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

Usage

data_rescale(x, ...)

change_scale(x, ...)

# S3 method for numeric
data_rescale(x, to = c(0, 100), range = NULL, verbose = TRUE, ...)

# S3 method for data.frame
data_rescale(
  x,
  select = NULL,
  exclude = NULL,
  to = c(0, 100),
  range = NULL,
  ignore_case = 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.

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(""),

  • 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.

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

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

Examples

data_rescale(c(0, 1, 5, -5, -2))
#> [1]  50  60 100   0  30
data_rescale(c(0, 1, 5, -5, -2), to = c(-5, 5))
#> [1]  0  1  5 -5 -2
data_rescale(c(1, 2, 3, 4, 5), to = c(-2, 2))
#> [1] -2 -1  0  1  2

# Specify the "theoretical" range of the input vector
data_rescale(c(1, 3, 4), to = c(0, 40), range = c(0, 4))
#> [1] 10 30 40

# Reverse-score a variable
data_rescale(c(1, 2, 3, 4, 5), to = c(5, 1))
#> [1] 5 4 3 2 1
data_rescale(c(1, 2, 3, 4, 5), to = c(2, -2))
#> [1]  2  1  0 -1 -2

# Data frames
head(data_rescale(iris, to = c(0, 1)))
#> Variables of class 'factor' can't be rescaled and remain unchanged.
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1   0.22222222   0.6250000   0.06779661  0.04166667  setosa
#> 2   0.16666667   0.4166667   0.06779661  0.04166667  setosa
#> 3   0.11111111   0.5000000   0.05084746  0.04166667  setosa
#> 4   0.08333333   0.4583333   0.08474576  0.04166667  setosa
#> 5   0.19444444   0.6666667   0.06779661  0.04166667  setosa
#> 6   0.30555556   0.7916667   0.11864407  0.12500000  setosa
head(data_rescale(iris, to = c(0, 1), select = "Sepal.Length"))
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1   0.22222222         3.5          1.4         0.2  setosa
#> 2   0.16666667         3.0          1.4         0.2  setosa
#> 3   0.11111111         3.2          1.3         0.2  setosa
#> 4   0.08333333         3.1          1.5         0.2  setosa
#> 5   0.19444444         3.6          1.4         0.2  setosa
#> 6   0.30555556         3.9          1.7         0.4  setosa

# One can specify a list of ranges
head(data_rescale(iris, to = list(
  "Sepal.Length" = c(0, 1),
  "Petal.Length" = c(-1, 0)
)))
#> Variables of class 'factor' can't be rescaled and remain unchanged.
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1   0.22222222         3.5   -0.9322034         0.2  setosa
#> 2   0.16666667         3.0   -0.9322034         0.2  setosa
#> 3   0.11111111         3.2   -0.9491525         0.2  setosa
#> 4   0.08333333         3.1   -0.9152542         0.2  setosa
#> 5   0.19444444         3.6   -0.9322034         0.2  setosa
#> 6   0.30555556         3.9   -0.8813559         0.4  setosa