scale_data_frame centers and/or
scales the columns of a data frame (or matrix).
scale_data_frame(x, center = TRUE, scale = TRUE)For scale.default, the centered, scaled data frame. Non-numeric columns are ignored.
Note that logicals are treated as 0/1-numerics to be consistent with scale().
The numeric centering and scalings used (if any) are returned as attributes
"scaled:center" and "scaled:scale" - but only for the numeric/logical columns.
a data frame or a numeric matrix (or vector). For matrices or vectors,
scale() is used.
either a logical value or numeric-alike vector of length
equal to the number of columns of x, where
‘numeric-alike’ means that as.numeric(.) will
be applied successfully if is.numeric(.) is not true.
either a logical value or a numeric-alike vector of length
equal to the number of columns of x.
The value of center determines how column centering is
performed. If center is a numeric-alike vector with length equal to
the number of numeric/logical columns of x, then each column of x has
the corresponding value from center subtracted from it. If
center is TRUE then centering is done by subtracting the
column means (omitting NAs) of x from their
corresponding columns, and if center is FALSE, no
centering is done.
The value of scale determines how column scaling is performed
(after centering). If scale is a numeric-alike vector with length
equal to the number of numeric/logiocal columns of x, then each column of
x is divided by the corresponding value from scale.
If scale is TRUE then scaling is done by dividing the
(centered) columns of x by their standard deviations if
center is TRUE, and the root mean square otherwise.
If scale is FALSE, no scaling is done.
The root-mean-square for a (possibly centered) column is defined as
\(\sqrt{\sum(x^2)/(n-1)}\), where \(x\) is
a vector of the non-missing values and \(n\) is the number of
non-missing values. In the case center = TRUE, this is the
same as the standard deviation, but in general it is not. (To scale
by the standard deviations without centering, use
scale(x, center = FALSE, scale = apply(x, 2, sd, na.rm = TRUE)).)
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
sweep which allows centering (and scaling) with
arbitrary statistics.
require(stats)
data(iris)
summary(scale_data_frame(iris))
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