General Variable Filter

step_rm creates a specification of a recipe step that will remove variables based on their name, type, or role.

step_rm(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  removals = NULL,
  skip = FALSE,
  id = rand_id("rm")
)

# S3 method for step_rm
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 be evaluated by the filtering bake. 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_rm 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.

Examples

library(modeldata)
data(biomass)

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

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

library(dplyr)
smaller_set <- rec %>%
  step_rm(contains("gen"))

smaller_set <- prep(smaller_set, training = biomass_tr)

filtered_te <- bake(smaller_set, biomass_te)
filtered_te
#> # A tibble: 80 x 3
#>    carbon sulfur   HHV
#>     <dbl>  <dbl> <dbl>
#>  1   46.4   0.22  18.3
#>  2   43.2   0.34  17.6
#>  3   42.7   0.3   17.2
#>  4   46.4   0.5   18.9
#>  5   48.8   0     20.5
#>  6   44.3   0.2   18.5
#>  7   38.9   0.51  15.1
#>  8   42.1   0.2   16.2
#>  9   29.2   4.9   11.1
#> 10   27.8   1.05  10.8
#> # … with 70 more rows

tidy(smaller_set, number = 1)
#> # A tibble: 3 x 2
#>   terms    id      
#>   <chr>    <chr>   
#> 1 hydrogen rm_NJe2n
#> 2 oxygen   rm_NJe2n
#> 3 nitrogen rm_NJe2n