per

#libraries
library(tidyverse)

## -- Attaching packages ---------------------------------------------- tidyverse 1.2.1 --

## v ggplot2 3.0.0     v purrr   0.2.5
## v tibble  1.4.2     v dplyr   0.7.6
## v tidyr   0.8.1     v stringr 1.3.1
## v readr   1.1.1     v forcats 0.3.0

## -- Conflicts ------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(haven)
library(dplyr)
library(sjPlot)
library(ggplot2)
library(list)

## Loading required package: sandwich

library(tidyr)
library(corrplot)

## corrplot 0.84 loaded

library(FactoMineR)
library(factoextra)

## Welcome! Related Books: `Practical Guide To Cluster Analysis in R` at https://goo.gl/13EFCZ

#data

data.eng <- read_sav("C:/Users/emretoros/OneDrive/Makale/ElectoralIntegrity/EI-T/Data_v1/Data_v01_Eng.sav")
dievt <- read.csv("C:/Users/emretoros/OneDrive/Makale/ElectoralIntegrity/EI-T/Data_v1/gmf/DIEV-T.csv", encoding="UTF-8")
lead <- read_sav("C:/Users/emretoros/OneDrive/Makale/ElectoralIntegrity/EI-T/Data_v1/personality/leaderscores.sav")
nai <- read_dta("C:/Users/emretoros/OneDrive/Makale/ElectoralIntegrity/EI-T/Data_v1/personality/nai.dta")

data.eng[data.eng == 99] <- NA



## CONSTRUCTING VARIABLES ##

#Language variable
data.eng$Kurdisha <- data.eng$z0801
data.eng$Kurdishb <- data.eng$z0802
data.eng$Kurdishc <- data.eng$z0803


data.eng <- data.eng %>% 
  mutate(Kurdish = case_when(
    Kurdisha == 1 ~ 1,
    Kurdishb == 1 ~ 1,
    Kurdishc == 1 ~ 1,
    TRUE ~ 0)
  )





#income variable
data.eng$income <- data.eng$z05
data.eng$income[data.eng$income == 99] <- NA

data.eng <- data.eng %>% 
  mutate(income.binary = case_when(
    income == 6 ~ 1,
    income == 7 ~ 1,
    income == 8 ~ 1,
    income == 9 ~ 1,
    TRUE ~ 0
  ))


# housewife
data.eng <- data.eng %>% 
  mutate(housewife = case_when(
    z01 == 5 ~ 1, 
    TRUE ~ 0
  ))



#interest in politics
data.eng <- data.eng %>% 
  mutate(pol.interest = case_when(
    c01 == 3 ~ 1,
    c01 == 4 ~ 1,
    TRUE ~ 0
  ))


#gender variable
data.eng$gender<- data.eng$cins

#age variable
data.eng <- data.eng %>% 
  mutate(age = 2018 -dtarih)

# esea region variable
data.eng <- data.eng %>% 
  mutate(esea = case_when(
    bolge == 10 ~ 1,
    bolge == 11 ~ 1,
    bolge == 12 ~ 1,
    TRUE ~ 0
  ))


# istanbul region variable
data.eng <- data.eng %>% 
  mutate(ist = case_when(
    bolge == 1 ~ 1,
    TRUE ~ 0
  ))


#Education variable
data.eng <- data.eng %>% 
  mutate(edu = case_when(
    z01 == 5 ~ 1,
    z01 == 6 ~ 1,
    z01 == 7 ~ 1,
    z01 == 8 ~ 1,
    TRUE ~ 0
  ))
data.eng$edu.factor <- as.factor(data.eng$edu)

# Top5 Party

data.eng <- data.eng %>% 
  mutate(feel.close.to = case_when(
    e05 == 1 ~ "AKP",
    e05 == 2 ~ "CHP",
    e05 == 3 ~ "HDP",
    e05 == 4 ~ "MHP",
    e05 == 5 ~ "IYI",
    TRUE ~ "Others"
  ))



