Apply modern tidyverse and rlang patterns to R development tasks.
npx skills add https://github.com/CodingKaiser/kaiser-skills --skill r-development
Please help me install this Agent Skill. Skill: r-development Source: https://github.com/CodingKaiser/kaiser-skills/tree/main/r-development Command: npx skills add https://github.com/CodingKaiser/kaiser-skills --skill r-development
This skill solves the challenge of consistently applying modern R development practices across projects to improve code quality and maintainability.
To start, review the core principles and apply the recommended patterns to a small R script, then scale to a package.
High-intent search queries and answers about installing and using this skill.
Modern tidyverse patterns in R use the native pipe and .by grouping for concise data manipulation. This approach enforces dplyr 1.1+ features and join_by specifications to improve code consistency and maintainability across projects.
Rlang metaprogramming works by leveraging embracing, data-masking patterns, and code generation to build robust R functions. It allows you to programmatically manipulate code and create flexible functions for complex data analysis tasks.
To create an R package and optimize performance, apply professional package-development guidelines alongside performance profiling techniques. This ensures your R code is efficient, well-structured, and maintained to modern development standards.
Yes, this approach fully supports dplyr 1.1+ features including join_by and data-masking patterns. It consistently enforces up-to-date tidyverse patterns to ensure your R code follows current best practices.
The best way to write efficient R code is combining tidyverse patterns with rlang metaprogramming. This includes using dplyr 1.1+ features, native pipes, and performance profiling to optimize data manipulation and package development.
Using the native pipe in modern R development aligns with up-to-date tidyverse patterns and reduces external dependencies. It integrates seamlessly with dplyr 1.1+ features and .by grouping for cleaner, more maintainable code.
Dmccarty30Create isolated Git worktrees with dependency installation and baseline tests.
drmowinckelsAutomate CRAN preparation with roxygen2, devtools, and testthat checks.
modu-aiGuide R 4.4+ data analysis with tidyverse, ggplot2, Shiny, and reproducible tooling.
jeremy-allenProfile and benchmark R code to identify performance bottlenecks.
