check_new_values {recipes} | R Documentation |
Check for new values
Description
check_new_values
creates a specification of a recipe
operation that will check if variables contain new values.
Usage
check_new_values(
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
ignore_NA = TRUE,
values = NULL,
skip = FALSE,
id = rand_id("new_values")
)
Arguments
recipe |
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 |
role |
Not used by this check since no new variables are created. |
trained |
A logical for whether the selectors in |
columns |
A character string of the selected variable names. This field
is a placeholder and will be populated once |
ignore_NA |
A logical that indicates if we should consider missing
values as value or not. Defaults to |
values |
A named list with the allowed values.
This is |
skip |
A logical. Should the check be skipped when the
recipe is baked by |
id |
A character string that is unique to this check to identify it. |
Details
This check will break the bake
function if any of the checked
columns does contain values it did not contain when prep
was called
on the recipe. If the check passes, nothing is changed to the data.
Value
An updated version of recipe
with the new check added to the
sequence of any existing operations.
Tidying
When you tidy()
this check, a tibble with columns
terms
(the selectors or variables selected) is returned.
Case weights
The underlying operation does not allow for case weights.
See Also
Other checks:
check_class()
,
check_cols()
,
check_missing()
,
check_range()
Examples
data(credit_data, package = "modeldata")
# If the test passes, `new_data` is returned unaltered
recipe(credit_data) %>%
check_new_values(Home) %>%
prep() %>%
bake(new_data = credit_data)
# If `new_data` contains values not in `x` at the [prep()] function,
# the [bake()] function will break.
## Not run:
recipe(credit_data %>% dplyr::filter(Home != "rent")) %>%
check_new_values(Home) %>%
prep() %>%
bake(new_data = credit_data)
## End(Not run)
# By default missing values are ignored, so this passes.
recipe(credit_data %>% dplyr::filter(!is.na(Home))) %>%
check_new_values(Home) %>%
prep() %>%
bake(credit_data)
# Use `ignore_NA = FALSE` if you consider missing values as a value,
# that should not occur when not observed in the train set.
## Not run:
recipe(credit_data %>% dplyr::filter(!is.na(Home))) %>%
check_new_values(Home, ignore_NA = FALSE) %>%
prep() %>%
bake(credit_data)
## End(Not run)