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 flaglibrary(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) 7.533 8.232 38.95704 8.6245 9.0640 3008.800 100
## cm(x) 1.914 2.256 13.51899 2.4255 2.6485 937.083 100
## cmq(x) 1.210 1.440 14.93670 1.5885 1.7525 1272.311 100
autoplot(mb)
x
is a numeric of length 1000 with no missing nor NaN valuesx = 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) 21.757 22.6730 90.15829 23.3060 33.6130 6239.554 100
## cm(x) 5.657 6.0290 25.24867 6.4965 6.9820 1599.212 100
## cmq(x) 6.549 6.9805 17.54003 7.2515 7.4635 1023.259 100
autoplot(mb)
x
is a character vector with no missing values nor empty stringsx = 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) 1709.571 1712.610 1990.60590 1737.979 1826.3505 5508.125 100
## cm(x) 56.607 57.529 79.01033 60.133 64.9905 1058.652 100
## cmq(x) 65.424 65.824 88.64710 67.234 75.6220 1178.504 100
autoplot(mb)
x
is a data frame with no missing valuesN = 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) 96.263 97.6595 184.70175 98.7560 150.2195 3682.601 100
## cm(x) 22.480 23.6645 37.38582 24.8995 26.4325 970.148 100
## cmq(x) 15.767 16.4220 30.98829 17.3695 18.2170 1256.514 100
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
## r(x) 80527 81933.0 142404.07 89060.5 104460.5 3634665 100
## cm(x) 5808 6964.5 8245.90 7822.5 8559.5 21414 100
## cmq(x) 940 1296.5 1956.83 1625.0 2101.5 12473 100
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:
checkmate
to your “Imports” and “LinkingTo” sections in your DESCRIPTION file."checkmate_stub.c"
. See example below.<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 3.4.1 (2017-06-30)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
##
## locale:
## [1] C/de_DE.UTF-8/de_DE.UTF-8/C/de_DE.UTF-8/de_DE.UTF-8
##
## 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_1.8.4
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.12 digest_0.6.12 rprojroot_1.2 plyr_1.8.4
## [5] grid_3.4.1 gtable_0.2.0 backports_1.1.1 magrittr_1.5
## [9] scales_0.5.0 evaluate_0.10.1 rlang_0.1.2.9000 stringi_1.1.5
## [13] lazyeval_0.2.0 rmarkdown_1.6 tools_3.4.1 stringr_1.2.0
## [17] munsell_0.4.3 yaml_2.1.14 compiler_3.4.1 colorspace_1.3-2
## [21] htmltools_0.3.6 knitr_1.17 tibble_1.3.4