Check for Missing Values
check_missing creates a specification of a recipe
operation that will check if variables contain missing values.
check_missing(
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("missing")
)
## S3 method for class 'check_missing'
tidy(x, ...)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. |
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. |
x |
A |
This check will break the bake function if any of the checked
columns does contain NA values. 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) is.na(credit_data) %>% colSums() # If the test passes, `new_data` is returned unaltered recipe(credit_data) %>% check_missing(Age, Expenses) %>% prep() %>% bake(credit_data) # If your training set doesn't pass, prep() will stop with an error ## Not run: recipe(credit_data) %>% check_missing(Income) %>% prep() ## End(Not run) # If `new_data` contain missing values, the check will stop bake() train_data <- credit_data %>% dplyr::filter(Income > 150) test_data <- credit_data %>% dplyr::filter(Income <= 150 | is.na(Income)) rp <- recipe(train_data) %>% check_missing(Income) %>% prep() bake(rp, train_data) ## Not run: bake(rp, test_data) ## End(Not run)
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