}] of has a mainimum value of 2, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Operation": "Resolute Support (Afghanistan, Uzbekistan)", + "Soldiers": 841 + }, + { + "Operation": "KFOR (Kosovo)", + "Soldiers": 657 + }, + { + "Operation": "Anti-ISIS operations (Middle East)", + "Soldiers": 437 + }, + { + "Operation": "EUNAVFOR MED (Mediterranean Sea)", + "Soldiers": 279 + }, + { + "Operation": "EUTM Mali (Mali)", + "Soldiers": 197 + }, + { + "Operation": "MINUSMA (Senegal, Mali)", + "Soldiers": 178 + }, + { + "Operation": "UNIFIL (Lebanon)", + "Soldiers": 119 + }, + { + "Operation": "Training support Iraq (Northern Iraq)", + "Soldiers": 114 + }, + { + "Operation": "ATALANTA (Horn of Africa)", + "Soldiers": 101 + }, + { + "Operation": "STRATAIRMEDEVAC* (Germany)", + "Soldiers": 41 + }, + { + "Operation": "UNMISS (South Sudan)", + "Soldiers": 16 + }, + { + "Operation": "EUTM SOM (Somalia)", + "Soldiers": 9 + }, + { + "Operation": "UNAMID (Sudan)", + "Soldiers": 8 + }, + { + "Operation": "MINURSO (Western Sahara)", + "Soldiers": 3 + }, + { + "Operation": "UNMIL (Liberia)", + "Soldiers": 3 + }, + { + "Operation": "UNAMA (Afghanistan)", + "Soldiers": 2 + } + ], + "columns": [ + { + "name": "Operation", + "importance": "primary", + "description": "Name of the international operation and location", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Soldiers", + "importance": "primary", + "description": "Number of Bundeswehr soldiers deployed", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2523": { + "metadata": { + "title": "Which Countries Produce The Most Books?", + "description": "Top publishing markets for new book titles in 2015", + "main_insight": "China produced the most new book titles in 2015, significantly more than the United States, which ranked second." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "China", + "New Book Titles": 470000 + } + ], + "score": 0.95, + "annotation": "The has a maximum value at China", + "reason": "The [{'Country': 'China', 'New Book Titles': 470000}] of has a maximum value of 470000, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "New Book Titles": 338986 + } + ], + "score": 0.76, + "annotation": "The shows a sudden jump in New Book Titles from United Kingdom to United States.", + "reason": "The New Book Titles for increases from 173000 at United Kingdom to 338986 at United States, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "South Korea", + "New Book Titles": 45212 + } + ], + "score": 0.48, + "annotation": "The has a minimum value at South Korea", + "reason": "The [{'Country': 'South Korea', 'New Book Titles': 45212}] of has a mainimum value of 45212, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "China", + "New Book Titles": 470000 + }, + { + "Country": "United States", + "New Book Titles": 338986 + }, + { + "Country": "United Kingdom", + "New Book Titles": 173000 + }, + { + "Country": "France", + "New Book Titles": 106760 + }, + { + "Country": "Germany", + "New Book Titles": 89506 + }, + { + "Country": "Brazil", + "New Book Titles": 88685 + }, + { + "Country": "Japan", + "New Book Titles": 76445 + }, + { + "Country": "Spain", + "New Book Titles": 73233 + }, + { + "Country": "Italy", + "New Book Titles": 65886 + }, + { + "Country": "South Korea", + "New Book Titles": 45212 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country producing books", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "New Book Titles", + "importance": "primary", + "description": "Number of new book titles produced in 2015", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "913": { + "metadata": { + "title": "Which Teams Played the Most Super Bowl Games?", + "description": "Super Bowl appearances and Super Bowl wins of NFL teams", + "main_insight": "The New England Patriots have the most Super Bowl appearances with 11, followed by the Pittsburgh Steelers and Dallas Cowboys with 8 appearances each. The Pittsburgh Steelers and New England Patriots have won most often, with six wins." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Team": "New England Patriots", + "Super Bowl wins": 6 + }, + { + "Team": "Pittsburgh Steelers", + "Super Bowl wins": 6 + } + ], + "score": 0.82, + "annotation": "The has maximum values at New England Patriots, Pittsburgh Steelers", + "reason": "The [{'Team': 'New England Patriots', 'Super Bowl wins': 6}, {'Team': 'Pittsburgh Steelers', 'Super Bowl wins': 6}] of have maximum values of 6, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Team": "Miami Dolphins", + "Super Bowl wins": 2 + }, + { + "Team": "Los Angeles Rams**", + "Super Bowl wins": 2 + } + ], + "score": 0.73, + "annotation": "The has minimum values at Miami Dolphins, Los Angeles Rams**", + "reason": "The [{'Team': 'Miami Dolphins', 'Super Bowl wins': 2}, {'Team': 'Los Angeles Rams**', 'Super Bowl wins': 2}] of have minimum values of 2, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Team": "Denver Broncos", + "Super Bowl wins": 3 + }, + { + "Team": "Green Bay Packers", + "Super Bowl wins": 4 + }, + { + "Team": "Dallas Cowboys", + "Super Bowl wins": 5 + }, + { + "Team": "New England Patriots", + "Super Bowl wins": 6 + } + ], + "score": 0.65, + "annotation": "The exhibits multiple sudden jumps in Super Bowl wins, transitioning from Los Angeles Rams**, Oakland Raiders*, New York Giants, San Francisco 49ers to Denver Broncos, Green Bay Packers, Dallas Cowboys, New England Patriots.", + "reason": "Across these points, the Super Bowl wins for increases from 2, 3, 4, 5 at Los Angeles Rams**, Oakland Raiders*, New York Giants, San Francisco 49ers to 3, 4, 5, 6 at Denver Broncos, Green Bay Packers, Dallas Cowboys, New England Patriots, suggesting significant shifts in value." + } + ], + "data": { + "data": [ + { + "Team": "New England Patriots", + "Super Bowl wins": 6 + }, + { + "Team": "Pittsburgh Steelers", + "Super Bowl wins": 6 + }, + { + "Team": "Dallas Cowboys", + "Super Bowl wins": 5 + }, + { + "Team": "San Francisco 49ers", + "Super Bowl wins": 5 + }, + { + "Team": "Denver Broncos", + "Super Bowl wins": 3 + }, + { + "Team": "Kansas City Chiefs", + "Super Bowl wins": 3 + }, + { + "Team": "Green Bay Packers", + "Super Bowl wins": 4 + }, + { + "Team": "New York Giants", + "Super Bowl wins": 4 + }, + { + "Team": "Washington Commanders", + "Super Bowl wins": 3 + }, + { + "Team": "Oakland Raiders*", + "Super Bowl wins": 3 + }, + { + "Team": "Miami Dolphins", + "Super Bowl wins": 2 + }, + { + "Team": "Los Angeles Rams**", + "Super Bowl wins": 2 + } + ], + "columns": [ + { + "name": "Team", + "importance": "primary", + "description": "NFL Team", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Super Bowl wins", + "importance": "primary", + "description": "Number of Super Bowl wins", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1460": { + "metadata": { + "title": "The Distrustful Consumer", + "description": "Share of British consumers who distrust brands", + "main_insight": "A significant portion of British consumers distrust advertising and brands, with 69% distrusting advertising, and a smaller, but still substantial percentages reporting that they trust advertising less than before (43%), or distrust brands in general (42%)." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "Distrust advertising", + "Distrust advertising": 69.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Distrust advertising from Distrust brands to Distrust advertising.", + "reason": "The Distrust advertising for increases from 0 at Distrust brands to 69.0 at Distrust advertising, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Distrust advertising", + "Distrust advertising": 69.0 + } + ], + "score": 0.67, + "annotation": "The has a maximum value at Distrust advertising", + "reason": "The [{'Category': 'Distrust advertising', 'Distrust advertising': 69.0}] of has a maximum value of 69.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Category": "Trust advertising less", + "Distrust advertising": 0 + }, + { + "Category": "Distrust brands", + "Distrust advertising": 0 + } + ], + "score": 0.39, + "annotation": "The has minimum values at Trust advertising less, Distrust brands", + "reason": "The [{'Category': 'Trust advertising less', 'Distrust advertising': 0}, {'Category': 'Distrust brands', 'Distrust advertising': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Category": "Distrust advertising", + "Distrust advertising": 69.0 + }, + { + "Category": "Trust advertising less", + "Distrust advertising": 0 + }, + { + "Category": "Distrust brands", + "Distrust advertising": 0 + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "The category of distrust", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Distrust advertising", + "importance": "primary", + "description": "Percentage of consumers who distrust advertising", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "232": { + "metadata": { + "title": "Inflation Heats Up for Third Consecutive Month", + "description": "Year-over-year change in the Consumer Price Index for All Urban Consumers in the U.S.", + "main_insight": "Inflation, as measured by the Consumer Price Index (CPI), has increased for the third month in a row, reaching 3.2% in December 2024. The CPI excluding shelter shows a lower, but similar, inflation rate of 2.9%, while excluding food and energy the inflation rate has plateaued at 1.9%." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Ratio": 3.2, + "Exclusion": "All items" + }, + { + "Year": 2024, + "Ratio": 1.9, + "Exclusion": "Excluding food and energy" + } + ], + "score": 0.94, + "annotation": "The Ratio of All items and Excluding food and energy show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between All items and Excluding food and energy is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Ratio": 1.9, + "Exclusion": "Excluding food and energy" + }, + { + "Year": 2024, + "Ratio": 2.9, + "Exclusion": "Excluding shelter" + } + ], + "score": 0.87, + "annotation": "The Ratio of Excluding food and energy and Excluding shelter show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Excluding food and energy and Excluding shelter is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2024, + "Ratio": 3.2, + "Exclusion": "All items" + }, + { + "Year": 2024, + "Ratio": 2.9, + "Exclusion": "Excluding shelter" + } + ], + "score": 0.84, + "annotation": "The Ratio of All items and Excluding shelter show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between All items and Excluding shelter is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2019, + "Ratio": 1.8, + "Exclusion": "All items" + }, + { + "Year": 2019, + "Ratio": 2.1, + "Exclusion": "Excluding food and energy" + }, + { + "Year": 2019, + "Ratio": 1.3, + "Exclusion": "Excluding shelter" + }, + { + "Year": 2020, + "Ratio": 1.2, + "Exclusion": "All items" + }, + { + "Year": 2020, + "Ratio": 1.6, + "Exclusion": "Excluding food and energy" + }, + { + "Year": 2020, + "Ratio": 1.0, + "Exclusion": "Excluding shelter" + }, + { + "Year": 2021, + "Ratio": 7.0, + "Exclusion": "All items" + }, + { + "Year": 2021, + "Ratio": 5.5, + "Exclusion": "Excluding food and energy" + }, + { + "Year": 2021, + "Ratio": 4.2, + "Exclusion": "Excluding shelter" + }, + { + "Year": 2022, + "Ratio": 6.5, + "Exclusion": "All items" + }, + { + "Year": 2022, + "Ratio": 5.7, + "Exclusion": "Excluding food and energy" + }, + { + "Year": 2022, + "Ratio": 7.5, + "Exclusion": "Excluding shelter" + }, + { + "Year": 2023, + "Ratio": 3.4, + "Exclusion": "All items" + }, + { + "Year": 2023, + "Ratio": 3.9, + "Exclusion": "Excluding food and energy" + }, + { + "Year": 2023, + "Ratio": 4.1, + "Exclusion": "Excluding shelter" + }, + { + "Year": 2024, + "Ratio": 3.2, + "Exclusion": "All items" + }, + { + "Year": 2024, + "Ratio": 1.9, + "Exclusion": "Excluding food and energy" + }, + { + "Year": 2024, + "Ratio": 2.9, + "Exclusion": "Excluding shelter" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year on the x-axis", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding exclusion", + "role": "y" + }, + { + "name": "Exclusion", + "importance": "primary", + "description": "The exclusion of the data point", + "role": "group" + } + ] + } + }, + "444": { + "metadata": { + "title": "America's Biggest Magazine Brands", + "description": "Average monthly cross-platform audience of the largest magazine brands in the U.S. in 2017", + "main_insight": "ESPN The Magazine had the largest average monthly cross-platform audience in 2017, significantly exceeding other major magazine brands, primarily due to its strong mobile web presence." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Magazine Brand": "Forbes", + "Units": 12.6, + "Reach Metrics": "Web (Desktop)" + } + ], + "score": 0.95, + "annotation": "The Web (Desktop) has a maximum value at Forbes", + "reason": "The [{'Magazine Brand': 'Forbes', 'Units': 12.6, 'Reach Metrics': 'Web (Desktop)'}] of Web (Desktop) has a maximum value of 12.6, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Magazine Brand": "ESPN The Magazine", + "Units": 8.6, + "Reach Metrics": "Video" + } + ], + "score": 0.94, + "annotation": "The Video has a maximum value at ESPN The Magazine", + "reason": "The [{'Magazine Brand': 'ESPN The Magazine', 'Units': 8.6, 'Reach Metrics': 'Video'}] of Video has a maximum value of 8.6, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Magazine Brand": "ESPN The Magazine", + "Units": 102.2, + "Reach Metrics": "Total Audience" + } + ], + "score": 0.94, + "annotation": "The Total Audience has a maximum value at ESPN The Magazine", + "reason": "The [{'Magazine Brand': 'ESPN The Magazine', 'Units': 102.2, 'Reach Metrics': 'Total Audience'}] of Total Audience has a maximum value of 102.2, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Magazine Brand": "Forbes", + "Units": 0.09, + "Reach Metrics": "Y-o-Y Change" + } + ], + "score": 0.92, + "annotation": "The Y-o-Y Change has a maximum value at Forbes", + "reason": "The [{'Magazine Brand': 'Forbes', 'Units': 0.09, 'Reach Metrics': 'Y-o-Y Change'}] of Y-o-Y Change has a maximum value of 0.09, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Magazine Brand": "ESPN The Magazine", + "Units": 69.4, + "Reach Metrics": "Mobile Web" + } + ], + "score": 0.92, + "annotation": "The Mobile Web has a maximum value at ESPN The Magazine", + "reason": "The [{'Magazine Brand': 'ESPN The Magazine', 'Units': 69.4, 'Reach Metrics': 'Mobile Web'}] of Mobile Web has a maximum value of 69.4, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Magazine Brand": "ESPN The Magazine", + "Units": 102.2, + "Reach Metrics": "Total Audience" + }, + { + "Magazine Brand": "ESPN The Magazine", + "Units": 16.4, + "Reach Metrics": "Print & Digital Editions" + }, + { + "Magazine Brand": "ESPN The Magazine", + "Units": 7.8, + "Reach Metrics": "Web (Desktop)" + }, + { + "Magazine Brand": "ESPN The Magazine", + "Units": 69.4, + "Reach Metrics": "Mobile Web" + }, + { + "Magazine Brand": "ESPN The Magazine", + "Units": 8.6, + "Reach Metrics": "Video" + }, + { + "Magazine Brand": "ESPN The Magazine", + "Units": -0.01, + "Reach Metrics": "Y-o-Y Change" + }, + { + "Magazine Brand": "People", + "Units": 79.7, + "Reach Metrics": "Total Audience" + }, + { + "Magazine Brand": "People", + "Units": 31.3, + "Reach Metrics": "Print & Digital Editions" + }, + { + "Magazine Brand": "People", + "Units": 5.9, + "Reach Metrics": "Web (Desktop)" + }, + { + "Magazine Brand": "People", + "Units": 39.7, + "Reach Metrics": "Mobile Web" + }, + { + "Magazine Brand": "People", + "Units": 2.7, + "Reach Metrics": "Video" + }, + { + "Magazine Brand": "Forbes", + "Units": 68.2, + "Reach Metrics": "Total Audience" + }, + { + "Magazine Brand": "Forbes", + "Units": 13.2, + "Reach Metrics": "Print & Digital Editions" + }, + { + "Magazine Brand": "Forbes", + "Units": 12.6, + "Reach Metrics": "Web (Desktop)" + }, + { + "Magazine Brand": "Forbes", + "Units": 37.6, + "Reach Metrics": "Mobile Web" + }, + { + "Magazine Brand": "Forbes", + "Units": 4.7, + "Reach Metrics": "Video" + }, + { + "Magazine Brand": "Forbes", + "Units": 0.09, + "Reach Metrics": "Y-o-Y Change" + }, + { + "Magazine Brand": "WebMD Magazine", + "Units": 59.5, + "Reach Metrics": "Total Audience" + }, + { + "Magazine Brand": "WebMD Magazine", + "Units": 12.2, + "Reach Metrics": "Print & Digital Editions" + }, + { + "Magazine Brand": "WebMD Magazine", + "Units": 5.8, + "Reach Metrics": "Web (Desktop)" + }, + { + "Magazine Brand": "WebMD Magazine", + "Units": 37.4, + "Reach Metrics": "Mobile Web" + }, + { + "Magazine Brand": "WebMD Magazine", + "Units": 4.1, + "Reach Metrics": "Video" + }, + { + "Magazine Brand": "WebMD Magazine", + "Units": 0.05, + "Reach Metrics": "Y-o-Y Change" + }, + { + "Magazine Brand": "Allrecipes", + "Units": 54.1, + "Reach Metrics": "Total Audience" + }, + { + "Magazine Brand": "Allrecipes", + "Units": 8.6, + "Reach Metrics": "Print & Digital Editions" + }, + { + "Magazine Brand": "Allrecipes", + "Units": 4.6, + "Reach Metrics": "Web (Desktop)" + }, + { + "Magazine Brand": "Allrecipes", + "Units": 37.2, + "Reach Metrics": "Mobile Web" + }, + { + "Magazine Brand": "Allrecipes", + "Units": 3.7, + "Reach Metrics": "Video" + }, + { + "Magazine Brand": "Allrecipes", + "Units": 0.01, + "Reach Metrics": "Y-o-Y Change" + }, + { + "Magazine Brand": "Time", + "Units": 47.8, + "Reach Metrics": "Total Audience" + }, + { + "Magazine Brand": "Time", + "Units": 17.2, + "Reach Metrics": "Print & Digital Editions" + }, + { + "Magazine Brand": "Time", + "Units": 5.2, + "Reach Metrics": "Web (Desktop)" + }, + { + "Magazine Brand": "Time", + "Units": 21.7, + "Reach Metrics": "Mobile Web" + }, + { + "Magazine Brand": "Time", + "Units": 3.8, + "Reach Metrics": "Video" + }, + { + "Magazine Brand": "Time", + "Units": 0.02, + "Reach Metrics": "Y-o-Y Change" + }, + { + "Magazine Brand": "Better Homes and Gardens", + "Units": 47.5, + "Reach Metrics": "Total Audience" + }, + { + "Magazine Brand": "Better Homes and Gardens", + "Units": 36.2, + "Reach Metrics": "Print & Digital Editions" + }, + { + "Magazine Brand": "Better Homes and Gardens", + "Units": 2.9, + "Reach Metrics": "Web (Desktop)" + }, + { + "Magazine Brand": "Better Homes and Gardens", + "Units": 7.6, + "Reach Metrics": "Mobile Web" + }, + { + "Magazine Brand": "Better Homes and Gardens", + "Units": 0.8, + "Reach Metrics": "Video" + }, + { + "Magazine Brand": "Better Homes and Gardens", + "Units": -0.01, + "Reach Metrics": "Y-o-Y Change" + }, + { + "Magazine Brand": "AARP", + "Units": 46.3, + "Reach Metrics": "Total Audience" + }, + { + "Magazine Brand": "AARP", + "Units": 43.5, + "Reach Metrics": "Print & Digital Editions" + }, + { + "Magazine Brand": "AARP", + "Units": 1.4, + "Reach Metrics": "Web (Desktop)" + }, + { + "Magazine Brand": "AARP", + "Units": 1.3, + "Reach Metrics": "Mobile Web" + }, + { + "Magazine Brand": "AARP", + "Units": 0.1, + "Reach Metrics": "Video" + }, + { + "Magazine Brand": "AARP", + "Units": 0.03, + "Reach Metrics": "Y-o-Y Change" + }, + { + "Magazine Brand": "National Geographic", + "Units": 40.9, + "Reach Metrics": "Total Audience" + }, + { + "Magazine Brand": "National Geographic", + "Units": 21.2, + "Reach Metrics": "Print & Digital Editions" + }, + { + "Magazine Brand": "National Geographic", + "Units": 3.9, + "Reach Metrics": "Web (Desktop)" + }, + { + "Magazine Brand": "National Geographic", + "Units": 14.7, + "Reach Metrics": "Mobile Web" + }, + { + "Magazine Brand": "National Geographic", + "Units": 1.2, + "Reach Metrics": "Video" + }, + { + "Magazine Brand": "National Geographic", + "Units": 0.03, + "Reach Metrics": "Y-o-Y Change" + }, + { + "Magazine Brand": "Sports Illustrated", + "Units": 36.2, + "Reach Metrics": "Total Audience" + }, + { + "Magazine Brand": "Sports Illustrated", + "Units": 17.9, + "Reach Metrics": "Print & Digital Editions" + }, + { + "Magazine Brand": "Sports Illustrated", + "Units": 3.1, + "Reach Metrics": "Web (Desktop)" + }, + { + "Magazine Brand": "Sports Illustrated", + "Units": 12.5, + "Reach Metrics": "Mobile Web" + }, + { + "Magazine Brand": "Sports Illustrated", + "Units": 2.7, + "Reach Metrics": "Video" + }, + { + "Magazine Brand": "Sports Illustrated", + "Units": 0.03, + "Reach Metrics": "Y-o-Y Change" + } + ], + "columns": [ + { + "name": "Magazine Brand", + "importance": "primary", + "description": "Name of the magazine brand", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding reach metrics", + "role": "y" + }, + { + "name": "Reach Metrics", + "importance": "primary", + "description": "The reach metrics of the data point", + "role": "group" + } + ] + } + }, + "1674": { + "metadata": { + "title": "Hey brands, cool it with the emojis!", + "description": "% of people who agree or disagree that businesses try too hard with emojis*", + "main_insight": "Across all age groups, a majority of people agree that businesses try too hard with emojis in advertising and communications. The sentiment is strongest in the 50-64 age group." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Age Group": "18-34", + "Units": 21.0, + "Agreement": "Disagree" + } + ], + "score": 0.83, + "annotation": "The Disagree shows a sudden jump in Units from 65+ to 18-34.", + "reason": "The Units for Disagree increases from 18.0 at 65+ to 21.0 at 18-34, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Age Group": "18-34", + "Units": 59.0, + "Agreement": "Agree" + }, + { + "Age Group": "50-64", + "Units": 62.0, + "Agreement": "Agree" + } + ], + "score": 0.75, + "annotation": "The Agree exhibits multiple sudden jumps in Units, transitioning from 35-49, 18-34 to 18-34, 50-64.", + "reason": "Across these points, the Units for Agree increases from 56.0, 59.0 at 35-49, 18-34 to 59.0, 62.0 at 18-34, 50-64, suggesting significant shifts in value." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "35-49", + "Units": 56.0, + "Agreement": "Agree" + } + ], + "score": 0.74, + "annotation": "The Agree accounts for the majority in 35-49.", + "reason": "The Units of Agree in 35-49 accounts for 0.7088607594936709 (more than 50%) of the total of Units of all Agreement." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Age Group": "18-34", + "Units": 59.0, + "Agreement": "Agree" + } + ], + "score": 0.73, + "annotation": "The Agree accounts for the majority in 18-34.", + "reason": "The Units of Agree in 18-34 accounts for 0.7375 (more than 50%) of the total of Units of all Agreement." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Agreement": "Agree", + "Age Group": "total", + "Units": 231.0 + } + ], + "score": 0.73, + "annotation": "The total value of Agree accounts for the majority in all Agreement.", + "reason": "The total value of Units of Agree accounts for 0.7451612903225806 (more than 50%) of the total of Units of all Agreement." + } + ], + "data": { + "data": [ + { + "Age Group": "18-34", + "Units": 59.0, + "Agreement": "Agree" + }, + { + "Age Group": "18-34", + "Units": 21.0, + "Agreement": "Disagree" + }, + { + "Age Group": "35-49", + "Units": 56.0, + "Agreement": "Agree" + }, + { + "Age Group": "35-49", + "Units": 23.0, + "Agreement": "Disagree" + }, + { + "Age Group": "50-64", + "Units": 62.0, + "Agreement": "Agree" + }, + { + "Age Group": "50-64", + "Units": 17.0, + "Agreement": "Disagree" + }, + { + "Age Group": "65+", + "Units": 54.0, + "Agreement": "Agree" + }, + { + "Age Group": "65+", + "Units": 18.0, + "Agreement": "Disagree" + } + ], + "columns": [ + { + "name": "Age Group", + "importance": "primary", + "description": "Age ranges of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding agreement", + "role": "y" + }, + { + "name": "Agreement", + "importance": "primary", + "description": "The agreement of the data point", + "role": "group" + } + ] + } + }, + "1125": { + "metadata": { + "title": "7 of the world's 10 busiest container ports are in China", + "description": "Busiest container ports in the world as of 2016, based on shipping volume (in million TEU*)", + "main_insight": "The chart shows that 7 of the top 10 busiest container ports are in China, with Shanghai being the busiest port globally, handling 37.13 million TEU in 2016." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Port": "Shanghai (China)", + "Shipping Volume (Million TEU)": 37.13 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at Shanghai (China)", + "reason": "The [{'Port': 'Shanghai (China)', 'Shipping Volume (Million TEU)': 37.13}] of has a maximum value of 37.13, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Port": "Singapore", + "Shipping Volume (Million TEU)": 30.9 + } + ], + "score": 0.7, + "annotation": "The shows a sudden jump in Shipping Volume (Million TEU) from Shenzhen (China) to Singapore.", + "reason": "The Shipping Volume (Million TEU) for increases from 23.97 at Shenzhen (China) to 30.9 at Singapore, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Port": "Tianjin (China)", + "Shipping Volume (Million TEU)": 14.49 + } + ], + "score": 0.63, + "annotation": "The has a minimum value at Tianjin (China)", + "reason": "The [{'Port': 'Tianjin (China)', 'Shipping Volume (Million TEU)': 14.49}] of has a mainimum value of 14.49, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Port": "Shanghai (China)", + "Shipping Volume (Million TEU)": 37.13 + }, + { + "Port": "Singapore", + "Shipping Volume (Million TEU)": 30.9 + }, + { + "Port": "Shenzhen (China)", + "Shipping Volume (Million TEU)": 23.97 + }, + { + "Port": "Ningbo-Zhoushan (China)", + "Shipping Volume (Million TEU)": 21.6 + }, + { + "Port": "Busan (South Korea)", + "Shipping Volume (Million TEU)": 19.85 + }, + { + "Port": "Hong Kong (China)", + "Shipping Volume (Million TEU)": 19.81 + }, + { + "Port": "Guangzhou (China)", + "Shipping Volume (Million TEU)": 18.85 + }, + { + "Port": "Qingdao (China)", + "Shipping Volume (Million TEU)": 18.01 + }, + { + "Port": "Jebel Ali (UAE)", + "Shipping Volume (Million TEU)": 15.73 + }, + { + "Port": "Tianjin (China)", + "Shipping Volume (Million TEU)": 14.49 + } + ], + "columns": [ + { + "name": "Port", + "importance": "primary", + "description": "Name of the container port and its country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Shipping Volume (Million TEU)", + "importance": "primary", + "description": "Shipping volume in million TEU", + "unit": "Million TEU", + "data_type": "number", + "role": "y" + } + ] + } + }, + "552": { + "metadata": { + "title": "A Quarter Of Britons & Dutch Wish They Still Had Empires", + "description": "\"Would you like your country to still have an empire?\"", + "main_insight": "A significant minority of people in several countries, most notably the United Kingdom and Netherlands, would like their country to still have an empire. However, in all surveyed countries, a majority do not wish for an empire." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Japan", + "Units": 7.0, + "Response": "Yes" + }, + { + "Country": "Japan", + "Units": 70.0, + "Response": "No" + } + ], + "score": 0.91, + "annotation": "The Units of Yes and No show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Yes and No is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Netherlands", + "Units": 45.0, + "Response": "No" + } + ], + "score": 0.8, + "annotation": "The No has a minimum value at Netherlands", + "reason": "The [{'Country': 'Netherlands', 'Units': 45.0, 'Response': 'No'}] of No has a mainimum value of 45.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Japan", + "Units": 7.0, + "Response": "Yes" + } + ], + "score": 0.77, + "annotation": "The Yes has a minimum value at Japan", + "reason": "The [{'Country': 'Japan', 'Units': 7.0, 'Response': 'Yes'}] of Yes has a mainimum value of 7.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Netherlands", + "Units": 45.0, + "Response": "No" + } + ], + "score": 0.76, + "annotation": "The No accounts for the majority in Netherlands.", + "reason": "The Units of No in Netherlands accounts for 0.6338028169014085 (more than 50%) of the total of Units of all Response." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "United Kingdom", + "Units": 50.0, + "Response": "No" + } + ], + "score": 0.75, + "annotation": "The No accounts for the majority in United Kingdom.", + "reason": "The Units of No in United Kingdom accounts for 0.6493506493506493 (more than 50%) of the total of Units of all Response." + } + ], + "data": { + "data": [ + { + "Country": "United Kingdom", + "Units": 27.0, + "Response": "Yes" + }, + { + "Country": "United Kingdom", + "Units": 50.0, + "Response": "No" + }, + { + "Country": "Netherlands", + "Units": 26.0, + "Response": "Yes" + }, + { + "Country": "Netherlands", + "Units": 45.0, + "Response": "No" + }, + { + "Country": "Belgium", + "Units": 21.0, + "Response": "Yes" + }, + { + "Country": "Belgium", + "Units": 52.0, + "Response": "No" + }, + { + "Country": "Spain", + "Units": 17.0, + "Response": "Yes" + }, + { + "Country": "Spain", + "Units": 62.0, + "Response": "No" + }, + { + "Country": "France", + "Units": 17.0, + "Response": "Yes" + }, + { + "Country": "France", + "Units": 59.0, + "Response": "No" + }, + { + "Country": "Italy", + "Units": 14.0, + "Response": "Yes" + }, + { + "Country": "Italy", + "Units": 71.0, + "Response": "No" + }, + { + "Country": "Germany", + "Units": 9.0, + "Response": "Yes" + }, + { + "Country": "Germany", + "Units": 66.0, + "Response": "No" + }, + { + "Country": "Japan", + "Units": 7.0, + "Response": "Yes" + }, + { + "Country": "Japan", + "Units": 70.0, + "Response": "No" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding response", + "role": "y" + }, + { + "name": "Response", + "importance": "primary", + "description": "The response of the data point", + "role": "group" + } + ] + } + }, + "2543": { + "metadata": { + "title": "Mediterranean Migrant Deaths on the Rise", + "description": "Number of dead and missing migrants recorded in the Mediterranean from 2014 to 2023", + "main_insight": "Migrant deaths in the Mediterranean peaked in 2016, and while decreasing since then, there was a notable increase from 2020 to 2022. Data for 2023 is incomplete, covering only up to June 9th." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2014, + "Migrant Deaths": 3289 + }, + { + "Year": 2015, + "Migrant Deaths": 4055 + }, + { + "Year": 2016, + "Migrant Deaths": 5136 + }, + { + "Year": 2017, + "Migrant Deaths": 3139 + }, + { + "Year": 2018, + "Migrant Deaths": 2337 + }, + { + "Year": 2019, + "Migrant Deaths": 1885 + }, + { + "Year": 2020, + "Migrant Deaths": 1449 + }, + { + "Year": 2021, + "Migrant Deaths": 2062 + }, + { + "Year": 2022, + "Migrant Deaths": 2406 + }, + { + "Year": 2023, + "Migrant Deaths": 1166 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of recorded deaths", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Migrant Deaths", + "importance": "primary", + "description": "Number of dead and missing migrants", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1753": { + "metadata": { + "title": "Americans Divided on Native American Policies", + "description": "Share of respondents who would strongly/somewhat support the following policies, by party alignment (in %)", + "main_insight": "There is significant division between Democrats and Republicans on policies related to Native Americans, with Democrats generally showing much higher support for these policies." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Policy": "Banning sports teams from using Native American-themed mascots", + "Score": 48.0, + "Political Party": "Democrats" + } + ], + "score": 0.94, + "annotation": "The Democrats has a minimum value at Banning sports teams from using Native American-themed mascots", + "reason": "The [{'Policy': 'Banning sports teams from using Native American-themed mascots', 'Score': 48.0, 'Political Party': 'Democrats'}] of Democrats has a mainimum value of 48.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Policy": "Banning sports teams from using Native American-themed mascots", + "Score": 12.0, + "Political Party": "Republicans" + } + ], + "score": 0.85, + "annotation": "The Republicans has a minimum value at Banning sports teams from using Native American-themed mascots", + "reason": "The [{'Policy': 'Banning sports teams from using Native American-themed mascots', 'Score': 12.0, 'Political Party': 'Republicans'}] of Republicans has a mainimum value of 12.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Policy": "Banning sports teams from using Native American-themed mascots", + "Score": 48.0, + "Political Party": "Democrats" + }, + { + "Policy": "Banning sports teams from using Native American-themed mascots", + "Score": 12.0, + "Political Party": "Republicans" + } + ], + "score": 0.83, + "annotation": "The Score of Democrats and Republicans show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Democrats and Republicans is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Policy": "Requiring federal agencies to involve tribes in decision-making that affects their lands", + "Score": 81.0, + "Political Party": "Democrats" + } + ], + "score": 0.8, + "annotation": "The Democrats accounts for the majority in Requiring federal agencies to involve tribes in decision-making that affects their lands.", + "reason": "The Score of Democrats in Requiring federal agencies to involve tribes in decision-making that affects their lands accounts for 0.50625 (more than 50%) of the total of Score of all Political Party." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Policy": "Giving Native American families and institutions priority in the adoption of Native children", + "Score": 79.0, + "Political Party": "Democrats" + } + ], + "score": 0.79, + "annotation": "The Democrats accounts for the majority in Giving Native American families and institutions priority in the adoption of Native children.", + "reason": "The Score of Democrats in Giving Native American families and institutions priority in the adoption of Native children accounts for 0.5197368421052632 (more than 50%) of the total of Score of all Political Party." + } + ], + "data": { + "data": [ + { + "Policy": "Increasing the amount that is taught about Native American history in public school curriculums", + "Score": 85.0, + "Political Party": "Democrats" + }, + { + "Policy": "Increasing the amount that is taught about Native American history in public school curriculums", + "Score": 78.0, + "Political Party": "Republicans" + }, + { + "Policy": "Strengthening laws to prevent the unwarranted removal of Native children from their families", + "Score": 85.0, + "Political Party": "Democrats" + }, + { + "Policy": "Strengthening laws to prevent the unwarranted removal of Native children from their families", + "Score": 75.0, + "Political Party": "Republicans" + }, + { + "Policy": "The government apologizing for its past treatment of Native Americans", + "Score": 84.0, + "Political Party": "Democrats" + }, + { + "Policy": "The government apologizing for its past treatment of Native Americans", + "Score": 60.0, + "Political Party": "Republicans" + }, + { + "Policy": "Requiring federal agencies to involve tribes in decision-making that affects their lands", + "Score": 81.0, + "Political Party": "Democrats" + }, + { + "Policy": "Requiring federal agencies to involve tribes in decision-making that affects their lands", + "Score": 79.0, + "Political Party": "Republicans" + }, + { + "Policy": "Increasing government funding for the preservation of Native American languages and traditions", + "Score": 80.0, + "Political Party": "Democrats" + }, + { + "Policy": "Increasing government funding for the preservation of Native American languages and traditions", + "Score": 58.0, + "Political Party": "Republicans" + }, + { + "Policy": "Giving Native American families and institutions priority in the adoption of Native children", + "Score": 79.0, + "Political Party": "Democrats" + }, + { + "Policy": "Giving Native American families and institutions priority in the adoption of Native children", + "Score": 73.0, + "Political Party": "Republicans" + }, + { + "Policy": "Halting or rerouting infrastructure projects based on concerns raised by Native tribes", + "Score": 70.0, + "Political Party": "Democrats" + }, + { + "Policy": "Halting or rerouting infrastructure projects based on concerns raised by Native tribes", + "Score": 44.0, + "Political Party": "Republicans" + }, + { + "Policy": "Removing monuments dedicated to historical figures who supported mistreatment of Native Americans", + "Score": 68.0, + "Political Party": "Democrats" + }, + { + "Policy": "Removing monuments dedicated to historical figures who supported mistreatment of Native Americans", + "Score": 18.0, + "Political Party": "Republicans" + }, + { + "Policy": "The government paying cash reparations to Native American tribal members", + "Score": 67.0, + "Political Party": "Democrats" + }, + { + "Policy": "The government paying cash reparations to Native American tribal members", + "Score": 30.0, + "Political Party": "Republicans" + }, + { + "Policy": "Giving Native Americans an advantage in college admissions decisions", + "Score": 67.0, + "Political Party": "Democrats" + }, + { + "Policy": "Giving Native Americans an advantage in college admissions decisions", + "Score": 32.0, + "Political Party": "Republicans" + }, + { + "Policy": "Expanding the jurisdiction of tribal courts for crimes committed on Native American lands", + "Score": 61.0, + "Political Party": "Democrats" + }, + { + "Policy": "Expanding the jurisdiction of tribal courts for crimes committed on Native American lands", + "Score": 56.0, + "Political Party": "Republicans" + }, + { + "Policy": "Banning sports teams from using Native American-themed mascots", + "Score": 48.0, + "Political Party": "Democrats" + }, + { + "Policy": "Banning sports teams from using Native American-themed mascots", + "Score": 12.0, + "Political Party": "Republicans" + } + ], + "columns": [ + { + "name": "Policy", + "importance": "primary", + "description": "The specific policy related to Native Americans", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding political party", + "role": "y" + }, + { + "name": "Political Party", + "importance": "primary", + "description": "The political party of the data point", + "role": "group" + } + ] + } + }, + "1443": { + "metadata": { + "title": "IMF Forecasts Growth in Asia To Slow in 2025", + "description": "Real GDP growth projections for selected countries, by year", + "main_insight": "The IMF forecasts that real GDP growth in most Asian countries will slow down in 2025 compared to 2024. India is projected to experience the highest growth, though it is also expected to decrease from 6.8% to 6.5%." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country/Region": "KR", + "Value": 2.3, + "Projection": "2024" + }, + { + "Country/Region": "KR", + "Value": 2.0, + "Projection": "2025" + } + ], + "score": 0.98, + "annotation": "The Value of 2024 and 2025 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Value between 2024 and 2025 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country/Region": "JP", + "Value": 0.8, + "Projection": "Change" + } + ], + "score": 0.92, + "annotation": "The Change has a maximum value at JP", + "reason": "The [{'Country/Region': 'JP', 'Value': 0.8, 'Projection': 'Change'}] of Change has a maximum value of 0.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country/Region": "JP", + "Value": 0.9, + "Projection": "2024" + } + ], + "score": 0.87, + "annotation": "The 2024 has a minimum value at JP", + "reason": "The [{'Country/Region': 'JP', 'Value': 0.9, 'Projection': '2024'}] of 2024 has a mainimum value of 0.9, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country/Region": "IN", + "Value": 6.8, + "Projection": "2024" + } + ], + "score": 0.82, + "annotation": "The 2024 has a maximum value at IN", + "reason": "The [{'Country/Region': 'IN', 'Value': 6.8, 'Projection': '2024'}] of 2024 has a maximum value of 6.8, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country/Region": "JP", + "Value": 1.7, + "Projection": "2025" + } + ], + "score": 0.78, + "annotation": "The 2025 has a minimum value at JP", + "reason": "The [{'Country/Region': 'JP', 'Value': 1.7, 'Projection': '2025'}] of 2025 has a mainimum value of 1.7, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country/Region": "World", + "Value": 3.2, + "Projection": "2024" + }, + { + "Country/Region": "World", + "Value": 3.2, + "Projection": "2025" + }, + { + "Country/Region": "Asia", + "Value": 4.5, + "Projection": "2024" + }, + { + "Country/Region": "Asia", + "Value": 4.3, + "Projection": "2025" + }, + { + "Country/Region": "Asia", + "Value": -0.2, + "Projection": "Change" + }, + { + "Country/Region": "IN", + "Value": 6.8, + "Projection": "2024" + }, + { + "Country/Region": "IN", + "Value": 6.3, + "Projection": "2025" + }, + { + "Country/Region": "IN", + "Value": -0.5, + "Projection": "Change" + }, + { + "Country/Region": "PH", + "Value": 6.0, + "Projection": "2024" + }, + { + "Country/Region": "PH", + "Value": 6.3, + "Projection": "2025" + }, + { + "Country/Region": "PH", + "Value": 0.3, + "Projection": "Change" + }, + { + "Country/Region": "ID", + "Value": 5.0, + "Projection": "2024" + }, + { + "Country/Region": "ID", + "Value": 5.1, + "Projection": "2025" + }, + { + "Country/Region": "ID", + "Value": 0.1, + "Projection": "Change" + }, + { + "Country/Region": "CN", + "Value": 4.6, + "Projection": "2024" + }, + { + "Country/Region": "CN", + "Value": 4.3, + "Projection": "2025" + }, + { + "Country/Region": "CN", + "Value": -0.3, + "Projection": "Change" + }, + { + "Country/Region": "MY", + "Value": 4.4, + "Projection": "2024" + }, + { + "Country/Region": "MY", + "Value": 4.0, + "Projection": "2025" + }, + { + "Country/Region": "MY", + "Value": -0.4, + "Projection": "Change" + }, + { + "Country/Region": "TH", + "Value": 2.7, + "Projection": "2024" + }, + { + "Country/Region": "TH", + "Value": 2.9, + "Projection": "2025" + }, + { + "Country/Region": "TH", + "Value": 0.2, + "Projection": "Change" + }, + { + "Country/Region": "JP", + "Value": 0.9, + "Projection": "2024" + }, + { + "Country/Region": "JP", + "Value": 1.7, + "Projection": "2025" + }, + { + "Country/Region": "JP", + "Value": 0.8, + "Projection": "Change" + }, + { + "Country/Region": "KR", + "Value": 2.3, + "Projection": "2024" + }, + { + "Country/Region": "KR", + "Value": 2.0, + "Projection": "2025" + }, + { + "Country/Region": "KR", + "Value": -0.3, + "Projection": "Change" + } + ], + "columns": [ + { + "name": "Country/Region", + "importance": "primary", + "description": "Country or region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Value", + "importance": "primary", + "description": "Value for the corresponding projection", + "role": "y" + }, + { + "name": "Projection", + "importance": "primary", + "description": "The projection of the data point", + "role": "group" + } + ] + } + }, + "1591": { + "metadata": { + "title": "Most Republicans Say Universities Negatively Impact U.S.", + "description": "% saying each has a positive/negative effect on the way things are going in the country", + "main_insight": "Republicans are significantly more likely than Democrats to view colleges and universities, and national news media, as having a negative impact on the country." