GitHub Data → Excel Analysis → Automated Report Generation
Transform GitHub repository data into economic insights and automated reports for research teams and policy analysts studying digital innovation patterns.
Workflow Steps
GitHub API
Extract repository and contribution data
Use GitHub's REST API or GraphQL API to pull repository data, commit histories, contributor information, and project complexity metrics for specific countries or regions. Set up authentication and create queries to gather innovation indicators.
Python
Clean and structure the raw data
Write Python scripts using pandas to clean the GitHub data, remove duplicates, normalize country codes, calculate digital complexity scores, and structure the data for analysis. Export cleaned datasets as CSV files.
Microsoft Excel
Perform correlation analysis
Import the cleaned data into Excel and use built-in statistical functions to analyze correlations between GitHub activity metrics and economic indicators like GDP, inequality measures, and emissions data. Create pivot tables and charts.
Power BI
Generate automated research reports
Connect Power BI to your Excel analysis and create interactive dashboards showing digital complexity rankings, economic correlations, and trend visualizations. Set up automatic refresh schedules and export options for stakeholders.
Workflow Flow
Step 1
GitHub API
Extract repository and contribution data
Step 2
Python
Clean and structure the raw data
Step 3
Microsoft Excel
Perform correlation analysis
Step 4
Power BI
Generate automated research reports
Why This Works
Combines GitHub's rich developer data with traditional analysis tools, enabling researchers to discover hidden economic patterns that conventional metrics miss while automating the reporting process.
Best For
Economic research teams studying the relationship between digital innovation and economic development
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