init commit

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/IS_445_Final_Project_Part2.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="JAVA_MODULE" version="4">
+  <component name="NewModuleRootManager" inherit-compiler-output="true">
+    <exclude-output />
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/encodings.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Encoding" native2AsciiForPropertiesFiles="true" />
+</project>

.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+  </profile>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.13 (final-project-part2-u8uBTYck-py3.13)" />
+  </component>
+  <component name="GOROOT" url="file:///opt/homebrew/opt/go/libexec" />
+  <component name="ProjectRootManager" version="2" languageLevel="JDK_11" default="true" project-jdk-name="Python 3.13 (final-project-part2-u8uBTYck-py3.13)" project-jdk-type="Python SDK">
+    <output url="file://$PROJECT_DIR$/out" />
+  </component>
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/IS_445_Final_Project_Part2.iml" filepath="$PROJECT_DIR$/.idea/IS_445_Final_Project_Part2.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

app.py

@@ -5,78 +5,108 @@
 # 4. Open a Simple Browswer with View --> Command Palette --> Simple Browser: Show
 # 5. use the URL from prior steps as intput into this simple browser
 
-
 import altair as alt
 import pandas as pd
 import streamlit as st
 
-st.title('Streamlit App for IS445: ID29724')
+st.title('Group 5 Final Project - Part 2 (Dashboard)')
+st.text("Group Members: Nabeel Bashir, Tony An, Devansh Kumar, Jiajun Li")
+
+st.text("The URL for this app is: https://huggingface.co/spaces/fa24-is445-group5/Final_Project_Part2")
+
+ev_data = pd.read_csv('data/Electric_Vehicle_Population_Data.csv')
 
-st.text("The URL for this app is: https://huggingface.co/spaces/FantasticTony/is445_demo")
+st.title('Electric Vehicle Population Data Dashboard')
+st.markdown('''Explore the electric vehicle data interactively using this dashboard.
+This dashboard helps experts and stakeholders understand the distribution and characteristics of electric vehicles
+in various counties and cities.''')
 
-licenses_data = pd.read_csv('data/licenses_fall2022.csv')
+county_list = ev_data['County'].unique()
+make_list = ev_data['Make'].unique()
 
-licenses_data_filtered = licenses_data[['License Type', 'License Status', 'LastModifiedDate']]
-licenses_data_filtered['LastModifiedDate'] = pd.to_datetime(licenses_data_filtered['LastModifiedDate'], errors='coerce')
-licenses_data_filtered.dropna(subset=['LastModifiedDate'], inplace=True)
+selected_county = st.sidebar.selectbox('Select County', county_list)
+selected_make = st.sidebar.multiselect('Select Make', make_list, default=make_list)
 
-# chart 1
-license_type_counts = licenses_data_filtered['License Type'].value_counts().head(10).reset_index()
-license_type_counts.columns = ['License Type', 'Count']
+filtered_data = ev_data[(ev_data['County'] == selected_county) & (ev_data['Make'].isin(selected_make))]
 
