Guide R 4.4+ data analysis with tidyverse, ggplot2, Shiny, and reproducible tooling.
npx skills add https://github.com/modu-ai/moai-adk --skill moai-lang-r
Please help me install this Agent Skill. Skill: moai-lang-r Source: https://github.com/modu-ai/moai-adk/tree/main/.claude/skills/moai-lang-r Command: npx skills add https://github.com/modu-ai/moai-adk --skill moai-lang-r
Performing robust data analysis and statistical modeling in R requires adherence to best practices for code quality, testing, and reproducibility. This Skill provides expert guidance for R 4.4+, integrating testthat 3.2 for testing and lintr 3.2 for code quality, ensuring your data science projects are reliable and maintainable.
testthat 3.2.lintr 3.2.install.packages(c("testthat", "lintr"))
testthat::test_dir("tests/")
lintr::lint("my_script.R")
High-intent search queries and answers about installing and using this skill.
R 4.4+ best practices integrate testthat 3.2 for unit testing and lintr 3.2 for code quality enforcement. Install both packages, write tests in tests/ directories, and run testthat::test_dir() and lintr::lint() to validate your data pipelines, visualizations, and statistical models before deployment.
Yes. R 4.4+ supports native pipes (|>) and lambda syntax that work seamlessly with dplyr and tidyr for data manipulation, ggplot2 and plotly for visualizations, and Shiny for interactive dashboards. Use renv or pak for reproducible package management across these frameworks.
Structure R code using R Markdown or Quarto for reproducible reports, renv for dependency locking, testthat for test-driven development, and lintr for consistent style. This ensures your data cleaning, transformation, modeling, and visualization pipelines remain maintainable across experiments and production deployments.
Use testthat 3.2 to write unit tests for data manipulation, statistical functions, and model outputs. Create test files in tests/ directories, write expectations that validate function behavior, and run testthat::test_dir() to verify your data pipelines work correctly before committing changes.
Yes. R 4.4+ introduces native pipes (|>), lambda syntax, and improved error messages that streamline tidyverse workflows. These features reduce boilerplate in data transformation chains and make statistical modeling code more readable and maintainable.
lintr 3.2 detects inconsistent naming conventions, trailing whitespace, unused variables, and style violations in R scripts. Running lintr::lint() on your data manipulation, visualization, and modeling code ensures adherence to best practices and prevents errors before deployment.
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