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 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

library(modeldata) data(credit_data) is.na(credit_data) %>% colSums()
#> Status Seniority Home Time Age Marital Records Job #> 0 0 6 0 0 1 0 2 #> Expenses Income Assets Debt Amount Price #> 0 381 47 18 0 0
# If the test passes, `new_data` is returned unaltered recipe(credit_data) %>% check_missing(Age, Expenses) %>% prep() %>% bake(credit_data)
#> # A tibble: 4,454 x 14 #> Status Seniority Home Time Age Marital Records Job Expenses Income #> <fct> <int> <fct> <int> <int> <fct> <fct> <fct> <int> <int> #> 1 good 9 rent 60 30 married no free… 73 129 #> 2 good 17 rent 60 58 widow no fixed 48 131 #> 3 bad 10 owner 36 46 married yes free… 90 200 #> 4 good 0 rent 60 24 single no fixed 63 182 #> 5 good 0 rent 36 26 single no fixed 46 107 #> 6 good 1 owner 60 36 married no fixed 75 214 #> 7 good 29 owner 60 44 married no fixed 75 125 #> 8 good 9 pare… 12 27 single no fixed 35 80 #> 9 good 0 owner 60 32 married no free… 90 107 #> 10 bad 0 pare… 48 41 married no part… 90 80 #> # … with 4,444 more rows, and 4 more variables: Assets <int>, Debt <int>, #> # Amount <int>, Price <int>
# 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)
#> # A tibble: 1,338 x 14 #> Status Seniority Home Time Age Marital Records Job Expenses Income #> <fct> <int> <fct> <int> <int> <fct> <fct> <fct> <int> <int> #> 1 bad 10 owner 36 46 married yes free… 90 200 #> 2 good 0 rent 60 24 single no fixed 63 182 #> 3 good 1 owner 60 36 married no fixed 75 214 #> 4 good 8 owner 60 30 married no fixed 75 199 #> 5 good 19 priv 36 37 married no fixed 75 170 #> 6 good 15 priv 24 52 single no free… 35 330 #> 7 good 33 rent 24 68 married no free… 65 200 #> 8 good 5 owner 60 22 single no fixed 45 324 #> 9 good 19 owner 60 43 single no fixed 75 180 #> 10 good 15 owner 36 43 married no fixed 75 251 #> # … with 1,328 more rows, and 4 more variables: Assets <int>, Debt <int>, #> # Amount <int>, Price <int>
if (FALSE) { bake(rp, test_data) }