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:

```
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
}
```

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`

:

```
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.

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.

```
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]")
})
```

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(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: nanoseconds
## expr min lq mean median uq max neval cld
## r(x) 6091 6372.0 30435.03 6564.5 6978 2330306 100 a
## cm(x) 1455 1656.0 9239.63 1812.5 1935 646236 100 a
## cmq(x) 880 1040.5 8152.48 1203.0 1329 640295 100 a
```

`autoplot(mb)`

`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) 18.118 18.7405 60.63018 19.2445 19.8155 4071.992 100 a
## cm(x) 5.833 6.0635 15.97368 6.4515 6.7350 869.217 100 a
## cmq(x) 5.661 5.8140 14.79382 6.1900 6.4180 853.256 100 a
```

`autoplot(mb)`

`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) 1303.934 1324.3330 1422.35016 1325.7085 1383.3445 4661.874 100
## cm(x) 36.816 37.3410 49.28407 38.0455 38.5895 860.683 100
## cmq(x) 47.377 47.5895 59.59740 48.0755 48.4435 988.761 100
## cld
## b
## a
## a
```

`autoplot(mb)`

`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) 72.346 77.469 134.00455 97.2735 102.2095 2932.458 100 b
## cm(x) 17.726 19.139 29.84926 20.1115 20.9190 805.182 100 a
## cmq(x) 13.289 13.959 22.55845 14.6280 14.9250 774.300 100 a
```

`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) 65567 66876.5 95745.68 92478.0 94265.0 1439443 100 b
## cm(x) 4001 4276.5 5082.41 5127.5 5507.5 14245 100 a
## cmq(x) 750 861.5 1220.12 1075.0 1477.0 6614 100 a
```

`autoplot(mb)`

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 = vname(x), add = NULL)
## {
## res = checkSquareMatrix(x, mode)
## makeAssertion(x, res, .var.name, add)
## }
```

```
# For tests:
test_square_matrix = testSquareMatrix = makeTestFunction(checkSquareMatrix)
print(testSquareMatrix)
```

```
## function (x, mode = NULL)
## {
## identical(checkSquareMatrix(x, mode), TRUE)
## }
```

```
# For expectations:
expect_square_matrix = makeExpectationFunction(checkSquareMatrix)
print(expect_square_matrix)
```

```
## function (x, mode = NULL, info = NULL, label = vname(x))
## {
## 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.

```
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 example below. - Include the provided header file
`<checkmate.h>`

in each compilation unit where you want to use checkmate.

```
/* Example for (2), "checkmate_stub.c":*/
#include <checkmate.h>
#include <checkmate_stub.c>
```

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 3.4.2 (2017-09-28)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Arch Linux
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib/libopenblas_haswellp-r0.2.20.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_1.4-2.1 ggplot2_2.2.1 checkmate_1.8.5
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.13 knitr_1.17 magrittr_1.5 MASS_7.3-47
## [5] splines_3.4.2 munsell_0.4.3 lattice_0.20-35 colorspace_1.3-2
## [9] rlang_0.1.2.9000 multcomp_1.4-7 stringr_1.2.0 plyr_1.8.4
## [13] tools_3.4.2 grid_3.4.2 gtable_0.2.0 TH.data_1.0-8
## [17] htmltools_0.3.6 survival_2.41-3 yaml_2.1.14 lazyeval_0.2.0
## [21] rprojroot_1.2 digest_0.6.12 tibble_1.3.4 Matrix_1.2-11
## [25] codetools_0.2-15 evaluate_0.10.1 rmarkdown_1.6 sandwich_2.4-0
## [29] stringi_1.1.5 compiler_3.4.2 scales_0.5.0 backports_1.1.2
## [33] mvtnorm_1.0-6 zoo_1.8-0
```