% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/center.R
\name{center}
\alias{center}
\alias{centre}
\alias{center.numeric}
\alias{center.data.frame}
\title{Centering (Grand-Mean Centering)}
\usage{
center(x, ...)

centre(x, ...)

\method{center}{numeric}(
  x,
  robust = FALSE,
  weights = NULL,
  reference = NULL,
  center = NULL,
  verbose = TRUE,
  ...
)

\method{center}{data.frame}(
  x,
  select = NULL,
  exclude = NULL,
  robust = FALSE,
  weights = NULL,
  reference = NULL,
  center = NULL,
  force = FALSE,
  remove_na = c("none", "selected", "all"),
  append = FALSE,
  ignore_case = FALSE,
  verbose = TRUE,
  regex = FALSE,
  ...
)
}
\arguments{
\item{x}{A (grouped) data frame, a (numeric or character) vector or a factor.}

\item{...}{Currently not used.}

\item{robust}{Logical, if \code{TRUE}, centering is done by subtracting the
median from the variables. If \code{FALSE}, variables are centered by
subtracting the mean.}

\item{weights}{Can be \code{NULL} (for no weighting), or:
\itemize{
\item For data frames: a numeric vector of weights, or a character of the
name of a column in the \code{data.frame} that contains the weights.
\item For numeric vectors: a numeric vector of weights.
}}

\item{reference}{A data frame or variable from which the centrality and
deviation will be computed instead of from the input variable. Useful for
standardizing a subset or new data according to another data frame.}

\item{center}{Numeric value, which can be used as alternative to
\code{reference} to define a reference centrality. If \code{center} is of length 1,
it will be recycled to match the length of selected variables for centering.
Else, \code{center} must be of same length as the number of selected variables.
Values in \code{center} will be matched to selected variables in the provided
order, unless a named vector is given. In this case, names are matched
against the names of the selected variables.}

\item{verbose}{Toggle warnings and messages.}

\item{select}{Variables that will be included when performing the required
tasks. Can be either
\itemize{
\item a variable specified as a literal variable name (e.g., \code{column_name}),
\item a string with the variable name (e.g., \code{"column_name"}), or a character
vector of variable names (e.g., \code{c("col1", "col2", "col3")}),
\item a formula with variable names (e.g., \code{~column_1 + column_2}),
\item a vector of positive integers, giving the positions counting from the left
(e.g. \code{1} or \code{c(1, 3, 5)}),
\item a vector of negative integers, giving the positions counting from the
right (e.g., \code{-1} or \code{-1:-3}),
\item one of the following select-helpers: \code{starts_with()}, \code{ends_with()},
\code{contains()}, a range using \code{:} or \code{regex("")}. \code{starts_with()},
\code{ends_with()}, and  \code{contains()} accept several patterns, e.g
\code{starts_with("Sep", "Petal")}.
\item or a function testing for logical conditions, e.g. \code{is.numeric()} (or
\code{is.numeric}), or any user-defined function that selects the variables
for which the function returns \code{TRUE} (like: \code{foo <- function(x) mean(x) > 3}),
\item ranges specified via literal variable names, select-helpers (except
\code{regex()}) and (user-defined) functions can be negated, i.e. return
non-matching elements, when prefixed with a \code{-}, e.g. \code{-ends_with("")},
\code{-is.numeric} or \code{-(Sepal.Width:Petal.Length)}. \strong{Note:} Negation means
that matches are \emph{excluded}, and thus, the \code{exclude} argument can be
used alternatively. For instance, \code{select=-ends_with("Length")} (with
\code{-}) is equivalent to \code{exclude=ends_with("Length")} (no \code{-}). In case
negation should not work as expected, use the \code{exclude} argument instead.
}

If \code{NULL}, selects all columns. Patterns that found no matches are silently
ignored, e.g. \code{find_columns(iris, select = c("Species", "Test"))} will just
return \code{"Species"}.}

\item{exclude}{See \code{select}, however, column names matched by the pattern
from \code{exclude} will be excluded instead of selected. If \code{NULL} (the default),
excludes no columns.}

\item{force}{Logical, if \code{TRUE}, forces centering of factors as
well. Factors are converted to numerical values, with the lowest level
being the value \code{1} (unless the factor has numeric levels, which are
converted to the corresponding numeric value).}

\item{remove_na}{How should missing values (\code{NA}) be treated: if \code{"none"}
(default): each column's standardization is done separately, ignoring
\code{NA}s. Else, rows with \code{NA} in the columns selected with \code{select} /
\code{exclude} (\code{"selected"}) or in all columns (\code{"all"}) are dropped before
standardization, and the resulting data frame does not include these cases.}

\item{append}{Logical or string. If \code{TRUE}, centered variables get new
column names (with the suffix \code{"_c"}) and are appended (column bind) to \code{x},
thus returning both the original and the centered variables. If \code{FALSE},
original variables in \code{x} will be overwritten by their centered versions.
If a character value, centered variables are appended with new column
names (using the defined suffix) to the original data frame.}

\item{ignore_case}{Logical, if \code{TRUE} and when one of the select-helpers or
a regular expression is used in \code{select}, ignores lower/upper case in the
search pattern when matching against variable names.}

\item{regex}{Logical, if \code{TRUE}, the search pattern from \code{select} will be
treated as regular expression. When \code{regex = TRUE}, select \emph{must} be a
character string (or a variable containing a character string) and is not
allowed to be one of the supported select-helpers or a character vector
of length > 1. \code{regex = TRUE} is comparable to using one of the two
select-helpers, \code{select = contains("")} or \code{select = regex("")}, however,
since the select-helpers may not work when called from inside other
functions (see 'Details'), this argument may be used as workaround.}
}
\value{
The centered variables.
}
\description{
Performs a grand-mean centering of data.
}
\note{
\strong{Difference between centering and standardizing}: Standardized variables
are computed by subtracting the mean of the variable and then dividing it by
the standard deviation, while centering variables involves only the
subtraction.
}
\section{Selection of variables - the \code{select} argument}{

For most functions that have a \code{select} argument (including this function),
the complete input data frame is returned, even when \code{select} only selects
a range of variables. That is, the function is only applied to those variables
that have a match in \code{select}, while all other variables remain unchanged.
In other words: for this function, \code{select} will not omit any non-included
variables, so that the returned data frame will include all variables
from the input data frame.
}

\examples{
data(iris)

# entire data frame or a vector
head(iris$Sepal.Width)
head(center(iris$Sepal.Width))
head(center(iris))
head(center(iris, force = TRUE))

# only the selected columns from a data frame
center(anscombe, select = c("x1", "x3"))
center(anscombe, exclude = c("x1", "x3"))

# centering with reference center and scale
d <- data.frame(
  a = c(-2, -1, 0, 1, 2),
  b = c(3, 4, 5, 6, 7)
)

# default centering at mean
center(d)

# centering, using 0 as mean
center(d, center = 0)

# centering, using -5 as mean
center(d, center = -5)
}
\seealso{
If centering within-clusters (instead of grand-mean centering)
is required, see \code{\link[=demean]{demean()}}. For standardizing, see \code{\link[=standardize]{standardize()}}, and
\code{\link[=makepredictcall.dw_transformer]{makepredictcall.dw_transformer()}} for use in model formulas.
}
