Percentile Transformation

step_percentile creates a specification of a recipe step that replaces the value of a variable with its percentile from the training set.

Usage

step_percentile(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  ref_dist = NULL,
  options = list(probs = (0:100)/100),
  skip = FALSE,
  id = rand_id("percentile")
)

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 variables for this step. See selections() for more details.

role

For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.

trained

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

ref_dist

The computed percentiles is stored here once this preprocessing step has be trained by prep().

options

A named list of options to pass to stats::quantile(). See Details for more information.

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 any existing operations.

Case weights

This step performs an unsupervised operation that can utilize case weights. As a result, case weights are only used with frequency weights. For more information, see the documentation in case_weights and the examples on tidymodels.org.

See also

Other individual transformation steps: step_BoxCox(), step_YeoJohnson(), step_bs(), step_harmonic(), step_hyperbolic(), step_inverse(), step_invlogit(), step_logit(), step_log(), step_mutate(), step_ns(), step_poly(), step_relu(), step_sqrt()

Examples

data(biomass, package = "modeldata")

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

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

prepped_rec <- prep(rec)

prepped_rec %>%
  bake(biomass_te)
#> # A tibble: 80 × 6
#>    carbon hydrogen oxygen nitrogen sulfur   HHV
#>     <dbl>    <dbl>  <dbl>    <dbl>  <dbl> <dbl>
#>  1 0.421      5.67   47.2     0.3    0.22  18.3
#>  2 0.18       5.5    48.1     2.85   0.34  17.6
#>  3 0.156      5.5    49.1     2.4    0.3   17.2
#>  4 0.423      6.1    37.3     1.8    0.5   18.9
#>  5 0.666      6.32   42.8     0.2    0     20.5
#>  6 0.218      5.5    41.7     0.7    0.2   18.5
#>  7 0.0803     5.23   54.1     1.19   0.51  15.1
#>  8 0.139      4.66   33.8     0.95   0.2   16.2
#>  9 0.0226     4.4    31.1     0.14   4.9   11.1
#> 10 0.0178     3.77   23.7     4.63   1.05  10.8
#> # … with 70 more rows

tidy(rec, 1)
#> # A tibble: 1 × 4
#>   terms  value percentile id              
#>   <chr>  <dbl>      <dbl> <chr>           
#> 1 carbon    NA         NA percentile_dwNDP
tidy(prepped_rec, 1)
#> # A tibble: 101 × 4
#>    term   value percentile id              
#>    <chr>  <dbl>      <dbl> <chr>           
#>  1 carbon  14.6          0 percentile_dwNDP
#>  2 carbon  25.9          1 percentile_dwNDP
#>  3 carbon  28.4          2 percentile_dwNDP
#>  4 carbon  31.6          3 percentile_dwNDP
#>  5 carbon  35.1          4 percentile_dwNDP
#>  6 carbon  35.9          5 percentile_dwNDP
#>  7 carbon  37.5          6 percentile_dwNDP
#>  8 carbon  38.3          7 percentile_dwNDP
#>  9 carbon  38.9          8 percentile_dwNDP
#> 10 carbon  39.6          9 percentile_dwNDP
#> # … with 91 more rows