Author: Michel Lang
Date: 2016-11-02

# Checkmate

#### Michel Lang

#### 2016-11-02

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.

## 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.

### Benchmark 1: Assert that `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: microseconds
    ##    expr   min     lq    mean median     uq     max neval cld
    ##    r(x) 6.297 6.6145 8.13963 6.8615 7.5025  35.795   100   b
    ##   cm(x) 1.603 1.7575 4.00172 1.9265 2.1035 169.700   100  a
    ##  cmq(x) 1.029 1.1395 1.66973 1.2515 1.4320  34.927   100  a

    autoplot(mb)

### 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) 17.949 18.5330 20.23758 18.8110 19.346 43.093   100   b
    ##   cm(x)  6.295  6.4850  7.80275  6.8155  7.094 75.240   100  a
    ##  cmq(x)  5.655  5.8005  6.67053  6.0675  6.245 25.272   100  a

    autoplot(mb)

### 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
    ##    r(x) 1166.335 1168.1965 1303.36054 1184.8910 1399.4770 2069.396   100
    ##   cm(x)   28.874   29.6705   34.96495   30.4835   34.3575  229.213   100
    ##  cmq(x)   40.891   43.5615   48.42563   44.3015   49.7410   90.307   100
    ##  cld
    ##    b
    ##   a
    ##   a

    autoplot(mb)

### Benchmark 4: Assert 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) 72.386 75.5275 105.74166 100.9505 105.4205 1312.374   100   b
    ##   cm(x) 18.697 19.5600  22.95351  20.4065  20.8585  177.918   100  a
    ##  cmq(x) 14.255 14.5525  15.50605  14.8595  15.2120   59.144   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) 64301 66390.0 91347.12 69911.0 95435.0 1224736   100   b
    ##   cm(x)  3916  4354.5  5843.08  4926.5  5341.0   23976   100  a
    ##  cmq(x)   762   921.5  1333.89  1256.0  1621.5    3985   100  a

    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 = 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).

## 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 file C source file which pulls in the provided C functions in order to compile them for your package. See example below.
3. 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>

## 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 3.3.1 (2016-06-21)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Arch Linux
    ##
    ## 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_2.1.0          checkmate_1.8.2
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] Rcpp_0.12.7        knitr_1.14         magrittr_1.5
    ##  [4] MASS_7.3-45        splines_3.3.1      munsell_0.4.3
    ##  [7] colorspace_1.2-7   lattice_0.20-34    multcomp_1.4-6
    ## [10] stringr_1.1.0      plyr_1.8.4         tools_3.3.1
    ## [13] grid_3.3.1         gtable_0.2.0       TH.data_1.0-7
    ## [16] htmltools_0.3.5    survival_2.40-1    yaml_2.1.13
    ## [19] assertthat_0.1     rprojroot_1.1      digest_0.6.10
    ## [22] tibble_1.2         Matrix_1.2-7.1     formatR_1.4
    ## [25] codetools_0.2-15   evaluate_0.10      rmarkdown_1.1.9012
    ## [28] sandwich_2.3-4     stringi_1.1.2      scales_0.4.0
    ## [31] backports_1.0.5    mvtnorm_1.0-5      zoo_1.7-13