order

0th

Percentile

Ordering Permutation

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.

Keywords
manip, univar
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 NAs. 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 NAs 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.

Aliases
  • order
  • sort.list
Examples
library(base) 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
Documentation reproduced from package base, version 3.1.3, License: Part of R 3.1.3

Community examples

mark@niemannross.com at Dec 5, 2018 base v3.5.1

[Exercise files for LinkedIn Learning](https://linkedin-learning.pxf.io/rweekly_sortmerge) ```r # Need a dataframe to play with data("ChickWeight") # a built-in dataset # data.frames: order ------------------------------------------------------- ChickWeight$weight # produces unsorted list of "weight" values sort(ChickWeight$weight) # sorts a vector order(ChickWeight$weight) # What is this? answer: sorted row numbers - not values ChickWeight[196,] #lists the observation with the smallest weight ChickWeight[order(ChickWeight$weight),] # comma because [ row - comma - column ] ```

maxinehe93@gmail.com at Oct 24, 2017 base v3.4.1

``` ##Note that order() is different from rank(). rank() will return you rank of the elements while order() returns the ranked element's position in the original list: > a <- c(45,50,10,96) > order(a) [1] 3 1 2 4 > rank(a) [1] 2 3 1 4 ```