check_cols() creates a specification of a recipe step that will check if
all the columns of the training frame are present in the new data.
check_cols(
recipe,
...,
role = NA,
trained = FALSE,
skip = FALSE,
id = rand_id("cols")
)A recipe object. The check will be added to the sequence of operations for this recipe.
One or more selector functions to choose variables for this check.
See selections() for more details.
Not used by this check since no new variables are created.
A logical for whether the selectors in ... have been
resolved by prep().
A logical. Should the check be skipped when the recipe is baked
by bake()? While all operations are baked when prep() is run, some
operations may not be able to be conducted on new data (e.g. processing the
outcome variable(s)). Care should be taken when using skip = TRUE as it
may affect the computations for subsequent operations.
A character string that is unique to this check to identify it.
An updated version of recipe with the new check added to the
sequence of any existing operations.
This check will break the bake() function if any of the specified columns
is not present in the data. If the check passes, nothing is changed to the
data.
When you tidy() this check, a tibble with columns terms
(the selectors or variables selected) and value (the type) is returned.
Other checks:
check_class(),
check_missing(),
check_new_values(),
check_range()
data(biomass, package = "modeldata")
biomass_rec <- recipe(HHV ~ ., data = biomass) |>
step_rm(sample, dataset) |>
check_cols(contains("gen")) |>
step_center(all_numeric_predictors())
if (FALSE) { # \dontrun{
bake(biomass_rec, biomass[, c("carbon", "HHV")])
} # }