Rescale variables to a new range. Can also be used to reverse-score variables (change the keying/scoring direction).
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
orc(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:
orregex("")
.starts_with()
,ends_with()
, andcontains()
accept several patterns, e.gstarts_with("Sep", "Petal")
.or a function testing for logical conditions, e.g.
is.numeric()
(oris.numeric
), or any user-defined function that selects the variables for which the function returnsTRUE
(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, theexclude
argument can be used alternatively. For instance,select=-ends_with("Length")
(with-
) is equivalent toexclude=ends_with("Length")
(no-
). In case negation should not work as expected, use theexclude
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 fromexclude
will be excluded instead of selected. IfNULL
(the default), excludes no columns.- ignore_case
Logical, if
TRUE
and when one of the select-helpers or a regular expression is used inselect
, ignores lower/upper case in the search pattern when matching against variable names.- regex
Logical, if
TRUE
, the search pattern fromselect
will be treated as regular expression. Whenregex = 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("")
orselect = regex("")
, however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.
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))
#> [1] 50 60 100 0 30
#> attr(,"min_value")
#> [1] -5
#> attr(,"max_value")
#> [1] 5
#> attr(,"new_min")
#> [1] 0
#> attr(,"new_max")
#> [1] 100
#> attr(,"range_difference")
#> [1] 10
#> attr(,"to_range")
#> [1] 0 100
#> attr(,"class")
#> [1] "dw_transformer" "numeric"
rescale(c(0, 1, 5, -5, -2), to = c(-5, 5))
#> [1] 0 1 5 -5 -2
#> attr(,"min_value")
#> [1] -5
#> attr(,"max_value")
#> [1] 5
#> attr(,"new_min")
#> [1] -5
#> attr(,"new_max")
#> [1] 5
#> attr(,"range_difference")
#> [1] 10
#> attr(,"to_range")
#> [1] -5 5
#> attr(,"class")
#> [1] "dw_transformer" "numeric"
rescale(c(1, 2, 3, 4, 5), to = c(-2, 2))
#> [1] -2 -1 0 1 2
#> attr(,"min_value")
#> [1] 1
#> attr(,"max_value")
#> [1] 5
#> attr(,"new_min")
#> [1] -2
#> attr(,"new_max")
#> [1] 2
#> attr(,"range_difference")
#> [1] 4
#> attr(,"to_range")
#> [1] -2 2
#> attr(,"class")
#> [1] "dw_transformer" "numeric"
# Specify the "theoretical" range of the input vector
rescale(c(1, 3, 4), to = c(0, 40), range = c(0, 4))
#> [1] 10 30 40
#> attr(,"min_value")
#> [1] 0
#> attr(,"max_value")
#> [1] 4
#> attr(,"new_min")
#> [1] 0
#> attr(,"new_max")
#> [1] 40
#> attr(,"range_difference")
#> [1] 4
#> attr(,"to_range")
#> [1] 0 40
#> attr(,"class")
#> [1] "dw_transformer" "numeric"
# Reverse-score a variable
rescale(c(1, 2, 3, 4, 5), to = c(5, 1))
#> [1] 5 4 3 2 1
#> attr(,"min_value")
#> [1] 1
#> attr(,"max_value")
#> [1] 5
#> attr(,"new_min")
#> [1] 5
#> attr(,"new_max")
#> [1] 1
#> attr(,"range_difference")
#> [1] 4
#> attr(,"to_range")
#> [1] 5 1
#> attr(,"class")
#> [1] "dw_transformer" "numeric"
rescale(c(1, 2, 3, 4, 5), to = c(2, -2))
#> [1] 2 1 0 -1 -2
#> attr(,"min_value")
#> [1] 1
#> attr(,"max_value")
#> [1] 5
#> attr(,"new_min")
#> [1] 2
#> attr(,"new_max")
#> [1] -2
#> attr(,"range_difference")
#> [1] 4
#> attr(,"to_range")
#> [1] 2 -2
#> attr(,"class")
#> [1] "dw_transformer" "numeric"
# Data frames
head(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(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(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