update to latest version
This commit is contained in:
@@ -1,281 +1,175 @@
|
||||
load(file = "tweets_untagged.RData")
|
||||
setwd("~/Dokumente/Uni/Aktuell/BA-Arbeit/uni-ba-issuecomp")
|
||||
results_files <- "matched-ids/all.csv"
|
||||
load(file = "tweets_untagged.RData")
|
||||
View(issues)
|
||||
issues <- data.frame(date = drange)
|
||||
issuelist <- readLines("issues.xml")
|
||||
issuelist <- str_replace_all(string = issuelist, pattern = ".*<!-- .+ -->", "")
|
||||
issuelist <- xmlToList(issuelist)
|
||||
issueheads <- names(issuelist)
|
||||
issues[issueheads] <- 0
|
||||
tweets$issue <- ""
|
||||
tweets$tags <- ""
|
||||
View(results)
|
||||
rm(r, results_temp, results_files)
|
||||
results <- results[!duplicated(results), ]
|
||||
names(results) <- c("date", "id_str", "issue", "tags")
|
||||
results <- results[order(results$id_str), ]
|
||||
row.names(results) <- NULL
|
||||
for(r in 1:nrow(results)) {
|
||||
curdate <- as.character(results$date[r])
|
||||
curid <- as.character(results$id_str[r])
|
||||
curissue <- as.character(results$issue[r])
|
||||
curtag <- as.character(results$tags[r])
|
||||
cat("Sorting match", r, "of 53383 \n")
|
||||
# Update issue counter (date and issue)
|
||||
issues[issues[, "date"] == curdate, curissue] <- issues[issues[, "date"] == curdate, curissue] + 1
|
||||
# Update tweet dataframe (id, issue and tags)
|
||||
oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
|
||||
tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ",")
|
||||
oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
|
||||
tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ",")
|
||||
}
|
||||
curdate
|
||||
curissue
|
||||
issues[issues[, "date"] == curdate, curissue]
|
||||
issueheads
|
||||
issuelist <- readLines("issues-v2.xml")
|
||||
issues <- data.frame(date = drange)
|
||||
issuelist <- readLines("issues-v2.xml")
|
||||
issuelist <- str_replace_all(string = issuelist, pattern = ".*<!-- .+ -->", "")
|
||||
issuelist <- xmlToList(issuelist)
|
||||
issueheads <- names(issuelist)
|
||||
issues[issueheads] <- 0
|
||||
tweets$issue <- ""
|
||||
tweets$tags <- ""
|
||||
for(r in 1:nrow(results)) {
|
||||
curdate <- as.character(results$date[r])
|
||||
curid <- as.character(results$id_str[r])
|
||||
curissue <- as.character(results$issue[r])
|
||||
curtag <- as.character(results$tags[r])
|
||||
cat("Sorting match", r, "of 53383 \n")
|
||||
# Update issue counter (date and issue)
|
||||
issues[issues[, "date"] == curdate, curissue] <- issues[issues[, "date"] == curdate, curissue] + 1
|
||||
# Update tweet dataframe (id, issue and tags)
|
||||
oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
|
||||
tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ",")
|
||||
oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
|
||||
tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ",")
|
||||
}
|
||||
results[33170,]
|
||||
results[33171,]
|
||||
results$date[33170]
|
||||
results$date[33170] <- "2014-08-21"
|
||||
for(r in 33170:nrow(results)) {
|
||||
curdate <- as.character(results$date[r])
|
||||
curid <- as.character(results$id_str[r])
|
||||
curissue <- as.character(results$issue[r])
|
||||
curtag <- as.character(results$tags[r])
|
||||
cat("Sorting match", r, "of 53383 \n")
|
||||
# Update issue counter (date and issue)
|
||||
issues[issues[, "date"] == curdate, curissue] <- issues[issues[, "date"] == curdate, curissue] + 1
|
||||
# Update tweet dataframe (id, issue and tags)
|
||||
oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
|
||||
tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ",")
|
||||
oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
|
||||
tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ",")
|
||||
}
|
||||
save(tweets, file="tweets_tagged.RData")
|
||||
write.csv(tweets, file="tweets.csv")
|
||||
save(issues, file="issues.RData")
|
||||
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.text = element_text(size = 18))
|
||||
g_perday
|
||||
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 = 18))
|
||||
g_perday
|
||||
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 = 12))
|
||||
g_perday
|
||||
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 = 13))
|
||||
