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Jeremy Foote

I'm a PhD candidate, studying Media, Technology, and Society at Northwestern University

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So, I thanks to some help from the very kind BrodieG on Stack Overflow, I was finally able to get some visualizations of the way that roles change over time. I am using the following code (as you can see, I tried to learn how to do melting and reshaping, then kind of gave up that hope - maybe another time).


#clusters.mlt <- melt(clusters, id.vars="id")
#clusters.agg <- aggregate(. ~ id + variable, clusters.mlt, sum)

# The minimum number of times a user has to be in a given group in order to
# be shown in the graph for that group
minMonths = 2

makeGraph <- function(clusters){
clus1 <- apply(clusters, 2, function(x) {sum(x=='1', na.rm=TRUE)})
clus2 <- apply(clusters, 2, function(x) {sum(x=='2', na.rm=TRUE)})
clus3 <- apply(clusters, 2, function(x) {sum(x=='3', na.rm=TRUE)})
clus0 <- apply(clusters, 2, function(x) {sum(x=='0', na.rm=TRUE)})
clusters2 <- data.frame(clus0, clus1, clus2, clus3)
c2 <- t(clusters2)
c3 <-
c3$id = c('Low Activity Cluster', 'Cluster 1', 'Cluster 2', 'Cluster 3')
c3 <- c3[order(c3$'id'),]
return(ggplot(melt(c3, id.vars="id")) +
geom_area(aes(x=variable, y=value, fill=id, group=id), position="fill"))
#print(ggplot(clusters.mlt) +
# stat_summary(aes(x=variable, y=value, fill=id, group=id), fun.y=sum, position="fill", geom="area"))

# Stats for just those who were in each group

clusterDF <-'clustersByID.csv'))
ggsave(file="../Results/allUsers.png", plot=makeGraph(clusterDF))
cl1 <- clusterDF[apply(clusterDF, 1, function(x) {sum(x[2:76] == "1", na.rm=TRUE) >= minMonths}),]
ggsave("../Results/Role1_2+.png", makeGraph(cl1))

And this is what the code produces (some new colors would probably be a good thing to work on next!) There are some interesting things going on here, but no clear movement into the central-type role (Role 1).