step_center() creates a specification of a recipe step that will
normalize numeric data to have a mean of zero.
step_center(
recipe,
...,
role = NA,
trained = FALSE,
means = NULL,
na_rm = TRUE,
skip = FALSE,
id = rand_id("center")
)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.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
A named numeric vector of means. This is NULL until computed
by prep().
A logical value indicating whether NA values should be removed
during computations.
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.
A character string that is unique to this step to identify it.
An updated version of recipe with the new step added to the
sequence of any existing operations.
Centering data means that the average of a variable is subtracted from the
data. step_center() estimates the variable means from the data used in the
training argument of prep(). bake() then applies the centering to new
data sets using these means.
When you tidy() this step, a tibble is returned with
columns terms, value, and id:
character, the selectors or variables selected
numeric, the means
character, id of this step
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.
Other normalization steps:
step_normalize(),
step_range(),
step_scale()
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
)
center_trans <- rec |>
step_center(carbon, contains("gen"), -hydrogen)
center_obj <- prep(center_trans, training = biomass_tr)
transformed_te <- bake(center_obj, biomass_te)
biomass_te[1:10, names(transformed_te)]
#> carbon hydrogen oxygen nitrogen sulfur HHV
#> 15 46.35 5.67 47.20 0.30 0.22 18.275
#> 20 43.25 5.50 48.06 2.85 0.34 17.560
#> 26 42.70 5.50 49.10 2.40 0.30 17.173
#> 31 46.40 6.10 37.30 1.80 0.50 18.851
#> 36 48.76 6.32 42.77 0.20 0.00 20.547
#> 41 44.30 5.50 41.70 0.70 0.20 18.467
#> 46 38.94 5.23 54.13 1.19 0.51 15.095
#> 51 42.10 4.66 33.80 0.95 0.20 16.240
#> 55 29.20 4.40 31.10 0.14 4.90 11.147
#> 65 27.80 3.77 23.69 4.63 1.05 10.750
transformed_te
#> # A tibble: 80 × 6
#> carbon hydrogen oxygen nitrogen sulfur HHV
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -2.00 5.67 8.68 -0.775 0.22 18.3
#> 2 -5.10 5.5 9.54 1.78 0.34 17.6
#> 3 -5.65 5.5 10.6 1.33 0.3 17.2
#> 4 -1.95 6.1 -1.22 0.725 0.5 18.9
#> 5 0.406 6.32 4.25 -0.875 0 20.5
#> 6 -4.05 5.5 3.18 -0.375 0.2 18.5
#> 7 -9.41 5.23 15.6 0.115 0.51 15.1
#> 8 -6.25 4.66 -4.72 -0.125 0.2 16.2
#> 9 -19.2 4.4 -7.42 -0.935 4.9 11.1
#> 10 -20.6 3.77 -14.8 3.56 1.05 10.8
#> # ℹ 70 more rows
tidy(center_trans, number = 1)
#> # A tibble: 3 × 3
#> terms value id
#> <chr> <dbl> <chr>
#> 1 "carbon" NA center_nb4eY
#> 2 "contains(\"gen\")" NA center_nb4eY
#> 3 "-hydrogen" NA center_nb4eY
tidy(center_obj, number = 1)
#> # A tibble: 3 × 3
#> terms value id
#> <chr> <dbl> <chr>
#> 1 carbon 48.4 center_nb4eY
#> 2 oxygen 38.5 center_nb4eY
#> 3 nitrogen 1.07 center_nb4eY