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Institution": "Colleges & universities", + "Units": 58.0, + "Sentiment": "Republicans_Negative" + }, + { + "Institution": "Colleges & universities", + "Units": 36.0, + "Sentiment": "Republicans_Positive" + } + ], + "score": 0.97, + "annotation": "The Units of Republicans_Negative and Republicans_Positive show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Republicans_Negative and Republicans_Positive is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Institution": "Colleges & universities", + "Units": 19.0, + "Sentiment": "Democrats_Negative" + }, + { + "Institution": "Colleges & universities", + "Units": 72.0, + "Sentiment": "Democrats_Positive" + } + ], + "score": 0.97, + "annotation": "The Units of Democrats_Negative and Democrats_Positive show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Democrats_Negative and Democrats_Positive is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Sentiment": "Democrats_Positive", + "Institution": "total", + "Units": 258.0 + } + ], + "score": 0.93, + "annotation": "The total value of Democrats_Positive has the largest proportion.", + "reason": "The total value of Units of Democrats_Positive accounts for 0.29553264604810997, larger than all other Sentiment, of the total of Units of all Sentiment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Institution": "Banks, financial institutions", + "Units": 54.0, + "Sentiment": "Democrats_Negative" + } + ], + "score": 0.93, + "annotation": "The Democrats_Negative has the largest proportion in Banks, financial institutions.", + "reason": "The Units of Democrats_Negative in Banks, financial institutions accounts for 0.3176470588235294, larger than all other Sentiment, of the total of Units of all Sentiment." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Institution": "Labor unions", + "Units": 59.0, + "Sentiment": "Democrats_Positive" + } + ], + "score": 0.92, + "annotation": "The Democrats_Positive has the largest proportion in Labor unions.", + "reason": "The Units of Democrats_Positive in Labor unions accounts for 0.36875, larger than all other Sentiment, of the total of Units of all Sentiment." + } + ], + "data": { + "data": [ + { + "Institution": "Churches, religious organizations", + "Units": 14.0, + "Sentiment": "Republicans_Negative" + }, + { + "Institution": "Churches, religious organizations", + "Units": 36.0, + "Sentiment": "Democrats_Negative" + }, + { + "Institution": "Churches, religious organizations", + "Units": 73.0, + "Sentiment": "Republicans_Positive" + }, + { + "Institution": "Churches, religious organizations", + "Units": 50.0, + "Sentiment": "Democrats_Positive" + }, + { + "Institution": "Banks, financial institutions", + "Units": 37.0, + "Sentiment": "Republicans_Negative" + }, + { + "Institution": "Banks, financial institutions", + "Units": 54.0, + "Sentiment": "Democrats_Negative" + }, + { + "Institution": "Banks, financial institutions", + "Units": 46.0, + "Sentiment": "Republicans_Positive" + }, + { + "Institution": "Banks, financial institutions", + "Units": 33.0, + "Sentiment": "Democrats_Positive" + }, + { + "Institution": "Labor unions", + "Units": 46.0, + "Sentiment": "Republicans_Negative" + }, + { + "Institution": "Labor unions", + "Units": 22.0, + "Sentiment": "Democrats_Negative" + }, + { + "Institution": "Labor unions", + "Units": 33.0, + "Sentiment": "Republicans_Positive" + }, + { + "Institution": "Labor unions", + "Units": 59.0, + "Sentiment": "Democrats_Positive" + }, + { + "Institution": "National news media", + "Units": 85.0, + "Sentiment": "Republicans_Negative" + }, + { + "Institution": "National news media", + "Units": 46.0, + "Sentiment": "Democrats_Negative" + }, + { + "Institution": "National news media", + "Units": 10.0, + "Sentiment": "Republicans_Positive" + }, + { + "Institution": "National news media", + "Units": 44.0, + "Sentiment": "Democrats_Positive" + }, + { + "Institution": "Colleges & universities", + "Units": 58.0, + "Sentiment": "Republicans_Negative" + }, + { + "Institution": "Colleges & universities", + "Units": 19.0, + "Sentiment": "Democrats_Negative" + }, + { + "Institution": "Colleges & universities", + "Units": 36.0, + "Sentiment": "Republicans_Positive" + }, + { + "Institution": "Colleges & universities", + "Units": 72.0, + "Sentiment": "Democrats_Positive" + } + ], + "columns": [ + { + "name": "Institution", + "importance": "primary", + "description": "The institution being evaluated", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sentiment", + "role": "y" + }, + { + "name": "Sentiment", + "importance": "primary", + "description": "The sentiment of the data point", + "role": "group" + } + ] + } + }, + "2283": { + "metadata": { + "title": "Finland's Most Popular Brands", + "description": "Ranking of brands in Finland based on recognition and public perception", + "main_insight": "Fiskars is the most popular brand in Finland, followed by Fazer and Arabia, based on a survey measuring brand recognition and public perception." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Ranking": 1, + "Brand": "Fiskars" + }, + { + "Ranking": 2, + "Brand": "Fazer" + }, + { + "Ranking": 3, + "Brand": "Arabia" + }, + { + "Ranking": 4, + "Brand": "Fazer Sininen" + }, + { + "Ranking": 5, + "Brand": "YLE" + }, + { + "Ranking": 6, + "Brand": "Hackman" + }, + { + "Ranking": 7, + "Brand": "Iittala" + }, + { + "Ranking": 8, + "Brand": "Valio" + } + ], + "columns": [ + { + "name": "Ranking", + "importance": "primary", + "description": "The rank of the brand in terms of popularity", + "unit": "none", + "data_type": "number", + "role": "x" + }, + { + "name": "Brand", + "importance": "primary", + "description": "The name of the brand", + "unit": "none", + "data_type": "string", + "role": "y" + } + ] + } + }, + "2361": { + "metadata": { + "title": "Europe Is Facing a Dementia Problem", + "description": "Estimated population with dementia in selected European countries per 1,000 inhabitants", + "main_insight": "The estimated number of people with dementia per 1,000 inhabitants is projected to increase significantly in all listed European countries between 2021 and 2050. Italy is expected to have the highest prevalence in 2050." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "IT", + "Units": 43.0, + "Year": "2050*" + } + ], + "score": 0.84, + "annotation": "The 2050* has a maximum value at IT", + "reason": "The [{'Country': 'IT', 'Units': 43.0, 'Year': '2050*'}] of 2050* has a maximum value of 43.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "GB", + "Units": 28.0, + "Year": "2050*" + }, + { + "Country": "GB", + "Units": 18.0, + "Year": "2021" + } + ], + "score": 0.81, + "annotation": "The Units of 2050* and 2021 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2050* and 2021 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "IT", + "Units": 23.0, + "Year": "2021" + } + ], + "score": 0.8, + "annotation": "The 2021 has a maximum value at IT", + "reason": "The [{'Country': 'IT', 'Units': 23.0, 'Year': '2021'}] of 2021 has a maximum value of 23.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "ES", + "Units": 21.0, + "Year": "2021" + } + ], + "score": 0.77, + "annotation": "The 2021 shows a sudden jump in Units from BE to ES.", + "reason": "The Units for 2021 increases from 19.0 at BE to 21.0 at ES, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "GB", + "Units": 28.0, + "Year": "2050*" + } + ], + "score": 0.77, + "annotation": "The 2050* accounts for the majority in GB.", + "reason": "The Units of 2050* in GB accounts for 0.6086956521739131 (more than 50%) of the total of Units of all Year." + } + ], + "data": { + "data": [ + { + "Country": "IT", + "Units": 43.0, + "Year": "2050*" + }, + { + "Country": "IT", + "Units": 23.0, + "Year": "2021" + }, + { + "Country": "ES", + "Units": 41.0, + "Year": "2050*" + }, + { + "Country": "ES", + "Units": 21.0, + "Year": "2021" + }, + { + "Country": "DE", + "Units": 36.0, + "Year": "2050*" + }, + { + "Country": "DE", + "Units": 22.0, + "Year": "2021" + }, + { + "Country": "FR", + "Units": 34.0, + "Year": "2050*" + }, + { + "Country": "FR", + "Units": 21.0, + "Year": "2021" + }, + { + "Country": "CH", + "Units": 33.0, + "Year": "2050*" + }, + { + "Country": "CH", + "Units": 18.0, + "Year": "2021" + }, + { + "Country": "NL", + "Units": 33.0, + "Year": "2050*" + }, + { + "Country": "NL", + "Units": 18.0, + "Year": "2021" + }, + { + "Country": "BE", + "Units": 31.0, + "Year": "2050*" + }, + { + "Country": "BE", + "Units": 19.0, + "Year": "2021" + }, + { + "Country": "GB", + "Units": 28.0, + "Year": "2050*" + }, + { + "Country": "GB", + "Units": 18.0, + "Year": "2021" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Selected European countries", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "693": { + "metadata": { + "title": "Study Finds Microplastics In 93% Of Bottled Water", + "description": "Lowest & highest number of plastic particles found per liter of bottled water (location & brand)", + "main_insight": "A study found a wide range of microplastic particle concentrations in bottled water across various brands, with Nestle Pure Life having the highest concentration." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Brand": "Gerolsteiner", + "Min Concentration": 9.0 + } + ], + "score": 0.96, + "annotation": "The has a maximum value at Gerolsteiner", + "reason": "The [{'Brand': 'Gerolsteiner', 'Min Concentration': 9.0}] of has a maximum value of 9.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Brand": "Nestle Pure Life", + "Min Concentration": 6.0 + } + ], + "score": 0.8, + "annotation": "The shows a sudden jump in Min Concentration from Dasani to Nestle Pure Life.", + "reason": "The Min Concentration for increases from 2.0 at Dasani to 6.0 at Nestle Pure Life, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Brand": "Bisleri", + "Min Concentration": 0.0 + }, + { + "Brand": "Aqua", + "Min Concentration": 0.0 + }, + { + "Brand": "Epura", + "Min Concentration": 0.0 + }, + { + "Brand": "Minalba", + "Min Concentration": 0.0 + }, + { + "Brand": "Evian", + "Min Concentration": 0.0 + }, + { + "Brand": "San Pellegrino", + "Min Concentration": 0.0 + } + ], + "score": 0.4, + "annotation": "The has minimum values at Bisleri, Aqua, Epura, Minalba, Evian, San Pellegrino", + "reason": "The [{'Brand': 'Bisleri', 'Min Concentration': 0.0}, {'Brand': 'Aqua', 'Min Concentration': 0.0}, {'Brand': 'Epura', 'Min Concentration': 0.0}, {'Brand': 'Minalba', 'Min Concentration': 0.0}, {'Brand': 'Evian', 'Min Concentration': 0.0}, {'Brand': 'San Pellegrino', 'Min Concentration': 0.0}] of have minimum values of 0.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Brand": "Nestle Pure Life", + "Min Concentration": 6.0 + }, + { + "Brand": "Bisleri", + "Min Concentration": 0.0 + }, + { + "Brand": "Gerolsteiner", + "Min Concentration": 9.0 + }, + { + "Brand": "Aqua", + "Min Concentration": 0.0 + }, + { + "Brand": "Epura", + "Min Concentration": 0.0 + }, + { + "Brand": "Aquafina", + "Min Concentration": 2.0 + }, + { + "Brand": "Minalba", + "Min Concentration": 0.0 + }, + { + "Brand": "Wahaha", + "Min Concentration": 1.0 + }, + { + "Brand": "Dasani", + "Min Concentration": 2.0 + }, + { + "Brand": "Evian", + "Min Concentration": 0.0 + }, + { + "Brand": "San Pellegrino", + "Min Concentration": 0.0 + } + ], + "columns": [ + { + "name": "Brand", + "importance": "primary", + "description": "Brand of bottled water", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Min Concentration", + "importance": "primary", + "description": "Lowest number of plastic particles found per liter", + "unit": "particles/liter", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1571": { + "metadata": { + "title": "Summer of 2024 Has Been a Rollercoaster Ride for Investors", + "description": "Daily change in closing prices of the S&P 500 and the Nasdaq Composite Index in July and August 2024", + "main_insight": "Both the S&P 500 and Nasdaq Composite experienced significant daily volatility in July and August 2024, with an overall negative change since July 1st." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "August 12", + "Ratio": -0.005, + "Index": "Nasdaq Composite" + } + ], + "score": 0.91, + "annotation": "The Nasdaq Composite shows a sudden jump in Ratio from Early August to August 12.", + "reason": "The Ratio for Nasdaq Composite increases from -0.031 at Early August to -0.005 at August 12, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "Early August", + "Ratio": -0.031, + "Index": "Nasdaq Composite" + } + ], + "score": 0.9, + "annotation": "The Nasdaq Composite has a minimum value at Early August", + "reason": "The [{'Date': 'Early August', 'Ratio': -0.031, 'Index': 'Nasdaq Composite'}] of Nasdaq Composite has a mainimum value of -0.031, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "Early August", + "Ratio": -0.029, + "Index": "S&P 500" + } + ], + "score": 0.89, + "annotation": "The S&P 500 has a minimum value at Early August", + "reason": "The [{'Date': 'Early August', 'Ratio': -0.029, 'Index': 'S&P 500'}] of S&P 500 has a mainimum value of -0.029, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Date": "August 12", + "Ratio": 0.002, + "Index": "S&P 500" + }, + { + "Date": "August 12", + "Ratio": -0.005, + "Index": "Nasdaq Composite" + } + ], + "score": 0.87, + "annotation": "The Ratio of S&P 500 and Nasdaq Composite show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between S&P 500 and Nasdaq Composite is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "August 1", + "Ratio": -0.01, + "Index": "S&P 500" + } + ], + "score": 0.85, + "annotation": "The S&P 500 shows a sudden jump in Ratio from Early August to August 1.", + "reason": "The Ratio for S&P 500 increases from -0.029 at Early August to -0.01 at August 1, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Date": "Early July", + "Ratio": 0.007, + "Index": "S&P 500" + }, + { + "Date": "Early July", + "Ratio": 0.006, + "Index": "Nasdaq Composite" + }, + { + "Date": "Mid July", + "Ratio": 0.004, + "Index": "S&P 500" + }, + { + "Date": "Mid July", + "Ratio": 0.001, + "Index": "Nasdaq Composite" + }, + { + "Date": "Late July", + "Ratio": -0.003, + "Index": "S&P 500" + }, + { + "Date": "Late July", + "Ratio": 0.008, + "Index": "Nasdaq Composite" + }, + { + "Date": "Early August", + "Ratio": -0.029, + "Index": "S&P 500" + }, + { + "Date": "Early August", + "Ratio": -0.031, + "Index": "Nasdaq Composite" + }, + { + "Date": "August 1", + "Ratio": -0.01, + "Index": "S&P 500" + }, + { + "Date": "August 1", + "Ratio": -0.002, + "Index": "Nasdaq Composite" + }, + { + "Date": "August 12", + "Ratio": 0.002, + "Index": "S&P 500" + }, + { + "Date": "August 12", + "Ratio": -0.005, + "Index": "Nasdaq Composite" + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Day in July and August 2024", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding index", + "role": "y" + }, + { + "name": "Index", + "importance": "primary", + "description": "The index of the data point", + "role": "group" + } + ] + } + }, + "2015": { + "metadata": { + "title": "India has burst onto the smartphone manufacturing scene", + "description": "Number of mobile phone manufacturing units in India", + "main_insight": "The number of mobile phone manufacturing units in India has significantly increased from 2014 to 2017, demonstrating rapid growth in the sector." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2014, + "Manufacturing Units": 2 + }, + { + "Year": 2015, + "Manufacturing Units": 18 + }, + { + "Year": 2016, + "Manufacturing Units": 50 + }, + { + "Year": 2017, + "Manufacturing Units": 123 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "The year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Manufacturing Units", + "importance": "primary", + "description": "Number of mobile phone manufacturing units", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1406": { + "metadata": { + "title": "The Present Balance of Left and Right", + "description": "Share of respondents who self-identified as holding the following political positions (in percent)", + "main_insight": "The visualization shows the political leaning distribution (Left, Center, Right) across several countries. In most of the surveyed countries, a greater percentage of respondents identified with Right-leaning political positions." + }, + "chart_type": "Dot Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "France", + "Right": 27.0 + } + ], + "score": 0.86, + "annotation": "The has a minimum value at France", + "reason": "The [{'Country': 'France', 'Right': 27.0}] of has a mainimum value of 27.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Japan", + "Right": 51.0 + } + ], + "score": 0.78, + "annotation": "The has a maximum value at Japan", + "reason": "The [{'Country': 'Japan', 'Right': 51.0}] of has a maximum value of 51.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Right": 35.0 + } + ], + "score": 0.72, + "annotation": "The shows a sudden jump in Right from France to United States.", + "reason": "The Right for increases from 27.0 at France to 35.0 at United States, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Brazil", + "Right": 37.0 + }, + { + "Country": "United Kingdom", + "Right": 37.0 + }, + { + "Country": "France", + "Right": 27.0 + }, + { + "Country": "Germany", + "Right": 44.0 + }, + { + "Country": "United States", + "Right": 35.0 + }, + { + "Country": "Australia", + "Right": 41.0 + }, + { + "Country": "South Korea", + "Right": 48.0 + }, + { + "Country": "Japan", + "Right": 51.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Right", + "importance": "primary", + "description": "Percentage of respondents identifying as Right-leaning", + "unit": "percent", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2535": { + "metadata": { + "title": "How the Covid-19 Pandemic Unfolded", + "description": "Countries with the highest number of cumulative Covid-19 cases", + "main_insight": "The United States had by far the highest number of cumulative Covid-19 cases as of April 2022, almost double the number of cases compared with India." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United States", + "Cumulative Cases": 81100599 + } + ], + "score": 0.97, + "annotation": "The has a maximum value at United States", + "reason": "The [{'Country': 'United States', 'Cumulative Cases': 81100599}] of has a maximum value of 81100599, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Cumulative Cases": 81100599 + } + ], + "score": 0.87, + "annotation": "The shows a sudden jump in Cumulative Cases from India to United States.", + "reason": "The Cumulative Cases for increases from 43065496 at India to 81100599 at United States, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Turkey", + "Cumulative Cases": 15023662 + } + ], + "score": 0.46, + "annotation": "The has a minimum value at Turkey", + "reason": "The [{'Country': 'Turkey', 'Cumulative Cases': 15023662}] of has a mainimum value of 15023662, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Cumulative Cases": 81100599 + }, + { + "Country": "India", + "Cumulative Cases": 43065496 + }, + { + "Country": "Brazil", + "Cumulative Cases": 30378061 + }, + { + "Country": "France", + "Cumulative Cases": 28469248 + }, + { + "Country": "Germany", + "Cumulative Cases": 24337394 + }, + { + "Country": "United Kingdom", + "Cumulative Cases": 22068585 + }, + { + "Country": "Russia", + "Cumulative Cases": 17887152 + }, + { + "Country": "South Korea", 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"type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Demographic": "Not married", + "Units": 37.0, + "Year": "2019" + }, + { + "Demographic": "Not married", + "Units": 27.0, + "Year": "2020" + } + ], + "score": 0.89, + "annotation": "The Units of 2019 and 2020 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2019 and 2020 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Demographic": "Male", + "Units": 41.0, + "Year": "2020" + } + ], + "score": 0.83, + "annotation": "The 2020 shows a sudden jump in Units from Total to Male.", + "reason": "The Units for 2020 increases from 34.0 at Total to 41.0 at Male, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Demographic": "Democrat", + "Units": 30.0, + "Year": "2019" + } + ], + "score": 0.83, + "annotation": "The 2019 has a 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"Units": 43.0, + "Year": "2019" + }, + { + "Demographic": "Total", + "Units": 34.0, + "Year": "2020" + }, + { + "Demographic": "Male", + "Units": 49.0, + "Year": "2019" + }, + { + "Demographic": "Male", + "Units": 41.0, + "Year": "2020" + }, + { + "Demographic": "Female", + "Units": 37.0, + "Year": "2019" + }, + { + "Demographic": "Female", + "Units": 27.0, + "Year": "2020" + }, + { + "Demographic": "Republican", + "Units": 56.0, + "Year": "2019" + }, + { + "Demographic": "Republican", + "Units": 41.0, + "Year": "2020" + }, + { + "Demographic": "Independent", + "Units": 44.0, + "Year": "2019" + }, + { + "Demographic": "Independent", + "Units": 32.0, + "Year": "2020" + }, + { + "Demographic": "Democrat", + "Units": 30.0, + "Year": "2019" + }, + { + "Demographic": "Democrat", + "Units": 29.0, + "Year": "2020" + }, + { + "Demographic": "Married", + "Units": 49.0, + "Year": "2019" + }, + { + "Demographic": "Married", + "Units": 41.0, + "Year": "2020" + }, + { + "Demographic": "Not 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Expectancy": 0.1 + }, + { + "Year": 2004, + "Change in Life Expectancy": 0.6 + }, + { + "Year": 2005, + "Change in Life Expectancy": 0.2 + }, + { + "Year": 2006, + "Change in Life Expectancy": 0.5 + }, + { + "Year": 2007, + "Change in Life Expectancy": 0.2 + }, + { + "Year": 2008, + "Change in Life Expectancy": 0.2 + }, + { + "Year": 2009, + "Change in Life Expectancy": 0.3 + }, + { + "Year": 2010, + "Change in Life Expectancy": 0.2 + }, + { + "Year": 2011, + "Change in Life Expectancy": 0.3 + }, + { + "Year": 2012, + "Change in Life Expectancy": 0.3 + }, + { + "Year": 2013, + "Change in Life Expectancy": 0.1 + }, + { + "Year": 2014, + "Change in Life Expectancy": 0.4 + }, + { + "Year": 2015, + "Change in Life Expectancy": -0.3 + }, + { + "Year": 2016, + "Change in Life Expectancy": 0.1 + }, + { + "Year": 2017, + "Change in Life Expectancy": 0.4 + }, + { + "Year": 2018, + "Change in Life Expectancy": 0.1 + }, + { + "Year": 2019, + "Change in Life Expectancy": 0.3 + }, + { + "Year": 2020, + "Change in Life Expectancy": -0.9 + }, + { + "Year": 2021, + "Change in Life Expectancy": -0.3 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Change in Life Expectancy", + "importance": "primary", + "description": "Year-on-year change in life expectancy (in years)", + "unit": "years", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1120": { + "metadata": { + "title": "Americans Don't Like Visiting the Doctor", + "description": "Annual average doctor visits per person in selected countries", + "main_insight": "Americans have significantly fewer doctor visits per year compared to other developed countries, with an average of 4.1 visits, while Japan has the highest at 13.0 visits." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Japan", + "Doctor Visits": 13.0 + } + ], + "score": 0.89, + "annotation": "The has a maximum value at Japan", + "reason": "The [{'Country': 'Japan', 'Doctor Visits': 13.0}] of has a maximum value of 13.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Japan", + "Doctor Visits": 13.0 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Doctor Visits from Germany to Japan.", + "reason": "The Doctor Visits for increases from 9.7 at Germany to 13.0 at Japan, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "United States", + "Doctor Visits": 4.1 + } + ], + "score": 0.66, + "annotation": "The has a minimum value at United States", + "reason": "The [{'Country': 'United States', 'Doctor Visits': 4.1}] of has a mainimum value of 4.1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Japan", + "Doctor Visits": 13.0 + }, + { + "Country": "Germany", + "Doctor Visits": 9.7 + }, + { + "Country": "Canada", + "Doctor Visits": 7.4 + }, + { + "Country": "France", + "Doctor Visits": 6.8 + }, + { + "Country": "Australia", + "Doctor Visits": 6.7 + }, + { + "Country": "United Kingdom", + "Doctor Visits": 5.0 + }, + { + "Country": "United States", + "Doctor Visits": 4.1 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "The country for which data is shown", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Doctor Visits", + "importance": "primary", + "description": "Annual average doctor visits per person", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "101": { + "metadata": { + "title": "Bewildering causes of death in the US", + "description": "Figures for 2015, * 2011 or ** multi-year averages", + "main_insight": "The infographic highlights the number of deaths from various unusual causes in the US, emphasizing the comparatively high number of deaths by police shootings." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Cause of Death": "Shot by police", + "Number of Deaths": 986.0 + } + ], + "score": 0.98, + "annotation": "The has a maximum value at Shot by police", + "reason": "The [{'Cause of Death': 'Shot by police', 'Number of Deaths': 986.0}] of has a maximum value of 986.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Cause of Death": "Shot by police", + "Number of Deaths": 986.0 + } + ], + "score": 0.96, + "annotation": "The shows a sudden jump in Number of Deaths from Deaths by lawnmower to Shot by police.", + "reason": "The Number of Deaths for increases from 75.0 at Deaths by lawnmower to 986.0 at Shot by police, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Cause of Death": "Autoerotic asphyxiation", + "Number of Deaths": 0 + }, + { + "Cause of Death": "Victims of serial killers", + "Number of Deaths": 0 + } + ], + "score": 0.3, + "annotation": "The has minimum values at Autoerotic asphyxiation, Victims of serial killers", + "reason": "The [{'Cause of Death': 'Autoerotic asphyxiation', 'Number of Deaths': 0}, {'Cause of Death': 'Victims of serial killers', 'Number of Deaths': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Cause of Death": "Shot by police", + "Number of Deaths": 986.0 + }, + { + "Cause of Death": "Autoerotic asphyxiation", + "Number of Deaths": 0 + }, + { + "Cause of Death": "Deaths by lawnmower", + "Number of Deaths": 75.0 + }, + { + "Cause of Death": "Victims of serial killers", + "Number of Deaths": 0 + }, + { + "Cause of Death": "Struck by lightning", + "Number of Deaths": 31.0 + }, + { + "Cause of Death": "People shot dead inadvertently by toddlers", + "Number of Deaths": 2.0 + }, + { + "Cause of Death": "Crushed by vending machines", + "Number of Deaths": 2.0 + }, + { + "Cause of Death": "Deaths by selfie", + "Number of Deaths": 2.0 + }, + { + "Cause of Death": "Trampled to death on Black Friday", + "Number of Deaths": 1.0 + } + ], + "columns": [ + { + "name": "Cause of Death", + "importance": "primary", + "description": "Different unusual causes of death", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Deaths", + "importance": "primary", + "description": "The number of deaths associated with each cause", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2172": { + "metadata": { + "title": "Twitter Suffers Sell-Off as Lock-Up Expires", + "description": "Stock price change on the first day early investors + employees could sell after selected IPOs", + "main_insight": "Twitter's stock price experienced a significant drop when its lock-up period expired, contrasting with the positive or smaller negative changes seen in some other tech companies on their first day of free trading." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Company": "Yelp (Aug. 29, 2012)", + "Stock Price Change": 22.5 + } + ], + "score": 0.81, + "annotation": "The has a maximum value at Yelp (Aug. 29, 2012)", + "reason": "The [{'Company': 'Yelp (Aug. 29, 2012)', 'Stock Price Change': 22.5}] of has a maximum value of 22.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "Facebook (Nov. 14, 2012)", + "Stock Price Change": 12.6 + } + ], + "score": 0.76, + "annotation": "The shows a sudden jump in Stock Price Change from LinkedIn (Nov. 21, 2011) to Facebook (Nov. 14, 2012).", + "reason": "The Stock Price Change for increases from -2.8 at LinkedIn (Nov. 21, 2011) to 12.6 at Facebook (Nov. 14, 2012), indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Company": "Twitter (May 5, 2014)", + "Stock Price Change": -17.8 + } + ], + "score": 0.68, + "annotation": "The has a minimum value at Twitter (May 5, 2014)", + "reason": "The [{'Company': 'Twitter (May 5, 2014)', 'Stock Price Change': -17.8}] of has a mainimum value of -17.8, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Company": "Yelp (Aug. 29, 2012)", + "Stock Price Change": 22.5 + }, + { + "Company": "Facebook (Nov. 14, 2012)", + "Stock Price Change": 12.6 + }, + { + "Company": "LinkedIn (Nov. 21, 2011)", + "Stock Price Change": -2.8 + }, + { + "Company": "Google (Nov. 16, 2004)", + "Stock Price Change": -6.7 + }, + { + "Company": "Groupon (Jun. 1, 2012)", + "Stock Price Change": -8.9 + }, + { + "Company": "Twitter (May 5, 2014)", + "Stock Price Change": -17.8 + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Name of the company and its IPO lock-up expiration date.", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Stock Price Change", + "importance": "primary", + "description": "Percentage change in stock price on the first day the lock-up expired.", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1486": { + "metadata": { + "title": "The E.U. Outstrips the U.S. in Transatlantic Meat Trade", + "description": "E.U. and U.S. meat imports and exports between 2010 and 2012 (in million U.S. dollars)", + "main_insight": "The E.U. exports significantly more meat to the U.S. than the U.S. exports to the E.U., and this gap widened between 2010 and 2012." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2010, + "E.U. Exports to U.S.": 1652.0 + }, + { + "Year": 2011, + "E.U. Exports to U.S.": 2031.0 + }, + { + "Year": 2012, + "E.U. Exports to U.S.": 2154.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of trade", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "E.U. Exports to U.S.", + "importance": "primary", + "description": "Value of meat exports from the E.U. to the U.S. in million U.S. dollars", + "unit": "Million U.S. dollars", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1555": { + "metadata": { + "title": "Apple Hit With €1.8 Billion EU Antitrust Fine", + "description": "Major antitrust fines imposed by the European Commission against U.S. tech companies", + "main_insight": "The European Commission has imposed significant antitrust fines on major U.S. tech companies, with Google receiving the largest fines and Apple receiving a recent €1.8 billion fine in 2024." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Company": "Microsoft", + "Year": 2004 + } + ], + "score": 0.86, + "annotation": "The has a minimum value at Microsoft", + "reason": "The [{'Company': 'Microsoft', 'Year': 2004}] of has a mainimum value of 2004, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "Google", + "Year": 2017 + } + ], + "score": 0.77, + "annotation": "The shows a sudden jump in Year from Intel to Google.", + "reason": "The Year for increases from 2009 at Intel to 2017 at Google, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Company": "Apple", + "Year": 2024 + } + ], + "score": 0.71, + "annotation": "The has a maximum value at Apple", + "reason": "The [{'Company': 'Apple', 'Year': 2024}] of has a maximum value of 2024, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Company": "Google", + "Year": 2018 + }, + { + "Company": "Google", + "Year": 2017 + }, + { + "Company": "Apple", + "Year": 2024 + }, + { + "Company": "Google", + "Year": 2019 + }, + { + "Company": "Intel", + "Year": 2009 + }, + { + "Company": "Qualcomm", + "Year": 2018 + }, + { + "Company": "Microsoft", + "Year": 2004 + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Name of the tech company", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Year", + "importance": "primary", + "description": "Year the fine was imposed", + "unit": "none", + "data_type": "time", + "role": "y" + } + ] + } + }, + "1348": { + "metadata": { + "title": "Campy Batman Beats Broody Batman", + "description": "Percentage of U.S. adults who said each actor was their favorite live-action portrayal of Batman", + "main_insight": "Christian Bale and Michael Keaton are the most popular choices for favorite live-action Batman, with a significant percentage of respondents also selecting 'Don't Know'." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Actor": "Christian Bale (2005-2012)", + "Percentage": 24.0 + } + ], + "score": 0.84, + "annotation": "The has a maximum value at Christian Bale (2005-2012)", + "reason": "The [{'Actor': 'Christian Bale (2005-2012)', 'Percentage': 24.0}] of has a maximum value of 24.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Actor": "Michael Keaton (1989-1992)", + "Percentage": 17.0 + } + ], + "score": 0.79, + "annotation": "The shows a sudden jump in Percentage from Val Kilmer (1995) to Michael Keaton (1989-1992).", + "reason": "The Percentage for increases from 7.0 at Val Kilmer (1995) to 17.0 at Michael Keaton (1989-1992), indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Actor": "Lewis Wilson (1943)", + "Percentage": 1.0 + } + ], + "score": 0.57, + "annotation": "The has a minimum value at Lewis Wilson (1943)", + "reason": "The [{'Actor': 'Lewis Wilson (1943)', 'Percentage': 1.0}] of has a mainimum value of 1.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Actor": "Adam West (1966)", + "Percentage": 4.0 + }, + { + "Actor": "Ben Affleck (2016)", + "Percentage": 3.0 + }, + { + "Actor": "Christian Bale (2005-2012)", + "Percentage": 24.0 + }, + { + "Actor": "George Clooney (1997)", + "Percentage": 5.0 + }, + { + "Actor": "Michael Keaton (1989-1992)", + "Percentage": 17.0 + }, + { + "Actor": "Val Kilmer (1995)", + "Percentage": 7.0 + }, + { + "Actor": "Robert Lowery (1949)", + "Percentage": 2.0 + }, + { + "Actor": "Lewis Wilson (1943)", + "Percentage": 1.0 + }, + { + "Actor": "Don't know", + "Percentage": 22.0 + } + ], + "columns": [ + { + "name": "Actor", + "importance": "primary", + "description": "Actor who played Batman", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of respondents who chose the actor as their favorite", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2262": { + "metadata": { + "title": "China Boosts New Coal Power Projects in 2022", + "description": "Countries with the most coal power plants in (pre-)construction (in megawatts of capacity)", + "main_insight": "China significantly leads in new coal power plant capacity in both announced/pre-permit/permitted and construction stages, dwarfing other countries." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "China", + "Dollars": 300000.0, + "Project Stage": "Announced, pre-permit, permitted" + } + ], + "score": 0.95, + "annotation": "The Announced, pre-permit, permitted shows a sudden jump in Dollars from India to China.", + "reason": "The Dollars for Announced, pre-permit, permitted increases from 50000.0 at India to 300000.0 at China, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "China", + "Dollars": 300000.0, + "Project Stage": "Announced, pre-permit, permitted" + } + ], + "score": 0.92, + "annotation": "The Announced, pre-permit, permitted has a maximum value at China", + "reason": "The [{'Country': 'China', 'Dollars': 300000.0, 'Project Stage': 'Announced, pre-permit, permitted'}] of Announced, pre-permit, permitted has a maximum value of 300000.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Vietnam", + "Dollars": -94.0, + "Project Stage": "Change from Jan. 2022 (Announced)" + } + ], + "score": 0.91, + "annotation": "The Change from Jan. 2022 (Announced) has a minimum value at Vietnam", + "reason": "The [{'Country': 'Vietnam', 'Dollars': -94.0, 'Project Stage': 'Change from Jan. 2022 (Announced)'}] of Change from Jan. 2022 (Announced) has a mainimum value of -94.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "China", + "Dollars": 80000.0, + "Project Stage": "Construction" + } + ], + "score": 0.91, + "annotation": "The Construction shows a sudden jump in Dollars from India to China.", + "reason": "The Dollars for Construction increases from 30000.0 at India to 80000.0 at China, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Turkey", + "Dollars": -90.0, + "Project Stage": "Change from Jan. 2022 (Construction)" + } + ], + "score": 0.91, + "annotation": "The Change from Jan. 2022 (Construction) has a minimum value at Turkey", + "reason": "The [{'Country': 'Turkey', 'Dollars': -90.0, 'Project Stage': 'Change from Jan. 2022 (Construction)'}] of Change from Jan. 2022 (Construction) has a mainimum value of -90.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "China", + "Dollars": 300000.0, + "Project Stage": "Announced, pre-permit, permitted" + }, + { + "Country": "China", + "Dollars": 80000.0, + "Project Stage": "Construction" + }, + { + "Country": "China", + "Dollars": 42.0, + "Project Stage": "Change from Jan. 2022 (Announced)" + }, + { + "Country": "China", + "Dollars": 29.0, + "Project Stage": "Change from Jan. 2022 (Construction)" + }, + { + "Country": "India", + "Dollars": 50000.0, + "Project Stage": "Announced, pre-permit, permitted" + }, + { + "Country": "India", + "Dollars": 30000.0, + "Project Stage": "Construction" + }, + { + "Country": "India", + "Dollars": 10.0, + "Project Stage": "Change from Jan. 2022 (Announced)" + }, + { + "Country": "India", + "Dollars": 2.0, + "Project Stage": "Change from Jan. 2022 (Construction)" + }, + { + "Country": "Indonesia", + "Dollars": 30000.0, + "Project Stage": "Announced, pre-permit, permitted" + }, + { + "Country": "Indonesia", + "Dollars": 10000.0, + "Project Stage": "Construction" + }, + { + "Country": "Indonesia", + "Dollars": -35.0, + "Project Stage": "Change from Jan. 2022 (Announced)" + }, + { + "Country": "Indonesia", + "Dollars": 9.0, + "Project Stage": "Change from Jan. 2022 (Construction)" + }, + { + "Country": "Bangladesh", + "Dollars": 10000.0, + "Project Stage": "Construction" + }, + { + "Country": "Bangladesh", + "Dollars": -38.0, + "Project Stage": "Change from Jan. 2022 (Announced)" + }, + { + "Country": "Bangladesh", + "Dollars": -1.0, + "Project Stage": "Change from Jan. 2022 (Construction)" + }, + { + "Country": "Turkey", + "Dollars": 10000.0, + "Project Stage": "Announced, pre-permit, permitted" + }, + { + "Country": "Turkey", + "Dollars": 4.0, + "Project Stage": "Change from Jan. 2022 (Announced)" + }, + { + "Country": "Turkey", + "Dollars": -90.0, + "Project Stage": "Change from Jan. 2022 (Construction)" + }, + { + "Country": "Vietnam", + "Dollars": 5000.0, + "Project Stage": "Construction" + }, + { + "Country": "Vietnam", + "Dollars": -94.0, + "Project Stage": "Change from Jan. 2022 (Announced)" + }, + { + "Country": "Vietnam", + "Dollars": -11.0, + "Project Stage": "Change from Jan. 2022 (Construction)" + }, + { + "Country": "Mongolia", + "Dollars": 5000.0, + "Project Stage": "Announced, pre-permit, permitted" + }, + { + "Country": "Mongolia", + "Dollars": -1.0, + "Project Stage": "Change from Jan. 2022 (Announced)" + }, + { + "Country": "Laos", + "Dollars": 5000.0, + "Project Stage": "Announced, pre-permit, permitted" + }, + { + "Country": "Laos", + "Dollars": 11.0, + "Project Stage": "Change from Jan. 2022 (Announced)" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country undertaking coal power projects", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding project stage", + "role": "y" + }, + { + "name": "Project Stage", + "importance": "primary", + "description": "The project stage of the data point", + "role": "group" + } + ] + } + }, + "2090": { + "metadata": { + "title": "England vs. Scotland - A History", + "description": "Results of matches between England and Scotland and key facts for both teams", + "main_insight": "England has historically outperformed Scotland in football matches, with more wins and goals overall, as well as in the last 10 matches." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Metric": "Total Goals", + "Points": 171, + "Nation": "Scotland" + } + ], + "score": 0.95, + "annotation": "The Scotland has a maximum value at Total Goals", + "reason": "The [{'Metric': 'Total Goals', 'Points': 171, 'Nation': 'Scotland'}] of Scotland has a maximum value of 171, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Metric": "Most Expensive Player Market Value", + "Points": 108, + "Nation": "England" + }, + { + "Metric": "Most Expensive Player Market Value", + "Points": 58.5, + "Nation": "Scotland" + } + ], + "score": 0.94, + "annotation": "The Points of England and Scotland show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Points between England and Scotland is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Metric": "Total Goals", + "Points": 195, + "Nation": "England" + } + ], + "score": 0.93, + "annotation": "The England has a maximum value at Total Goals", + "reason": "The [{'Metric': 'Total Goals', 'Points': 195, 'Nation': 'England'}] of England has a maximum value of 195, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Metric": "Total Goals", + "Points": 171, + "Nation": "Scotland" + } + ], + "score": 0.9, + "annotation": "The Scotland shows a sudden jump in Points from Most Expensive Player Market Value to Total Goals.", + "reason": "The Points for Scotland increases from 58.5 at Most Expensive Player Market Value to 171 at Total Goals, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Metric": "Total Goals", + "Points": 195, + "Nation": "England" + } + ], + "score": 0.8, + "annotation": "The England shows a sudden jump in Points from Most Expensive Player Market Value to Total Goals.", + "reason": "The Points for England increases from 108 at Most Expensive Player Market Value to 195 at Total Goals, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Metric": "Total Match Wins", + "Points": 48, + "Nation": "England" + }, + { + "Metric": "Total Match Wins", + "Points": 41, + "Nation": "Scotland" + }, + { + "Metric": "Total Match Draws", + "Points": 24, + "Nation": "England" + }, + { + "Metric": "Total Match Draws", + "Points": 24, + "Nation": "Scotland" + }, + { + "Metric": "Total Goals", + "Points": 195, + "Nation": "England" + }, + { + "Metric": "Total Goals", + "Points": 171, + "Nation": "Scotland" + }, + { + "Metric": "Last 10 Matches Wins", + "Points": 7, + "Nation": "England" + }, + { + "Metric": "Last 10 Matches Wins", + "Points": 1, + "Nation": "Scotland" + }, + { + "Metric": "Last 10 Matches Draws", + "Points": 2, + "Nation": "England" + }, + { + "Metric": "Last 10 Matches Draws", + "Points": 2, + "Nation": "Scotland" + }, + { + "Metric": "Last 10 Matches Goals", + "Points": 18, + "Nation": "England" + }, + { + "Metric": "Last 10 Matches Goals", + "Points": 6, + "Nation": "Scotland" + }, + { + "Metric": "FIFA World Ranking", + "Points": 4, + "Nation": "England" + }, + { + "Metric": "FIFA World Ranking", + "Points": 44, + "Nation": "Scotland" + }, + { + "Metric": "Most Expensive Player Market Value", + "Points": 108, + "Nation": "England" + }, + { + "Metric": "Most Expensive Player Market Value", + "Points": 58.5, + "Nation": "Scotland" + } + ], + "columns": [ + { + "name": "Metric", + "importance": "primary", + "description": "Category of comparison", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding nation", + "role": "y" + }, + { + "name": "Nation", + "importance": "primary", + "description": "The nation of the data point", + "role": "group" + } + ] + } + }, + "1799": { + "metadata": { + "title": "Eastern Europe Is Pumping Arms Into The Middle East", + "description": "Arms exports from Central & Eastern Europe to the Middle East since 2012*", + "main_insight": "Central and Eastern European countries have exported over 1.2 billion euros worth of arms to the Middle East since 2012, with Saudi Arabia being the largest importer." