Reverse-score variables (change the keying/scoring direction).

## Usage

reverse(x, ...)

reverse_scale(x, ...)

# S3 method for numeric
reverse(x, range = NULL, verbose = TRUE, ...)

# S3 method for data.frame
reverse(
x,
select = NULL,
exclude = NULL,
range = NULL,
ignore_case = FALSE,
regex = FALSE,
verbose = FALSE,
...
)

## Arguments

x

A (grouped) data frame, numeric vector or factor.

...

Arguments passed to or from other methods.

range

Range of values that is used as reference for reversing the scale. For numeric variables, can be NULL or a numeric vector of length two, indicating the lowest and highest value of the reference range. If NULL, will take the range of the input vector (range(x)). For factors, range can be NULL, a numeric vector of length two, or a (numeric) vector of at least the same length as factor levels (i.e. must be equal to or larger than nlevels(x)). Note that providing a range for factors usually only makes sense when factor levels are numeric, not characters.

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. 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 reverse-scored 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.

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

## Examples

reverse(c(1, 2, 3, 4, 5))
#> [1] 5 4 3 2 1
reverse(c(-2, -1, 0, 2, 1))
#> [1]  2  1  0 -2 -1

# Specify the "theoretical" range of the input vector
reverse(c(1, 3, 4), range = c(0, 4))
#> [1] 3 1 0

# Factor variables
reverse(factor(c(1, 2, 3, 4, 5)))
#> [1] 5 4 3 2 1
#> Levels: 1 2 3 4 5
reverse(factor(c(1, 2, 3, 4, 5)), range = 0:10)
#> [1] 9 8 7 6 5
#> Levels: 0 1 2 3 4 5 6 7 8 9 10

# Data frames
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
#> 1          7.1         2.9          6.5         2.4 virginica
#> 2          7.3         3.4          6.5         2.4 virginica
#> 3          7.5         3.2          6.6         2.4 virginica
#> 4          7.6         3.3          6.4         2.4 virginica
#> 5          7.2         2.8          6.5         2.4 virginica
#> 6          6.8         2.5          6.2         2.2 virginica