step_normalize creates a specification of a recipe step that will normalize numeric data to have a standard deviation of one and a mean of zero.

step_normalize(
recipe,
...,
role = NA,
trained = FALSE,
means = NULL,
sds = NULL,
na_rm = TRUE,
skip = FALSE,
id = rand_id("normalize")
)

## 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. 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 named numeric vector of standard deviations This is NULL until computed by prep.recipe(). A logical value indicating whether NA values should be removed when computing the standard deviation and mean. 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.

## Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

## Details

Centering data means that the average of a variable is subtracted from the data. Scaling data means that the standard deviation of a variable is divided out of the data. step_normalize estimates the variable standard deviations and means from the data used in the training argument of prep.recipe. bake.recipe then applies the scaling to new data sets using these estimates.

When you tidy() this step, a tibble with columns terms (the selectors or variables selected), value (the standard deviations and means), and statistic for the type of value is returned.

## 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)

norm_trans <- rec %>%
step_normalize(carbon, hydrogen)

norm_obj <- prep(norm_trans, training = biomass_tr)

transformed_te <- bake(norm_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 x 6
#>     carbon hydrogen oxygen nitrogen sulfur   HHV
#>      <dbl>    <dbl>  <dbl>    <dbl>  <dbl> <dbl>
#>  1 -0.193    0.176    47.2     0.3    0.22  18.3
#>  2 -0.490    0.0342   48.1     2.85   0.34  17.6
#>  3 -0.543    0.0342   49.1     2.4    0.3   17.2
#>  4 -0.188    0.535    37.3     1.8    0.5   18.9
#>  5  0.0390   0.719    42.8     0.2    0     20.5
#>  6 -0.390    0.0342   41.7     0.7    0.2   18.5
#>  7 -0.904   -0.191    54.1     1.19   0.51  15.1
#>  8 -0.601   -0.668    33.8     0.95   0.2   16.2
#>  9 -1.84    -0.885    31.1     0.14   4.9   11.1
#> 10 -1.97    -1.41     23.7     4.63   1.05  10.8
#> # … with 70 more rows
tidy(norm_trans, number = 1)
#> # A tibble: 2 x 4
#>   terms    statistic value id
#>   <chr>    <chr>     <dbl> <chr>
#> 1 carbon   NA           NA normalize_u8gfY
#> 2 hydrogen NA           NA normalize_u8gfY
tidy(norm_obj, number = 1)
#> # A tibble: 4 x 4
#>   terms    statistic value id
#>   <chr>    <chr>     <dbl> <chr>
#> 1 carbon   mean      48.4  normalize_u8gfY
#> 2 hydrogen mean       5.46 normalize_u8gfY
#> 3 carbon   sd        10.4  normalize_u8gfY
#> 4 hydrogen sd         1.20 normalize_u8gfY

# To keep the original variables in the output, use step_mutate_at:
norm_keep_orig <- rec %>%
step_mutate_at(all_numeric_predictors(), fn = list(orig = ~.)) %>%
step_normalize(-contains("orig"), -all_outcomes())

keep_orig_obj <- prep(norm_keep_orig, training = biomass_tr)
keep_orig_te <- bake(keep_orig_obj, biomass_te)
keep_orig_te
#> # A tibble: 80 x 11
#>     carbon hydrogen oxygen nitrogen   sulfur   HHV carbon_orig hydrogen_orig
#>      <dbl>    <dbl>  <dbl>    <dbl>    <dbl> <dbl>       <dbl>         <dbl>
#>  1 -0.193    0.176   0.801  -0.643   0.00755  18.3        46.4          5.67
#>  2 -0.490    0.0342  0.881   1.47    0.281    17.6        43.2          5.5
#>  3 -0.543    0.0342  0.977   1.10    0.190    17.2        42.7          5.5
#>  4 -0.188    0.535  -0.113   0.602   0.646    18.9        46.4          6.1
#>  5  0.0390   0.719   0.392  -0.726  -0.494    20.5        48.8          6.32
#>  6 -0.390    0.0342  0.293  -0.311  -0.0380   18.5        44.3          5.5
#>  7 -0.904   -0.191   1.44    0.0958  0.668    15.1        38.9          5.23
#>  8 -0.601   -0.668  -0.436  -0.103  -0.0380   16.2        42.1          4.66
#>  9 -1.84    -0.885  -0.686  -0.776  10.7      11.1        29.2          4.4
#> 10 -1.97    -1.41   -1.37    2.95    1.90     10.8        27.8          3.77
#> # … with 70 more rows, and 3 more variables: oxygen_orig <dbl>,
#> #   nitrogen_orig <dbl>, sulfur_orig <dbl>