Check for New Values
check_new_values creates a specification of a recipe
operation that will check if variables contain new values.
check_new_values(
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
ignore_NA = TRUE,
values = NULL,
skip = FALSE,
id = rand_id("new_values")
)recipe |
A recipe object. The check will be added to the sequence of operations for this recipe. |
... |
One or more selector functions to choose which
variables are checked in the 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 variable names that will be populated (eventually) by the terms argument. |
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 step to identify it. |
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.
An updated version of recipe with the new check
added to the sequence of existing operations (if any). For the
tidy method, a tibble with columns terms (the
selectors or variables selected).
library(modeldata) data(credit_data) # 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)
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