Checkmate

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.

• qassert
• qassertr

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.

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

print(mb)

## Unit: nanoseconds
##    expr  min   lq     mean median   uq     max neval cld
##    r(x) 2296 2337 16416.40   2378 2460 1390105   100   a
##   cm(x) 1558 1599  6275.46   1640 1722  403645   100   a
##  cmq(x)  984 1025  7812.55   1066 1148  612663   100   a

autoplot(mb)

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) 12.710 13.038 32.76064 13.202 13.325 1942.498   100   a
##   cm(x)  4.674  4.797 10.48124  4.879  4.961  494.050   100   a
##  cmq(x)  3.936  4.018  9.92774  4.059  4.141  575.353   100   a

autoplot(mb)

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) 178.227 186.9395 205.36818 189.3380 192.987 1616.261   100  a 
##   cm(x)  70.725  71.1350  79.09310  71.3195  71.545  671.416   100   b
##  cmq(x)  77.695  77.9000  85.11477  81.4875  81.672  520.249   100   b

autoplot(mb)

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) 75.276 83.681 101.34626 84.624 86.1205 1635.449   100  a 
##   cm(x) 29.684 29.971  36.43137 30.094 30.2170  471.090   100   b
##  cmq(x) 24.846 24.969  31.29325 25.010 25.1330  585.726   100   b

autoplot(mb)

# 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) 62689 68347 72084.15 71668.0 73144 113939   100 a  
##   cm(x)  3772  3977  4513.28  4202.5  4387  21976   100  b 
##  cmq(x)   615   656   861.41   779.0   902   8487   100   c

autoplot(mb)

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) 33.497 36.7975 38.15337 37.2075 37.8430   56.334   100 ab 
##    cm(x.sexp) 14.596 14.7600 15.53326 14.8830 15.1290   73.308   100 a c
##   r(x.altrep) 36.982 40.3030 55.73212 40.6720 41.6970 1481.781   100  b 
##  cm(x.altrep)  2.501  2.6240  7.40378  2.8290  3.0135  456.822   100   c

autoplot(mb)

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:

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).

sessionInfo()

## R version 4.4.1 (2024-06-14)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sonoma 14.5
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.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_1.4.10 ggplot2_3.5.1         checkmate_2.3.2      
## 
## loaded via a namespace (and not attached):
##  [1] Matrix_1.5-4         gtable_0.3.5         jsonlite_1.8.8      
##  [4] highr_0.11           dplyr_1.1.4          compiler_4.4.1      
##  [7] tidyselect_1.2.1     jquerylib_0.1.4      splines_4.4.1       
## [10] scales_1.3.0         yaml_2.3.10          fastmap_1.2.0       
## [13] TH.data_1.1-2        lattice_0.22-6       R6_2.5.1            
## [16] generics_0.1.3       knitr_1.48           MASS_7.3-59         
## [19] backports_1.4.1-9001 tibble_3.2.1         munsell_0.5.1       
## [22] bslib_0.7.0          pillar_1.9.0         rlang_1.1.4         
## [25] utf8_1.2.4           multcomp_1.4-26      cachem_1.1.0        
## [28] xfun_0.46            sass_0.4.9           cli_3.6.3           
## [31] withr_3.0.0          magrittr_2.0.3       digest_0.6.36       
## [34] grid_4.4.1           mvtnorm_1.2-5        sandwich_3.1-0      
## [37] lifecycle_1.0.4      vctrs_0.6.5          evaluate_0.24.0     
## [40] glue_1.7.0           farver_2.1.2         codetools_0.2-20    
## [43] zoo_1.8-12           survival_3.6-4       fansi_1.0.6         
## [46] colorspace_2.1-1     rmarkdown_2.27       tools_4.4.1         
## [49] pkgconfig_2.0.3      htmltools_0.5.8.1