Zero variance filter

step_zv() creates a specification of a recipe step that will remove variables that contain only a single value.

step_zv(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  group = NULL,
  removals = NULL,
  skip = FALSE,
  id = rand_id("zv")
)

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.
group: An optional character string or call to dplyr::vars() that can be used to specify a group(s) within which to identify variables that contain only a single value. If the grouping variables are contained in terms selector, they will not be considered for removal.
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.

Tidying

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

terms: character, names of the columns that will be removed
id: character, id of this step

Sparse data

This step can be applied to sparse_data such that it is preserved. Nothing needs to be done for this to happen as it is done automatically.

Case weights

The underlying operation does not allow for case weights.

Examples

data(biomass, package = "modeldata")

biomass$one_value <- 1

biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]

rec <- recipe(HHV ~ carbon + hydrogen + oxygen +
  nitrogen + sulfur + one_value,
data = biomass_tr
)

zv_filter <- rec |>
  step_zv(all_predictors())

filter_obj <- prep(zv_filter, training = biomass_tr)

filtered_te <- bake(filter_obj, biomass_te)
any(names(filtered_te) == "one_value")
#> [1] FALSE

tidy(zv_filter, number = 1)
#> # A tibble: 1 × 2
#>   terms            id      
#>   <chr>            <chr>   
#> 1 all_predictors() zv_VT1qF
tidy(filter_obj, number = 1)
#> # A tibble: 1 × 2
#>   terms     id      
#>   <chr>     <chr>   
#> 1 one_value zv_VT1qF