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, removals = NULL,
  skip = FALSE, id = rand_id("zv"))

# S3 method for step_zv
tidy(x, ...)

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 which variables that will evaluated by the filtering. See selections() for more details. For the tidy method, these are not currently used.

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.

removals

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

skip

A logical. Should the step 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 step_zv object.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms which is the columns that will be removed.

See also

Examples

data(biomass) 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 x 2 #> terms id #> <chr> <chr> #> 1 <NA> zv_mQoHJ
tidy(filter_obj, number = 1)
#> # A tibble: 1 x 2 #> terms id #> <chr> <chr> #> 1 one_value zv_mQoHJ