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
Benedikt Klein developed end-to-end data analysis and visualization workflows in the Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub repository, focusing on projects such as wildfire impact analysis and movie data analytics. He established reproducible project scaffolding and clear documentation using R, R Markdown, and Tidyverse, enabling rapid experimentation and collaborative review. His work included data cleaning, manipulation, and extraction, as well as the design of time-series and comparative visualizations to support data-driven decisions. By integrating automated reporting and version-controlled development, Benedikt ensured transparency and maintainability, demonstrating depth in statistical modeling and workflow design while aligning technical outputs with project and stakeholder needs.
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
39 people
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
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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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