How does this skill handle dynamic column names in functions?

It utilizes modern rlang patterns such as 'embracing' with curly-curly ({{ }}) to properly handle data-masking and metaprogramming within custom functions.

Does this skill support the magrittr pipe (%>%)?

While it can interpret legacy code, this skill specifically prioritizes the native R pipe (|>) introduced in R 4.1 for improved performance and reduced dependency overhead.

Is this skill suitable for R package developers?

Extensively. It provides guidance on API design, dependency management, unit testing with testthat, and documentation to ensure packages meet CRAN-quality standards.

Can this skill help with optimizing slow R code?

Yes, it includes strategies for performance optimization, including profiling with profvis, benchmarking alternatives with bench, and using dtplyr for large-scale data operations.

Modern R Development

Name: Modern R Development
Author: CodingKaiser

byCodingKaiser

0•

Data Science & ML

Implements modern R programming patterns focusing on tidyverse efficiency, metaprogramming, and performance-optimized package development.

This skill provides Claude with the expertise to write professional, modern R code following the latest industry standards. It prioritizes the native pipe operator, advanced dplyr 1.1+ syntax like join_by() and .by grouping, and robust metaprogramming with rlang. Beyond simple scripting, it offers comprehensive guidance for R package development, performance profiling with profvis, and memory-efficient data processing, making it an essential tool for data scientists and developers looking to modernize their R codebase and optimize computational workflows.

Key Features

010 GitHub stars

02Metaprogramming with rlang and data-masking patterns ({{ }})

03Professional R package development and API design best practices

04Modern Tidyverse implementation (dplyr 1.1+, native pipe, join_by)

05Performance profiling and benchmarking using profvis and bench

06Migration patterns from legacy Base R and old Tidyverse to modern standards

Use Cases

01Modernizing legacy R scripts to utilize the native pipe and current dplyr 1.1+ APIs.

02Developing high-performance R packages with robust unit testing and clear documentation.

03Building complex data transformation pipelines using rlang for dynamic column handling and metaprogramming.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add codingkaiser/kaiser-skills r-development

For use in Claude.ai and ChatGPT

Key Features

010 GitHub stars

02Metaprogramming with rlang and data-masking patterns ({{ }})

03Professional R package development and API design best practices

04Modern Tidyverse implementation (dplyr 1.1+, native pipe, join_by)

05Performance profiling and benchmarking using profvis and bench

06Migration patterns from legacy Base R and old Tidyverse to modern standards

Use Cases

01Modernizing legacy R scripts to utilize the native pipe and current dplyr 1.1+ APIs.

02Developing high-performance R packages with robust unit testing and clear documentation.

03Building complex data transformation pipelines using rlang for dynamic column handling and metaprogramming.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add codingkaiser/kaiser-skills r-development

For use in Claude.ai and ChatGPT