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Exporter": "Croatia", + "Exports (€m)": 302.0 + } + ], + "score": 0.86, + "annotation": "The shows a sudden jump in Exports (€m) from Czech Republic to Croatia.", + "reason": "The Exports (€m) for increases from 240.0 at Czech Republic to 302.0 at Croatia, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Exporter": "Croatia", + "Exports (€m)": 302.0 + } + ], + "score": 0.75, + "annotation": "The has a maximum value at Croatia", + "reason": "The [{'Exporter': 'Croatia', 'Exports (€m)': 302.0}] of has a maximum value of 302.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Exporter": "Slovakia", + "Exports (€m)": 192.0 + } + ], + "score": 0.49, + "annotation": "The has a minimum value at Slovakia", + "reason": "The [{'Exporter': 'Slovakia', 'Exports (€m)': 192.0}] of has a mainimum value of 192.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Exporter": "Croatia", + "Exports (€m)": 302.0 + }, + { + "Exporter": "Czech Republic", + "Exports (€m)": 240.0 + }, + { + "Exporter": "Serbia", + "Exports (€m)": 194.0 + }, + { + "Exporter": "Slovakia", + "Exports (€m)": 192.0 + } + ], + "columns": [ + { + "name": "Exporter", + "importance": "primary", + "description": "Central & Eastern European countries exporting arms", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Exports (€m)", + "importance": "primary", + "description": "Value of arms exports in millions of euros", + "unit": "€m", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1568": { + "metadata": { + "title": "Stagnation & Progress in Global Covid-19 Vaccinations", + "description": "Share of people fully vaccinated against Covid-19 in selected countries/regions (as of Nov 21, 2021)", + "main_insight": "The chart shows the progress of COVID-19 vaccination rates across different countries and regions throughout 2021. There is significant variation, with countries like South Korea and Japan achieving high vaccination rates, while others like Kenya lag significantly." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "Nov", + "Ratio": 60.0, + "Country": "Brazil" + }, + { + "Month": "Nov", + "Ratio": 28.8, + "Country": "India" + } + ], + "score": 0.99, + "annotation": "The Ratio of Brazil and India show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Brazil and India is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "Nov", + "Ratio": 78.9, + "Country": "South Korea" + }, + { + "Month": "Nov", + "Ratio": 76.5, + "Country": "Japan" + } + ], + "score": 0.98, + "annotation": "The Ratio of South Korea and Japan show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between South Korea and Japan is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "United States", + "Month": "total", + "Ratio": 207.9 + } + ], + "score": 0.97, + "annotation": "The total value of United States has the largest proportion.", + "reason": "The total value of Ratio of United States accounts for 0.20810810810810815, larger than all other Country, of the total of Ratio of all Country." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Nov", + "Ratio": 78.9, + "Country": "South Korea" + } + ], + "score": 0.97, + "annotation": "The South Korea has the largest proportion in Sep.", + "reason": "The Ratio of South Korea in Sep accounts for 0.22244149985903583, larger than all other Country, of the total of Ratio of all Country." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "Nov", + "Ratio": 60.0, + "Country": "Brazil" + }, + { + "Month": "Nov", + "Ratio": 45.9, + "Country": "Colombia" + } + ], + "score": 0.97, + "annotation": "The Ratio of Brazil and Colombia show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Brazil and Colombia is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Month": "Mar", + "Ratio": 5.0, + "Country": "European Union" + }, + { + "Month": "Mar", + "Ratio": 2.0, + "Country": "Brazil" + }, + { + "Month": "Mar", + "Ratio": 10.0, + "Country": "United States" + }, + { + "Month": "Mar", + "Ratio": 2.0, + "Country": "Colombia" + }, + { + "Month": "Mar", + "Ratio": 2.0, + "Country": "India" + }, + { + "Month": "May", + "Ratio": 5.0, + "Country": "South Korea" + }, + { + "Month": "May", + "Ratio": 2.0, + "Country": "Japan" + }, + { + "Month": "May", + "Ratio": 20.0, + "Country": "European Union" + }, + { + "Month": "May", + "Ratio": 10.0, + "Country": "Brazil" + }, + { + "Month": "May", + "Ratio": 35.0, + "Country": "United States" + }, + { + "Month": "May", + "Ratio": 5.0, + "Country": "Colombia" + }, + { + "Month": "May", + "Ratio": 3.0, + "Country": "India" + }, + { + "Month": "May", + "Ratio": 1.0, + "Country": "Kenya" + }, + { + "Month": "Jul", + "Ratio": 15.0, + "Country": "South Korea" + }, + { + "Month": "Jul", + "Ratio": 20.0, + "Country": "Japan" + }, + { + "Month": "Jul", + "Ratio": 45.0, + "Country": "European Union" + }, + { + "Month": "Jul", + "Ratio": 15.0, + "Country": "Brazil" + }, + { + "Month": "Jul", + "Ratio": 50.0, + "Country": "United States" + }, + { + "Month": "Jul", + "Ratio": 20.0, + "Country": "Colombia" + }, + { + "Month": "Jul", + "Ratio": 5.0, + "Country": "India" + }, + { + "Month": "Jul", + "Ratio": 1.0, + "Country": "Kenya" + }, + { + "Month": "Sep", + "Ratio": 50.0, + "Country": "South Korea" + }, + { + "Month": "Sep", + "Ratio": 60.0, + "Country": "Japan" + }, + { + "Month": "Sep", + "Ratio": 60.0, + "Country": "European Union" + }, + { + "Month": "Sep", + "Ratio": 35.0, + "Country": "Brazil" + }, + { + "Month": "Sep", + "Ratio": 55.0, + "Country": "United States" + }, + { + "Month": "Sep", + "Ratio": 30.0, + "Country": "Colombia" + }, + { + "Month": "Sep", + "Ratio": 15.0, + "Country": "India" + }, + { + "Month": "Sep", + "Ratio": 2.0, + "Country": "Kenya" + }, + { + "Month": "Nov", + "Ratio": 78.9, + "Country": "South Korea" + }, + { + "Month": "Nov", + "Ratio": 76.5, + "Country": "Japan" + }, + { + "Month": "Nov", + "Ratio": 66.7, + "Country": "European Union" + }, + { + "Month": "Nov", + "Ratio": 60.0, + "Country": "Brazil" + }, + { + "Month": "Nov", + "Ratio": 57.9, + "Country": "United States" + }, + { + "Month": "Nov", + "Ratio": 45.9, + "Country": "Colombia" + }, + { + "Month": "Nov", + "Ratio": 28.8, + "Country": "India" + }, + { + "Month": "Nov", + "Ratio": 4.3, + "Country": "Kenya" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year 2021", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding country", + "role": "y" + }, + { + "name": "Country", + "importance": "primary", + "description": "The country of the data point", + "role": "group" + } + ] + } + }, + "505": { + "metadata": { + "title": "What Do You Want To Be When You Grow Up?", + "description": "Share of children who want to take up the following professions one day, by country (in %)", + "main_insight": "Children in the US and UK are more interested in becoming Vloggers/YouTubers than Astronauts, while Chinese children show a strong preference for becoming Astronauts." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "China", + "Units": 52.0, + "Occupations": "Teacher" + }, + { + "Country": "China", + "Units": 47.0, + "Occupations": "Musician" + } + ], + "score": 1.0, + "annotation": "The Units of Teacher and Musician show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Teacher and Musician is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "China", + "Units": 47.0, + "Occupations": "Musician" + }, + { + "Country": "China", + "Units": 56.0, + "Occupations": "Astronaut" + } + ], + "score": 1.0, + "annotation": "The Units of Musician and Astronaut show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Musician and Astronaut is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "China", + "Units": 52.0, + "Occupations": "Teacher" + }, + { + "Country": "China", + "Units": 56.0, + "Occupations": "Astronaut" + } + ], + "score": 1.0, + "annotation": "The Units of Teacher and Astronaut show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Teacher and Astronaut is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "China", + "Units": 18.0, + "Occupations": "Vlogger/YouTuber" + }, + { + "Country": "China", + "Units": 37.0, + "Occupations": "Professional athlete" + } + ], + "score": 1.0, + "annotation": "The Units of Vlogger/YouTuber and Professional athlete show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Vlogger/YouTuber and Professional athlete is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "China", + "Units": 18.0, + "Occupations": "Vlogger/YouTuber" + }, + { + "Country": "China", + "Units": 52.0, + "Occupations": "Teacher" + } + ], + "score": 1.0, + "annotation": "The Units of Vlogger/YouTuber and Teacher show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Vlogger/YouTuber and Teacher is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Country": "USA", + "Units": 29.0, + "Occupations": "Vlogger/YouTuber" + }, + { + "Country": "USA", + "Units": 26.0, + "Occupations": "Teacher" + }, + { + "Country": "USA", + "Units": 23.0, + "Occupations": "Professional athlete" + }, + { + "Country": "USA", + "Units": 19.0, + "Occupations": "Musician" + }, + { + "Country": "USA", + "Units": 11.0, + "Occupations": "Astronaut" + }, + { + "Country": "UK", + "Units": 30.0, + "Occupations": "Vlogger/YouTuber" + }, + { + "Country": "UK", + "Units": 25.0, + "Occupations": "Teacher" + }, + { + "Country": "UK", + "Units": 21.0, + "Occupations": "Professional athlete" + }, + { + "Country": "UK", + "Units": 18.0, + "Occupations": "Musician" + }, + { + "Country": "UK", + "Units": 11.0, + "Occupations": "Astronaut" + }, + { + "Country": "China", + "Units": 18.0, + "Occupations": "Vlogger/YouTuber" + }, + { + "Country": "China", + "Units": 52.0, + "Occupations": "Teacher" + }, + { + "Country": "China", + "Units": 37.0, + "Occupations": "Professional athlete" + }, + { + "Country": "China", + "Units": 47.0, + "Occupations": "Musician" + }, + { + "Country": "China", + "Units": 56.0, + "Occupations": "Astronaut" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country where the survey was conducted", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding occupations", + "role": "y" + }, + { + "name": "Occupations", + "importance": "primary", + "description": "The occupations of the data point", + "role": "group" + } + ] + } + }, + "1226": { + "metadata": { + "title": "Chinese Car Exports Continue to Soar as EV Share Grows", + "description": "Automobile exports from China (in million units)", + "main_insight": "Chinese car exports have increased significantly from 2021 to 2023, with a growing share of New Energy Vehicles (NEVs)." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Units": 4.9, + "Export Metrics": "Total Exports" + }, + { + "Year": 2023, + "Units": 3.7, + "Export Metrics": "Others" + } + ], + "score": 1.0, + "annotation": "The Units of Total Exports and Others show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Exports and Others is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Units": 4.9, + "Export Metrics": "Total Exports" + }, + { + "Year": 2023, + "Units": 1.2, + "Export Metrics": "New energy vehicles" + } + ], + "score": 1.0, + "annotation": "The Units of Total Exports and New energy vehicles show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Exports and New energy vehicles is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Units": 1.2, + "Export Metrics": "New energy vehicles" + }, + { + "Year": 2023, + "Units": 3.7, + "Export Metrics": "Others" + } + ], + "score": 0.99, + "annotation": "The Units of New energy vehicles and Others show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between New energy vehicles and Others is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Units": 1.2, + "Export Metrics": "New energy vehicles" + }, + { + "Year": 2023, + "Units": 25.0, + "Export Metrics": "NEV share (in %)" + } + ], + "score": 0.96, + "annotation": "The Units of New energy vehicles and NEV share (in %) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between New energy vehicles and NEV share (in %) is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Units": 4.9, + "Export Metrics": "Total Exports" + }, + { + "Year": 2023, + "Units": 25.0, + "Export Metrics": "NEV share (in %)" + } + ], + "score": 0.93, + "annotation": "The Units of Total Exports and NEV share (in %) show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Exports and NEV share (in %) is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2021, + "Units": 2.0, + "Export Metrics": "Total Exports" + }, + { + "Year": 2021, + "Units": 0.3, + "Export Metrics": "New energy vehicles" + }, + { + "Year": 2021, + "Units": 1.7, + "Export Metrics": "Others" + }, + { + "Year": 2021, + "Units": 15.0, + "Export Metrics": "NEV share (in %)" + }, + { + "Year": 2022, + "Units": 3.1, + "Export Metrics": "Total Exports" + }, + { + "Year": 2022, + "Units": 0.7, + "Export Metrics": "New energy vehicles" + }, + { + "Year": 2022, + "Units": 2.4, + "Export Metrics": "Others" + }, + { + "Year": 2022, + "Units": 22.0, + "Export Metrics": "NEV share (in %)" + }, + { + "Year": 2023, + "Units": 4.9, + "Export Metrics": "Total Exports" + }, + { + "Year": 2023, + "Units": 1.2, + "Export Metrics": "New energy vehicles" + }, + { + "Year": 2023, + "Units": 3.7, + "Export Metrics": "Others" + }, + { + "Year": 2023, + "Units": 25.0, + "Export Metrics": "NEV share (in %)" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of automobile export", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding export metrics", + "role": "y" + }, + { + "name": "Export Metrics", + "importance": "primary", + "description": "The export metrics of the data point", + "role": "group" + } + ] + } + }, + "29": { + "metadata": { + "title": "The UK trails Europe in holiday allowance", + "description": "Total paid annual leave (statutory minimum leave plus public holidays)*", + "main_insight": "The UK has one of the lowest total paid annual leave allowances compared to other European countries, and the US has significantly less." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Switzerland", + "Units": 4.0, + "Leave Type": "Paid public holidays" + } + ], + "score": 0.97, + "annotation": "The Paid public holidays has a minimum value at Switzerland", + "reason": "The [{'Country': 'Switzerland', 'Units': 4.0, 'Leave Type': 'Paid public holidays'}] of Paid public holidays has a mainimum value of 4.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Switzerland", + "Units": 24.0, + "Leave Type": "Total Paid Leave" + } + ], + "score": 0.93, + "annotation": "The Total Paid Leave has a minimum value at Switzerland", + "reason": "The [{'Country': 'Switzerland', 'Units': 24.0, 'Leave Type': 'Total Paid Leave'}] of Total Paid Leave has a mainimum value of 24.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Spain", + "Units": 14.0, + "Leave Type": "Paid public holidays" + } + ], + "score": 0.88, + "annotation": "The Paid public holidays has a maximum value at Spain", + "reason": "The [{'Country': 'Spain', 'Units': 14.0, 'Leave Type': 'Paid public holidays'}] of Paid public holidays has a maximum value of 14.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Spain", + "Units": 36.0, + "Leave Type": "Total Paid Leave" + } + ], + "score": 0.86, + "annotation": "The Total Paid Leave has the largest proportion in Spain.", + "reason": "The Units of Total Paid Leave in Spain accounts for 0.5, larger than all other Leave Type, of the total of Units of all Leave Type." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Sweden", + "Units": 36.0, + "Leave Type": "Total Paid Leave" + } + ], + "score": 0.86, + "annotation": "The Total Paid Leave has the largest proportion in Sweden.", + "reason": "The Units of Total Paid Leave in Sweden accounts for 0.5, larger than all other Leave Type, of the total of Units of all Leave Type." + } + ], + "data": { + "data": [ + { + "Country": "Spain", + "Units": 22.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "Spain", + "Units": 14.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "Spain", + "Units": 36.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "Sweden", + "Units": 25.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "Sweden", + "Units": 11.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "Sweden", + "Units": 36.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "Austria", + "Units": 22.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "Austria", + "Units": 13.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "Austria", + "Units": 35.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "Finland", + "Units": 24.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "Finland", + "Units": 11.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "Finland", + "Units": 35.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "France", + "Units": 25.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "France", + "Units": 10.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "France", + "Units": 35.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "Denmark", + "Units": 25.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "Denmark", + "Units": 9.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "Denmark", + "Units": 34.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "Italy", + "Units": 20.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "Italy", + "Units": 12.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "Italy", + "Units": 32.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "Greece", + "Units": 20.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "Greece", + "Units": 11.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "Greece", + "Units": 31.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "Norway", + "Units": 21.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "Norway", + "Units": 10.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "Norway", + "Units": 31.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "Belgium", + "Units": 20.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "Belgium", + "Units": 10.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "Belgium", + "Units": 30.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "Germany", + "Units": 20.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "Germany", + "Units": 9.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "Germany", + "Units": 29.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "Ireland", + "Units": 20.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "Ireland", + "Units": 9.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "Ireland", + "Units": 29.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "Portugal", + "Units": 20.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "Portugal", + "Units": 9.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "Portugal", + "Units": 29.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "Netherlands", + "Units": 20.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "Netherlands", + "Units": 8.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "Netherlands", + "Units": 28.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "U.K.", + "Units": 20.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "U.K.", + "Units": 8.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "U.K.", + "Units": 28.0, + "Leave Type": "Total Paid Leave" + }, + { + "Country": "Switzerland", + "Units": 20.0, + "Leave Type": "Paid annual leave (working days)" + }, + { + "Country": "Switzerland", + "Units": 4.0, + "Leave Type": "Paid public holidays" + }, + { + "Country": "Switzerland", + "Units": 24.0, + "Leave Type": "Total Paid Leave" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country Name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding leave type", + "role": "y" + }, + { + "name": "Leave Type", + "importance": "primary", + "description": "The leave type of the data point", + "role": "group" + } + ] + } + }, + "266": { + "metadata": { + "title": "The Olympics Attract Millions of Spectators", + "description": "Number of tickets sold for the past five Summer Olympic Games", + "main_insight": "The London 2012 Olympics had the highest number of tickets sold among the past five Summer Olympic Games, significantly exceeding the other games." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Olympic Games": "Rio de Janeiro 2016", + "Tickets Sold (Millions)": 6.2 + } + ], + "score": 0.87, + "annotation": "The shows a sudden jump in Tickets Sold (Millions) from Athens 2004 to Rio de Janeiro 2016.", + "reason": "The Tickets Sold (Millions) for increases from 3.6 at Athens 2004 to 6.2 at Rio de Janeiro 2016, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Olympic Games": "Athens 2004", + "Tickets Sold (Millions)": 3.6 + } + ], + "score": 0.83, + "annotation": "The has a minimum value at Athens 2004", + "reason": "The [{'Olympic Games': 'Athens 2004', 'Tickets Sold (Millions)': 3.6}] of has a mainimum value of 3.6, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Olympic Games": "London 2012", + "Tickets Sold (Millions)": 8.2 + } + ], + "score": 0.68, + "annotation": "The has a maximum value at London 2012", + "reason": "The [{'Olympic Games': 'London 2012', 'Tickets Sold (Millions)': 8.2}] of has a maximum value of 8.2, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Olympic Games": "Sydney 2000", + "Tickets Sold (Millions)": 6.7 + }, + { + "Olympic Games": "Athens 2004", + "Tickets Sold (Millions)": 3.6 + }, + { + "Olympic Games": "Beijing 2008", + "Tickets Sold (Millions)": 6.5 + }, + { + "Olympic Games": "London 2012", + "Tickets Sold (Millions)": 8.2 + }, + { + "Olympic Games": "Rio de Janeiro 2016", + "Tickets Sold (Millions)": 6.2 + } + ], + "columns": [ + { + "name": "Olympic Games", + "importance": "primary", + "description": "The host city and year of the Summer Olympic Games", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Tickets Sold (Millions)", + "importance": "primary", + "description": "Number of tickets sold in millions", + "unit": "Millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1017": { + "metadata": { + "title": "Google Spends Millions to Fend Off Antitrust Case", + "description": "Lobbying expenditure of tech companies in the United States in 2012", + "main_insight": "Google spent significantly more on lobbying in 2012 than other tech companies, with a total expenditure of $16.5 million." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Company": "Apple", + "Units": 2.0, + "Lobbying Data": "Total Lobbying Spend" + }, + { + "Company": "Apple", + "Units": 0.6, + "Lobbying Data": "Q4 2012" + } + ], + "score": 0.99, + "annotation": "The Units of Total Lobbying Spend and Q4 2012 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Lobbying Spend and Q4 2012 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Company": "Apple", + "Units": 2.0, + "Lobbying Data": "Total Lobbying Spend" + }, + { + "Company": "Apple", + "Units": 0.5, + "Lobbying Data": "Q1 2012" + } + ], + "score": 0.99, + "annotation": "The Units of Total Lobbying Spend and Q1 2012 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Lobbying Spend and Q1 2012 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Company": "Apple", + "Units": 2.0, + "Lobbying Data": "Total Lobbying Spend" + }, + { + "Company": "Apple", + "Units": 0.5, + "Lobbying Data": "Q2 2012" + } + ], + "score": 0.99, + "annotation": "The Units of Total Lobbying Spend and Q2 2012 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Lobbying Spend and Q2 2012 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Company": "Apple", + "Units": 2.0, + "Lobbying Data": "Total Lobbying Spend" + }, + { + "Company": "Apple", + "Units": 0.4, + "Lobbying Data": "Q3 2012" + } + ], + "score": 0.98, + "annotation": "The Units of Total Lobbying Spend and Q3 2012 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Lobbying Spend and Q3 2012 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Company": "Apple", + "Units": 0.4, + "Lobbying Data": "Q3 2012" + }, + { + "Company": "Apple", + "Units": 0.6, + "Lobbying Data": "Q4 2012" + } + ], + "score": 0.98, + "annotation": "The Units of Q3 2012 and Q4 2012 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Q3 2012 and Q4 2012 is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Company": "Google", + "Units": 16.5, + "Lobbying Data": "Total Lobbying Spend" + }, + { + "Company": "Google", + "Units": 4.8, + "Lobbying Data": "Q1 2012" + }, + { + "Company": "Google", + "Units": 3.9, + "Lobbying Data": "Q2 2012" + }, + { + "Company": "Google", + "Units": 3.8, + "Lobbying Data": "Q3 2012" + }, + { + "Company": "Google", + "Units": 4.0, + "Lobbying Data": "Q4 2012" + }, + { + "Company": "Microsoft", + "Units": 8.1, + "Lobbying Data": "Total Lobbying Spend" + }, + { + "Company": "Microsoft", + "Units": 1.9, + "Lobbying Data": "Q1 2012" + }, + { + "Company": "Microsoft", + "Units": 2.3, + "Lobbying Data": "Q2 2012" + }, + { + "Company": "Microsoft", + "Units": 1.7, + "Lobbying Data": "Q3 2012" + }, + { + "Company": "Microsoft", + "Units": 2.2, + "Lobbying Data": "Q4 2012" + }, + { + "Company": "Hewlett-Packard", + "Units": 7.1, + "Lobbying Data": "Total Lobbying Spend" + }, + { + "Company": "Hewlett-Packard", + "Units": 1.6, + "Lobbying Data": "Q1 2012" + }, + { + "Company": "Hewlett-Packard", + "Units": 1.9, + "Lobbying Data": "Q2 2012" + }, + { + "Company": "Hewlett-Packard", + "Units": 1.9, + "Lobbying Data": "Q3 2012" + }, + { + "Company": "Hewlett-Packard", + "Units": 1.7, + "Lobbying Data": "Q4 2012" + }, + { + "Company": "IBM", + "Units": 4.9, + "Lobbying Data": "Total Lobbying Spend" + }, + { + "Company": "IBM", + "Units": 1.4, + "Lobbying Data": "Q1 2012" + }, + { + "Company": "IBM", + "Units": 1.3, + "Lobbying Data": "Q2 2012" + }, + { + "Company": "IBM", + "Units": 1.1, + "Lobbying Data": "Q3 2012" + }, + { + "Company": "IBM", + "Units": 1.1, + "Lobbying Data": "Q4 2012" + }, + { + "Company": "Facebook", + "Units": 4.0, + "Lobbying Data": "Total Lobbying Spend" + }, + { + "Company": "Facebook", + "Units": 0.7, + "Lobbying Data": "Q1 2012" + }, + { + "Company": "Facebook", + "Units": 0.6, + "Lobbying Data": "Q2 2012" + }, + { + "Company": "Facebook", + "Units": 1.4, + "Lobbying Data": "Q3 2012" + }, + { + "Company": "Facebook", + "Units": 1.3, + "Lobbying Data": "Q4 2012" + }, + { + "Company": "Intel", + "Units": 3.7, + "Lobbying Data": "Total Lobbying Spend" + }, + { + "Company": "Intel", + "Units": 0.9, + "Lobbying Data": "Q1 2012" + }, + { + "Company": "Intel", + "Units": 0.9, + "Lobbying Data": "Q2 2012" + }, + { + "Company": "Intel", + "Units": 0.9, + "Lobbying Data": "Q3 2012" + }, + { + "Company": "Intel", + "Units": 1.0, + "Lobbying Data": "Q4 2012" + }, + { + "Company": "Yahoo!", + "Units": 2.8, + "Lobbying Data": "Total Lobbying Spend" + }, + { + "Company": "Yahoo!", + "Units": 0.7, + "Lobbying Data": "Q1 2012" + }, + { + "Company": "Yahoo!", + "Units": 0.7, + "Lobbying Data": "Q2 2012" + }, + { + "Company": "Yahoo!", + "Units": 0.7, + "Lobbying Data": "Q3 2012" + }, + { + "Company": "Yahoo!", + "Units": 0.7, + "Lobbying Data": "Q4 2012" + }, + { + "Company": "Amazon", + "Units": 2.5, + "Lobbying Data": "Total Lobbying Spend" + }, + { + "Company": "Amazon", + "Units": 0.5, + "Lobbying Data": "Q1 2012" + }, + { + "Company": "Amazon", + "Units": 0.6, + "Lobbying Data": "Q2 2012" + }, + { + "Company": "Amazon", + "Units": 0.6, + "Lobbying Data": "Q3 2012" + }, + { + "Company": "Amazon", + "Units": 0.8, + "Lobbying Data": "Q4 2012" + }, + { + "Company": "Dell", + "Units": 2.4, + "Lobbying Data": "Total Lobbying Spend" + }, + { + "Company": "Dell", + "Units": 0.6, + "Lobbying Data": "Q1 2012" + }, + { + "Company": "Dell", + "Units": 0.6, + "Lobbying Data": "Q2 2012" + }, + { + "Company": "Dell", + "Units": 0.7, + "Lobbying Data": "Q3 2012" + }, + { + "Company": "Dell", + "Units": 0.5, + "Lobbying Data": "Q4 2012" + }, + { + "Company": "Apple", + "Units": 2.0, + "Lobbying Data": "Total Lobbying Spend" + }, + { + "Company": "Apple", + "Units": 0.5, + "Lobbying Data": "Q1 2012" + }, + { + "Company": "Apple", + "Units": 0.5, + "Lobbying Data": "Q2 2012" + }, + { + "Company": "Apple", + "Units": 0.4, + "Lobbying Data": "Q3 2012" + }, + { + "Company": "Apple", + "Units": 0.6, + "Lobbying Data": "Q4 2012" + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Name of the tech company", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding lobbying data", + "role": "y" + }, + { + "name": "Lobbying Data", + "importance": "primary", + "description": "The lobbying data of the data point", + "role": "group" + } + ] + } + }, + "596": { + "metadata": { + "title": "Gender pay gap? Yes, but not here", + "description": "Opinion of financial decision makers on pay gap in own company and UK companies generally", + "main_insight": "A large majority of financial decision makers believe there is a gender pay gap in favor of men in UK companies generally, but most believe there is no gender pay gap in their own company." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Statement": "There is a gender pay gap in favour of men", + "UK Companies": 78.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in UK Companies from There is no gender pay gap to There is a gender pay gap in favour of men.", + "reason": "The UK Companies for increases from 13.0 at There is no gender pay gap to 78.0 at There is a gender pay gap in favour of men, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Statement": "There is a gender pay gap in favour of men", + "UK Companies": 78.0 + } + ], + "score": 0.45, + "annotation": "The has a maximum value at There is a gender pay gap in favour of men", + "reason": "The [{'Statement': 'There is a gender pay gap in favour of men', 'UK Companies': 78.0}] of has a maximum value of 78.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Statement": "There is no gender pay gap", + "UK Companies": 13.0 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at There is no gender pay gap", + "reason": "The [{'Statement': 'There is no gender pay gap', 'UK Companies': 13.0}] of has a mainimum value of 13.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Statement": "There is a gender pay gap in favour of men", + "UK Companies": 78.0 + }, + { + "Statement": "There is no gender pay gap", + "UK Companies": 13.0 + } + ], + "columns": [ + { + "name": "Statement", + "importance": "primary", + "description": "The statement about gender pay gap", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "UK Companies", + "importance": "primary", + "description": "Percentage of respondents who agree with the statement for UK companies", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1966": { + "metadata": { + "title": "Serena On The Brink", + "description": "Comparison on selected points of Serena Williams and Steffi Graf", + "main_insight": "Serena Williams and Steffi Graf have the same number of Grand Slam singles titles and consecutive weeks at no.1, although Graf was a Grand Slam runner-up more times." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Metric": "Consecutive weeks at no.1", + "Williams": 186 + } + ], + "score": 0.96, + "annotation": "The shows a sudden jump in Williams from Grand Slam singles titles to Consecutive weeks at no.1.", + "reason": "The Williams for increases from 22 at Grand Slam singles titles to 186 at Consecutive weeks at no.1, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Metric": "Consecutive weeks at no.1", + "Williams": 186 + } + ], + "score": 0.67, + "annotation": "The has a maximum value at Consecutive weeks at no.1", + "reason": "The [{'Metric': 'Consecutive weeks at no.1', 'Williams': 186}] of has a maximum value of 186, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Metric": "Times Grand Slam runner-up", + "Williams": 6 + } + ], + "score": 0.43, + "annotation": "The has a minimum value at Times Grand Slam runner-up", + "reason": "The [{'Metric': 'Times Grand Slam runner-up', 'Williams': 6}] of has a mainimum value of 6, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Metric": "Grand Slam singles titles", + "Williams": 22 + }, + { + "Metric": "Consecutive weeks at no.1", + "Williams": 186 + }, + { + "Metric": "Times Grand Slam runner-up", + "Williams": 6 + } + ], + "columns": [ + { + "name": "Metric", + "importance": "primary", + "description": "The tennis statistic being compared", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Williams", + "importance": "primary", + "description": "Serena Williams' value for the given metric", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1123": { + "metadata": { + "title": "Back-to-Back Wins Bump Kansas City Chiefs to Super Bowl Top 3", + "description": "NFL teams by number of Super Bowl wins from 1967 to 2024", + "main_insight": "The Kansas City Chiefs are now in the top 3 teams with the most Super Bowl wins after winning back-to-back championships, tied with the Green Bay Packers and New York Giants at 4 wins, while the Pittsburgh Steelers and New England Patriots remain at the top with 6 wins." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "NFL Team": "Pittsburgh Steelers", + "Super Bowl Wins": 6 + }, + { + "NFL Team": "New England Patriots", + "Super Bowl Wins": 6 + } + ], + "score": 0.89, + "annotation": "The has maximum values at Pittsburgh Steelers, New England Patriots", + "reason": "The [{'NFL Team': 'Pittsburgh Steelers', 'Super Bowl Wins': 6}, {'NFL Team': 'New England Patriots', 'Super Bowl Wins': 6}] of have maximum values of 6, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "NFL Team": "New York Jets", + "Super Bowl Wins": 1 + }, + { + "NFL Team": "New Orleans Saints", + "Super Bowl Wins": 1 + }, + { + "NFL Team": "Chicago Bears", + "Super Bowl Wins": 1 + }, + { + "NFL Team": "Seattle Seahawks", + "Super Bowl Wins": 1 + }, + { + "NFL Team": "Philadelphia Eagles", + "Super Bowl Wins": 1 + } + ], + "score": 0.66, + "annotation": "The has minimum values at New York Jets, New Orleans Saints, Chicago Bears, Seattle Seahawks, Philadelphia Eagles", + "reason": "The [{'NFL Team': 'New York Jets', 'Super Bowl Wins': 1}, {'NFL Team': 'New Orleans Saints', 'Super Bowl Wins': 1}, {'NFL Team': 'Chicago Bears', 'Super Bowl Wins': 1}, {'NFL Team': 'Seattle Seahawks', 'Super Bowl Wins': 1}, {'NFL Team': 'Philadelphia Eagles', 'Super Bowl Wins': 1}] of have minimum values of 1, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "NFL Team": "Indianapolis Colts", + "Super Bowl Wins": 2 + }, + { + "NFL Team": "Oakland Raiders", + "Super Bowl Wins": 3 + }, + { + "NFL Team": "New York Giants", + "Super Bowl Wins": 4 + }, + { + "NFL Team": "San Francisco 49ers", + "Super Bowl Wins": 5 + }, + { + "NFL Team": "Pittsburgh Steelers", + "Super Bowl Wins": 6 + } + ], + "score": 0.6, + "annotation": "The exhibits multiple sudden jumps in Super Bowl Wins, transitioning from Philadelphia Eagles, Tampa Bay Buccaneers, Denver Broncos, Kansas City Chiefs, Dallas Cowboys to Indianapolis Colts, Oakland Raiders, New York Giants, San Francisco 49ers, Pittsburgh Steelers.", + "reason": "Across these points, the Super Bowl Wins for increases from 1, 2, 3, 4, 5 at Philadelphia Eagles, Tampa Bay Buccaneers, Denver Broncos, Kansas City Chiefs, Dallas Cowboys to 2, 3, 4, 5, 6 at Indianapolis Colts, Oakland Raiders, New York Giants, San Francisco 49ers, Pittsburgh Steelers, suggesting significant shifts in value." + } + ], + "data": { + "data": [ + { + "NFL Team": "Pittsburgh Steelers", + "Super Bowl Wins": 6 + }, + { + "NFL Team": "New England Patriots", + "Super Bowl Wins": 6 + }, + { + "NFL Team": "San Francisco 49ers", + "Super Bowl Wins": 5 + }, + { + "NFL Team": "Dallas Cowboys", + "Super Bowl Wins": 5 + }, + { + "NFL Team": "New York Giants", + "Super Bowl Wins": 4 + }, + { + "NFL Team": "Green Bay Packers", + "Super Bowl 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"importance": "primary", + "description": "Name of the NFL Team", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Super Bowl Wins", + "importance": "primary", + "description": "Number of Super Bowl wins", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1131": { + "metadata": { + "title": "Facebook's Reach Among Young Adults Is Still Unmatched", + "description": "% of 18- to 29-year-olds in the United States who have an account on the following platforms", + "main_insight": "Facebook has significantly greater reach among 18-29 year olds in the US compared to other social media platforms as of Spring 2014." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Platform": "Facebook", + "Reach": 84.0 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at Facebook", + "reason": "The [{'Platform': 'Facebook', 'Reach': 84.0}] of has a maximum value of 84.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Platform": "Facebook", + "Reach": 84.0 + } + ], + "score": 0.84, + "annotation": "The shows a sudden jump in Reach from Google+ to Facebook.", + "reason": "The Reach for increases from 44.0 at Google+ to 84.0 at Facebook, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Platform": "Foursquare", + "Reach": 6.0 + } + ], + "score": 0.64, + "annotation": "The has a minimum value at Foursquare", + "reason": "The [{'Platform': 'Foursquare', 'Reach': 6.0}] of has a mainimum value of 6.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Platform": "Facebook", + "Reach": 84.0 + }, + { + "Platform": "Google+", + "Reach": 44.0 + }, + { + "Platform": "Twitter", + "Reach": 40.0 + }, + { + "Platform": "Instagram", + "Reach": 36.0 + }, + { + "Platform": "Pinterest", + "Reach": 33.0 + }, + { + "Platform": "Snapchat", + "Reach": 23.0 + }, + { + "Platform": "Tumblr", + "Reach": 14.0 + }, + { + "Platform": "WhatsApp", + "Reach": 9.0 + }, + { + "Platform": "Foursquare", + "Reach": 6.0 + } + ], + "columns": [ + { + "name": "Platform", + "importance": "primary", + "description": "Social media platform", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Reach", + "importance": "primary", + "description": "Percentage of 18-29 year olds in the US with an account", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "100": { + "metadata": { + "title": "ChatGPT Leads an Already Crowded Market", + "description": "Estimated share of worldwide users by AI text generation tool in 2022", + "main_insight": "ChatGPT had the largest market share (19.7%) among AI text generation tools in 2022, followed by Jasper Chat (13.4%) and YouChat (12.3%)." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "AI Tool": "ChatGPT", + "Market Share": 19.7 + } + ], + "score": 0.94, + "annotation": "The has a maximum value at ChatGPT", + "reason": "The [{'AI Tool': 'ChatGPT', 'Market Share': 19.7}] of has a maximum value of 19.7, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "AI Tool": "ChatGPT", + "Market Share": 19.7 + } + ], + "score": 0.83, + "annotation": "The shows a sudden jump in Market Share from Jasper Chat to ChatGPT.", + "reason": "The Market Share for increases from 13.4 at Jasper Chat to 19.7 at ChatGPT, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "AI Tool": "Simplified", + "Market Share": 7.1 + } + ], + "score": 0.61, + "annotation": "The has a minimum value at Simplified", + "reason": "The [{'AI Tool': 'Simplified', 'Market Share': 7.1}] of has a mainimum value of 7.1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "AI Tool": "ChatGPT", + "Market Share": 19.7 + }, + { + "AI Tool": "Jasper Chat", + "Market Share": 13.4 + }, + { + "AI Tool": "YouChat", + "Market Share": 12.3 + }, + { + "AI Tool": "DeepL", + "Market Share": 12.1 + }, + { + "AI Tool": "Perplexity", + "Market Share": 9.7 + }, + { + "AI Tool": "Typehero", + "Market Share": 9.4 + }, + { + "AI Tool": "Writesonic", + "Market Share": 8.7 + }, + { + "AI Tool": "Neuroflash", + "Market Share": 7.5 + }, + { + "AI Tool": "Simplified", + "Market Share": 7.1 + } + ], + "columns": [ + { + "name": "AI Tool", + "importance": "primary", + "description": "Name of the AI text generation tool", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Market Share", + "importance": "primary", + "description": "Percentage of worldwide users in 2022", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "87": { + "metadata": { + "title": "Digital Audio is Reeling in Radio", + "description": "(Forecast) Reach of digital audio and traditional radio advertising in the United States (in million users)", + "main_insight": "Digital audio advertising reach in the US is rapidly growing and is projected to nearly reach the level of traditional radio by 2025." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2025, + "Units": 215.4, + "Media Type": "Digital audio" + }, + { + "Year": 2025, + "Units": 221.9, + "Media Type": "Traditional radio" + } + ], + "score": 0.98, + "annotation": "The Units of Digital audio and Traditional radio show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Digital audio and Traditional radio is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2017, + "Units": 99.7, + "Media Type": "Digital audio" + }, + { + "Year": 2017, + "Units": 213.0, + "Media Type": "Traditional radio" + }, + { + "Year": 2019, + "Units": 128.8, + "Media Type": "Digital audio" + }, + { + "Year": 2019, + "Units": 214.6, + "Media Type": "Traditional radio" + }, + { + "Year": 2021, + "Units": 167.7, + "Media Type": "Digital audio" + }, + { + "Year": 2021, + "Units": 216.7, + "Media Type": "Traditional radio" + }, + { + "Year": 2023, + "Units": 195.5, + "Media Type": "Digital audio" + }, + { + "Year": 2023, + "Units": 219.3, + "Media Type": "Traditional radio" + }, + { + "Year": 2025, + "Units": 215.4, + "Media Type": "Digital audio" + }, + { + "Year": 2025, + "Units": 221.9, + "Media Type": "Traditional radio" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the forecast", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding media type", + "role": "y" + }, + { + "name": "Media Type", + "importance": "primary", + "description": "The media type of the data point", + "role": "group" + } + ] + } + }, + "1763": { + "metadata": { + "title": "World's Third-Largest Economy Enters Recession", + "description": "Compounded annual rate of change for GDP in Japan by quarter", + "main_insight": "Japan's GDP growth rate declined sharply from Q1 2019 to Q1 2020, entering a recession with negative growth in Q4 2019 and Q1 2020." