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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/center.R
\name{step_center}
\alias{step_center}
\alias{tidy.step_center}
\title{Centering numeric data}
\usage{
step_center(
recipe,
...,
role = NA,
trained = FALSE,
means = NULL,
na_rm = TRUE,
skip = FALSE,
id = rand_id("center")
)
\method{tidy}{step_center}(x, ...)
}
\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 which
variables are affected by the step. See \code{\link[=selections]{selections()}}
for more details. For the \code{tidy} method, these are not
currently used.}
\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.recipe]{prep.recipe()}}.}
\item{na_rm}{A logical value indicating whether \code{NA}
values should be removed during computations.}
\item{skip}{A logical. Should the step be skipped when the
recipe is baked by \code{\link[=bake.recipe]{bake.recipe()}}? While all operations are baked
when \code{\link[=prep.recipe]{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 \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.}
\item{x}{A \code{step_center} object.}
}
\value{
An updated version of \code{recipe} with the new step
added to the sequence of existing steps (if any). For the
\code{tidy} method, a tibble with columns \code{terms} (the
selectors or variables selected) and \code{value} (the means).
}
\description{
\code{step_center} creates a \emph{specification} of a recipe
step that will normalize numeric data to have a mean of zero.
}
\details{
Centering data means that the average of a variable is
subtracted from the data. \code{step_center} estimates the
variable means from the data used in the \code{training}
argument of \code{prep.recipe}. \code{bake.recipe} then applies
the centering to new data sets using these means.
}
\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)]
transformed_te
tidy(center_trans, number = 1)
tidy(center_obj, number = 1)
}
\seealso{
\code{\link[=recipe]{recipe()}} \code{\link[=prep.recipe]{prep.recipe()}}
\code{\link[=bake.recipe]{bake.recipe()}}
}
\concept{normalization_methods}
\concept{preprocessing}
\keyword{datagen}
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