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(Signed) rank transformation

Source: R/ranktransform.R
ranktransform.Rd

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 numeric
ranktransform(x, sign = FALSE, method = "average", verbose = TRUE, ...)

# S3 method for data.frame
ranktransform(
  x,
  select = NULL,
  exclude = NULL,
  sign = FALSE,
  method = "average",
  ignore_case = FALSE,
  regex = FALSE,
  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". See rank() for details.

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 rank-transformed 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.

See also

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

Examples

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

# Won't work
# ranktransform(c(0, 1, 5, -5, -2), sign = TRUE)

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