setwd("~/Desktop/BLOG")

library(haven)

#vertrauen 2007 - 2017
sicherheit_2017_gesamtdatensatz <- read_sav("~/Desktop/BLOG/data/sicherheit_2017_gesamtdatensatz.sav")

sicherheit <- sicherheit_2017_gesamtdatensatz

#summary(sicherheit)

library(ggplot2)
library(scales)

#set_vertrauen: jahr, 01=Bundesrat, 02=Parlament, 03=Gerichte, 04=Polizei, 05=Armee, 06=Medien, 07=Wirtschaft, 16=Parteien
vertrauen <- c("jahr", "st_q01.01", "st_q01.02", "st_q01.03", "st_q01.04", "st_q01.05", "st_q01.06", 
               "st_q01.07", "st_q01.16")
set_vertrauen <- sicherheit[vertrauen]

#remove NA's
set_vertrauen_c <- na.omit(set_vertrauen)

#mean 8 vertrauen 2007 - 2017
attach(set_vertrauen_c)
agg_vertrauen <- aggregate(set_vertrauen_c, by=list(jahr), FUN=mean, na.rm=TRUE)
print(agg_vertrauen)
detach(set_vertrauen_c)


#clean set
group_vertrauen <- c("jahr", "st_q01.01", "st_q01.02", "st_q01.03", "st_q01.04", "st_q01.05", "st_q01.06", 
               "st_q01.07", "st_q01.16")
set_group <- agg_vertrauen[group_vertrauen]

#calc overall mean
set_group$mean <- with(set_group,(st_q01.01+st_q01.02+st_q01.03+st_q01.04+st_q01.05+st_q01.06+st_q01.07+st_q01.16)/8)

set_group

#tmp
vertrauen_plot <- data.frame() 

vars <- c("st_q01.01", "st_q01.02", "st_q01.03", "st_q01.04", "st_q01.05", "st_q01.06", 
          "st_q01.07", "st_q01.16", "mean") 

for (var in vars)
{tmp <- subset(set_group, select = c("jahr", var)) 
tmp$institution <- var 
colnames(tmp) <- c("jahr", "vertrauen", "institution") 
vertrauen_plot <- rbind(vertrauen_plot, tmp) }

#x-axis breaks
years <- 2007:2017

#colour variables
colours <- c("st_q01.01" = "burlywood4", "st_q01.02" = "burlywood3", "st_q01.03" = "cadetblue4", "st_q01.04" = "cadetblue3", 
             "st_q01.05" = "cadetblue1", "st_q01.06" ="red3", "st_q01.07" = "rosybrown2", "st_q01.16" = "ivory3", "mean"= "black")


#plot mean 8 vertrauen + overall mean 2007 - 2017
p <- ggplot(data=vertrauen_plot, aes(x=jahr, y=vertrauen, group=institution, colour=institution)) + 
  geom_line(data=vertrauen_plot[vertrauen_plot$institution!="st_q01.06" & vertrauen_plot$institution!="mean",], 
            aes(x=jahr, y=vertrauen, group=institution, colour=institution), linetype="solid", size=0.7) + 
  geom_line(data=vertrauen_plot[vertrauen_plot$institution=="st_q01.06",], aes(x=jahr, y=vertrauen, group=institution, colour=institution), 
            linetype="solid", size=0.8) + 
  geom_line(data=vertrauen_plot[vertrauen_plot$institution=="mean",], aes(x=jahr, y=vertrauen, group=institution, colour=institution), 
            linetype="dashed", size=0.5, show.legend = TRUE) + 
  geom_point(data=vertrauen_plot[vertrauen_plot$institution!="mean",], size=0.9, show.legend = FALSE) + scale_x_continuous(breaks=years) + 
  theme(axis.line = element_line(colour = "black"), panel.grid.major = element_line(colour="lightgrey", size=0.2),
  panel.grid.minor = element_line(colour="lightgrey", size=0.2), panel.border = element_blank(), panel.background = element_blank(), 
  legend.position="right" , legend.title=element_blank(), strip.background = element_rect(fill = "transparent"), 
  legend.key=element_blank(), plot.title = element_text(hjust = 0)) + 
  labs(title = "Vertrauen in Institutionen", x="", y="Mittelwert" ) + 
  scale_colour_manual(values = colours,
                     breaks =c("st_q01.01", "st_q01.02", "st_q01.03", "st_q01.04", "st_q01.05", "st_q01.16", "st_q01.07", "st_q01.06",  "mean"), 
                     labels=c("Bundesrat", "Parlament", "Gerichte", "Polizei", "Armee", "Parteien", "Wirtschaft", "Medien", "Gesamtmittelwert")) +
  guides(colour=guide_legend(override.aes=list(linetype=c(rep(1, times=8), 2))))