# AKP voter
data.eng <- data.eng %>% 
  mutate(akp = case_when(
    e05 == 1 ~ 1,
    TRUE ~ 0
  ))


# CHP voter
data.eng <- data.eng %>% 
  mutate(chp = case_when(
    e05 == 2 ~ 1,
    TRUE ~ 0
  ))


# HDP voter
data.eng <- data.eng %>% 
  mutate(hdp = case_when(
    e05 == 3 ~ 1,
    TRUE ~ 0
  ))

# MHP voter
data.eng <- data.eng %>% 
  mutate(mhp = case_when(
    e05 == 4 ~ 1,
    TRUE ~ 0
  ))


# IYI voter
data.eng <- data.eng %>% 
  mutate(iyi = case_when(
    e05 == 5 ~ 1,
    TRUE ~ 0
  ))







# Strong AKP partisan
data.eng <- data.eng %>% 
  mutate(akp.strong = case_when(
    e02 == 1 ~ 1,
    TRUE ~ 0
  ))

# Strong AKP partisan2
data.eng <- data.eng %>% 
  mutate(akp.strong2 = case_when(
    e0101 == 3 ~ 1,
    e0101 == 4 ~ 1,
    TRUE ~ 0
  ))

# Weak AKP partisan2
data.eng <- data.eng %>% 
  mutate(akp.weak2 = case_when(
    e0101 == 3 ~ 1,
    e0101 == 2 ~ 1,
    TRUE ~ 0
  ))


# Strong CHP partisan
data.eng <- data.eng %>% 
  mutate(chp.strong = case_when(
    e02 == 2 ~ 1,
    TRUE ~ 0
  ))
# Strong CHP partisan2
data.eng <- data.eng %>% 
  mutate(chp.strong2 = case_when(
    e0103 == 3 ~ 1,
    e0103 == 4 ~ 1,
    TRUE ~ 0
  ))

# Weak CHP partisan2
data.eng <- data.eng %>% 
  mutate(chp.weak2 = case_when(
    e0103 == 3 ~ 1,
    e0103 == 2 ~ 1,
    TRUE ~ 0
  ))


# Strong HDP partisan
data.eng <- data.eng %>% 
  mutate(hdp.strong = case_when(
    e02 == 3 ~ 1,
    TRUE ~ 0
  ))

# Strong HDP partisan2
data.eng <- data.eng %>% 
  mutate(hdp.strong2 = case_when(
    e0102 == 3 ~ 1,
    e0102 == 4 ~ 1,
    TRUE ~ 0
  ))

# Weak HDP partisan2
data.eng <- data.eng %>% 
  mutate(hdp.weak2 = case_when(
    e0102 == 3 ~ 1,
    e0102 == 2 ~ 1,
    TRUE ~ 0
  ))


# Strong MHP partisan
data.eng <- data.eng %>% 
  mutate(mhp.strong = case_when(
    e02 == 4 ~ 1,
    TRUE ~ 0
  ))
# Strong MHP partisan2
data.eng <- data.eng %>% 
  mutate(mhp.strong2 = case_when(
    e0104 == 3 ~ 1,
    e0104 == 4 ~ 1,
    TRUE ~ 0
  ))

# Weak MHP partisan2
data.eng <- data.eng %>% 
  mutate(mhp.weak2 = case_when(
    e0104 == 3 ~ 1,
    e0104 == 2 ~ 1,
    TRUE ~ 0
  ))


# Strong IYI partisan
data.eng <- data.eng %>% 
  mutate(iyi.strong = case_when(
    e02 == 5 ~ 1,
    TRUE ~ 0
  ))
# Strong IYI partisan2
data.eng <- data.eng %>% 
  mutate(iyi.strong2 = case_when(
    e0105 == 3 ~ 1,
    e0105 == 4 ~ 1,
    TRUE ~ 0
  ))

# Weak IYI partisan2
data.eng <- data.eng %>% 
  mutate(iyi.weak2 = case_when(
    e0105 == 3 ~ 1,
    e0105 == 2 ~ 1,
    TRUE ~ 0
  ))


