Frequently Asked Questions

General

How do I test error conditions?

Use the ut_cmp_error function. For example, here is a function that will throw an error for a bad argument:

add_four <- function(x) {
    if( ! is.numeric(x) ) stop("x must be numeric")
    return( x+4 )
}

We can test the argument check like this:

ok(ut_cmp_error(add_four("a"), "must be numeric"), "add_four() argument not numeric throws error")

## ok - add_four() argument not numeric throws error

How do I test if a warning is issued?

Use the ut_cmp_warning function. For example, here is a function that will issue a warning if an argument contains NA:

has_similar_mean <- function(x, y, tol = 0.5) {
    if( any(is.na(x)) ) warning("x contains NAs", call. = FALSE)
    if( any(is.na(y)) ) warning("y contains NAs", call. = FALSE)
    return( isTRUE(all.equal(mean(x), mean(y), tolerance = tol)) )
}

We can test for a warning like this:

ok(ut_cmp_warning(has_similar_mean(c(1,2,3,4), c(1,NA,5)), "y contains NAs"), "has_similar_mean() NAs in y issues a warning")

## ok - has_similar_mean() NAs in y issues a warning

We can check for multiple distinct warnings

ok(ut_cmp_warning(has_similar_mean(c(NA,2,3,4), c(1,NA,5)), expected_regexp = c("x contains NAs", "y contains NAs"), expected_count = 2L),
   "has_similar_mean() NAs in arguments issue warnings")

## ok - has_similar_mean() NAs in arguments issue warnings

Here we could use the same regexp to match both wrnings

ok(ut_cmp_warning(has_similar_mean(c(NA,2,3,4), c(1,NA,5)), "^[xy] contains NAs", expected_count = 2L),
   "has_similar_mean() NAs in arguments issue warnings")

## ok - has_similar_mean() NAs in arguments issue warnings

How do I test multivalue results, or see differences?

Use ut_cmp_equal(...) or ut_cmp_identical(...) as replacements for all.equal(...) and identical(...) respectively:

a <- c(1,2,3)
b <- 1:3
ok(ut_cmp_equal(a,b), "a and b are equal")

## ok - a and b are equal

ut_cmp_identical will make sure your objects are identical, and is more useful when comparing e.g. a list of strings which should be exactly the same.

ut_cmp_equal will test for ‘near equality’, and is more useful when comparing numeric values which may be slightly different due to floating-point accuracy.

Either way, if your test fails you will get verbose output showing you how they differ, and if you have git installed the output will be coloured. For example:

> ok(ut_cmp_equal(c(1,2,3,4,5), c(1,8,8,4,5)))
not ok - ut_cmp_equal(c(1, 2, 3, 4, 5), c(1, 8, 8, 4, 5))
# Test returned non-TRUE value:
# Mean relative difference: 2.2
# --- c(1, 2, 3, 4, 5)
# +++ c(1, 8, 8, 4, 5)
# [1] 1 [-2 3-]{+8 8+} 4 5

Grouping tests

When dealing with many unit tests in one file it can be useful to group related unit tests.

The ok_group() function is used like this:

ok_group("Test addition", {
    ok(1 + 1 == 2, "Can add 1")
    ok(1 + 3 == 4, "Can add 3")
})

## # Test addition
## ok - Can add 1
## ok - Can add 3

ok_group("Test subtraction", {
    ok(1 - 1 == 0, "Can subtract 1")
    ok(1 - 3 == -2, "Can subtract 3")
})

## # Test subtraction
## ok - Can subtract 1
## ok - Can subtract 3

x You can use local() to ensure that state is localized within an ok_group

ok_group("Test adding integers", local({
    x <- 1L; y <- 2L
    ok(x + y == 3L, "Can add integer variables")
}))

## # Test adding integers
## ok - Can add integer variables

I am sure I do not need to test my code. Is this true?

No. Sit down and have a cup of tea. Hopefully the feeling will go away.