# rank

##### Sample Ranks

Returns the sample ranks of the values in a vector. Ties (i.e., equal values) and missing values can be handled in several ways.

- Keywords
- univar

##### Usage

```
rank(x, na.last = TRUE,
ties.method = c("average", "first", "last", "random", "max", "min"))
```

##### Arguments

- x
a numeric, complex, character or logical vector.

- na.last
for controlling the treatment of

`NA`

s. 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`

.- ties.method
a character string specifying how ties are treated, see ‘Details’; can be abbreviated.

##### Details

If all components are different (and no `NA`

s), the ranks are
well defined, with values in `seq_along(x)`

. With some values equal
(called ‘ties’), the argument `ties.method`

determines the
result at the corresponding indices. The `"first"`

method results
in a permutation with increasing values at each index set of ties, and
analogously `"last"`

with decreasing values. The
`"random"`

method puts these in random order whereas the
default, `"average"`

, replaces them by their mean, and
`"max"`

and `"min"`

replaces them by their maximum and
minimum respectively, the latter being the typical sports
ranking.

`NA`

values are never considered to be equal: for ```
na.last =
TRUE
```

and `na.last = FALSE`

they are given distinct ranks in
the order in which they occur in `x`

.

**NB**: `rank`

is not itself generic but `xtfrm`

is, and `rank(xtfrm(x), ....)`

will have the desired result if
there is a `xtfrm`

method. Otherwise, `rank`

will make use
of `==`

, `>`

, `is.na`

and extraction methods for
classed objects, possibly rather slowly.

##### Value

A numeric vector of the same length as `x`

with names copied from
`x`

(unless `na.last = NA`

, when missing values are
removed). The vector is of integer type unless `x`

is a long
vector or `ties.method = "average"`

when it is of double type
(whether or not there are any ties).

##### References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
*The New S Language*.
Wadsworth & Brooks/Cole.

##### See Also

##### Examples

`library(base)`

```
# NOT RUN {
(r1 <- rank(x1 <- c(3, 1, 4, 15, 92)))
x2 <- c(3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5)
names(x2) <- letters[1:11]
(r2 <- rank(x2)) # ties are averaged
## rank() is "idempotent": rank(rank(x)) == rank(x) :
stopifnot(rank(r1) == r1, rank(r2) == r2)
## ranks without averaging
rank(x2, ties.method= "first") # first occurrence wins
rank(x2, ties.method= "last") # last occurrence wins
rank(x2, ties.method= "random") # ties broken at random
rank(x2, ties.method= "random") # and again
## keep ties ties, no average
(rma <- rank(x2, ties.method= "max")) # as used classically
(rmi <- rank(x2, ties.method= "min")) # as in Sports
stopifnot(rma + rmi == round(r2 + r2))
## Comparing all tie.methods:
tMeth <- eval(formals(rank)$ties.method)
rx2 <- sapply(tMeth, function(M) rank(x2, ties.method=M))
cbind(x2, rx2)
## ties.method's does not matter w/o ties:
x <- sample(47)
rx <- sapply(tMeth, function(MM) rank(x, ties.method=MM))
stopifnot(all(rx[,1] == rx))
# }
```

*Documentation reproduced from package base, version 3.5.3, License: Part of R 3.5.3*