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:

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")
    sqrt(2 * pi * n) * (n / exp(1))^n

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

fact <- function(n, method = "stirling") {
  assertChoice(method, c("stirling", "factorial"))

  if (method == "factorial")
    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) {
    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(checkmate) # for testthat extensions

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


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))
## Unit: microseconds
##    expr   min    lq     mean median     uq      max neval
##    r(x) 3.607 3.727 26.71447  3.837 3.9770 2270.939   100
##   cm(x) 2.344 2.464 10.73274  2.565 2.6600  724.242   100
##  cmq(x) 1.603 1.704 11.81899  1.788 1.8735  939.544   100

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))
## Unit: microseconds
##    expr    min      lq     mean median      uq      max neval
##    r(x) 12.303 12.7585 44.32854 13.064 13.5155 3103.021   100
##   cm(x)  5.430  5.6655 16.51297  5.816  5.9810  960.342   100
##  cmq(x)  6.321  6.4620 13.68411  6.572  6.6975  709.524   100

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))
## Unit: microseconds
##    expr     min       lq     mean  median       uq      max neval
##    r(x) 280.915 281.6910 314.9706 282.362 289.3750 2715.088   100
##   cm(x) 289.831 290.5875 305.4370 291.459 294.2295 1234.795   100
##  cmq(x) 125.214 125.4995 133.4021 125.765 126.2355  829.378   100

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))
## Unit: microseconds
##    expr    min     lq      mean  median     uq      max neval
##    r(x) 74.579 75.571 105.75408 76.0620 76.853 2887.750   100
##   cm(x) 35.566 36.228  46.31586 37.2550 37.825  763.104   100
##  cmq(x) 28.764 28.980  37.16867 29.3845 29.550  699.857   100

# checkmate tries to stop as early as possible
x$a[1] = NA
mb = microbenchmark(r(x), cm(x), cmq(x))
## Unit: nanoseconds
##    expr   min      lq     mean median      uq    max neval
##    r(x) 61534 62586.5 64682.88  63037 64235.0 108192   100
##   cm(x)  5300  5705.0  6620.78   6527  6978.0  25237   100
##  cmq(x)   992  1182.5  1533.24   1553  1793.5   4559   100

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))
## Unit: microseconds
##          expr    min      lq     mean  median      uq      max neval
##     r(x.sexp) 29.635 29.8405 30.31053 29.9460 30.1015   46.256   100
##    cm(x.sexp) 12.944 13.0840 20.88232 13.2650 13.4450  772.722   100
##   r(x.altrep) 41.898 42.0830 65.87599 42.1890 42.3540 2384.431   100
##  cm(x.altrep)  3.576  3.7770  4.84550  3.9625  4.0925   78.096   100

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))
  if (nrow(x) != ncol(x))
    return("Must be square")

# a quick test:
X = matrix(1:9, nrow = 3)
## [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)
## 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)
## function (x, mode = NULL) 
## {
##     isTRUE(checkSquareMatrix(x, mode))
## }
# For expectations:
expect_square_matrix = makeExpectationFunction(checkSquareMatrix)
## 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:

  1. Add checkmate to your “Imports” and “LinkingTo” sections in your DESCRIPTION file.
  2. Create a stub C source file "checkmate_stub.c", see below.
  3. 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 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
## locale:
##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
## time zone: UTC
## tzcode source: system (glibc)
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## other attached packages:
## [1] microbenchmark_1.4.10 ggplot2_3.4.4         checkmate_2.3.1      
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.4      jsonlite_1.8.8    highr_0.10        compiler_4.3.2   
##  [5] stringr_1.5.1     jquerylib_0.1.4   systemfonts_1.0.5 scales_1.3.0     
##  [9] textshaping_0.3.7 yaml_2.3.7        fastmap_1.1.1     R6_2.5.1         
## [13] knitr_1.45        backports_1.4.1   tibble_3.2.1      desc_1.4.2       
## [17] munsell_0.5.0     rprojroot_2.0.4   bslib_0.6.1       pillar_1.9.0     
## [21] rlang_1.1.2       utf8_1.2.4        cachem_1.0.8      stringi_1.8.2    
## [25] xfun_0.41         fs_1.6.3          sass_0.4.7        memoise_2.0.1    
## [29] cli_3.6.1         withr_2.5.2       pkgdown_2.0.7     magrittr_2.0.3   
## [33] digest_0.6.33     grid_4.3.2        lifecycle_1.0.4   vctrs_0.6.5      
## [37] evaluate_0.23     glue_1.6.2        farver_2.1.1      ragg_1.2.6       
## [41] fansi_1.0.5       colorspace_2.1-0  rmarkdown_2.25    purrr_1.0.2      
## [45] pkgconfig_2.0.3   tools_4.3.2       htmltools_0.5.7