r-development

Apply modern tidyverse and rlang patterns to R development tasks.

6|1|Updated Nov 26, 2025

Search Tags:#performance #metaprogramming #tidyverse #R #rlang #package-development

Ready to run

npx skills add https://github.com/CodingKaiser/kaiser-skills --skill r-development

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill solves the challenge of consistently applying modern R development practices across projects to improve code quality and maintainability.

Core Features & Use Cases

Modern tidyverse patterns: embrace dplyr 1.1+ features, native pipe, join_by, and .by grouping.
Rlang metaprogramming: leverage embracing, data-masking patterns, and code generation for robust functions.
Performance and package development: guidance on profiling, optimization, and professional package creation.
Use Case: when Claude needs to write efficient R code, build R packages, or optimize performance.

Quick Start

To start, review the core principles and apply the recommended patterns to a small R script, then scale to a package.

Frequently Asked Questions about r-development

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I apply modern tidyverse patterns like the native pipe and .by grouping in R?▼

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.

What is rlang metaprogramming and how does it work for R functions?▼

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.

How do I create an R package and optimize its performance?▼

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.

Does this approach support dplyr 1.1+ features like join_by for data masking?▼

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.

What is the best way to write efficient R code with tidyverse and rlang?▼

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.

Why should I use native pipe instead of magrittr pipe in modern R 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.