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Quarter": "Q3 2019", + "GDP Growth Rate": 2.0 + } + ], + "score": 0.79, + "annotation": "The shows a sudden jump in GDP Growth Rate from Q1 2020 to Q3 2019.", + "reason": "The GDP Growth Rate for increases from -3.08 at Q1 2020 to 2.0 at Q3 2019, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Quarter": "Q4 2019", + "GDP Growth Rate": -7.1 + } + ], + "score": 0.78, + "annotation": "The has a minimum value at Q4 2019", + "reason": "The [{'Quarter': 'Q4 2019', 'GDP Growth Rate': -7.1}] of has a mainimum value of -7.1, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Quarter": "Q1 2019", + "GDP Growth Rate": 4.61 + } + ], + "score": 0.59, + "annotation": "The has a maximum value at Q1 2019", + "reason": "The [{'Quarter': 'Q1 2019', 'GDP Growth Rate': 4.61}] of has a maximum value of 4.61, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Quarter": "Q1 2019", + "GDP Growth Rate": 4.61 + }, + { + "Quarter": "Q2 2019", + "GDP Growth Rate": 2.9 + }, + { + "Quarter": "Q3 2019", + "GDP Growth Rate": 2.0 + }, + { + "Quarter": "Q4 2019", + "GDP Growth Rate": -7.1 + }, + { + "Quarter": "Q1 2020", + "GDP Growth Rate": -3.08 + } + ], + "columns": [ + { + "name": "Quarter", + "importance": "primary", + "description": "Quarter of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "GDP Growth Rate", + "importance": "primary", + "description": "Compounded annual rate of change for GDP in Japan", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2426": { + "metadata": { + "title": "Why Australians Are Being Urged To Eat Kangaroo Meat", + "description": "Estimated population of kangaroos in Australia (2010-2016)", + "main_insight": "The kangaroo population in Australia has grown significantly from 2010 to 2016, and in each of those years it has been higher than human population which stayed at 24.6m" + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2010, + "Kangaroo Population": 27.04 + }, + { + "Year": 2011, + "Kangaroo Population": 25.16 + }, + { + "Year": 2012, + "Kangaroo Population": 34.3 + }, + { + "Year": 2013, + "Kangaroo Population": 39.41 + }, + { + "Year": 2014, + "Kangaroo Population": 53.2 + }, + { + "Year": 2015, + "Kangaroo Population": 49.31 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[{'Country': 'Burundi', 'GHI Score': 38.8}] of has a maximum value of 38.8, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Comoros", + "GHI Score": 33.6 + } + ], + "score": 0.67, + "annotation": "The shows a sudden jump in GHI Score from Timor-Leste to Comoros.", + "reason": "The GHI Score for increases from 29.6 at Timor-Leste to 33.6 at Comoros, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Zambia", + "GHI Score": 24.1 + } + ], + "score": 0.62, + "annotation": "The has a minimum value at Zambia", + "reason": "The [{'Country': 'Zambia', 'GHI Score': 24.1}] of has a mainimum value of 24.1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Burundi", + "GHI Score": 38.8 + }, + { + "Country": "Eritrea", + "GHI Score": 35.0 + }, + { + "Country": "Comoros", + "GHI Score": 33.6 + }, + { + "Country": "Timor-Leste", + "GHI Score": 29.6 + }, + { + "Country": "Sudan (former)", + "GHI Score": 27.0 + }, + { + "Country": "Chad", + "GHI Score": 26.9 + }, + { + "Country": "Yemen", + "GHI Score": 26.5 + }, + { + "Country": "Ethiopia", + "GHI Score": 25.7 + }, + { + "Country": "Madagascar", + "GHI Score": 25.2 + }, + { + "Country": "Zambia", + "GHI Score": 24.1 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country Name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "GHI Score", + "importance": "primary", + "description": "Global Hunger Index Score", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "683": { + "metadata": { + "title": "How Skeptical is Europe About Vaccinations?", + "description": "Share of respondents who have little trust in the safety of vaccinations", + "main_insight": "Poland has the highest percentage of respondents with little trust in vaccination safety among the surveyed European countries, at 31%. The UK and Spain have the lowest, at 16%." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Poland", + "Skepticism Percentage": 31.0 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at Poland", + "reason": "The [{'Country': 'Poland', 'Skepticism Percentage': 31.0}] of has a maximum value of 31.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Poland", + "Skepticism Percentage": 31.0 + } + ], + "score": 0.77, + "annotation": "The shows a sudden jump in Skepticism Percentage from Austria to Poland.", + "reason": "The Skepticism Percentage for increases from 25.0 at Austria to 31.0 at Poland, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Spain", + "Skepticism Percentage": 16.0 + }, + { + "Country": "United Kingdom", + 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categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Germany", + "Score": 100.0 + } + ], + "score": 0.66, + "annotation": "The has a maximum value at Germany", + "reason": "The [{'Country': 'Germany', 'Score': 100.0}] of has a maximum value of 100.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Japan", + "Score": 93.5 + } + ], + "score": 0.63, + "annotation": "The shows a sudden jump in Score from France to Japan.", + "reason": "The Score for increases from 90.1 at France to 93.5 at Japan, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Germany", + "Score": 100.0 + }, + { + "Country": "Canada", + "Score": 99.7 + }, + { + "Country": "United Kingdom", + "Score": 97.5 + }, + { + "Country": "United States", + "Score": 97.4 + }, + { + "Country": "Sweden", + "Score": 96.9 + }, + { + "Country": "Australia", + "Score": 94.9 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sources." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Recipient": "AstraZeneca/University of Oxford", + "Units": 501.0, + "Funding Source": "CEPI" + } + ], + "score": 0.97, + "annotation": "The CEPI shows a sudden jump in Units from Sichuan Clover Biopharmaceuticals to AstraZeneca/University of Oxford.", + "reason": "The Units for CEPI increases from 328.0 at Sichuan Clover Biopharmaceuticals to 501.0 at AstraZeneca/University of Oxford, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Recipient": "Curevac", + "Units": 741.7, + "Funding Source": "Public" + } + ], + "score": 0.93, + "annotation": "The Public shows a sudden jump in Units from Sinopharm to Curevac.", + "reason": "The Units for Public increases from 145.0 at Sinopharm to 741.7 at Curevac, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Recipient": "Janssen", + "Units": 910.6, + "Funding Source": "Public" + }, + { + "Recipient": "Janssen", + "Units": 910.6, + "Funding Source": "Total" + } + ], + "score": 0.88, + "annotation": "The Public, Total all have the largest proportion in Janssen.", + "reason": "The Units of Public, Total in Janssen all account for 0.3921281543364052, larger than all other Funding Source, of the total of Units of all Funding Source." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Recipient": "Moderna Therapeutics", + "Units": 956.3, + "Funding Source": "Public" + }, + { + "Recipient": "Moderna Therapeutics", + "Units": 956.3, + "Funding Source": "Total" + } + ], + "score": 0.88, + "annotation": "The Public, Total all have the largest proportion in Moderna Therapeutics.", + "reason": "The Units of Public, Total in Moderna Therapeutics all account for 0.39506733867636123, larger than all other 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Advertising is the primary revenue source for both Google and Facebook, while Hardware, Retail, and Software, are primary for Apple, Amazon and Microsoft, respectively." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [], + "data": { + "data": [ + { + "Company": "Apple", + "Units": 229.2, + "Revenue Profile": "Total Revenue (billions)" + }, + { + "Company": "Apple", + "Units": "Hardware", + "Revenue Profile": "Main Revenue Source" + }, + { + "Company": "Apple", + "Units": 81.0, + "Revenue Profile": "Main Source Revenue Share (%)" + }, + { + "Company": "Amazon", + "Units": 177.9, + "Revenue Profile": "Total Revenue (billions)" + }, + { + "Company": "Amazon", + "Units": "Retail", + "Revenue Profile": "Main Revenue Source" + }, + { + "Company": "Amazon", + "Units": 82.0, + "Revenue Profile": "Main Source Revenue Share (%)" + }, + { + "Company": "Google", + "Units": 110.9, + "Revenue Profile": "Total Revenue (billions)" + }, + { + "Company": "Google", + "Units": "Advertising", + "Revenue Profile": "Main Revenue Source" + }, + { + "Company": "Google", + "Units": 86.0, + "Revenue Profile": "Main Source Revenue Share (%)" + }, + { + "Company": "Microsoft", + "Units": 90.0, + "Revenue Profile": "Total Revenue (billions)" + }, + { + "Company": "Microsoft", + "Units": "Software", + "Revenue Profile": "Main Revenue Source" + }, + { + "Company": "Microsoft", + "Units": 62.0, + "Revenue Profile": "Main Source Revenue Share (%)" + }, + { + "Company": "Facebook", + "Units": 40.7, + "Revenue Profile": "Total Revenue (billions)" + }, + { + "Company": "Facebook", + "Units": "Advertising", + "Revenue Profile": "Main Revenue Source" + }, + { + "Company": "Facebook", + "Units": 98.0, + "Revenue Profile": "Main Source Revenue Share (%)" + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "Name of the tech company", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding revenue profile", + "role": "y" + }, + { + "name": "Revenue Profile", + "importance": "primary", + "description": "The revenue profile of the data point", + "role": "group" + } + ] + } + }, + "1509": { + "metadata": { + "title": "The rise and fall of the Brexit Party", + "description": "Share saying they would vote for the Brexit Party in a general election", + "main_insight": "Support for the Brexit Party peaked in late May/early June 2019 at 26%, then declined significantly, falling to 4% by November 2019." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "June", + "Brexit Party Support": 26.0 + } + ], + "score": 0.84, + "annotation": "The has a maximum value at June", + "reason": "The [{'Month': 'June', 'Brexit Party Support': 26.0}] of has a maximum value of 26.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "Mar", + "Brexit Party Support": 2.0 + } + ], + "score": 0.79, + "annotation": "The has a minimum value at Mar", + "reason": "The [{'Month': 'Mar', 'Brexit Party Support': 2.0}] of has a mainimum value of 2.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Apr", + "Brexit Party Support": 12.0 + }, + { + "Month": "May", + "Brexit Party Support": 23.0 + } + ], + "score": 0.72, + "annotation": "The exhibits multiple sudden jumps in Brexit Party Support, transitioning from Nov, Aug to Apr, May.", + "reason": "Across these points, the Brexit Party Support for increases from 4.0, 15.0 at Nov, Aug to 12.0, 23.0 at Apr, May, suggesting significant shifts in value." + } + ], + "data": { + "data": [ + { + "Month": "Mar", + "Brexit Party Support": 2.0 + }, + { + "Month": "Apr", + "Brexit Party Support": 12.0 + }, + { + "Month": "May", + "Brexit Party Support": 23.0 + }, + { + "Month": "June", + "Brexit Party Support": 26.0 + }, + { + "Month": "July", + "Brexit Party Support": 15.0 + }, + { + "Month": "Aug", + "Brexit Party Support": 15.0 + }, + { + "Month": "Sep", + "Brexit Party Support": 12.0 + }, + { + "Month": "Oct", + "Brexit Party Support": 12.0 + }, + { + "Month": "Nov", + "Brexit Party Support": 4.0 + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year 2019", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Brexit Party Support", + "importance": "primary", + "description": "Percentage of people saying they would vote for the Brexit Party", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1371": { + "metadata": { + "title": "How Chinese New Year Compares With Thanksgiving", + "description": "Key numbers regarding Thanksgiving in the US and Spring Festival in China", + "main_insight": "Chinese New Year (Spring Festival) sees significantly higher numbers in terms of trips and TV viewership compared to Thanksgiving, while Thanksgiving has a significantly higher retail/restaurant expenditure." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Metric": "Holiday TV viewership", + "China/Spring Festival": 700.0 + } + ], + "score": 0.95, + "annotation": "The shows a sudden jump in China/Spring Festival from Holiday retail/restaurant expenditure to Holiday TV viewership.", + "reason": "The China/Spring Festival for increases from 100.0 at Holiday retail/restaurant expenditure to 700.0 at Holiday TV viewership, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Metric": "Holiday TV viewership", + "China/Spring Festival": 700.0 + } + ], + "score": 0.67, + "annotation": "The has a maximum value at Holiday TV viewership", + "reason": "The [{'Metric': 'Holiday TV viewership', 'China/Spring Festival': 700.0}] of has a maximum value of 700.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Metric": "Number of trips during holiday", + "China/Spring Festival": 2.8 + } + ], + "score": 0.45, + "annotation": "The has a minimum value at Number of trips during holiday", + "reason": "The [{'Metric': 'Number of trips during holiday', 'China/Spring Festival': 2.8}] of has a mainimum value of 2.8, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Metric": "Number of trips during holiday", + "China/Spring Festival": 2.8 + }, + { + "Metric": "Holiday retail/restaurant expenditure", + "China/Spring Festival": 100.0 + }, + { + "Metric": "Holiday TV viewership", + "China/Spring Festival": 700.0 + } + ], + "columns": [ + { + "name": "Metric", + "importance": "primary", + "description": "The category being compared", + "unit": "none", + "data_type": "string", + "role": "x" + }, + 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Korean Baseball in the Spotlight", + "description": "Key stats for the U.S. Major Baseball and the Korean Professional Baseball Leagues", + "main_insight": "The infographic compares key statistics between US Major League Baseball and Korean Professional Baseball. 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While Asian and African regions show mixed results, with some subregions having significant growth, EMEA and American regions are mostly below 2019 levels." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "African regions", + "Units": 27.0, + "Metric": "Subregion 3" + } + ], + "score": 0.97, + "annotation": "The Subregion 3 shows a sudden jump in Units from American regions to African regions.", + "reason": "The Units for Subregion 3 increases from 18.0 at American regions to 27.0 at African regions, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "Asian regions", + "Units": 28.0, + "Metric": "Subregion 2" + } + ], + "score": 0.95, + "annotation": "The Subregion 2 shows a sudden jump in Units from African regions to Asian regions.", + "reason": "The Units for Subregion 2 increases from 9.0 at African regions to 28.0 at Asian regions, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "American regions", + "Units": -4.0, + "Metric": "Total Change" + } + ], + "score": 0.91, + "annotation": "The Total Change shows a sudden jump in Units from Asian regions to American regions.", + "reason": "The Units for Total Change increases from -15.0 at Asian regions to -4.0 at American regions, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Region": "African regions", + "Units": 5.0, + "Metric": "Subregion 1" + } + ], + "score": 0.9, + "annotation": "The Subregion 1 shows a sudden jump in Units from EMEA regions to African regions.", + "reason": "The Units for Subregion 1 increases from -4.0 at EMEA regions to 5.0 at African regions, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Region": "Asian regions", + "Units": 28.0, + "Metric": "Subregion 2" + } + ], + "score": 0.8, + "annotation": "The Subregion 2 has a maximum value at Asian regions", + "reason": "The [{'Region': 'Asian regions', 'Units': 28.0, 'Metric': 'Subregion 2'}] of Subregion 2 has a maximum value of 28.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Region": "Asian regions", + "Units": -15.0, + "Metric": "Total Change" + }, + { + "Region": "Asian regions", + "Units": 10.0, + "Metric": "Subregion 1" + }, + { + "Region": "Asian regions", + "Units": 28.0, + "Metric": "Subregion 2" + }, + { + "Region": "African regions", + "Units": -18.0, + "Metric": "Total Change" + }, + { + "Region": "African regions", + "Units": 5.0, + "Metric": "Subregion 1" + }, + { + "Region": "African regions", + "Units": 9.0, + "Metric": "Subregion 2" + }, + { + "Region": "African regions", + "Units": 27.0, + "Metric": "Subregion 3" + }, + { + "Region": "EMEA regions", + "Units": -20.0, + "Metric": "Total Change" + }, + { + "Region": "EMEA regions", + "Units": -4.0, + "Metric": "Subregion 1" + }, + { + "Region": "EMEA regions", + "Units": 7.0, + "Metric": "Subregion 2" + }, + { + "Region": "American regions", + "Units": -4.0, + "Metric": "Total Change" + }, + { + "Region": "American regions", + "Units": 8.0, + "Metric": "Subregion 1" + }, + { + "Region": "American regions", + "Units": 5.0, + "Metric": "Subregion 2" + }, + { + "Region": "American regions", + "Units": 18.0, + "Metric": "Subregion 3" + } + ], + "columns": [ + { + "name": "Region", + "importance": "primary", + "description": "Geographic region", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding metric", + "role": "y" + }, + { + "name": "Metric", + "importance": "primary", + "description": "The metric of the data point", + "role": "group" + } + ] + } + }, + "2446": { + "metadata": { + "title": "Netflix Falls Short of Own Record at the Emmys", + "description": "Number of Emmy nominations and wins for Netflix", + "main_insight": "Netflix's Emmy nominations and wins peaked in 2021. In 2023, Netflix has seen a decline. The exact win/nomination ratio for 2023 are not disclosed." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2015, + "Nominations": 34 + }, + { + "Year": 2016, + "Nominations": 54 + }, + { + "Year": 2017, + "Nominations": 91 + }, + { + "Year": 2018, + "Nominations": 112 + }, + { + "Year": 2019, + "Nominations": 118 + }, + { + "Year": 2020, + "Nominations": 160 + }, + { + "Year": 2021, + "Nominations": 129 + }, + { + "Year": 2022, + "Nominations": 105 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of Emmy nominations and wins", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Nominations", + "importance": "primary", + "description": "Number of Emmy nominations", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2074": { + "metadata": { + "title": "Religion is on the decline in the UK", + "description": "Current religious affiliation in England and Wales", + "main_insight": "Almost half of the population in England and Wales identifies as having no religion, significantly surpassing any single religious affiliation." + }, + "chart_type": "Donut Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Religious Affiliation": "No religion", + "Percentage": 48.5 + } + ], + "score": 0.92, + "annotation": "The shows a sudden jump in Percentage from Anglican to No religion.", + "reason": "The Percentage for increases from 19.8 at Anglican to 48.5 at No religion, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Religious Affiliation": "No religion", + "Percentage": 48.5 + } + ], + "score": 0.86, + "annotation": "The has a maximum value at No religion", + "reason": "The [{'Religious Affiliation': 'No religion', 'Percentage': 48.5}] of has a maximum value of 48.5, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Religious Affiliation": "Non-Christian religion", + "Percentage": 7.7 + } + ], + "score": 0.47, + "annotation": "The has a minimum value at Non-Christian religion", + "reason": "The [{'Religious Affiliation': 'Non-Christian religion', 'Percentage': 7.7}] of has a mainimum value of 7.7, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Religious Affiliation": "No religion", + "Percentage": 48.5 + }, + { + "Religious Affiliation": "Anglican", + "Percentage": 19.8 + }, + { + "Religious Affiliation": "Other Christian", + "Percentage": 15.7 + }, + { + "Religious Affiliation": "Catholic", + "Percentage": 8.3 + }, + { + "Religious Affiliation": "Non-Christian religion", + "Percentage": 7.7 + } + ], + "columns": [ + { + "name": "Religious Affiliation", + "importance": "primary", + "description": "Category of religious or non-religious affiliation", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Percentage", + "importance": "primary", + "description": "Percentage of the population affiliated with each category", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2396": { + "metadata": { + "title": "Climbing the Ladder in the Global Innovation Race", + "description": "Countries that gained the most ranks in the Global Innovation Index 2022 vs. 2021", + "main_insight": "Botswana gained the most ranks in the Global Innovation Index between 2021 and 2022, followed by Ghana and Qatar." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Botswana", + "Rank Gained": 20 + } + ], + "score": 0.86, + "annotation": "The has a maximum value at Botswana", + "reason": "The [{'Country': 'Botswana', 'Rank Gained': 20}] of has a maximum value of 20, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Botswana", + "Rank Gained": 20 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Rank Gained from Ghana to Botswana.", + "reason": "The Rank Gained for increases from 17 at Ghana to 20 at Botswana, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Indonesia", + "Rank Gained": 12 + }, + { + "Country": "Pakistan", + "Rank Gained": 12 + } + ], + "score": 0.65, + "annotation": "The has minimum values at Indonesia, Pakistan", + "reason": "The [{'Country': 'Indonesia', 'Rank Gained': 12}, {'Country': 'Pakistan', 'Rank Gained': 12}] of have minimum values of 12, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Botswana", + "Rank Gained": 20 + }, + { + "Country": "Ghana", + "Rank Gained": 17 + }, + { + "Country": "Qatar", + "Rank Gained": 16 + }, + { + "Country": "Saudi Arabia", + "Rank Gained": 15 + }, + { + "Country": "Bangladesh", + "Rank Gained": 14 + }, + { + "Country": "Indonesia", + "Rank Gained": 12 + }, + { + "Country": "Pakistan", + "Rank Gained": 12 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country Name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Rank Gained", + "importance": "primary", + "description": "Number of ranks gained in the Global Innovation Index between 2021 and 2022", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1066": { + "metadata": { + "title": "Countries With the Highest GDP per Capita in Africa", + "description": "Gross domestic product per capita in 2023 (in current U.S. dollars)", + "main_insight": "Seychelles has the highest GDP per capita in Africa in 2023, significantly exceeding other African nations." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Seychelles", + "GDP per capita (USD)": 21580.0 + } + ], + "score": 0.97, + "annotation": "The has a maximum value at Seychelles", + "reason": "The [{'Country': 'Seychelles', 'GDP per capita (USD)': 21580.0}] of has a maximum value of 21580.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Seychelles", + "GDP per capita (USD)": 21580.0 + } + ], + "score": 0.88, + "annotation": "The shows a sudden jump in GDP per capita (USD) from Mauritius to Seychelles.", + "reason": "The GDP per capita (USD) for increases from 11400.0 at Mauritius to 21580.0 at Seychelles, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Cabo Verde", + "GDP per capita (USD)": 4370.0 + } + ], + "score": 0.49, + "annotation": "The has a minimum value at Cabo Verde", + "reason": "The [{'Country': 'Cabo Verde', 'GDP per capita (USD)': 4370.0}] of has a mainimum value of 4370.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "Seychelles", + "GDP per capita (USD)": 21580.0 + }, + { + "Country": "Mauritius", + "GDP per capita (USD)": 11400.0 + }, + { + "Country": "Gabon", + "GDP per capita (USD)": 9290.0 + }, + { + "Country": "Botswana", + "GDP per capita (USD)": 7640.0 + }, + { + "Country": "Equatorial Guinea", + "GDP per capita (USD)": 6660.0 + }, + { + "Country": "Libya", + "GDP per capita (USD)": 6580.0 + }, + { + "Country": "South Africa", + "GDP per capita (USD)": 6140.0 + }, + { + "Country": "Algeria", + "GDP per capita (USD)": 5320.0 + }, + { + "Country": "Namibia", + "GDP per capita (USD)": 4660.0 + }, + { + "Country": "Cabo Verde", + "GDP per capita (USD)": 4370.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Name of the African country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "GDP per capita (USD)", + "importance": "primary", + "description": "Gross Domestic Product per capita in 2023 in current U.S. dollars", + "unit": "USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2453": { + "metadata": { + "title": "The Cities With The Most Five Star Hotels", + "description": "Number of five star hotels by city in 2017", + "main_insight": "London had the most five-star hotels in 2017, followed by Dubai and New York. There is a significant drop off after the top four cities." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "City": "London", + "Number of Five Star Hotels": 75 + } + ], + "score": 0.89, + "annotation": "The has a maximum value at London", + "reason": "The [{'City': 'London', 'Number of Five Star Hotels': 75}] of has a maximum value of 75, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "City": "London", + "Number of Five Star Hotels": 75 + } + ], + "score": 0.71, + "annotation": "The shows a sudden jump in Number of Five Star Hotels from Dubai to London.", + "reason": "The Number of Five Star Hotels for increases from 61 at Dubai to 75 at London, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "City": "Washington D.C.", + "Number of Five Star Hotels": 32 + }, + { + "City": "Rome", + "Number of Five Star Hotels": 32 + } + ], + "score": 0.58, + "annotation": "The has minimum values at Washington D.C., Rome", + "reason": "The [{'City': 'Washington D.C.', 'Number of Five Star Hotels': 32}, {'City': 'Rome', 'Number of Five Star Hotels': 32}] of have minimum values of 32, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "City": "London", + "Number of Five Star Hotels": 75 + }, + { + "City": "Dubai", + "Number of Five Star Hotels": 61 + }, + { + "City": "New York", + "Number of Five Star Hotels": 59 + }, + { + "City": "Paris", + "Number of Five Star Hotels": 56 + }, + { + "City": "Miami", + "Number of Five Star Hotels": 46 + }, + { + "City": "Los Angeles", + "Number of Five Star Hotels": 39 + }, + { + "City": "Bangkok", + "Number of Five Star Hotels": 33 + }, + { + "City": "Shanghai", + "Number of Five Star Hotels": 33 + }, + { + "City": "Washington D.C.", + "Number of Five Star Hotels": 32 + }, + { + "City": "Rome", + "Number of Five Star Hotels": 32 + } + ], + "columns": [ + { + "name": "City", + "importance": "primary", + "description": "Name of the city", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Five Star Hotels", + "importance": "primary", + "description": "The total number of five-star hotels", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "147": { + "metadata": { + "title": "The Places That Rich People Are Leaving", + "description": "Countries/territories with the highest net number of millionaires emigrating in 2022-2023*", + "main_insight": "China and India are projected to have the highest net outflow of millionaires in 2023, significantly higher than other countries." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country/Territory": "China", + "Dollars": 13500, + "Year": "2023*" + } + ], + "score": 0.94, + "annotation": "The 2023* has a maximum value at China", + "reason": "The [{'Country/Territory': 'China', 'Dollars': 13500, 'Year': '2023*'}] of 2023* has a maximum value of 13500, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country/Territory": "China", + "Dollars": 13500, + "Year": "2023*" + } + ], + "score": 0.86, + "annotation": "The 2023* shows a sudden jump in Dollars from India to China.", + "reason": "The Dollars for 2023* increases from 6500 at India to 13500 at China, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country/Territory": "China", + "Dollars": 10800, + "Year": "2022" + } + ], + "score": 0.84, + "annotation": "The 2022 has a maximum value at China", + "reason": "The [{'Country/Territory': 'China', 'Dollars': 10800, 'Year': '2022'}] of 2022 has a maximum value of 10800, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country/Territory": "Mexico", + "Dollars": 700, + "Year": "2023*" + }, + { + "Country/Territory": "Mexico", + "Dollars": 800, + "Year": "2022" + } + ], + "score": 0.84, + "annotation": "The Dollars of 2023* and 2022 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Dollars between 2023* and 2022 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country/Territory": "India", + "Dollars": 7500, + "Year": "2022" + } + ], + "score": 0.83, + "annotation": "The 2022 shows a sudden jump in Dollars from Hong Kong to India.", + "reason": "The Dollars for 2022 increases from 2400 at Hong Kong to 7500 at India, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country/Territory": "China", + "Dollars": 13500, + "Year": "2023*" + }, + { + "Country/Territory": "China", + "Dollars": 10800, + "Year": "2022" + }, + { + "Country/Territory": "India", + "Dollars": 6500, + "Year": "2023*" + }, + { + "Country/Territory": "India", + "Dollars": 7500, + "Year": "2022" + }, + { + "Country/Territory": "United Kingdom", + "Dollars": 3200, + "Year": "2023*" + }, + { + "Country/Territory": "United Kingdom", + "Dollars": 1600, + "Year": "2022" + }, + { + "Country/Territory": "Russia", + "Dollars": 3000, + "Year": "2023*" + }, + { + "Country/Territory": "Russia", + "Dollars": 8500, + "Year": "2022" + }, + { + "Country/Territory": "Brazil", + "Dollars": 1200, + "Year": "2023*" + }, + { + "Country/Territory": "Brazil", + "Dollars": 1800, + "Year": "2022" + }, + { + "Country/Territory": "Hong Kong", + "Dollars": 1000, + "Year": "2023*" + }, + { + "Country/Territory": "Hong Kong", + "Dollars": 2400, + "Year": "2022" + }, + { + "Country/Territory": "South Korea", + "Dollars": 800, + "Year": "2023*" + }, + { + "Country/Territory": "South Korea", + "Dollars": 400, + "Year": "2022" + }, + { + "Country/Territory": "Mexico", + "Dollars": 700, + "Year": "2023*" + }, + { + "Country/Territory": "Mexico", + "Dollars": 800, + "Year": "2022" + } + ], + "columns": [ + { + "name": "Country/Territory", + "importance": "primary", + "description": "Country or territory of origin", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding year", + "role": "y" + }, + { + "name": "Year", + "importance": "primary", + "description": "The year of the data point", + "role": "group" + } + ] + } + }, + "2512": { + "metadata": { + "title": "eBook Pricing Around the World", + "description": "Average price of the respective country's Top 10 bestsellers on Amazon", + "main_insight": "eBook prices are significantly lower than hardcover prices, with the largest discount in China. Germany has the highest average prices for both hardcover and eBooks." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Bestseller List": "Open Book Bestsellers", + "Units": 3.99, + "Price Metrics": "Hardcover Price" + }, + { + "Bestseller List": "Open Book Bestsellers", + "Units": 1.02, + "Price Metrics": "eBook Price" + } + ], + "score": 1.0, + "annotation": "The Units of Hardcover Price and eBook Price show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Hardcover Price and eBook Price is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Bestseller List": "NY Times Bestsellers", + "Units": -22, + "Price Metrics": "eBook Discount" + } + ], + "score": 0.97, + "annotation": "The eBook Discount shows a sudden jump in Units from Open Book Bestsellers to NY Times Bestsellers.", + "reason": "The Units for eBook Discount increases from -74 at Open Book Bestsellers to -22 at NY Times Bestsellers, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Bestseller List": "Open Book Bestsellers", + "Units": 1.02, + "Price Metrics": "eBook Price" + }, + { + "Bestseller List": "Open Book Bestsellers", + "Units": -74, + "Price Metrics": "eBook Discount" + } + ], + "score": 0.94, + "annotation": "The Units of eBook Price and eBook Discount show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between eBook Price and eBook Discount is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Bestseller List": "Open Book Bestsellers", + "Units": 3.99, + "Price Metrics": "Hardcover Price" + }, + { + "Bestseller List": "Open Book Bestsellers", + "Units": -74, + "Price Metrics": "eBook Discount" + } + ], + "score": 0.93, + "annotation": "The Units of Hardcover Price and eBook Discount show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Hardcover Price and eBook Discount is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Bestseller List": "NY Times Bestsellers", + "Units": 12.44, + "Price Metrics": "eBook Price" + } + ], + "score": 0.88, + "annotation": "The eBook Price shows a sudden jump in Units from Open Book Bestsellers to NY Times Bestsellers.", + "reason": "The Units for eBook Price increases from 1.02 at Open Book Bestsellers to 12.44 at NY Times Bestsellers, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Bestseller List": "NY Times Bestsellers", + "Units": 16.02, + "Price Metrics": "Hardcover Price" + }, + { + "Bestseller List": "NY Times Bestsellers", + "Units": 12.44, + "Price Metrics": "eBook Price" + }, + { + "Bestseller List": "NY Times Bestsellers", + "Units": -22, + "Price Metrics": "eBook Discount" + }, + { + "Bestseller List": "Spiegel Bestsellers", + "Units": 24.29, + "Price Metrics": "Hardcover Price" + }, + { + "Bestseller List": "Spiegel Bestsellers", + "Units": 19.79, + "Price Metrics": "eBook Price" + }, + { + "Bestseller List": "Spiegel Bestsellers", + "Units": -19, + "Price Metrics": "eBook Discount" + }, + { + "Bestseller List": "Open Book Bestsellers", + "Units": 3.99, + "Price Metrics": "Hardcover Price" + }, + { + "Bestseller List": "Open Book Bestsellers", + "Units": 1.02, + "Price Metrics": "eBook Price" + }, + { + "Bestseller List": "Open Book Bestsellers", + "Units": -74, + "Price Metrics": "eBook Discount" + } + ], + "columns": [ + { + "name": "Bestseller List", + "importance": "primary", + "description": "Bestseller list by country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding price metrics", + "role": "y" + }, + { + "name": "Price Metrics", + "importance": "primary", + "description": "The price metrics of the data point", + "role": "group" + } + ] + } + }, + "881": { + "metadata": { + "title": "The Economics Behind the iPhone SE", + "description": "Estimated material / manufacturing costs and retail prices of recent iPhone models", + "main_insight": "The iPhone SE has significantly lower material and manufacturing costs, and a lower retail price, compared to the iPhone 6S Plus." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Category": "Retail Price", + "Units": 399.0, + "Phone Model": "iPhone SE" + }, + { + "Category": "Retail Price", + "Units": 749.0, + "Phone Model": "iPhone 6S Plus" + } + ], + "score": 1.0, + "annotation": "The Units of iPhone SE and iPhone 6S Plus show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between iPhone SE and iPhone 6S Plus is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "Retail Price", + "Units": 399.0, + "Phone Model": "iPhone SE" + } + ], + "score": 0.97, + "annotation": "The iPhone SE shows a sudden jump in Units from Material and Manufacturing Costs to Retail Price.", + "reason": "The Units for iPhone SE increases from 160.0 at Material and Manufacturing Costs to 399.0 at Retail Price, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "Retail Price", + "Units": 749.0, + "Phone Model": "iPhone 6S Plus" + } + ], + "score": 0.97, + "annotation": "The iPhone 6S Plus shows a sudden jump in Units from Material and Manufacturing Costs to Retail Price.", + "reason": "The Units for iPhone 6S Plus increases from 236.0 at Material and Manufacturing Costs to 749.0 at Retail Price, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Category": "Material and Manufacturing Costs", + "Units": 236.0, + "Phone Model": "iPhone 6S Plus" + } + ], + "score": 0.77, + "annotation": "The iPhone 6S Plus accounts for the majority in Material and Manufacturing Costs.", + "reason": "The Units of iPhone 6S Plus in Material and Manufacturing Costs accounts for 0.5959595959595959 (more than 50%) of the total of Units of all Phone Model." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Phone Model": "iPhone 6S Plus", + "Category": "total", + "Units": 985.0 + } + ], + "score": 0.76, + "annotation": "The total value of iPhone 6S Plus accounts for the majority in all Phone Model.", + "reason": "The total value of Units of iPhone 6S Plus accounts for 0.6379533678756477 (more than 50%) of the total of Units of all Phone Model." + } + ], + "data": { + "data": [ + { + "Category": "Material and Manufacturing Costs", + "Units": 160.0, + "Phone Model": "iPhone SE" + }, + { + "Category": "Material and Manufacturing Costs", + "Units": 236.0, + "Phone Model": "iPhone 6S Plus" + }, + { + "Category": "Retail Price", + "Units": 399.0, + "Phone Model": "iPhone SE" + }, + { + "Category": "Retail Price", + "Units": 749.0, + "Phone Model": "iPhone 6S Plus" + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Cost Category", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding phone model", + "role": "y" + }, + { + "name": "Phone Model", + "importance": "primary", + "description": "The phone model of the data point", + "role": "group" + } + ] + } + }, + "1219": { + "metadata": { + "title": "Adani Stock Still Ailing Three Weeks After Hindenburg Report", + "description": "Price of Adani Enterprises shares on the Indian National Stock Exchange in 2023 (in Indian rupees)", + "main_insight": "Adani Enterprises' stock price significantly decreased after the Hindenburg Research report was published and, despite some fluctuations, remained considerably lower than its pre-report levels." + }, + "chart_type": "Spline Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "3 Feb", + "Stock Price": 1550.0 + } + ], + "score": 0.8, + "annotation": "The has a minimum value at 3 Feb", + "reason": "The [{'Date': '3 Feb', 'Stock Price': 1550.0}] of has a mainimum value of 1550.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "19 Jan", + "Stock Price": 3620.0 + } + ], + "score": 0.72, + "annotation": "The has a maximum value at 19 Jan", + "reason": "The [{'Date': '19 Jan', 'Stock Price': 3620.0}] of has a maximum value of 3620.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "27 Jan", + "Stock Price": 2750.0 + } + ], + "score": 0.66, + "annotation": "The shows a sudden jump in Stock Price from 8 Feb to 27 Jan.", + "reason": "The Stock Price for increases from 2200.0 at 8 Feb to 2750.0 at 27 Jan, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Date": "16 Jan", + "Stock Price": 3600.0 + }, + { + "Date": "17 Jan", + "Stock Price": 3550.0 + }, + { + "Date": "18 Jan", + "Stock Price": 3570.0 + }, + { + "Date": "19 Jan", + "Stock Price": 3620.0 + }, + { + "Date": "20 Jan", + "Stock Price": 3580.0 + }, + { + "Date": "23 Jan", + "Stock Price": 3590.0 + }, + { + "Date": "24 Jan", + "Stock Price": 3400.0 + }, + { + "Date": "25 Jan", + "Stock Price": 3000.0 + }, + { + "Date": "27 Jan", + "Stock Price": 2750.0 + }, + { + "Date": "30 Jan", + "Stock Price": 2800.0 + }, + { + "Date": "31 Jan", + "Stock Price": 2950.0 + }, + { + "Date": "1 Feb", + "Stock Price": 2150.0 + }, + { + "Date": "2 Feb", + "Stock Price": 2100.0 + }, + { + "Date": "3 Feb", + "Stock Price": 1550.0 + }, + { + "Date": "6 Feb", + "Stock Price": 1700.0 + }, + { + "Date": "7 Feb", + "Stock Price": 2000.0 + }, + { + "Date": "8 Feb", + "Stock Price": 2200.0 + }, + { + "Date": "9 Feb", + "Stock Price": 2100.0 + }, + { + "Date": "10 Feb", + "Stock Price": 1900.0 + }, + { + "Date": "13 Feb", + "Stock Price": 1950.0 + }, + { + "Date": "14 Feb", + "Stock Price": 2000.0 + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Date in January and February 2023", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Stock Price", + "importance": "primary", + "description": "Price of Adani Enterprises shares in Indian rupees", + "unit": "Indian rupees", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1820": { + "metadata": { + "title": "Russia's Friends and Foes", + "description": "Countries identified by the highest shares of Russians to be their nation's friends or enemies", + "main_insight": "The chart shows which countries Russians consider to be friends and enemies. The US, GB, DE, PL and UA are viewed as the top enemies, while BY, CN, IN, KZ and AM are viewed as the top friends." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "IN", + "Score": 18.0, + "Perception": "Change Friend" + } + ], + "score": 0.96, + "annotation": "The Change Friend shows a sudden jump in Score from KZ to IN.", + "reason": "The Score for Change Friend increases from -8.0 at KZ to 18.0 at IN, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "UA", + "Score": 26.0, + "Perception": "Perceived as Enemy" + }, + { + "Country": "AM", + "Score": 16.0, + "Perception": "Perceived as Friend" + } + ], + "score": 0.93, + "annotation": "The Score of Perceived as Enemy and Perceived as Friend show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Perceived as Enemy and Perceived as Friend is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "UA", + "Score": -14.0, + "Perception": "Change Enemy" + } + ], + "score": 0.82, + "annotation": "The Change Enemy has a minimum value at UA", + "reason": "The [{'Country': 'UA', 'Score': -14.0, 'Perception': 'Change Enemy'}] of Change Enemy has a mainimum value of -14.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "US", + "Score": 8.0, + "Perception": "Change Enemy" + } + ], + "score": 0.82, + "annotation": "The Change Enemy shows a sudden jump in Score from UA to US.", + "reason": "The Score for Change Enemy increases from -14.0 at UA to 8.0 at US, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "US", + "Score": 72.0, + "Perception": "Perceived as Enemy" + } + ], + "score": 0.81, + "annotation": "The Perceived as Enemy shows a sudden jump in Score from GB to US.", + "reason": "The Score for Perceived as Enemy increases from 51.0 at GB to 72.0 at US, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "US", + "Score": 72.0, + "Perception": "Perceived as Enemy" + }, + { + "Country": "US", + "Score": 8.0, + "Perception": "Change Enemy" + }, + { + "Country": "GB", + "Score": 51.0, + "Perception": "Perceived as Enemy" + }, + { + "Country": "GB", + "Score": 23.0, + "Perception": "Change Enemy" + }, + { + "Country": "DE", + "Score": 48.0, + "Perception": "Perceived as Enemy" + }, + { + "Country": "DE", + "Score": 32.0, + "Perception": "Change Enemy" + }, + { + "Country": "PL", + "Score": 42.0, + "Perception": "Perceived as Enemy" + }, + { + "Country": "PL", + "Score": 21.0, + "Perception": "Change Enemy" + }, + { + "Country": "UA", + "Score": 26.0, + "Perception": "Perceived as Enemy" + }, + { + "Country": "UA", + "Score": -14.0, + "Perception": "Change Enemy" + }, + { + "Country": "BY", + "Score": 77.0, + "Perception": "Perceived as Friend" + }, + { + "Country": "BY", + "Score": 19.0, + "Perception": "Change Friend" + }, + { + "Country": "CN", + "Score": 58.0, + "Perception": "Perceived as Friend" + }, + { + "Country": "CN", + "Score": 20.0, + "Perception": "Change Friend" + }, + { + "Country": "IN", + "Score": 31.0, + "Perception": "Perceived as Friend" + }, + { + "Country": "IN", + "Score": 18.0, + "Perception": "Change Friend" + }, + { + "Country": "KZ", + "Score": 26.0, + "Perception": "Perceived as Friend" + }, + { + "Country": "KZ", + "Score": -8.0, + "Perception": "Change Friend" + }, + { + "Country": "AM", + "Score": 16.0, + "Perception": "Perceived as Friend" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding perception", + "role": "y" + }, + { + "name": "Perception", + "importance": "primary", + "description": "The perception of the data point", + "role": "group" + } + ] + } + }, + "185": { + "metadata": { + "title": "The War On Terror Has Cost Taxpayers Nearly $1.7 Trillion", + "description": "Estimated war funding by agency from 2001 to 2015 (in billion U.S. dollars)", + "main_insight": "The War on Terror has cost nearly $1.7 trillion between 2001 and 2015, with the vast majority of funding going to the Department of Defense (DOD). Funding peaked in FY08 and has generally decreased since." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Agency": "DOD", + "Fiscal Year": "total", + "Points": 1610.0 + } + ], + "score": 0.94, + "annotation": "The total value of DOD has the smallest proportion.", + "reason": "The total value of Points of DOD accounts for 0.011890606420927468, smaller than all other Agency, of the total of Points of all Agency." