EIES_data.R

# For description, see: https://www.stats.ox.ac.uk/~snijders/siena/EIES_data.htm
    # The Electronic Information Exchange System (EIES) data were collected by Freeman and Freeman (1980). This data set is discussed also in Wasserman and Faust (1994:62-63). This is a network of 32 researchers who participated in an early study on the effects of electronic information exchange, a precursor of email communication.

# References:
# Freeman, L.C., and Freeman, S.C., A semi-visible college: Structural effects on a social networks group. Pp. 77-85 in Henderson, M.M., and McNaughton, M.J. (eds.) _Electronic Communication: Technology and Impacts Boulder_, CO: Westview Press, 1980.
# Wasserman, Stanley, and Katherine Faust. _Social Network Analysis: Methods and Applications._ New York and Cambridge: Cambridge University Press, 1994.

# To load this dataset, enter the following into R:
#    source("http://pastebin.com/raw.php?i=thqLfnY4")

library(igraph)
library(utils)

firstdir <- getwd()
newdir <- paste(firstdir, "EIES_data", sep = "/")
download.file("https://www.stats.ox.ac.uk/~snijders/siena/EIES_data.zip", "EIES_data.zip", method = "wget")
unzip("EIES_data.zip", exdir = newdir)
setwd(newdir)

eiesatts <- read.table("eies.dat", as.is = TRUE, stringsAsFactors = FALSE, strip.white = TRUE, skip = 3, quote = "", nrows = 32)
colnames(eiesatts) <- c("Researcher", "Original_Code", "Citations", "Discipline")
eiesatts$Discipline_Name <- c("Sociology", "Anthropology", "Statistics", "Psychology")[eiesatts$Discipline]
eiesatts$Discipline_Name[3] <- "Communication"
eiesatts$Discipline_Name[27] <- "Mathematics"

friend1 <- read.table("EIES.1", sep = " ", as.is = TRUE, strip.white = TRUE)
friend2 <- read.table("EIES.2", sep = " ", as.is = TRUE, strip.white = TRUE)
eiesmessages <- read.table("messages.asc", nrows = 32, strip.white = TRUE)
eies <- list(t1 = friend1, t2 = friend2, messages = eiesmessages)
rm(friend1, friend2, eiesmessages)
eies <- lapply(eies, as.matrix)
eies$t1 <- graph.adjacency(eies$t1, mode = "directed", weighted = TRUE, diag = FALSE)

# The acquaintance description at Snijders's page is incorrect, as it omits tie strength of 0 and "close friend"
# A code of 0 indicates "do not know" (I can't imagine what would be less) and 4 indicates "close friend."
E(eies$t1)$etype <- c("Do not know", "Heard of", "Met", "Friend", "Close Friend")[E(eies$t1)$weight + 1]
eies$t2 <- graph.adjacency(eies$t2, mode = "directed", weighted = TRUE, diag = FALSE)
E(eies$t2)$etype <- c("Do not know", "Heard of", "Met", "Friend", "Close Friend")[E(eies$t2)$weight + 1]

eies$messages <- graph.adjacency(eies$messages, mode = "directed", weighted = TRUE, diag = TRUE)

eiesvatts <- function(g, atts = eiesatts){
    V(g)$name <- as.character(1:vcount(g))
    for (i in 1:ncol(atts))
        g <- set.vertex.attribute(g, name = colnames(atts)[i], value = atts[, i])
    return(g)
    }
eies <- lapply(eies, eiesvatts)
rm(eiesvatts, eiesatts)

sapply(list.files(), file.remove)
setwd(firstdir)
file.remove(newdir)
file.remove("EIES_data.zip")
rm(firstdir, newdir)