iramler/slu_score_module_development
Mar 2025 – May 2025
2 Months active
Languages Used
CSVQuartoRText
Technical Skills
Basic File OperationsData AnalysisData EngineeringData ManagementData VisualizationData Wrangling
mgwahlquist
PROFILE
Mgwahlquist
Developed a Gymnastics Data Analysis Suite within the iramler/slu_score_module_development repository, delivering a reproducible workflow for analyzing and modeling gymnastics scores. The work centered on building end-to-end data pipelines using R and R Markdown, incorporating data wrangling, exploratory analysis, and predictive modeling to estimate scores from difficulty and execution metrics. Leveraging tools such as dplyr, tidyr, and ggplot2, the developer created visualizations and implemented both linear and multi-level statistical models. The suite consolidated data assets and reporting, enabling scalable analytics for coaching and judging insights while establishing a foundation for future extensibility and reproducible, data-driven evaluation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
3
Lines of code
24,811
Activity Months2
Your Network
29 peopleShared Repositories
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Brendan KaradenesMember
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Cooper OlneyMember
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emiliaagostinelliMember
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amsiko21Member
Work History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for iramler/slu_score_module_development: Key feature delivered a Gymnastics Scores Analysis Notebook (R Markdown) encapsulating data wrangling, exploratory data analysis with visualizations, and predictive modeling to estimate scores from difficulty and execution. No major bugs fixed this month. Impact: provides an end-to-end, reproducible analytics workflow that enables data-driven evaluation of gymnastics scoring, supports coaching and judging insights, and lays groundwork for scalable extension. Technologies demonstrated: R, R Markdown, data wrangling with dplyr/tidyr, visualization with ggplot2, and linear and multi-level modeling (e.g., lm and mixed-effects models).
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for iramler/slu_score_module_development: Key feature delivered a Gymnastics Scores Analysis Notebook (R Markdown) encapsulating data wrangling, exploratory data analysis with visualizations, and predictive modeling to estimate scores from difficulty and execution. No major bugs fixed this month. Impact: provides an end-to-end, reproducible analytics workflow that enables data-driven evaluation of gymnastics scoring, supports coaching and judging insights, and lays groundwork for scalable extension. Technologies demonstrated: R, R Markdown, data wrangling with dplyr/tidyr, visualization with ggplot2, and linear and multi-level modeling (e.g., lm and mixed-effects models).
May 2025
March 2025
5 Commits • 2 Features
Mar 1, 2025March 2025 performance summary for iramler/slu_score_module_development: Delivered a cohesive Gymnastics Data Analysis Suite that consolidates gymnastics data assets and analyses into a reproducible data analysis workflow, enabling faster data-driven scoring insights and scalable reporting. Established initial data wrangling, analysis, and modeling components and validated the repository's commit workflow with a lightweight test artifact.
March 2025
5 Commits • 2 Features
Mar 1, 2025March 2025 performance summary for iramler/slu_score_module_development: Delivered a cohesive Gymnastics Data Analysis Suite that consolidates gymnastics data assets and analyses into a reproducible data analysis workflow, enabling faster data-driven scoring insights and scalable reporting. Established initial data wrangling, analysis, and modeling components and validated the repository's commit workflow with a lightweight test artifact.
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture86.6%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVQuartoRText
Technical Skills
Basic File OperationsData AnalysisData EngineeringData ManagementData VisualizationData WranglingR MarkdownR ProgrammingStatistical AnalysisStatistical Modeling
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