Perform Min Max Scaling — step_minmax • extrasteps

step_minmax() creates a specification of a recipe step that will perform Min Max scaling.

Usage

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

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 min and max 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.
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)

rec <- recipe(~., data = mtcars) %>%
  step_minmax(all_predictors()) %>%
  prep()

rec %>%
  bake(new_data = NULL)
#> # A tibble: 32 × 11
#>      mpg   cyl   disp     hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 0.451   0.5 0.222  0.205  0.525 0.283 0.233     0     1   0.5 0.429
#>  2 0.451   0.5 0.222  0.205  0.525 0.348 0.3       0     1   0.5 0.429
#>  3 0.528   0   0.0920 0.145  0.502 0.206 0.489     1     1   0.5 0    
#>  4 0.468   0.5 0.466  0.205  0.147 0.435 0.588     1     0   0   0    
#>  5 0.353   1   0.721  0.435  0.180 0.493 0.3       0     0   0   0.143
#>  6 0.328   0.5 0.384  0.187  0     0.498 0.681     1     0   0   0    
#>  7 0.166   1   0.721  0.682  0.207 0.526 0.160     0     0   0   0.429
#>  8 0.596   0   0.189  0.0353 0.429 0.429 0.655     1     0   0.5 0.143
#>  9 0.528   0   0.174  0.152  0.535 0.419 1         1     0   0.5 0.143
#> 10 0.374   0.5 0.241  0.251  0.535 0.493 0.452     1     0   0.5 0.429
#> # ℹ 22 more rows

tidy(rec, 1)
#> # A tibble: 22 × 4
#>    terms statistic value id          
#>    <chr> <chr>     <dbl> <chr>       
#>  1 mpg   min       10.4  minmax_qZ3vE
#>  2 cyl   min        4    minmax_qZ3vE
#>  3 disp  min       71.1  minmax_qZ3vE
#>  4 hp    min       52    minmax_qZ3vE
#>  5 drat  min        2.76 minmax_qZ3vE
#>  6 wt    min        1.51 minmax_qZ3vE
#>  7 qsec  min       14.5  minmax_qZ3vE
#>  8 vs    min        0    minmax_qZ3vE
#>  9 am    min        0    minmax_qZ3vE
#> 10 gear  min        3    minmax_qZ3vE
#> # ℹ 12 more rows