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Fiscal Year": "FY12", + "Points": 4.0, + "Agency": "VA Medical" + } + ], + "score": 0.91, + "annotation": "The VA Medical shows a sudden jump in Points from FY11 to FY12.", + "reason": "The Points for VA Medical increases from 2.0 at FY11 to 4.0 at FY12, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Fiscal Year": "FY01-02", + "Points": 36.0, + "Agency": "DOD" + } + ], + "score": 0.88, + "annotation": "The DOD has a minimum value at FY01-02", + "reason": "The [{'Fiscal Year': 'FY01-02', 'Points': 36.0, 'Agency': 'DOD'}] of DOD has a mainimum value of 36.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Fiscal Year": "FY13", + "Points": 4.0, + "Agency": "VA Medical" + } + ], + "score": 0.84, + "annotation": "The VA Medical has the smallest proportion in FY04.", + "reason": "The Points of VA Medical in FY04 accounts for 0.037037037037037035, smaller than all other Agency, of the total of Points of all Agency." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Fiscal Year": "FY11", + "Points": 2.0, + "Agency": "VA Medical" + } + ], + "score": 0.84, + "annotation": "The VA Medical has a minimum value at FY11", + "reason": "The [{'Fiscal Year': 'FY11', 'Points': 2.0, 'Agency': 'VA Medical'}] of VA Medical has a mainimum value of 2.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Fiscal Year": "FY01-02", + "Points": 36.0, + "Agency": "DOD" + }, + { + "Fiscal Year": "FY03", + "Points": 75.0, + "Agency": "DOD" + }, + { + "Fiscal Year": "FY04", + "Points": 96.0, + "Agency": "DOD" + }, + { + "Fiscal Year": "FY05", + "Points": 109.0, + "Agency": "DOD" + }, + { + "Fiscal Year": "FY06", + "Points": 121.0, + "Agency": "DOD" + }, + { + "Fiscal Year": "FY06", + "Points": 3.0, + "Agency": "State/USAID" + }, + { + "Fiscal Year": "FY07", + "Points": 166.0, + "Agency": "DOD" + }, + { + "Fiscal Year": "FY07", + "Points": 4.0, + "Agency": "State/USAID" + }, + { + "Fiscal Year": "FY08", + "Points": 187.0, + "Agency": "DOD" + }, + { + "Fiscal Year": "FY08", + "Points": 8.0, + "Agency": "State/USAID" + }, + { + "Fiscal Year": "FY09", + "Points": 151.0, + "Agency": "DOD" + }, + { + "Fiscal Year": "FY09", + "Points": 5.0, + "Agency": "State/USAID" + }, + { + "Fiscal Year": "FY10", + "Points": 161.0, + "Agency": "DOD" + }, + { + "Fiscal Year": "FY10", + "Points": 5.0, + "Agency": "State/USAID" + }, + { + "Fiscal Year": "FY11", + "Points": 153.0, + "Agency": "DOD" + }, + { + "Fiscal Year": "FY11", + "Points": 3.0, + "Agency": "State/USAID" + }, + { + "Fiscal Year": "FY11", + "Points": 2.0, + "Agency": "VA Medical" + }, + { + "Fiscal Year": "FY12", + "Points": 122.0, + "Agency": "DOD" + }, + { + "Fiscal Year": "FY12", + "Points": 3.0, + "Agency": "State/USAID" + }, + { + "Fiscal Year": "FY12", + "Points": 4.0, + "Agency": "VA Medical" + }, + { + "Fiscal Year": "FY13", + "Points": 87.0, + "Agency": "DOD" + }, + { + "Fiscal Year": "FY13", + "Points": 8.0, + "Agency": "State/USAID" + }, + { + "Fiscal Year": "FY13", + "Points": 4.0, + "Agency": "VA Medical" + }, + { + "Fiscal Year": "FY14", + "Points": 84.0, + "Agency": "DOD" + }, + { + "Fiscal Year": "FY14", + "Points": 6.0, + "Agency": "State/USAID" + }, + { + "Fiscal Year": "FY14", + "Points": 5.0, + "Agency": "VA Medical" + }, + { + "Fiscal Year": "FY15", + "Points": 62.0, + "Agency": "DOD" + }, + { + "Fiscal Year": "FY15", + "Points": 7.0, + "Agency": "State/USAID" + }, + { + "Fiscal Year": "FY15", + "Points": 5.0, + "Agency": "VA Medical" + } + ], + "columns": [ + { + "name": "Fiscal Year", + "importance": "primary", + "description": "Fiscal Year, ranging from 2001-2002 to 2015", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Points", + "importance": "primary", + "description": "Points for the corresponding agency", + "role": "y" + }, + { + "name": "Agency", + "importance": "primary", + "description": "The agency of the data point", + "role": "group" + } + ] + } + }, + "772": { + "metadata": { + "title": "Facebook Handily Beats Competition in Terms of Engagement", + "description": "Breakdown of social network users in the United States by frequency of use", + "main_insight": "Facebook has significantly higher daily user engagement compared to other major social networks in the US." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Social Network": "LinkedIn", + "Units": 22.0, + "Frequency": "Daily" + }, + { + "Social Network": "LinkedIn", + "Units": 46.0, + "Frequency": "Less often" + } + ], + "score": 0.98, + "annotation": "The Units of Daily and Less often show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Daily and Less often is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Social Network": "Twitter", + "Units": 40.0, + "Frequency": "Less often" + } + ], + "score": 0.88, + "annotation": "The Less often has the largest proportion in Twitter.", + "reason": "The Units of Less often in Twitter accounts for 0.40404040404040403, larger than all other Frequency, of the total of Units of all Frequency." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Frequency": "Daily", + "Social Network": "total", + "Units": 216.0 + } + ], + "score": 0.87, + "annotation": "The total value of Daily has the largest proportion.", + "reason": "The total value of Units of Daily accounts for 0.43636363636363634, larger than all other Frequency, of the total of Units of all Frequency." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Social Network": "Pinterest", + "Units": 44.0, + "Frequency": "Less often" + } + ], + "score": 0.87, + "annotation": "The Less often has the largest proportion in Pinterest.", + "reason": "The Units of Less often in Pinterest accounts for 0.4444444444444444, larger than all other Frequency, of the total of Units of all Frequency." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Social Network": "LinkedIn", + "Units": 46.0, + "Frequency": "Less often" + } + ], + "score": 0.86, + "annotation": "The Less often has the largest proportion in LinkedIn.", + "reason": "The Units of Less often in LinkedIn accounts for 0.46938775510204084, larger than all other Frequency, of the total of Units of all Frequency." + } + ], + "data": { + "data": [ + { + "Social Network": "Facebook", + "Units": 70.0, + "Frequency": "Daily" + }, + { + "Social Network": "Facebook", + "Units": 21.0, + "Frequency": "Weekly" + }, + { + "Social Network": "Facebook", + "Units": 9.0, + "Frequency": "Less often" + }, + { + "Social Network": "Instagram", + "Units": 59.0, + "Frequency": "Daily" + }, + { + "Social Network": "Instagram", + "Units": 17.0, + "Frequency": "Weekly" + }, + { + "Social Network": "Instagram", + "Units": 23.0, + "Frequency": "Less often" + }, + { + "Social Network": "Twitter", + "Units": 38.0, + "Frequency": "Daily" + }, + { + "Social Network": "Twitter", + "Units": 21.0, + "Frequency": "Weekly" + }, + { + "Social Network": "Twitter", + "Units": 40.0, + "Frequency": "Less often" + }, + { + "Social Network": "Pinterest", + "Units": 27.0, + "Frequency": "Daily" + }, + { + "Social Network": "Pinterest", + "Units": 28.0, + "Frequency": "Weekly" + }, + { + "Social Network": "Pinterest", + "Units": 44.0, + "Frequency": "Less often" + }, + { + "Social Network": "LinkedIn", + "Units": 22.0, + "Frequency": "Daily" + }, + { + "Social Network": "LinkedIn", + "Units": 30.0, + "Frequency": "Weekly" + }, + { + "Social Network": "LinkedIn", + "Units": 46.0, + "Frequency": "Less often" + } + ], + "columns": [ + { + "name": "Social Network", + "importance": "primary", + "description": "Name of the social media platform", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding frequency", + "role": "y" + }, + { + "name": "Frequency", + "importance": "primary", + "description": "The frequency of the data point", + "role": "group" + } + ] + } + }, + "516": { + "metadata": { + "title": "American Steel Imports", + "description": "Volume and value of U.S. steel imports from selected countries in 2017", + "main_insight": "Canada was the largest source of U.S. steel imports in 2017 by volume, and second by value after the European Union. South Korea had by far the highest value per million metric tons." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Mexico", + "Units": 2.5, + "Unit": "Billion U.S. dollars" + } + ], + "score": 0.88, + "annotation": "The Billion U.S. dollars has the largest proportion in Mexico.", + "reason": "The Units of Billion U.S. dollars in Mexico accounts for 0.4230118443316413, larger than all other Unit, of the total of Units of all Unit." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "South Korea", + "Units": 2.8, + "Unit": "Billion U.S. dollars" + } + ], + "score": 0.87, + "annotation": "The Billion U.S. dollars has the largest proportion in South Korea.", + "reason": "The Units of Billion U.S. dollars in South Korea accounts for 0.4508856682769727, larger than all other Unit, of the total of Units of all Unit." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "South Korea", + "Units": 2.1, + "Unit": "Billion dollars per million tons" + } + ], + "score": 0.86, + "annotation": "The Billion dollars per million tons has a maximum value at South Korea", + "reason": "The [{'Country': 'South Korea', 'Units': 2.1, 'Unit': 'Billion dollars per million tons'}] of Billion dollars per million tons has a maximum value of 2.1, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "European Union", + "Units": 6.2, + "Unit": "Billion U.S. dollars" + } + ], + "score": 0.86, + "annotation": "The Billion U.S. dollars has the largest proportion in European Union.", + "reason": "The Units of Billion U.S. dollars in European Union accounts for 0.4979919678714859, larger than all other Unit, of the total of Units of all Unit." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Unit": "Billion U.S. dollars", + "Country": "total", + "Units": 26.35 + } + ], + "score": 0.85, + "annotation": "The total value of Billion U.S. dollars accounts for the majority in all Unit.", + "reason": "The total value of Units of Billion U.S. dollars accounts for 0.5109559821601707 (more than 50%) of the total of Units of all Unit." + } + ], + "data": { + "data": [ + { + "Country": "Canada", + "Units": 6.85, + "Unit": "Million metric tons" + }, + { + "Country": "Canada", + "Units": 5.1, + "Unit": "Billion U.S. dollars" + }, + { + "Country": "Canada", + "Units": 0.7, + "Unit": "Billion dollars per million tons" + }, + { + "Country": "Russia", + "Units": 1.4, + "Unit": "Million metric tons" + }, + { + "Country": "Russia", + "Units": 5.16, + "Unit": "Billion U.S. dollars" + }, + { + "Country": "Russia", + "Units": 0.3, + "Unit": "Billion dollars per million tons" + }, + { + "Country": "European Union", + "Units": 4.95, + "Unit": "Million metric tons" + }, + { + "Country": "European Union", + "Units": 6.2, + "Unit": "Billion U.S. dollars" + }, + { + "Country": "European Union", + "Units": 1.3, + "Unit": "Billion dollars per million tons" + }, + { + "Country": "Brazil", + "Units": 2.4, + "Unit": "Million metric tons" + }, + { + "Country": "Brazil", + "Units": 4.59, + "Unit": "Billion U.S. dollars" + }, + { + "Country": "Brazil", + "Units": 0.5, + "Unit": "Billion dollars per million tons" + }, + { + "Country": "Mexico", + "Units": 2.41, + "Unit": "Million metric tons" + }, + { + "Country": "Mexico", + "Units": 2.5, + "Unit": "Billion U.S. dollars" + }, + { + "Country": "Mexico", + "Units": 1.0, + "Unit": "Billion dollars per million tons" + }, + { + "Country": "South Korea", + "Units": 1.31, + "Unit": "Million metric tons" + }, + { + "Country": "South Korea", + "Units": 2.8, + "Unit": "Billion U.S. dollars" + }, + { + "Country": "South Korea", + "Units": 2.1, + "Unit": "Billion dollars per million tons" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Source country of U.S. steel imports", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding unit", + "role": "y" + }, + { + "name": "Unit", + "importance": "primary", + "description": "The unit of the data point", + "role": "group" + } + ] + } + }, + "1399": { + "metadata": { + "title": "Ireland: cases taken by TSB against customers", + "description": "Number of cases taken by Permanent TSB in the first six months of the year", + "main_insight": "The number of cases taken by Permanent TSB against customers increased dramatically in 2018 compared to the previous three years." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2015, + "Cases": 15 + }, + { + "Year": 2016, + "Cases": 3 + }, + { + "Year": 2017, + "Cases": 15 + }, + { + "Year": 2018, + "Cases": 102 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "The first six months of the year", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Cases", + "importance": "primary", + "description": "Number of cases taken by Permanent TSB", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1831": { + "metadata": { + "title": "NFL Still MVP in the Sponsorship Game", + "description": "Total sponsorship revenue of major U.S. sports leagues in 2022/23", + "main_insight": "The NFL had the highest sponsorship revenue among major U.S. sports leagues in the 2022/23 season, significantly exceeding the revenue of other leagues like the NBA, MLB, NHL, and MLS." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "League": "NBA", + "Sponsorship Revenue": 1.64 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Sponsorship Revenue from MLB to NBA.", + "reason": "The Sponsorship Revenue for increases from 1.19 at MLB to 1.64 at NBA, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "League": "NFL", + "Sponsorship Revenue": 1.88 + } + ], + "score": 0.7, + "annotation": "The has a maximum value at NFL", + "reason": "The [{'League': 'NFL', 'Sponsorship Revenue': 1.88}] of has a maximum value of 1.88, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "League": "MLS", + "Sponsorship Revenue": 0.68 + } + ], + "score": 0.61, + "annotation": "The has a minimum value at MLS", + "reason": "The [{'League': 'MLS', 'Sponsorship Revenue': 0.68}] of has a mainimum value of 0.68, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "League": "NFL", + "Sponsorship Revenue": 1.88 + }, + { + "League": "NBA", + "Sponsorship Revenue": 1.64 + }, + { + "League": "MLB", + "Sponsorship Revenue": 1.19 + }, + { + "League": "NHL", + "Sponsorship Revenue": 0.75 + }, + { + "League": "MLS", + "Sponsorship Revenue": 0.68 + } + ], + "columns": [ + { + "name": "League", + "importance": "primary", + "description": "Major U.S. sports leagues", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Sponsorship Revenue", + "importance": "primary", + "description": "Total sponsorship revenue in billions of U.S. dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1161": { + "metadata": { + "title": "How Often Do U.S. Workers Experience Abuse & Harassment?", + "description": "Share of U.S. workers who have been subjected to the following (2015)", + "main_insight": "The chart shows the percentage of US workers, separated by gender, who experienced different kinds of abuse and harassment in 2015. Verbal abuse and threats were most common for both, While unwanted sexual attention had the biggest disparity between genders" + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Type of Abuse/Harassment": "Physical violence (past year)", + "Units": 2.0, + "Gender": "Men" + }, + { + "Type of Abuse/Harassment": "Physical violence (past year)", + "Units": 1.1, + "Gender": "Women" + } + ], + "score": 0.9, + "annotation": "The Units of Men and Women show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Men and Women is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Type of Abuse/Harassment": "Bullying/harassment including sexual (past year)", + "Units": 9.6, + "Gender": "Men" + } + ], + "score": 0.89, + "annotation": "The Men shows a sudden jump in Units from Physical violence (past year) to Bullying/harassment including sexual (past year).", + "reason": "The Units for Men increases from 2.0 at Physical violence (past year) to 9.6 at Bullying/harassment including sexual (past year), indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Type of Abuse/Harassment": "Verbal abuse or threats (past month)", + "Units": 13.1, + "Gender": "Men" + } + ], + "score": 0.8, + "annotation": "The Men accounts for the majority in Verbal abuse or threats (past month).", + "reason": "The Units of Men in Verbal abuse or threats (past month) accounts for 0.5137254901960784 (more than 50%) of the total of Units of all Gender." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Gender": "Women", + "Type of Abuse/Harassment": "total", + "Units": 37.3 + } + ], + "score": 0.8, + "annotation": "The total value of Women accounts for the majority in all Gender.", + "reason": "The total value of Units of Women accounts for 0.5137741046831956 (more than 50%) of the total of Units of all Gender." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Type of Abuse/Harassment": "Bullying/harassment including sexual (past year)", + "Units": 11.0, + "Gender": "Women" + } + ], + "score": 0.79, + "annotation": "The Women accounts for the majority in Bullying/harassment including sexual (past year).", + "reason": "The Units of Women in Bullying/harassment including sexual (past year) accounts for 0.5339805825242718 (more than 50%) of the total of Units of all Gender." + } + ], + "data": { + "data": [ + { + "Type of Abuse/Harassment": "Verbal abuse or threats (past month)", + "Units": 13.1, + "Gender": "Men" + }, + { + "Type of Abuse/Harassment": "Verbal abuse or threats (past month)", + "Units": 12.4, + "Gender": "Women" + }, + { + "Type of Abuse/Harassment": "Bullying/harassment including sexual (past year)", + "Units": 9.6, + "Gender": "Men" + }, + { + "Type of Abuse/Harassment": "Bullying/harassment including sexual (past year)", + "Units": 11.0, + "Gender": "Women" + }, + { + "Type of Abuse/Harassment": "Humiliating behavior (past month)", + "Units": 9.8, + "Gender": "Men" + }, + { + "Type of Abuse/Harassment": "Humiliating behavior (past month)", + "Units": 7.9, + "Gender": "Women" + }, + { + "Type of Abuse/Harassment": "Unwanted sexual attention (past month)", + "Units": 0.8, + "Gender": "Men" + }, + { + "Type of Abuse/Harassment": "Unwanted sexual attention (past month)", + "Units": 4.9, + "Gender": "Women" + }, + { + "Type of Abuse/Harassment": "Physical violence (past year)", + "Units": 2.0, + "Gender": "Men" + }, + { + "Type of Abuse/Harassment": "Physical violence (past year)", + "Units": 1.1, + "Gender": "Women" + } + ], + "columns": [ + { + "name": "Type of Abuse/Harassment", + "importance": "primary", + "description": "Different types of abuse and harassment experienced", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding gender", + "role": "y" + }, + { + "name": "Gender", + "importance": "primary", + "description": "The gender of the data point", + "role": "group" + } + ] + } + }, + "678": { + "metadata": { + "title": "Where U.S. Home Prices Are Rising the Fastest (and Slowest)", + "description": "Percentage change in house prices in the U.S. between Q1 2018 and Q1 2019", + "main_insight": "Home prices in Idaho rose the fastest between Q1 2018 and Q1 2019, with a 13.4% increase, significantly higher than the national average of 5.1%. Maryland saw the slowest growth at 0.5%." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "State": "Idaho", + "Price Change": 13.4 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at Idaho", + "reason": "The [{'State': 'Idaho', 'Price Change': 13.4}] of has a maximum value of 13.4, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "State": "Maryland", + "Price Change": 0.5 + } + ], + "score": 0.65, + "annotation": "The has a minimum value at Maryland", + "reason": "The [{'State': 'Maryland', 'Price Change': 0.5}] of has a mainimum value of 0.5, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "State": "U.S.", + "Price Change": 5.1 + } + ], + "score": 0.63, + "annotation": "The shows a sudden jump in Price Change from Alaska to U.S..", + "reason": "The Price Change for increases from 2.1 at Alaska to 5.1 at U.S., indicating a sharp change." + } + ], + "data": { + "data": [ + { + "State": "Idaho", + "Price Change": 13.4 + }, + { + "State": "Nevada", + "Price Change": 10.6 + }, + { + "State": "Utah", + "Price Change": 8.9 + }, + { + "State": "Tennessee", + "Price Change": 7.7 + }, + { + "State": "Georgia", + "Price Change": 7.5 + }, + { + "State": "U.S.", + "Price Change": 5.1 + }, + { + "State": "Wyoming", + "Price Change": 2.1 + }, + { + "State": "Alaska", + "Price Change": 2.1 + }, + { + "State": "Louisiana", + "Price Change": 1.0 + }, + { + "State": "Delaware", + "Price Change": 0.7 + }, + { + "State": "Maryland", + "Price Change": 0.5 + } + ], + "columns": [ + { + "name": "State", + "importance": "primary", + "description": "US State or National Average", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Price Change", + "importance": "primary", + "description": "Percentage change in house prices between Q1 2018 and Q1 2019", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1522": { + "metadata": { + "title": "One European Country Is Not Excited About Christmas", + "description": "\"I am looking forward to Christmas this year\" - Agree/Disagree?", + "main_insight": "France shows significantly lower enthusiasm for Christmas compared to other European countries surveyed, with only 57% agreeing they are looking forward to it." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "France", + "Units": 57.0, + "Agreement": "Agree" + }, + { + "Country": "France", + "Units": 37.0, + "Agreement": "Disagree" + } + ], + "score": 0.99, + "annotation": "The Units of Agree and Disagree show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Agree and Disagree is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "France", + "Units": 57.0, + "Agreement": "Agree" + } + ], + "score": 0.91, + "annotation": "The Agree has a minimum value at France", + "reason": "The [{'Country': 'France', 'Units': 57.0, 'Agreement': 'Agree'}] of Agree has a mainimum value of 57.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "France", + "Units": 37.0, + "Agreement": "Disagree" + } + ], + "score": 0.9, + "annotation": "The Disagree has a maximum value at France", + "reason": "The [{'Country': 'France', 'Units': 37.0, 'Agreement': 'Disagree'}] of Disagree has a maximum value of 37.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Finland", + "Units": 67.0, + "Agreement": "Agree" + } + ], + "score": 0.85, + "annotation": "The Agree shows a sudden jump in Units from France to Finland.", + "reason": "The Units for Agree increases from 57.0 at France to 67.0 at Finland, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "France", + "Units": 37.0, + "Agreement": "Disagree" + } + ], + "score": 0.8, + "annotation": "The Disagree shows a sudden jump in Units from Finland to France.", + "reason": "The Units for Disagree increases from 29.0 at Finland to 37.0 at France, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Denmark", + "Units": 76.0, + "Agreement": "Agree" + }, + { + "Country": "Denmark", + "Units": 19.0, + "Agreement": "Disagree" + }, + { + "Country": "Norway", + "Units": 74.0, + "Agreement": "Agree" + }, + { + "Country": "Norway", + "Units": 21.0, + "Agreement": "Disagree" + }, + { + "Country": "Germany", + "Units": 71.0, + "Agreement": "Agree" + }, + { + "Country": "Germany", + "Units": 25.0, + "Agreement": "Disagree" + }, + { + "Country": "Sweden", + "Units": 71.0, + "Agreement": "Agree" + }, + { + "Country": "Sweden", + "Units": 25.0, + "Agreement": "Disagree" + }, + { + "Country": "United Kingdom", + "Units": 69.0, + "Agreement": "Agree" + }, + { + "Country": "United Kingdom", + "Units": 25.0, + "Agreement": "Disagree" + }, + { + "Country": "Finland", + "Units": 67.0, + "Agreement": "Agree" + }, + { + "Country": "Finland", + "Units": 29.0, + "Agreement": "Disagree" + }, + { + "Country": "France", + "Units": 57.0, + "Agreement": "Agree" + }, + { + "Country": "France", + "Units": 37.0, + "Agreement": "Disagree" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "European country", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding agreement", + "role": "y" + }, + { + "name": "Agreement", + "importance": "primary", + "description": "The agreement of the data point", + "role": "group" + } + ] + } + }, + "1719": { + "metadata": { + "title": "The Grass Is Looking Greener", + "description": "Projected legal and illegal sales of cannabis in the U.S. in 2016 and 2026", + "main_insight": "Legal cannabis sales in the U.S. are projected to increase significantly between 2016 and 2026, while illegal sales are expected to decrease dramatically." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2016, + "Units": 6.0, + "Sales Type": "Legal sales" + }, + { + "Year": 2016, + "Units": 25.0, + "Sales Type": "Illegal sales" + }, + { + "Year": 2026, + "Units": 50.0, + "Sales Type": "Legal sales" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of sales data", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding sales type", + "role": "y" + }, + { + "name": "Sales Type", + "importance": "primary", + "description": "The sales type of the data point", + "role": "group" + } + ] + } + }, + "1738": { + "metadata": { + "title": "Travel Funds in International Comparison", + "description": "Average budget for summer holidays by country in 2018*", + "main_insight": "Switzerland, Austria, and the U.S. have the highest average summer holiday budgets, significantly exceeding countries like Brazil and Spain." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Brazil", + "Average Budget (€)": 1238.0 + } + ], + "score": 0.92, + "annotation": "The has a minimum value at Brazil", + "reason": "The [{'Country': 'Brazil', 'Average Budget (€)': 1238.0}] of has a mainimum value of 1238.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Switzerland", + "Average Budget (€)": 2710.0 + } + ], + "score": 0.72, + "annotation": "The has a maximum value at Switzerland", + "reason": "The [{'Country': 'Switzerland', 'Average Budget (€)': 2710.0}] of has a maximum value of 2710.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Spain", + "Average Budget (€)": 1658.0 + } + ], + "score": 0.68, + "annotation": "The shows a sudden jump in Average Budget (€) from Brazil to Spain.", + "reason": "The Average Budget (€) for increases from 1238.0 at Brazil to 1658.0 at Spain, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Switzerland", + "Average Budget (€)": 2710.0 + }, + { + "Country": "Austria", + "Average Budget (€)": 2645.0 + }, + { + "Country": "U.S.", + "Average Budget (€)": 2643.0 + }, + { + "Country": "Germany", + "Average Budget (€)": 2376.0 + }, + { + "Country": "Belgium", + "Average Budget (€)": 2318.0 + }, + { + "Country": "India", + "Average Budget (€)": 2234.0 + }, + { + "Country": "UK", + "Average Budget (€)": 2230.0 + }, + { + "Country": "China", + "Average Budget (€)": 2035.0 + }, + { + "Country": "France", + "Average Budget (€)": 1933.0 + }, + { + "Country": "Italy", + "Average Budget (€)": 1776.0 + }, + { + "Country": "Spain", + "Average Budget (€)": 1658.0 + }, + { + "Country": "Brazil", + "Average Budget (€)": 1238.0 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of residence", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Average Budget (€)", + "importance": "primary", + "description": "Average summer holiday budget in Euros", + "unit": "€", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2542": { + "metadata": { + "title": "Cloud Boom Drives Data Center Investment", + "description": "Estimated annual investment in data centers in North America", + "main_insight": "Annual investment in North American data centers has significantly increased from 2011 to 2017, with a dramatic jump in 2017." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2011, + "Investment": 4.7 + }, + { + "Year": 2012, + "Investment": 2.4 + }, + { + "Year": 2013, + "Investment": 4.6 + }, + { + "Year": 2014, + "Investment": 3.9 + }, + { + "Year": 2015, + "Investment": 5.7 + }, + { + "Year": 2016, + "Investment": 7.7 + }, + { + "Year": 2017, + "Investment": 20.0 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of investment", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Investment", + "importance": "primary", + "description": "Estimated annual investment in billions of dollars", + "unit": "Billion USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1502": { + "metadata": { + "title": "The Majority of Americans Believe U.S. Global Power is in Decline", + "description": "Does the U.S. play a more or less important role as world leader compared to 10 years ago?", + "main_insight": "In 2013, a majority of Americans (53%) believed the U.S. played a less important role as a world leader compared to 10 years ago, a significant increase from previous years." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2013, + "Units": 53.0, + "Importance": "Less important than 10 years ago" + }, + { + "Year": 2013, + "Units": 17.0, + "Importance": "More important than 10 years ago" + } + ], + "score": 0.79, + "annotation": "The Units of Less important than 10 years ago and More important than 10 years ago show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Less important than 10 years ago and More important than 10 years ago is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 1974, + "Units": 39.0, + "Importance": "Less important than 10 years ago" + }, + { + "Year": 1974, + "Units": 28.0, + "Importance": "More important than 10 years ago" + }, + { + "Year": 1978, + "Units": 41.0, + "Importance": "Less important than 10 years ago" + }, + { + "Year": 1978, + "Units": 30.0, + "Importance": "More important than 10 years ago" + }, + { + "Year": 1982, + "Units": 26.0, + "Importance": "Less important than 10 years ago" + }, + { + "Year": 1982, + "Units": 27.0, + "Importance": "More important than 10 years ago" + }, + { + "Year": 1986, + "Units": 32.0, + "Importance": "Less important than 10 years ago" + }, + { + "Year": 1986, + "Units": 41.0, + "Importance": "More important than 10 years ago" + }, + { + "Year": 1990, + "Units": 35.0, + "Importance": "Less important than 10 years ago" + }, + { + "Year": 1990, + "Units": 38.0, + "Importance": "More important than 10 years ago" + }, + { + "Year": 1993, + "Units": 33.0, + "Importance": "Less important than 10 years ago" + }, + { + "Year": 1993, + "Units": 39.0, + "Importance": "More important than 10 years ago" + }, + { + "Year": 1994, + "Units": 31.0, + "Importance": "Less important than 10 years ago" + }, + { + "Year": 1994, + "Units": 40.0, + "Importance": "More important than 10 years ago" + }, + { + "Year": 1997, + "Units": 26.0, + "Importance": "Less important than 10 years ago" + }, + { + "Year": 1997, + "Units": 39.0, + "Importance": "More important than 10 years ago" + }, + { + "Year": 2001, + "Units": 25.0, + "Importance": "Less important than 10 years ago" + }, + { + "Year": 2001, + "Units": 38.0, + "Importance": "More important than 10 years ago" + }, + { + "Year": 2004, + "Units": 20.0, + "Importance": "Less important than 10 years ago" + }, + { + "Year": 2004, + "Units": 45.0, + "Importance": "More important than 10 years ago" + }, + { + "Year": 2009, + "Units": 41.0, + "Importance": "Less important than 10 years ago" + }, + { + "Year": 2009, + "Units": 25.0, + "Importance": "More important than 10 years ago" + }, + { + "Year": 2013, + "Units": 53.0, + "Importance": "Less important than 10 years ago" + }, + { + "Year": 2013, + "Units": 17.0, + "Importance": "More important than 10 years ago" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding importance", + "role": "y" + }, + { + "name": "Importance", + "importance": "primary", + "description": "The importance of the data point", + "role": "group" + } + ] + } + }, + "1205": { + "metadata": { + "title": "Can Sonos Avoid the Fate of Fitbit and GoPro?", + "description": "IPO and long-term stock performance of hardware companies that recently went public", + "main_insight": "GoPro and Fitbit saw significant drops in stock price since their IPO, raising concerns about the long-term performance of recently public hardware companies like Sonos." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Company": "Sonos\nAugust 2018", + "Units": 19.91, + "Stock Prices": "Closing price on first trading day" + }, + { + "Company": "Sonos\nAugust 2018", + "Units": 19.35, + "Stock Prices": "Closing price on August 6, 2018" + } + ], + "score": 0.98, + "annotation": "The Units of Closing price on first trading day and Closing price on August 6, 2018 show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Closing price on first trading day and Closing price on August 6, 2018 is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "Sonos\nAugust 2018", + "Units": 19.35, + "Stock Prices": "Closing price on August 6, 2018" + } + ], + "score": 0.96, + "annotation": "The Closing price on August 6, 2018 shows a sudden jump in Units from GoPro\nJune 2014 to Sonos\nAugust 2018.", + "reason": "The Units for Closing price on August 6, 2018 increases from 6.68 at GoPro\nJune 2014 to 19.35 at Sonos\nAugust 2018, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Company": "Fitbit\nJune 2015", + "Units": 29.68, + "Stock Prices": "Closing price on first trading day" + } + ], + "score": 0.95, + "annotation": "The Closing price on first trading day shows a sudden jump in Units from Sonos\nAugust 2018 to Fitbit\nJune 2015.", + "reason": "The Units for Closing price on first trading day increases from 19.91 at Sonos\nAugust 2018 to 29.68 at Fitbit\nJune 2015, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Company": "Sonos\nAugust 2018", + "Units": 15.0, + "Stock Prices": "IPO price" + }, + { + "Company": "Sonos\nAugust 2018", + "Units": 19.91, + "Stock Prices": "Closing price on first trading day" + } + ], + "score": 0.95, + "annotation": "The Units of IPO price and Closing price on first trading day show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between IPO price and Closing price on first trading day is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Company": "Sonos\nAugust 2018", + "Units": 19.91, + "Stock Prices": "Closing price on first trading day" + } + ], + "score": 0.89, + "annotation": "The Closing price on first trading day has the largest proportion in Sonos\nAugust 2018.", + "reason": "The Units of Closing price on first trading day in Sonos\nAugust 2018 accounts for 0.3669369701437523, larger than all other Stock Prices, of the total of Units of all Stock Prices." + } + ], + "data": { + "data": [ + { + "Company": "GoPro\nJune 2014", + "Units": 24.0, + "Stock Prices": "IPO price" + }, + { + "Company": "GoPro\nJune 2014", + "Units": 31.34, + "Stock Prices": "Closing price on first trading day" + }, + { + "Company": "GoPro\nJune 2014", + "Units": 6.68, + "Stock Prices": "Closing price on August 6, 2018" + }, + { + "Company": "Fitbit\nJune 2015", + "Units": 20.0, + "Stock Prices": "IPO price" + }, + { + "Company": "Fitbit\nJune 2015", + "Units": 29.68, + "Stock Prices": "Closing price on first trading day" + }, + { + "Company": "Fitbit\nJune 2015", + "Units": 5.45, + "Stock Prices": "Closing price on August 6, 2018" + }, + { + "Company": "Sonos\nAugust 2018", + "Units": 15.0, + "Stock Prices": "IPO price" + }, + { + "Company": "Sonos\nAugust 2018", + "Units": 19.91, + "Stock Prices": "Closing price on first trading day" + }, + { + "Company": "Sonos\nAugust 2018", + "Units": 19.35, + "Stock Prices": "Closing price on August 6, 2018" + } + ], + "columns": [ + { + "name": "Company", + "importance": "primary", + "description": "The company name and IPO date", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding stock prices", + "role": "y" + }, + { + "name": "Stock Prices", + "importance": "primary", + "description": "The stock prices of the data point", + "role": "group" + } + ] + } + }, + "1661": { + "metadata": { + "title": "The airlines forcing passengers to sit separately", + "description": "Chances of being separated if not paying extra to guarantee seats by airline*", + "main_insight": "Ryanair has the highest chance (35%) of separating passengers who don't pay extra for guaranteed seating, significantly higher than other airlines. UK passengers spend an estimated £390 million annually to ensure they sit together." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Airline": "RYANAIR", + "Separation Chance": 35.0 + } + ], + "score": 0.97, + "annotation": "The has a maximum value at RYANAIR", + "reason": "The [{'Airline': 'RYANAIR', 'Separation Chance': 35.0}] of has a maximum value of 35.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Airline": "RYANAIR", + "Separation Chance": 35.0 + } + ], + "score": 0.87, + "annotation": "The shows a sudden jump in Separation Chance from Emirates to RYANAIR.", + "reason": "The Separation Chance for increases from 22.0 at Emirates to 35.0 at RYANAIR, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Airline": "flybe.com", + "Separation Chance": 12.0 + }, + { + "Airline": "Monarch", + "Separation Chance": 12.0 + }, + { + "Airline": "TUI", + "Separation Chance": 12.0 + } + ], + "score": 0.47, + "annotation": "The has minimum values at flybe.com, Monarch, TUI", + "reason": "The [{'Airline': 'flybe.com', 'Separation Chance': 12.0}, {'Airline': 'Monarch', 'Separation Chance': 12.0}, {'Airline': 'TUI', 'Separation Chance': 12.0}] of have minimum values of 12.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Airline": "RYANAIR", + "Separation Chance": 35.0 + }, + { + "Airline": "Emirates", + "Separation Chance": 22.0 + }, + { + "Airline": "virgin atlantic", + "Separation Chance": 18.0 + }, + { + "Airline": "Jet2.com", + "Separation Chance": 16.0 + }, + { + "Airline": "BRITISH AIRWAYS", + "Separation Chance": 15.0 + }, + { + "Airline": "easyJet", + "Separation Chance": 15.0 + }, + { + "Airline": "Thomas Cook", + "Separation Chance": 15.0 + }, + { + "Airline": "flybe.com", + "Separation Chance": 12.0 + }, + { + "Airline": "Monarch", + "Separation Chance": 12.0 + }, + { + "Airline": "TUI", + "Separation Chance": 12.0 + } + ], + "columns": [ + { + "name": "Airline", + "importance": "primary", + "description": "Name of the airline", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Separation Chance", + "importance": "primary", + "description": "Percentage chance of being separated from traveling group if not paying extra for seat selection", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2315": { + "metadata": { + "title": "What is important for young Europeans?", + "description": "Share of respondents saying the following social aspects are important for them personally", + "main_insight": "Human rights are consistently ranked as the most important social aspect for young Europeans across all surveyed countries, with peace also being a significant concern." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Greece", + "Score": 71.0, + "Core Values": "Human rights" + } + ], + "score": 0.98, + "annotation": "The Human rights has the largest proportion in Greece.", + "reason": "The Score of Human rights in Greece accounts for 0.1658878504672897, larger than all other Core Values, of the total of Score of all Core Values." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Spain", + "Score": 71.0, + "Core Values": "Human rights" + } + ], + "score": 0.98, + "annotation": "The Human rights has the largest proportion in Spain.", + "reason": "The Score of Human rights in Spain accounts for 0.16705882352941176, larger than all other Core Values, of the total of Score of all Core Values." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Germany", + "Score": 65.0, + "Core Values": "Human rights" + } + ], + "score": 0.98, + "annotation": "The Human rights has the largest proportion in Germany.", + "reason": "The Score of Human rights in Germany accounts for 0.17105263157894737, larger than all other Core Values, of the total of Score of all Core Values." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Core Values": "Human rights", + "Country": "total", + "Score": 486.0 + } + ], + "score": 0.98, + "annotation": "The total value of Human rights has the largest proportion.", + "reason": "The total value of Score of Human rights accounts for 0.178348623853211, larger than all other Core Values, of the total of Score of all Core Values." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Italy", + "Score": 70.0, + "Core Values": "Human rights" + } + ], + "score": 0.98, + "annotation": "The Human rights has the largest proportion in Italy.", + "reason": "The Score of Human rights in Italy accounts for 0.18041237113402062, larger than all other Core Values, of the total of Score of all Core Values." + } + ], + "data": { + "data": [ + { + "Country": "Germany", + "Score": 65.0, + "Core Values": "Human rights" + }, + { + "Country": "Germany", + "Score": 62.0, + "Core Values": "Peace" + }, + { + "Country": "Germany", + "Score": 49.0, + "Core Values": "Individual freedom" + }, + { + "Country": "Germany", + "Score": 48.0, + "Core Values": "Tolerance" + }, + { + "Country": "Germany", + "Score": 50.0, + "Core Values": "Safety" + }, + { + "Country": "Germany", + "Score": 41.0, + "Core Values": "Democracy" + }, + { + "Country": "Germany", + "Score": 27.0, + "Core Values": "Prosperity and economic success" + }, + { + "Country": "Germany", + "Score": 15.0, + "Core Values": "Solidarity" + }, + { + "Country": "Germany", + "Score": 23.0, + "Core Values": "Stability and reliability" + }, + { + "Country": "France", + "Score": 74.0, + "Core Values": "Human rights" + }, + { + "Country": "France", + "Score": 58.0, + "Core Values": "Peace" + }, + { + "Country": "France", + "Score": 47.0, + "Core Values": "Individual freedom" + }, + { + "Country": "France", + "Score": 40.0, + "Core Values": "Tolerance" + }, + { + "Country": "France", + "Score": 57.0, + "Core Values": "Safety" + }, + { + "Country": "France", + "Score": 35.0, + "Core Values": "Democracy" + }, + { + "Country": "France", + "Score": 20.0, + "Core Values": "Prosperity and economic success" + }, + { + "Country": "France", + "Score": 43.0, + "Core Values": "Solidarity" + }, + { + "Country": "France", + "Score": 21.0, + "Core Values": "Stability and reliability" + }, + { + "Country": "Greece", + "Score": 71.0, + "Core Values": "Human rights" + }, + { + "Country": "Greece", + "Score": 55.0, + "Core Values": "Peace" + }, + { + "Country": "Greece", + "Score": 54.0, + "Core Values": "Individual freedom" + }, + { + "Country": "Greece", + "Score": 35.0, + "Core Values": "Tolerance" + }, + { + "Country": "Greece", + "Score": 60.0, + "Core Values": "Safety" + }, + { + "Country": "Greece", + "Score": 44.0, + "Core Values": "Democracy" + }, + { + "Country": "Greece", + "Score": 36.0, + "Core Values": "Prosperity and economic success" + }, + { + "Country": "Greece", + "Score": 39.0, + "Core Values": "Solidarity" + }, + { + "Country": "Greece", + "Score": 34.0, + "Core Values": "Stability and reliability" + }, + { + "Country": "Italy", + "Score": 70.0, + "Core Values": "Human rights" + }, + { + "Country": "Italy", + "Score": 42.0, + "Core Values": "Peace" + }, + { + "Country": "Italy", + "Score": 41.0, + "Core Values": "Individual freedom" + }, + { + "Country": "Italy", + "Score": 41.0, + "Core Values": "Tolerance" + }, + { + "Country": "Italy", + "Score": 62.0, + "Core Values": "Safety" + }, + { + "Country": "Italy", + "Score": 35.0, + "Core Values": "Democracy" + }, + { + "Country": "Italy", + "Score": 32.0, + "Core Values": "Prosperity and economic success" + }, + { + "Country": "Italy", + "Score": 32.0, + "Core Values": "Solidarity" + }, + { + "Country": "Italy", + "Score": 33.0, + "Core Values": "Stability and reliability" + }, + { + "Country": "Poland", + "Score": 70.0, + "Core Values": "Human rights" + }, + { + "Country": "Poland", + "Score": 58.0, + "Core Values": "Peace" + }, + { + "Country": "Poland", + "Score": 48.0, + "Core Values": "Individual freedom" + }, + { + "Country": "Poland", + "Score": 40.0, + "Core Values": "Tolerance" + }, + { + "Country": "Poland", + "Score": 58.0, + "Core Values": "Safety" + }, + { + "Country": "Poland", + "Score": 33.0, + "Core Values": "Democracy" + }, + { + "Country": "Poland", + "Score": 27.0, + "Core Values": "Prosperity and economic success" + }, + { + "Country": "Poland", + "Score": 24.0, + "Core Values": "Solidarity" + }, + { + "Country": "Poland", + "Score": 26.0, + "Core Values": "Stability and reliability" + }, + { + "Country": "Spain", + "Score": 71.0, + "Core Values": "Human rights" + }, + { + "Country": "Spain", + "Score": 59.0, + "Core Values": "Peace" + }, + { + "Country": "Spain", + "Score": 45.0, + "Core Values": "Individual freedom" + }, + { + "Country": "Spain", + "Score": 54.0, + "Core Values": "Tolerance" + }, + { + "Country": "Spain", + "Score": 64.0, + "Core Values": "Safety" + }, + { + "Country": "Spain", + "Score": 42.0, + "Core Values": "Democracy" + }, + { + "Country": "Spain", + "Score": 32.0, + "Core Values": "Prosperity and economic success" + }, + { + "Country": "Spain", + "Score": 28.0, + "Core Values": "Solidarity" + }, + { + "Country": "Spain", + "Score": 30.0, + "Core Values": "Stability and reliability" + }, + { + "Country": "United Kingdom", + "Score": 65.0, + "Core Values": "Human rights" + }, + { + "Country": "United Kingdom", + "Score": 54.0, + "Core Values": "Peace" + }, + { + "Country": "United Kingdom", + "Score": 44.0, + "Core Values": "Individual freedom" + }, + { + "Country": "United Kingdom", + "Score": 41.0, + "Core Values": "Tolerance" + }, + { + "Country": "United Kingdom", + "Score": 36.0, + "Core Values": "Safety" + }, + { + "Country": "United Kingdom", + "Score": 25.0, + "Core Values": "Democracy" + }, + { + "Country": "United Kingdom", + "Score": 19.0, + "Core Values": "Prosperity and economic success" + }, + { + "Country": "United Kingdom", + "Score": 17.0, + "Core Values": "Solidarity" + }, + { + "Country": "United Kingdom", + "Score": 24.0, + "Core Values": "Stability and reliability" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of the respondents", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding core values", + "role": "y" + }, + { + "name": "Core Values", + "importance": "primary", + "description": "The core values of the data point", + "role": "group" + } + ] + } + }, + "1178": { + "metadata": { + "title": "Environmental Protection Awareness Rising in U.S.", + "description": "Percentage of U.S. adults by age group who believe a given issue should be the top priority for Trump and Congress", + "main_insight": "Older Americans (65+) are significantly more likely to prioritize the environment and climate change than younger Americans (18-29)." