checkArray(x, mode = NULL, any.missing = TRUE, d = NULL, min.d = NULL,
max.d = NULL)assertArray(x, mode = NULL, any.missing = TRUE, d = NULL, min.d = NULL,
max.d = NULL, add = NULL, .var.name = NULL)
assert_array(x, mode = NULL, any.missing = TRUE, d = NULL, min.d = NULL,
max.d = NULL, add = NULL, .var.name = NULL)
testArray(x, mode = NULL, any.missing = TRUE, d = NULL, min.d = NULL,
max.d = NULL)
test_array(x, mode = NULL, any.missing = TRUE, d = NULL, min.d = NULL,
max.d = NULL)
expect_array(x, mode = NULL, any.missing = TRUE, d = NULL, min.d = NULL,
max.d = NULL, info = NULL, label = NULL)
character(1)]
Storage mode of the array. Arrays can hold vectors, i.e. logical(1)]
Are missing values allowed? Default is TRUE.integer(1)]
Exact number of dimensions of array x.
Default is NULL (no check).integer(1)]
Minimum number of dimensions of array x.
Default is NULL (no check).integer(1)]
Maximum number of dimensions of array x.
Default is NULL (no check).AssertCollection]
Collection to store assertions. See AssertCollection.NULL || character(1)]
Name of the checked object to print in error message. If NULL,
the name will be heuristically determined via substitute.expect_that..var.name, but passed down to expect_that.TRUE. If the check
is not successful, assertArray/assert_array
throws an error message, testArray/test_array
returns FALSE,
and checkArray returns a string with the error message.
The function expect_array always returns an
expectation.checkAtomicVector,
checkAtomic, checkCharacter,
checkComplex, checkDataFrame,
checkDataTable,
checkEnvironment,
checkFactor, checkFunction,
checkIntegerish,
checkInteger, checkList,
checkLogical, checkMatrix,
checkNumeric, checkVectorcheckArray(array(1:27, dim = c(3, 3, 3)), d = 3)Run the code above in your browser using DataLab