Ever used an R function that produced a not-very-helpful error message, just to discover after minutes of debugging that you simply passed a wrong argument?

Blaming the laziness of the package author for not doing such standard checks (in a dynamically typed language such as R) is at least partially unfair, as R makes theses types of checks cumbersome and annoying. Well, that’s how it was in the past.

Enter checkmate.

Virtually **every standard type of user error** when passing arguments into function can be caught with a simple, readable line which produces an **informative error message** in case. A substantial part of the package was written in C to **minimize any worries about execution time overhead**.

As a motivational example, consider you have a function to calculate the faculty of a natural number and the user may choose between using either the stirling approximation or R’s `factorial`

function (which internally uses the gamma function). Thus, you have two arguments, `n`

and `method`

. Argument `n`

must obviously be a positive natural number and `method`

must be either `"stirling"`

or `"factorial"`

. Here is a version of all the hoops you need to jump through to ensure that these simple requirements are met:

```
fact <- function(n, method = "stirling") {
if (length(n) != 1)
stop("Argument 'n' must have length 1")
if (!is.numeric(n))
stop("Argument 'n' must be numeric")
if (is.na(n))
stop("Argument 'n' may not be NA")
if (is.double(n)) {
if (is.nan(n))
stop("Argument 'n' may not be NaN")
if (is.infinite(n))
stop("Argument 'n' must be finite")
if (abs(n - round(n, 0)) > sqrt(.Machine$double.eps))
stop("Argument 'n' must be an integerish value")
n <- as.integer(n)
}
if (n < 0)
stop("Argument 'n' must be >= 0")
if (length(method) != 1)
stop("Argument 'method' must have length 1")
if (!is.character(method) || !method %in% c("stirling", "factorial"))
stop("Argument 'method' must be either 'stirling' or 'factorial'")
if (method == "factorial")
factorial(n)
else
sqrt(2 * pi * n) * (n / exp(1))^n
}
```

And for comparison, here is the same function using checkmate:

```
fact <- function(n, method = "stirling") {
assertCount(n)
assertChoice(method, c("stirling", "factorial"))
if (method == "factorial")
factorial(n)
else
sqrt(2 * pi * n) * (n / exp(1))^n
}
```

The functions can be split into four functional groups, indicated by their prefix.

If prefixed with `assert`

, an error is thrown if the corresponding check fails. Otherwise, the checked object is returned invisibly. There are many different coding styles out there in the wild, but most R programmers stick to either `camelBack`

or `underscore_case`

. Therefore, `checkmate`

offers all functions in both flavors: `assert_count`

is just an alias for `assertCount`

but allows you to retain your favorite style.

The family of functions prefixed with `test`

always return the check result as logical value. Again, you can use `test_count`

and `testCount`

interchangeably.

Functions starting with `check`

return the error message as a string (or `TRUE`

otherwise) and can be used if you need more control and, e.g., want to grep on the returned error message.

`expect`

is the last family of functions and is intended to be used with the testthat package. All performed checks are logged into the `testthat`

reporter. Because `testthat`

uses the `underscore_case`

, the extension functions only come in the underscore style.

All functions are categorized into objects to check on the package help page.

You can use assert to perform multiple checks at once and throw an assertion if all checks fail.

Here is an example where we check that x is either of class `foo`

or class `bar`

:

```
f <- function(x) {
assert(
checkClass(x, "foo"),
checkClass(x, "bar")
)
}
```

Note that `assert(, combine = "or")`

and `assert(, combine = "and")`

allow to control the logical combination of the specified checks, and that the former is the default.

The following functions allow a special syntax to define argument checks using a special format specification. E.g., `qassert(x, "I+")`

asserts that `x`

is an integer vector with at least one element and no missing values. This very simple domain specific language covers a large variety of frequent argument checks with only a few keystrokes. You choose what you like best.

To extend testthat, you need to IMPORT, DEPEND or SUGGEST on the `checkmate`

package. Here is a minimal example:

```
# file: tests/test-all.R
library(testthat)
library(checkmate) # for testthat extensions
test_check("mypkg")
```

Now you are all set and can use more than 30 new expectations in your tests.

```
test_that("checkmate is a sweet extension for testthat", {
x = runif(100)
expect_numeric(x, len = 100, any.missing = FALSE, lower = 0, upper = 1)
# or, equivalent, using the lazy style:
qexpect(x, "N100[0,1]")
})
```

In comparison with tediously writing the checks yourself in R (c.f. factorial example at the beginning of the vignette), R is sometimes a tad faster while performing checks on scalars. This seems odd at first, because checkmate is mostly written in C and should be comparably fast. Yet many of the functions in the `base`

package are not regular functions, but primitives. While primitives jump directly into the C code, checkmate has to use the considerably slower `.Call`

interface. As a result, it is possible to write (very simple) checks using only the base functions which, under some circumstances, slightly outperform checkmate. However, if you go one step further and wrap the custom check into a function to convenient re-use it, the performance gain is often lost (see benchmark 1).