g_perday
|
||||
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, face="plain")) +
|
||||
theme(legend.text = element_text(size=12)) +
|
||||
theme(axis.title = element_text(size = 13))
|
||||
g_perday
|
||||
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 = 13))
|
||||
g_perday
|
||||
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 = 13))
|
||||
g_entrop
|
||||
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 = 13))
|
||||
g_entrop
|
||||
detach("package:ggplot2", unload=TRUE)
|
||||
library("ggplot2", lib.loc="/usr/lib/R/site-library")
|
||||
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 = 13))
|
||||
g_entrop
|
||||
theme()
|
||||
require(stringr)
|
||||
require(reshape2)
|
||||
require(ggplot2)
|
||||
require(vars)
|
||||
drop_s <- which(str_detect(names(issues), "^s"))
|
||||
drop_i <- which(str_detect(names(issues), "^i"))
|
||||
issues_i <- issues[,-drop_s]
|
||||
issues_s <- issues[,-drop_i]
|
||||
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$entropy[r] <- sum(-1 * curp * log(curp))
|
||||
}
|
||||
issues_s$total <- rowSums(issues_s[2:ncol(issues_s)])
|
||||
issues_s$entropy <- 0
|
||||
for(r in 1:nrow(issues_s)) {
|
||||
curtotal <- as.numeric(issues_s$total[r])
|
||||
curp <- 0
|
||||
for(c in 2:ncol(issues_s)) {
|
||||
curcount <- as.numeric(issues_s[r,c])
|
||||
curp[c] <- curcount / curtotal
|
||||
}
|
||||
curp <- curp [2:length(curp)-2]
|
||||
curdrop <- which(curp==0)
|
||||
curp <- curp[-curdrop]
|
||||
issues_s$entropy[r] <- sum(-1 * curp * log(curp))
|
||||
}
|
||||
stats_total <- data.frame(date=drange)
|
||||
stats_total$tpd <- 0
|
||||
stats_total$ipd <- issues_i$total
|
||||
stats_total$spd <- issues_s$total
|
||||
# Total number of tweets per day over time
|
||||
for(r in 1:length(drange)) {
|
||||
stats_total$tpd[r] <- length(tweets[tweets[, "created_at"] == drange[r], "id_str"])
|
||||
}
|
||||
g1 <- ggplot(data = stats_melt, aes(x=date,y=value,colour=variable, group=variable)) +
|
||||
theme()
|
||||
g_entrop <- ggplot(data = stats_entropy, aes(x=date,y=value,colour=variable, group=variable)) +
|
||||
geom_line() +
|
||||
geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color=1) +
|
||||
xlab("Zeitraum") + ylab("Entropie") +
|
||||
scale_colour_discrete(name = "", labels = "Entropie")# +
|
||||
# theme(legend.title = element_text(size=14)) +
|
||||
# theme(legend.text = element_text(size=12)) +
|
||||
# theme(axis.title = element_text(size = 13))
|
||||
g_entrop
|
||||
g_entrop <- ggplot(data = stats_entropy, aes(x=date,y=value,colour=variable, group=variable)) +
|
||||
geom_line() +
|
||||
geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color=1) +
|
||||
xlab("Zeitraum") + ylab("Entropie") +
|
||||
scale_colour_discrete(name = "", labels = "Entropie")
|
||||
g_entrop
|
||||
g_perday <- ggplot(data = stats_melt, aes(x=date,y=value,colour=variable, group=variable)) +
|
||||
geom_line()+
|
||||
geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color=1) +
|
||||
xlab("Zeitraum") + ylab("Tweets pro Tag") +
|
||||
scale_colour_discrete(name = "Tweets", labels = c("Gesamt", "Allgemein", "Sensation"))
|
||||
g_perday
|
||||
g_entrop <- ggplot(data = stats_entropy, aes(x=date,y=value,colour=variable, group=variable)) +
|
||||
geom_line() +
|
||||
geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color=1) +
|
||||
xlab("Zeitraum") + ylab("Entropie") +
|
||||
scale_colour_discrete(name = "", labels = "Entropie")
|
||||
g_entrop
|
||||
g_entrop <- ggplot(data = stats_entropy, aes(x=date,y=value,colour=variable, group=variable)) +
|
||||
geom_line() +
|
||||
geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color=1)
|
||||
g1
|
||||
g_entrop
|
||||
stats_entropy <- data.frame(date=drange)
|
||||
stats_entropy$entropy <- issues_i$entropy
|
||||
stats_entropy <- melt(stats_entropy, id="date")
|
||||
g1 <- ggplot(data = stats_entropy, aes(x=date,y=value,colour=variable, group=variable)) +
|
||||
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)
|
||||
g1
|
||||
test <- VAR(issues[,2:32], p=1, type="none")
|
||||
View(issues_i)
|
||||
View(issues_s)
|
||||
View(issues)
|
||||
test <- VAR(issues[,2:44], p=1, type="none")
|
||||
