library(unittest)
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")
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")
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")
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")
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")
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
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") }) ok_group("Test subtraction", { ok(1 - 1 == 0, "Can subtract 1") ok(1 - 3 == -2, "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") }))
No. Sit down and have a cup of tea. Hopefully the feeling will go away.
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