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.recipe. bake.recipe 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