VAR(issues_s[,2:23], p=1, type=c("const", "trend", "both", "none"), season=NULL, exogen = issues_i[2:22])
|
||||
plot(irf(test, impulse = names(issues_s[2:23]), response = names(issues_i[2:22])))
|
||||
rm(c_correct, c_curissue, c_errcode, c_errid, c_errissue, c_error1, c_error2, c_errors)
|
||||
rm(c_issues, c_issuelist, c_issueheads)
|
||||
rm(c_errtags, c_errtext, c_result, c_tag, c_tmp, c_tweets)
|
||||
geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color=1) +
|
||||
xlab("Zeitraum") + ylab("Entropie") +
|
||||
scale_colour_discrete(name = "", labels = "Entropie")
|
||||
g_entrop
|
||||
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 = 13))
|
||||
g_entrop
|
||||
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
|
||||
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
|
||||
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
|
||||
acc_parties <- data.frame(party = c("cducsu", "spd", "linke", "gruene"))
|
||||
acc_parties$btw13 <- c(49.3, 30.6, 10.1, 10.0) # 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)
|
||||
}
|
||||
require(jsonlite)
|
||||
require(stringr)
|
||||
require(XML)
|
||||
readYN <- function(question) {
|
||||
n <- readline(prompt=question)
|
||||
n <- as.character(n)
|
||||
return(n)
|
||||
}
|
||||
checkIssue <- function(string, issuelist) {
|
||||
status <- any(str_detect(string, issuelist))
|
||||
return(status)
|
||||
}
|
||||
checkAllIssues <- function(string, issuelist) {
|
||||
status <- NULL
|
||||
for(i in 1:length(string)) {
|
||||
if(checkIssue(string[i], issuelist)) {
|
||||
status[i] <- TRUE
|
||||
}
|
||||
else {
|
||||
cat("Issue",string[i],"does not exist. Please try again.\n")
|
||||
status[i] <- FALSE
|
||||
}
|
||||
}
|
||||
return(status)
|
||||
}
|
||||
View(tweets)
|
||||
c_tweets <- read.csv("tweets.csv", colClasses="character")
|
||||
for(r in 1:nrow(c_tweets)) {
|
||||
curtext <- as.character(c_tweets$text[r])
|
||||
if(str_detect(curtext, "\"")) {
|
||||
c_tweets$text[r] <- str_replace(curtext, "\"", "")
|
||||
}
|
||||
}
|
||||
c_tweets$X <- NULL
|
||||
c_issues <- data.frame(date = drange)
|
||||
c_issuelist <- xmlToList("issues-v2.xml")
|
||||
c_issueheads <- names(issuelist)
|
||||
c_issues[issueheads] <- 0
|
||||
source("issuecomp-codingsample-function.R")
|
||||
require(stringr)
|
||||
curtext
|
||||
curtext <- str_replace_all(curtext, "#", "")
|
||||
curtext <- str_replace_all(curtext, "-", " ")
|
||||
curtext
|
||||
curtext
|
||||
str_replace_all(curtext, "[^[:alnum:]]", "")
|
||||
str_replace_all(curtext, "[^[:alnum:]\s]", "")
|
||||
str_replace_all(curtext, "[^[:alnum:]\\s]", "")
|
||||
str_replace_all(curtext, "[^[:alnum:]^\\s]", "")
|
||||
str_replace_all(curtext, "[^[:alnum:]^\\S]", "")
|
||||
str_replace_all(curtext, "[^[:alnum:]][^\\s]", "")
|
||||
str_replace_all(curtext, "[^[:alnum:]][^\\S]", "")
|
||||
str_replace_all(curtext, "[^[:alnum:]][^[:blank]]", "")
|
||||
str_replace_all(curtext, "[^[:alnum:]][^[:blank:]]", "")
|
||||
str_replace_all(curtext, "[^[:alnum:]]", "")
|
||||
str_replace_all(curtext, "\\W", "")
|
||||
str_replace_all(curtext, "[\\W|\\S]", "")
|
||||
str_replace_all(curtext, "(\\W|\\S)", "")
|
||||
str_replace_all(curtext, "\\W|\\S", "")
|
||||
str_replace_all(curtext, "\\W", "")
|
||||
str_replace_all(curtext, "[\\W\\S]", "")
|
||||
str_replace_all(curtext, "[\\S\\W]", "")
|
||||
str_replace_all(curtext, "[\\s\\W]", "")
|
||||
str_replace_all(curtext, "[\\W\\s]", "")
|
||||
str_replace_all(curtext, "[\\W\s]", "")
|
||||
str_replace_all(curtext, "[\\Ws]", "")
|
||||
str_replace_all(curtext, "[\\W]", "")
|
||||
str_replace_all(curtext, "\\W", "")
|
||||
str_replace_all(curtext, "\\W|\\S", "")
|
||||
str_replace_all(curtext, "\\W|\\s", "")
|
||||
str_replace_all(curtext, "[^[:alnum:]]", "")
|
||||
str_replace_all(curtext, "[^[:alnum:] ]", "")
|
||||
str_replace_all(curtext, "[^[:alnum:]\\s]", "")
|
||||
str_replace_all(curtext, "[^[:alnum:] ]", "")
|
||||
curtext
|
||||
curtext <- "liebe @cdu, wir finden #Steuer gut, aber die KFZ-Steuer nicht!"
|
||||
curtext <- str_replace_all(curtext, "-", " ")
|
||||
curtext <- str_replace_all(curtext, "[^[:alnum:] ]", "")
|
||||
curtext
|
||||
curtext <- "liebe @cdu, wir finden #Steuer gut, aber die KFZ--Steuer nicht!"
