Michel Lang


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 (
    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 CamelCase 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.




Choices and Subsets

Matrices, Arrays and Data Frame

Safe Coercion to integer

Other builtin

File IO:

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") andassert(, 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 || ! }
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) 11.221 12.324 16.53132 14.142 17.396  74.113   100
##   cm(x)  2.989  3.550  8.64275  4.037  4.919 365.210   100
##  cmq(x)  1.937  2.377  4.15354  2.673  3.258  92.479   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(! & 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) 33.097 45.9615 49.89004 46.5795 47.6425  122.367   100
##   cm(x) 12.480 13.8430 99.49034 14.4000 15.4945 8380.918   100
##  cmq(x) 11.154 14.3360 15.54349 14.8185 15.4510   39.930   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( && 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) 1752.978 1767.4850 2046.84741 1841.4035 1960.1180 5672.231   100
##   cm(x)   61.134   62.9010   72.12497   64.6695   67.4005  354.562   100
##  cmq(x)   65.588   66.0565   67.86057   66.4990   67.6885   97.791   100

Benchmark 4: Assert 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) && !any(sapply(x, function(x) any(
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) 106.796 110.256 142.18350 111.5910 113.464 2571.558   100
##   cm(x)  27.956  29.344  33.97551  30.6910  32.186  212.699   100
##  cmq(x)  20.888  21.492  23.83829  21.9685  22.770   87.842   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) 93644 112553.0 186016.02 114586.0 154421.0 2827604   100
##   cm(x)  5960   7567.0  10745.98   8798.5  10084.0   56015   100
##  cmq(x)   984   1545.5   2669.89   2078.0   2708.5   19261   100

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. Include the provided header file checkmate.h. Unfortunately, the include is a bit tedious at the moment and you have to make sure to include the header only once. To work around this issue, write your own header file “include_checkmate.h” (see below for an example).
  3. In every file you want to use qtest or qassert, include checkmate.h or include_checkmate.h, respectively.
/* Examplary header file as workaround for (2) */
#include <checkmate.h>
#include "include_checkmate.h"
/* alternative: #include <checkmate.h> */

SEXP double(SEXP x) {
  /* x must be a numeric, not NA and must have length 1 */
  qassert(x, "N1");

  num = REAL(x)[0];
  return ScalarReal(num * num);

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 3.3.1 (2016-06-21)
## Platform: x86_64-apple-darwin15.5.0 (64-bit)
## Running under: OS X 10.11.5 (El Capitan)
## locale:
## [1] de_DE.UTF-8/en_US.UTF-8/de_DE.UTF-8/C/de_DE.UTF-8/en_US.UTF-8
## attached base packages:
## [1] methods   stats     graphics  grDevices utils     datasets  base     
## other attached packages:
## [1] microbenchmark_1.4-2.1 ggplot2_2.1.0          checkmate_1.8.1       
## [4] rt_0.1                
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.5      knitr_1.13       magrittr_1.5     devtools_1.11.1 
##  [5] munsell_0.4.3    docopt_0.4.5     colorspace_1.2-6 plyr_1.8.4      
##  [9] stringr_1.0.0    tools_3.3.1      parallel_3.3.1   grid_3.3.1      
## [13] data.table_1.9.6 gtable_0.2.0     withr_1.0.2      htmltools_0.3.5 
## [17] yaml_2.1.13      digest_0.6.9     crayon_1.3.1     formatR_1.4     
## [21] memoise_1.0.0    evaluate_0.9     rmarkdown_0.9.6  stringi_1.1.1   
## [25] scales_0.4.0     backports_1.0.2  chron_2.3-47