For larger objects the tide has turned because checkmate avoids many unnecessary intermediate variables. Also note that the quick/lazy implementation in `qassert`

/`qtest`

/`qexpect`

is often a tad faster because only two arguments have to be evaluated (the object and the rule) to determine the set of checks to perform.

Below you find some (probably unrepresentative) benchmark. But also note that this one here has been executed from inside `knitr`

which is often the cause for outliers in the measured execution time. Better run the benchmark yourself to get unbiased results.

`x`

is a flag```
library(checkmate)
library(ggplot2)
library(microbenchmark)
x = TRUE
r = function(x, na.ok = FALSE) { stopifnot(is.logical(x), length(x) == 1, na.ok || !is.na(x)) }
cm = function(x) assertFlag(x)
cmq = function(x) qassert(x, "B1")
mb = microbenchmark(r(x), cm(x), cmq(x))
print(mb)
```

```
## Unit: microseconds
## expr min lq mean median uq max neval
## r(x) 5.251 5.6160 175.31084 5.7850 5.965 16931.787 100
## cm(x) 3.498 3.7205 16.89482 4.0100 4.179 1123.842 100
## cmq(x) 2.535 2.6820 49.81749 2.9155 3.126 4627.816 100
```

`autoplot(mb)`

`## Coordinate system already present. Adding new coordinate system, which will replace the existing one.`

`x`

is a numeric of length 1000 with no missing nor NaN values```
x = runif(1000)
r = function(x) stopifnot(is.numeric(x), length(x) == 1000, all(!is.na(x) & x >= 0 & x <= 1))
cm = function(x) assertNumeric(x, len = 1000, any.missing = FALSE, lower = 0, upper = 1)
cmq = function(x) qassert(x, "N1000[0,1]")
mb = microbenchmark(r(x), cm(x), cmq(x))
print(mb)
```

```
## Unit: microseconds
## expr min lq mean median uq max neval
## r(x) 18.592 20.6575 92.16226 26.6315 33.1000 6227.570 100
## cm(x) 8.029 8.6795 30.18130 9.8305 11.2345 1739.368 100
## cmq(x) 7.726 8.1335 22.70707 9.1835 10.1870 1241.411 100
```

`autoplot(mb)`

`## Coordinate system already present. Adding new coordinate system, which will replace the existing one.`

`x`

is a character vector with no missing values nor empty strings```
x = sample(letters, 10000, replace = TRUE)
r = function(x) stopifnot(is.character(x), !any(is.na(x)), all(nchar(x) > 0))
cm = function(x) assertCharacter(x, any.missing = FALSE, min.chars = 1)
cmq = function(x) qassert(x, "S+[1,]")
mb = microbenchmark(r(x), cm(x), cmq(x))
print(mb)
```

```
## Unit: microseconds
## expr min lq mean median uq max neval
## r(x) 1343.663 1421.1435 1656.7193 1443.869 1532.3315 10128.188 100
## cm(x) 101.990 105.6155 131.1643 109.284 119.2535 1461.645 100
## cmq(x) 114.415 117.5250 144.1785 118.736 127.0350 1684.160 100
```

`autoplot(mb)`

`## Coordinate system already present. Adding new coordinate system, which will replace the existing one.`

`x`

is a data frame with no missing values```
N = 10000
x = data.frame(a = runif(N), b = sample(letters[1:5], N, replace = TRUE), c = sample(c(FALSE, TRUE), N, replace = TRUE))
r = function(x) is.data.frame(x) && !any(sapply(x, function(x) any(is.na(x))))
cm = function(x) testDataFrame(x, any.missing = FALSE)
cmq = function(x) qtest(x, "D")
mb = microbenchmark(r(x), cm(x), cmq(x))
print(mb)
```

```
## Unit: microseconds
## expr min lq mean median uq max neval
## r(x) 137.140 172.0190 218.23345 174.0405 177.6215 4275.365 100
## cm(x) 49.142 51.4285 70.60613 53.0165 54.5490 1530.961 100
## cmq(x) 37.447 38.6460 51.36826 39.4460 39.8985 1132.218 100
```

`autoplot(mb)`

`## Coordinate system already present. Adding new coordinate system, which will replace the existing one.`

```
# checkmate tries to stop as early as possible
x$a[1] = NA
mb = microbenchmark(r(x), cm(x), cmq(x))
print(mb)
```

```
## Unit: microseconds
## expr min lq mean median uq max neval
## r(x) 94.467 151.4685 155.32901 154.0175 156.5345 209.756 100
## cm(x) 7.626 8.4795 10.61491 11.0470 12.4250 28.137 100
## cmq(x) 1.467 1.8405 2.75603 2.5805 3.6205 14.948 100
```

`autoplot(mb)`

`## Coordinate system already present. Adding new coordinate system, which will replace the existing one.`