|
||||
curtext <- str_replace_all(curtext, "-", " ")
|
||||
curtext <- str_replace_all(curtext, "[^[:alnum:] ]", "")
|
||||
curtext
|
||||
str_replace_all(curtext, " ", " ")
|
||||
smartPatternMatch
|
||||
require(vars)
|
||||
require(stringr)
|
||||
adf1 <- summary(ur.df(issues))
|
||||
issues
|
||||
summary(issues)
|
||||
summary(issues[2:44])
|
||||
summary(issues[2:44], digits = 2)
|
||||
adf1 <- summary(ur.df(issues[, 2:44]), type ="trend", lags=1)
|
||||
data("Canda")
|
||||
data("Canada")
|
||||
class(Canada)
|
||||
class(issues)
|
||||
view(Canada)
|
||||
View(Canada)
|
||||
as.ts(issues)
|
||||
issues_ts <- as.ts(issues)
|
||||
class(issues_ts)
|
||||
View(issues_ts)
|
||||
View(issues)
|
||||
adf1 <- summary(ur.df(issues_ts[, 2:44]), type ="trend", lags=1)
|
||||
adf1 <- summary(ur.df(issues_ts[, 2]), type ="trend", lags=1)
|
||||
adf1 <- summary(ur.df(issues_ts[, 2], type ="trend", lags=1))
|
||||
adf1 <- summary(ur.df(issues_ts[, 2:44], type ="trend", lags=1))
|
||||
adf1 <- summary(ur.df(issues_ts[, 2], type ="trend", lags=1))
|
||||
adf1
|
||||
adf1 <- summary(ur.df(issues_ts[, 3], type ="trend", lags=1))
|
||||
adf1
|
||||
adf1 <- summary(ur.df(issues_ts[, 2], type ="none", lags=1))
|
||||
adf1
|
||||
adf1 <- summary(ur.df(issues_ts[, 2], type ="trend", lags=1))
|
||||
adf1
|
||||
summary(ur.df(issues_ts[, 2], type ="none", lags=1))
|
||||
VARselect(issues_ts[2:44], lag.max = 8, type = "both")
|
||||
VARselect(issues_ts[1:44], lag.max = 8, type = "both")
|
||||
VARselect(issues[1:44], lag.max = 8, type = "both")
|
||||
VARselect(issues[2:44], lag.max = 8, type = "both")
|
||||
VARselect(issues_ts[2:44], lag.max = 8, type = "both")
|
||||
VARselect(issues[2:44], lag.max = 8, type = "none")
|
||||
VARselect(issues[2:44], lag.max = 8, type = "trend")
|
||||
VARselect(issues[2:44], lag.max = 8, type = "const")
|
||||
VARselect(issues[2:44], lag.max = 8, type = "both")
|
||||
test <- VAR(issues[,2:44], p=1, type="both")
|
||||
# VAR(issues_s[,2:23], p=1, type=c("const", "trend", "both", "none"), season=NULL, exogen = issues_i[2:22])
|
||||
plot(irf(test, impulse = names(issues_s[2:23]), response = names(issues_i[2:22])))
|
||||
summary(ur.df(issues_ts[, 2], type ="both", lags=1))
|
||||
summary(ur.df(issues_ts[, 2], type ="none", lags=1))
|
||||
test <- VAR(issues_ts[,2:44], p=1, type="both")
|
||||
plot(irf(test, impulse = names(issues_s[2:23]), response = names(issues_i[2:22])))
|
||||
require(devtools)
|
||||
require(RTwitterAPI)
|
||||
acc_df <- read.csv("MdB-twitter.csv")
|
||||
delrow <- NULL
|
||||
for(r in 1:nrow(acc_df)) {
|
||||
@@ -288,225 +182,331 @@ acc_df <- acc_df[-delrow, ]
|
||||
rm(delrow, r, acc)
|
||||
acc_df$row.names <- NULL
|
||||
row.names(acc_df) <- NULL
|
||||
View(acc_df)
|
||||
View(c_tweets)
|
||||
issueheads
|
||||
length(issueheads)
|
||||
issuelist
|
||||
length(issuelist)
|
||||
length(issuelist[*])
|
||||
length(issuelist[[*]])
|
||||
length(issuelist[1:43])
|
||||
length(issuelist[1)
|
||||
length(issuelist[1])
|
||||
length(issuelist[2])
|
||||
length(issuelist[[1]])
|
||||
length(issuelist[[2]])
|
||||
length(issuelist[[70]])
|
||||
length(issuelist[[43]])
|
||||
length(issuelist[[44]])
|
||||
length(issuelist[[1:43]])
|
||||
length(issuelist[[1-43]])
|
||||
length(issuelist[[2]])
|
||||
test <- 0
|
||||
num <- 0
|
||||
for(i in 1:length(issuelist)) {
|
||||
j <- length(issuelist[[i]])
|
||||
num <- num + j
|
||||
rm(j)
|
||||
acc_parties <- data.frame(party = c("cducsu", "spd", "linke", "gruene"))
|
||||
acc_parties$btw13 <- c(49.3, 30.6, 10.1, 10.0) # seats of party / 631 seats
|
||||
acc_parties$twitter <- 0
|
||||
for(p in 1:nrow(acc_parties)) {
|
||||
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
||||
}
|
||||
num
|
||||
drop_s <- which(str_detect(names(issues), "^s"))
|
||||
drop_i <- which(str_detect(names(issues), "^i"))
|
||||
issues_i <- issues[,-drop_s]
|
||||
issues_s <- issues[,-drop_i]
|
||||
require(stringr)
|
||||
drop_s <- which(str_detect(names(issues), "^s"))
|
||||
drop_i <- which(str_detect(names(issues), "^i"))
|
||||
issues_i <- issues[,-drop_s]
|
||||
issues_s <- issues[,-drop_i]
|
||||
issues_i$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
