recipes (version 1.0.0)

check_missing: 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")
)

Value

An updated version of recipe with the new check added to the sequence of any existing operations.

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 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 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.

tidy() results

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

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.

See Also

Other checks: check_class(), check_cols(), check_new_values(), check_range()

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
if (FALSE) {
recipe(credit_data) %>%
  check_missing(Income) %>%
  prep()
}

# 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)
if (FALSE) {
bake(rp, test_data)
}