`x`

is an increasing sequence of integers with no missing values```
N = 10000
x.altrep = seq_len(N) # this is an ALTREP in R version >= 3.5.0
x.sexp = c(x.altrep) # this is a regular SEXP OTOH
r = function(x) stopifnot(is.integer(x), !any(is.na(x)), !is.unsorted(x))
cm = function(x) assertInteger(x, any.missing = FALSE, sorted = TRUE)
mb = microbenchmark(r(x.sexp), cm(x.sexp), r(x.altrep), cm(x.altrep))
print(mb)
```

```
## Unit: microseconds
## expr min lq mean median uq max neval
## r(x.sexp) 47.345 65.7290 102.99725 66.6845 68.1475 3541.364 100
## cm(x.sexp) 19.031 19.7945 21.16492 21.2755 22.1755 35.944 100
## r(x.altrep) 56.966 76.2870 79.29206 77.1715 78.7520 138.955 100
## cm(x.altrep) 5.102 5.9010 19.81671 7.7730 8.3825 1175.151 100
```

`autoplot(mb)`

`## Coordinate system already present. Adding new coordinate system, which will replace the existing one.`

To extend checkmate a custom `check*`

function has to be written. For example, to check for a square matrix one can re-use parts of checkmate and extend the check with additional functionality:

```
checkSquareMatrix = function(x, mode = NULL) {
# check functions must return TRUE on success
# and a custom error message otherwise
res = checkMatrix(x, mode = mode)
if (!isTRUE(res))
return(res)
if (nrow(x) != ncol(x))
return("Must be square")
return(TRUE)
}
# a quick test:
X = matrix(1:9, nrow = 3)
checkSquareMatrix(X)
```

`## [1] TRUE`

`## [1] "Must store characters"`

`## [1] "Must be square"`

The respective counterparts to the `check`

-function can be created using the constructors makeAssertionFunction, makeTestFunction and makeExpectationFunction:

```
# For assertions:
assert_square_matrix = assertSquareMatrix = makeAssertionFunction(checkSquareMatrix)
print(assertSquareMatrix)
```

```
## function (x, mode = NULL, .var.name = checkmate::vname(x), add = NULL)
## {
## if (missing(x))
## stop(sprintf("argument \"%s\" is missing, with no default",
## .var.name))
## res = checkSquareMatrix(x, mode)
## checkmate::makeAssertion(x, res, .var.name, add)
## }
```

```
# For tests:
test_square_matrix = testSquareMatrix = makeTestFunction(checkSquareMatrix)
print(testSquareMatrix)
```

```
## function (x, mode = NULL)
## {
## isTRUE(checkSquareMatrix(x, mode))
## }
```

```
# For expectations:
expect_square_matrix = makeExpectationFunction(checkSquareMatrix)
print(expect_square_matrix)
```

```
## function (x, mode = NULL, info = NULL, label = vname(x))
## {
## if (missing(x))
## stop(sprintf("Argument '%s' is missing", label))
## res = checkSquareMatrix(x, mode)
## makeExpectation(x, res, info, label)
## }
```

Note that all the additional arguments `.var.name`

, `add`

, `info`

and `label`

are automatically joined with the function arguments of your custom check function. Also note that if you define these functions inside an R package, the constructors are called at build-time (thus, there is no negative impact on the runtime).

The package registers two functions which can be used in other packages’ C/C++ code for argument checks.

These are the counterparts to qassert and qtest. Due to their simplistic interface, they perfectly suit the requirements of most type checks in C/C++.

For detailed background information on the register mechanism, see the Exporting C Code section in Hadley’s Book “R Packages” or WRE. Here is a step-by-step guide to get you started:

- Add
`checkmate`

to your “Imports” and “LinkingTo” sections in your DESCRIPTION file. - Create a stub C source file
`"checkmate_stub.c"`

, see below. - Include the provided header file
`<checkmate.h>`

in each compilation unit where you want to use checkmate.

File contents for (2):

For the sake of completeness, here the `sessionInfo()`

for the benchmark (but remember the note before on `knitr`

possibly biasing the results).

```
## R version 4.0.0 (2020-04-24)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] microbenchmark_1.4-7 ggplot2_3.3.1 checkmate_2.0.0-9000
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.4.6 knitr_1.28 magrittr_1.5 MASS_7.3-51.5
## [5] munsell_0.5.0 colorspace_1.4-1 R6_2.4.1 rlang_0.4.6
## [9] stringr_1.4.0 tools_4.0.0 grid_4.0.0 gtable_0.3.0
## [13] xfun_0.14 withr_2.2.0 ellipsis_0.3.1 htmltools_0.4.0
## [17] yaml_2.2.1 assertthat_0.2.1 digest_0.6.25 rprojroot_1.3-2
## [21] tibble_3.0.1 lifecycle_0.2.0 pkgdown_1.4.1.9000 crayon_1.3.4
## [25] farver_2.0.3 vctrs_0.3.0 fs_1.4.1 glue_1.4.1
## [29] memoise_1.1.0 evaluate_0.14 rmarkdown_2.2 stringi_1.4.6
## [33] pillar_1.4.4 compiler_4.0.0 scales_1.1.1 desc_1.2.0
## [37] backports_1.1.7 pkgconfig_2.0.3
```