|
||||
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T,
|
||||
main = "Seats of parties in the parliament")
|
||||
pie(acc_parties$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T,
|
||||
main = "Percentage of parties' MdBs of all Twitter accounts")
|
||||
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T)
|
||||
pie(acc_parties$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T)
|
||||
View(acc_parties)
|
||||
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"),
|
||||
labels = c("CDU/CSU (49.3%)", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T)
|
||||
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"),
|
||||
labels = c("CDU/CSU (49,3%)", "SPD (30,6%)", "Die LINKE (10,1%)", "Bündnis 90/Grüne(10.0%)"),
|
||||
clockwise = T)
|
||||
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$party[p]), ]) / 280 * 100)
|
||||
}
|
||||
curp <- curp [2:length(curp)-2]
|
||||
curdrop <- which(curp==0)
|
||||
curp <- curp[-curdrop]
|
||||
issues_i$entropy[r] <- sum(-1 * curp * log(curp))
|
||||
}
|
||||
issues_s$total <- rowSums(issues_s[2:ncol(issues_s)])
|
||||
issues_s$entropy <- 0
|
||||
for(r in 1:nrow(issues_s)) {
|
||||
curtotal <- as.numeric(issues_s$total[r])
|
||||
curp <- 0
|
||||
for(c in 2:ncol(issues_s)) {
|
||||
curcount <- as.numeric(issues_s[r,c])
|
||||
curp[c] <- curcount / curtotal
|
||||
}
|
||||
curp <- curp [2:length(curp)-2]
|
||||
curdrop <- which(curp==0)
|
||||
curp <- curp[-curdrop]
|
||||
issues_s$entropy[r] <- sum(-1 * curp * log(curp))
|
||||
}
|
||||
stats_total <- data.frame(date=drange)
|
||||
stats_total$tpd <- 0
|
||||
stats_total$ipd <- issues_i$total
|
||||
stats_total$spd <- issues_s$total
|
||||
# Total number of tweets per day over time
|
||||
for(r in 1:length(drange)) {
|
||||
stats_total$tpd[r] <- length(tweets[tweets[, "created_at"] == drange[r], "id_str"])
|
||||
}
|
||||
stats_melt <- melt(stats_total, 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
|
||||
require(ggplot2)
|
||||
stats_melt <- melt(stats_total, 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
|
||||
g1 <- ggplot(data = stats_melt, aes(x=date,y=value,colour=variable, group=variable)) +
|
||||
geom_line()+
|
||||
geom_smooth(size=1,formula = y ~ x, method="lm", se=FALSE, color=1)
|
||||
g1
|
||||
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
|
||||
# Visuals for entropy in time series
|
||||
stats_entropy <- data.frame(date=drange)
|
||||
stats_entropy$entropy <- issues_i$entropy
|
||||
stats_entropy <- melt(stats_entropy, id="date")
|
||||
require(reshape2)
|
||||
stats_melt <- melt(stats_total, 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
|
||||
stats_entropy <- data.frame(date=drange)
|
||||
stats_entropy$entropy <- issues_i$entropy
|
||||
stats_entropy <- melt(stats_entropy, id="date")
|
||||
g1 <- 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)
|
||||
g1
|
||||
g1 <- ggplot(data = stats_entropy, aes(x=date,y=value,colour=variable, group=variable)) +
|
||||
geom_line() +
|
||||
geom_smooth(size=1,formula = y ~ x, method="lm", se=FALSE, color=1)
|
||||
g1
|
||||
vIssues <- VAR(issues_ts[,2:44], p=1, type="both")
|
||||
require(vars)
|
||||
VARselect(issues_ts, lag.max = 8, type = "both")
|
||||
vIssues <- VAR(issues_ts[,2:44], p=1, type="both")
|
||||
VARselect(issues_ts, lag.max = 16, type = "both")
|
||||
VARselect(issues_ts, lag.max = 4, type = "both")
|
||||
VARselect(issues_ts, lag.max = 5, type = "both")
|
||||
VARselect(issues_ts, lag.max = 6, type = "both")
|
||||
VARselect(issues_ts, lag.max = 5, type = "both")
|
||||
names(issues_ts)
|
||||
issues_ts
|
||||
issues_ts[2:44]
|
||||
issues_ts <- as.ts(issues[,2:44])
|
||||
issues_ts[1:1]
|
||||
issues_ts[,1]
|
||||
issues_ts[1,1]
|
||||
issues_ts2,1]
|
||||
issues_ts[2,1]
|
||||
pie(acc_parties$btw13, col=c("black", "red", "green", "purple"),
|
||||
labels = c("CDU/CSU (49,3%)", "SPD (30,6%)", "Bündnis 90/Grüne(10.0%)", "Die LINKE (10,1%)"),
|
||||
clockwise = T)
|
||||
pie(acc_parties$btw13, col=c("black", "red", "green", "purple"),
|
||||
pie(acc_parties$btw13, col=c("black", "red", "green", "purple"),
|
||||
labels = c("CDU/CSU (49,3%)", "SPD (30,6%)", "Bündnis 90/Grüne(10,0%)", "Die LINKE (10,1%)"),
