library(dplyr)
## 
## Attaching package: 'dplyr'
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(igraph)

Take a toy dataset of members of organizations. Numbers are people; letters are organizations.

test <- data_frame(people = c(1, 2, 3, 4, 5, 1, 2, 3, 3, 4, 5),
                   orgs = c("A", "A", "A", "A", "A", "B", "B", "B", "C", "C", "C"))

Turn that data into a graph:

g <- graph.data.frame(test)

From that graph were can get an adjacency matrix:

adj <- get.adjacency(g, sparse = FALSE)
adj
##   1 2 3 4 5 A B C
## 1 0 0 0 0 0 1 1 0
## 2 0 0 0 0 0 1 1 0
## 3 0 0 0 0 0 1 1 1
## 4 0 0 0 0 0 1 0 1
## 5 0 0 0 0 0 1 0 1
## A 0 0 0 0 0 0 0 0
## B 0 0 0 0 0 0 0 0
## C 0 0 0 0 0 0 0 0

The difficulty with this adjacency matrix is that it includes both people and organizations as both rows and columns. Since this is a bipartite graph, that is not necessary. We should subset the rows so we get only people, and we should subset the columns so we get only organizations.

adj_simple <- adj[rownames(adj) %in% test$people, colnames(adj) %in% test$orgs]
adj_simple
##   A B C
## 1 1 1 0
## 2 1 1 0
## 3 1 1 1
## 4 1 0 1
## 5 1 0 1

That was what we wanted. Note that we can turn this into a data frame if we want.

as.data.frame(adj_simple)
##   A B C
## 1 1 1 0
## 2 1 1 0
## 3 1 1 1
## 4 1 0 1
## 5 1 0 1

This is not necessarily the most elegant solution, but it works.