`order`

returns a permutation which rearranges its first
argument into ascending or descending order, breaking ties by further
arguments. `sort.list`

is the same, using only one argument.
See the examples for how to use these functions to sort data frames,
etc.

```
order(…, na.last = TRUE, decreasing = FALSE,
method = c("auto", "shell", "radix"))
```sort.list(x, partial = NULL, na.last = TRUE, decreasing = FALSE,
method = c("auto", "shell", "quick", "radix"))

…

a sequence of numeric, complex, character or logical vectors, all of the same length, or a classed R object.

x

an atomic vector.

partial

vector of indices for partial sorting.
(Non-`NULL`

values are not implemented.)

decreasing

logical. Should the sort order be increasing or
decreasing? For the `"radix"`

method, this can be a vector of
length equal to the number of arguments in `…`

. For the
other methods, it must be length one.

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
(see ‘Note’.)

method

the method to be used: partial matches are allowed. The
default (`"auto"`

) implies `"radix"`

for short numeric vectors,
integer vectors, logical vectors and factors.
Otherwise, it implies `"shell"`

.
For details of methods `"shell"`

,
`"quick"`

, and `"radix"`

,
see the help for `sort`

.

An integer vector unless any of the inputs has \(2^{31}\) or more elements, when it is a double vector.

In programmatic use it is unsafe to name the `…`

arguments,
as the names could match current or future control
arguments such as `decreasing`

. A sometimes-encountered unsafe
practice is to call `do.call('order', df_obj)`

where
`df_obj`

might be a data frame: copy `df_obj`

and
remove any names, for example using `unname`

.

In the case of ties in the first vector, values in the second are used
to break the ties. If the values are still tied, values in the later
arguments are used to break the tie (see the first example).
The sort used is *stable* (except for `method = "quick"`

),
so any unresolved ties will be left in their original ordering.

Complex values are sorted first by the real part, then the imaginary part.

Except for method `"radix"`

, the sort order for character vectors
will depend on the collating sequence of the locale in use: see
`Comparison`

.

The `"shell"`

method is generally the safest bet and is the
default method, except for short factors, numeric vectors,
integer vectors and logical vectors, where `"radix"`

is assumed.
Method `"radix"`

stably sorts logical,
numeric and character vectors in linear time. It outperforms the other
methods, although there are caveats (see `sort`

). Method
`"quick"`

for `sort.list`

is only supported for numeric
`x`

with `na.last = NA`

, is not stable, and is slower than
`"radix"`

.

`partial = NULL`

is supported for compatibility with other
implementations of S, but no other values are accepted and ordering is
always complete.

For a classed R object, the sort order is taken from
`xtfrm`

: as its help page notes, this can be slow unless a
suitable method has been defined or `is.numeric(x)`

is
true. For factors, this sorts on the internal codes, which is
particularly appropriate for ordered factors.

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

Knuth, D. E. (1998)
*The Art of Computer Programming, Volume 3: Sorting and
Searching.* 2nd ed. Addison-Wesley.

# NOT RUN { require(stats) (ii <- order(x <- c(1,1,3:1,1:4,3), y <- c(9,9:1), z <- c(2,1:9))) ## 6 5 2 1 7 4 10 8 3 9 rbind(x, y, z)[,ii] # shows the reordering (ties via 2nd & 3rd arg) ## Suppose we wanted descending order on y. ## A simple solution for numeric 'y' is rbind(x, y, z)[, order(x, -y, z)] ## More generally we can make use of xtfrm cy <- as.character(y) rbind(x, y, z)[, order(x, -xtfrm(cy), z)] ## The radix sort supports multiple 'decreasing' values: rbind(x, y, z)[, order(x, cy, z, decreasing = c(FALSE, TRUE, FALSE), method="radix")] ## Sorting data frames: dd <- transform(data.frame(x, y, z), z = factor(z, labels = LETTERS[9:1])) ## Either as above {for factor 'z' : using internal coding}: dd[ order(x, -y, z), ] ## or along 1st column, ties along 2nd, ... *arbitrary* no.{columns}: dd[ do.call(order, dd), ] set.seed(1) # reproducible example: d4 <- data.frame(x = round( rnorm(100)), y = round(10*runif(100)), z = round( 8*rnorm(100)), u = round(50*runif(100))) (d4s <- d4[ do.call(order, d4), ]) (i <- which(diff(d4s[, 3]) == 0)) # in 2 places, needed 3 cols to break ties: d4s[ rbind(i, i+1), ] ## rearrange matched vectors so that the first is in ascending order x <- c(5:1, 6:8, 12:9) y <- (x - 5)^2 o <- order(x) rbind(x[o], y[o]) ## tests of na.last a <- c(4, 3, 2, NA, 1) b <- c(4, NA, 2, 7, 1) z <- cbind(a, b) (o <- order(a, b)); z[o, ] (o <- order(a, b, na.last = FALSE)); z[o, ] (o <- order(a, b, na.last = NA)); z[o, ] # } # NOT RUN { ## speed examples on an average laptop for long vectors: ## factor/small-valued integers: x <- factor(sample(letters, 1e7, replace = TRUE)) system.time(o <- sort.list(x, method = "quick", na.last = NA)) # 0.1 sec stopifnot(!is.unsorted(x[o])) system.time(o <- sort.list(x, method = "radix")) # 0.05 sec, 2X faster stopifnot(!is.unsorted(x[o])) ## large-valued integers: xx <- sample(1:200000, 1e7, replace = TRUE) system.time(o <- sort.list(xx, method = "quick", na.last = NA)) # 0.3 sec system.time(o <- sort.list(xx, method = "radix")) # 0.2 sec ## character vectors: xx <- sample(state.name, 1e6, replace = TRUE) system.time(o <- sort.list(xx, method = "shell")) # 2 sec system.time(o <- sort.list(xx, method = "radix")) # 0.007 sec, 300X faster ## double vectors: xx <- rnorm(1e6) system.time(o <- sort.list(xx, method = "shell")) # 0.4 sec system.time(o <- sort.list(xx, method = "quick", na.last = NA)) # 0.1 sec system.time(o <- sort.list(xx, method = "radix")) # 0.05 sec, 2X faster # }