assertions: Additional assertions to use with `checkmate`

Description

Additional assertion functions which can be used together with the checkmate package.

Usage

assert_list_of_variables(x, .var.name = checkmate::vname(x), add = NULL)
assert_df_with_variables(
  df,
  variables,
  na_level = NULL,
  .var.name = checkmate::vname(df),
  add = NULL
)
assert_valid_factor(
  x,
  min.levels = 1,
  max.levels = NULL,
  null.ok = TRUE,
  any.missing = TRUE,
  n.levels = NULL,
  len = NULL,
  .var.name = checkmate::vname(x),
  add = NULL
)
assert_df_with_factors(
  df,
  variables,
  min.levels = 1,
  max.levels = NULL,
  any.missing = TRUE,
  na_level = NULL,
  .var.name = checkmate::vname(df),
  add = NULL
)
assert_proportion_value(x, include_boundaries = FALSE)

Value

Nothing if assertion passes, otherwise prints the error message.

Arguments

x: (any)
object to test.
.var.name: [character(1)]
Name of the checked object to print in assertions. Defaults to the heuristic implemented in vname.
add: [AssertCollection]
Collection to store assertion messages. See AssertCollection.
df: (data.frame)
data set to test.
variables: (named list of character)
list of variables to test.
na_level: (string)
the string you have been using to represent NA or missing data. For NA values please consider using directly is.na() or similar approaches.
min.levels: [integer(1)]
Minimum number of factor levels. Default is NULL (no check).
max.levels: [integer(1)]
Maximum number of factor levels. Default is NULL (no check).
null.ok: [logical(1)]
If set to TRUE, x may also be NULL. In this case only a type check of x is performed, all additional checks are disabled.
any.missing: [logical(1)]
Are vectors with missing values allowed? Default is TRUE.
n.levels: [integer(1)]
Exact number of factor levels. Default is NULL (no check).
len: [integer(1)]
Exact expected length of x.
include_boundaries: (flag)
whether to include boundaries when testing for proportions.

Functions

assert_list_of_variables(): Checks whether x is a valid list of variable names. NULL elements of the list x are dropped with Filter(Negate(is.null), x).
assert_df_with_variables(): Check whether df is a data frame with the analysis variables. Please notice how this produces an error when not all variables are present in the data.frame while the opposite is not required.
assert_valid_factor(): Check whether x is a valid factor (i.e. has levels and no empty string levels). Note that NULL and NA elements are allowed.
assert_df_with_factors(): Check whether df is a data frame where the analysis variables are all factors. Note that the creation of NA by direct call of factor() will trim NA levels out of the vector list itself.
assert_proportion_value(): Check whether x is a proportion: number between 0 and 1.