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"))NULL values are not implemented.)NAs.
If TRUE, missing values in the data are put last; if
FALSE, they are put first; if NA, they are removed
(see ‘Note’.)"shell" except for some special cases: see
‘Details’.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.
sort, rank, xtfrm.
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, ]
## Not run:
# ## 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
# ## End(Not run)Run the code above in your browser using DataLab