# RTE voter
data.eng <- data.eng %>% 
  mutate(rte = case_when(
    e06 == 1 ~ 1,
    TRUE ~ 0
  ))

# İnce Voter

data.eng <- data.eng %>% 
  mutate(mi = case_when(
    e06 == 2 ~ 1,
    TRUE ~ 0
  ))

# Aksener Voter

data.eng <- data.eng %>% 
  mutate(ma = case_when(
    e06 == 3 ~ 1,
    TRUE ~ 0
  ))


# Demirtas Voter

data.eng <- data.eng %>% 
  mutate(sd = case_when(
    e06 == 4 ~ 1,
    TRUE ~ 0
  ))




#region variable

data.eng <- data.eng %>% 
  mutate(region = case_when(
    bolge == 1 ~ "Marmara",
    bolge == 2 ~ "Marmara",
    bolge == 3 ~ "Ege",
    bolge == 4 ~ "Marmara",
    bolge == 5 ~ "İç Anadolu",
    bolge == 6 ~ "Akdeniz",
    bolge == 7 ~ "İç Anadolu",
    bolge == 8 ~ "Karadeniz",
    bolge == 9 ~ "Karadeniz",
    TRUE ~ "Doğu ve Güneydoğu Anadolu"
    
  ))
data.eng$region <- as.factor(data.eng$region)
data.eng$region <-relevel(data.eng$region , ref="Doğu ve Güneydoğu Anadolu")

#big5 variables

data.eng$extraversion.enthusiastic <- data.eng$i0301
data.eng$extraversion.reserved <- data.eng$i0306

data.eng$agreeableness.critical <- data.eng$i0302
data.eng$agreeableness.warm <- data.eng$i0307

data.eng$consciousness.dependeble <- data.eng$i0303

data.eng$emotional.anxious <- data.eng$i0304
data.eng$emotional.calm <- data.eng$i0308

data.eng$openness.newexpriences <- data.eng$i0305

data.eng$extraversion <- ((data.eng$extraversion.enthusiastic + data.eng$extraversion.reserved)/2) 
data.eng$agreeableness <- ((data.eng$agreeableness.critical + data.eng$agreeableness.warm)/2) 
data.eng$consciousness <- data.eng$consciousness.dependeble
data.eng$emotional.stability <- ((data.eng$emotional.anxious + data.eng$emotional.calm)/2)
data.eng$openness <- data.eng$openness.newexpriences


sjt.xtab(data.eng$feel.close.to, data.eng$extraversion.enthusiastic,  
         show.col.prc = T)

feel.close.to	I0301 - Enthusiastic, extrovert - Do you agree that Recep Tayyip ErdoÄŸan has this feature?						Total
	Completely disagree	Somewhat disagree	Neither agree nor disagree	Somewhat agree	Completely agree	Do not know/No answer
AKP	13 6.6 %	15 12.7 %	47 27.6 %	127 48.3 %	380 85.6 %	0 0 %	582 48.8 %
CHP	117 59.1 %	56 47.5 %	48 28.2 %	30 11.4 %	8 1.8 %	0 0 %	259 21.7 %
HDP	38 19.2 %	16 13.6 %	12 7.1 %	24 9.1 %	2 0.5 %	0 0 %	92 7.7 %
IYI	10 5.1 %	10 8.5 %	13 7.6 %	6 2.3 %	2 0.5 %	0 0 %	41 3.4 %
MHP	3 1.5 %	5 4.2 %	9 5.3 %	35 13.3 %	27 6.1 %	0 0 %	79 6.6 %
Others	17 8.6 %	16 13.6 %	41 24.1 %	41 15.6 %	25 5.6 %	0 0 %	140 11.7 %
Total	198 100 %	118 100 %	170 100 %	263 100 %	444 100 %	0 100 %	1193 100 %
χ2=673.975 · df=20 · Cramer’s V=0.376 · Fisher’s p=0.000

sjt.xtab(data.eng$feel.close.to, data.eng$extraversion.reserved,  
         show.col.prc = T)

