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.

manip, univar
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.


an atomic vector.


vector of indices for partial sorting. (Non-NULL values are not implemented.)


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.


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’.)


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.


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.


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


sort.list can get called by mistake as a method for sort with a list argument: it 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 3.3.0 method = "radix" was only supported for integers of range less than 100,000.


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.


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.

See Also

sort, rank, xtfrm.

  • order
  • sort.list
library(base) # 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 # }
Documentation reproduced from package base, version 3.6.2, License: Part of R 3.6.2

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 ```