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.
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.tables as input. In addition to the
ties.method possibilities provided by
base::rank, it also provides
The present function
Rank is merely a somewhat customized parameterization of the
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A vector, or list with all its elements identical in length or
Control treatment of
A character string specifying how ties are treated, see
To be consistent with other
NAs are considered identical to other
NaNs to other
base::rank. Therefore, for
NaNs) are given identical ranks, unlike
Rank is not limited to vectors. It accepts
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
In addition to the
ties.method values possible using base's
rank, it also provides another additional argument
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.
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
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).
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# 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 Rank(x, na.last=FALSE) # ties.method = min Rank(x, ties.method="min") # ties.method = dense Rank(x, ties.method="dense") # 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") Rank(d.set, ties.method="dense", na.last=NA) # decreasing argument Rank(d.set, decreasing=c(FALSE, TRUE), ties.method="first")
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