step_filter_missing creates a specification of a recipe step that will potentially remove variables that have too many missing values.

Usage

step_filter_missing(
recipe,
...,
role = NA,
trained = FALSE,
threshold = 0.1,
removals = NULL,
skip = FALSE,
id = rand_id("filter_missing")
)

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

Not used by this step since no new variables are created.

trained

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

threshold

A value for the threshold of missing values in column. The step will remove the columns where the proportion of missing values exceeds the threshold.

removals

A character string that contains the names of columns that should be removed. These values are not determined until prep() is called.

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.

Details

This step can potentially remove columns from the data set. This may cause issues for subsequent steps in your recipe if the missing columns are specifically referenced by name. To avoid this, see the advice in the Tips for saving recipes and filtering columns section of selections.

This step will remove variables if the proportion of missing values exceeds the threshold.

All variables with missing values will be removed for threshold = 0.

Tidying

When you tidy() this step, a tibble with column terms (the columns that will be removed) is returned.

Case weights

This step performs an unsupervised operation that can utilize case weights. As a result, case weights are only used with frequency weights. For more information, see the documentation in case_weights and the examples on tidymodels.org.

Other variable filter steps: step_corr(), step_lincomb(), step_nzv(), step_rm(), step_select(), step_zv()

Examples

data(credit_data, package = "modeldata")

rec <- recipe(Status ~ ., data = credit_data) %>%
step_filter_missing(all_predictors(), threshold = 0)

filter_obj <- prep(rec)

filtered_te <- bake(filter_obj, new_data = NULL)

tidy(rec, number = 1)
#> # A tibble: 1 × 2
#>   terms            id
#>   <chr>            <chr>
#> 1 all_predictors() filter_missing_IYaDd
tidy(filter_obj, number = 1)
#> # A tibble: 6 × 2
#>   terms   id
#>   <chr>   <chr>
#> 1 Home    filter_missing_IYaDd
#> 2 Marital filter_missing_IYaDd
#> 3 Job     filter_missing_IYaDd
#> 4 Income  filter_missing_IYaDd
#> 5 Assets  filter_missing_IYaDd
#> 6 Debt    filter_missing_IYaDd