-chart1 = alt.Chart(license_type_counts).mark_bar().encode(
-    x=alt.X('License Type:N', sort='-y', title='Top 10 License Types'),
-    y=alt.Y('Count:Q', title='Number of Licenses'),
-    color=alt.Color('License Type:N', legend=None)
+st.subheader('Filtered Data')
+st.write(filtered_data)
+
+st.subheader('Distribution of Electric Vehicle Types')
+ev_type_count = filtered_data['Electric Vehicle Type'].value_counts().reset_index()
+ev_type_count.columns = ['Electric Vehicle Type', 'Count']
+
+bar_chart = alt.Chart(ev_type_count).mark_bar().encode(
+    x=alt.X('Electric Vehicle Type', sort='-y', title='Electric Vehicle Type'),
+    y=alt.Y('Count', title='Number of Vehicles'),
+    tooltip=['Electric Vehicle Type', 'Count'],
+    color='Electric Vehicle Type'
 ).properties(
-    title='Distribution of Top 10 License Types',
     width=600,
-    height=400
-)
-
-# chart 2
-licenses_data_filtered.loc[:, 'Year'] = licenses_data_filtered['LastModifiedDate'].dt.year
-license_status_counts = licenses_data_filtered[
-    licenses_data_filtered['License Status'].isin(['ACTIVE', 'NOT RENEWED', 'EXPIRED'])]
-license_status_counts = license_status_counts.groupby(['Year', 'License Status']).size().reset_index(name='Count')
-
-chart2 = alt.Chart(license_status_counts).mark_line().encode(
-    x=alt.X('Year:O', title='Year (YYYY)'),
-    y=alt.Y('Count:Q', title='Number of Licenses Issued'),
-    color=alt.Color('License Status:N', title='License Status')
+    height=400,
+    title='Distribution of Electric Vehicle Types in Selected County'
+).interactive()
+
+st.altair_chart(bar_chart)
+
+st.subheader('Electric Range Distribution')
+electric_range_chart = alt.Chart(filtered_data).mark_bar().encode(
+    alt.X('Electric Range', bin=alt.Bin(maxbins=30), title='Electric Range (Miles)'),
+    alt.Y('count()', title='Frequency'),
+    tooltip=['count()'],
+    color=alt.value('steelblue')
+).properties(
+    width=600,
+    height=400,
+    title='Electric Range Distribution in Selected County'
+).interactive()
+
+st.altair_chart(electric_range_chart)
+
+st.subheader('Vehicle Count by Model Year')
+model_year_count = filtered_data['Model Year'].value_counts().reset_index()
+model_year_count.columns = ['Model Year', 'Count']
+
+year_chart = alt.Chart(model_year_count).mark_bar().encode(
+    x=alt.X('Model Year:N', sort='-y', title='Model Year'),
+    y=alt.Y('Count', title='Number of Vehicles'),
+    tooltip=['Model Year', 'Count'],
+    color='Model Year:N'
 ).properties(
-    title='License Status Over Time (Filtered for Key Statuses)',
     width=600,
-    height=400
-)
-
-# APP
-st.title('Licenses Data Visualization')
-
-st.write("## Introduction")
-st.text("""
-This app provides visual insights into licensing data by focusing on the distribution of license types and
-tracking the trends of license statuses over time. The visualizations aim to simplify the exploration
-of the dataset and highlight key insights into how licenses are distributed and change year over year.
-""")
-st.write("## Visualization 1: Distribution of License Types")
-st.altair_chart(chart1, use_container_width=True)
-
-st.text("""
-This bar chart shows the distribution of the top 10 license types in the dataset. I chose a bar chart
-because it is effective for comparing categorical data like license types. The colors help to visually
-differentiate each category, and focusing on the top 10 types makes the chart more readable and
-less cluttered. If I had more time, I would add interactive tooltips to display additional
-information about each license type, such as the percentage of the total.
-""")
-
-st.write("## Visualization 2: License Status Over Time")
-st.altair_chart(chart2, use_container_width=True)
-
-st.text("""
-This line chart shows the number of licenses by key statuses over time.
-The chart helps to visualize trends in the renewal and activity status of licenses. I chose different colors
-for each status to make it easy to distinguish between them. Filtering for key statuses allows for a clearer
-view of the most important trends without overwhelming the viewer. If I had more time, I would include an option
-to filter by license type to see how specific types have changed over time.
-""")
+    height=400,
+    title='Number of Vehicles by Model Year'
+).interactive()
+
+st.altair_chart(year_chart)
+
+st.subheader('Map of Vehicle Locations')
+
+ev_data['Latitude'] = pd.to_numeric(ev_data['Latitude'], errors='coerce')
+ev_data['Longitude'] = pd.to_numeric(ev_data['Longitude'], errors='coerce')
+
+filtered_data[['Longitude', 'Latitude']] = filtered_data['Vehicle Location'].str.extract(r'POINT \((-?\d+\.\d+) (-?\d+\.\d+)\)')
+filtered_data['Latitude'] = pd.to_numeric(filtered_data['Latitude'], errors='coerce')
+filtered_data['Longitude'] = pd.to_numeric(filtered_data['Longitude'], errors='coerce')
+
+filtered_data = filtered_data.dropna(subset=['Latitude', 'Longitude'])
+filtered_data.rename(columns={'Latitude': 'latitude', 'Longitude': 'longitude'}, inplace=True)
+st.map(filtered_data[['latitude', 'longitude']])
+
+st.markdown('''### How to Use This Dashboard
+To explore the dataset, use the sidebar options to filter by county and make. The filtered dataset is displayed in the 
+table, and corresponding charts will help you explore the distribution of electric vehicle types, their electric ranges, 
+and model years. The map also provides a visualization of the locations of the vehicles in the selected county.
+
+All charts are interactive. You can click on bars to highlight corresponding data points in other charts, making it easier to discover patterns and relationships.''')
+
+st.markdown('''### Contextual Datasets
+A potentially useful contextual dataset could be the [Electric Charging Stations Locations](https://afdc.energy.gov/fuels/electricity_locations.html).
+This dataset will provide information about the availability of charging stations in each county, allowing for a deeper
+analysis of the convenience and accessibility of electric vehicles in different regions.
+''')
+
+st.markdown('''### Dataset Size
+This dataset is not larger than the GitHub file upload limit, so there is no need to revise the plan for hosting this data.
+''')
+

data/Electric_Vehicle_Population_Data.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:18ee71403c210c570bbe6786e88436bc21ad7748ab1f5683d94b5bffb711072d
+size 51381679