unitizerWrites To Your Filesystem
all.equalStored Reference Values
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:
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:
Or check out the screencast to see
unitizer in action.
Are you tired of the
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?
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.
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:
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.
unitizer requires you to review test outputs and confirm they are as expected.
testthat requires you to assert what the test outputs should be beforehand. There are trade-offs between these strategies that we illustrate here, first with
vec <- c(10, -10, 0, .1, Inf, NA) expect_error( log10(letters), "Error in log10\\(letters\\) : non-numeric argument to mathematical function\n" ) expect_equal(log10(vec), c(1, NaN, -Inf, -1, Inf, NA)) expect_warning(log10(vec), "NaNs produced")
vec <- c(10, -10, 0, .1, Inf, NA) log10(letters) # input error log10(vec) # succeed with warnings
These two unit test implementations are functionally equivalent. There are benefits to both approaches. In favor of
In favor of
unitizeryou still need to
unitizeand review the tests.
unitizerstores reference values in binary RDSes (see Collaborating with Unitizer).
unitizer is particularly convenient when the tests return complex objects (e.g as
lm does) and/or produce conditions. There is no need for complicated assertions involving deparsed objects, or different workflows for snapshots.
If you have a stable set of tests it is probably not worth trying to convert them to
unitizer unless you expect the code those tests cover to change substantially. If you do decide to convert tests you can use the provided
testthat_translate* functions (see
The simplest way to use
unitizer as part of your package development process is to create a
tests/unitizer folder for all your
unitizer test scripts. Here is a sample test structure from the demo package:
unitizer.fastlm/ # top level package directory R/ tests/ run.R # <- calls `unitize` or `unitize_dir` unitizer/ fastlm.R cornerCases.R
And this is what the
tests/run.R file would look like
library(unitizer) unitize("unitizer/fastlm.R") unitize("unitizer/cornerCases.R")
The path specification for test files should be relative to the
tests directory as that is what
R CMD check uses. When
unitize is run by
R CMD check it will run in a non-interactive mode that will succeed only if all tests pass.
You can use any folder name for your tests, but if you use “tests/unitizer”
unitize will look for files automatically, so the following work assuming your working directory is a folder within the package:
unitize_dir() # same as `unitize_dir("unitizer")` unitize("fast") # same as `unitize("fastlm.R")` unitize() # Will prompt for a file to `unitize`
Remember to include
unitizer as a “suggests” package in your DESCRIPTION file.
unitizerWrites To Your Filesystem
unitized tests need to be saved someplace, and the default action is to save to the same directory as the test file. You will always be prompted by
unitizer before it writes to your file system. See storing
unitized tests for implications and alternatives.
all.equalStored Reference Values
Once you have created your first
unitize, subsequent calls to
unitize will compare the old stored value to the new one using
all.equal. You can change the comparison function by using
unitizer_sect (see tests vignette).
This means you need to be careful with expressions that may deparse differently on different machines. For example, in order to avoid round issues with numerics, it is better to use:
<- 14523.2342520 # assignments are not considered tests num.var test_me(num.var) # safe
test_me(14523.2342520) # could be deparsed differently
Similarly issues may arise with non-ASCII characters, so use:
<- "hello\u044F" chr fun_to_test(chr)
fun_to_test("hello\u044F") # could be deparsed differently
unitizer can track and manage many aspects of state to make your tests more reproducible. For example,
unitizer can reset your search path to what is is found in a fresh R session prior to running tests to avoid conflicts with whatever libraries you happen to have loaded at the time. Your session state is restored when
unitizer exits. The following aspects of state can be actively tracked and managed:
State management is turned off by default because it requires tracing some base functions which is against CRAN policy. If you wish to enable this feature use
unitize(..., state='suggested') or
options(unitizer.state='suggested'). For more details see
?unitizerState and the reproducible tests vignette.
If you interrupt evaluation with CTRL+C (or with ESC in RStudio), or if you
debug and quit with ‘Q’, you will exit
unitizer with no opportunity to save any modifications you made during
unitizer review. Make sure you quit by typing ‘Q’ at the
unitizer prompt. If you are in
browser, you will need to let the browsed function finish evaluation to return to the
unitizer prompt, and only then quit.
Tests that modify objects by reference are not perfectly suited for use with
unitizer. The tests will work fine, but
unitizer will only be able to show you the most recent version of the reference object when you review a test, not what it was like when the test was evaluated. This is only an issue with reference objects that are modified (e.g. environments, RC objects,
data.table modified with
In order to re-create the feel of the R prompt within
unitizer we resorted to a fair bit of trickery. For the most part this should be transparent to the user, but you should be aware it exists in the event something unexpected happens that exposes it. Here is a non-exhaustive list of some of the tricky things we do:
ls(see esoteric topics vignette) at the
tracebackshould work when reviewing tests that produce errors, but only because we capture the trace with
sys.callsand write it to
.Last.valuewill not work
stderrduring test evaluation to capture those streams (see details on tests vignette), though we take care to do so responsibly
unitizerinteractions do not pollute it
In particular, you should avoid evaluating tests that invoke
debugged functions, or introducing interactivity by using something like
readline, or some such. Tests will work, but the interaction will be challenging because you will have to do it with
Doing so will cause
unitize to quit if any test expressions throw conditions. See discussion in error handling.
quit are masked to give the user an opportunity to cancel the quit action in case they meant to quit from
unitizer instead of R. Use Q to quit from
unitizer, as you would from
ls is masked with a specialized version for use in
In both cases you can still access the original functions by preceding them with