normalize: Normalize numeric variable to 0-1 range

In datawizard: Easy Data Wrangling and Statistical Transformations

Normalize numeric variable to 0-1 range

Description

Performs a normalization of data, i.e., it scales variables in the range 0 - 1. This is a special case of rescale(). unnormalize() is the counterpart, but only works for variables that have been normalized with normalize().

Usage

normalize(x, ...)

## S3 method for class 'numeric'
normalize(x, include_bounds = TRUE, verbose = TRUE, ...)

## S3 method for class '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 class 'numeric'
unnormalize(x, verbose = TRUE, ...)

## S3 method for class 'data.frame'
unnormalize(
  x,
  select = NULL,
  exclude = NULL,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = TRUE,
  ...
)

## S3 method for class 'grouped_df'
unnormalize(
  x,
  select = NULL,
  exclude = NULL,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = TRUE,
  ...
)

Arguments

x	A numeric vector, (grouped) data frame, or matrix. See 'Details'.
...	Arguments passed to or from other methods.
include_bounds	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., 0.001), include_bounds defines the "distance" to the lower and upper bound, i.e. the normalized vectors are rescaled to a range from 0 + include_bounds to 1 - include_bounds.
verbose	Toggle warnings and messages on or off.
select	Variables that will be included when performing the required tasks. Can be either a variable specified as a literal variable name (e.g., column_name), a string with the variable name (e.g., "column_name"), a character vector of variable names (e.g., c("col1", "col2", "col3")), or a character vector of variable names including ranges specified via : (e.g., c("col1:col3", "col5")), for some functions, like data_select() or data_rename(), select can be a named character vector. In this case, the names are used to rename the columns in the output data frame. See 'Details' in the related functions to see where this option applies. 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. 1 or c(1, 3, 5)), a vector of negative integers, giving the positions counting from the right (e.g., -1 or -1:-3), one of the following select-helpers: starts_with(), ends_with(), contains(), a range using :, or regex(). starts_with(), ends_with(), and contains() accept several patterns, e.g starts_with("Sep", "Petal"). regex() can be used to define regular expression patterns. a function testing for logical conditions, e.g. is.numeric() (or is.numeric), or any user-defined function that selects the variables for which the function returns TRUE (like: 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 -, e.g. -ends_with(), -is.numeric or -(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the exclude argument can be used alternatively. For instance, select=-ends_with("Length") (with -) is equivalent to exclude=ends_with("Length") (no -). In case negation should not work as expected, use the exclude argument instead. If NULL, selects all columns. Patterns that found no matches are silently ignored, e.g. extract_column_names(iris, select = c("Species", "Test")) will just return "Species".
exclude	See select, however, column names matched by the pattern from exclude will be excluded instead of selected. If NULL (the default), excludes no columns.
append	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 x will 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.
ignore_case	Logical, if TRUE and 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.
regex	Logical, if TRUE, the search pattern from select will 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 = TRUE is 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.

Details

• If x is a matrix, normalization is performed across all values (not column- or row-wise). For column-wise normalization, convert the matrix to a data.frame.

• If x is a grouped data frame (grouped_df), normalization is performed separately for each group.

Value

A normalized object.

Selection of variables - the select argument

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.

References

Smithson M, Verkuilen J (2006). A Better Lemon Squeezer? Maximum-Likelihood Regression with Beta-Distributed Dependent Variables. Psychological Methods, 11(1), 54–71.

See Also

See makepredictcall.dw_transformer() for use in model formulas.

Other transform utilities: ranktransform(), rescale(), reverse(), standardize()

Examples

normalize(c(0, 1, 5, -5, -2))
normalize(c(0, 1, 5, -5, -2), include_bounds = FALSE)
# use a value defining the bounds
normalize(c(0, 1, 5, -5, -2), include_bounds = .001)

head(normalize(trees))

datawizard documentation built on Aug. 8, 2025, 6:36 p.m.