Checkmate
Michel Lang
2025-08-18
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.
Intro
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
}
Function overview
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.
In case you miss flexibility
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.
Argument Checks for the Lazy
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.
checkmate as testthat extension
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]")
})
Speed considerations
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.
Benchmark 1: Assert that 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))
## Warning in microbenchmark(r(x), cm(x), cmq(x)): less accurate nanosecond times
## to avoid potential integer overflows
## Unit: nanoseconds
## expr min lq mean median uq max neval cld
## r(x) 1681 1763 13141.73 1845 1886 1117004 100 a
## cm(x) 1230 1271 7458.31 1312 1394 526563 100 a
## cmq(x) 738 820 4580.93 820 861 348459 100 a
Benchmark 2: Assert that 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 cld
## r(x) 9.061 9.6145 25.64140 9.963 10.455 1545.741 100 a
## cm(x) 2.952 3.0340 7.13974 3.157 3.239 356.946 100 a
## cmq(x) 2.911 3.0340 6.49522 3.075 3.157 334.437 100 a
Benchmark 3: Assert that 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 cld
## r(x) 129.601 136.735 149.86730 138.3135 142.024 1174.896 100 a
## cm(x) 53.710 53.956 60.25811 54.1610 54.489 525.333 100 b
## cmq(x) 58.999 61.828 65.18385 62.0125 62.402 387.122 100 b
Benchmark 4: Test that 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 cld
## r(x) 53.177 58.5685 74.46502 61.3155 62.9350 1251.853 100 a
## cm(x) 22.550 22.8370 29.08622 23.0010 24.2925 467.318 100 b
## cmq(x) 18.860 19.0035 24.93005 19.3315 20.2540 503.111 100 b
# checkmate tries to stop as early as possible
x$a[1] = NA
mb = microbenchmark(r(x), cm(x), cmq(x))
print(mb)
## Unit: nanoseconds
## expr min lq mean median uq max neval cld
## r(x) 40877 51434.5 53962.56 53566.5 55985.5 86264 100 a
## cm(x) 2870 3116.0 3601.03 3280.0 3505.5 12710 100 b
## cmq(x) 451 492.0 657.23 574.0 697.0 6437 100 c
Benchmark 5: Assert that 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 cld
## r(x.sexp) 23.247 25.420 25.85009 25.6250 25.8095 37.269 100 ab
## cm(x.sexp) 9.553 9.635 13.87317 9.7990 9.9220 356.618 100 a c
## r(x.altrep) 23.657 25.789 37.31205 26.0145 26.3220 1137.627 100 b
## cm(x.altrep) 1.886 1.968 2.12954 2.1115 2.2140 2.665 100 c
Extending checkmate
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
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:
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).
Calling checkmate from C/C++
The package registers two functions which can be used in other
packages’ C/C++ code for argument checks.
SEXP qassert(SEXP x, const char *rule, const char *name);
Rboolean qtest(SEXP x, const char *rule);
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>
Session Info
For the sake of completeness, here the sessionInfo() for
the benchmark (but remember the note before on knitr
possibly biasing the results).
## R version 4.5.1 (2025-06-13)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] C/de_DE.UTF-8/de_DE.UTF-8/C/de_DE.UTF-8/de_DE.UTF-8
##
## time zone: Europe/Berlin
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] microbenchmark_1.5.0 ggplot2_3.5.2 checkmate_2.3.3
##
## loaded via a namespace (and not attached):
## [1] Matrix_1.7-0 gtable_0.3.6 jsonlite_2.0.0 dplyr_1.1.4
## [5] compiler_4.5.1 tidyselect_1.2.1 jquerylib_0.1.4 splines_4.5.1
## [9] scales_1.4.0 yaml_2.3.10 fastmap_1.2.0 TH.data_1.1-3
## [13] lattice_0.22-7 R6_2.6.1 generics_0.1.4 knitr_1.50
## [17] MASS_7.3-59 backports_1.5.0 tibble_3.3.0 bslib_0.9.0
## [21] pillar_1.11.0 RColorBrewer_1.1-3 rlang_1.1.6 multcomp_1.4-28
## [25] cachem_1.1.0 xfun_0.52 sass_0.4.10 cli_3.6.5
## [29] withr_3.0.2 magrittr_2.0.3 digest_0.6.37 grid_4.5.1
## [33] mvtnorm_1.3-3 sandwich_3.1-1 lifecycle_1.0.4 vctrs_0.6.5
## [37] evaluate_1.0.4 glue_1.8.0 farver_2.1.2 codetools_0.2-20
## [41] zoo_1.8-14 survival_3.8-3 rmarkdown_2.29 tools_4.5.1
## [45] pkgconfig_2.0.3 htmltools_0.5.8.1