Testing R Packages

September 29, 2013
By

(This article was first published on Yihui Xie, and kindly contributed to R-bloggers)

This guy th3james claimed Testing Code Is Simple, and I agree. In the R world, this is not anything new. As far as I can see, there are three schools of R users with different testing techniques:

  1. tests are put under package/tests/, and a foo-test.Rout.save from R CMD BATCH foo-test.R; testing is done by comparing foo-test.Rout from R CMD check with your foo-test.Rout.save; R notifies you when it sees text differences; this is typically used by R core and followers
  2. RUnit and its followers: formal ideas were borrowed from other languages and frameworks and it looks there is a lot to learn before you can get started
  3. the testthat family: tests are expressed as expect_something() like a natural human language

At its core, testing is nothing but "tell me if something unexpected happened". The usual way to tell you is to signal an error. In R, that means stop(). A very simple way to write a test for the function FUN() is:

if (!identical(FUN(arg1 = val1, arg2 = val2, ...), expected_value)) {
  stop('FUN() did not return the expected value!')
}

That is, when we pass the values val1 and val2 to the arguments arg1 and arg2, respectively, the function FUN() should return a value identical to our expected value, otherwise we signal an error. If R CMD check sees an error, it will stop and fail.

For me, I only want one thing for unit testing: I want the non-exported functions to be visible to me during testing; unit testing should have all "units" available, but R's namespace has intentionally restricted the objects that are visible to the end users of a package, which is a Very Good Thing to end users. It is less convenient to the package author, since he/she will have to use the triple colon syntax such as foo:::hidden_fun() when testing the function hidden_fun().

I wrote a tiny package called testit after John Ramey dropped by my office one afternoon while I was doing intern at Fred Hutchinson Cancer Research Center last year. I thought a while about the three testing approaches, and decided to write my own package because I did not like the first approach (text comparison), and I did not want to learn or remember the new vocabulary of RUnit or testthat. There is only one function for the testing purpose in this package: assert().

assert(
  "1 plus 1 is equal to 2",
  1 + 1 == 2
)

You can write multiple testing conditions, e.g.

assert(
  "1 plus 1 is equal to 2",
  1 + 1 == 2,
  identical(1 + 1, 2),
  (1 + 1 >= 2) && (1 + 1 <= 2), # mathematician's proof
  c(is.numeric(1 + 1), is.numeric(2))
)

There is another function test_pkg() to run all tests of a package using an empty environment with the package namespace as its parent environment, which means all objects in the package, exported or not, are directly available without ::: in the test scripts. See the CRAN page for a list of packages that use testit, for example, my highr package, where you can find some examples of tests.

While I do not like the text comparison approach, it does not mean it is not useful. Actually it is extremely useful when testing text document output. It is just a little awkward when testing function output. The text comparison approach plays an important role in the development of knitr: I have a Github repository knitr-examples, which serves as both an example repo and a testing repo. When I push new commits to Github, I use Travis CI to test the package, and there are two parts of the tests: one is to run R CMD check on the package, which uses testit to run the test R scripts, and the other is to re-compile all the examples, and do git diff to see if there are changes. I have more than 100 examples, which should have reasonable coverage of possible problems in the new changes in knitr. This way, I feel comfortable when I bring new features or make changes in knitr because I know they are unlikely to break old documents.

If you are new to testing and only have 3 minutes, I'd strongly recommend you to read at least the first two sections of Hadley's testthat article.

To leave a comment for the author, please follow the link and comment on his blog: Yihui Xie.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.