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Issue": "Environment", + "Units": 57.0, + "Age Group": "50-64" + }, + { + "Issue": "Environment", + "Units": 55.0, + "Age Group": "65+" + } + ], + "score": 0.96, + "annotation": "The Units of 50-64 and 65+ show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 50-64 and 65+ is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Issue": "Environment", + "Units": 77.0, + "Age Group": "18-29" + }, + { + "Issue": "Environment", + "Units": 67.0, + "Age Group": "30-49" + } + ], + "score": 0.95, + "annotation": "The Units of 18-29 and 30-49 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 18-29 and 30-49 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Issue": "Environment", + "Units": 77.0, + "Age Group": "18-29" + }, + { + "Issue": "Environment", + "Units": 22.0, + "Age Group": "Young-old diff" + } + ], + "score": 0.91, + "annotation": "The Units of 18-29 and Young-old diff show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 18-29 and Young-old diff is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "average_small", + "data_points": [ + { + "Age Group": "Young-old diff", + "Issue": "avg", + "Units": -3.6 + } + ], + "score": 0.88, + "annotation": "The average value of Young-old diff is the smallest in all groups.", + "reason": "The average value of Units of Young-old diff has a value of -3.6, which is smaller than all other Age Group." + }, + { + "type": "difference", + "subtype": "minimum_small", + "data_points": [ + { + "Issue": "Military", + "Units": -30.0, + "Age Group": "Young-old diff" + } + ], + "score": 0.87, + "annotation": "The minimum value of Young-old diff is the smallest in all groups.", + "reason": "The minimum value of Units of Young-old diff has a value of -30.0, which is smaller than all other Age Group." + } + ], + "data": { + "data": [ + { + "Issue": "Military", + "Units": 28.0, + "Age Group": "18-29" + }, + { + "Issue": "Military", + "Units": 42.0, + "Age Group": "30-49" + }, + { + "Issue": "Military", + "Units": 56.0, + "Age Group": "50-64" + }, + { + "Issue": "Military", + "Units": 58.0, + "Age Group": "65+" + }, + { + "Issue": "Military", + "Units": -30.0, + "Age Group": "Young-old diff" + }, + { + "Issue": "Crime", + "Units": 44.0, + "Age Group": "18-29" + }, + { + "Issue": "Crime", + "Units": 51.0, + "Age Group": "30-49" + }, + { + "Issue": "Crime", + "Units": 61.0, + "Age Group": "50-64" + }, + { + "Issue": "Crime", + "Units": 68.0, + "Age Group": "65+" + }, + { + "Issue": "Crime", + "Units": -24.0, + "Age Group": "Young-old diff" + }, + { + "Issue": "Race Relations", + "Units": 42.0, + "Age Group": "18-29" + }, + { + "Issue": "Race Relations", + "Units": 44.0, + "Age Group": "30-49" + }, + { + "Issue": "Race Relations", + "Units": 47.0, + "Age Group": "50-64" + }, + { + "Issue": "Race Relations", + "Units": 44.0, + "Age Group": "65+" + }, + { + "Issue": "Race Relations", + "Units": -2.0, + "Age Group": "Young-old diff" + }, + { + "Issue": "Climate Change", + "Units": 61.0, + "Age Group": "18-29" + }, + { + "Issue": "Climate Change", + "Units": 53.0, + "Age Group": "30-49" + }, + { + "Issue": "Climate Change", + "Units": 50.0, + "Age Group": "50-64" + }, + { + "Issue": "Climate Change", + "Units": 45.0, + "Age Group": "65+" + }, + { + "Issue": "Climate Change", + "Units": 16.0, + "Age Group": "Young-old diff" + }, + { + "Issue": "Environment", + "Units": 77.0, + "Age Group": "18-29" + }, + { + "Issue": "Environment", + "Units": 67.0, + "Age Group": "30-49" + }, + { + "Issue": "Environment", + "Units": 57.0, + "Age Group": "50-64" + }, + { + "Issue": "Environment", + "Units": 55.0, + "Age Group": "65+" + }, + { + "Issue": "Environment", + "Units": 22.0, + "Age Group": "Young-old diff" + } + ], + "columns": [ + { + "name": "Issue", + "importance": "primary", + "description": "Political issue considered", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding age group", + "role": "y" + }, + { + "name": "Age Group", + "importance": "primary", + "description": "The age group of the data point", + "role": "group" + } + ] + } + }, + "1729": { + "metadata": { + "title": "Where Women Face the Highest Risk of Maternal Death", + "description": "Lifetime risk of maternal death (one death per number of women stated)*", + "main_insight": "The visualization shows the countries with the highest lifetime risk of maternal death, with Chad having the highest risk at 1 in 15 women. All listed countries are in Africa." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Chad", + "Maternal Death Risk": 15 + } + ], + "score": 0.86, + "annotation": "The has a minimum value at Chad", + "reason": "The [{'Country': 'Chad', 'Maternal Death Risk': 15}] of has a mainimum value of 15, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Ivory Coast", + "Maternal Death Risk": 29 + } + ], + "score": 0.81, + "annotation": "The has a maximum value at Ivory Coast", + "reason": "The [{'Country': 'Ivory Coast', 'Maternal Death Risk': 29}] of has a maximum value of 29, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Somalia", + "Maternal Death Risk": 18 + }, + { + "Country": "Mali", + "Maternal Death Risk": 26 + } + ], + "score": 0.61, + "annotation": "The exhibits multiple sudden jumps in Maternal Death Risk, transitioning from Chad, Congo to Somalia, Mali.", + "reason": "Across these points, the Maternal Death Risk for increases from 15, 23 at Chad, Congo to 18, 26 at Somalia, Mali, suggesting significant shifts in value." + } + ], + "data": { + "data": [ + { + "Country": "Chad", + "Maternal Death Risk": 15 + }, + { + "Country": "Somalia", + "Maternal Death Risk": 18 + }, + { + "Country": "Niger", + "Maternal Death Risk": 20 + }, + { + "Country": "Sierra Leone", + "Maternal Death Risk": 21 + }, + { + "Country": "Burundi", + "Maternal Death Risk": 22 + }, + { + "Country": "Central African Republic", + "Maternal Death Risk": 22 + }, + { + "Country": "Congo", + "Maternal Death Risk": 23 + }, + { + "Country": "Mali", + "Maternal Death Risk": 26 + }, + { + "Country": "South Sudan", + "Maternal Death Risk": 28 + }, + { + "Country": "Ivory Coast", + "Maternal Death Risk": 29 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country name", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Maternal Death Risk", + "importance": "primary", + "description": "Lifetime risk of maternal death, represented as 1 death per number of women", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "181": { + "metadata": { + "title": "Russia Tightens Squeeze on Gas Flow to Europe", + "description": "Daily natural gas flow from Russia to the EU via the Nord Stream pipeline (in million cubic meters)", + "main_insight": "Russia has significantly reduced gas flow to Europe via the Nord Stream pipeline, with dramatic drops in June and July 2022." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "Jul 15", + "Gas Flow": 0.0 + } + ], + "score": 0.82, + "annotation": "The has a minimum value at Jul 15", + "reason": "The [{'Date': 'Jul 15', 'Gas Flow': 0.0}] of has a mainimum value of 0.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "May 1", + "Gas Flow": 170.0 + }, + { + "Date": "May 15", + "Gas Flow": 170.0 + } + ], + "score": 0.75, + "annotation": "The has maximum values at May 1, May 15", + "reason": "The [{'Date': 'May 1', 'Gas Flow': 170.0}, {'Date': 'May 15', 'Gas Flow': 170.0}] of have maximum values of 170.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "Jun 1", + "Gas Flow": 160.0 + } + ], + "score": 0.73, + "annotation": "The shows a sudden jump in Gas Flow from Jun 15 to Jun 1.", + "reason": "The Gas Flow for increases from 100.0 at Jun 15 to 160.0 at Jun 1, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Date": "May 1", + "Gas Flow": 170.0 + }, + { + "Date": "May 15", + "Gas Flow": 170.0 + }, + { + "Date": "Jun 1", + "Gas Flow": 160.0 + }, + { + "Date": "Jun 15", + "Gas Flow": 100.0 + }, + { + "Date": "Jun 20", + "Gas Flow": 70.0 + }, + { + "Date": "Jul 1", + "Gas Flow": 70.0 + }, + { + "Date": "Jul 10", + "Gas Flow": 70.0 + }, + { + "Date": "Jul 15", + "Gas Flow": 0.0 + }, + { + "Date": "Jul 22", + "Gas Flow": 67.0 + }, + { + "Date": "Jul 27", + "Gas Flow": 33.0 + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Date representing daily gas flow", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Gas Flow", + "importance": "primary", + "description": "Daily natural gas flow in million cubic meters", + "unit": "million cubic meters", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1314": { + "metadata": { + "title": "Top 5 Cloud Infrastructure Service Providers", + "description": "Worldwide cloud infrastructure services market share in 2015*", + "main_insight": "Amazon was the dominant cloud infrastructure service provider in 2015, holding a significantly larger market share than its competitors." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Provider": "Amazon", + "Market Share": 31.0 + } + ], + "score": 0.95, + "annotation": "The shows a sudden jump in Market Share from Microsoft to Amazon.", + "reason": "The Market Share for increases from 9.0 at Microsoft to 31.0 at Amazon, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Provider": "Amazon", + "Market Share": 31.0 + } + ], + "score": 0.87, + "annotation": "The has a maximum value at Amazon", + "reason": "The [{'Provider': 'Amazon', 'Market Share': 31.0}] of has a maximum value of 31.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Provider": "Google", + "Market Share": 4.0 + }, + { + "Provider": "Salesforce", + "Market Share": 4.0 + } + ], + "score": 0.41, + "annotation": "The has minimum values at Google, Salesforce", + "reason": "The [{'Provider': 'Google', 'Market Share': 4.0}, {'Provider': 'Salesforce', 'Market Share': 4.0}] of have minimum values of 4.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Provider": "Amazon", + "Market Share": 31.0 + }, + { + "Provider": "Microsoft", + "Market Share": 9.0 + }, + { + "Provider": "IBM", + "Market Share": 7.0 + }, + { + "Provider": "Google", + "Market Share": 4.0 + }, + { + "Provider": "Salesforce", + "Market Share": 4.0 + } + ], + "columns": [ + { + "name": "Provider", + "importance": "primary", + "description": "Cloud infrastructure service provider", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Market Share", + "importance": "primary", + "description": "Percentage of the market share in 2015", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2122": { + "metadata": { + "title": "Facebook and Twitter Are Old News to Young People", + "description": "% of U.S. teens who consider the following social networks their favorite", + "main_insight": "Snapchat and Instagram are the most popular social networks among U.S. teens, while Facebook and Twitter have significantly declined in popularity." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Social Network": "Facebook", + "Units": 11.0, + "Spring Semesters": "Spring 2017" + }, + { + "Social Network": "Facebook", + "Units": 8.0, + "Spring Semesters": "Spring 2018" + } + ], + "score": 1.0, + "annotation": "The Units of Spring 2017 and Spring 2018 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Spring 2017 and Spring 2018 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Spring Semesters": "Spring 2018", + "Social Network": "total", + "Units": 88.0 + }, + { + "Spring Semesters": "Spring 2019", + "Social Network": "total", + "Units": 88.0 + } + ], + "score": 0.96, + "annotation": "The total value of Spring 2018, Spring 2019 all have the largest proportion.", + "reason": "The total value of Units of Spring 2018, Spring 2019 all account for 0.2141119221411192, larger than all other Spring Semesters, of the total of Units of all Spring Semesters." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Social Network": "Instagram", + "Units": 35.0, + "Spring Semesters": "Spring 2019" + } + ], + "score": 0.95, + "annotation": "The Spring 2019 has the largest proportion in Instagram.", + "reason": "The Units of Spring 2019 in Instagram accounts for 0.25735294117647056, larger than all other Spring Semesters, of the total of Units of all Spring Semesters." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Social Network": "Instagram", + "Units": 35.0, + "Spring Semesters": "Spring 2019" + } + ], + "score": 0.95, + "annotation": "The Spring 2019 shows a sudden jump in Units from Facebook to Instagram.", + "reason": "The Units for Spring 2019 increases from 6.0 at Facebook to 35.0 at Instagram, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Social Network": "Snapchat", + "Units": 45.0, + "Spring Semesters": "Spring 2018" + } + ], + "score": 0.95, + "annotation": "The Spring 2018 has the largest proportion in Snapchat.", + "reason": "The Units of Spring 2018 in Snapchat accounts for 0.28125, larger than all other Spring Semesters, of the total of Units of all Spring Semesters." + } + ], + "data": { + "data": [ + { + "Social Network": "Snapchat", + "Units": 11.0, + "Spring Semesters": "Spring 2015" + }, + { + "Social Network": "Snapchat", + "Units": 24.0, + "Spring Semesters": "Spring 2016" + }, + { + "Social Network": "Snapchat", + "Units": 39.0, + "Spring Semesters": "Spring 2017" + }, + { + "Social Network": "Snapchat", + "Units": 45.0, + "Spring Semesters": "Spring 2018" + }, + { + "Social Network": "Snapchat", + "Units": 41.0, + "Spring Semesters": "Spring 2019" + }, + { + "Social Network": "Instagram", + "Units": 23.0, + "Spring Semesters": "Spring 2015" + }, + { + "Social Network": "Instagram", + "Units": 29.0, + "Spring Semesters": "Spring 2016" + }, + { + "Social Network": "Instagram", + "Units": 23.0, + "Spring Semesters": "Spring 2017" + }, + { + "Social Network": "Instagram", + "Units": 26.0, + "Spring Semesters": "Spring 2018" + }, + { + "Social Network": "Instagram", + "Units": 35.0, + "Spring Semesters": "Spring 2019" + }, + { + "Social Network": "Twitter", + "Units": 21.0, + "Spring Semesters": "Spring 2015" + }, + { + "Social Network": "Twitter", + "Units": 16.0, + "Spring Semesters": "Spring 2016" + }, + { + "Social Network": "Twitter", + "Units": 11.0, + "Spring Semesters": "Spring 2017" + }, + { + "Social Network": "Twitter", + "Units": 9.0, + "Spring Semesters": "Spring 2018" + }, + { + "Social Network": "Twitter", + "Units": 6.0, + "Spring Semesters": "Spring 2019" + }, + { + "Social Network": "Facebook", + "Units": 15.0, + "Spring Semesters": "Spring 2015" + }, + { + "Social Network": "Facebook", + "Units": 12.0, + "Spring Semesters": "Spring 2016" + }, + { + "Social Network": "Facebook", + "Units": 11.0, + "Spring Semesters": "Spring 2017" + }, + { + "Social Network": "Facebook", + "Units": 8.0, + "Spring Semesters": "Spring 2018" + }, + { + "Social Network": "Facebook", + "Units": 6.0, + "Spring Semesters": "Spring 2019" + } + ], + "columns": [ + { + "name": "Social Network", + "importance": "primary", + "description": "The social media platform", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding spring semesters", + "role": "y" + }, + { + "name": "Spring Semesters", + "importance": "primary", + "description": "The spring semesters of the data point", + "role": "group" + } + ] + } + }, + "1581": { + "metadata": { + "title": "Harley-Davidson in Trouble", + "description": "Key financial figures for Harley-Davidson in Q1 2019 and change on Q1 2018", + "main_insight": "Harley-Davidson's key financial figures for Q1 2019 showed significant declines compared to Q1 2018, with the largest drop in net income." + }, + "chart_type": "Proportional Circle Area Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Financial Metric": "Revenue", + "Q1 2019 Value": 1380000000.0 + } + ], + "score": 0.96, + "annotation": "The shows a sudden jump in Q1 2019 Value from Net income to Revenue.", + "reason": "The Q1 2019 Value for increases from 127900000.0 at Net income to 1380000000.0 at Revenue, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Financial Metric": "Revenue", + "Q1 2019 Value": 1380000000.0 + } + ], + "score": 0.67, + "annotation": "The has a maximum value at Revenue", + "reason": "The [{'Financial Metric': 'Revenue', 'Q1 2019 Value': 1380000000.0}] of has a maximum value of 1380000000.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Financial Metric": "Earnings per share", + "Q1 2019 Value": 0.8 + } + ], + "score": 0.43, + "annotation": "The has a minimum value at Earnings per share", + "reason": "The [{'Financial Metric': 'Earnings per share', 'Q1 2019 Value': 0.8}] of has a mainimum value of 0.8, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Financial Metric": "Revenue", + "Q1 2019 Value": 1380000000.0 + }, + { + "Financial Metric": "Net income", + "Q1 2019 Value": 127900000.0 + }, + { + "Financial Metric": "Earnings per share", + "Q1 2019 Value": 0.8 + } + ], + "columns": [ + { + "name": "Financial Metric", + "importance": "primary", + "description": "The financial metric being measured", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Q1 2019 Value", + "importance": "primary", + "description": "The value of the metric in Q1 2019", + "unit": "USD", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1242": { + "metadata": { + "title": "Love and Hate in the Premier League", + "description": "Premier League clubs which the most fans say they love or hate in 2021", + "main_insight": "Manchester United and Liverpool are the most loved teams, but also among the most hated. There's a clear difference in the distribution of 'love' versus 'hate'; more people express positive sentiment towards the top teams than negative sentiment." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Team": "Tottenham Hotspur", + "Love Them": 0 + } + ], + "score": 0.86, + "annotation": "The has a minimum value at Tottenham Hotspur", + "reason": "The [{'Team': 'Tottenham Hotspur', 'Love Them': 0}] of has a mainimum value of 0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Team": "Manchester City", + "Love Them": 12.0 + } + ], + "score": 0.84, + "annotation": "The shows a sudden jump in Love Them from Tottenham Hotspur to Manchester City.", + "reason": "The Love Them for increases from 0 at Tottenham Hotspur to 12.0 at Manchester City, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Team": "Manchester United", + "Love Them": 23.0 + } + ], + "score": 0.6, + "annotation": "The has a maximum value at Manchester United", + "reason": "The [{'Team': 'Manchester United', 'Love Them': 23.0}] of has a maximum value of 23.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Team": "Manchester United", + "Love Them": 23.0 + }, + { + "Team": "Liverpool", + "Love Them": 22.0 + }, + { + "Team": "Arsenal", + "Love Them": 17.0 + }, + { + "Team": "Chelsea", + "Love Them": 13.0 + }, + { + "Team": "Manchester City", + "Love Them": 12.0 + }, + { + "Team": "Tottenham Hotspur", + "Love Them": 0 + } + ], + "columns": [ + { + "name": "Team", + "importance": "primary", + "description": "Premier League Club", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Love Them", + "importance": "primary", + "description": "Percentage of UK respondents who say the team is their favorite", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "184": { + "metadata": { + "title": "Jobless Claims Hit Pandemic Low Despite Delta", + "description": "Weekly initial jobless claims in the United States (seasonally adjusted)", + "main_insight": "Weekly initial jobless claims in the US have significantly decreased since the start of the pandemic, reaching a low of 348,000 by August 14, 2021, despite the Delta variant." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Week ended": "Mar 28, 2020", + "Jobless Claims": 6200000 + } + ], + "score": 0.97, + "annotation": "The has a maximum value at Mar 28, 2020", + "reason": "The [{'Week ended': 'Mar 28, 2020', 'Jobless Claims': 6200000}] of has a maximum value of 6200000, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Week ended": "Apr 9, 2020", + "Jobless Claims": 5200000 + } + ], + "score": 0.7, + "annotation": "The shows a sudden jump in Jobless Claims from Mar 20, 2020 to Apr 9, 2020.", + "reason": "The Jobless Claims for increases from 3300000 at Mar 20, 2020 to 5200000 at Apr 9, 2020, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Week ended": "Jul 31, 2021", + "Jobless Claims": 0 + } + ], + "score": 0.52, + "annotation": "The has a minimum value at Jul 31, 2021", + "reason": "The [{'Week ended': 'Jul 31, 2021', 'Jobless Claims': 0}] of has a mainimum value of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Week ended": "Jan 4, 2020", + "Jobless Claims": 220000 + }, + { + "Week ended": "Jan 15, 2020", + "Jobless Claims": 210000 + }, + { + "Week ended": "Feb 28, 2020", + "Jobless Claims": 220000 + }, + { + "Week ended": "Mar 9, 2020", + "Jobless Claims": 220000 + }, + { + "Week ended": "Mar 20, 2020", + "Jobless Claims": 3300000 + }, + { + "Week ended": "Mar 28, 2020", + "Jobless Claims": 6200000 + }, + { + "Week ended": "Apr 9, 2020", + "Jobless Claims": 5200000 + }, + { + "Week ended": "May 1, 2020", + "Jobless Claims": 3200000 + }, + { + "Week ended": "Jun 12, 2020", + "Jobless Claims": 1400000 + }, + { + "Week ended": "Jul 24, 2020", + "Jobless Claims": 1300000 + }, + { + "Week ended": "Aug 5, 2020", + "Jobless Claims": 1150000 + }, + { + "Week ended": "Sep 16, 2020", + "Jobless Claims": 850000 + }, + { + "Week ended": "Oct 27, 2020", + "Jobless Claims": 750000 + }, + { + "Week ended": "Dec 10, 2020", + "Jobless Claims": 880000 + }, + { + "Week ended": "Jan 22, 2021", + "Jobless Claims": 800000 + }, + { + "Week ended": "Mar 3, 2021", + "Jobless Claims": 750000 + }, + { + "Week ended": "Apr 14, 2021", + "Jobless Claims": 348000 + }, + { + "Week ended": "Jul 31, 2021", + "Jobless Claims": 0 + } + ], + "columns": [ + { + "name": "Week ended", + "importance": "primary", + "description": "Week-ending dates from January 2020 to August 2021", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Jobless Claims", + "importance": "primary", + "description": "Weekly initial jobless claims (seasonally adjusted)", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "669": { + "metadata": { + "title": "What Americans Made Of Trump's Tweet About \"The Squad\"", + "description": "Share of the U.S. public rating Trump's Sunday tweet as...", + "main_insight": "There is a significant difference in how different political groups rated Trump's tweet. While a large majority of Democrats rated the tweet as terrible, Republicans and Independents had more varied opinions, with a greater distribution over the 'OK, 'Good' and 'Great' categories." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Group": "Independents", + "Units": 10.0, + "Performance Rating": "Good" + }, + { + "Group": "Independents", + "Units": 34.0, + "Performance Rating": "Terrible" + } + ], + "score": 1.0, + "annotation": "The Units of Good and Terrible show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Good and Terrible is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Group": "Independents", + "Units": 17.0, + "Performance Rating": "OK" + }, + { + "Group": "Independents", + "Units": 34.0, + "Performance Rating": "Terrible" + } + ], + "score": 0.99, + "annotation": "The Units of OK and Terrible show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between OK and Terrible is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Group": "Independents", + "Units": 20.0, + "Performance Rating": "Great" + }, + { + "Group": "Independents", + "Units": 10.0, + "Performance Rating": "Good" + } + ], + "score": 0.99, + "annotation": "The Units of Great and Good show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Great and Good is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Group": "Independents", + "Units": 10.0, + "Performance Rating": "Good" + }, + { + "Group": "Independents", + "Units": 17.0, + "Performance Rating": "OK" + } + ], + "score": 0.99, + "annotation": "The Units of Good and OK show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Good and OK is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Group": "Independents", + "Units": 20.0, + "Performance Rating": "Great" + }, + { + "Group": "Independents", + "Units": 34.0, + "Performance Rating": "Terrible" + } + ], + "score": 0.98, + "annotation": "The Units of Great and Terrible show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Great and Terrible is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Group": "U.S. adults", + "Units": 21.0, + "Performance Rating": "Great" + }, + { + "Group": "U.S. adults", + "Units": 9.0, + "Performance Rating": "Good" + }, + { + "Group": "U.S. adults", + "Units": 18.0, + "Performance Rating": "OK" + }, + { + "Group": "U.S. adults", + "Units": 15.0, + "Performance Rating": "Bad" + }, + { + "Group": "U.S. adults", + "Units": 37.0, + "Performance Rating": "Terrible" + }, + { + "Group": "Democrats", + "Units": 1.0, + "Performance Rating": "Great" + }, + { + "Group": "Democrats", + "Units": 3.0, + "Performance Rating": "Good" + }, + { + "Group": "Democrats", + "Units": 9.0, + "Performance Rating": "OK" + }, + { + "Group": "Democrats", + "Units": 16.0, + "Performance Rating": "Bad" + }, + { + "Group": "Democrats", + "Units": 71.0, + "Performance Rating": "Terrible" + }, + { + "Group": "Republicans", + "Units": 48.0, + "Performance Rating": "Great" + }, + { + "Group": "Republicans", + "Units": 16.0, + "Performance Rating": "Good" + }, + { + "Group": "Republicans", + "Units": 24.0, + "Performance Rating": "OK" + }, + { + "Group": "Republicans", + "Units": 8.0, + "Performance Rating": "Bad" + }, + { + "Group": "Republicans", + "Units": 5.0, + "Performance Rating": "Terrible" + }, + { + "Group": "Independents", + "Units": 20.0, + "Performance Rating": "Great" + }, + { + "Group": "Independents", + "Units": 10.0, + "Performance Rating": "Good" + }, + { + "Group": "Independents", + "Units": 17.0, + "Performance Rating": "OK" + }, + { + "Group": "Independents", + "Units": 19.0, + "Performance Rating": "Bad" + }, + { + "Group": "Independents", + "Units": 34.0, + "Performance Rating": "Terrible" + } + ], + "columns": [ + { + "name": "Group", + "importance": "primary", + "description": "Political affiliation or US adults", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding performance rating", + "role": "y" + }, + { + "name": "Performance Rating", + "importance": "primary", + "description": "The performance rating of the data point", + "role": "group" + } + ] + } + }, + "941": { + "metadata": { + "title": "Party Affiliation Defines News Sources", + "description": "Percentage viewership by partisan affiliation for each news media outlet", + "main_insight": "News sources show a strong correlation with political party affiliation, with Fox News favored by Republicans and MSNBC, New York Times, NPR, and CNN favored by Democrats." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "News Media Outlet": "MSNBC", + "Score": 5.0, + "Party Affiliation": "Republican" + }, + { + "News Media Outlet": "MSNBC", + "Score": 95.0, + "Party Affiliation": "Democrat" + } + ], + "score": 1.0, + "annotation": "The Score of Republican and Democrat show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Republican and Democrat is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "News Media Outlet": "Fox News", + "Score": 93.0, + "Party Affiliation": "Republican" + } + ], + "score": 0.93, + "annotation": "The Republican has a maximum value at Fox News", + "reason": "The [{'News Media Outlet': 'Fox News', 'Score': 93.0, 'Party Affiliation': 'Republican'}] of Republican has a maximum value of 93.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "News Media Outlet": "Fox News", + "Score": 6.0, + "Party Affiliation": "Democrat" + } + ], + "score": 0.92, + "annotation": "The Democrat has a minimum value at Fox News", + "reason": "The [{'News Media Outlet': 'Fox News', 'Score': 6.0, 'Party Affiliation': 'Democrat'}] of Democrat has a mainimum value of 6.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "News Media Outlet": "Fox News", + "Score": 93.0, + "Party Affiliation": "Republican" + } + ], + "score": 0.86, + "annotation": "The Republican shows a sudden jump in Score from ABC News to Fox News.", + "reason": "The Score for Republican increases from 44.0 at ABC News to 93.0 at Fox News, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "News Media Outlet": "ABC News", + "Score": 53.0, + "Party Affiliation": "Democrat" + } + ], + "score": 0.85, + "annotation": "The Democrat shows a sudden jump in Score from Fox News to ABC News.", + "reason": "The Score for Democrat increases from 6.0 at Fox News to 53.0 at ABC News, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "News Media Outlet": "Fox News", + "Score": 93.0, + "Party Affiliation": "Republican" + }, + { + "News Media Outlet": "Fox News", + "Score": 6.0, + "Party Affiliation": "Democrat" + }, + { + "News Media Outlet": "ABC News", + "Score": 44.0, + "Party Affiliation": "Republican" + }, + { + "News Media Outlet": "ABC News", + "Score": 53.0, + "Party Affiliation": "Democrat" + }, + { + "News Media Outlet": "CBS News", + "Score": 41.0, + "Party Affiliation": "Republican" + }, + { + "News Media Outlet": "CBS News", + "Score": 55.0, + "Party Affiliation": "Democrat" + }, + { + "News Media Outlet": "NBC News", + "Score": 38.0, + "Party Affiliation": "Republican" + }, + { + "News Media Outlet": "NBC News", + "Score": 57.0, + "Party Affiliation": "Democrat" + }, + { + "News Media Outlet": "CNN", + "Score": 17.0, + "Party Affiliation": "Republican" + }, + { + "News Media Outlet": "CNN", + "Score": 79.0, + "Party Affiliation": "Democrat" + }, + { + "News Media Outlet": "NPR", + "Score": 12.0, + "Party Affiliation": "Republican" + }, + { + "News Media Outlet": "NPR", + "Score": 87.0, + "Party Affiliation": "Democrat" + }, + { + "News Media Outlet": "New York Times", + "Score": 7.0, + "Party Affiliation": "Republican" + }, + { + "News Media Outlet": "New York Times", + "Score": 91.0, + "Party Affiliation": "Democrat" + }, + { + "News Media Outlet": "MSNBC", + "Score": 5.0, + "Party Affiliation": "Republican" + }, + { + "News Media Outlet": "MSNBC", + "Score": 95.0, + "Party Affiliation": "Democrat" + } + ], + "columns": [ + { + "name": "News Media Outlet", + "importance": "primary", + "description": "Name of the news organization", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding party affiliation", + "role": "y" + }, + { + "name": "Party Affiliation", + "importance": "primary", + "description": "The party affiliation of the data point", + "role": "group" + } + ] + } + }, + "1077": { + "metadata": { + "title": "America's One-Sided Love of European Wine", + "description": "Wine trade between the U.S. and major EU wine producers from Aug. 2022 to Jul. 2023 (in 1,000 hectoliters)", + "main_insight": "The U.S. imports significantly more wine from major European producers than it exports to them, with Italy and France being the largest exporters to the U.S." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Italy", + "Units": 3.0, + "Trade Flow": "Imports from the U.S." + } + ], + "score": 1.0, + "annotation": "The Imports from the U.S. has the smallest proportion in Italy.", + "reason": "The Units of Imports from the U.S. in Italy accounts for 0.0008665511265164644, smaller than all other Trade Flow, of the total of Units of all Trade Flow." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Spain", + "Units": 3.0, + "Trade Flow": "Imports from the U.S." + } + ], + "score": 0.98, + "annotation": "The Imports from the U.S. has the smallest proportion in Spain.", + "reason": "The Units of Imports from the U.S. in Spain accounts for 0.004424778761061947, smaller than all other Trade Flow, of the total of Units of all Trade Flow." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Germany", + "Units": 252.0, + "Trade Flow": "Imports from the U.S." + } + ], + "score": 0.94, + "annotation": "The Imports from the U.S. shows a sudden jump in Units from France to Germany.", + "reason": "The Units for Imports from the U.S. increases from 54.0 at France to 252.0 at Germany, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "France", + "Units": 54.0, + "Trade Flow": "Imports from the U.S." + } + ], + "score": 0.87, + "annotation": "The Imports from the U.S. has the smallest proportion in France.", + "reason": "The Units of Imports from the U.S. in France accounts for 0.030033370411568408, smaller than all other Trade Flow, of the total of Units of all Trade Flow." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "Italy", + "Units": 3459.0, + "Trade Flow": "Exports to the U.S." + } + ], + "score": 0.84, + "annotation": "The Exports to the U.S. shows a sudden jump in Units from France to Italy.", + "reason": "The Units for Exports to the U.S. increases from 1744.0 at France to 3459.0 at Italy, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "Italy", + "Units": 3459.0, + "Trade Flow": "Exports to the U.S." + }, + { + "Country": "Italy", + "Units": 3.0, + "Trade Flow": "Imports from the U.S." + }, + { + "Country": "France", + "Units": 1744.0, + "Trade Flow": "Exports to the U.S." + }, + { + "Country": "France", + "Units": 54.0, + "Trade Flow": "Imports from the U.S." + }, + { + "Country": "Spain", + "Units": 675.0, + "Trade Flow": "Exports to the U.S." + }, + { + "Country": "Spain", + "Units": 3.0, + "Trade Flow": "Imports from the U.S." + }, + { + "Country": "Portugal", + "Units": 234.0, + "Trade Flow": "Exports to the U.S." + }, + { + "Country": "Germany", + "Units": 162.0, + "Trade Flow": "Exports to the U.S." + }, + { + "Country": "Germany", + "Units": 252.0, + "Trade Flow": "Imports from the U.S." + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Major EU wine producers", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding trade flow", + "role": "y" + }, + { + "name": "Trade Flow", + "importance": "primary", + "description": "The trade flow of the data point", + "role": "group" + } + ] + } + }, + "1897": { + "metadata": { + "title": "Trump's Approval Rating Hits New Low", + "description": "\"Do you approve/disapprove of the job Donald Trump is doing as president?\"", + "main_insight": "Donald Trump's disapproval rating reached a new high of 58% on March 18, 2017, while his approval rating hit a low of 37% on the same date." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "2017-03-18", + "Units": 37.0, + "Approval Rating": "Approve" + } + ], + "score": 0.94, + "annotation": "The Approve has a minimum value at 2017-03-18", + "reason": "The [{'Date': '2017-03-18', 'Units': 37.0, 'Approval Rating': 'Approve'}] of Approve has a mainimum value of 37.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "2017-01-22", + "Units": 45.0, + "Approval Rating": "Disapprove" + } + ], + "score": 0.94, + "annotation": "The Disapprove has a minimum value at 2017-01-22", + "reason": "The [{'Date': '2017-01-22', 'Units': 45.0, 'Approval Rating': 'Disapprove'}] of Disapprove has a mainimum value of 45.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "2017-02-11", + "Units": 41.0, + "Approval Rating": "Approve" + } + ], + "score": 0.83, + "annotation": "The Approve shows a sudden jump in Units from 2017-03-18 to 2017-02-11.", + "reason": "The Units for Approve increases from 37.0 at 2017-03-18 to 41.0 at 2017-02-11, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Date": "2017-03-18", + "Units": 58.0, + "Approval Rating": "Disapprove" + }, + { + "Date": "2017-03-18", + "Units": 37.0, + "Approval Rating": "Approve" + } + ], + "score": 0.81, + "annotation": "The Units of Disapprove and Approve show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Disapprove and Approve is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "2017-01-28", + "Units": 51.0, + "Approval Rating": "Disapprove" + } + ], + "score": 0.81, + "annotation": "The Disapprove shows a sudden jump in Units from 2017-01-22 to 2017-01-28.", + "reason": "The Units for Disapprove increases from 45.0 at 2017-01-22 to 51.0 at 2017-01-28, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Date": "2017-01-22", + "Units": 45.0, + "Approval Rating": "Disapprove" + }, + { + "Date": "2017-01-22", + "Units": 45.0, + "Approval Rating": "Approve" + }, + { + "Date": "2017-01-28", + "Units": 51.0, + "Approval Rating": "Disapprove" + }, + { + "Date": "2017-01-28", + "Units": 43.0, + "Approval Rating": "Approve" + }, + { + "Date": "2017-02-04", + "Units": 53.0, + "Approval Rating": "Disapprove" + }, + { + "Date": "2017-02-04", + "Units": 43.0, + "Approval Rating": "Approve" + }, + { + "Date": "2017-02-11", + "Units": 55.0, + "Approval Rating": "Disapprove" + }, + { + "Date": "2017-02-11", + "Units": 41.0, + "Approval Rating": "Approve" + }, + { + "Date": "2017-02-18", + "Units": 55.0, + "Approval Rating": "Disapprove" + }, + { + "Date": "2017-02-18", + "Units": 41.0, + "Approval Rating": "Approve" + }, + { + "Date": "2017-02-25", + "Units": 54.0, + "Approval Rating": "Disapprove" + }, + { + "Date": "2017-02-25", + "Units": 43.0, + "Approval Rating": "Approve" + }, + { + "Date": "2017-03-04", + "Units": 51.0, + "Approval Rating": "Disapprove" + }, + { + "Date": "2017-03-04", + "Units": 45.0, + "Approval Rating": "Approve" + }, + { + "Date": "2017-03-11", + "Units": 53.0, + "Approval Rating": "Disapprove" + }, + { + "Date": "2017-03-11", + "Units": 44.0, + "Approval Rating": "Approve" + }, + { + "Date": "2017-03-18", + "Units": 58.0, + "Approval Rating": "Disapprove" + }, + { + "Date": "2017-03-18", + "Units": 37.0, + "Approval Rating": "Approve" + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Date of the poll", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding approval rating", + "role": "y" + }, + { + "name": "Approval Rating", + "importance": "primary", + "description": "The approval rating of the data point", + "role": "group" + } + ] + } + }, + "317": { + "metadata": { + "title": "Putin Is Held In Low Regard Around The World", + "description": "% confident about President Vladimir Putin doing the right thing regarding world affairs", + "main_insight": "A large majority of people across most of the surveyed countries lack confidence in President Vladimir Putin's handling of world affairs. Vietnam is a notable outlier, with a majority expressing confidence." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "Vietnam", + "Score": 70.0, + "Confidence Level": "Confidence" + } + ], + "score": 0.97, + "annotation": "The Confidence has a maximum value at Vietnam", + "reason": "The [{'Country': 'Vietnam', 'Score': 70.0, 'Confidence Level': 'Confidence'}] of Confidence has a maximum value of 70.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Vietnam", + "Score": 11.0, + "Confidence Level": "No confidence" + } + ], + "score": 0.91, + "annotation": "The No confidence has a minimum value at Vietnam", + "reason": "The [{'Country': 'Vietnam', 'Score': 11.0, 'Confidence Level': 'No confidence'}] of No confidence has a mainimum value of 11.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Country": "Vietnam", + "Score": 11.0, + "Confidence Level": "No confidence" + }, + { + "Country": "Vietnam", + "Score": 70.0, + "Confidence Level": "Confidence" + } + ], + "score": 0.9, + "annotation": "The Score of No confidence and Confidence show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between No confidence and Confidence is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "South Africa", + "Score": 41.0, + "Confidence Level": "No confidence" + } + ], + "score": 0.77, + "annotation": "The No confidence accounts for the majority in South Africa.", + "reason": "The Score of No confidence in South Africa accounts for 0.5942028985507246 (more than 50%) of the total of Score of all Confidence Level." