Perform binary encoding of factor variables

step_encoding_binary() creates a specification of a recipe step that will perform binary encoding of factor variables.

Usage

step_encoding_binary(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  res = NULL,
  columns = NULL,
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("encoding_binary")
)

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 are affected by the step. See recipes::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.

res

A list containing levels of training variables is stored here once this preprocessing step has be trained by recipes::prep().

columns

A character string of variable names that will be populated (eventually) by the terms argument.

keep_original_cols

A logical to keep the original variables in the output. Defaults to FALSE.

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 existing steps (if any). For the tidy method, a tibble with columns terms (the columns that will be affected) and base.

Examples

library(recipes)
library(modeldata)

data(ames)

rec <- recipe(~ Land_Contour + Neighborhood, data = ames) %>%
  step_encoding_binary(all_nominal_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)
#> # A tibble: 2,930 × 9
#>    Land_Contour_1 Land_Contour_2 Land_Contour_4 Neighborhood_1 Neighborhood_2
#>             <int>          <int>          <int>          <int>          <int>
#>  1              0              0              1              1              0
#>  2              0              0              1              1              0
#>  3              0              0              1              1              0
#>  4              0              0              1              1              0
#>  5              0              0              1              1              1
#>  6              0              0              1              1              1
#>  7              0              0              1              1              0
#>  8              0              1              0              1              0
#>  9              0              0              1              1              0
#> 10              0              0              1              1              1
#> # ℹ 2,920 more rows
#> # ℹ 4 more variables: Neighborhood_4 <int>, Neighborhood_8 <int>,
#> #   Neighborhood_16 <int>, Neighborhood_32 <int>

tidy(rec, 1)
#> # A tibble: 2 × 3
#>   terms        value id                   
#>   <chr>        <int> <chr>                
#> 1 Land_Contour     4 encoding_binary_HIyvQ
#> 2 Neighborhood    29 encoding_binary_HIyvQ