p




#vertrauen 2017
Sicherheit2017_nur2017 <- read_sav("~/Desktop/BLOG/Sicherheit2017_nur2017.sav")

sicherheit <- Sicherheit2017_nur2017 

#summary(sicherheit)

library(Zelig)
library(nnet)
library(MASS)
library(RCurl)
library(reshape2)
library(gridExtra)
library(dplyr)
library(stargazer)

#set_vertrauen17: jahr, 06=Medien, 18=Internet, 19=SocMed, 08=polEinstellung, s3=Geschlecht, age_qr7=Alter, bildung3=Bildung, reg=Region, 01=Bundesrat, 02=Parlament, 03=Gerichte, 04=Polizei, 05=Armee, 07=Wirtschaft, 16=Parteien
vertrauen17 <- c("jahr","st_q01.06", "st_q01.18", "st_q01.19", "st_q08", "s3", "age_qr7", "bildung3", "reg", "st_q01.01", "st_q01.02", 
                 "st_q01.03", "st_q01.04", "st_q01.05", "st_q01.07", "st_q01.16")
set_vertrauen17<- sicherheit[vertrauen17]
set_vertrauen17_c <- na.omit(set_vertrauen17)


#socialmedia
m1 <- zelig(st_q01.19 ~ st_q08 + I(st_q08^2) + st_q01.01 + st_q01.02 + st_q01.03 + st_q01.04 + 
              st_q01.05 + st_q01.06 + st_q01.07 + st_q01.16 + st_q01.18 + s3 + age_qr7 + bildung3 + reg, 
            model = "ls", data = set_vertrauen17_c, cite = FALSE)

summary(m1)

mpred1 <- setx(m1, st_q08=c(0:10))

mpred11 <- sim(m1, x = mpred1)

summary(mpred11)

plot(mpred11)

sims.full1 <- set_vertrauen17_c %>% 
  zelig(st_q01.19 ~ st_q08 + I(st_q08^2) + st_q01.01 + st_q01.02 + st_q01.03 + st_q01.04 + st_q01.05 + st_q01.06 + st_q01.07 + st_q01.16 + st_q01.18 + s3 + age_qr7 + bildung3 + reg, 
        model = "ls", data =., cite = FALSE) %>%
  setx(st_q08 = c(0:10))  %>%
  sim() %>%
  zelig_qi_to_df()

head(sims.full1)

sims.slimmed1 <- qi_slimmer(sims.full1)

"Mittelwert1" <- mean(set_vertrauen17_c$st_q01.19)
Mittelwert1

ggplot(sims.slimmed1, aes(st_q08, qi_ci_median)) +
  geom_ribbon(aes(ymin = qi_ci_min, ymax = qi_ci_max), alpha = 0.3, fill="lightpink") +
  geom_line(color="lightpink4") + 
  geom_line(aes(y=Mittelwert1), color="black", linetype="dotted")+
  scale_x_continuous(breaks=seq(0,10,1), labels=c("links","","","","","mitte","","","","","rechts")) +
  ylab('expected values') +
  theme_bw()+
  labs(title = "Social Media", x="", y="Vertrauen") +
  annotate("text", x = 1, y = 3.6, size=2.6, label = "Mittelwert = 3.53")


