R: Ordering Permutation

order {base} | R Documentation

## Ordering Permutation

### Description

`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.

### Usage

    order(..., na.last = TRUE, decreasing = FALSE)

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

### Arguments

`...` | 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?
`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 is `"shell"` except for some special cases: see ‘Details’.

### Details

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.

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

The default method for `sort.list` is a good compromise. Method `"quick"` is only supported for numeric `x` with `na.last = NA`, and is not stable, but will be substantially faster for long vectors. Method `"radix"` is only implemented for integer `x` with a range of less than 100,000. For such `x` it is very fast (and stable), and hence is ideal for sorting factors—as from R 3.0.0 it is the default method for factors with less than 100,000 levels. (This is also known as counting sorting.)

`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.

### Value

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

### Note

`sort.list` can get called by mistake as a method for `sort` with a list argument, and gives a suitable error message for list `x`.

There is a historical difference in behaviour for `na.last = NA`: `sort.list` removes the `NA`s and then computes the order amongst the remaining elements: `order` computes the order amongst the non-`NA` elements of the original vector. Thus

       x[order(x, na.last = NA)]
       zz <- x[!is.na(x)]; zz[sort.list(x, na.last = NA)]

both sort the non-`NA` values of `x`. Prior to R 3.1.0 `method = "radix"` was only supported for non-negative integers.

### References

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

### See Also

`sort`, `rank`, `xtfrm`.

### Examples

    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)]

    ## 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, ]

    ##  speed examples for long vectors:
    x <- factor(sample(letters, 1e6, replace = TRUE))
    system.time(o <- sort.list(x)) ## 0.4 secs
    stopifnot(!is.unsorted(x[o]))
    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")) # 0.01 sec
    stopifnot(!is.unsorted(x[o]))
    xx <- sample(1:26, 1e7, replace = TRUE)
    system.time(o <- sort.list(xx, method = "radix")) # 0.1 sec
    xx <- sample(1:100000, 1e7, replace = TRUE)
    system.time(o <- sort.list(xx, method = "radix")) # 0.5 sec
    system.time(o <- sort.list(xx, method = "quick", na.last = NA)) # 1.3 sec

[Package base version 3.1.2 Index]