order Ordering Permutation
Description
order returns a permutation which rearranges its first argument into ascending or descending order, breaking ties by further arguments. sort.list does 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,
method = c("auto", "shell", "radix"))
sort.list(x, partial = NULL, na.last = TRUE, decreasing = FALSE,
method = c("auto", "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 for |
partial | vector of indices for partial sorting. (Non- |
decreasing | logical. Should the sort order be increasing or decreasing? For the |
na.last | for controlling the treatment of |
method | the method to be used: partial matches are allowed. The default ( |
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.
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.
Value
An integer vector unless any of the inputs has 2^31 or more elements, when it is a double vector.
Warning
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.
Note
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.
References
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
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)]
## 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, ]
## 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
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Licensed under the GNU General Public License.