order {base}  R Documentation 
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) sort.list(x, partial = NULL, na.last = TRUE, decreasing = FALSE, method = c("shell", "quick", "radix"))
... 
a sequence of numeric, complex, character or logical vectors, all of the same length. 
x 
a vector. 
partial 
vector of indices for partial sorting. 
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.

method 
the method to be used: partial matches are allowed. 
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.
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 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.
partial
is supplied for compatibility with other
implementations of S, but no other values are accepted and ordering is
always complete.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
(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 is rbind(x,y,z)[, order(x, y, z)] ## For character vectors we can make use of rank: cy < as.character(y) rbind(x,y,z)[, order(x, rank(y), 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, ] ## Not run: ## speed examples for long vectors: timings are immediately after gc() x < factor(sample(letters, 1e6, replace=TRUE)) system.time(o < sort.list(x)) ## 4 secs stopifnot(!is.unsorted(x[o])) system.time(o < sort.list(x, method="quick", na.last=NA)) # 0.4 sec stopifnot(!is.unsorted(x[o])) system.time(o < sort.list(x, method="radix")) # 0.04 sec stopifnot(!is.unsorted(x[o])) xx < sample(1:26, 1e7, replace=TRUE) system.time(o < sort.list(xx, method="radix")) # 0.4 sec xx < sample(1:100000, 1e7, replace=TRUE) system.time(o < sort.list(xx, method="radix")) # 4 sec ## End(Not run)