R: how to sample without replacement AND without consecutive same values

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I have spent over a day trying to accomplish what seems to be a very simple thing. I have to create 300 'random' sequences in which the numbers 1,2,3 and 4 all appear exactly 12 times, but the same number is never used twice 'in a row'/consecutively.

My best attempts (I guess) were:

  1. have R sample 48 items without replacement, test whether there are consecutive values with rle, then use only the sequences that do not contain consecutive values. Problem: there are almost no random sequences that meet this criterion, so it takes forever.

  2. have R create sequences without consecutive values (see code).

pop<-rep(1:4,12)
y=c()
while(length(y)!=48)
  {
  y= c(y,sample(pop,48-length(y),replace=F))
  y=y[!c(FALSE, diff(y) == 0)]
  }

Problem: this creates sequences with varying numbers of each value. I then tried to use only those sequences with exactly 12 of each value, but that only brought me back to problem 1: takes forever.

There must be some easy way to do this, right? Any help is greatly appreciated!

sample r
3 Answers

Maybe using replicate() with a repeat loop is faster. here an example with 3 sequences. Looks like this would take approx. 1490 seconds with 300 (not tested). 

set.seed(42)
seqc <- rep(1:4, each=12)  # starting sequence

system.time(
  res <- replicate(3, {
    repeat {
      seqcs <- sample(seqc, 48, replace=FALSE) 
      if (!any(diff(seqcs) == 0)) break
    }
    seqcs
  })
)
#  user  system elapsed 
# 14.88    0.00   14.90 

res[1:10, ]
#       [,1] [,2] [,3]
#  [1,]    4    2    3
#  [2,]    1    1    4
#  [3,]    3    2    1
#  [4,]    1    1    4
#  [5,]    2    3    1
#  [6,]    4    1    2
#  [7,]    3    4    4
#  [8,]    2    1    1
#  [9,]    3    4    4
# [10,]    4    3    2
user_65740380

You can take out consecutive values and placing them where they are not consecutive.

unConsecutive  <- function(x) {
    repeat{
        tt <- c(FALSE, diff(x)==0)
        if(any(tt)) {
            y <- x[which(tt)]
            x <- x[which(!tt)]
            i <- x != y[1]
            i  <- which(c(c(TRUE, diff(i)==0) & i,FALSE)
                        | c(FALSE, c(diff(i)==0, TRUE) & i))
            if(length(i) > 0) {
                i <- i[1]-1
                x <- c(x[seq_len(i)], y, x[i+seq_len(length(x)-i)])
            } else {
                x  <- c(x, y)
                break
            }
        } else {break}
    }
    x
}

unConsecutive(c(1,1,2))
#[1] 1 2 1
unConsecutive(c(1,1,1))
#[1] 1 1 1

set.seed(7)
system.time(
    res <- replicate(300, unConsecutive(sample(rep(1:4,12))))
)
#   user  system elapsed 
#  0.058   0.011   0.069 
all(apply(res, 2, table) == 12)
#[1] TRUE
all(apply(res, 2, diff) != 0)
#[1] TRUE
user_104885040

Another option is to use a Markov Chain Monte-Carlo method to swap 2 numbers randomly and move to the new sample only when 1) we are not swapping the same number and 2) no 2 identical numbers are adjacent. To address correlated samples, we can generate a lot of samples and then randomly select 300 of them:

v <- rep(1:4, 12)
l <- 48
nr <- 3e5
m <- matrix(0, nrow=nr, ncol=l)
count <- 0
while(count < nr) {
    i <- sample(l, 2)
    if (i[1L] != i[2L]) {
        v[i] = v[i[2:1]]
        if (!any(diff(v)==0)) {
            count <- count + 1
            m[count, ] <- v
        } else {
            v[i] = v[i[2:1]]
        }
    }
}
a <- m[sample(nr, 300),]
a
user_19894800
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