Reverse-score variables (change the keying/scoring direction).
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, append = FALSE, ignore_case = FALSE, regex = FALSE, verbose = FALSE, ... )
A (grouped) data frame, numeric vector or factor.
Arguments passed to or from other methods.
Range of values that is used as reference for reversing the scale. For numeric variables, can be
NULLor 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,
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
rangefor factors usually only makes sense when factor levels are numeric, not characters.
Variables that will be included when performing the required tasks. Can be either
a variable specified as a literal variable name (e.g.,
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.
c(1, 3, 5)),
a vector of negative integers, giving the positions counting from the right (e.g.,
one of the following select-helpers:
contains(), a range using
contains()accept several patterns, e.g
or a function testing for logical conditions, e.g.
is.numeric), or any user-defined function that selects the variables for which the function returns
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
-(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the
excludeargument can be used alternatively. For instance,
-) is equivalent to
-). In case negation should not work as expected, use the
NULL, selects all columns. Patterns that found no matches are silently ignored, e.g.
find_columns(iris, select = c("Species", "Test"))will just return
select, however, column names matched by the pattern from
excludewill be excluded instead of selected. If
NULL(the default), excludes no columns.
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,
append=FALSE, original variables in
xwill 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.
TRUEand 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.
TRUE, the search pattern from
selectwill 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 = TRUEis 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.
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.
reverse(c(1, 2, 3, 4, 5)) #>  5 4 3 2 1 reverse(c(-2, -1, 0, 2, 1)) #>  2 1 0 -2 -1 # Specify the "theoretical" range of the input vector reverse(c(1, 3, 4), range = c(0, 4)) #>  3 1 0 # Factor variables reverse(factor(c(1, 2, 3, 4, 5))) #>  5 4 3 2 1 #> Levels: 1 2 3 4 5 reverse(factor(c(1, 2, 3, 4, 5)), range = 0:10) #>  9 8 7 6 5 #> Levels: 0 1 2 3 4 5 6 7 8 9 10 # Data frames head(reverse(iris)) #> 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 head(reverse(iris, select = "Sepal.Length")) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 7.1 3.5 1.4 0.2 setosa #> 2 7.3 3.0 1.4 0.2 setosa #> 3 7.5 3.2 1.3 0.2 setosa #> 4 7.6 3.1 1.5 0.2 setosa #> 5 7.2 3.6 1.4 0.2 setosa #> 6 6.8 3.9 1.7 0.4 setosa