Load the data
# read in the edge list from our github
el <- read.table("https://raw.githubusercontent.com/mahoffman/stanford_networks/main/data/comm59.dat.txt", header = T)
# Read in attributes from our github
attributes <- read.table("https://raw.githubusercontent.com/mahoffman/stanford_networks/main/data/comm59_att.dat.txt", header = T)
# add an ID column
attributes$ID <- 1:nrow(attributes)
# Indexing data so that you only put in certain columns
el_no_weight <- el[,1:2] # We will ignore the ranking variable for now.
colnames(el_no_weight) <- c("from", "to")
el_no_weight <- as.matrix(el_no_weight) # igraph requires a matrix
# convert ids to characters so they are preserved as names
el_no_weight <- apply(el_no_weight,2,as.character)
# Graph the network
net59 <- graph.edgelist(el_no_weight, directed = T)
# Finally, add attributes
# First link vertex names to their place in the attribute dataset
linked_ids <- match(V(net59)$name, attributes$ID)
# Then we can use that to assign a variable to each user in the network
V(net59)$race <- attributes$race[linked_ids]
V(net59)$sex <- attributes$sex[linked_ids]
V(net59)$grade <- attributes$grade[linked_ids]
V(net59)$school <- attributes$school[linked_ids]
net59
## IGRAPH 79f70cc DN-- 975 4160 --
## + attr: name (v/c), race (v/n), sex (v/n), grade (v/n), school (v/n)
## + edges from 79f70cc (vertex names):
## [1] 1 ->191 1 ->245 1 ->272 1 ->413 1 ->447 3 ->21 3 ->221 3 ->480 3 ->495
## [10] 3 ->574 5 ->96 5 ->258 5 ->335 6 ->271 6 ->374 6 ->400 6 ->489 6 ->491
## [19] 6 ->573 6 ->586 7 ->134 7 ->159 7 ->464 7 ->478 8 ->221 8 ->284 8 ->378
## [28] 8 ->557 9 ->137 9 ->442 9 ->473 9 ->498 10->20 10->22 10->64 10->75
## [37] 10->89 10->219 10->272 10->276 11->101 11->155 11->190 11->273 11->337
## [46] 11->339 11->353 11->616 12->188 12->475 14->20 14->151 14->597 15->106
## [55] 15->189 15->233 15->325 15->333 15->449 15->491 15->552 15->624 15->627
## [64] 16->30 16->201 16->217 16->267 16->268 16->466 16->569 17->625 19->45
## + ... omitted several edges