Rank.RdThe function base::rank has various weaknesses. Apart from the fact that it is not very fast, the option to calculate dense ranks is not implemented. Then, an argument for specifying the ranking direction is missing (assuming that this can be done with the ranking of the negative variables) and finally, multiple columns cannot be used in the case of ties for further ranking.
The function data.table::frankv provides a more elaborated interface and convinces by very performant calculations and is much faster than the original.
It further accepts vectors, lists, data.frames or data.tables as input. In addition to the ties.method possibilities provided by base::rank, it also provides ties.method="dense".
The present function Rank is merely a somewhat customized parameterization of the data.table function.
Rank(..., decreasing = FALSE, na.last = TRUE,
ties.method = c("average", "first", "last", "random",
"max", "min", "dense"))A vector, or list with all its elements identical in length or data.frame or data.table.
An logical vector corresponding to ascending and descending order. decreasing is recycled to length(...).
Control treatment of NAs. If TRUE, missing values in the data are put last; if FALSE, they are put first; if NA, they are removed; if "keep" they are kept with rank NA.
A character string specifying how ties are treated, see Details.
To be consistent with other data.table operations, NAs are considered identical to other NAs (and NaNs to other NaNs), unlike base::rank. Therefore, for na.last=TRUE and na.last=FALSE, NAs (and NaNs) are given identical ranks, unlike rank.
Rank is not limited to vectors. It accepts data.tables (and lists and data.frames) as well. It accepts unquoted column names (with names preceded with a - sign for descending order, even on character vectors), for e.g., Rank(DT, a, -b, c, ties.method="first") where a,b,c are columns in DT.
In addition to the ties.method values possible using base's rank, it also provides another additional argument "dense".
Dense ranks are consecutive integers beginning with 1. No ranks are skipped if there are ranks with multiple items. So the largest rank value is the number of unique values of x. See examples.
Like forder, sorting is done in "C-locale"; in particular, this may affect how capital/lowercase letters are ranked. See Details on forder for more.
bit64::integer64 type is also supported.
A numeric vector of length equal to NROW(x) (unless na.last = NA, when missing values are removed). The vector is of integer type unless ties.method = "average" when it is of double type (irrespective of ties).
# on vectors
x <- c(4, 1, 4, NA, 1, NA, 4)
# NAs are considered identical (unlike base R)
# default is average
Rank(x) # na.last=TRUE
#> [1] 4.0 1.5 4.0 6.5 1.5 6.5 4.0
Rank(x, na.last=FALSE)
#> [1] 6.0 3.5 6.0 1.5 3.5 1.5 6.0
# ties.method = min
Rank(x, ties.method="min")
#> [1] 3 1 3 6 1 6 3
# ties.method = dense
Rank(x, ties.method="dense")
#> [1] 2 1 2 3 1 3 2
# on data.frame, using both columns
d.set <- data.frame(x, y=c(1, 1, 1, 0, NA, 0, 2))
Rank(d.set, na.last="keep")
#> [1] 2.5 1.0 2.5 NA NA NA 4.0
Rank(d.set, ties.method="dense", na.last=NA)
#> [1] 2 1 2 3
# decreasing argument
Rank(d.set, decreasing=c(FALSE, TRUE), ties.method="first")
#> [1] 4 1 5 6 2 7 3