Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub
Mar 2026 – Mar 2026
1 Month active
Languages Used
MarkdownR
Technical Skills
R programmingdata analysisdata visualizationdocumentationproject managementstatistical analysis
kamess1
PROFILE
Kamess1
Karim Messaoud developed the Global Education Attainment Project within the Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub repository, focusing on the Barro-Lee Educational Attainment Dataset. He designed and documented a comprehensive project plan, detailing data cleaning steps, statistical hypotheses, and visualization objectives using R programming and data analysis techniques. Karim updated the project’s README in Markdown to enhance visibility and facilitate future collaboration. While no bugs were addressed during this period, his work emphasized clear documentation, structured planning, and reproducible workflows. The project’s depth is reflected in its thorough preparation for subsequent analysis and its readiness for collaborative development and extension.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
29,060
Activity Months1
Your Network
78 peopleSame Organization
@student.uni-tuebingen.de18
Shared Repositories
60
Work History
March 2026
3 Commits • 1 Features
Mar 1, 2026March 2026: Global Education Attainment Project drafted, planned, and visible within the Data-projects-with-R-and-GitHub repository. Consolidated efforts around the Barro-Lee Educational Attainment Dataset, outlining data cleaning steps, hypotheses, and visualization goals; updated the README to improve discoverability and collaboration readiness. No major bug fixes were recorded this month; the focus was on feature delivery, documentation, and project visibility to accelerate next-phase work.
3 Commits • 1 Features
Mar 1, 2026March 2026: Global Education Attainment Project drafted, planned, and visible within the Data-projects-with-R-and-GitHub repository. Consolidated efforts around the Barro-Lee Educational Attainment Dataset, outlining data cleaning steps, hypotheses, and visualization goals; updated the README to improve discoverability and collaboration readiness. No major bug fixes were recorded this month; the focus was on feature delivery, documentation, and project visibility to accelerate next-phase work.
March 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability93.4%
Architecture93.4%
Performance93.4%
AI Usage26.6%
Skills & Technologies
Programming Languages
MarkdownR
Technical Skills
R programmingdata analysisdata visualizationdocumentationproject managementstatistical analysisstatistical modeling
Repositories Contributed To
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