recipes (version 0.1.5)

check_missing: Check for Missing Values

Description

check_missing creates a 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"))
# S3 method for check_missing
tidy(x, ...)

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 which variables are checked in the check See selections() for more details. For the tidy method, these are not currently used.

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.recipe()? While all operations are baked when prep.recipe() 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 step to identify it.

x

A check_missing object.

Value

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).

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.

Examples

# NOT RUN {
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()
# }
# 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)
# }

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