#internet
m2 <- zelig(st_q01.18 ~ st_q08 + I(st_q08^2) + st_q01.01 + st_q01.02 + st_q01.03 + st_q01.04 + 
              st_q01.05 + st_q01.06 + st_q01.07 + st_q01.16 + st_q01.19 + s3 + age_qr7 + bildung3 + reg, 
            model = "ls", data = set_vertrauen17_c, cite = FALSE)

summary(m2)

mpred2 <- setx(m2, st_q08=c(0:10))

mpred22 <- sim(m2, x = mpred2)

summary(mpred22)

plot(mpred22)

sims.full2 <- set_vertrauen17_c %>% 
  zelig(st_q01.18 ~ st_q08 + I(st_q08^2) + st_q01.01 + st_q01.02 + st_q01.03 + st_q01.04 + st_q01.05 + st_q01.06 + st_q01.07 + st_q01.16 + st_q01.19 + s3 + age_qr7 + bildung3 + reg, 
        model = "ls", data =., cite = FALSE) %>%
  setx(st_q08 = c(0:10)) %>%
  sim() %>%
  zelig_qi_to_df()

head(sims.full2)

sims.slimmed2 <- qi_slimmer(sims.full2)

"Mittelwert2" <- mean(set_vertrauen17_c$st_q01.18)
Mittelwert2

ggplot(sims.slimmed2, aes(st_q08, qi_ci_median)) +
  geom_ribbon(aes(ymin = qi_ci_min, ymax = qi_ci_max), alpha = 0.3, fill="lightgoldenrod") +
  geom_line(color="lightgoldenrod4") + 
  geom_line(aes(y=Mittelwert2), color="black", linetype="dotted")+
  scale_x_continuous(breaks=seq(0,10,1), labels=c("links","","","","","mitte","","","","","rechts")) +
  ylab('expected values') +
  theme_bw() +
  labs(title = "Internet", x="", y="Vertrauen") +
  annotate("text", x = 1, y = 5.4, size=2.6, label = "Mittelwert = 5.44")


#medien generell
m3 <- zelig(st_q01.06 ~ st_q08 + I(st_q08^2) + st_q01.01 + st_q01.02 + st_q01.03 + st_q01.04 + 
              st_q01.05 + st_q01.19 + st_q01.07 + st_q01.16 + st_q01.18 + s3 + age_qr7 + bildung3 + reg, 
            model = "ls", data = set_vertrauen17_c, cite = FALSE)

summary(m3)

mpred3 <- setx(m3, st_q08=c(0:10))

mpred33 <- sim(m3, x = mpred3)

summary(mpred33)

plot(mpred33)

sims.full3 <- set_vertrauen17_c %>% 
  zelig(st_q01.06 ~ st_q08 + I(st_q08^2) + st_q01.01 + st_q01.02 + st_q01.03 + st_q01.04 + st_q01.05 + st_q01.19 + st_q01.07 + st_q01.16 + st_q01.18 + s3 + age_qr7 + bildung3 + reg, 
        model = "ls", data =., cite = FALSE) %>%
  setx(st_q08 = c(0:10)) %>%
  sim() %>%
  zelig_qi_to_df()

head(sims.full3)

sims.slimmed3 <- qi_slimmer(sims.full3)

"Mittelwert3" <- mean(set_vertrauen17_c$st_q01.06)
Mittelwert3

ggplot(sims.slimmed3, aes(st_q08, qi_ci_median)) +
  geom_ribbon(aes(ymin = qi_ci_min, ymax = qi_ci_max), alpha = 0.3, fill="lightblue") +
  geom_line(color="lightblue4") + 
  geom_line(aes(y=Mittelwert3), color="black", linetype="dotted")+
  scale_x_continuous(breaks=seq(0,10,1), labels=c("links","","","","","mitte","","","","","rechts")) +
  ylab('expected values') +
  theme_bw()  + 
  labs(title = "Medien generell", x="", y="Vertrauen")+
  annotate("text", x = 1, y = 5.42, size=2.6, label = "Mittelwert = 5.46")