|
||||
clockwise = T)
|
||||
pie(acc_parties$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$twitter, col=c("black", "red", "green", "purple"),
|
||||
labels = c("CDU/CSU (36%)", "SPD (30%)", "Bündnis 90/Grüne(19%)", "Die LINKE (15%)"),
|
||||
clockwise = T)
|
||||
pie(acc_parties$twitter, col=c("black", "red", "green", "purple"),
|
||||
pie(acc_parties$twitter, col=c("black", "red", "green", "purple"),
|
||||
labels = c("CDU/CSU (36%)", "SPD (30%)", "Bündnis 90/Grüne(19%)", "Die LINKE (15%)"),
|
||||
clockwise = T, init.angle = 90)
|
||||
pie(acc_parties$twitter, col=c("black", "red", "green", "purple"),
|
||||
labels = c("CDU/CSU (36%)", "SPD (30%)", "Bündnis 90/Grüne(19%)", "Die LINKE (15%)"),
|
||||
clockwise = T)
|
||||
pie(acc_parties$twitter, col=c("black", "red", "green", "purple"),
|
||||
labels = c("CDU/CSU (36%)", "SPD (30%)", "Bündnis 90/Grüne(19%)", "Die LINKE (15%)"),
|
||||
clockwise = T, init.angle = 180)
|
||||
pie(acc_parties$twitter, col=c("black", "red", "green", "purple"),
|
||||
labels = c("CDU/CSU (36%)", "SPD (30%)", "Bündnis 90/Grüne(19%)", "Die LINKE (15%)"),
|
||||
clockwise = T, init.angle = 270)
|
||||
pie(acc_parties$twitter, col=c("black", "red", "green", "purple"),
|
||||
labels = c("CDU/CSU (36%)", "SPD (30%)", "Bündnis 90/Grüne(19%)", "Die LINKE (15%)"),
|
||||
clockwise = T, init.angle = 360)
|
||||
pie(acc_parties$twitter, col=c("black", "red", "green", "purple"),
|
||||
labels = c("CDU/CSU (36%)", "SPD (30%)", "Bündnis 90/Grüne(19%)", "Die LINKE (15%)"),
|
||||
clockwise = T, init.angle = 20)
|
||||
pie(acc_parties$twitter, col=c("black", "red", "green", "purple"),
|
||||
labels = c("CDU/CSU (36%)", "SPD (30%)", "Bündnis 90/Grüne(19%)", "Die LINKE (15%)"),
|
||||
clockwise = T, init.angle = 20)
|
||||
pie(acc_parties$twitter, col=c("black", "red", "green", "purple"),
|
||||
labels = c("CDU/CSU (36%)", "SPD (30%)", "Bündnis 90/Grüne(19%)", "Die LINKE (15%)"),
|
||||
clockwise = T, init.angle = 90)
|
||||
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)
|
||||
2359 / 200 * 100
|
||||
issues_ts <- as.ts(issues[,2:44])
|
||||
VARselect(issues_ts, lag.max = 5, type = "both")
|
||||
VARselect(issues_ts, lag.max = 8, type = "both")
|
||||
VARselect(issues_ts, lag.max = 7, type = "both")
|
||||
VARselect(issues_ts, lag.max = 5, type = "both")
|
||||
vIssues <- VAR(issues_ts[,2:44], p=5, type="both")
|
||||
vIssues <- VAR(issues_ts, p=5, type="both")
|
||||
plot(irf(vIssues, impulse = names(issues_s[2:23]), response = names(issues_i[2:22])))
|
||||
require(stringr)
|
||||
require(XML)
|
||||
c_errors <- read.csv("issuecomp-codingsample-error.csv", header = F, sep=",", colClasses="character")
|
||||
names(c_errors) <- c("str_id", "code", "issue", "tags", "text")
|
||||
for(r in 1:nrow(c_errors)) {
|
||||
c_errcode <- as.character(c_errors$code[r])
|
||||
c_errissue <- as.character(c_errors$issue[r])
|
||||
c_errtags <- as.character(c_errors$tags[r])
|
||||
c_errtext <- as.character(c_errors$text[r])
|
||||
c_errid <- as.character(c_errors$str_id[r])
|
||||
cat("===============\n\n[TWEET]: ",c_errtext,"\n[ISSUES]: ", c_errissue, " (", c_errtags, ")\n", sep="")
|
||||
source("issuecomp-codingsample-function2.R")
|
||||
vIssues <- VAR(issues_ts, p=1, type="both")
|
||||
issues_ts <- as.ts(issues)
|
||||
VARselect(issues[2:44], lag.max = 8, type = "both")
|
||||
summary(ur.df(issues_ts[, 2], type ="none", lags=1))
|
||||
VARselect(issues_ts, lag.max = 5, type = "both")
|
||||
issues_ts <- as.ts(issues[,2:44])
|
||||
VARselect(issues_ts, lag.max = 5, type = "both")
|
||||
VARselect(issues_ts, lag.max = 5, type = "both")
|
||||
VARselect(issues_ts, lag.max = 5, type = "both")
|
||||
VARselect(issues_ts, lag.max = 5, type = "both")
|
||||
VARselect(issues_ts, lag.max = 5, type = "both")
|
||||
VARselect(issues_ts, lag.max = 5, type = "both")
|
||||
VARselect(issues_ts, lag.max = 5, type = "both")
|
||||
summary(ur.df(issues_ts[, 2], type ="none", lags=1))
|
||||
ur.df(issues_ts[, 2], type ="none", lags=1)
|
||||
head(issues_ts)
|
||||
issues_ts$i1.macro
|
||||
issues_ts[, "i1.macro"]
|
||||
summary(ur.df(issues_ts[, "i1.macro"], type ="none", lags=1))
