Percent Rank of a Numeric Vector

Computes the percent rank of each element in a numeric vector. The percent rank is defined as: $$(rank(x) - 1) / (n - 1)$$ where \(n\) is the number of non-missing observations.

Usage

percentRank(x)

Arguments

x

a numeric (or comparable) vector.

Value

a numeric vector of the same length as x, containing values between 0 and 1.

Details

This corresponds to the definition used in SQL and dplyr::percent_rank().

The smallest value in x receives a percent rank of 0, and the largest value receives a percent rank of 1 (if there are at least two non-missing values).

Ties are handled using ties.method = "min" via rankX(), meaning tied values receive the same minimal rank.

Missing values (NA) are preserved in the output.

If x contains fewer than two non-missing values, all results are NA.

See also

rank(), rankX()

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

Examples

x <- c(10, 20, 20, 30)
percentRank(x)
#> [1] 0.0000000 0.3333333 0.3333333 1.0000000

# With ties
x <- c(1, 2, 2, 3)
percentRank(x)
#> [1] 0.0000000 0.3333333 0.3333333 1.0000000

# With missing values
x <- c(3, NA, 1, 2)
percentRank(x)
#> [1] 1.0  NA 0.0 0.5

# Single non-missing value
percentRank(c(5, NA, NA))
#> [1] NA NA NA