Transform numeric values with the integers of their rank (i.e., 1st smallest,
2nd smallest, 3rd smallest, etc.). Setting the sign
argument to TRUE
will
give you signed ranks, where the ranking is done according to absolute size
but where the sign is preserved (i.e., 2, 1, -3, 4).
Usage
ranktransform(x, ...)
# S3 method for class 'numeric'
ranktransform(
x,
sign = FALSE,
method = "average",
zeros = "na",
verbose = TRUE,
...
)
# S3 method for class 'data.frame'
ranktransform(
x,
select = NULL,
exclude = NULL,
sign = FALSE,
method = "average",
ignore_case = FALSE,
regex = FALSE,
zeros = "na",
verbose = TRUE,
...
)
Arguments
- x
Object.
- ...
Arguments passed to or from other methods.
- sign
Logical, if
TRUE
, return signed ranks.- method
Treatment of ties. Can be one of
"average"
(default),"first"
,"last"
,"random"
,"max"
or"min"
. Seerank()
for details.- zeros
How to handle zeros. If
"na"
(default), they are marked asNA
. If"signrank"
, they are kept during the ranking and marked as zeros. This is only used whensign = TRUE
.- 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.- 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
Other transform utilities:
normalize()
,
rescale()
,
reverse()
,
standardize()
Examples
ranktransform(c(0, 1, 5, -5, -2))
#> [1] 3 4 5 1 2
# By default, zeros are converted to NA
suppressWarnings(
ranktransform(c(0, 1, 5, -5, -2), sign = TRUE)
)
#> [1] NA 1.0 3.5 -3.5 -2.0
ranktransform(c(0, 1, 5, -5, -2), sign = TRUE, zeros = "signrank")
#> [1] 0.0 2.0 4.5 -4.5 -3.0
head(ranktransform(trees))
#> Girth Height Volume
#> 1 1 6.0 2.5
#> 2 2 3.0 2.5
#> 3 3 1.0 1.0
#> 4 4 8.5 5.0
#> 5 5 25.5 7.0
#> 6 6 28.0 9.0