|
||||
ncol(issues_ts)
|
||||
for(i in 2:ncol(issues_ts)) {
|
||||
summary(ur.df(issues_ts[, i], type ="none", lags=1))
|
||||
}
|
||||
for(r in 1:nrow(c_errors)) {
|
||||
c_errcode <- as.character(c_errors$code[r])
|
||||
c_errissue <- as.character(c_errors$issue[r])
|
||||
c_errtags <- as.character(c_errors$tags[r])
|
||||
c_errtext <- as.character(c_errors$text[r])
|
||||
c_errid <- as.character(c_errors$str_id[r])
|
||||
cat("===============\n\n[TWEET]: ",c_errtext,"\n[ISSUES]: ", c_errissue, " (", c_errtags, ")\n", sep="")
|
||||
source("issuecomp-codingsample-function2.R")
|
||||
cat(summary(ur.df(issues_ts[, "i1.macro"], type ="none", lags=1)))
|
||||
paste(summary(ur.df(issues_ts[, "i1.macro"], type ="none", lags=1)))
|
||||
i
|
||||
summary(ur.df(issues_ts[, i], type ="none", lags=1))
|
||||
summary(ur.df(issues_ts[, 1], type ="none", lags=1))
|
||||
summary(ur.df(issues_ts[, 2], type ="none", lags=1))
|
||||
summary(ur.df(issues_ts[, 1], type ="none", lags=1))
|
||||
summary(ur.df(issues_ts[, 1], type ="none", lags=1))
|
||||
names(issues_ts)
|
||||
issues_ts[1,]
|
||||
summary(ur.df(issues_ts[, 1], type ="none", lags=1))
|
||||
summary(ur.df(issues_ts[, 2], type ="none", lags=1))
|
||||
summary(ur.df(issues_ts[, 43], type ="none", lags=1))
|
||||
summary(ur.df(issues_ts[, 43], type ="trend", lags=1))
|
||||
summary(ur.df(issues_ts[, 43], type ="none", lags=1))
|
||||
summary(ur.df(issues_ts[, 1], type ="none", lags=1))
|
||||
ur.df(issues_ts[, 1], type ="none", lags=1)
|
||||
for(i in 2:ncol(issues_ts)) {
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
}
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
test <- ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
test
|
||||
for(i in 2:ncol(issues_ts)) {
|
||||
test <- ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
cat(test)
|
||||
}
|
||||
class(test)
|
||||
as.character(test)
|
||||
for(i in 2:ncol(issues_ts)) {
|
||||
test[i] <- ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
}
|
||||
ur.df(issues_ts[, 1], type ="none", lags=1)
|
||||
ur.df(issues_ts[, sprintf("%s", c(1,2,3))], type ="none", lags=1)
|
||||
ur.df(issues_ts[, sprintf("%i", c(1,2,3))], type ="none", lags=1)
|
||||
ur.df(issues_ts[, 2], type ="none", lags=1)
|
||||
ur.df(issues_ts[, 3], type ="none", lags=1)
|
||||
i <- 0
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i <- i + 1
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
i
|
||||
issueheads
|
||||
for(i in 1:length(issueheads)) {paste(issueheads[i])}
|
||||
for(i in 1:length(issueheads)) {cat(issueheads[i], "\n")}
|
||||
for(r in 1:nrow(c_errors)) {
|
||||
c_errcode <- as.character(c_errors$code[r])
|
||||
c_errissue <- as.character(c_errors$issue[r])
|
||||
c_errtags <- as.character(c_errors$tags[r])
|
||||
c_errtext <- as.character(c_errors$text[r])
|
||||
c_errid <- as.character(c_errors$str_id[r])
|
||||
cat("===============\n\n[TWEET]: ",c_errtext,"\n[ISSUES]: ", c_errissue, " (", c_errtags, ")\n", sep="")
|
||||
source("issuecomp-codingsample-function2.R")
|
||||
length(issuelist[[2]])
|
||||
# List all issues in one row
|
||||
for(i in 1:length(issueheads)) {
|
||||
cat(issueheads[i], "\n")
|
||||
}
|
||||
for(r in 1:nrow(c_errors)) {
|
||||
c_errcode <- as.character(c_errors$code[r])
|
||||
c_errissue <- as.character(c_errors$issue[r])
|
||||
c_errtags <- as.character(c_errors$tags[r])
|
||||
c_errtext <- as.character(c_errors$text[r])
|
||||
c_errid <- as.character(c_errors$str_id[r])
|
||||
cat("===============\n\n[TWEET]: ",c_errtext,"\n[ISSUES]: ", c_errissue, " (", c_errtags, ")\n", sep="")
|
||||
source("issuecomp-codingsample-function2.R")
|
||||
}
|
||||
c_errors <- read.csv("issuecomp-codingsample-error.csv", header = F, sep=",", colClasses="character")
|
||||
names(c_errors) <- c("str_id", "code", "issue", "tags", "text")
|
||||
for(r in 1:nrow(c_errors)) {
|
||||
c_errcode <- as.character(c_errors$code[r])
|
||||
c_errissue <- as.character(c_errors$issue[r])
|
||||
c_errtags <- as.character(c_errors$tags[r])
|
||||
c_errtext <- as.character(c_errors$text[r])
|
||||
c_errid <- as.character(c_errors$str_id[r])
|
||||
