Create Dashboard.py

pages/Dashboard.py

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+import os
+import sys
+import subprocess
+
+required_libraries = ["streamlit", "pandas", "plotly", "altair"]
+
+for lib in required_libraries:
+    try:
+        __import__(lib)
+    except ImportError:
+        subprocess.check_call([sys.executable, "-m", "pip", "install", lib])
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import altair as alt
+
+child_mortality_url = "https://huggingface.co/spaces/jiyachachan/fp2/resolve/main/child_mortality_0_5_year_olds_dying_per_1000_born.csv"
+life_expectancy_url = "https://huggingface.co/spaces/jiyachachan/fp2/resolve/main/life_expectancy.csv"
+population_url = "https://huggingface.co/spaces/jiyachachan/fp2/resolve/main/pop.csv"
+income_url = "https://huggingface.co/spaces/jiyachachan/fp2/resolve/main/mincpcap_cppp.csv"
+
+child_mortality = pd.read_csv(child_mortality_url)
+life_expectancy = pd.read_csv(life_expectancy_url)
+population = pd.read_csv(population_url)
+income = pd.read_csv(income_url)
+
+def convert_population(value):
+    if isinstance(value, str):
+        if 'B' in value:
+            return float(value.replace('B', '')) * 1_000_000_000
+        elif 'M' in value:
+            return float(value.replace('M', '')) * 1_000_000
+        elif 'k' in value:
+            return float(value.replace('k', '')) * 1_000
+        else:
+            return float(value)
+    return value
+
+population.iloc[:, 1:] = population.iloc[:, 1:].applymap(convert_population)
+
+# 1. Choropleth Map
+st.title("Analyzing Global Child Mortality")
+st.write("By Jiya Chachan, Smeet Patel, Ji Eun Kim, Miloni Shah, Chenzhao Wang")
+data_melted = child_mortality.melt(id_vars=["country"], var_name="year", value_name="mortality_rate")
+data_melted["year"] = pd.to_numeric(data_melted["year"])
+min_year = int(data_melted["year"].min())
+max_year = int(data_melted["year"].max())
+selected_year = st.slider("Select Year", min_value=min_year, max_value=max_year, value=2024, step=5)
+filtered_data = data_melted[data_melted["year"] == selected_year]
+
+st.write("""
+### Child Mortality Map - per 1000 children born
+This chart reveals an important trend of how the child mortality rate has been changing across the years. 
+It provides insights into how developed countries have successfully reduced the rate, while underdeveloped countries still face challenges in curbing child mortality.
+We can utilize the trends in the graph to understand the factors responsible for high or low mortality.
+""")
+
+fig1 = px.choropleth(
+    filtered_data,
+    locations="country",
+    locationmode="country names",
+    color="mortality_rate",
+    title=f"Child Mortality Rate in {selected_year}",
+    color_continuous_scale=px.colors.sequential.OrRd,
+)
+st.plotly_chart(fig1)
+
+# 2. Visual 2: Child Mortality Trends (Altair Line Chart)
+
+countries = list(child_mortality['country'].unique())
+selected_countries = st.sidebar.multiselect("Select Countries", countries, default=["Argentina", "Australia", "India", "USA"])
+years = [str(year) for year in range(1900, 2025)]
+filtered_mortality = child_mortality[child_mortality['country'].isin(selected_countries)][['country'] + years]
+mortality_melted = filtered_mortality.melt(id_vars="country", var_name="year", value_name="mortality_rate")
+
+st.write("""
+### Child Mortality Trends
+This line chart shows the trends in child mortality rates over the years for selected countries.
+""")
+
+mortality_chart = alt.Chart(mortality_melted).mark_line().encode(
+    x="year:O",
+    y="mortality_rate:Q",
+    color="country:N",
+    tooltip=["country", "year", "mortality_rate"]
+).properties(width=700, height=400)
+st.altair_chart(mortality_chart)
+
+# 3. Visual 3: Child Mortality vs. Population
+
+selected_country = st.selectbox("Country for Population vs. Mortality", countries, index=0)
+mortality_country = child_mortality[child_mortality['country'] == selected_country].melt(
+    id_vars='country', var_name='year', value_name='child_mortality'
+)
+population_country = population[population['country'] == selected_country].melt(
+    id_vars='country', var_name='year', value_name='population'
+)
+merged_country_data = pd.merge(mortality_country, population_country, on=['country', 'year'], how='inner')
+merged_country_data['year'] = merged_country_data['year'].astype(int)
+
+st.write("""
+### Child Mortality vs Population
+This visualization shows the relationship between child mortality rates (per 1,000 live births) and population size over time for a selected country. 
+Displayed on a dual-y-axis chart, it allows users to explore how both metrics have evolved across decades. 
+The interactive feature lets users choose a country, and hovering over the chart reveals detailed tooltips with year, population size, and child mortality rate information.
