The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

unitizeR - Interactive R Unit Tests

TL;DR

unitizer simplifies creation, review, and debugging of tests in R. It automatically stores R expressions and the values they produce, so explicit expectations are unnecessary. Every test is easy to write with unitizer because testing and using a function are the same. This encourages non-trivial tests that better represent actual usage.

Tests fail when the value associated with an expression changes. In interactive mode you are dropped directly into the failing test environment so you may debug it.

unitizer is on CRAN:

install.packages('unitizer')

It bakes in a lot of contextual help so you can get started without reading all the documentation. Try the demo to get an idea:

library(unitizer)
demo(unitizer)

Or check out the screencast to see unitizer in action.

Why Another Testing Framework?

Automated Test Formalization

Are you tired of the deparse/dput then copy-paste R objects into test file dance, or do you use testthat::expect_equal_to_reference or other snapshot testing a lot?

With unitizer you interactively review your code as you would when typing it at the R prompt. Then, with a single keystroke, you tell unitizer to store the code, and any values, warnings, or errors it produced, thereby creating a formal regression test.

Streamlined Debugging

Do you wish the nature of a test failure was more immediately obvious?

When tests fail, you are shown a proper diff so you can clearly identify how the test failed:

Do you wish that you could start debugging your failed tests without additional set-up work?

unitizer drops you in the test environment so you can debug why the test failed without further ado:

Fast Test Updates

Do you avoid improvements to your functions because that would require painstakingly updating many tests?

The diffs for the failed tests let you immediately confirm only what you intended changed. Then you can update each test with a single keystroke.

Usage

unitizer stores R expressions and the result of evaluating them so that it can detect code regressions. This is akin to saving test output to a .Rout.save file as documented in Writing R Extensions, except that we’re storing the actual R objects and it is much easier to review them.

To use unitizer:

• Write test expressions as you would when informally testing code on the command line, and save them to a file (e.g. “my_file_name.R”).
• Run unitize("my_file_name.R") and follow the prompts.
• Continue developing your package.
• Re-run unitize("my_file_name.R"); if any tests fail you will be able to review and debug them in an interactive prompt.

unitizer can run in a non-interactive mode for use with R CMD check.

Documentation

• help(package="unitizer"), in particular ?unitize
• demo(package="unitizer")
• browseVignettes("unitizer") for a list of vignettes, or skip straight to the Introduction vignette

Related Packages

• aammrtf, a minimal version of unitizer, used by unitizer for its own tests.
• testthat.
• tinytest, which is extended by ttdo for diffobj diffs.
• RUnit.

Acknowledgments

Thank you to:

• R Core for developing and maintaining such a wonderfully language.
• CRAN maintainers, for patiently shepherding packages onto CRAN and maintaining the repository, and Uwe Ligges in particular for maintaining Winbuilder.
• Gábor Csárdi for crayon through which we can add a new dimension to the R experience.
• Jim Hester because covr rocks.
• Hadley Wickham for testthat from which we borrow many concepts, and for his many other packages.
• Dirk Eddelbuettel and Carl Boettiger for the rocker project, and Gábor Csárdi and the R-consortium for Rhub, without which testing bugs on R-devel and other platforms would be a nightmare.
• Yihui Xie for knitr and J.J. Allaire et al. for rmarkdown, and by extension John MacFarlane for pandoc.
• @kohler for gifsicle and the ffmpeg team for ffmpeg.
• All open source developers out there that make their work freely available for others to use.
• Github, Codecov, Vagrant, Docker, Ubuntu, Brew for providing infrastructure that greatly simplifies open source development.
• Free Software Foundation for developing the GPL license and promotion of the free software movement.

About the Author

Brodie Gaslam is a hobbyist programmer based in the US East Coast.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.