Perform frequency encoding

step_encoding_frequency() creates a specification of a recipe step that will perform frequency encoding.

Usage

step_encoding_frequency(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = rand_id("encoding_frequency")
)

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 frequencies of the levels of the 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.

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_frequency(all_nominal_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)
#> # A tibble: 2,930 × 2
#>    Land_Contour Neighborhood
#>           <dbl>        <dbl>
#>  1       0.899        0.151 
#>  2       0.899        0.151 
#>  3       0.899        0.151 
#>  4       0.899        0.151 
#>  5       0.899        0.0563
#>  6       0.899        0.0563
#>  7       0.899        0.0174
#>  8       0.0410       0.0174
#>  9       0.899        0.0174
#> 10       0.899        0.0563
#> # ℹ 2,920 more rows

tidy(rec, 1)
#> # A tibble: 33 × 4
#>    terms        level              frequency id                      
#>    <chr>        <chr>                  <dbl> <chr>                   
#>  1 Land_Contour Bnk                   0.0399 encoding_frequency_3vEaf
#>  2 Land_Contour HLS                   0.0410 encoding_frequency_3vEaf
#>  3 Land_Contour Low                   0.0205 encoding_frequency_3vEaf
#>  4 Land_Contour Lvl                   0.899  encoding_frequency_3vEaf
#>  5 Neighborhood North_Ames            0.151  encoding_frequency_3vEaf
#>  6 Neighborhood College_Creek         0.0911 encoding_frequency_3vEaf
#>  7 Neighborhood Old_Town              0.0816 encoding_frequency_3vEaf
#>  8 Neighborhood Edwards               0.0662 encoding_frequency_3vEaf
#>  9 Neighborhood Somerset              0.0621 encoding_frequency_3vEaf
#> 10 Neighborhood Northridge_Heights    0.0567 encoding_frequency_3vEaf
#> # ℹ 23 more rows