+""")
+dual_axis_chart = alt.layer(
+    alt.Chart(merged_country_data).mark_line().encode(
+        x="year:O",
+        y="child_mortality:Q",
+        tooltip=["year", "child_mortality"]
+    ),
+    alt.Chart(merged_country_data).mark_line(color='orange').encode(
+        x="year:O",
+        y="population:Q",
+        tooltip=["year", "population"]
+    )
+).resolve_scale(y='independent').properties(width=700, height=400)
+st.altair_chart(dual_axis_chart)
+
+# 4. Visual 4: Child Mortality vs. Income
+income_long = pd.melt(income, id_vars=['country'], var_name='year', value_name='income')
+mortality_long = pd.melt(child_mortality, id_vars=['country'], var_name='year', value_name='mortality')
+income_long['year'] = income_long['year'].astype(int)
+mortality_long['year'] = mortality_long['year'].astype(int)
+yay = pd.merge(income_long, mortality_long, on=['country', 'year'])
+yeyear = st.slider("Year for Income vs. Mortality", min_value=1800, max_value=2024, value=2024)
+filtered_yay = yay[yay["year"] == yeyear]
+
+st.write("""
+### Child Mortality vs Daily Income
+The interactive scatterplot compares child mortality to daily income for 24 countries, with a slider to select a year between 1800 and 2024. 
+The x-axis represents daily income (USD) on a logarithmic scale, and the y-axis shows child mortality (per 1,000). 
+Each country is color-coded, and hover tooltips display details for each data point. 
+Our analysis reveals that in earlier years (e.g., 1800), countries have tightly clustered mortality and income. 
+By 2024, a negative correlation emerges, showing that as child mortality decreases, daily income increases.
+""")
+scatter_plot = alt.Chart(filtered_yay).mark_circle(size=60).encode(
+    x=alt.X('income', title='Daily Income (USD)', scale=alt.Scale(type='log')),
+    y=alt.Y('mortality', title='Child Mortality (per 1,000)'),
+    color='country',
+    tooltip=['country', 'year', 'income', 'mortality']
+).properties(width=700, height=400)
+st.altair_chart(scatter_plot)
+
+# 5. Visual 5: Life Expectancy Trends
+filtered_expectancy = life_expectancy[life_expectancy['country'].isin(selected_countries)]
+expectancy_melted = filtered_expectancy.melt(id_vars="country", var_name="year", value_name="life_expectancy")
+st.write("""
+### Life Expectancy Trends
+The scatter plot reveals a strong inverse relationship between child mortality and life expectancy, emphasizing how improvements in healthcare, nutrition, and living conditions enhance both indicators. 
+Notably, countries like India and South Africa show steep curves, reflecting rapid progress in life expectancy as child mortality declines. 
+Developed nations such as Australia and the USA exhibit a plateau, with incremental life expectancy gains as child mortality remains low.
+""")
+expectancy_chart = alt.Chart(expectancy_melted).mark_line().encode(
+    x="year:O",
+    y="life_expectancy:Q",
+    color="country:N",
+    tooltip=["country", "year", "life_expectancy"]
+).properties(width=700, height=400)
+st.altair_chart(expectancy_chart)
+
+
+st.subheader("Child Mortality vs. Life Expectancy")
+st.write("""
+This scatter plot illustrates the strong inverse relationship between child mortality 
+rates and life expectancy at birth, underscoring how advancements in healthcare, 
+nutrition, and living conditions improve both indicators simultaneously. A particularly 
+striking observation is how the data for countries like India and South Africa forms a 
+steep curve, signifying rapid improvements in life expectancy as child mortality declines. 
+Developed nations such as Australia and the USA show a plateau in the later stages, where 
+child mortality rates are already low, and life expectancy improvements are incremental. 
+The trajectory of China’s data during the mid-20th century highlights a rapid transition, 
+likely due to systemic public health efforts.
+""")
+
+
+mortality_long = pd.melt(child_mortality, id_vars=["country"], var_name="year", value_name="child_mortality")
+expectancy_long = pd.melt(life_expectancy, id_vars=["country"], var_name="year", value_name="life_expectancy")
+
+mortality_long["year"] = mortality_long["year"].astype(int)
+expectancy_long["year"] = expectancy_long["year"].astype(int)
+
+merged_data = pd.merge(mortality_long, expectancy_long, on=["country", "year"])
+merged_data = merged_data.dropna(subset=["child_mortality", "life_expectancy"])
+
+default_countries = ["Argentina", "Australia", "China", "India", "South Africa", "UK", "USA"]
+
+countries = merged_data["country"].unique()
+selected_countries = st.multiselect("Select Countries", countries, default=default_countries)
+filtered_data = merged_data[merged_data["country"].isin(selected_countries)]
+
+scatter_chart = alt.Chart(filtered_data).mark_circle(size=60).encode(
+    x=alt.X("life_expectancy:Q", title="Life Expectancy at Birth"),
+    y=alt.Y("child_mortality:Q", title="Child Mortality (0–5 years per 1000 births)"),
+    color="country:N",
+    tooltip=["country", "year", "child_mortality", "life_expectancy"]
+).properties(width=700, height=400)
+
+st.altair_chart(scatter_chart)