Sequentially re-ordering sections of a vector around NA values - r

Question

I have a large set of data that I want to reorder in groups of twelve using the sample() function in R to generate randomised data sets with which I can carry out a permutation test. However, this data has NA characters where data could not be collected and I would like them to stay in their respective original positions when the data is shuffled.
With help on a previous question I have managed to shuffle the data around the NA values for a single vector of 24 values with the code:
example.data <- c(0.33, 0.12, NA, 0.25, 0.47, 0.83, 0.90, 0.64, NA, NA, 1.00, 0.42)
example.data[!is.na(example.data)] <- sample(example.data[!is.na(example.data)], replace = F, prob = NULL)
[1] 0.64 0.83 NA 0.33 0.47 0.90 0.25 0.12 NA NA 0.42 1.00
Extending from this, if I have a set of data with a length of 24 how would I go about re-ordering the first and second set of 12 values as individual cases in a loop?
For example, a vector extending from the first example:
example.data <- c(0.33, 0.12, NA, 0.25, 0.47, 0.83, 0.90, 0.64, NA, NA, 1.00, 0.42, 0.73, NA, 0.56, 0.12, 1.0, 0.47, NA, 0.62, NA, 0.98, NA, 0.05)
Where example.data[1:12] and example.data[13:24] are shuffled separately within their own respective groups around their NA values.
The code I am trying to work this solution into is as follows:
shuffle.data = function(input.data,nr,ns){
simdata <- input.data
for(i in 1:nr){
start.row <- (ns*(i-1))+1
end.row <- start.row + actual.length[i] - 1
newdata = sample(input.data[start.row:end.row], size=actual.length[i], replace=F)
simdata[start.row:end.row] <- newdata
}
return(simdata)}
Where input.data is the raw input data (example.data); nr is the number of groups (2), ns is the size of each sample (12); and actual.length is the length of each group exluding NAs stored in a vector (actual.length <- c(9, 8) for the example above).
Would anyone know how to go about achieving this?
Thank you again for your help!

Answer 1

I agree with Gregor's comment that it may be a better approach to work with the data in another form. However, what you need to accomplish can still be done easily enough even if all the data is in one vector.
First make a function that shuffles only non-NA values of an entire vector:
shuffle_real <- function(data){
# Sample from only the non-NA values,
# and store the result only in indices of non-NA values
data[!is.na(data)] <- sample(data[!is.na(data)])
# Then return the shuffled data
return(data)
}
Now write a function that takes in a larger vector, and applies this function to each group in the vector:
shuffle_groups <- function(data, groupsize){
# It will be convenient to store the length of the data vector
N <- length(data)
# Do a sanity check to make sure there's a match between N and groupsize
if ( N %% groupsize != 0 ) {
stop('The length of the data is not a multiple of the group size.',
call.=FALSE)
}
# Get the index of every first element of a new group
starts <- seq(from=1, to=N, by=groupsize)
# and for every segment of the data of group 'groupsize',
# apply shuffle_real to it;
# note the use of c() -- otherwise a matrix would be returned,
# where each column is one group of length 'groupsize'
# (which I note because that may be more convenient)
return(c(sapply(starts, function(x) shuffle_real(data[x:(x+groupsize-1)]))))
}
For example,
example.data <- c(0.33, 0.12, NA, 0.25, 0.47, 0.83, 0.90, 0.64, NA, NA, 1.00,
0.42, 0.73, NA, 0.56, 0.12, 1.0, 0.47, NA, 0.62, NA, 0.98,
NA, 0.05)
set.seed(1234)
shuffle_groups(example.data, 12)
which results in
> shuffle_groups(example.data, 12)
[1] 0.12 0.83 NA 1.00 0.47 0.64 0.25 0.33 NA NA 0.90 0.42 0.47 NA
[15] 0.05 1.00 0.56 0.62 NA 0.73 NA 0.98 NA 0.12
or try shuffle_groups(example.data[1:23], 12), which results in Error: The length of the data is not a multiple of the group size.