cat("===============\n\n[TWEET]: ",c_errtext,"\n[ISSUES]: ", c_errissue, " (", c_errtags, ")\n", sep="")
|
||||
source("issuecomp-codingsample-function2.R")
|
||||
}
|
||||
c_tmp <- read.csv("issuecomp-codingsample-error1.csv", header = F, colClasses="character")
|
||||
names(c_tmp) <- c("str_id", "all", "wrong", "tagged", "text")
|
||||
c_error1 <- c_tmp[, c("wrong", "tagged", "all", "text")]
|
||||
c_tmp <- read.csv("issuecomp-codingsample-error2.csv", header = F, colClasses="character")
|
||||
names(c_tmp) <- c("str_id", "all", "missing", "tagged", "text")
|
||||
c_error2 <- c_tmp[, c("missing", "text", "tagged", "all")]
|
||||
View(c_error2)
|
||||
summary(ur.df(issues_ts[, 2], type ="none", lags=1))
|
||||
vIssues <- VAR(issues_ts, p=1, type="both")
|
||||
issues_ts[1:20,1]
|
||||
issues_ts[,1]
|
||||
issues_ts[1,]
|
||||
issues_ts[1:21,]
|
||||
issues_ts[,1:21]
|
||||
issues_ts[1,1:21]
|
||||
issues_ts[1,22:43]
|
||||
issues_ts[1,22:44]
|
||||
issues_ts[1,22:43]
|
||||
plot(irf(vIssues, impulse = names(issues_ts[1:21]), response = names(issues_i[22:43])))
|
||||
require(stringr)
|
||||
require(reshape2)
|
||||
require(ggplot2)
|
||||
require(vars)
|
||||
summary(ur.df(issues_ts[, 2], type ="none", lags=1))
|
||||
stability(vIssues)
|
||||
stability(vIssues[2:])
|
||||
stability(vIssues[2:44])
|
||||
plot(stability(vIssues))
|
||||
class(vIssues)
|
||||
summary(vIssues)
|
||||
plot(stability(vIssues[2]))
|
||||
plot(stability(vIssues), nc=2)
|
||||
plot(stability(vIssues), h=0.15)
|
||||
stability(vIssues)
|
||||
efp(formula = formula, data = data, type = type, h = h, dynamic = dynamic,
|
||||
rescale = rescale)
|
||||
plot(stability(vIssues), h=0.15)
|
||||
plot(stability(vIssues, h=0.15))
|
||||
plot(stability(vIssues, h=0.15, rescale = TRUE))
|
||||
plot(stability(vIssues, h=0.15, rescale = TRUE), nc=2)
|
||||
par("mar")
|
||||
par(mar=c(1,1,1,1))
|
||||
plot(stability(vIssues, h=0.15, rescale = TRUE), nc=2)
|
||||
vIssues
|
||||
plot(irf(vIssues, impulse = names(issues_ts[1:21]), response = names(issues_i[22:43])))
|
||||
plot(irf(vIssues, impulse = names(issues_ts[1:21]), response = names(issues_ts[22:43])))
|
||||
issues_s
|
||||
names(issues_s)
|
||||
names(issues_s[2:23])
|
||||
names(issuesi[2:22])
|
||||
names(issues_i[2:22])
|
||||
plot(irf(vIssues, impulse = names(issues_s[2:23]), response = names(issues_i[2:22])))
|
||||
plot(irf(vIssues, impulse = names(issues_s[2:23]), response = names(issues_i[2:22])))
|
||||
plot(irf(vIssues, impulse = names(issues_s[2:23]), response = names(issues_i[2:22])))
|
||||
irf(vIssues, impulse = names(issues_s[2:23]), response = names(issues_i[2:22]))
|
||||
vIRF <- irf(vIssues, impulse = names(issues_s[2:23]), response = names(issues_i[2:22]))
|
||||
summary(vIRF)
|
||||
vIRF$irf
|
||||
vIRF$boot
|
||||
vIRF$ortho
|
||||
vIRF$Lower
|
||||
vIRF$irf[1]
|
||||
vIRF$irf["s.boko"]
|
||||
summary(issues$hk)
|
||||
summary(issues$s.hk)
|
||||
summary(issues$s.nsa)
|
||||
summary(issues$s.gaza)
|
||||
summary(issues$s.boko)
|
||||
summary(issues$s.ebola)
|
||||
summary(issues$s.edathy)
|
||||
summary(issues$s.ferguson)
|
||||
summary(issues$s.gurlitt)
|
||||
summary(issues$s.is)
|
||||
summary(issues$s.pegida)
|
||||
summary(issues$s.schumi)
|
||||
summary(issues$s.tebartz)
|
||||
summary(issues$s.wm)
|
||||
summary(issues$s.wulff)
|
||||
plot(vIRF)
|
||||
names(issues)
|
||||
summary(issues[2:44])
|
||||
plot(vIRF)
|
||||
plot(vIRF, x=300, y=200)
|
||||
plot(vIRF, res = 300)
|
||||
plot(vIRF[1])
|
||||
plot(vIRF$irf[1])
|
||||
summary(issues[2:44])
|
||||
ur.df(issues_ts[, i], type ="none", lags=1)
|
||||
summary(ur.df(issues_ts[, i], type ="none", lags=1))
|
||||
summary(ur.df(issues_ts[,30], type ="none", lags=1))
|
||||
summary(issues[2])
|
||||
stats_entropy
|
||||
names(issues)
|
||||
issues_bak <- issues
|
||||
issues$total <- rowSums(issues[2:ncol(issues)])
|
||||
issues$entropy <- 0
|
||||
names(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))
|
||||
}
|
||||
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
|
||||
View(issues)
|
||||
summary(issues$entropy)
|
||||
summary(issues$total)
|
||||
stats_total
|
||||
summary(issues[2:44])
|
||||
|
||||
Reference in New Issue
Block a user