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 andmultiply
will be used, giving the factor by which the actual range ofx
should be expanded. For example, if a vector ranges from 5 to 15 andmultiply = 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 andadd
will be used, giving the amount by which the minimum and maximum of the actual range ofx
should be expanded. For example, if a vector ranges from 5 to 15 andadd = 1
, the range will be expanded from 4 to 16. Ifadd
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"
), 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()
ordata_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
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")
.regex()
can be used to define regular expression patterns.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.extract_column_names(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.- append
Logical or string. If
TRUE
, recoded or converted variables get new column names and are appended (column bind) tox
, 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"
forto_numeric()
,"_f"
forto_factor()
, or"_s"
forslide()
. Ifappend=FALSE
, original variables inx
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 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
#> (original range = -5 to 5)
#>
rescale(c(0, 1, 5, -5, -2), to = c(-5, 5))
#> [1] 0 1 5 -5 -2
#> (original range = -5 to 5)
#>
rescale(c(1, 2, 3, 4, 5), to = c(-2, 2))
#> [1] -2 -1 0 1 2
#> (original range = 1 to 5)
#>
# 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
#> (original range = 0 to 4)
#>
# Reverse-score a variable
rescale(c(1, 2, 3, 4, 5), to = c(5, 1))
#> [1] 5 4 3 2 1
#> (original range = 1 to 5)
#>
rescale(c(1, 2, 3, 4, 5), to = c(2, -2))
#> [1] 2 1 0 -1 -2
#> (original range = 1 to 5)
#>
# 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
# "expand" ranges by a factor or a given value
x <- 5:15
x
#> [1] 5 6 7 8 9 10 11 12 13 14 15
# both will expand the range by 10%
rescale(x, multiply = 1.1)
#> [1] 4.5 5.6 6.7 7.8 8.9 10.0 11.1 12.2 13.3 14.4 15.5
#> (original range = 5 to 15)
#>
rescale(x, add = 0.5)
#> [1] 4.5 5.6 6.7 7.8 8.9 10.0 11.1 12.2 13.3 14.4 15.5
#> (original range = 5 to 15)
#>
# expand range by different values
rescale(x, add = c(1, 3))
#> [1] 4.0 5.4 6.8 8.2 9.6 11.0 12.4 13.8 15.2 16.6 18.0
#> (original range = 5 to 15)
#>
# Specify list of multipliers
d <- data.frame(x = 5:15, y = 5:15)
rescale(d, multiply = list(x = 1.1, y = 0.5))
#> x y
#> 1 4.5 7.5
#> 2 5.6 8.0
#> 3 6.7 8.5
#> 4 7.8 9.0
#> 5 8.9 9.5
#> 6 10.0 10.0
#> 7 11.1 10.5
#> 8 12.2 11.0
#> 9 13.3 11.5
#> 10 14.4 12.0
#> 11 15.5 12.5