iramler/slu_score_module_development
Feb 2026 – Feb 2026
1 Month active
Languages Used
MarkdownR
Technical Skills
R programmingdata analysisdata processingdata scrapingdata visualizationdocumentation
Lillian Li
PROFILE
Lillian Li
Developed the Table Tennis Data Processing Toolkit feature for the iramler/slu_score_module_development repository, delivering a comprehensive Olympic table tennis medal results dataset spanning 1988 to 2024. Leveraged R programming for data preparation, analysis, and statistical testing, establishing a reproducible data pipeline that supports scalable analytics for future score module integration. Authored clear documentation in Markdown, including a README that details the SCORE folder structure and data processing scripts, streamlining onboarding and ongoing development. The work focused on robust data analysis and visualization, laying a solid technical foundation for future enhancements while ensuring clarity and maintainability in both code and documentation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
370
Activity Months1
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22 peopleShared Repositories
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Work History
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026 — Delivered the Table Tennis Data Processing Toolkit feature in iramler/slu_score_module_development. Implemented an Olympic table tennis medal results dataset (1988–2024) with data preparation and analysis code. Added a README documenting the SCORE folder contents and the data processing scripts, establishing a clear data pipeline foundation for reproducible analytics. These efforts enable scalable analytics for score module development and position the repo for future features.
2 Commits • 1 Features
Feb 1, 2026February 2026 — Delivered the Table Tennis Data Processing Toolkit feature in iramler/slu_score_module_development. Implemented an Olympic table tennis medal results dataset (1988–2024) with data preparation and analysis code. Added a README documenting the SCORE folder contents and the data processing scripts, establishing a clear data pipeline foundation for reproducible analytics. These efforts enable scalable analytics for score module development and position the repo for future features.
February 2026
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
MarkdownR
Technical Skills
R programmingdata analysisdata processingdata scrapingdata visualizationdocumentationstatistical testing
Repositories Contributed To
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