Performs a normalization of data, i.e., it scales variables in the range
0 - 1. This is a special case of
unnormalize() is the
counterpart, but only works for variables that have been normalized with
normalize(x, ...) # S3 method for numeric normalize(x, include_bounds = TRUE, verbose = TRUE, ...) # S3 method for data.frame normalize( x, select = NULL, exclude = NULL, include_bounds = TRUE, append = FALSE, ignore_case = FALSE, regex = FALSE, verbose = TRUE, ... ) unnormalize(x, ...) # S3 method for numeric unnormalize(x, verbose = TRUE, ...) # S3 method for data.frame unnormalize( x, select = NULL, exclude = NULL, ignore_case = FALSE, regex = FALSE, verbose = TRUE, ... ) # S3 method for grouped_df unnormalize( x, select = NULL, exclude = NULL, ignore_case = FALSE, regex = FALSE, verbose = TRUE, ... )
A numeric vector, (grouped) data frame, or matrix. See 'Details'.
Arguments passed to or from other methods.
Numeric or logical. Using this can be useful in case of beta-regression, where the response variable is not allowed to include zeros and ones. If
TRUE, the input is normalized to a range that includes zero and one. If
FALSE, the return value is compressed, using Smithson and Verkuilen's (2006) formula
(x * (n - 1) + 0.5) / n, to avoid zeros and ones in the normalized variables. Else, if numeric (e.g.,
include_boundsdefines the "distance" to the lower and upper bound, i.e. the normalized vectors are rescaled to a range from
0 + include_boundsto
1 - include_bounds.
Toggle warnings and messages on or off.
Variables that will be included when performing the required tasks. Can be either
a variable specified as a literal variable name (e.g.,
a string with the variable name (e.g.,
"column_name"), or a character vector of variable names (e.g.,
c("col1", "col2", "col3")),
a formula with variable names (e.g.,
~column_1 + column_2),
a vector of positive integers, giving the positions counting from the left (e.g.
c(1, 3, 5)),
a vector of negative integers, giving the positions counting from the right (e.g.,
one of the following select-helpers:
contains(), a range using
contains()accept several patterns, e.g
or a function testing for logical conditions, e.g.
is.numeric), or any user-defined function that selects the variables for which the function returns
foo <- function(x) mean(x) > 3),
ranges specified via literal variable names, select-helpers (except
regex()) and (user-defined) functions can be negated, i.e. return non-matching elements, when prefixed with a
-(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the
excludeargument can be used alternatively. For instance,
-) is equivalent to
-). In case negation should not work as expected, use the
NULL, selects all columns. Patterns that found no matches are silently ignored, e.g.
find_columns(iris, select = c("Species", "Test"))will just return
select, however, column names matched by the pattern from
excludewill be excluded instead of selected. If
NULL(the default), excludes no columns.
Logical or string. If
TRUE, standardized variables get new column names (with the suffix
"_z") and are appended (column bind) to
x, thus returning both the original and the standardized variables. If
FALSE, original variables in
xwill be overwritten by their standardized versions. If a character value, standardized variables are appended with new column names (using the defined suffix) to the original data frame.
TRUEand when one of the select-helpers or a regular expression is used in
select, ignores lower/upper case in the search pattern when matching against variable names.
TRUE, the search pattern from
selectwill be treated as regular expression. When
regex = TRUE, select 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.
regex = TRUEis comparable to using one of the two select-helpers,
select = contains("")or
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.
xis a matrix, normalization is performed across all values (not column- or row-wise). For column-wise normalization, convert the matrix to a data.frame.
xis a grouped data frame (
grouped_df), normalization is performed separately for each group.
For most functions that have a
select argument (including this function),
the complete input data frame is returned, even when
select only selects
a range of variables. That is, the function is only applied to those variables
that have a match in
select, while all other variables remain unchanged.
In other words: for this function,
select will not omit any non-included
variables, so that the returned data frame will include all variables
from the input data frame.
Smithson M, Verkuilen J (2006). A Better Lemon Squeezer? Maximum-Likelihood Regression with Beta-Distributed Dependent Variables. Psychological Methods, 11(1), 54–71.
normalize(c(0, 1, 5, -5, -2)) #>  0.5 0.6 1.0 0.0 0.3 #> (original range = -5 to 5) #> normalize(c(0, 1, 5, -5, -2), include_bounds = FALSE) #>  0.50 0.58 0.90 0.10 0.34 #> (original range = -5 to 5) #> # use a value defining the bounds normalize(c(0, 1, 5, -5, -2), include_bounds = .001) #>  0.5000 0.5998 0.9990 0.0010 0.3004 #> (original range = -5 to 5) #> head(normalize(trees)) #> Girth Height Volume #> 1 0.00000000 0.29166667 0.001497006 #> 2 0.02439024 0.08333333 0.001497006 #> 3 0.04065041 0.00000000 0.000000000 #> 4 0.17886179 0.37500000 0.092814371 #> 5 0.19512195 0.75000000 0.128742515 #> 6 0.20325203 0.83333333 0.142215569