R中的成对交互矩阵

Question

I am trying to compute a pairwise matrix in R that counts the number of times individuals interact with other individuals (so the matrix will include N number of rows and columns corresponding to number of individuals). I have a dataframe that lists "actors" and "partners" in separate columns.

nn <- data.frame(actors=c('DOL','DOL','DOL','DOL','DOL','NOR','NOR','NOR','NIN','JOJ'),partners=c('JOJ','JOJ','NOR','NOR','NIN','NIN','DOL','JOJ','NOR','NOR'))

The data are such that direction of the interaction is irrelevant, so each cell should count the number of times individual X acts on Y plus the number of times Y acts on X. Ideally, the data frame above should give a matrix that looks like this:

     DOL JOJ NOR NIN
DOL    0   2   3   1
JOJ    2   0   2   0
NOR    3   2   0   2
NIN    1   0   2   0

I started writing a loop to cycle through each individual in my dataset and to count his/her interactions both from actor->partner and partner->actor. I'm sure this would work, but is not ideal as the full dataset is quite large. Is there a better way?

---

Update: Thanks for the responses! Both solutions work great! I'm posting my implementation of Josh's suggestion, which was very helpful.

x <- with(nn, table(actors, partners))
y <- t(x)

# unique individuals
u <- unique(c(rownames(x),colnames(x)))

m <- matrix(0,ncol=length(u),nrow=length(u),dimnames=list(u,u))

i1 <- as.matrix(expand.grid(rownames(x),colnames(x)))
i2 <- as.matrix(expand.grid(rownames(y),colnames(y)))

m[i1] <- x[i1]
m[i2] <- m[i2] + y[i2]

Answer 1

Base R's table() will get you what you're after:

x <- with(nn, table(actors, partners))
x + t(x)
#       partners
# actors DOL JOJ NIN NOR
#    DOL   0   2   1   3
#    JOJ   2   0   0   2
#    NIN   1   0   0   2
#    NOR   3   2   2   0

Answer 2

In the field of graph theory, what you are looking for is an adjacency matrix :

library(igraph)
g <- graph.edgelist(as.matrix(nn), directed = FALSE)
get.adjacency(g)
#     DOL JOJ NOR NIN
# DOL   0   2   3   1
# JOJ   2   0   2   0
# NOR   3   2   0   2
# NIN   1   0   2   0