Normalize numeric variable to 0-1 range

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 numeric
normalize(x, include_bounds = TRUE, verbose = TRUE, ...)

# S3 method for data.frame
normalize(
  x,
  select = NULL,
  exclude = NULL,
  include_bounds = TRUE,
  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,
  ...
)

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"), 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. 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").
or 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. find_columns(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.

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.

Value

A normalized object.

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.

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.

Examples


normalize(c(0, 1, 5, -5, -2))
#> [1] 0.5 0.6 1.0 0.0 0.3
#> attr(,"include_bounds")
#> [1] TRUE
#> attr(,"flag_bounds")
#> [1] TRUE
#> attr(,"min_value")
#> [1] -5
#> attr(,"vector_length")
#> [1] 5
#> attr(,"range_difference")
#> [1] 10
#> attr(,"class")
#> [1] "dw_transformer" "numeric"       
normalize(c(0, 1, 5, -5, -2), include_bounds = FALSE)
#> [1] 0.50 0.58 0.90 0.10 0.34
#> attr(,"include_bounds")
#> [1] FALSE
#> attr(,"flag_bounds")
#> [1] TRUE
#> attr(,"min_value")
#> [1] -5
#> attr(,"vector_length")
#> [1] 5
#> attr(,"range_difference")
#> [1] 10
#> attr(,"class")
#> [1] "dw_transformer" "numeric"       
# use a value defining the bounds
normalize(c(0, 1, 5, -5, -2), include_bounds = .001)
#> [1] 0.5000 0.5998 0.9990 0.0010 0.3004
#> attr(,"min_value")
#> [1] -5
#> attr(,"max_value")
#> [1] 1
#> attr(,"new_min")
#> [1] 0.001
#> attr(,"new_max")
#> [1] 0.999
#> attr(,"range_difference")
#> [1] 10
#> attr(,"to_range")
#> [1] 0.001 0.999
#> attr(,"class")
#> [1] "dw_transformer" "dw_transformer" "numeric"       
#> attr(,"include_bounds")
#> [1] 0.001
#> attr(,"flag_bounds")
#> [1] TRUE
#> attr(,"vector_length")
#> [1] 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