The goal of a unit test is to record the expected output of a function using code. This is a powerful technique because not only does it ensure that code doesn’t change unexpectedly, it also expresses the desired behaviour in a way that a human can understand.

However, it’s not always convenient to record the expected behaviour with code. Some challenges include:

  • Text output that includes many characters like quotes and newlines that require special handling in a string.

  • Output that is large, making it painful to define the reference output, and bloating the size of the test file and making it hard to navigate.

  • Binary formats like plots or images, which are very difficult to describe in code: i.e. the plot looks right, the error message is useful to a human, the print method uses colour effectively.

For these situations, testthat provides an alternative mechanism: snapshot tests. Instead of using code to describe expected output, snapshot tests (also known as golden tests) record results in a separate human readable file. Snapshot tests in testthat are inspired primarily by Jest, thanks to a number of very useful discussions with Joe Cheng.

Basic workflow

We’ll illustrate the basic workflow with a simple function that generates an HTML heading. It can optionally include an id attribute, which allows you to construct a link directly to that heading.

bullets <- function(text, id = NULL) {
  paste0(
    "<ul", if (!is.null(id)) paste0(" id=\"", id, "\""), ">\n", 
    paste0("  <li>", text, "</li>\n", collapse = ""),
    "</ul>\n"
  )
}
cat(bullets("a", id = "x"))
#> <ul id="x">
#>   <li>a</li>
#> </ul>

Testing this simple function is relatively painful. To write the test you have to carefully escape the newlines and quotes. And then when you re-read the test in the future, all that escaping makes it hard to tell exactly what it’s supposed to return.

test_that("bullets", {
  expect_equal(bullets("a"), "<ul>\n  <li>a</li>\n</ul>\n")
  expect_equal(bullets("a", id = "x"), "<ul id=\"x\">\n  <li>a</li>\n</ul>\n")
})
#> Test passed 🥇

This is a great place to use snapshot testing. To do this we make two changes to our code:

This yields the following test:

test_that("bullets", {
  expect_snapshot(cat(bullets("a")))
  expect_snapshot(cat(bullets("a", "b")))
})
#> ── Warning (<text>:2:3): bullets ───────────────────────────────────────────────
#> Adding new snapshot:
#> Code
#>   cat(bullets("a"))
#> Output
#>   <ul>
#>     <li>a</li>
#>   </ul>
#> 
#> ── Warning (<text>:3:3): bullets ───────────────────────────────────────────────
#> Adding new snapshot:
#> Code
#>   cat(bullets("a", "b"))
#> Output
#>   <ul id="b">
#>     <li>a</li>
#>   </ul>

When we run the test for the first time, it automatically generates reference output, and prints it, so that you can visually confirm that it’s correct. The output is automatically saved in _snaps/{name}.R. The name of the snapshot matches your test file name — e.g. if your test is test-pizza.R then your snapshot will be saved in test/testthat/_snaps/pizza.md. As the file name suggests, this is a markdown file, which I’ll explain shortly.

If you run the test again, it’ll succeed:

test_that("bullets", {
  expect_snapshot(cat(bullets("a")))
  expect_snapshot(cat(bullets("a", "b")))
})
#> Test passed 🎊

But if you change the underlying code, say to tweak the indenting, the test will fail:

bullets <- function(text, id = NULL) {
  paste0(
    "<ul", if (!is.null(id)) paste0(" id=\"", id, "\""), ">\n", 
    paste0("<li>", text, "</li>\n", collapse = ""),
    "</ul>\n"
  )
}
test_that("bullets", {
  expect_snapshot(cat(bullets("a")))
  expect_snapshot(cat(bullets("a", "b")))
})
#> ── Failure (<text>:9:3): bullets ───────────────────────────────────────────────
#> Snapshot of code has changed:
#> `old[2:6]`: "  cat(bullets(\"a\"))" "Output" "  <ul>" "    <li>a</li>" "  </ul>"
#> `new[2:6]`: "  cat(bullets(\"a\"))" "Output" "  <ul>" "  <li>a</li>"   "  </ul>"
#> 
#> Run `snapshot_accept('snapshotting.Rmd')` if this is a deliberate change
#> 
#> ── Failure (<text>:10:3): bullets ──────────────────────────────────────────────
#> Snapshot of code has changed:
#>     old                            | new                               
#> [2] "  cat(bullets(\"a\", \"b\"))" | "  cat(bullets(\"a\", \"b\"))" [2]
#> [3] "Output"                       | "Output"                       [3]
#> [4] "  <ul id=\"b\">"              | "  <ul id=\"b\">"              [4]
#> [5] "    <li>a</li>"               - "  <li>a</li>"                 [5]
#> [6] "  </ul>"                      | "  </ul>"                      [6]
#> 
#> Run `snapshot_accept('snapshotting.Rmd')` if this is a deliberate change

If this is a deliberate change, you can follow the advice in the message and update the snapshots for that file by running snapshot_accept("pizza"); otherwise you can fix the bug and your tests will pass once more. (You can also accept snapshot for all files with snapshot_accept()).

Snapshot format

Snapshots are recorded using a subset of markdown. You might wonder why we use markdown? It’s important that snapshots be readable by humans, because humans have to look at it during code reviews. Reviewers often don’t run your code but still want to understand the changes.

