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 these 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:

```
<- function(n, method = "stirling") {
fact 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")
<- as.integer(n)
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:

```
<- function(n, method = "stirling") {
fact 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`

:

```
<- function(x) {
f 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", {
= runif(100)
x 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)
= TRUE
x = function(x, na.ok = FALSE) { stopifnot(is.logical(x), length(x) == 1, na.ok || !is.na(x)) }
r = function(x) assertFlag(x)
cm = function(x) qassert(x, "B1")
cmq = microbenchmark(r(x), cm(x), cmq(x))
mb print(mb)
```

```
## Unit: microseconds
## expr min lq mean median uq max neval cld
## r(x) 2.757 3.0665 19.71121 3.1765 3.3020 1637.434 100 a
## cm(x) 1.753 2.2065 11.18866 2.3520 2.4935 806.694 100 a
## cmq(x) 1.160 1.3690 7.37568 1.4760 1.5800 549.017 100 a
```

`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```
= runif(1000)
x = function(x) stopifnot(is.numeric(x), length(x) == 1000, all(!is.na(x) & x >= 0 & x <= 1))
r = function(x) assertNumeric(x, len = 1000, any.missing = FALSE, lower = 0, upper = 1)
cm = function(x) qassert(x, "N1000[0,1]")
cmq = microbenchmark(r(x), cm(x), cmq(x))
mb print(mb)
```

```
## Unit: microseconds
## expr min lq mean median uq max neval cld
## r(x) 9.416 9.7410 29.43165 10.0970 10.4260 1920.628 100 a
## cm(x) 4.119 4.2725 13.27240 4.3805 4.5140 802.139 100 a
## cmq(x) 3.353 3.4760 8.51441 3.5315 3.5925 484.240 100 a
```

`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```
= sample(letters, 10000, replace = TRUE)
x = function(x) stopifnot(is.character(x), !any(is.na(x)), all(nchar(x) > 0))
r = function(x) assertCharacter(x, any.missing = FALSE, min.chars = 1)
cm = function(x) qassert(x, "S+[1,]")
cmq = microbenchmark(r(x), cm(x), cmq(x))
mb print(mb)
```

```
## Unit: microseconds
## expr min lq mean median uq max neval cld
## r(x) 186.306 189.3985 224.23717 209.2895 219.5775 2015.763 100 b
## cm(x) 181.824 182.5230 196.71609 189.2055 195.5500 726.782 100 b
## cmq(x) 58.878 59.0245 65.66076 60.4660 61.7060 504.555 100 a
```

`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```
= 10000
N = data.frame(a = runif(N), b = sample(letters[1:5], N, replace = TRUE), c = sample(c(FALSE, TRUE), N, replace = TRUE))
x = function(x) is.data.frame(x) && !any(sapply(x, function(x) any(is.na(x))))
r = function(x) testDataFrame(x, any.missing = FALSE)
cm = function(x) qtest(x, "D")
cmq = microbenchmark(r(x), cm(x), cmq(x))
mb print(mb)
```

```
## Unit: microseconds
## expr min lq mean median uq max neval cld
## r(x) 44.789 46.3885 66.95317 47.1555 47.9530 1990.472 100 a
## cm(x) 22.332 23.1965 31.08940 23.4505 23.9630 612.482 100 a
## cmq(x) 16.805 16.9545 25.94709 17.0345 17.1975 842.227 100 a
```

`autoplot(mb)`

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

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

```
## Unit: nanoseconds
## expr min lq mean median uq max neval cld
## r(x) 36398 37071.5 38625.12 37615.0 39012.5 91541 100 c
## cm(x) 3699 3957.5 4530.33 4115.5 4290.0 23727 100 b
## cmq(x) 635 727.5 928.91 794.5 861.5 10372 100 a
```

`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```
= 10000
N = seq_len(N) # this is an ALTREP in R version >= 3.5.0
x.altrep = c(x.altrep) # this is a regular SEXP OTOH
x.sexp = function(x) stopifnot(is.integer(x), !any(is.na(x)), !is.unsorted(x))
r = function(x) assertInteger(x, any.missing = FALSE, sorted = TRUE)
cm = microbenchmark(r(x.sexp), cm(x.sexp), r(x.altrep), cm(x.altrep))
mb print(mb)
```

```
## Unit: microseconds
## expr min lq mean median uq max neval cld
## r(x.sexp) 18.874 19.6350 34.62320 20.452 21.6885 1410.413 100 a
## cm(x.sexp) 8.669 8.8205 9.13639 8.968 9.2235 13.908 100 a
## r(x.altrep) 20.230 20.6250 21.42175 20.953 21.4235 40.031 100 a
## cm(x.altrep) 2.422 2.5730 11.36307 2.712 2.8625 773.655 100 a
```

`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:

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

`## [1] TRUE`

`checkSquareMatrix(X, mode = "character")`

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

`checkSquareMatrix(X[1:2, ])`

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

The respective counterparts to the `check`

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

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

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

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

```
qassert(SEXP x, const char *rule, const char *name);
SEXP qtest(SEXP x, const char *rule); Rboolean
```

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):

```
#include <checkmate.h>
#include <checkmate_stub.c>
```

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

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

possibly biasing the results).

`sessionInfo()`

```
## R version 4.1.3 (2022-03-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Arch Linux
##
## Matrix products: default
## BLAS: /usr/lib/libopenblasp-r0.3.20.so
## LAPACK: /usr/lib/liblapack.so.3.10.1
##
## locale:
## [1] LC_CTYPE=de_DE.utf8 LC_NUMERIC=C
## [3] LC_TIME=de_DE.utf8 LC_COLLATE=C
## [5] LC_MONETARY=de_DE.utf8 LC_MESSAGES=de_DE.utf8
## [7] LC_PAPER=de_DE.utf8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=de_DE.utf8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] microbenchmark_1.4.9 ggplot2_3.3.5 checkmate_2.1.0
##
## loaded via a namespace (and not attached):
## [1] zoo_1.8-10 tidyselect_1.1.2 xfun_0.30 bslib_0.3.1
## [5] purrr_0.3.4 splines_4.1.3 lattice_0.20-45 colorspace_2.0-3
## [9] vctrs_0.4.1 generics_0.1.2 htmltools_0.5.2 yaml_2.3.5
## [13] utf8_1.2.2 survival_3.2-13 rlang_1.0.2 jquerylib_0.1.4
## [17] pillar_1.7.0 glue_1.6.2 withr_2.5.0 DBI_1.1.2
## [21] multcomp_1.4-18 lifecycle_1.0.1 stringr_1.4.0 munsell_0.5.0
## [25] gtable_0.3.0 mvtnorm_1.1-3 codetools_0.2-18 evaluate_0.15
## [29] knitr_1.38 fastmap_1.1.0 fansi_1.0.3 highr_0.9
## [33] TH.data_1.1-0 scales_1.2.0 backports_1.4.1 jsonlite_1.8.0
## [37] farver_2.1.0 digest_0.6.29 stringi_1.7.6 dplyr_1.0.8
## [41] grid_4.1.3 cli_3.2.0 tools_4.1.3 sandwich_3.0-1
## [45] magrittr_2.0.3 sass_0.4.1 tibble_3.1.6 crayon_1.5.1
## [49] pkgconfig_2.0.3 ellipsis_0.3.2 MASS_7.3-55 Matrix_1.4-0
## [53] assertthat_0.2.1 rmarkdown_2.13 R6_2.5.1 compiler_4.1.3
```