writing-tidyverse-r

Modernize R code by replacing legacy tidyverse patterns with current dplyr APIs.

2|Updated Jan 20, 2026
One-click install
npx skills add https://github.com/justanesta/claude-code-resources --skill writing-tidyverse-r
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: writing-tidyverse-r
Source: https://github.com/justanesta/claude-code-resources/tree/main/skills/R/writing-tidyverse-r
Command: npx skills add https://github.com/justanesta/claude-code-resources --skill writing-tidyverse-r

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Modernizes R code by replacing legacy tidyverse patterns with up-to-date practices.

Core Features & Use Cases

  • Modern pipe usage with |> and current dplyr 1.1+ APIs to improve readability
  • Migration guidance from base R and older tidyverse patterns to modern equivalents
  • Comprehensive references on join syntax, grouping, across, and stringr usage to standardize coding style
  • Use cases include refactoring data analysis scripts, data pipelines, and reproducible research projects

Quick Start

Refactor an example R script to use modern tidyverse patterns and run tests to verify behavior.

Frequently Asked Questions about writing-tidyverse-r

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

FAQPage Schema
How do I migrate base R code to modern tidyverse APIs?

Migrate base R code to modern tidyverse APIs by replacing legacy patterns with up-to-date practices for data analysis scripts and pipelines. It provides practical migration references to enforce consistency across codebases.

What is the modern pipe usage syntax in dplyr 1.1+?

Modern pipe usage in dplyr 1.1+ utilizes the native |> operator to improve readability. It replaces older tidyverse patterns and ensures code adheres to current style conventions.

How do I refactor data analysis scripts to use across and join syntax?

Refactor data analysis scripts using comprehensive references on across, join syntax, and grouping to standardize coding style. It modernizes R code by enforcing practical patterns for reproducible research projects.

Can I use this to enforce tidyverse code-style conventions across reproducible research projects?

Yes, enforce tidyverse code-style conventions across reproducible research projects by applying practical patterns and migration references. It standardizes coding style for modern pipe usage, join syntax, and stringr.

What is the best way to update legacy tidyverse patterns in R data pipelines?

Update legacy tidyverse patterns in R data pipelines by replacing them with current dplyr 1.1+ APIs and modern tidyverse equivalents. This approach improves readability and enforces consistency across codebases.

Why should I replace older tidyverse patterns with modern equivalents?

Replace older tidyverse patterns with modern equivalents to improve readability and enforce consistency across data pipelines. It aligns codebases with dplyr 1.1+ APIs and current style conventions.