Optimize R 4.4+ workflows for data analytics, visualization, and Shiny applications.
npx skills add https://github.com/GoosLab/moai-rank --skill moai-lang-r-gooslab
Please help me install this Agent Skill. Skill: moai-lang-r Source: https://github.com/GoosLab/moai-rank/tree/main/.claude/skills/moai-lang-r Command: npx skills add https://github.com/GoosLab/moai-rank --skill moai-lang-r-gooslab
R 4.4+ development environment patterns and best practices for data analysis, visualization, and Shiny apps, enabling consistent, reproducible results in R projects.
Load an R project that uses tidyverse and Shiny components and run a minimal analysis to verify pattern support.
High-intent search queries and answers about installing and using this skill.
Build reproducible R data analysis pipelines using renv for dependency management and tidyverse for data manipulation. This approach ensures consistent project environments and reproducible analytical results across R 4.4+ development workflows.
Structure Shiny modules using modern R 4.4+ patterns to build interactive dashboards. Applying modular Shiny components alongside ggplot2 visualizations creates scalable, maintainable interactive applications with consistent UI and server logic separation.
Yes, tidyverse and ggplot2 work together seamlessly for data visualization in R. You manipulate data using tidyverse functions and pass the results directly into ggplot2 to generate reproducible visualizations within the same workflow.
Yes, renv manages R package dependencies for reproducible workflows. It creates project-specific local environments that capture exact package versions, ensuring your R data analysis remains consistent and reproducible across different systems.
Validate data manipulation logic in R using testthat by writing automated tests for your tidyverse pipelines. This testing framework checks your data transformations and ensures analytical code correctness throughout development.
Use renv for R dependency management to avoid version conflicts inherent in global package installations. renv isolates project dependencies, guaranteeing that your data analysis environment remains stable and fully reproducible over time.
windyskyApply modern R 4.4+ development patterns for data science projects.
hnabyz-botStandardize modern R 4.4+ data analysis and Shiny app development patterns.
taewook486Develop R data pipelines, ggplot2 visualizations, and Shiny web applications.
ScientiaCapitalCompute SaaS metrics and generate Streamlit dashboards from ingested data.
