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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/normalize.R
\name{step_normalize}
\alias{step_normalize}
\title{Center and scale numeric data}
\usage{
step_normalize(
recipe,
...,
role = NA,
trained = FALSE,
means = NULL,
sds = NULL,
na_rm = TRUE,
skip = FALSE,
id = rand_id("normalize")
)
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the
sequence of operations for this recipe.}
\item{...}{One or more selector functions to choose variables
for this step. See \code{\link[=selections]{selections()}} for more details.}
\item{role}{Not used by this step since no new variables are
created.}
\item{trained}{A logical to indicate if the quantities for
preprocessing have been estimated.}
\item{means}{A named numeric vector of means. This is \code{NULL} until computed
by \code{\link[=prep]{prep()}}.}
\item{sds}{A named numeric vector of standard deviations This is \code{NULL} until
computed by \code{\link[=prep]{prep()}}.}
\item{na_rm}{A logical value indicating whether \code{NA} values should be removed
when computing the standard deviation and mean.}
\item{skip}{A logical. Should the step be skipped when the
recipe is baked by \code{\link[=bake]{bake()}}? While all operations are baked
when \code{\link[=prep]{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 \code{skip = TRUE} as it may affect
the computations for subsequent operations.}
\item{id}{A character string that is unique to this step to identify it.}
}
\value{
An updated version of \code{recipe} with the new step added to the
sequence of any existing operations.
}
\description{
\code{step_normalize} creates a \emph{specification} of a recipe
step that will normalize numeric data to have a standard
deviation of one and a mean of zero.
}
\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. \code{step_normalize} estimates the variable standard
deviations and means from the data used in the \code{training} argument of
\code{prep.recipe}. \code{\link{bake.recipe}} then applies the scaling to new data sets using
these estimates.
}
\section{Tidying}{
When you \code{\link[=tidy.recipe]{tidy()}} this step, a tibble with columns
\code{terms} (the selectors or variables selected), \code{value} (the standard
deviations and means), and \code{statistic} for the type of value is
returned.
}
\section{Case weights}{
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 \link{case_weights} and the examples on
\code{tidymodels.org}.
}
\examples{
\dontshow{if (rlang::is_installed("modeldata")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
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
)
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)]
transformed_te
tidy(norm_trans, number = 1)
tidy(norm_obj, number = 1)
# 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
\dontshow{\}) # examplesIf}
}
\seealso{
Other normalization steps:
\code{\link{step_center}()},
\code{\link{step_range}()},
\code{\link{step_scale}()}
}
\concept{normalization steps}
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