Can this skill run my R tests automatically?

Yes, it integrates with testthat 3.2 to execute test suites, analyze results, and help you maintain a Test-Driven Development workflow.

How does it handle code quality?

It uses lintr 3.2 for static analysis and enforces a minimum test coverage of 85% to ensure your R code is maintainable and reliable.

What version of R does this skill support?

This skill is specifically optimized for R 4.4.2 and later, ensuring compatibility with modern language features and best practices.

Is this skill suitable for professional data analysis?

Absolutely; it is designed with specific data analysis patterns and TRUST 5 principles to ensure statistical accuracy and project integrity.

Does it require external tools to be installed?

Yes, for full functionality, you should have R, testthat, and lintr installed in your environment so the skill can interact with them via the terminal.

R Data Science & Testing

Name: R Data Science & Testing
Author: kivo360

bykivo360

Data Science & ML

Streamlines R development using modern data analysis patterns, automated testing with testthat, and strict linting standards.

About

The moai-lang-r skill equips Claude Code with deep expertise in the R programming language, specifically optimized for version 4.4 and above. It enforces industry-standard best practices through deep integration with testthat and lintr, ensuring high code quality and data integrity. This skill is ideal for data scientists and statisticians who need to maintain rigorous TDD workflows, achieve high test coverage, and implement TRUST 5 principles in their data analysis pipelines.

Key Features

Automates unit testing with testthat 3.2 integration
Implements TRUST 5 principles for reliable data analysis
Enforces R 4.4+ best practices and modern coding standards
0 GitHub stars
Maintains high quality gates with a target of 85% test coverage
Performs static code analysis using lintr 3.2

Use Cases

Automating TDD workflows for statistical modeling and data processing
Performing code reviews to ensure compliance with R linting standards
Migrating legacy R scripts to modern, tested data analysis patterns

About

Key Features

Automates unit testing with testthat 3.2 integration
Implements TRUST 5 principles for reliable data analysis
Enforces R 4.4+ best practices and modern coding standards
0 GitHub stars
Maintains high quality gates with a target of 85% test coverage
Performs static code analysis using lintr 3.2

Use Cases

Automating TDD workflows for statistical modeling and data processing
Performing code reviews to ensure compliance with R linting standards
Migrating legacy R scripts to modern, tested data analysis patterns