Hadley Wickham said it best, in his 2011 paper on his testthat package:

It’s not that we don’t test our code, it’s that we don’t store our tests so they can be re-run automatically.

How do you know that your code works? You try it out.

How do you know that your code still works a year later? Well, ideally you saved those initial tests. Even better, you didn’t just save them, but you structured them in a way that you could run them regularly. This gives you confidence that later changes haven’t broken things that worked.

R CMD check

The simplest (but most crude) tests are the examples in your documentation. These are run each time you use R CMD check. But that doesn’t check to see if your code is actually giving the right answers. You’re just notified if the code produces errors.

As mentioned in the page on getting your package on CRAN, your examples should be quick to run. On CRAN, every package is tested daily on multiple systems.

You might subset a dataset so that the subsequent code is much faster; you could put such code with \dontshow{ }, so that it will be run when the examples are run but won’t be shown to the user. Computationally intensive code could also be placed in \dontrun{ } or \donttest{ }. Code in \dontrun{ } will never be run automatically; code in \donttest{ } will be run when a user calls example( ) but won’t be run with R CMD check. Of course, a disadvantage of \dontrun{ } and \donttest{ } is that then the code won’t be tested automatically.

testthat package

For proper tests, in which you actually assess whether your code is giving the correct answers, use the testthat package. You’d be best off reading the testing section in Hadley’s book on R packages, but let me give a brief synopsis with a few examples.

Create a subdirectory tests and within that create another subdirectory testthat plus a file testthat.R containing the following code:


Then, within the tests/testthat/ directory, put a bunch of files like test-something.R containing code to be run by the testthat package. This code will be run with R CMD check, or also with devtools::test(). (Generally, you’d load devtools with library(devtools) and type test().)

Here’s a portion of the test-runningmean.R file from my R/broman package.

context("running mean")

test_that("running mean stops when it should", {

  expect_error( runningmean(0, c(0,0)) )


test_that("running mean with constant x or position", {

  n <- 100
  x <- rnorm(n)
  pos <- rep(0, n)
  expect_equal( runningmean(pos, x, window=1), rep(mean(x), n) )

  mu <- mean(x)
  x <- rep(mu, n)
  pos <- runif(n, 0, 5)
  expect_equal( runningmean(pos, x, window=1), x)


Begin each test-blah.R file a with call to context; the string here will be printed in the output of test(). Follow this with a series of calls to test_that(), each containing a group of related tests. Each of these calls has a character string and then a bunch of code within { }. That code will be much like what one always writes in typical informal tests: run a bit of code with known result, and then check if the result actually matches that expected.

The testthat package includes a number of helper functions for checking whether results match expectation.

  • expect_error() if the code should throw an error
  • expect_warning() if the code should throw a warning
  • expect_equal(a, b) if a and b should be the same (up to numeric tolerance)
  • expect_equivalent(a, b) if a and b should contain the same values but may have different attributes (e.g., names and dimnames)

There are a number of others.

When you run devtools::test(), you’ll get output like this:

Testing broman
Loading broman
running mean : ........
winsorize : ......

Each dot indicates a test that passed. If there are failures, there’ll be a bunch of output indicating what failed and how.

When you write a new function for your package, write some tests, and then put them within a call to testthat() within a file in tests/testthat/.

When you find a bug, write a test

If you find a bug in your package, the first thing you should do is write a test for it: write some code that reproduces the problem, either by throwing an error or by giving results that you know are incorrect. Don’t start working to fix the bug until after you’ve written the test!

If you don’t have test code that demonstrates the bug, how will you know that you’ve really fixed it? And if you take the time to write test code for the bug, you should add that to your battery of tests.

Find a bug, write a test, and then fix the bug.

Automated testing with Travis CI

If you poke around GitHub, you’ll often see R packages whose ReadMe files contain badges like this:

Build Status

The package author is using the Travis CI site to test their package whenever they push changes to GitHub. This is an example of what’s called Continuous Integration (that’s the “CI” in “Travis CI”): after every change, the package is automatically built and tested.

This is another great reason to place your package on GitHub. And use of Travis CI is remarkably easy to set up, particularly with the devtools function use_travis().

If you invoke R within your package directory, use_travis() will add a file .travis.yml with the relevant information for Travis CI, and then will add that file name to the .Rbuildignore file, so that R CMD build will ignore it.

You then need to sign in to Travis CI with your GitHub account, giving it limited access to your GitHub repositories. Then select which of your repositories you want Travis CI to monitor and test. That’s it. Whenever you push to GitHub, Travis CI will grab the package, build it, run R CMD check, and send you an email with a message about whether it worked or not.

The final step: add a bit of code like the following to the ReadMe file for your package:

[![Build Status](https://travis-ci.org/user/pkg.svg?branch=master)](https://travis-ci.org/user/pkg)

Replace user with your GitHub user name and pkg with the name of your package repository.

That will give you the little badge shown above (showing either “passing” or “failing”).

You’ll want to be careful to not push to GitHub too often, as every push will cause Travis to build and check your package.

If you know that you want Travis CI to skip a build (e.g., you’ve just edited the ReadMe file), include [ci skip] or [skip ci] anywhere in the commit message.

Note: Also see Julia Silge’s Beginner’s guide to Travis for R and L. Collado Torres’s slightly out of date but still useful Protocol on Travis for R packages.

Automated testing with Github Actions

There’s been a big change to Travis: travis-ci.org is shutting down and travis-ci.com is focusing on commercial efforts. It seems generally recommended to move to using Github Actions for automated package testing. See this blog post at ROpenSci.

The basic suggestion is to use one of the following commands from the usethis package. The following sets things up to run R CMD check on the three major operating systems (mac, windows, linux) on the release and devel versions of R:


The following sets it up for testing with just the release version of R and just on mac:


Now go to the page about including datasets.