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require(stringr)
require(reshape2)
require(ggplot2)
require(vars)
# Create dataframes with only non-sensational (i) and sensational (s) issue columns
drop_s <- which(str_detect(names(issues), "^s"))
drop_i <- which(str_detect(names(issues), "^i"))
issues_i <- issues[,-drop_s]
issues <- issues[,-drop_i]
# #
# ENTROPY
# #
# Entropy non-sensational issues
issues_i$total <- rowSums(issues_i[2:ncol(issues_i)])
issues_i$entropy <- 0
for(r in 1:nrow(issues_i)) {
curtotal <- as.numeric(issues_i$total[r])
curp <- 0
for(c in 2:ncol(issues_i)) {
curcount <- as.numeric(issues_i[r,c])
curp[c] <- curcount / curtotal
}
curp <- curp [2:length(curp)-2]
curdrop <- which(curp==0)
curp <- curp[-curdrop]
issues_i$entropy[r] <- sum(-1 * curp * log(curp))
}
# Entropy sensational issues
issues$total <- rowSums(issues[2:ncol(issues)])
issues$entropy <- 0
for(r in 1:nrow(issues)) {
curtotal <- as.numeric(issues$total[r])
curp <- 0
for(c in 2:ncol(issues)) {
curcount <- as.numeric(issues[r,c])
curp[c] <- curcount / curtotal
}
curp <- curp [2:length(curp)-2]
curdrop <- which(curp==0)
curp <- curp[-curdrop]
issues$entropy[r] <- sum(-1 * curp * log(curp))
}
# Entropy overall
issues$total <- rowSums(issues[2:ncol(issues)])
issues$entropy <- 0
for(r in 1:nrow(issues)) {
curtotal <- as.numeric(issues$total[r])
curp <- 0
for(c in 2:ncol(issues)) {
curcount <- as.numeric(issues[r,c])
curp[c] <- curcount / curtotal
}
curp <- curp [2:length(curp)-2]
curdrop <- which(curp==0)
curp <- curp[-curdrop]
issues$entropy[r] <- sum(-1 * curp * log(curp))
}
# Compare total tweets vs. total sensational & total unsensational
stats_total <- data.frame(date=drange)
stats_total$tpd <- 0
stats_total$ipd <- issues_i$total
stats_total$spd <- issues$total
# Total number of tweets per day over time
for(r in 1:length(drange)) {
stats_total$tpd[r] <- length(tweets[tweets[, "created_at"] == drange[r], "id_str"])
}
# VISUALS: Tweets per day vs. sensational vs. general findings
stats_melt <- melt(stats_total, id="date")
g_perday <- ggplot(data = stats_melt, aes(x=date,y=value,colour=variable, group=variable)) +
geom_line()+
geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color=1) +
xlab("Zeitraum") + ylab("Tweets pro Tag") +
scale_colour_discrete(name = "Tweets", labels = c("Gesamt", "Allgemein", "Sensation")) +
theme(legend.title = element_text(size=14)) +
theme(legend.text = element_text(size=12)) +
theme(axis.title = element_text(size = 14))
g_perday
# Visuals for entropy in time series
stats_entropy <- data.frame(date=drange)
stats_entropy$entropy <- issues$entropy
stats_entropy <- melt(stats_entropy, id="date")
g_entrop <- ggplot(data = stats_entropy, aes(x=date,y=value,colour=variable, group=variable)) +
geom_line() +
geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color=1) +
xlab("Zeitraum") + ylab("Entropie") +
scale_colour_discrete(name = "", labels = "Entropie") +
theme(legend.title = element_text(size=14)) +
theme(legend.text = element_text(size=12)) +
theme(axis.title = element_text(size = 14))
g_entrop
# VAR ---------------------------------------------------------------------
# test <- VAR(issues[,2:32], p=1, type=c("const", "trend", "both", "none"), season=NULL, exogen = NULL, lag.max = NULL, ic = c("AIC", "HQ", "SC", "FPE"))
# test <- VAR(issues_i[,2:22], p=1, type="none", exogen = issues[,2:3])
# test <- VAR(issues[,2:11], p=1, type="none")
# VAR(issues[,2:23], p=1, type=c("const", "trend", "both", "none"), season=NULL, exogen = issues_i[2:22])
issues_ts <- as.ts(issues[,2:44])
# Tests
VARselect(issues_ts, lag.max = 5, type = "both")
i <- 0
i <- i + 1
ur.df(issues_ts[, i], type ="none", lags=1)
summary(issues[2:44])
# VAR and IRF
vIssues <- VAR(issues_ts, p=1, type="both")
vIRF <- irf(vIssues, impulse = names(issues[2:23]), response = names(issues_i[2:22]))
plot(vIRF)
# capture.output(print(summary(test), prmsd=TRUE, digits=1), file="out.txt")
# SOME TESTS --------------------------------------------------------------
stats <- data.frame(date=drange)
stats$tpd <- 0
# Total number of tweets per day over time
for(r in 1:length(drange)) {
stats$tpd[r] <- length(tweets[tweets[, "created_at"] == drange[r], "id_str"])
}
stats_melt <- melt(stats, id="date")
g1 <- ggplot(data = stats_melt, aes(x=date,y=value,colour=variable, group=variable)) +
geom_line() +
geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color=1)
g1
rm(g1, r)
# Show party percentage of twitter users
acc_parties <- data.frame(party = c("cducsu", "spd", "gruene", "linke"))
acc_parties$btw13 <- c(49.3, 30.6, 10.0, 10.1) # seats of party / 631 seats
acc_parties$twitter <- 0
for(p in 1:nrow(acc_parties)) {
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
}
pie(acc_parties$btw13, col=c("black", "red", "green", "purple"),
labels = c("CDU/CSU (49,3%)", "SPD (30,6%)", "Bündnis 90/Grüne(10,0%)", "Die LINKE (10,1%)"),
clockwise = T)
pie(acc_parties$twitter, col=c("black", "red", "green", "purple"),
labels = c("CDU/CSU (36%)", "SPD (30%)", "Bündnis 90/Grüne(19%)", "Die LINKE (15%)"),
clockwise = T)
rm(acc_parties, p)
# Count all tags
num <- 0
for(i in 1:length(issuelist)) {
j <- length(issuelist[[i]])
num <- num + j
rm(j)
}
num
# VISUALS -----------------------------------------------------------------
# Level: days
issues_melt <- melt(issues,id="date")
ggplot(issues_melt,aes(x=date,y=value,colour=variable,group=variable)) + geom_line(size=1)
ggplot(issues_melt,aes(x=date,y=value,colour=variable,group=variable)) + geom_smooth(size=1,method="loess",formula = y ~ x, se=FALSE)
# POSSIBLY USEFUL CODE ----------------------------------------------------
# List all issues in one row
for(i in 1:length(issueheads)) {
cat(issueheads[i], "\n")
}
# Limits of list
length(issuelist)
length(issuelist[[2]])
# Select all tweets from current day in drange
tweets_curday <- tweets[tweets[, "created_at"] == drange[5], ]
# Is column a issue counting column?
str_detect(names(issues[2]), "^issue")