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Fast Ranking with Extended Tie Handling

Source: R/rankX.R
rankX.Rd

Computes ranks for vectors or multiple inputs using a fast implementation based on data.table::frankv. Supports additional tie-handling methods such as "dense" and multi-column ranking via ....

Usage

rankX(
  ...,
  decreasing = FALSE,
  na.last = TRUE,
  ties.method = c("average", "first", "last", "random", "max", "min", "dense")
)

Arguments

...

one or more vectors to be ranked. If multiple vectors are provided, they are ranked lexicographically (like order). All inputs must have the same length.

decreasing

logical; if TRUE, larger values receive smaller ranks (i.e., ranking in descending order). When ranking multiple inputs, a logical vector may be given to control the direction per input.

na.last

logical or "keep"; determines the placement of NA values. Passed to data.table::frankv.

ties.method

character string specifying how ties are handled. One of:

"average"

average of the ranks for tied values (default).

"first"

ranks assigned in order of appearance.

"last"

ranks assigned in reverse order of appearance.

"random"

ranks assigned at random.

"max"

maximum rank for tied values.

"min"

minimum rank for tied values.

"dense"

like "min", but ranks are consecutive integers without gaps.

Value

an integer or numeric vector of ranks with the same length as the input.

Details

This function is a fast alternative to rank(), powered by data.table::frankv. It extends base functionality by:

  • Supporting dense ranking (ties.method = "dense")

  • Allowing multiple input vectors for lexicographic ranking

  • Providing improved performance for large datasets

When multiple inputs are supplied, ranking is performed jointly, similar to:


order(x1, x2, ...)

See also

rank(), data.table::frankv()

Other math.transform: linScale(), logit(), percentRank(), winsorize()

Examples

x <- c(10, 20, 20, 30)

# Basic ranking
rankX(x)
#> [1] 1.0 2.5 2.5 4.0

# Dense ranking
rankX(x, ties.method = "dense")
#> [1] 1 2 2 3

# Descending order
rankX(x, decreasing = TRUE)
#> [1] 4.0 2.5 2.5 1.0

# Handling NA values
x2 <- c(3, NA, 1, 2)
rankX(x2, na.last = "keep")
#> [1]  3 NA  1  2

# Multi-column ranking
a <- c(1, 1, 2, 2)
b <- c(2, 1, 2, 1)
rankX(a, b)
#> [1] 2 1 4 3