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")
)

# S3 method for step_center
tidy(x, ...)

## 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 selections() for more details. For the tidy method, these are not currently used. 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.recipe(). 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.recipe()? While all operations are baked when prep.recipe() 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. A step_center object.

## 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 selectors or variables selected) and value (the means).

## Details

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.

recipe() prep.recipe() bake.recipe()

## Examples

library(modeldata)
data(biomass)

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 10.750transformed_te
#> # A tibble: 80 x 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
#> # … with 70 more rows
tidy(center_trans, number = 1)
#> # A tibble: 3 x 3
#>   terms               value id
#>   <chr>               <dbl> <chr>
#> 1 "carbon"               NA center_wECZ3
#> 2 "contains(\"gen\")"    NA center_wECZ3
#> 3 "-hydrogen"            NA center_wECZ3tidy(center_obj, number = 1)
#> # A tibble: 3 x 3
#>   terms    value id
#>   <chr>    <dbl> <chr>
#> 1 carbon   48.4  center_wECZ3
#> 2 oxygen   38.5  center_wECZ3
#> 3 nitrogen  1.07 center_wECZ3