Here’s the snapshot file generated by the test above:

# bullets

    <ul>
      <li>a</li>
    </ul>
  
---

    <ul id="x">
      <li>a</li>
    </ul>

Each test starts with # {test name}, a level 1 heading. Within a test, each snapshot expectation is indented by four spaces, i.e. as code, and are separated by ---, a horizontal rule.

Interactive usage

Because the snapshot output uses the name of the current test file and the current test, snapshot expectations don’t really work when run interactively at the console. Since they can’t automatically find the reference output, they instead just print the current value for manual inspection.

Other types of output

So far we’ve focussed on snapshot tests for output printed to the console. But expect_snapshot() also captures messages, errors, and warnings. The following function generates a some output, a message, and a warning:

f <- function() {
  print("Hello")
  message("Hi!")
  warning("How are you?")
}

And expect_snapshot() captures them all:

test_that("f() makes lots of noice", {
  expect_snapshot(f())
})
#> ── Warning (<text>:2:3): f() makes lots of noice ───────────────────────────────
#> Adding new snapshot:
#> Code
#>   f()
#> Output
#>   [1] "Hello"
#> Message <simpleMessage>
#>   Hi!
#> Warning <simpleWarning>
#>   How are you?

Capturing errors is slightly more difficult because expect_snapshot() will fail when there’s an error:

test_that("you can't add a number and a letter", {
  expect_snapshot(1 + "a")
})
#> ── Failure (<text>:2:3): you can't add a number and a letter ───────────────────
#> `1 + "a"` threw an unexpected error.
#> Message: non-numeric argument to binary operator
#> Class:   simpleError/error/condition

This is a safety valve that ensures that you don’t accidentally write broken code. To deliberately snapshot an error, you’ll have to specifically request it with error = TRUE:

test_that("you can't add a number and a letter", {
  expect_snapshot(1 + "a", error = TRUE)
})
#> ── Warning (<text>:2:3): you can't add a number and a letter ───────────────────
#> Adding new snapshot:
#> Code
#>   1 + "a"
#> Error <simpleError>
#>   non-numeric argument to binary operator

When the code gets longer, I like to put error = TRUE up front so it’s a little more obvious:

test_that("you can't add weird thngs", {
  expect_snapshot(error = TRUE, {
    1 + "a"
    mtcars + iris
    mean + sum
  })
})
#> ── Warning (<text>:2:3): you can't add weird thngs ─────────────────────────────
#> Adding new snapshot:
#> Code
#>   1 + "a"
#> Error <simpleError>
#>   non-numeric argument to binary operator
#> Code
#>   mtcars + iris
#> Error <simpleError>
#>   '+' only defined for equally-sized data frames
#> Code
#>   mean + sum
#> Error <simpleError>
#>   non-numeric argument to binary operator

Other types of snapshot

expect_snapshot() is the most used snapshot function because it records everything: the code you run, printed output, messages, warnings, and errors. But sometimes you just want to capture the output or errors in which you might want to use expect_snapshot_output() or expect_snapshot_error().

Or rather than caring about side-effects, you may want to check that the value of an R object stays the same. In this case, you can use expect_snapshot_value() which offers a number of serialisation approaches that provide a tradeoff between accuracy and human readability.

Whole file snapshotting

expect_snapshot(), expect_snapshot_output(), expect_snapshot_error(), and expect_snapshot_value() all store their snapshots in a single file per test. But that doesn’t work for all file types — for example, what happens if you want to snapshot an image? expect_snapshot_file() provides an alternative workflow that generates one snapshot per expectation, rather than one file per test. Assuming you’re in test-burger.R then the snapshot created by expect_snapshot_file(code_that_returns_path_to_file(), "toppings.png") would be saved in tests/testthat/_snaps/burger/toppings.png. If a future change in the code creates a different file it will be saved in tests/testthat/_snaps/burger/toppings.new.png.

Unlike expect_snapshot() and friends, expect_snapshot_file() can’t provide an automatic diff when the test fails. Instead you’ll need to call snapshot_review(). This launches a Shiny app that allows you to visually review each change and approve it if it’s deliberate:

The display varies based on the file type (currently text files, common image files, and csv files are supported).

Sometimes the failure occurs in a non-interactive environment where you can’t run snapshot_review(), e.g. in R CMD check. In this case, the easiest fix is to retrieve the .new file, copy it into the appropriate directory, then run snapshot_review() locally. If your code was run on a CI platform, you’ll need to start by downloading the run “artifact”, which contains the check folder.

In most cases, we don’t expect you to use expect_snapshot_file() directly. Instead, you’ll use it via a wrapper that does its best to gracefully skip tests when differences in platform or package versions make it unlikely to generate perfectly reproducible output.

Previous work

This is not the first time that testthat has attempted to provide snapshot testing (although it’s the first time I knew what other languages called them). This section describes some of the previous attempts and why we believe the new approach is better.

  • verify_output() has three main drawbacks:

    • You have to supply a path where the output will be saved. This seems like a small issue, but thinking of a good name, and managing the difference between interactive and test-time paths introduces a suprising amount of friction.

    • It always overwrites the previous result; automatically assuming that the changes are correct. That means you have to use it with git and it’s easy to accidentally accept unwanted changes.

    • It’s relatively coarse grained, which means tests that use it tend to keep growing and growing.

  • expect_known_output() is finer grained version of verify_output() that captures output from a single function. The requirement to produce a path for each individual expectation makes it even more painful to use.

  • expect_known_value() and expect_known_hash() have all the disadvantages of expect_known_output(), but also produce binary output meaning that you can’t easily review test differences in pull requests.