producing a full adjacency matrix from partial information

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