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r_twitter_cluster.r
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r_twitter_cluster.r
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# run this if you don't have one or more of the next packages:
# install.packages(c("devtools", "RCurl", "rjson", "bit64","httr","ROAuth"))
# library(devtools)
# install_github("twitteR", username="geoffjentry")
# install_github('rCharts','ramnathv')
library(RCurl)
# Set SSL certs globally
options(RCurlOptions = list(cainfo = system.file("CurlSSL", "cacert.pem", package = "RCurl")))
library(twitteR)
#Authentication
#http://thinktostart.wordpress.com/2013/05/22/twitter-authentification-with-r/
library(rCharts)
user <- getUser("ACCOUNT-TO-BE-ANALYZED")
userFriends <- user$getFriends(n=5000) #put () if you want to get all friends and followers
userFollowers <- user$getFollowers(n=5000)
userNeighbors <- union(userFollowers, userFriends)
userNeighbors.df = twListToDF(userNeighbors)
userNeighbors.df[userNeighbors.df=="0"]<-1
userNeighbors.df$logFollowersCount <-log(userNeighbors.df$followersCount)
userNeighbors.df$logFriendsCount <-log(userNeighbors.df$friendsCount)
kObject.log <- data.frame(userNeighbors.df$logFriendsCount,userNeighbors.df$logFollowersCount)
###elbow
mydata <- kObject.log
wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))
for (i in 2:15) wss[i] <- sum(kmeans(mydata,
centers=i)$withinss)
plot(1:15, wss, type="b", xlab="Number of Clusters",
ylab="Within groups sum of squares")
###k-means
##Run the K Means algorithm, remember to specify centers from 'elbow plot'
userMeans.log <- kmeans(kObject.log, centers=4, iter.max=10, nstart=100)
##Add the vector of specified clusters back to the original vector as a factor
kObject.log$cluster=factor(userMeans.log$cluster)
userNeighbors.df$cluster <- kObject.log$cluster
p2 <- nPlot(logFollowersCount ~ logFriendsCount, group = 'cluster', data = userNeighbors.df, type = 'scatterChart')
p2$xAxis(axisLabel = 'Followers Count')
p2$yAxis(axisLabel = 'Friends Count')
p2$chart(tooltipContent = "#! function(key, x, y, e){
return e.point.screenName + ' Followers: ' + e.point.followersCount +' Friends: ' + e.point.friendsCount
} !#")
p2