How does it assist with R package creation?

It provides comprehensive guidance on API design, managing dependencies, writing unit tests, and documenting functions to meet professional CRAN-like standards.

Can it help me migrate from the magrittr pipe?

Absolutely. The skill emphasizes the use of the native R pipe ( |> ) over the magrittr pipe ( %>% ) and provides clear patterns for updating legacy code.

Does it include performance optimization tools?

Yes, it integrates best practices for profiling code using profvis and benchmarking different approaches with the bench package to ensure maximum efficiency.

Does this skill support the latest tidyverse features?

Yes, it specifically prioritizes modern patterns introduced in dplyr 1.1.0 and later, including the .by grouping argument and join_by() syntax.

Modern R Development

Name: Modern R Development
Author: CodingKaiser

byCodingKaiser

데이터 과학 및 ML

Implements modern R programming practices, tidyverse patterns, and performance optimizations for data science workflows.

소개

This skill equips Claude with the latest R programming standards, focusing on the tidyverse ecosystem (dplyr 1.1+), native pipe operations, and efficient data-masking techniques with rlang. It provides a comprehensive framework for writing clean, performant R code, developing professional-grade packages, and optimizing existing scripts through rigorous profiling and benchmarking. Whether you are migrating legacy code or building complex data pipelines, this skill ensures your R projects adhere to current industry best practices and maintainable design patterns.

주요 기능

Modern tidyverse implementation including dplyr 1.1+ and native pipe syntax
Performance profiling and benchmarking with profvis and bench
Automated migration patterns from legacy base R to modern tidyverse
Advanced data-masking and metaprogramming patterns using rlang
Standardized R package development and API design guidance
0 GitHub stars

사용 사례

Optimizing data processing bottlenecks in omics or large-scale analytics
Refactoring legacy R scripts to use modern, readable tidyverse syntax
Building and documenting professional R packages with robust testing

소개

주요 기능

Modern tidyverse implementation including dplyr 1.1+ and native pipe syntax
Performance profiling and benchmarking with profvis and bench
Automated migration patterns from legacy base R to modern tidyverse
Advanced data-masking and metaprogramming patterns using rlang
Standardized R package development and API design guidance
0 GitHub stars

사용 사례

Optimizing data processing bottlenecks in omics or large-scale analytics
Refactoring legacy R scripts to use modern, readable tidyverse syntax
Building and documenting professional R packages with robust testing