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.

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))
print(mb)

## Unit: microseconds
##    expr   min    lq     mean median     uq      max neval cld
##    r(x) 3.137 3.391 29.30048  3.484 3.6435 2561.891   100   a
##   cm(x) 2.257 2.478 14.89461  2.622 2.7495 1130.207   100   a
##  cmq(x) 1.496 1.640 11.08153  1.739 1.8525  849.814   100   a

autoplot(mb)

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

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) 15.639 16.4210 55.97355 16.8465 17.4790 3882.721   100   a
##   cm(x)  6.231  6.5905 17.35744  6.8870  7.1920  955.978   100   a
##  cmq(x)  6.299  6.6545 15.22232  6.8495  7.0295  819.705   100   a

autoplot(mb)

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

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) 1286.972 1320.4990 1424.82785 1371.4105 1428.7495 4531.028   100   b
##   cm(x)   68.164   70.8985   85.41703   73.7685   77.3755  890.016   100  a 
##  cmq(x)   82.929   86.4200   98.53113   90.2655   93.1135  839.488   100  a

autoplot(mb)

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

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) 80.901 85.2400 118.5151 87.607 92.262 2996.602   100   b
##   cm(x) 20.973 22.3405  39.4694 23.245 24.806 1128.040   100  a 
##  cmq(x) 12.972 13.6090  23.0715 14.171 14.913  861.294   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
x$a[1] = NA
mb = microbenchmark(r(x), cm(x), cmq(x))
print(mb)

## Unit: microseconds
##    expr    min      lq     mean  median      uq     max neval cld
##    r(x) 80.389 83.5725 89.72053 85.8405 89.1400 380.493   100   c
##   cm(x)  5.862  6.6600  7.83138  7.7150  8.3435  30.829   100  b 
##  cmq(x)  1.004  1.1790  1.63684  1.4705  1.9945   7.321   100 a

autoplot(mb)

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

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) 28.219 29.1210 30.42030 29.4565 30.3535   45.163   100  ab
##    cm(x.sexp) 12.004 12.5320 21.26764 12.8390 13.2845  847.534   100  ab
##   r(x.altrep) 36.237 37.4785 63.11861 37.9735 38.8405 2475.242   100   b
##  cm(x.altrep)  3.217  3.5995  4.97869  3.8950  4.1180  114.613   100  a

autoplot(mb)

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

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).

sessionInfo()

## R version 3.6.2 (2019-12-12)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Arch Linux
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib/libopenblas_haswellp-r0.3.7.so
## 
## locale:
##  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
##  [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] microbenchmark_1.4-7 ggplot2_3.2.1        checkmate_2.0.0     
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.3       pillar_1.4.3     compiler_3.6.2   tools_3.6.2     
##  [5] digest_0.6.23    evaluate_0.14    lifecycle_0.1.0  tibble_2.1.3    
##  [9] gtable_0.3.0     lattice_0.20-38  pkgconfig_2.0.3  rlang_0.4.4     
## [13] Matrix_1.2-18    yaml_2.2.1       mvtnorm_1.0-12   xfun_0.12       
## [17] withr_2.1.2      stringr_1.4.0    dplyr_0.8.4      knitr_1.27      
## [21] grid_3.6.2       tidyselect_1.0.0 glue_1.3.1       R6_2.4.1        
## [25] survival_3.1-7   rmarkdown_2.1    multcomp_1.4-12  TH.data_1.0-10  
## [29] farver_2.0.3     purrr_0.3.3      magrittr_1.5     codetools_0.2-16
## [33] MASS_7.3-51.4    splines_3.6.2    backports_1.1.5  scales_1.1.0    
## [37] htmltools_0.4.0  assertthat_0.2.1 colorspace_1.4-1 sandwich_2.5-1  
## [41] stringi_1.4.5    lazyeval_0.2.2   munsell_0.5.0    crayon_1.3.4    
## [45] zoo_1.8-7

Checkmate

Michel Lang

2020-02-05

Intro

Function overview

In case you miss flexibility

Argument Checks for the Lazy

checkmate as testthat extension

Speed considerations

Benchmark 1: Assert that `x` is a flag

Benchmark 2: Assert that `x` is a numeric of length 1000 with no missing nor NaN values

Benchmark 3: Assert that `x` is a character vector with no missing values nor empty strings

Benchmark 4: Test that `x` is a data frame with no missing values

Benchmark 5: Assert that `x` is an increasing sequence of integers with no missing values

Extending checkmate

Calling checkmate from C/C++

Session Info

Checkmate

Michel Lang

2020-02-05

Intro

Function overview

In case you miss flexibility

Argument Checks for the Lazy

checkmate as testthat extension

Speed considerations

Benchmark 1: Assert that x is a flag

Benchmark 2: Assert that x is a numeric of length 1000 with no missing nor NaN values

Benchmark 3: Assert that x is a character vector with no missing values nor empty strings

Benchmark 4: Test that x is a data frame with no missing values

Benchmark 5: Assert that x is an increasing sequence of integers with no missing values

Extending checkmate

Calling checkmate from C/C++

Session Info

Benchmark 1: Assert that `x` is a flag

Benchmark 2: Assert that `x` is a numeric of length 1000 with no missing nor NaN values

Benchmark 3: Assert that `x` is a character vector with no missing values nor empty strings

Benchmark 4: Test that `x` is a data frame with no missing values

Benchmark 5: Assert that `x` is an increasing sequence of integers with no missing values