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Country": "Spain", + "Score": 6.0, + "Confidence Level": "Confidence" + } + ], + "score": 0.77, + "annotation": "The Confidence has the smallest proportion in Spain.", + "reason": "The Score of Confidence in Spain accounts for 0.061224489795918366, smaller than all other Confidence Level, of the total of Score of all Confidence Level." + } + ], + "data": { + "data": [ + { + "Country": "Spain", + "Score": 92.0, + "Confidence Level": "No confidence" + }, + { + "Country": "Spain", + "Score": 6.0, + "Confidence Level": "Confidence" + }, + { + "Country": "Poland", + "Score": 87.0, + "Confidence Level": "No confidence" + }, + { + "Country": "Poland", + "Score": 9.0, + "Confidence Level": "Confidence" + }, + { + "Country": "France", + "Score": 85.0, + "Confidence Level": "No confidence" + }, + { + "Country": "France", + "Score": 15.0, + "Confidence Level": "Confidence" + }, + { + "Country": "Ukraine", + "Score": 84.0, + "Confidence Level": "No confidence" + }, + { + "Country": "Ukraine", + "Score": 10.0, + "Confidence Level": "Confidence" + }, + { + "Country": "Australia", + "Score": 81.0, + "Confidence Level": "No confidence" + }, + { + "Country": "Australia", + "Score": 15.0, + "Confidence Level": "Confidence" + }, + { + "Country": "Canada", + "Score": 76.0, + "Confidence Level": "No confidence" + }, + { + "Country": "Canada", + "Score": 17.0, + "Confidence Level": "Confidence" + }, + { + "Country": "Germany", + "Score": 76.0, + "Confidence Level": "No confidence" + }, + { + "Country": "Germany", + "Score": 23.0, + "Confidence Level": "Confidence" + }, + { + "Country": "United States", + "Score": 75.0, + "Confidence Level": "No confidence" + }, + { + "Country": "United States", + "Score": 17.0, + "Confidence Level": "Confidence" + }, + { + "Country": "Israel", + "Score": 74.0, + "Confidence Level": "No confidence" + }, + { + "Country": "Israel", + "Score": 24.0, + "Confidence Level": "Confidence" + }, + { + "Country": "Japan", + "Score": 71.0, + "Confidence Level": "No confidence" + }, + { + "Country": "Japan", + "Score": 22.0, + "Confidence Level": "Confidence" + }, + { + "Country": "Turkey", + "Score": 60.0, + "Confidence Level": "No confidence" + }, + { + "Country": "Turkey", + "Score": 29.0, + "Confidence Level": "Confidence" + }, + { + "Country": "South Africa", + "Score": 41.0, + "Confidence Level": "No confidence" + }, + { + "Country": "South Africa", + "Score": 28.0, + "Confidence Level": "Confidence" + }, + { + "Country": "China", + "Score": 29.0, + "Confidence Level": "No confidence" + }, + { + "Country": "China", + "Score": 54.0, + "Confidence Level": "Confidence" + }, + { + "Country": "India", + "Score": 15.0, + "Confidence Level": "No confidence" + }, + { + "Country": "India", + "Score": 36.0, + "Confidence Level": "Confidence" + }, + { + "Country": "Vietnam", + "Score": 11.0, + "Confidence Level": "No confidence" + }, + { + "Country": "Vietnam", + "Score": 70.0, + "Confidence Level": "Confidence" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country surveyed", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding confidence level", + "role": "y" + }, + { + "name": "Confidence Level", + "importance": "primary", + "description": "The confidence level of the data point", + "role": "group" + } + ] + } + }, + "2020": { + "metadata": { + "title": "Asian Stock Markets Start 2021 Strong", + "description": "Daily change in closing prices of selected Asian stock indices in 2020/21 (in percent)", + "main_insight": "Asian stock markets, especially the Kospi, experienced significant growth towards the end of 2020 and into early 2021, after a downturn earlier in the year. The Hang Seng index had the lowest performance and Kospi performed the best." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "2021-01-04", + "Ratio": 35.4, + "Stock Indices": "Kospi" + }, + { + "Month": "2021-01-04", + "Ratio": 17.5, + "Stock Indices": "Nikkei" + } + ], + "score": 0.97, + "annotation": "The Ratio of Kospi and Nikkei show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Kospi and Nikkei is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Month": "2020-03-01", + "Ratio": -30.0, + "Stock Indices": "Kospi" + } + ], + "score": 0.9, + "annotation": "The Kospi has a minimum value at 2020-03-01", + "reason": "The [{'Month': '2020-03-01', 'Ratio': -30.0, 'Stock Indices': 'Kospi'}] of Kospi has a mainimum value of -30.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "2021-01-04", + "Ratio": 35.4, + "Stock Indices": "Kospi" + }, + { + "Month": "2021-01-04", + "Ratio": 13.5, + "Stock Indices": "SSE Composite" + } + ], + "score": 0.89, + "annotation": "The Ratio of Kospi and SSE Composite show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Kospi and SSE Composite is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Month": "2021-01-04", + "Ratio": 35.4, + "Stock Indices": "Kospi" + } + ], + "score": 0.87, + "annotation": "The Kospi has a maximum value at 2021-01-04", + "reason": "The [{'Month': '2021-01-04', 'Ratio': 35.4, 'Stock Indices': 'Kospi'}] of Kospi has a maximum value of 35.4, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "2021-01-04", + "Ratio": 13.5, + "Stock Indices": "SSE Composite" + }, + { + "Month": "2021-01-04", + "Ratio": 17.5, + "Stock Indices": "Nikkei" + } + ], + "score": 0.85, + "annotation": "The Ratio of SSE Composite and Nikkei show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between SSE Composite and Nikkei is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Month": "2020-02-01", + "Ratio": -5.0, + "Stock Indices": "Kospi" + }, + { + "Month": "2020-02-01", + "Ratio": -7.0, + "Stock Indices": "SSE Composite" + }, + { + "Month": "2020-02-01", + "Ratio": -5.0, + "Stock Indices": "Nikkei" + }, + { + "Month": "2020-02-01", + "Ratio": -5.0, + "Stock Indices": "Hang Seng" + }, + { + "Month": "2020-03-01", + "Ratio": -30.0, + "Stock Indices": "Kospi" + }, + { + "Month": "2020-03-01", + "Ratio": -10.0, + "Stock Indices": "SSE Composite" + }, + { + "Month": "2020-03-01", + "Ratio": -15.0, + "Stock Indices": "Nikkei" + }, + { + "Month": "2020-03-01", + "Ratio": -18.0, + "Stock Indices": "Hang Seng" + }, + { + "Month": "2020-04-01", + "Ratio": -10.0, + "Stock Indices": "Kospi" + }, + { + "Month": "2020-04-01", + "Ratio": -8.0, + "Stock Indices": "SSE Composite" + }, + { + "Month": "2020-04-01", + "Ratio": -8.0, + "Stock Indices": "Nikkei" + }, + { + "Month": "2020-04-01", + "Ratio": -13.0, + "Stock Indices": "Hang Seng" + }, + { + "Month": "2020-05-01", + "Ratio": -8.0, + "Stock Indices": "Kospi" + }, + { + "Month": "2020-05-01", + "Ratio": -7.0, + "Stock Indices": "SSE Composite" + }, + { + "Month": "2020-05-01", + "Ratio": -6.0, + "Stock Indices": "Nikkei" + }, + { + "Month": "2020-05-01", + "Ratio": -17.0, + "Stock Indices": "Hang Seng" + }, + { + "Month": "2020-06-01", + "Ratio": -3.0, + "Stock Indices": "Kospi" + }, + { + "Month": "2020-06-01", + "Ratio": 2.0, + "Stock Indices": "SSE Composite" + }, + { + "Month": "2020-06-01", + "Ratio": -4.0, + "Stock Indices": "Nikkei" + }, + { + "Month": "2020-06-01", + "Ratio": -12.0, + "Stock Indices": "Hang Seng" + }, + { + "Month": "2020-07-01", + "Ratio": 7.0, + "Stock Indices": "Kospi" + }, + { + "Month": "2020-07-01", + "Ratio": 12.0, + "Stock Indices": "SSE Composite" + }, + { + "Month": "2020-07-01", + "Ratio": -2.0, + "Stock Indices": "Nikkei" + }, + { + "Month": "2020-07-01", + "Ratio": -10.0, + "Stock Indices": "Hang Seng" + }, + { + "Month": "2020-08-01", + "Ratio": 5.0, + "Stock Indices": "Kospi" + }, + { + "Month": "2020-08-01", + "Ratio": 5.0, + "Stock Indices": "SSE Composite" + }, + { + "Month": "2020-08-01", + "Ratio": 5.0, + "Stock Indices": "Nikkei" + }, + { + "Month": "2020-08-01", + "Ratio": -8.0, + "Stock Indices": "Hang Seng" + }, + { + "Month": "2020-09-01", + "Ratio": 8.0, + "Stock Indices": "Kospi" + }, + { + "Month": "2020-09-01", + "Ratio": 1.0, + "Stock Indices": "SSE Composite" + }, + { + "Month": "2020-09-01", + "Ratio": 3.0, + "Stock Indices": "Nikkei" + }, + { + "Month": "2020-09-01", + "Ratio": -13.0, + "Stock Indices": "Hang Seng" + }, + { + "Month": "2020-10-01", + "Ratio": 5.0, + "Stock Indices": "Kospi" + }, + { + "Month": "2020-10-01", + "Ratio": 1.0, + "Stock Indices": "SSE Composite" + }, + { + "Month": "2020-10-01", + "Ratio": 2.0, + "Stock Indices": "Nikkei" + }, + { + "Month": "2020-10-01", + "Ratio": -12.0, + "Stock Indices": "Hang Seng" + }, + { + "Month": "2020-11-01", + "Ratio": 18.0, + "Stock Indices": "Kospi" + }, + { + "Month": "2020-11-01", + "Ratio": 8.0, + "Stock Indices": "SSE Composite" + }, + { + "Month": "2020-11-01", + "Ratio": 12.0, + "Stock Indices": "Nikkei" + }, + { + "Month": "2020-11-01", + "Ratio": -8.0, + "Stock Indices": "Hang Seng" + }, + { + "Month": "2020-12-01", + "Ratio": 30.0, + "Stock Indices": "Kospi" + }, + { + "Month": "2020-12-01", + "Ratio": 13.5, + "Stock Indices": "SSE Composite" + }, + { + "Month": "2020-12-01", + "Ratio": 17.5, + "Stock Indices": "Nikkei" + }, + { + "Month": "2020-12-01", + "Ratio": -6.0, + "Stock Indices": "Hang Seng" + }, + { + "Month": "2021-01-04", + "Ratio": 35.4, + "Stock Indices": "Kospi" + }, + { + "Month": "2021-01-04", + "Ratio": 13.5, + "Stock Indices": "SSE Composite" + }, + { + "Month": "2021-01-04", + "Ratio": 17.5, + "Stock Indices": "Nikkei" + }, + { + "Month": "2021-01-04", + "Ratio": -3.8, + "Stock Indices": "Hang Seng" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year, from January 2020 to January 4, 2021", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding stock indices", + "role": "y" + }, + { + "name": "Stock Indices", + "importance": "primary", + "description": "The stock indices of the data point", + "role": "group" + } + ] + } + }, + "2133": { + "metadata": { + "title": "Indian Rail Entering the 21st Century?", + "description": "Projected number of AC train sets required in India to fill demand", + "main_insight": "The projected number of AC train sets required in India is expected to more than quadruple between 2021 and 2051, increasing from 70 to 306." + }, + "chart_type": "Pictorial Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2021, + "AC Train Sets": 70 + }, + { + "Year": 2031, + "AC Train Sets": 144 + }, + { + "Year": 2041, + "AC Train Sets": 214 + }, + { + "Year": 2051, + "AC Train Sets": 306 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of projection", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "AC Train Sets", + "importance": "primary", + "description": "Projected number of AC train sets required", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "991": { + "metadata": { + "title": "Apple's App Store: A Success Story", + "description": "Cumulative app downloads from Apple's App Store (in billions)", + "main_insight": "Cumulative app downloads from Apple's App Store have consistently increased from 2010 to 2013, reaching over 40 billion by January 2013." + }, + "chart_type": "Line Graph", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Date": "Jan 2013", + "Cumulative Downloads (billions)": 40.0 + } + ], + "score": 0.9, + "annotation": "The has a maximum value at Jan 2013", + "reason": "The [{'Date': 'Jan 2013', 'Cumulative Downloads (billions)': 40.0}] of has a maximum value of 40.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Date": "Jan 2010", + "Cumulative Downloads (billions)": 3.0 + } + ], + "score": 0.63, + "annotation": "The has a minimum value at Jan 2010", + "reason": "The [{'Date': 'Jan 2010', 'Cumulative Downloads (billions)': 3.0}] of has a mainimum value of 3.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Date": "Mar 2012", + "Cumulative Downloads (billions)": 25.0 + } + ], + "score": 0.59, + "annotation": "The shows a sudden jump in Cumulative Downloads (billions) from Oct 2011 to Mar 2012.", + "reason": "The Cumulative Downloads (billions) for increases from 18.0 at Oct 2011 to 25.0 at Mar 2012, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Date": "Jan 2010", + "Cumulative Downloads (billions)": 3.0 + }, + { + "Date": "Apr 2010", + "Cumulative Downloads (billions)": 4.0 + }, + { + "Date": "Jun 2010", + "Cumulative Downloads (billions)": 5.0 + }, + { + "Date": "Sept 2010", + "Cumulative Downloads (billions)": 6.5 + }, + { + "Date": "Oct 2010", + "Cumulative Downloads (billions)": 7.0 + }, + { + "Date": "Jan 2011", + "Cumulative Downloads (billions)": 10.0 + }, + { + "Date": "Jun 2011", + "Cumulative Downloads (billions)": 14.0 + }, + { + "Date": "Jul 2011", + "Cumulative Downloads (billions)": 15.0 + }, + { + "Date": "Oct 2011", + "Cumulative Downloads (billions)": 18.0 + }, + { + "Date": "Mar 2012", + "Cumulative Downloads (billions)": 25.0 + }, + { + "Date": "Jun 2012", + "Cumulative Downloads (billions)": 30.0 + }, + { + "Date": "Oct 2012", + "Cumulative Downloads (billions)": 35.0 + }, + { + "Date": "Jan 2013", + "Cumulative Downloads (billions)": 40.0 + } + ], + "columns": [ + { + "name": "Date", + "importance": "primary", + "description": "Timeline of app downloads", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Cumulative Downloads (billions)", + "importance": "primary", + "description": "Cumulative number of app downloads in billions", + "unit": "billions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "565": { + "metadata": { + "title": "Young US Consumers Are Willing to Pay More for Flexibility When Booking Trips", + "description": "Share of US adults who said they would pay the following extra amounts for a hotel booking that offers free cancellation", + "main_insight": "Younger generations (Gen Z and Millennials) are significantly more willing to pay extra for free cancellation on hotel bookings compared to older generations (Gen Xers and Baby Boomers)." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Generation": "Baby boomers", + "Units": 9.0, + "Donation Amount": "$50 or more" + }, + { + "Generation": "Baby boomers", + "Units": 59.0, + "Donation Amount": "Nothing" + } + ], + "score": 0.97, + "annotation": "The Units of $50 or more and Nothing show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between $50 or more and Nothing is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Generation": "Millennials", + "Units": 28.0, + "Donation Amount": "$50 or more" + } + ], + "score": 0.9, + "annotation": "The $50 or more shows a 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in Gen Z adults accounts for 0.37, larger than all other Donation Amount, of the total of Units of all Donation Amount." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Donation Amount": "Nothing", + "Generation": "total", + "Units": 175.0 + } + ], + "score": 0.87, + "annotation": "The total value of Nothing has the largest proportion.", + "reason": "The total value of Units of Nothing accounts for 0.4375, larger than all other Donation Amount, of the total of Units of all Donation Amount." + } + ], + "data": { + "data": [ + { + "Generation": "Gen Z adults", + "Units": 29.0, + "Donation Amount": "$50 or more" + }, + { + "Generation": "Gen Z adults", + "Units": 37.0, + "Donation Amount": "$1-49" + }, + { + "Generation": "Gen Z adults", + "Units": 34.0, + "Donation Amount": "Nothing" + }, + { + "Generation": "Millennials", + "Units": 28.0, + "Donation Amount": "$50 or more" + }, + { + "Generation": "Millennials", + "Units": 36.0, + "Donation 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"primary", + "description": "The donation amount of the data point", + "role": "group" + } + ] + } + }, + "1293": { + "metadata": { + "title": "India's TV Industry Remains Resilient Despite Cord Cutting", + "description": "Estimated number of TV households in India, by year (in millions)", + "main_insight": "Despite cord-cutting, the number of TV households in India is projected to increase between 2022 and 2026. The impact of cord-cutting reduces the total number of TV households, but the overall trend is still growth." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2026, + "Units": 262.0, + "Cord-Cutting": "Without cord cutting" + }, + { + "Year": 2026, + "Units": 248.0, + "Cord-Cutting": "With cord cutting" + } + ], + "score": 0.99, + "annotation": "The Units of Without cord cutting and With cord cutting show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Without cord cutting and With cord cutting is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2022, + "Units": 226.0, + "Cord-Cutting": "Without cord cutting" + }, + { + "Year": 2022, + "Units": 226.0, + "Cord-Cutting": "With cord cutting" + }, + { + "Year": 2023, + "Units": 235.0, + "Cord-Cutting": "Without cord cutting" + }, + { + "Year": 2023, + "Units": 232.0, + "Cord-Cutting": "With cord cutting" + }, + { + "Year": 2024, + "Units": 243.0, + "Cord-Cutting": "Without cord cutting" + }, + { + "Year": 2024, + "Units": 239.0, + "Cord-Cutting": "With cord cutting" + }, + { + "Year": 2025, + "Units": 252.0, + "Cord-Cutting": "Without cord cutting" + }, + { + "Year": 2025, + "Units": 244.0, + "Cord-Cutting": "With cord cutting" + }, + { + "Year": 2026, + "Units": 262.0, + "Cord-Cutting": "Without cord cutting" + }, + { + "Year": 2026, + "Units": 248.0, + "Cord-Cutting": "With cord cutting" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of estimate", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding cord-cutting", + "role": "y" + }, + { + "name": "Cord-Cutting", + "importance": "primary", + "description": "The cord-cutting of the data point", + "role": "group" + } + ] + } + }, + "1883": { + "metadata": { + "title": "Worldwide Domain Names Top 250 Million", + "description": "Number of registered domain names across all top-level domains at year-end (in millions)", + "main_insight": "The number of registered domain names worldwide grew significantly from 2007 to 2012, reaching 252 million. Other TLDs consistently make up the majority of registered domain names." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2012, + "Units": 252.0, + "TLDs": "Total Domain Names" + }, + { + "Year": 2012, + "Units": 221.8, + "TLDs": "Other TLDs" + } + ], + "score": 0.97, + "annotation": "The Units of Total Domain Names and Other TLDs show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Domain Names and Other TLDs is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2012, + "Units": 221.8, + "TLDs": "Other TLDs" + }, + { + "Year": 2012, + "Units": 30.2, + "TLDs": "Country Code TLDs" + } + ], + "score": 0.56, + "annotation": "The Units of Other TLDs and Country Code TLDs show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Other TLDs and Country Code TLDs is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Year": 2012, + "Units": 252.0, + "TLDs": "Total Domain Names" + }, + { + "Year": 2012, + "Units": 30.2, + "TLDs": "Country Code TLDs" + } + ], + "score": 0.34, + "annotation": "The Units of Total Domain Names and Country Code TLDs show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between Total Domain Names and Country Code TLDs is {r:.2f}, indicating a strong negative relationship." + } + ], + "data": { + "data": [ + { + "Year": 2007, + "Units": 153.0, + "TLDs": "Total Domain Names" + }, + { + "Year": 2007, + "Units": 111.7, + "TLDs": "Other TLDs" + }, + { + "Year": 2007, + "Units": 41.3, + "TLDs": "Country Code TLDs" + }, + { + "Year": 2008, + "Units": 177.0, + "TLDs": "Total Domain Names" + }, + { + "Year": 2008, + "Units": 148.7, + "TLDs": "Other TLDs" + }, + { + "Year": 2008, + "Units": 28.3, + "TLDs": "Country Code TLDs" + }, + { + "Year": 2009, + "Units": 192.0, + "TLDs": "Total Domain Names" + }, + { + "Year": 2009, + "Units": 176.6, + "TLDs": "Other TLDs" + }, + { + "Year": 2009, + "Units": 15.4, + "TLDs": "Country Code TLDs" + }, + { + "Year": 2010, + "Units": 205.0, + "TLDs": "Total Domain Names" + }, + { + "Year": 2010, + "Units": 190.7, + "TLDs": "Other TLDs" + }, + { + "Year": 2010, + "Units": 14.4, + "TLDs": "Country Code TLDs" + }, + { + "Year": 2011, + "Units": 225.0, + "TLDs": "Total Domain Names" + }, + { + "Year": 2011, + "Units": 202.5, + "TLDs": "Other TLDs" + }, + { + "Year": 2011, + "Units": 22.5, + "TLDs": "Country Code TLDs" + }, + { + "Year": 2012, + "Units": 252.0, + "TLDs": "Total Domain Names" + }, + { + "Year": 2012, + "Units": 221.8, + "TLDs": "Other TLDs" + }, + { + "Year": 2012, + "Units": 30.2, + "TLDs": "Country Code TLDs" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of measurement", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding tlds", + "role": "y" + }, + { + "name": "TLDs", + "importance": "primary", + "description": "The tlds of the data point", + "role": "group" + } + ] + } + }, + "471": { + "metadata": { + "title": "How Americans View Anonymous Sources", + "description": "Percentage of U.S. adults who say the use of anonymous sources in news stories is acceptable, by party", + "main_insight": "Both Republicans and Democrats are more likely to accept the use of anonymous sources only in special cases. Democrats are slightly more accepting of anonymous sources always, while Republicans are more likely to say they are never acceptable." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Party": "Democrats", + "Score": 70.0, + "Frequency": "Only in special cases" + }, + { + "Party": "Democrats", + "Score": 22.0, + "Frequency": "Always" + } + ], + "score": 1.0, + "annotation": "The Score of Only in special cases and Always show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between Only in special cases and Always is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Party": "Democrats", + "Score": 70.0, + "Frequency": "Only in special cases" + }, + { + "Party": "Democrats", + "Score": 8.0, + "Frequency": "Never" + } + ], + "score": 1.0, + "annotation": "The Score of Only in special cases and Never show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Only in special cases and Never is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Party": "Democrats", + "Score": 22.0, + "Frequency": "Always" + }, + { + "Party": "Democrats", + "Score": 8.0, + "Frequency": "Never" + } + ], + "score": 1.0, + "annotation": "The Score of Always and Never show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Always and Never is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Party": "Democrats", + "Score": 70.0, + "Frequency": "Only in special cases" + } + ], + "score": 0.97, + "annotation": "The Only in special cases shows a sudden jump in Score from Republicans to Democrats.", + "reason": "The Score for Only in special cases increases from 63.0 at Republicans to 70.0 at Democrats, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Party": "Democrats", + "Score": 22.0, + "Frequency": "Always" + } + ], + "score": 0.97, + "annotation": "The Always shows a sudden jump in Score from Republicans to Democrats.", + "reason": "The Score for Always increases from 8.0 at Republicans to 22.0 at Democrats, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Party": "Republicans", + "Score": 63.0, + "Frequency": "Only in special cases" + }, + { + "Party": "Republicans", + "Score": 8.0, + "Frequency": "Always" + }, + { + "Party": "Republicans", + "Score": 29.0, + "Frequency": "Never" + }, + { + "Party": "Democrats", + "Score": 70.0, + "Frequency": "Only in special cases" + }, + { + "Party": "Democrats", + "Score": 22.0, + "Frequency": "Always" + }, + { + "Party": "Democrats", + "Score": 8.0, + "Frequency": "Never" + } + ], + "columns": [ + { + "name": "Party", + "importance": "primary", + "description": "Political party affiliation", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding frequency", + "role": "y" + }, + { + "name": "Frequency", + "importance": "primary", + "description": "The frequency of the data point", + "role": "group" + } + ] + } + }, + "756": { + "metadata": { + "title": "Apple's iPhone cash cow", + "description": "Material and production costs of Apple iPhones compared to retail prices*", + "main_insight": "Apple's iPhone has consistently maintained a high markup percentage, generally above 200%, despite variations in material and manufacturing costs and retail prices over different models and years." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "iPhone Model": "iPhone 6 Plus 2014", + "Units": 4.5, + "Cost Structure": "Manufacturing Costs" + } + ], + "score": 0.98, + "annotation": "The Manufacturing Costs has the smallest proportion in iPhone 6 Plus 2014.", + "reason": "The Units of Manufacturing Costs in iPhone 6 Plus 2014 accounts for 0.003714097061736547, smaller than all other Cost Structure, of the total of Units of all Cost Structure." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "iPhone Model": "iPhone 6 2014", + "Units": 4.0, + "Cost Structure": "Manufacturing Costs" + } + ], + "score": 0.98, + "annotation": "The Manufacturing Costs has the smallest proportion in iPhone 6 2014.", + "reason": "The Units of Manufacturing Costs in iPhone 6 2014 accounts for 0.003727518404622123, smaller than all other Cost Structure, of the total of Units of all Cost Structure." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "iPhone Model": "iPhone 6S Plus 2015", + "Units": 4.5, + "Cost Structure": "Manufacturing Costs" + } + ], + "score": 0.98, + "annotation": "The Manufacturing Costs has the smallest proportion in iPhone 6S Plus 2015.", + "reason": "The Units of Manufacturing Costs in iPhone 6S Plus 2015 accounts for 0.0037437603993344427, smaller than all other Cost Structure, of the total of Units of all Cost Structure." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "iPhone Model": "iPhone SE 2016", + "Units": 3.8, + "Cost Structure": "Manufacturing Costs" + } + ], + "score": 0.97, + "annotation": "The Manufacturing Costs has the smallest proportion in iPhone SE 2016.", + "reason": "The Units of Manufacturing Costs in iPhone SE 2016 accounts for 0.005367231638418079, smaller than all other Cost Structure, of the total of Units of all Cost Structure." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Cost Structure": "Retail Price", + "iPhone Model": "total", + "Units": 5042.0 + } + ], + "score": 0.97, + "annotation": "The total value of Retail Price has the smallest proportion.", + "reason": "The total value of Units of Retail Price accounts for 0.00572153595710045, smaller than all other Cost Structure, of the total of Units of all Cost Structure." + } + ], + "data": { + "data": [ + { + "iPhone Model": "iPhone 4S 2011", + "Units": 188.0, + "Cost Structure": "Material Costs" + }, + { + "iPhone Model": "iPhone 4S 2011", + "Units": 8.0, + "Cost Structure": "Manufacturing Costs" + }, + { + "iPhone Model": "iPhone 4S 2011", + "Units": 649.0, + "Cost Structure": "Retail Price" + }, + { + "iPhone Model": "iPhone 4S 2011", + "Units": 231.0, + "Cost Structure": "Markup %" + }, + { + "iPhone Model": "iPhone 5 2012", + "Units": 199.0, + "Cost Structure": "Material Costs" + }, + { + "iPhone Model": "iPhone 5 2012", + "Units": 8.0, + "Cost Structure": "Manufacturing Costs" + }, + { + "iPhone Model": "iPhone 5 2012", + "Units": 649.0, + "Cost Structure": "Retail Price" + }, + { + "iPhone Model": "iPhone 5 2012", + "Units": 214.0, + "Cost Structure": "Markup %" + }, + { + "iPhone Model": "iPhone 5S 2013", + "Units": 190.7, + "Cost Structure": "Material Costs" + }, + { + "iPhone Model": "iPhone 5S 2013", + "Units": 8.0, + "Cost Structure": "Manufacturing Costs" + }, + { + "iPhone Model": "iPhone 5S 2013", + "Units": 649.0, + "Cost Structure": "Retail Price" + }, + { + "iPhone Model": "iPhone 5S 2013", + "Units": 227.0, + "Cost Structure": "Markup %" + }, + { + "iPhone Model": "iPhone 5C 2013", + "Units": 166.0, + "Cost Structure": "Material Costs" + }, + { + "iPhone Model": "iPhone 5C 2013", + "Units": 7.0, + "Cost Structure": "Manufacturing Costs" + }, + { + "iPhone Model": "iPhone 5C 2013", + "Units": 549.0, + "Cost Structure": "Retail Price" + }, + { + "iPhone Model": "iPhone 5C 2013", + "Units": 217.0, + "Cost Structure": "Markup %" + }, + { + "iPhone Model": "iPhone 6 Plus 2014", + "Units": 211.1, + "Cost Structure": "Material Costs" + }, + { + "iPhone Model": "iPhone 6 Plus 2014", + "Units": 4.5, + "Cost Structure": "Manufacturing Costs" + }, + { + "iPhone Model": "iPhone 6 Plus 2014", + "Units": 749.0, + "Cost Structure": "Retail Price" + }, + { + "iPhone Model": "iPhone 6 Plus 2014", + "Units": 247.0, + "Cost Structure": "Markup %" + }, + { + "iPhone Model": "iPhone 6 2014", + "Units": 196.1, + "Cost Structure": "Material Costs" + }, + { + "iPhone Model": "iPhone 6 2014", + "Units": 4.0, + "Cost Structure": "Manufacturing Costs" + }, + { + "iPhone Model": "iPhone 6 2014", + "Units": 649.0, + "Cost Structure": "Retail Price" + }, + { + "iPhone Model": "iPhone 6 2014", + "Units": 224.0, + "Cost Structure": "Markup %" + }, + { + "iPhone Model": "iPhone 6S Plus 2015", + "Units": 231.5, + "Cost Structure": "Material Costs" + }, + { + "iPhone Model": "iPhone 6S Plus 2015", + "Units": 4.5, + "Cost Structure": "Manufacturing Costs" + }, + { + "iPhone Model": "iPhone 6S Plus 2015", + "Units": 749.0, + "Cost Structure": "Retail Price" + }, + { + "iPhone Model": "iPhone 6S Plus 2015", + "Units": 217.0, + "Cost Structure": "Markup %" + }, + { + "iPhone Model": "iPhone SE 2016", + "Units": 156.2, + "Cost Structure": "Material Costs" + }, + { + "iPhone Model": "iPhone SE 2016", + "Units": 3.8, + "Cost Structure": "Manufacturing Costs" + }, + { + "iPhone Model": "iPhone SE 2016", + "Units": 399.0, + "Cost Structure": "Retail Price" + }, + { + "iPhone Model": "iPhone SE 2016", + "Units": 149.0, + "Cost Structure": "Markup %" + } + ], + "columns": [ + { + "name": "iPhone Model", + "importance": "primary", + "description": "The model and year of the iPhone", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding cost structure", + "role": "y" + }, + { + "name": "Cost Structure", + "importance": "primary", + "description": "The cost structure of the data point", + "role": "group" + } + ] + } + }, + "1928": { + "metadata": { + "title": "Can the Tour de France Shake Its Tarnished Past?", + "description": "List of Tour de France winners in the past 20 years", + "main_insight": "The Tour de France has been plagued by doping scandals, with many winners over the past 20 years either testing positive, admitting to doping, or being retroactively stripped of their titles." + }, + "chart_type": "Dot Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 1997, + "Winner": "Jan Ullrich" + }, + { + "Year": 1998, + "Winner": "Marco Pantani" + }, + { + "Year": 1999, + "Winner": "Lance Armstrong" + }, + { + "Year": 2000, + "Winner": "Lance Armstrong" + }, + { + "Year": 2001, + "Winner": "Lance Armstrong" + }, + { + "Year": 2002, + "Winner": "Lance Armstrong" + }, + { + "Year": 2003, + "Winner": "Lance Armstrong" + }, + { + "Year": 2004, + "Winner": "Lance Armstrong" + }, + { + "Year": 2005, + "Winner": "Lance Armstrong" + }, + { + "Year": 2006, + "Winner": "Floyd Landis" + }, + { + "Year": 2007, + "Winner": "Alberto Contador" + }, + { + "Year": 2008, + "Winner": "Carlos Sastre" + }, + { + "Year": 2009, + "Winner": "Alberto Contador" + }, + { + "Year": 2010, + "Winner": "Alberto Contador" + }, + { + "Year": 2011, + "Winner": "Cadel Evans" + }, + { + "Year": 2012, + "Winner": "Bradley Wiggins" + }, + { + "Year": 2013, + "Winner": "Chris Froome" + }, + { + "Year": 2014, + "Winner": "Vincenzo Nibali" + }, + { + "Year": 2015, + "Winner": "Chris Froome" + }, + { + "Year": 2016, + "Winner": "Chris Froome" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the Tour de France", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Winner", + "importance": "primary", + "description": "Name of the winner of the Tour de France", + "unit": "none", + "data_type": "string", + "role": "y" + } + ] + } + }, + "414": { + "metadata": { + "title": "U.S. Dominates Global Arms Exports", + "description": "Share of global exports of major conventional weapons systems and components*", + "main_insight": "The US has significantly increased its dominance in global arms exports from 2013-2017 to 2018-2022, while Russia's share has declined." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "US", + "Units": 40.0, + "Timeframe": "2018-2022" + } + ], + "score": 0.96, + "annotation": "The 2018-2022 has a maximum value at US", + "reason": "The [{'Country': 'US', 'Units': 40.0, 'Timeframe': '2018-2022'}] of 2018-2022 has a maximum value of 40.0, which is the largest in all categories." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Country": "Others", + "Units": 15.8, + "Timeframe": "2013-2017" + }, + { + "Country": "Others", + "Units": 14.0, + "Timeframe": "2018-2022" + } + ], + "score": 0.95, + "annotation": "The Units of 2013-2017 and 2018-2022 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between 2013-2017 and 2018-2022 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "US", + "Units": 33.0, + "Timeframe": "2013-2017" + } + ], + "score": 0.93, + "annotation": "The 2013-2017 has a maximum value at US", + "reason": "The [{'Country': 'US', 'Units': 33.0, 'Timeframe': '2013-2017'}] of 2013-2017 has a maximum value of 33.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "US", + "Units": 40.0, + "Timeframe": "2018-2022" + } + ], + "score": 0.9, + "annotation": "The 2018-2022 shows a sudden jump in Units from RU to US.", + "reason": "The Units for 2018-2022 increases from 16.0 at RU to 40.0 at US, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Timeframe": "2018-2022", + "Country": "total", + "Units": 101.5 + } + ], + "score": 0.8, + "annotation": "The total value of 2018-2022 accounts for the majority in all Timeframe.", + "reason": "The total value of Units of 2018-2022 accounts for 0.5075 (more than 50%) of the total of Units of all Timeframe." + } + ], + "data": { + "data": [ + { + "Country": "US", + "Units": 33.0, + "Timeframe": "2013-2017" + }, + { + "Country": "US", + "Units": 40.0, + "Timeframe": "2018-2022" + }, + { + "Country": "RU", + "Units": 22.0, + "Timeframe": "2013-2017" + }, + { + "Country": "RU", + "Units": 16.0, + "Timeframe": "2018-2022" + }, + { + "Country": "FR", + "Units": 7.1, + "Timeframe": "2013-2017" + }, + { + "Country": "FR", + "Units": 11.0, + "Timeframe": "2018-2022" + }, + { + "Country": "CN", + "Units": 6.3, + "Timeframe": "2013-2017" + }, + { + "Country": "CN", + "Units": 5.2, + "Timeframe": "2018-2022" + }, + { + "Country": "DE", + "Units": 6.1, + "Timeframe": "2013-2017" + }, + { + "Country": "DE", + "Units": 4.2, + "Timeframe": "2018-2022" + }, + { + "Country": "IT", + "Units": 2.5, + "Timeframe": "2013-2017" + }, + { + "Country": "IT", + "Units": 3.8, + "Timeframe": "2018-2022" + }, + { + "Country": "GB", + "Units": 3.2, + "Timeframe": "2013-2017" + }, + { + "Country": "GB", + "Units": 4.7, + "Timeframe": "2018-2022" + }, + { + "Country": "ES", + "Units": 2.5, + "Timeframe": "2013-2017" + }, + { + "Country": "ES", + "Units": 2.6, + "Timeframe": "2018-2022" + }, + { + "Country": "Others", + "Units": 15.8, + "Timeframe": "2013-2017" + }, + { + "Country": "Others", + "Units": 14.0, + "Timeframe": "2018-2022" + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country or category", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding timeframe", + "role": "y" + }, + { + "name": "Timeframe", + "importance": "primary", + "description": "The timeframe of the data point", + "role": "group" + } + ] + } + }, + "1891": { + "metadata": { + "title": "Refugees Arriving Across the Mediterranean", + "description": "Migrant sea arrivals in Europe by selected countries of arrival since January 2015", + "main_insight": "Migrant sea arrivals peaked sharply in 2015, primarily in Greece, and have since significantly decreased. Italy has consistently received a substantial number of arrivals throughout the period." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Year": "2015-10", + "Dollars": 210000, + "Country": "Greece" + } + ], + "score": 0.97, + "annotation": "The Greece has a maximum value at 2015-10", + "reason": "The [{'Year': '2015-10', 'Dollars': 210000, 'Country': 'Greece'}] of Greece has a maximum value of 210000, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Year": "2015-10", + "Dollars": 210000, + "Country": "Greece" + } + ], + "score": 0.96, + "annotation": "The Greece shows a sudden jump in Dollars from 2016-01 to 2015-10.", + "reason": "The Dollars for Greece increases from 20000 at 2016-01 to 210000 at 2015-10, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Year": "2015-10", + "Dollars": 3000, + "Country": "Spain" + } + ], + "score": 0.94, + "annotation": "The Spain has the smallest proportion in 2015-10.", + "reason": "The Dollars of Spain in 2015-10 accounts for 0.012875536480686695, smaller than all other Country, of the total of Dollars of all Country." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Year": "2016-10", + "Dollars": 5000, + "Country": "Spain" + } + ], + "score": 0.87, + "annotation": "The Spain has a maximum value at 2016-10", + "reason": "The [{'Year': '2016-10', 'Dollars': 5000, 'Country': 'Spain'}] of Spain has a maximum value of 5000, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Year": "2015-01", + "Dollars": 5000, + "Country": "Greece" + }, + { + "Year": "2015-01", + "Dollars": 5000, + "Country": "Italy" + } + ], + "score": 0.87, + "annotation": "The Greece, Italy all have the largest proportion in 2015-01.", + "reason": "The Dollars of Greece, Italy in 2015-01 all account for 0.45454545454545453, larger than all other Country, of the total of Dollars of all Country." + } + ], + "data": { + "data": [ + { + "Year": "2015-01", + "Dollars": 5000, + "Country": "Greece" + }, + { + "Year": "2015-01", + "Dollars": 5000, + "Country": "Italy" + }, + { + "Year": "2015-01", + "Dollars": 1000, + "Country": "Spain" + }, + { + "Year": "2015-06", + "Dollars": 20000, + "Country": "Greece" + }, + { + "Year": "2015-06", + "Dollars": 15000, + "Country": "Italy" + }, + { + "Year": "2015-06", + "Dollars": 2000, + "Country": "Spain" + }, + { + "Year": "2015-10", + "Dollars": 210000, + "Country": "Greece" + }, + { + "Year": "2015-10", + "Dollars": 20000, + "Country": "Italy" + }, + { + "Year": "2015-10", + "Dollars": 3000, + "Country": "Spain" + }, + { + "Year": "2016-01", + "Dollars": 20000, + "Country": "Greece" + }, + { + "Year": "2016-01", + "Dollars": 10000, + "Country": "Italy" + }, + { + "Year": "2016-01", + "Dollars": 1000, + "Country": "Spain" + }, + { + "Year": "2016-06", + "Dollars": 5000, + "Country": "Greece" + }, + { + "Year": "2016-06", + "Dollars": 20000, + "Country": "Italy" + }, + { + "Year": "2016-06", + "Dollars": 2000, + "Country": "Spain" + }, + { + "Year": "2016-10", + "Dollars": 2000, + "Country": "Greece" + }, + { + "Year": "2016-10", + "Dollars": 22000, + "Country": "Italy" + }, + { + "Year": "2016-10", + "Dollars": 5000, + "Country": "Spain" + }, + { + "Year": "2017-01", + "Dollars": 1000, + "Country": "Greece" + }, + { + "Year": "2017-01", + "Dollars": 10000, + "Country": "Italy" + }, + { + "Year": "2017-01", + "Dollars": 2000, + "Country": "Spain" + }, + { + "Year": "2017-06", + "Dollars": 5000, + "Country": "Greece" + }, + { + "Year": "2017-06", + "Dollars": 20000, + "Country": "Italy" + }, + { + "Year": "2017-06", + "Dollars": 4000, + "Country": "Spain" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of migrant sea arrivals", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Dollars", + "importance": "primary", + "description": "Dollars for the corresponding country", + "role": "y" + }, + { + "name": "Country", + "importance": "primary", + "description": "The country of the data point", + "role": "group" + } + ] + } + }, + "670": { + "metadata": { + "title": "America's Most & Least Trusted Professions", + "description": "Share of U.S. respondents rating the honesty/ethical standards of the following professions as high/low", + "main_insight": "Nurses are the most trusted profession in America, with 79% of respondents rating their honesty/ethical standards as high/very high, while Members of Congress are the least trusted, with only 9% giving them the same rating." + }, + "chart_type": "Horizontal Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Profession": "Members of Congress", + "Units": 9.0, + "Risk Level": "High/Very high" + }, + { + "Profession": "Members of Congress", + "Units": 62.0, + "Risk Level": "Low/Very low" + } + ], + "score": 0.91, + "annotation": "The Units of High/Very high and Low/Very low show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Units between High/Very high and Low/Very low is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Profession": "Nurses", + "Units": 79.0, + "Risk Level": "High/Very high" + } + ], + "score": 0.91, + "annotation": "The High/Very high has a maximum value at Nurses", + "reason": "The [{'Profession': 'Nurses', 'Units': 79.0, 'Risk Level': 'High/Very high'}] of High/Very high has a maximum value of 79.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Profession": "Members of Congress", + "Units": 62.0, + "Risk Level": "Low/Very low" + } + ], + "score": 0.85, + "annotation": "The Low/Very low has a maximum value at Members of Congress", + "reason": "The [{'Profession': 'Members of Congress', 'Units': 62.0, 'Risk Level': 'Low/Very low'}] of Low/Very low has a maximum value of 62.0, which is the largest in all categories." + }, + { + "type": "proportion", + "subtype": "value_minority", + "data_points": [ + { + "Profession": "Nurses", + "Units": 4.0, + "Risk Level": "Low/Very low" + } + ], + "score": 0.81, + "annotation": "The Low/Very low has the smallest proportion in Nurses.", + "reason": "The Units of Low/Very low in Nurses accounts for 0.04819277108433735, smaller than all other Risk Level, of the total of Units of all Risk Level." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Risk Level": "High/Very high", + "Profession": "total", + "Units": 390.0 + } + ], + "score": 0.8, + "annotation": "The total value of High/Very high accounts for the majority in all Risk Level.", + "reason": "The total value of Units of High/Very high accounts for 0.5145118733509235 (more than 50%) of the total of Units of all Risk Level." + } + ], + "data": { + "data": [ + { + "Profession": "Nurses", + "Units": 79.0, + "Risk Level": "High/Very high" + }, + { + "Profession": "Nurses", + "Units": 4.0, + "Risk Level": "Low/Very low" + }, + { + "Profession": "Medical doctors", + "Units": 62.0, + "Risk Level": "High/Very high" + }, + { + "Profession": "Medical doctors", + "Units": 10.0, + "Risk Level": "Low/Very low" + }, + { + "Profession": "High school teachers", + "Units": 53.0, + "Risk Level": "High/Very high" + }, + { + "Profession": "High school teachers", + "Units": 15.0, + "Risk Level": "Low/Very low" + }, + { + "Profession": "Police officers", + "Units": 50.0, + "Risk Level": "High/Very high" + }, + { + "Profession": "Police officers", + "Units": 18.0, + "Risk Level": "Low/Very low" + }, + { + "Profession": "Judges", + "Units": 39.0, + "Risk Level": "High/Very high" + }, + { + "Profession": "Judges", + "Units": 19.0, + "Risk Level": "Low/Very low" + }, + { + "Profession": "Lawyers", + "Units": 21.0, + "Risk Level": "High/Very high" + }, + { + "Profession": "Lawyers", + "Units": 28.0, + "Risk Level": "Low/Very low" + }, + { + "Profession": "Labor union leaders", + "Units": 24.0, + "Risk Level": "High/Very high" + }, + { + "Profession": "Labor union leaders", + "Units": 31.0, + "Risk Level": "Low/Very low" + }, + { + "Profession": "Business executives", + "Units": 14.0, + "Risk Level": "High/Very high" + }, + { + "Profession": "Business executives", + "Units": 36.0, + "Risk Level": "Low/Very low" + }, + { + "Profession": "Journalists", + "Units": 23.0, + "Risk Level": "High/Very high" + }, + { + "Profession": "Journalists", + "Units": 42.0, + "Risk Level": "Low/Very low" + }, + { + "Profession": "Car salespeople", + "Units": 10.0, + "Risk Level": "High/Very high" + }, + { + "Profession": "Car salespeople", + "Units": 44.0, + "Risk Level": "Low/Very low" + }, + { + "Profession": "Telemarketers", + "Units": 6.0, + "Risk Level": "High/Very high" + }, + { + "Profession": "Telemarketers", + "Units": 59.0, + "Risk Level": "Low/Very low" + }, + { + "Profession": "Members of Congress", + "Units": 9.0, + "Risk Level": "High/Very high" + }, + { + "Profession": "Members of Congress", + "Units": 62.0, + "Risk Level": "Low/Very low" + } + ], + "columns": [ + { + "name": "Profession", + "importance": "primary", + "description": "The profession being rated", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding risk level", + "role": "y" + }, + { + "name": "Risk Level", + "importance": "primary", + "description": "The risk level of the data point", + "role": "group" + } + ] + } + }, + "1843": { + "metadata": { + "title": "Russia: low confidence in honesty of elections", + "description": "Share of Russians saying they are confident in the honesty of elections in their country", + "main_insight": "Confidence in the honesty of elections in Russia has generally increased from 2012 to 2017, though there were slight dips in some years." + }, + "chart_type": "Multiple Donut Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2012, + "Units": 23.0, + "Confidence Metric": "Confidence in Election Honesty" + }, + { + "Year": 2013, + "Units": 23.0, + "Confidence Metric": "Confidence in Election Honesty" + }, + { + "Year": 2014, + "Units": 39.0, + "Confidence Metric": "Confidence in Election Honesty" + }, + { + "Year": 2015, + "Units": 34.0, + "Confidence Metric": "Confidence in Election Honesty" + }, + { + "Year": 2016, + "Units": 30.0, + "Confidence Metric": "Confidence in Election Honesty" + }, + { + "Year": 2017, + "Units": 40.0, + "Confidence Metric": "Confidence in Election Honesty" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of the survey", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding confidence metric", + "role": "y" + }, + { + "name": "Confidence Metric", + "importance": "primary", + "description": "The confidence metric of the data point", + "role": "group" + } + ] + } + }, + "1505": { + "metadata": { + "title": "Forget Nvidia! Here Comes Cocoa!", + "description": "Change in the ICCO daily cocoa price and Nvidia's stock price in 2024 (as of Mar. 27)", + "main_insight": "Cocoa prices have significantly outperformed Nvidia's stock price in the first three months of 2024, with cocoa increasing by 124% and Nvidia by 82%." