Create missing data column indicators

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",
  sparse = "auto",
  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".
sparse: A single string. Should the columns produced be sparse vectors. Can take the values "yes", "no", and "auto". If sparse = "auto" then workflows can determine the best option. Defaults to "auto".
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

Sparse data

This step produces sparse columns if sparse = "yes" is being set. The default value "auto" won't trigger production fo sparse columns if a recipe is prep()ed, but allows for a workflow to toggle to "yes" or "no" depending on whether the model supports sparse_data and if the model is is expected to run faster with the data.

The mechanism for determining how much sparsity is produced isn't perfect, and there will be times when you want to manually overwrite by setting sparse = "yes" or sparse = "no".

Case weights

The underlying operation does not allow for case weights.

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)