Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub
Feb 2025 – Apr 2025
3 Months active
Languages Used
MarkdownR MarkdownR
Technical Skills
Data AnalysisData VisualizationDocumentationProject PlanningData ManipulationProject Documentation
Benedikt Klein
PROFILE
Benedikt Klein
Over a three-month period, contributed to the Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub repository by designing and documenting end-to-end data analysis workflows in R and R Markdown. Established project scaffolding for wildfire impact studies, defined reproducible data manipulation and visualization plans, and delivered a comprehensive movie data analysis pipeline. The work emphasized clear documentation, reproducibility, and collaborative development, leveraging Tidyverse tools such as dplyr, ggplot2, and tidyr for data wrangling and visualization. Automated reporting and version-controlled commits ensured transparency and maintainability, while the integration of Markdown artifacts improved onboarding, review processes, and cross-project analytics within the repository’s evolving codebase.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
15Total
Bugs
0
Commits
15
Features
4
Lines of code
1,220
Activity Months3
Your Network
60 peopleShared Repositories
60
Work History
April 2025
7 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub: Delivered an end-to-end Movie Data Analysis and Visualization workflow in R Markdown, including data cleaning, extraction of genres and stars, time-series visualizations comparing critic and audience feedback over time, ranking of actors by average ratings and appearance frequency, and automated PDF reports. Implemented via a seven-commit sequence to enhance reproducibility and traceability.
7 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub: Delivered an end-to-end Movie Data Analysis and Visualization workflow in R Markdown, including data cleaning, extraction of genres and stars, time-series visualizations comparing critic and audience feedback over time, ranking of actors by average ratings and appearance frequency, and automated PDF reports. Implemented via a seven-commit sequence to enhance reproducibility and traceability.
April 2025
March 2025
6 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for the Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub repo: focused on documenting and setting up reproducible data-analysis work for two projects, establishing clear artifacts for ongoing analytics, and improving documentation quality across commits. The work underscores the ability to plan, document, and visualize data-driven insights with R and Markdown, enabling faster review, onboarding, and cross-project collaboration.
March 2025
6 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for the Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub repo: focused on documenting and setting up reproducible data-analysis work for two projects, establishing clear artifacts for ongoing analytics, and improving documentation quality across commits. The work underscores the ability to plan, document, and visualize data-driven insights with R and Markdown, enabling faster review, onboarding, and cross-project collaboration.
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025: Established the Wildfire Impacts Analysis project scaffolding in the Data-projects-with-R-and-GitHub repository, enabling rapid experimentation and consistent documentation. Focused on packaging a clear data workflow: sourcing data, defining data manipulation goals (including extrapolation to 2050), and outlining visualization plans for 2023 and projected 2030. This work lays a solid foundation for end-to-end analysis, reproducibility, and collaboration across the team.
2 Commits • 1 Features
Feb 1, 2025February 2025: Established the Wildfire Impacts Analysis project scaffolding in the Data-projects-with-R-and-GitHub repository, enabling rapid experimentation and consistent documentation. Focused on packaging a clear data workflow: sourcing data, defining data manipulation goals (including extrapolation to 2050), and outlining visualization plans for 2023 and projected 2030. This work lays a solid foundation for end-to-end analysis, reproducibility, and collaboration across the team.
February 2025
Activity
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Quality Metrics
Correctness88.0%
Maintainability86.6%
Architecture81.4%
Performance78.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownRR Markdown
Technical Skills
Data AnalysisData ManipulationData VisualizationData WranglingDocumentationProject DocumentationProject PlanningR MarkdownR ProgrammingStatistical ModelingTidyversedplyrggplot2tidyr
Repositories Contributed To
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