rescale: Rescale Variables to a New Range

In datawizard: Easy Data Wrangling and Statistical Transformations

Rescale Variables to a New Range

Description

Rescale variables to a new range. Can also be used to reverse-score variables (change the keying/scoring direction), or to expand a range.

Usage

rescale(x, ...)

change_scale(x, ...)

## S3 method for class 'numeric'
rescale(
  x,
  to = c(0, 100),
  multiply = NULL,
  add = NULL,
  range = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'data.frame'
rescale(
  x,
  select = NULL,
  exclude = NULL,
  to = c(0, 100),
  multiply = NULL,
  add = NULL,
  range = NULL,
  append = FALSE,
  ignore_case = FALSE,
  regex = FALSE,
  verbose = FALSE,
  ...
)

Arguments

x	A (grouped) data frame, numeric vector or factor.
...	Arguments passed to or from other methods.
to	Numeric vector of length 2 giving the new range that the variable will have after rescaling. To reverse-score a variable, the range should be given with the maximum value first. See examples.
multiply	If not NULL, to is ignored and multiply will be used, giving the factor by which the actual range of x should be expanded. For example, if a vector ranges from 5 to 15 and multiply = 1.1, the current range of 10 will be expanded by the factor of 1.1, giving a new range of 11. Thus, the rescaled vector would range from 4.5 to 15.5.
add	A vector of length 1 or 2. If not NULL, to is ignored and add will be used, giving the amount by which the minimum and maximum of the actual range of x should be expanded. For example, if a vector ranges from 5 to 15 and add = 1, the range will be expanded from 4 to 16. If add is of length 2, then the first value is used for the lower bound and the second value for the upper bound.
range	Initial (old) range of values. If NULL, will take the range of the input vector (range(x)).
verbose	Toggle warnings.
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. 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, recoded or converted variables get new column names and are appended (column bind) to x, thus returning both the original and the recoded variables. The new columns get a suffix, based on the calling function: "_r" for recode functions, "_n" for to_numeric(), "_f" for to_factor(), or "_s" for slide(). If append=FALSE, original variables in x will be overwritten by their recoded versions. If a character value, recoded 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.

Value

A rescaled 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.

See Also

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

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

Examples

rescale(c(0, 1, 5, -5, -2))
rescale(c(0, 1, 5, -5, -2), to = c(-5, 5))
rescale(c(1, 2, 3, 4, 5), to = c(-2, 2))

# Specify the "theoretical" range of the input vector
rescale(c(1, 3, 4), to = c(0, 40), range = c(0, 4))

# Reverse-score a variable
rescale(c(1, 2, 3, 4, 5), to = c(5, 1))
rescale(c(1, 2, 3, 4, 5), to = c(2, -2))

# Data frames
head(rescale(iris, to = c(0, 1)))
head(rescale(iris, to = c(0, 1), select = "Sepal.Length"))

# One can specify a list of ranges
head(rescale(iris, to = list(
  "Sepal.Length" = c(0, 1),
  "Petal.Length" = c(-1, 0)
)))

# "expand" ranges by a factor or a given value
x <- 5:15
x
# both will expand the range by 10%
rescale(x, multiply = 1.1)
rescale(x, add = 0.5)

# expand range by different values
rescale(x, add = c(1, 3))

# Specify list of multipliers
d <- data.frame(x = 5:15, y = 5:15)
rescale(d, multiply = list(x = 1.1, y = 0.5))

datawizard documentation built on Oct. 6, 2024, 1:08 a.m.