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Month": "Mar.", + "Ratio": 124.0, + "Company": "Cocoa" + }, + { + "Month": "Mar.", + "Ratio": 82.0, + "Company": "Nvidia" + } + ], + "score": 1.0, + "annotation": "The Ratio of Cocoa and Nvidia show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Ratio between Cocoa and Nvidia is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Mar.", + "Ratio": 124.0, + "Company": "Cocoa" + } + ], + "score": 0.97, + "annotation": "The Cocoa shows a sudden jump in Ratio from Feb. to Mar..", + "reason": "The Ratio for Cocoa increases from 35.0 at Feb. to 124.0 at Mar., indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Month": "Mar.", + "Ratio": 82.0, + "Company": "Nvidia" + } + ], + "score": 0.97, + "annotation": "The Nvidia shows a sudden jump in Ratio from Feb. to Mar..", + "reason": "The Ratio for Nvidia increases from 45.0 at Feb. to 82.0 at Mar., indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Company": "Cocoa", + "Month": "total", + "Ratio": 159.0 + } + ], + "score": 0.78, + "annotation": "The total value of Cocoa accounts for the majority in all Company.", + "reason": "The total value of Ratio of Cocoa accounts for 0.5559440559440559 (more than 50%) of the total of Ratio of all Company." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Month": "Feb.", + "Ratio": 45.0, + "Company": "Nvidia" + } + ], + "score": 0.78, + "annotation": "The Nvidia accounts for the majority in Feb..", + "reason": "The Ratio of Nvidia in Feb. accounts for 0.5625 (more than 50%) of the total of Ratio of all Company." + } + ], + "data": { + "data": [ + { + "Month": "Feb.", + "Ratio": 35.0, + "Company": "Cocoa" + }, + { + "Month": "Feb.", + "Ratio": 45.0, + "Company": "Nvidia" + }, + { + "Month": "Mar.", + "Ratio": 124.0, + "Company": "Cocoa" + }, + { + "Month": "Mar.", + "Ratio": 82.0, + "Company": "Nvidia" + } + ], + "columns": [ + { + "name": "Month", + "importance": "primary", + "description": "Month of the year 2024", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Ratio", + "importance": "primary", + "description": "Ratio for the corresponding company", + "role": "y" + }, + { + "name": "Company", + "importance": "primary", + "description": "The company of the data point", + "role": "group" + } + ] + } + }, + "827": { + "metadata": { + "title": "Americans Set to Spend $487 Billion on Tech in 2021", + "description": "Estimated consumer technology sales in the United States in 2021 (selected categories)", + "main_insight": "Americans are projected to spend $487 billion on consumer technology in 2021, with smartphones being the largest category. Gaming consoles show the highest year-over-year growth." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Category": "Smartphones", + "Sales (Billion $)": 73.0 + } + ], + "score": 0.93, + "annotation": "The has a maximum value at Smartphones", + "reason": "The [{'Category': 'Smartphones', 'Sales (Billion $)': 73.0}] of has a maximum value of 73.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Category": "Smartphones", + "Sales (Billion $)": 73.0 + } + ], + "score": 0.77, + "annotation": "The shows a sudden jump in Sales (Billion $) from Laptops to Smartphones.", + "reason": "The Sales (Billion $) for increases from 45.0 at Laptops to 73.0 at Smartphones, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Category": "E-Toys", + "Sales (Billion $)": 3.6 + } + ], + "score": 0.52, + "annotation": "The has a minimum value at E-Toys", + "reason": "The [{'Category': 'E-Toys', 'Sales (Billion $)': 3.6}] of has a mainimum value of 3.6, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Category": "Smartphones", + "Sales (Billion $)": 73.0 + }, + { + "Category": "Laptops", + "Sales (Billion $)": 45.0 + }, + { + "Category": "Televisions", + "Sales (Billion $)": 24.0 + }, + { + "Category": "Smart Home", + "Sales (Billion $)": 15.0 + }, + { + "Category": "Health/Fitness Tech", + "Sales (Billion $)": 13.0 + }, + { + "Category": "True Wireless Earbuds", + "Sales (Billion $)": 8.7 + }, + { + "Category": "Gaming Consoles", + "Sales (Billion $)": 6.0 + }, + { + "Category": "E-Toys", + "Sales (Billion $)": 3.6 + } + ], + "columns": [ + { + "name": "Category", + "importance": "primary", + "description": "Tech product category", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Sales (Billion $)", + "importance": "primary", + "description": "Estimated 2021 sales in billions of US dollars", + "unit": "Billion $", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1717": { + "metadata": { + "title": "Book Market Expected to Rally After Covid Slump", + "description": "Worldwide estimated revenue with eBooks and physical books", + "main_insight": "The book market experienced a dip in 2020, likely due to the Covid pandemic, but is expected to recover and grow steadily from 2021 onwards, with both physical and eBooks contributing to the growth." + }, + "chart_type": "Vertical Stacked Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Year": 2023, + "Units": 112.1, + "Book Format": "Physical books" + }, + { + "Year": 2023, + "Units": 17.8, + "Book Format": "eBooks" + } + ], + "score": 0.48, + "annotation": "The Units of Physical books and eBooks show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Physical books and eBooks is {r:.2f}, indicating a strong positive relationship." + } + ], + "data": { + "data": [ + { + "Year": 2018, + "Units": 110.8, + "Book Format": "Physical books" + }, + { + "Year": 2018, + "Units": 14.5, + "Book Format": "eBooks" + }, + { + "Year": 2019, + "Units": 108.4, + "Book Format": "Physical books" + }, + { + "Year": 2019, + "Units": 15.0, + "Book Format": "eBooks" + }, + { + "Year": 2020, + "Units": 101.0, + "Book Format": "Physical books" + }, + { + "Year": 2020, + "Units": 14.9, + "Book Format": "eBooks" + }, + { + "Year": 2021, + "Units": 107.1, + "Book Format": "Physical books" + }, + { + "Year": 2021, + "Units": 16.1, + "Book Format": "eBooks" + }, + { + "Year": 2022, + "Units": 109.9, + "Book Format": "Physical books" + }, + { + "Year": 2022, + "Units": 17.0, + "Book Format": "eBooks" + }, + { + "Year": 2023, + "Units": 112.1, + "Book Format": "Physical books" + }, + { + "Year": 2023, + "Units": 17.8, + "Book Format": "eBooks" + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of revenue", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding book format", + "role": "y" + }, + { + "name": "Book Format", + "importance": "primary", + "description": "The book format of the data point", + "role": "group" + } + ] + } + }, + "1532": { + "metadata": { + "title": "How Many Cups of Coffee Do Americans Drink Each Day?", + "description": "Share of U.S. coffee drinkers by level/frequency of coffee consumption", + "main_insight": "The infographic shows the distribution of coffee consumption among US coffee drinkers, differentiating between at-home weekday consumption and to-go consumption, and frequency. The largest share of at-home weekday drinkers consume 2 cups, whereas most to-go consumers drink coffee daily." + }, + "chart_type": "Multiple Pie Chart", + "datafacts": [], + "data": { + "data": [ + { + "Cups of Coffee (At home on a weekday)": 1, + "Units": 21.0, + "Consumption": "Percentage of Drinkers (At home)" + }, + { + "Cups of Coffee (At home on a weekday)": 1, + "Units": "Daily", + "Consumption": "Frequency of Coffee Consumption (To go)" + }, + { + "Cups of Coffee (At home on a weekday)": 1, + "Units": 20.0, + "Consumption": "Percentage of Drinkers (To Go)" + }, + { + "Cups of Coffee (At home on a weekday)": 2, + "Units": 27.0, + "Consumption": "Percentage of Drinkers (At home)" + }, + { + "Cups of Coffee (At home on a weekday)": 2, + "Units": "Several times per week", + "Consumption": "Frequency of Coffee Consumption (To go)" + }, + { + "Cups of Coffee (At home on a weekday)": 2, + "Units": 24.0, + "Consumption": "Percentage of Drinkers (To Go)" + }, + { + "Cups of Coffee (At home on a weekday)": 3, + "Units": 20.0, + "Consumption": "Percentage of Drinkers (At home)" + }, + { + "Cups of Coffee (At home on a weekday)": 3, + "Units": "Once per week", + "Consumption": "Frequency of Coffee Consumption (To go)" + }, + { + "Cups of Coffee (At home on a weekday)": 3, + "Units": 21.0, + "Consumption": "Percentage of Drinkers (To Go)" + }, + { + "Cups of Coffee (At home on a weekday)": "4 to 5", + "Units": 18.0, + "Consumption": "Percentage of Drinkers (At home)" + }, + { + "Cups of Coffee (At home on a weekday)": "4 to 5", + "Units": "Less often", + "Consumption": "Frequency of Coffee Consumption (To go)" + }, + { + "Cups of Coffee (At home on a weekday)": "4 to 5", + "Units": 25.0, + "Consumption": "Percentage of Drinkers (To Go)" + }, + { + "Cups of Coffee (At home on a weekday)": "6 or more", + "Units": 13.0, + "Consumption": "Percentage of Drinkers (At home)" + }, + { + "Cups of Coffee (At home on a weekday)": "6 or more", + "Units": "Never", + "Consumption": "Frequency of Coffee Consumption (To go)" + }, + { + "Cups of Coffee (At home on a weekday)": "6 or more", + "Units": 13.0, + "Consumption": "Percentage of Drinkers (To Go)" + } + ], + "columns": [ + { + "name": "Cups of Coffee (At home on a weekday)", + "importance": "primary", + "description": "Number of cups consumed at home on a weekday", + "unit": "cups", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding consumption", + "role": "y" + }, + { + "name": "Consumption", + "importance": "primary", + "description": "The consumption of the data point", + "role": "group" + } + ] + } + }, + "2088": { + "metadata": { + "title": "Who Makes The Most Reliable Electrical Appliances?", + "description": "Average predicted reliability score (100 = most reliable)", + "main_insight": "Speed Queen has the highest average predicted reliability score among electrical appliance brands, significantly outperforming many other well-known brands." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Brand": "Speed Queen", + "Reliability Score": 83.0 + } + ], + "score": 0.93, + "annotation": "The has a maximum value at Speed Queen", + "reason": "The [{'Brand': 'Speed Queen', 'Reliability Score': 83.0}] of has a maximum value of 83.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Brand": "Speed Queen", + "Reliability Score": 83.0 + } + ], + "score": 0.71, + "annotation": "The shows a sudden jump in Reliability Score from Miele to Speed Queen.", + "reason": "The Reliability Score for increases from 75.0 at Miele to 83.0 at Speed Queen, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Brand": "Maytag", + "Reliability Score": 58.0 + } + ], + "score": 0.59, + "annotation": "The has a minimum value at Maytag", + "reason": "The [{'Brand': 'Maytag', 'Reliability Score': 58.0}] of has a mainimum value of 58.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Brand": "Speed Queen", + "Reliability Score": 83.0 + }, + { + "Brand": "Miele", + "Reliability Score": 75.0 + }, + { + "Brand": "Ikea", + "Reliability Score": 70.0 + }, + { + "Brand": "LG", + "Reliability Score": 68.0 + }, + { + "Brand": "Thermador", + "Reliability Score": 63.0 + }, + { + "Brand": "Bosch", + "Reliability Score": 63.0 + }, + { + "Brand": "GE", + "Reliability Score": 60.0 + }, + { + "Brand": "Kenmore", + "Reliability Score": 60.0 + }, + { + "Brand": "Whirlpool", + "Reliability Score": 59.0 + }, + { + "Brand": "Maytag", + "Reliability Score": 58.0 + } + ], + "columns": [ + { + "name": "Brand", + "importance": "primary", + "description": "The brand of electrical appliance", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Reliability Score", + "importance": "primary", + "description": "Average predicted reliability score (100 = most reliable)", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1015": { + "metadata": { + "title": "Is Game Of Thrones Fueling Dubrovnik's Tourism?", + "description": "Number of tourist arrivals in Dubrovnik by year (in million)", + "main_insight": "Tourist arrivals in Dubrovnik have increased steadily from 2011 to 2016, with a notable rise after 2012, coinciding with the airing of Game of Thrones." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [], + "data": { + "data": [ + { + "Year": 2011, + "Tourist Arrivals": 1.1 + }, + { + "Year": 2012, + "Tourist Arrivals": 1.1 + }, + { + "Year": 2013, + "Tourist Arrivals": 1.2 + }, + { + "Year": 2014, + "Tourist Arrivals": 1.3 + }, + { + "Year": 2015, + "Tourist Arrivals": 1.4 + }, + { + "Year": 2016, + "Tourist Arrivals": 1.6 + } + ], + "columns": [ + { + "name": "Year", + "importance": "primary", + "description": "Year of tourist arrivals", + "unit": "none", + "data_type": "time", + "role": "x" + }, + { + "name": "Tourist Arrivals", + "importance": "primary", + "description": "Number of tourist arrivals in millions", + "unit": "millions", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2129": { + "metadata": { + "title": "The U.S. Leads the World in Toilet Paper Consumption", + "description": "Estimated annual per capita toilet paper consumption in selected countries (2018)", + "main_insight": "The United States has the highest per capita toilet paper consumption among the selected countries, significantly exceeding other nations." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United States", + "Consumption (kg)": 12.7 + } + ], + "score": 0.8, + "annotation": "The has a maximum value at United States", + "reason": "The [{'Country': 'United States', 'Consumption (kg)': 12.7}] of has a maximum value of 12.7, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "Brazil", + "Consumption (kg)": 3.4 + } + ], + "score": 0.79, + "annotation": "The has a minimum value at Brazil", + "reason": "The [{'Country': 'Brazil', 'Consumption (kg)': 3.4}] of has a mainimum value of 3.4, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United Kingdom", + "Consumption (kg)": 11.4 + } + ], + "score": 0.73, + "annotation": "The shows a sudden jump in Consumption (kg) from Japan to United Kingdom.", + "reason": "The Consumption (kg) for increases from 8.2 at Japan to 11.4 at United Kingdom, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Consumption (kg)": 12.7 + }, + { + "Country": "Germany", + "Consumption (kg)": 12.1 + }, + { + "Country": "United Kingdom", + "Consumption (kg)": 11.4 + }, + { + "Country": "Japan", + "Consumption (kg)": 8.2 + }, + { + "Country": "Australia", + "Consumption (kg)": 7.9 + }, + { + "Country": "Spain", + "Consumption (kg)": 7.3 + }, + { + "Country": "France", + "Consumption (kg)": 6.4 + }, + { + "Country": "Italy", + "Consumption (kg)": 6.3 + }, + { + "Country": "China", + "Consumption (kg)": 4.4 + }, + { + "Country": "Brazil", + "Consumption (kg)": 3.4 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Selected countries for comparison", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Consumption (kg)", + "importance": "primary", + "description": "Annual per capita toilet paper consumption in kilograms", + "unit": "kg", + "data_type": "number", + "role": "y" + } + ] + } + }, + "2139": { + "metadata": { + "title": "Women Behind the Scenes in Hollywood", + "description": "Share of women in behind-the-scenes roles in Hollywood on top 250 films* (in percent)", + "main_insight": "The chart shows the percentage of women in various behind-the-scenes roles in Hollywood, comparing 1998 and 2017. While there has been some progress in certain roles, women remain significantly underrepresented in most key positions." + }, + "chart_type": "Horizontal Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "negative", + "data_points": [ + { + "Role": "Overall", + "Score": 18.0, + "Group": "Women_Overall" + }, + { + "Role": "Overall", + "Score": 82.0, + "Group": "Men_Overall" + } + ], + "score": 1.0, + "annotation": "The Score of Women_Overall and Men_Overall show a negative correlation.", + "reason": "The Pearson correlation coefficient of the Score between Women_Overall and Men_Overall is {r:.2f}, indicating a strong negative relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Role": "Overall", + "Score": 18.0, + "Group": "Women_Overall" + } + ], + "score": 0.97, + "annotation": "The Women_Overall shows a sudden jump in Score from Cinematographers to Overall.", + "reason": "The Score for Women_Overall increases from 17.0 at Cinematographers to 18.0 at Overall, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Role": "Producers", + "Score": 83.0, + "Group": "Men_Overall" + } + ], + "score": 0.97, + "annotation": "The Men_Overall shows a sudden jump in Score from Overall to Producers.", + "reason": "The Score for Men_Overall increases from 82.0 at Overall to 83.0 at Producers, indicating a sharp change." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Role": "Cinematographers", + "Score": 4.0, + "Group": "1998" + }, + { + "Role": "Cinematographers", + "Score": 4.0, + "Group": "2017" + } + ], + "score": 0.95, + "annotation": "The Score of 1998 and 2017 show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Score between 1998 and 2017 is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Role": "Overall", + "Score": 82.0, + "Group": "Men_Overall" + } + ], + "score": 0.95, + "annotation": "The Men_Overall has a minimum value at Overall", + "reason": "The [{'Role': 'Overall', 'Score': 82.0, 'Group': 'Men_Overall'}] of Men_Overall has a mainimum value of 82.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Role": "Producers", + "Score": 24.0, + "Group": "1998" + }, + { + "Role": "Producers", + "Score": 25.0, + "Group": "2017" + }, + { + "Role": "Producers", + "Score": 17.0, + "Group": "Women_Overall" + }, + { + "Role": "Producers", + "Score": 83.0, + "Group": "Men_Overall" + }, + { + "Role": "Executive producers", + "Score": 18.0, + "Group": "1998" + }, + { + "Role": "Executive producers", + "Score": 19.0, + "Group": "2017" + }, + { + "Role": "Executive producers", + "Score": 17.0, + "Group": "Women_Overall" + }, + { + "Role": "Executive producers", + "Score": 83.0, + "Group": "Men_Overall" + }, + { + "Role": "Editors", + "Score": 20.0, + "Group": "1998" + }, + { + "Role": "Editors", + "Score": 16.0, + "Group": "2017" + }, + { + "Role": "Editors", + "Score": 17.0, + "Group": "Women_Overall" + }, + { + "Role": "Editors", + "Score": 83.0, + "Group": "Men_Overall" + }, + { + "Role": "Directors", + "Score": 9.0, + "Group": "1998" + }, + { + "Role": "Directors", + "Score": 11.0, + "Group": "2017" + }, + { + "Role": "Directors", + "Score": 17.0, + "Group": "Women_Overall" + }, + { + "Role": "Directors", + "Score": 83.0, + "Group": "Men_Overall" + }, + { + "Role": "Writers", + "Score": 13.0, + "Group": "1998" + }, + { + "Role": "Writers", + "Score": 11.0, + "Group": "2017" + }, + { + "Role": "Writers", + "Score": 17.0, + "Group": "Women_Overall" + }, + { + "Role": "Writers", + "Score": 83.0, + "Group": "Men_Overall" + }, + { + "Role": "Cinematographers", + "Score": 4.0, + "Group": "1998" + }, + { + "Role": "Cinematographers", + "Score": 4.0, + "Group": "2017" + }, + { + "Role": "Cinematographers", + "Score": 17.0, + "Group": "Women_Overall" + }, + { + "Role": "Cinematographers", + "Score": 83.0, + "Group": "Men_Overall" + }, + { + "Role": "Overall", + "Score": 18.0, + "Group": "Women_Overall" + }, + { + "Role": "Overall", + "Score": 82.0, + "Group": "Men_Overall" + } + ], + "columns": [ + { + "name": "Role", + "importance": "primary", + "description": "Behind-the-scenes roles in Hollywood", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Score", + "importance": "primary", + "description": "Score for the corresponding group", + "role": "y" + }, + { + "name": "Group", + "importance": "primary", + "description": "The group of the data point", + "role": "group" + } + ] + } + }, + "1954": { + "metadata": { + "title": "Left Wins But Right Gains After French Elections", + "description": "Distribution of seats after France's 2024 legislative elections, by political party/group", + "main_insight": "While the New Popular Front (left-leaning) secured the most seats in the 2024 French legislative elections, the National Rally and allies (right-leaning) experienced the largest gain in seats compared to 2022. No party achieved the absolute majority of 289 seats." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Party/Group": "National Rally and allies", + "Seats": 143 + } + ], + "score": 0.81, + "annotation": "The shows a sudden jump in Seats from Republicans and allies to National Rally and allies.", + "reason": "The Seats for increases from 66 at Republicans and allies to 143 at National Rally and allies, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Party/Group": "Other left-wing parties", + "Seats": 12 + } + ], + "score": 0.65, + "annotation": "The has a minimum value at Other left-wing parties", + "reason": "The [{'Party/Group': 'Other left-wing parties', 'Seats': 12}] of has a mainimum value of 12, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Party/Group": "New Popular Front", + "Seats": 180 + } + ], + "score": 0.65, + "annotation": "The has a maximum value at New Popular Front", + "reason": "The [{'Party/Group': 'New Popular Front', 'Seats': 180}] of has a maximum value of 180, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Party/Group": "New Popular Front", + "Seats": 180 + }, + { + "Party/Group": "Ensemble", + "Seats": 163 + }, + { + "Party/Group": "National Rally and allies", + "Seats": 143 + }, + { + "Party/Group": "Republicans and allies", + "Seats": 66 + }, + { + "Party/Group": "Others", + "Seats": 13 + }, + { + "Party/Group": "Other left-wing parties", + "Seats": 12 + } + ], + "columns": [ + { + "name": "Party/Group", + "importance": "primary", + "description": "Political party or group", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Seats", + "importance": "primary", + "description": "Number of seats won in the 2024 election", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + }, + "1038": { + "metadata": { + "title": "Gold Reserves in Latin America", + "description": "Latin American countries with the largest gold reserves (in metric tons)", + "main_insight": "Venezuela has the largest gold reserves in Latin America, significantly exceeding other countries in the region, followed by Brazil and Mexico." + }, + "chart_type": "Vertical Bar Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "VE", + "Gold Reserves": 161.0 + } + ], + "score": 0.88, + "annotation": "The has a maximum value at VE", + "reason": "The [{'Country': 'VE', 'Gold Reserves': 161.0}] of has a maximum value of 161.0, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "MX", + "Gold Reserves": 120.0 + } + ], + "score": 0.75, + "annotation": "The shows a sudden jump in Gold Reserves from AR to MX.", + "reason": "The Gold Reserves for increases from 61.7 at AR to 120.0 at MX, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "CO", + "Gold Reserves": 4.7 + } + ], + "score": 0.59, + "annotation": "The has a minimum value at CO", + "reason": "The [{'Country': 'CO', 'Gold Reserves': 4.7}] of has a mainimum value of 4.7, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "VE", + "Gold Reserves": 161.0 + }, + { + "Country": "BR", + "Gold Reserves": 130.0 + }, + { + "Country": "MX", + "Gold Reserves": 120.0 + }, + { + "Country": "AR", + "Gold Reserves": 61.7 + }, + { + "Country": "BO", + "Gold Reserves": 42.5 + }, + { + "Country": "PE", + "Gold Reserves": 34.7 + }, + { + "Country": "EC", + "Gold Reserves": 33.8 + }, + { + "Country": "PY", + "Gold Reserves": 8.2 + }, + { + "Country": "GT", + "Gold Reserves": 6.9 + }, + { + "Country": "CO", + "Gold Reserves": 4.7 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country abbreviation", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Gold Reserves", + "importance": "primary", + "description": "Gold reserves in metric tons", + "unit": "metric tons", + "data_type": "number", + "role": "y" + } + ] + } + }, + "865": { + "metadata": { + "title": "Social Web's infiltration of World Wide Web", + "description": "Share of global websites* who link to Social Networks (in %)", + "main_insight": "Facebook has the highest share of global websites linking to it, significantly more than other social networks. A larger proportion of websites have links (All Links) to social networks compared to the percentage that just use plugins/widgets." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Social Network": "Linked in", + "Units": 3.9, + "Traffic Source": "All Links" + }, + { + "Social Network": "Linked in", + "Units": 0.6, + "Traffic Source": "Plugins/Widgets" + } + ], + "score": 0.83, + "annotation": "The Units of All Links and Plugins/Widgets show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between All Links and Plugins/Widgets is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Social Network": "facebook", + "Units": 24.3, + "Traffic Source": "Plugins/Widgets" + } + ], + "score": 0.81, + "annotation": "The Plugins/Widgets shows a sudden jump in Units from Google+ to facebook.", + "reason": "The Units for Plugins/Widgets increases from 13.3 at Google+ to 24.3 at facebook, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Social Network": "twitter", + "Units": 41.7, + "Traffic Source": "All Links" + } + ], + "score": 0.8, + "annotation": "The All Links shows a sudden jump in Units from Google+ to twitter.", + "reason": "The Units for All Links increases from 21.5 at Google+ to 41.7 at twitter, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Social Network": "Google+", + "Units": 21.5, + "Traffic Source": "All Links" + } + ], + "score": 0.76, + "annotation": "The All Links accounts for the majority in Google+.", + "reason": "The Units of All Links in Google+ accounts for 0.6178160919540231 (more than 50%) of the total of Units of all Traffic Source." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Social Network": "facebook", + "Units": 49.3, + "Traffic Source": "All Links" + } + ], + "score": 0.75, + "annotation": "The All Links accounts for the majority in facebook.", + "reason": "The Units of All Links in facebook accounts for 0.6698369565217391 (more than 50%) of the total of Units of all Traffic Source." + } + ], + "data": { + "data": [ + { + "Social Network": "facebook", + "Units": 49.3, + "Traffic Source": "All Links" + }, + { + "Social Network": "facebook", + "Units": 24.3, + "Traffic Source": "Plugins/Widgets" + }, + { + "Social Network": "Google+", + "Units": 21.5, + "Traffic Source": "All Links" + }, + { + "Social Network": "Google+", + "Units": 13.3, + "Traffic Source": "Plugins/Widgets" + }, + { + "Social Network": "twitter", + "Units": 41.7, + "Traffic Source": "All Links" + }, + { + "Social Network": "twitter", + "Units": 10.0, + "Traffic Source": "Plugins/Widgets" + }, + { + "Social Network": "Linked in", + "Units": 3.9, + "Traffic Source": "All Links" + }, + { + "Social Network": "Linked in", + "Units": 0.6, + "Traffic Source": "Plugins/Widgets" + } + ], + "columns": [ + { + "name": "Social Network", + "importance": "primary", + "description": "The social network platform", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Units", + "importance": "primary", + "description": "Units for the corresponding traffic source", + "role": "y" + }, + { + "name": "Traffic Source", + "importance": "primary", + "description": "The traffic source of the data point", + "role": "group" + } + ] + } + }, + "700": { + "metadata": { + "title": "How Back to School Shoppers Use Technology", + "description": "Device usage in back to school shopping in the United States 2017", + "main_insight": "PC is the most used device for back-to-school shopping, primarily for accessing retailer websites. Smartphones are commonly used for collecting discounts and getting price information." + }, + "chart_type": "Horizontal Bar Chart", + "datafacts": [ + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Device": "PC", + "PC_Users": 57.0 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in PC_Users from Tablet to PC.", + "reason": "The PC_Users for increases from 0 at Tablet to 57.0 at PC, indicating a sharp change." + }, + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Device": "PC", + "PC_Users": 57.0 + } + ], + "score": 0.67, + "annotation": "The has a maximum value at PC", + "reason": "The [{'Device': 'PC', 'PC_Users': 57.0}] of has a maximum value of 57.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Device": "Smartphone", + "PC_Users": 0 + }, + { + "Device": "Tablet", + "PC_Users": 0 + } + ], + "score": 0.39, + "annotation": "The has minimum values at Smartphone, Tablet", + "reason": "The [{'Device': 'Smartphone', 'PC_Users': 0}, {'Device': 'Tablet', 'PC_Users': 0}] of have minimum values of 0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Device": "PC", + "PC_Users": 57.0 + }, + { + "Device": "Smartphone", + "PC_Users": 0 + }, + { + "Device": "Tablet", + "PC_Users": 0 + } + ], + "columns": [ + { + "name": "Device", + "importance": "primary", + "description": "Type of device used for shopping", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "PC_Users", + "importance": "primary", + "description": "Percentage of shoppers using a PC", + "unit": "%", + "data_type": "number", + "role": "y" + } + ] + } + }, + "196": { + "metadata": { + "title": "Restaurant Industry Collapses Due to Widespread Shutdowns", + "description": "Year-over-year change in seated diners at restaurants on the OpenTable network", + "main_insight": "The restaurant industry experienced a dramatic decline in seated diners in March, with near-total collapse by the end of the month across all tracked regions due to widespread shutdowns." + }, + "chart_type": "Multiple Line Graph", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Day in March": "21", + "Units": -100.0, + "Region": "United States" + }, + { + "Day in March": "21", + "Units": -100.0, + "Region": "United Kingdom" + } + ], + "score": 1.0, + "annotation": "The Units of United States and United Kingdom show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between United States and United Kingdom is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Day in March": "21", + "Units": -100.0, + "Region": "United States" + }, + { + "Day in March": "21", + "Units": -96.0, + "Region": "Germany" + } + ], + "score": 0.99, + "annotation": "The Units of United States and Germany show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between United States and Germany is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Day in March": "21", + "Units": -100.0, + "Region": "United Kingdom" + }, + { + "Day in March": "21", + "Units": -96.0, + "Region": "Germany" + } + ], + "score": 0.99, + "annotation": "The Units of United Kingdom and Germany show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between United Kingdom and Germany is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Day in March": "20", + "Units": -100.0, + "Region": "United Kingdom" + }, + { + "Day in March": "21", + "Units": -100.0, + "Region": "United Kingdom" + } + ], + "score": 0.86, + "annotation": "The United Kingdom has minimum values at 20, 21", + "reason": "The [{'Day in March': '20', 'Units': -100.0, 'Region': 'United Kingdom'}, {'Day in March': '21', 'Units': -100.0, 'Region': 'United Kingdom'}] of United Kingdom have minimum values of -100.0, which is the largest in all categories." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Day in March": "20", + "Units": -100.0, + "Region": "United States" + }, + { + "Day in March": "21", + "Units": -100.0, + "Region": "United States" + } + ], + "score": 0.85, + "annotation": "The United States has minimum values at 20, 21", + "reason": "The [{'Day in March': '20', 'Units': -100.0, 'Region': 'United States'}, {'Day in March': '21', 'Units': -100.0, 'Region': 'United States'}] of United States have minimum values of -100.0, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Day in March": "1", + "Units": 5.0, + "Region": "Global" + }, + { + "Day in March": "1", + "Units": -1.0, + "Region": "United States" + }, + { + "Day in March": "1", + "Units": 2.0, + "Region": "United Kingdom" + }, + { + "Day in March": "1", + "Units": 6.0, + "Region": "Germany" + }, + { + "Day in March": "2", + "Units": -4.0, + "Region": "Global" + }, + { + "Day in March": "2", + "Units": -5.0, + "Region": "United States" + }, + { + "Day in March": "2", + "Units": -7.0, + "Region": "United Kingdom" + }, + { + "Day in March": "2", + "Units": -6.0, + "Region": "Germany" + }, + { + "Day in March": "3", + "Units": -6.0, + "Region": "Global" + }, + { + "Day in March": "3", + "Units": -8.0, + "Region": "United States" + }, + { + "Day in March": "3", + "Units": -8.0, + "Region": "United Kingdom" + }, + { + "Day in March": "3", + "Units": -2.0, + "Region": "Germany" + }, + { + "Day in March": "4", + "Units": -6.0, + "Region": "Global" + }, + { + "Day in March": "4", + "Units": -8.0, + "Region": "United States" + }, + { + "Day in March": "4", + "Units": -10.0, + "Region": "United Kingdom" + }, + { + "Day in March": "4", + "Units": 2.0, + "Region": "Germany" + }, + { + "Day in March": "5", + "Units": -9.0, + "Region": "Global" + }, + { + "Day in March": "5", + "Units": -11.0, + "Region": "United States" + }, + { + "Day in March": "5", + "Units": -10.0, + "Region": "United Kingdom" + }, + { + "Day in March": "5", + "Units": 4.0, + "Region": "Germany" + }, + { + "Day in March": "6", + "Units": -6.0, + "Region": "Global" + }, + { + "Day in March": "6", + "Units": -8.0, + "Region": "United States" + }, + { + "Day in March": "6", + "Units": -7.0, + "Region": "United Kingdom" + }, + { + "Day in March": "6", + "Units": 6.0, + "Region": "Germany" + }, + { + "Day in March": "7", + "Units": -1.0, + "Region": "Global" + }, + { + "Day in March": "7", + "Units": -4.0, + "Region": "United States" + }, + { + "Day in March": "7", + "Units": 2.0, + "Region": "United Kingdom" + }, + { + "Day in March": "7", + "Units": 13.0, + "Region": "Germany" + }, + { + "Day in March": "8", + "Units": -1.0, + "Region": "United States" + }, + { + "Day in March": "8", + "Units": 1.0, + "Region": "United Kingdom" + }, + { + "Day in March": "8", + "Units": 9.0, + "Region": "Germany" + }, + { + "Day in March": "9", + "Units": -13.0, + "Region": "Global" + }, + { + "Day in March": "9", + "Units": -16.0, + "Region": "United States" + }, + { + "Day in March": "9", + "Units": -15.0, + "Region": "United Kingdom" + }, + { + "Day in March": "9", + "Units": -5.0, + "Region": "Germany" + }, + { + "Day in March": "10", + "Units": -16.0, + "Region": "Global" + }, + { + "Day in March": "10", + "Units": -18.0, + "Region": "United States" + }, + { + "Day in March": "10", + "Units": -17.0, + "Region": "United Kingdom" + }, + { + "Day in March": "10", + "Units": -7.0, + "Region": "Germany" + }, + { + "Day in March": "11", + "Units": -17.0, + "Region": "Global" + }, + { + "Day in March": "11", + "Units": -18.0, + "Region": "United States" + }, + { + "Day in March": "11", + "Units": -25.0, + "Region": "United Kingdom" + }, + { + "Day in March": 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Operating System (Android) increases from 49.0 at UK to 69.0 at France, indicating a sharp change." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Spain", + "Score": 95.0, + "Market Share": "Search Engine (Google)" + } + ], + "score": 0.89, + "annotation": "The Search Engine (Google) has the largest proportion in Spain.", + "reason": "The Score of Search Engine (Google) in Spain accounts for 0.3877551020408163, larger than all other Market Share, of the total of Score of all Market Share." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Italy", + "Score": 95.0, + "Market Share": "Search Engine (Google)" + } + ], + "score": 0.88, + "annotation": "The Search Engine (Google) has the largest proportion in Italy.", + "reason": "The Score of Search Engine (Google) in Italy accounts for 0.41125541125541126, larger than all other Market Share, of the total of Score of all Market Share." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Country": "Germany", + "Score": 91.0, + "Market Share": "Search Engine (Google)" + } + ], + "score": 0.88, + "annotation": "The Search Engine (Google) has the largest proportion in Germany.", + "reason": "The Score of Search Engine (Google) in Germany accounts for 0.4252336448598131, larger than all other Market Share, of the total of Score of all Market Share." + }, + { + "type": "proportion", + "subtype": "value_majority", + "data_points": [ + { + "Market Share": "Search Engine (Google)", + "Country": "total", + "Score": 464.0 + } + ], + "score": 0.88, + "annotation": "The total value of Search Engine (Google) has the largest proportion.", + "reason": "The total value of Score of Search Engine (Google) accounts for 0.42843951985226225, larger than all other Market Share, of the total of Score of all Market Share." + } + ], + "data": { + "data": [ + { + "Country": "Spain", + "Score": 95.0, + 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endorsements in U.S. politics", + "main_insight": "A significant portion of respondents, especially Democrats, reported that a celebrity's position on a political issue has negatively affected their opinion of the celebrity. However, celebrity endorsements have a relatively small impact on changing opinions on issues, encouraging votes, or supporting candidates." + }, + "chart_type": "Vertical Grouped Bar Chart", + "datafacts": [ + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Statement": "A celebrity's position on a political issue has negatively affected my opinion of him/her", + "Units": 55.0, + "Political Affiliation": "Democrats" + }, + { + "Statement": "A celebrity's position on a political issue has negatively affected my opinion of him/her", + "Units": 51.0, + "Political Affiliation": "All respondents" + } + ], + "score": 1.0, + "annotation": "The Units of Democrats and All respondents show a positive correlation.", + "reason": "The Pearson correlation coefficient of the Units between Democrats and All respondents is {r:.2f}, indicating a strong positive relationship." + }, + { + "type": "correlation", + "subtype": "positive", + "data_points": [ + { + "Statement": "A 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negatively affected my opinion of him/her, indicating a sharp change." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Statement": "A celebrity's position on a political issue has negatively affected my opinion of him/her", + "Units": 55.0, + "Political Affiliation": "Democrats" + } + ], + "score": 0.96, + "annotation": "The Democrats shows a sudden jump in Units from A celebrity has caused me to reconsider my opinion on a political issue to A celebrity's position on a political issue has negatively affected my opinion of him/her.", + "reason": "The Units for Democrats increases from 16.0 at A celebrity has caused me to reconsider my opinion on a political issue to 55.0 at A celebrity's position on a political issue has negatively affected my opinion of him/her, indicating a sharp change." + } + ], + "data": { + "data": [ + { + "Statement": "A celebrity has caused me to reconsider my opinion on a political issue", + "Units": 16.0, + "Political 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Russia/USSR is a distant second with 4." + }, + "chart_type": "Pictorial Unit Chart", + "datafacts": [ + { + "type": "value", + "subtype": "max", + "data_points": [ + { + "Country": "United States", + "Number of Female Astronauts": 46 + } + ], + "score": 0.98, + "annotation": "The has a maximum value at United States", + "reason": "The [{'Country': 'United States', 'Number of Female Astronauts': 46}] of has a maximum value of 46, which is the largest in all categories." + }, + { + "type": "difference", + "subtype": "sudden_change", + "data_points": [ + { + "Country": "United States", + "Number of Female Astronauts": 46 + } + ], + "score": 0.97, + "annotation": "The shows a sudden jump in Number of Female Astronauts from Russia/USSR to United States.", + "reason": "The Number of Female Astronauts for increases from 4 at Russia/USSR to 46 at United States, indicating a sharp change." + }, + { + "type": "value", + "subtype": "min", + "data_points": [ + { + "Country": "France", + "Number of Female Astronauts": 1 + }, + { + "Country": "Italy", + "Number of Female Astronauts": 1 + }, + { + "Country": "South Korea", + "Number of Female Astronauts": 1 + }, + { + "Country": "United Kingdom", + "Number of Female Astronauts": 1 + } + ], + "score": 0.3, + "annotation": "The has minimum values at France, Italy, South Korea, United Kingdom", + "reason": "The [{'Country': 'France', 'Number of Female Astronauts': 1}, {'Country': 'Italy', 'Number of Female Astronauts': 1}, {'Country': 'South Korea', 'Number of Female Astronauts': 1}, {'Country': 'United Kingdom', 'Number of Female Astronauts': 1}] of have minimum values of 1, which is the largest in all categories." + } + ], + "data": { + "data": [ + { + "Country": "United States", + "Number of Female Astronauts": 46 + }, + { + "Country": "Russia/USSR", + "Number of Female Astronauts": 4 + }, + { + "Country": "Canada", + "Number of Female Astronauts": 2 + }, + { + "Country": "China", + "Number of Female Astronauts": 2 + }, + { + "Country": "Japan", + "Number of Female Astronauts": 2 + }, + { + "Country": "France", + "Number of Female Astronauts": 1 + }, + { + "Country": "Italy", + "Number of Female Astronauts": 1 + }, + { + "Country": "South Korea", + "Number of Female Astronauts": 1 + }, + { + "Country": "United Kingdom", + "Number of Female Astronauts": 1 + } + ], + "columns": [ + { + "name": "Country", + "importance": "primary", + "description": "Country of origin for female astronauts/cosmonauts", + "unit": "none", + "data_type": "string", + "role": "x" + }, + { + "name": "Number of Female Astronauts", + "importance": "primary", + "description": "The total number of female astronauts/cosmonauts from each country", + "unit": "none", + "data_type": "number", + "role": "y" + } + ] + } + } +} \ No newline at end of file