check_missing: Check for missing values
In tidymodels/recipes: Preprocessing and Feature Engineering Steps for Modeling

check_missing

R Documentation

Check for missing values

Description

check_missing() creates a specification of a recipe operation that will check if variables contain missing values.

Usage

check_missing(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  skip = FALSE,
  id = rand_id("missing")
)

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 `selections()` for more details.
`role`	Not used by this check since no new variables are created.
`trained`	A logical for whether the selectors in `...` have been resolved by `prep()`.
`columns`	A character string of the selected variable names. This field is a placeholder and will be populated once `prep()` is used.
`skip`	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.
`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 NA values. 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.

tidy() results

When you tidy() this check, a tibble with column terms (the selectors or variables selected) is returned.

Examples


data(credit_data, package = "modeldata")
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)

tidymodels/recipes documentation built on April 17, 2025, 6:17 p.m.