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## Most of this code was adapted near-verbatim from Neil's post about ISMB 2012.
## Modify this. This is where I keep this repo.
repoDir <- ("~/workprojects/twitterchive/")
## Go to the analysis directory
setwd(paste(repoDir, "analysis", sep=""))
## Function needs better documentation
twitterchivePlots <- function (filename=NULL) {
## Load required packages
if (class(filename)!="character") stop("filename must be character")
if (!file.exists(filename)) stop(paste("File does not exist:", filename))
searchTerm <- sub("\\.txt", "", basename(filename))
message(paste("Filename:", filename))
message(paste("Search Term: ", searchTerm))
## Read in the data and munge around the dates.
## I can't promise the fixed widths will always work out for you.
message("Reading in data.")
trim.whitespace <- function(x) gsub("^\\s+|\\s+$", "", x) # Function to trim leading and trailing whitespace from character vectors.
d <- read.fwf(filename, widths=c(18, 14, 18, 1000), stringsAsFactors=FALSE, comment.char="")
d <-, trim.whitespace), stringsAsFactors=FALSE)
names(d) <- c("id", "datetime", "user", "text")
d$user <- sub("@", "", d$user)
d$datetime <- as.POSIXlt(d$datetime, format="%b %d %H:%M")
d$date <- as.Date(d$datetime)
d$hour <- d$datetime$hour
d <- na.omit(d) # CRs cause a problem. explain this later.
write.csv(d, file=sub("\\.txt", "\\.csv", filename))
## Number of tweets by date for the last n days
recentDays <- 30
message(paste("Plotting number of tweets by date in the last", recentDays, "days."))
recent <- subset(d, date>=(max(date)-recentDays))
byDate <-$date))
names(byDate) <- c("date", "tweets")
png(paste(searchTerm, "barplot-tweets-by-date.png", sep="--"), w=1000, h=700)
with(byDate, barplot(tweets, names=date, col="black", las=2, cex.names=1.2, cex.axis=1.2, mar=c(10,4,4,1), main=paste("Number of Tweets by Date", paste("Term:", searchTerm), sep="\n")))
# ggplot(byDate) + geom_bar(aes(date, tweets), stat="identity", fill="black") + theme_bw() + ggtitle("Number of Tweets by Date") + theme(axis.text.x=element_text(angle=90, hjust=1))
## Number of tweets by hour
message("Plotting number of tweets by hour.")
byHour <-$hour))
names(byHour) <- c("hour", "tweets")
png(paste(searchTerm, "barplot-tweets-by-hour.png", sep="--"), w=1000, h=700)
with(byHour, barplot(tweets, names.arg=hour, col="black", las=1, cex.names=1.2, cex.axis=1.2, main=paste("Number of Tweets by Hour", paste("Term:", searchTerm), paste("Date:", Sys.Date()), sep="\n")))
# ggplot(byHour) + geom_bar(aes(hour, tweets), stat="identity", fill="black") + theme_bw() + ggtitle("Number of Tweets by Hour")
## Barplot of top 20 hashtags
message("Plotting top 20 hashtags.")
words <- unlist(strsplit(d$text, " "))
ht <- words[grep("^#", words)]
ht <- tolower(ht)
ht <- gsub("[^A-Za-z0-9]", "", ht) # remove anything not starting with a letter or number
ht <-
ht <- subset(ht, ht!="") # remove blanks
ht <- ht[sort.list(ht$Freq, decreasing=TRUE), ]
ht <- ht[-1, ] # remove the term you're searching for? it usually dominates the results.
ht <- head(ht, 20)
png(paste(searchTerm, "barplot-top-hashtags.png", sep="--"), w=1000, h=700)
with(ht[order(ht$Freq), ], barplot(Freq, names=ht, horiz=T, col="black", las=1, cex.names=1.2, cex.axis=1.2, main=paste("Top Hashtags", paste("Term:", searchTerm), paste("Date:", Sys.Date()), sep="\n")))
# ggplot(ht) + geom_bar(aes(ht, Freq), fill = "black", stat="identity") + coord_flip() + theme_bw() + ggtitle("Top hashtags")
## Top Users
message("Plotting most prolific users.")
users <-$user))
colnames(users) <- c("user", "tweets")
users <- users[order(users$tweets, decreasing=T), ]
users <- subset(users, user!=searchTerm)
users <- head(users, 20)
png(paste(searchTerm, "barplot-top-users.png", sep="--"), w=1000, h=700)
with(users[order(users$tweets), ], barplot(tweets, names=user, horiz=T, col="black", las=1, cex.names=1.2, cex.axis=1.2, main=paste("Most prolific users", paste("Term:", searchTerm), paste("Date:", Sys.Date()), sep="\n")))
## Word clouds
message("Plotting a wordcloud.")
words <- unlist(strsplit(d$text, " "))
words <- grep("^[A-Za-z0-9]+$", words, value=T)
words <- tolower(words)
words <- words[-grep("^[rm]t$", words)] # remove "RT"
words <- words[!(words %in% stopwords("en"))] # remove stop words
words <- words[!(words %in% c("mt", "rt", "via", "using", 1:9))] # remove RTs, MTs, via, and single digits.
wordstable <-
wordstable <- wordstable[order(wordstable$Freq, decreasing=T), ]
wordstable <- wordstable[-1, ] # remove the hashtag you're searching for? need to functionalize this.
png(paste(searchTerm, "wordcloud.png", sep="--"), w=800, h=800)
wordcloud(wordstable$words, wordstable$Freq, scale = c(8, .2), min.freq = 3, max.words = 200, random.order = FALSE, rot.per = .15, colors = brewer.pal(8, "Dark2"))
#mtext(paste(paste("Term:", searchTerm), paste("Date:", Sys.Date()), sep=";"), cex=1.5)
message(paste(searchTerm, ": All done!\n"))
filelist <- as.list(list.files("..", pattern="bog14.txt", full.names=T))
#filelist <- list("../bioinformatics.txt", "../metagenomics.txt", "../rstats.txt", "../rna-seq.txt", "../cville.txt", "../SFAF2013.txt")
lapply(filelist, twitterchivePlots)
# Using imagemagick:
# system("montage bog14--barplot-tweets-by-date.png bog14--barplot-tweets-by-hour.png bog14--barplot-top-hashtags.png bog14--barplot-top-users.png -tile 2x -geometry -0-0 bog14--montage.jpg")