order                  package:base                  R Documentation

_O_r_d_e_r_i_n_g _P_e_r_m_u_t_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     '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.

_U_s_a_g_e:

     order(..., na.last = TRUE, decreasing = FALSE)

     sort.list(x, partial = NULL, na.last = TRUE, decreasing = FALSE,
               method = c("shell", "quick", "radix"))

_A_r_g_u_m_e_n_t_s:

     ...: a sequence of numeric, complex, character or logical vectors,
          all of the same length.

       x: a 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 '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.

_D_e_t_a_i_l_s:

     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 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.

     Note that these functions are only defined for vectors, so any
     class of the object supplied is ignored: this means factors are
     sorted on their internal codes and not their printed
     representation.

_N_o_t_e:

     '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'.

_R_e_f_e_r_e_n_c_e_s:

     Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) _The New S
     Language_. Wadsworth & Brooks/Cole.

_S_e_e _A_l_s_o:

     'sort', 'rank', 'xtfrm'.

_E_x_a_m_p_l_e_s:

     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)) ## 1.2 secs
     stopifnot(!is.unsorted(x[o]))
     system.time(o <- sort.list(x, method="quick", na.last=NA)) # 0.15 sec
     stopifnot(!is.unsorted(x[o]))
     system.time(o <- sort.list(x, method="radix")) # 0.02 sec
     stopifnot(!is.unsorted(x[o]))
     xx <- sample(1:26, 1e7, replace=TRUE)
     system.time(o <- sort.list(xx, method="radix")) # 0.2 sec
     xx <- sample(1:100000, 1e7, replace=TRUE)
     system.time(o <- sort.list(xx, method="radix")) # 0.8 sec
     system.time(o <- sort.list(xx, method="quick", na.last=NA)) # 1.4 sec
     ## End(Not run)

