NewsAppsUMD/maryland_voter_data
Apr 2025 – May 2025
2 Months active
Languages Used
JavaScriptMarkdownPNGPythonShellTypeScript
Technical Skills
API IntegrationBuild ToolsCode OptimizationData AnalysisData ManipulationData Visualization
johnny123-code
PROFILE
Johnny123-code
Jianyan Yan developed a comprehensive data analytics and visualization pipeline for the NewsAppsUMD/maryland_voter_data repository, focusing on voter demographics and turnout trends. Over two months, Jianyan delivered end-to-end Python-based solutions using pandas and Plotly, including demographic reshaping, static and interactive charts, and a reusable visualization suite for the 2020 and 2024 elections. The work included API integration, frontend dependency upgrades, and the creation of automated test tooling to streamline data validation. By standardizing chart titles and enhancing interactivity, Jianyan improved reporting accuracy and maintainability, enabling stakeholders to efficiently analyze participation patterns and scale analytics for future election cycles.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
18Total
Bugs
0
Commits
18
Features
4
Lines of code
923,088
Activity Months2
Your Network
33 peopleShared Repositories
3
Work History
May 2025
5 Commits • 1 Features
May 1, 2025May 2025 monthly summary for NewsAppsUMD/maryland_voter_data: Delivered a comprehensive voter turnout visualization suite for the 2020 and 2024 elections, including age group, gender, and party breakdowns, with separate visuals for unaffiliated voters and enhanced hover/percentage summaries. Resolved chart title inconsistencies and refined the three combined charts to ensure accurate, consistent labeling across demographics. The work leveraged Python-based visualizations to produce interactive insights, supporting data-driven analysis of participation trends and informing stakeholders about turnout dynamics in key electoral cycles. This foundation enables scalable analytics for future election data reviews and reporting.
5 Commits • 1 Features
May 1, 2025May 2025 monthly summary for NewsAppsUMD/maryland_voter_data: Delivered a comprehensive voter turnout visualization suite for the 2020 and 2024 elections, including age group, gender, and party breakdowns, with separate visuals for unaffiliated voters and enhanced hover/percentage summaries. Resolved chart title inconsistencies and refined the three combined charts to ensure accurate, consistent labeling across demographics. The work leveraged Python-based visualizations to produce interactive insights, supporting data-driven analysis of participation trends and informing stakeholders about turnout dynamics in key electoral cycles. This foundation enables scalable analytics for future election data reviews and reporting.
May 2025
April 2025
13 Commits • 3 Features
Apr 1, 2025April 2025 delivered a cohesive data-to-insight sequence for NewsAppsUMD/maryland_voter_data, focusing on API testing, demographic analytics, and frontend tooling upgrades. The Datawrapper API Test Tooling feature adds a dedicated test_script.py to exercise the Datawrapper API, validates authentication via the DATAWRAPPER_MD_DATA environment variable, reorganizes tests into a dedicated directory, and lays groundwork for pandas-based data manipulation to support future testing. The Demographic Data Processing and Visualization Suite introduces end-to-end analytics: reshaping voter data by age, gender, and party, and generating both static and interactive visualizations (bar charts and per-age-group metrics) using pandas, matplotlib, seaborn, and Plotly. The Frontend Dependency Upgrade and Tooling Refresh modernizes the JS stack and build tooling (svg-arc-to-cubic-bezier, supports-color, string-width, terser-webpack-plugin) and adds libraries such as string-split-by and strongly-connected-components to broaden visualization capabilities. Minor chart title fixes improve accuracy and consistency across visuals. Collectively, these efforts raise data quality, reduce manual testing effort, accelerate reporting cycles, and strengthen the platform's ability to deliver actionable demographics insights for stakeholders.
April 2025
13 Commits • 3 Features
Apr 1, 2025April 2025 delivered a cohesive data-to-insight sequence for NewsAppsUMD/maryland_voter_data, focusing on API testing, demographic analytics, and frontend tooling upgrades. The Datawrapper API Test Tooling feature adds a dedicated test_script.py to exercise the Datawrapper API, validates authentication via the DATAWRAPPER_MD_DATA environment variable, reorganizes tests into a dedicated directory, and lays groundwork for pandas-based data manipulation to support future testing. The Demographic Data Processing and Visualization Suite introduces end-to-end analytics: reshaping voter data by age, gender, and party, and generating both static and interactive visualizations (bar charts and per-age-group metrics) using pandas, matplotlib, seaborn, and Plotly. The Frontend Dependency Upgrade and Tooling Refresh modernizes the JS stack and build tooling (svg-arc-to-cubic-bezier, supports-color, string-width, terser-webpack-plugin) and adds libraries such as string-split-by and strongly-connected-components to broaden visualization capabilities. Minor chart title fixes improve accuracy and consistency across visuals. Collectively, these efforts raise data quality, reduce manual testing effort, accelerate reporting cycles, and strengthen the platform's ability to deliver actionable demographics insights for stakeholders.
Activity
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Quality Metrics
Correctness90.0%
Maintainability88.8%
Architecture85.6%
Performance86.6%
AI Usage24.4%
Skills & Technologies
Programming Languages
JavaScriptMarkdownPNGPythonShellTypeScript
Technical Skills
API IntegrationBuild ToolsCode OptimizationData AnalysisData ManipulationData VisualizationData Visualization LibrariesDependency ManagementEnvironment VariablesJavaScriptMatplotlibNode.jsPandasPlotlyPython
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