producing a full adjacency matrix from partial information - r

Question

I have a matrix that contains all the info necessary to construct 5x5 adjacency matrices. Each row represents one matrix:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1 1 1 1 1 1 1 0 1 0
[2,] 0 0 0 1 1 1 1 0 1 0
...
I want to create an adjacency matrix from the nth row of data. For the first row of have, the want matrix would look like this:
[,1] [,2] [,3] [,4] [,5]
[1,] 0 1 1 1 1
[2,] 1 0 1 1 1
[3,] 1 1 0 0 1
[4,] 1 1 0 0 0
[5,] 1 1 1 0 0
How do I get from have to want?

Answer 1

Here is an option using lower.tri and upper.tri
unlist(apply(mat, 1, function(x) {
m <- matrix(0, nrow = 5, ncol = 5)
m[lower.tri(m)] <- x
m[upper.tri(m)] <- x
list(m)
}), recursive = F)
#[[1]]
# [,1] [,2] [,3] [,4] [,5]
#[1,] 0 1 1 1 1
#[2,] 1 0 1 1 0
#[3,] 1 1 0 1 1
#[4,] 1 1 0 0 0
#[5,] 1 1 1 0 0
#
#[[2]]
# [,1] [,2] [,3] [,4] [,5]
#[1,] 0 0 0 1 1
#[2,] 0 0 0 1 0
#[3,] 0 1 0 1 1
#[4,] 0 1 0 0 0
#[5,] 1 1 1 0 0
The unlist(..., recursive = F) part seems somewhat awkward but is necessary to prevent apply from simplifying the result and dropping dims. An alternative would be to use lapply on a data.frame instead of a matrix:
lapply(as.data.frame(t(mat)), function(x) {
m <- matrix(0, nrow = 5, ncol = 5)
m[lower.tri(m)] <- x
m[upper.tri(m)] <- x
return(m)
})
giving the same result.
Sample data
mat <- as.matrix(read.table(text =
"1 1 1 1 1 1 1 0 1 0
0 0 0 1 1 1 1 0 1 0", header = F))
colnames(mat) <- NULL