Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub
Feb 2025 – Apr 2025
3 Months active
Languages Used
MarkdownRR Markdown
Technical Skills
Data AnalysisData VisualizationDocumentationProject DocumentationProject ManagementR
Madeleine
PROFILE
Madeleine
Madeleine Koerdel developed and enhanced data analysis pipelines for the Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub repository, focusing on robust data ingestion, cleaning, and visualization workflows. She implemented end-to-end solutions in R and R Markdown, including deduplication, improved JSON parsing, and error handling to ensure reliable reporting. Her work included building visualizations such as ridgeline plots and heatmaps using ggplot2 and ggridges, and she maintained comprehensive documentation to support onboarding and reproducibility. By refining project scaffolding and automating CI validation, Madeleine improved maintainability and collaboration, delivering actionable analytics that support data-driven decision-making for content strategy and stakeholder reporting.
Overall Statistics
Feature vs Bugs
82%Features
Repository Contributions
49Total
Bugs
2
Commits
49
Features
9
Lines of code
5,329
Activity Months3
Your Network
53 peopleSame Organization
@student.uni-tuebingen.de14
Work History
April 2025
19 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub. Delivered end-to-end enhancements to data analysis pipelines in R and R Markdown, focusing on reliability, clarity, and actionable reporting. Implemented robust data loading (suppressing non-critical messages/warnings), deduplication, improved JSON parsing error handling, updated data wrangling and visualization with cleaned data, and added a new report with an initial plot. Expanded federation analytics with age-based rating calculations, ridgeline plots, and heatmaps, plus improved data preparation, top-federation filtering, clearer plots/titles, and comprehensive visualization documentation.
19 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub. Delivered end-to-end enhancements to data analysis pipelines in R and R Markdown, focusing on reliability, clarity, and actionable reporting. Implemented robust data loading (suppressing non-critical messages/warnings), deduplication, improved JSON parsing error handling, updated data wrangling and visualization with cleaned data, and added a new report with an initial plot. Expanded federation analytics with age-based rating calculations, ridgeline plots, and heatmaps, plus improved data preparation, top-federation filtering, clearer plots/titles, and comprehensive visualization documentation.
April 2025
March 2025
3 Commits • 2 Features
Mar 1, 2025Month: 2025-03 | Repository: Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub Summary: Delivered end-to-end data analysis and visualization capabilities for movie metadata, and strengthened project scaffolding and documentation to improve onboarding and reproducibility. No major bugs reported this month. Key outcomes: - Movie Metadata Data Analysis and Visualization: implemented loading, cleaning, preprocessing, wrangling into grouped formats, and visualizations to explore trends in film production by country and year, genre distribution over time, and the relationship between budget and revenue. - Project Setup, Documentation, and Scaffolding: clarified data points, analysis goals, and visualization objectives; added R Markdown config and placeholder Markdown for yhykelly. Impact: - Enables data-driven decision making for content strategy and budgeting; improves reproducibility, onboarding, and cross-team collaboration; provides a foundation for future analytics. Technologies/skills demonstrated: - R (data wrangling, analysis, visualization) - Data governance and reproducible analytics - Documentation and project scaffolding
March 2025
3 Commits • 2 Features
Mar 1, 2025Month: 2025-03 | Repository: Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub Summary: Delivered end-to-end data analysis and visualization capabilities for movie metadata, and strengthened project scaffolding and documentation to improve onboarding and reproducibility. No major bugs reported this month. Key outcomes: - Movie Metadata Data Analysis and Visualization: implemented loading, cleaning, preprocessing, wrangling into grouped formats, and visualizations to explore trends in film production by country and year, genre distribution over time, and the relationship between budget and revenue. - Project Setup, Documentation, and Scaffolding: clarified data points, analysis goals, and visualization objectives; added R Markdown config and placeholder Markdown for yhykelly. Impact: - Enables data-driven decision making for content strategy and budgeting; improves reproducibility, onboarding, and cross-team collaboration; provides a foundation for future analytics. Technologies/skills demonstrated: - R (data wrangling, analysis, visualization) - Data governance and reproducible analytics - Documentation and project scaffolding
February 2025
27 Commits • 5 Features
Feb 1, 2025February 2025: Delivered foundational work for Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub, focusing on scaffolding, documentation, CI readiness, and repo hygiene. Key outcomes include initial project structure and assets, consolidated project/API docs, a CI/testing validation commit, and cleanup of obsolete assets and naming inconsistencies. These efforts improve maintainability, onboarding, and readiness for future feature work, while showcasing solid proficiency in R-based project setup, GitHub workflows, and documentation tooling.
27 Commits • 5 Features
Feb 1, 2025February 2025: Delivered foundational work for Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub, focusing on scaffolding, documentation, CI readiness, and repo hygiene. Key outcomes include initial project structure and assets, consolidated project/API docs, a CI/testing validation commit, and cleanup of obsolete assets and naming inconsistencies. These efforts improve maintainability, onboarding, and readiness for future feature work, while showcasing solid proficiency in R-based project setup, GitHub workflows, and documentation tooling.
February 2025
Activity
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Quality Metrics
Correctness95.0%
Maintainability94.8%
Architecture93.6%
Performance91.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownRR Markdown
Technical Skills
Data AnalysisData CleaningData VisualizationData WranglingDocumentationJSON ParsingProject DocumentationProject ManagementRR MarkdownR ProgrammingReport GenerationReportingTechnical WritingTidyverse
Repositories Contributed To
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