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 (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:

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.

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`

:

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.

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.

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.

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.

`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
## r(x) 34.100 35.7750 71.35144 36.2065 36.8215 3477.414 100
## cm(x) 2.754 3.2005 16.86151 3.4265 3.6560 1202.327 100
## cmq(x) 2.020 2.2410 16.90276 2.5825 2.8680 1330.526 100
```

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

`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
## r(x) 51.834 59.5775 121.21002 60.533 62.0590 5949.569 100
## cm(x) 7.029 8.0135 25.48076 8.631 9.5120 1437.352 100
## cmq(x) 8.140 9.2535 32.14792 9.695 10.3815 2169.813 100
```

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

`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
## r(x) 1717.864 1772.0325 1866.9911 1788.226 1825.924 5105.189 100
## cm(x) 117.743 119.9805 143.8170 123.355 129.399 1480.445 100
## cmq(x) 133.462 136.1815 153.9877 141.288 144.665 1195.723 100
```

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

`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
## r(x) 178.898 184.334 236.81761 191.2360 199.6105 4223.329 100
## cm(x) 25.656 26.684 42.99187 28.4890 29.7210 1134.436 100
## cmq(x) 17.098 17.818 29.50008 18.7425 19.6075 1040.400 100
```

`## 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
## r(x) 126.419 167.3140 183.53572 173.6855 186.2875 265.165 100
## cm(x) 6.480 7.4060 9.55978 9.6645 10.5200 31.350 100
## cmq(x) 1.158 1.5145 2.43574 2.0300 3.0180 19.371 100
```

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

`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
## r(x.sexp) 93.914 123.2420 171.91943 125.2220 136.4130 3361.278 100
## cm(x.sexp) 19.543 20.4210 24.88823 22.9725 23.8495 59.140 100
## r(x.altrep) 115.601 148.7020 167.23889 151.4205 160.5720 411.929 100
## cm(x.altrep) 4.666 5.5155 21.73968 8.0425 9.0600 1294.410 100
```

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

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`

`## [1] "Must store characters"`

`## [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).

The package registers two functions which can be used in other packages’ C/C++ code for argument checks.

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):

For the sake of completeness, here the `sessionInfo()`

for the benchmark (but remember the note before on `knitr`

possibly biasing the results).

```
## R version 3.5.3 (2019-03-11)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] microbenchmark_1.4-6 ggplot2_3.1.1 checkmate_1.9.3
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.1 knitr_1.22 magrittr_1.5 munsell_0.5.0
## [5] colorspace_1.4-1 rlang_0.3.4 stringr_1.4.0 plyr_1.8.4
## [9] tools_3.5.3 grid_3.5.3 gtable_0.3.0 xfun_0.6
## [13] withr_2.1.2 htmltools_0.3.6 yaml_2.2.0 lazyeval_0.2.2
## [17] digest_0.6.18 tibble_2.1.1 crayon_1.3.4 evaluate_0.13
## [21] rmarkdown_1.12 stringi_1.4.3 compiler_3.5.3 pillar_1.3.1
## [25] scales_1.0.0 backports_1.1.4 pkgconfig_2.0.2
```