6 Conclusion

openstatsware short course: Good Software Engineering Practice for R Packages

Daniel Sabanés Bové

July 17, 2026

감사합니다 !

  • to you for your engagement and participation 🙏
  • to the SAIHST organizers, in particular Sang Ho Park, for making this event possible 👏
  • to our fellow openstatsware authors of the course materials 📚

Take away messages

R package structure

  • R package = folder structure with (many) conventions
  • With modern tools usethis, roxygen2 it is easy to start a new package
  • Packaging a set of functions is an ideal way to share with collaborators and the public
  • Start small and simple and over time you can learn additional options

Ensuring quality

  • Quality by Design: use a workable workflow:
    Idea \(\rightarrow\) Design docs \(\rightarrow\) Programming \(\rightarrow\) Quality check \(\rightarrow\) Publication
  • Apply common clean code rules:
    • Use clear names for functions and variables
    • Don’t repeat yourself!
    • Use styler to optimize the code styling
  • Write tests for your functions
    • Use testthat to test, test, and test
    • Use covr to improve the test coverage

Collaboration

  • Version control is key
    • Multiple people working on code without strong VC \(\leadsto\) disaster!
    • There are different options, but git is the defacto standard for R packages
    • Git needs a friend - use platforms like GitHub or GitLab
  • Automated CI/CD allows for much quicker iteration
    • Automate tests to avoid bugs slipping back in
  • Technology does not solve everything - foster a positive culture
    • Keep internal and external contributors engaged
    • Invest in documentation to make it easier to contribute

Publication

  • pkgdown can help you easily create a nice website for your package
  • Versions and licenses along with NEWS updates are important
  • GitHub helps with tagging of release versions
  • R-Hub helps with checking before CRAN submission

Possible next steps

  • Bookmark the openstatsguide as a quick reference for good practices for R package development
  • Bring the information back to your colleagues in your organization
  • Start building your first own package and share internally first
  • Later publish it open source on GitHub and submit it to CRAN
  • Learn about more tips and tricks how to extend R

Photo by Pixabay on pexels.com

License information