step_zv: Zero variance filter

View source: R/zv.R

step_zvR Documentation

Zero variance filter


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


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



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.


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


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


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.


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


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.


A character string that is unique to this step to identify it.


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.


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


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


character, names of the columns that will be removed


character, id of this step

Case weights

The underlying operation does not allow for case weights.

See Also

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


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 %>%

filter_obj <- prep(zv_filter, training = biomass_tr)

filtered_te <- bake(filter_obj, biomass_te)
any(names(filtered_te) == "one_value")

tidy(zv_filter, number = 1)
tidy(filter_obj, number = 1)

recipes documentation built on May 29, 2024, 2:21 a.m.