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Usually, when we develop software, we know very well how to use it and how it works. This is not true for external users, people who will make mistakes and won't use your software correctly on the fir...

Snapshot tests (aka golden tests) are similar to unit tests except that the expected result is stored in a separate file that is managed by testthat. Snapshot tests are useful for when the expected va...

Join us for an online workshop designed for PhD students, postdoctoral researchers, and professional researchers who want to turn R analysis scripts into robust, testable, and maintainable R packages. Coordinated by the Biostatistics section of the Statistical Society of Canada, with materials developed by the Quantitative Life Sciences - McGill Initiative in Computational Medicine (QLS-MiCM), this hands-on seminar will guide participants through package structure, unit testing with testthat, code coverage, documentation with roxygen2, and GitHub-based workflows for version control, collaboration, and automated testing with GitHub Actions. Led by Adrien Osakwe, a PhD candidate in Quantitative Life Sciences at McGill University and founding member of the QLS-MiCM student organization, the workshop is ideal for researchers developing statistical software, reproducible workflows, simulation studies, or bespoke analytical tools in R. Participants should have experience writing R scripts, working with Git, and using GitHub and RStudio. The seminar will be delivered via Zoom, with recordings, materials, and a monitored Q&A forum available for 30 days afterward, making it easy to attend live or asynchronously. An official certificate of completion is provided at the conclusion of the seminar. #SoftwareEngineering #Statistics #ComputerScience #Git #Github #R #Research #ResearchTraining #Instats

Hadley is Chief Scientist at Posit PBC, winner of the 2019 COPSS award, and a member of the R Foundation. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. His work includes packages for data science (like the tidyverse, which includes ggplot2, dplyr, and tidyr)and principled software development (e.g. roxygen2, testthat, and pkgdown). He is also a writer, educator, and speaker promoting the use of R for data science.

A collection of Claude Skills from Posit. Contribute to posit-dev/skills development by creating an account on GitHub.

Positron Assistant provides inline completions with GitHub Copilot and chat using Claude 4 Sonnet. Demonstration: using agent mode to create an R package with Roxygen2 docs and testthat unit tests.

The testthat package is the most widely used tool for unit testing in R. However, many users may not be aware of the possibility to nest test blocks within each other. In this post, I demonstrate how ...

A minimalistic, dependency-free alternative to 'roxygen2'. Reads '#” comment blocks above R functions and objects and turns them into '.Rd' documentation files and a 'NAMESPACE' file. 'tinyroxygen' is to 'roxygen2' what 'tinytest' is to 'testthat', a lightweight alternative built with base R only, with no recursive dependencies to install. 'Roxygen' stands for R documentation inspired by the 'Doxygen' for 'C++' documentation tools.

Extends 'roxygen2' to support '@meta' tags for documenting 'testthat' test cases and function specifications. Includes a custom 'JUnit' reporter that exports test metadata as XML properties and validation functions to ensure all exported functions and tests contain required tags. Designed for traceability between requirements and tests in regulated industries such as pharma and finance.