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Create missing data column indicators

Source: R/indicate_na.R
step_indicate_na.Rd

step_indicate_na() creates a specification of a recipe step that will create and append additional binary columns to the data set to indicate which observations are missing.

Usage

step_indicate_na(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  columns = NULL,
  prefix = "na_ind",
  keep_original_cols = TRUE,
  skip = FALSE,
  id = rand_id("indicate_na")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose variables for this step. See selections() for more details.

role

For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

columns

A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.

prefix

A character string that will be the prefix to the resulting new variables. Defaults to "na_ind".

keep_original_cols

A logical to keep the original variables in the output. Defaults to TRUE.

skip

A logical. Should the step 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 step to identify it.

Value

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

Tidying

When you tidy() this step, a tibble is returned with columns terms and id:

terms

character, the selectors or variables selected

id

character, id of this step

Case weights

The underlying operation does not allow for case weights.

See also

Other dummy variable and encoding steps: step_bin2factor(), step_count(), step_date(), step_dummy(), step_dummy_extract(), step_dummy_multi_choice(), step_factor2string(), step_holiday(), step_integer(), step_novel(), step_num2factor(), step_ordinalscore(), step_other(), step_regex(), step_relevel(), step_string2factor(), step_time(), step_unknown(), step_unorder()

Examples

data("credit_data", package = "modeldata")

## missing data per column
purrr::map_dbl(credit_data, function(x) mean(is.na(x)))
#>       Status    Seniority         Home         Time          Age 
#> 0.0000000000 0.0000000000 0.0013471037 0.0000000000 0.0000000000 
#>      Marital      Records          Job     Expenses       Income 
#> 0.0002245173 0.0000000000 0.0004490346 0.0000000000 0.0855410867 
#>       Assets         Debt       Amount        Price 
#> 0.0105523125 0.0040413112 0.0000000000 0.0000000000 

set.seed(342)
in_training <- sample(1:nrow(credit_data), 2000)

credit_tr <- credit_data[in_training, ]
credit_te <- credit_data[-in_training, ]

rec <- recipe(Price ~ ., data = credit_tr)

impute_rec <- rec %>%
  step_indicate_na(Income, Assets, Debt)

imp_models <- prep(impute_rec, training = credit_tr)

imputed_te <- bake(imp_models, new_data = credit_te)