feel.close.to	I0306 - Introvert, quiet - Do you agree that Recep Tayyip ErdoÄŸan has this feature?						Total
	Completely disagree	Somewhat disagree	Neither agree nor disagree	Somewhat agree	Completely agree	Do not know/No answer
AKP	327 55.2 %	150 52.6 %	62 32 %	25 36.8 %	20 40.8 %	0 0 %	584 49.2 %
CHP	106 17.9 %	48 16.8 %	68 35.1 %	18 26.5 %	18 36.7 %	0 0 %	258 21.7 %
HDP	52 8.8 %	20 7 %	12 6.2 %	5 7.4 %	2 4.1 %	0 0 %	91 7.7 %
IYI	9 1.5 %	17 6 %	10 5.2 %	5 7.4 %	0 0 %	0 0 %	41 3.5 %
MHP	41 6.9 %	18 6.3 %	11 5.7 %	6 8.8 %	3 6.1 %	0 0 %	79 6.6 %
Others	57 9.6 %	32 11.2 %	31 16 %	9 13.2 %	6 12.2 %	0 0 %	135 11.4 %
Total	592 100 %	285 100 %	194 100 %	68 100 %	49 100 %	0 100 %	1188 100 %
χ2=75.196 · df=20 · Cramer’s V=0.126 · Fisher’s p=0.000

sjt.xtab(data.eng$feel.close.to, data.eng$agreeableness.critical,  
         show.col.prc = T)

feel.close.to	I0302 - Criticising, fighter - Do you agree that Recep Tayyip ErdoÄŸan has this feature?						Total
	Completely disagree	Somewhat disagree	Neither agree nor disagree	Somewhat agree	Completely agree	Do not know/No answer
AKP	339 84.3 %	121 61.7 %	63 34.6 %	35 18.4 %	27 12 %	0 0 %	585 49 %
CHP	13 3.2 %	17 8.7 %	42 23.1 %	75 39.5 %	113 50.2 %	0 0 %	260 21.8 %
HDP	5 1.2 %	5 2.6 %	17 9.3 %	27 14.2 %	38 16.9 %	0 0 %	92 7.7 %
IYI	3 0.7 %	5 2.6 %	11 6 %	15 7.9 %	7 3.1 %	0 0 %	41 3.4 %
MHP	24 6 %	16 8.2 %	15 8.2 %	12 6.3 %	12 5.3 %	0 0 %	79 6.6 %
Others	18 4.5 %	32 16.3 %	34 18.7 %	26 13.7 %	28 12.4 %	0 0 %	138 11.5 %
Total	402 100 %	196 100 %	182 100 %	190 100 %	225 100 %	0 100 %	1195 100 %
χ2=526.434 · df=20 · Cramer’s V=0.332 · p=0.000

sjt.xtab(data.eng$feel.close.to, data.eng$agreeableness.warm,  
         show.col.prc = T)

feel.close.to	I0307 - Sympathetic, sincere - Do you agree that Recep Tayyip ErdoÄŸan has this feature?						Total
	Completely disagree	Somewhat disagree	Neither agree nor disagree	Somewhat agree	Completely agree	Do not know/No answer
AKP	10 4.5 %	17 12.1 %	54 30.3 %	154 63.9 %	350 85 %	0 0 %	585 48.9 %
CHP	129 57.6 %	68 48.2 %	40 22.5 %	16 6.6 %	5 1.2 %	0 0 %	258 21.6 %
HDP	42 18.8 %	23 16.3 %	17 9.6 %	7 2.9 %	3 0.7 %	0 0 %	92 7.7 %
IYI	9 4 %	14 9.9 %	13 7.3 %	2 0.8 %	3 0.7 %	0 0 %	41 3.4 %
MHP	6 2.7 %	4 2.8 %	14 7.9 %	29 12 %	26 6.3 %	0 0 %	79 6.6 %
Others	28 12.5 %	15 10.6 %	40 22.5 %	33 13.7 %	25 6.1 %	0 0 %	141 11.8 %
Total	224 100 %	141 100 %	178 100 %	241 100 %	412 100 %	0 100 %	1196 100 %
χ2=719.192 · df=20 · Cramer’s V=0.388 · p=0.000

sjt.xtab(data.eng$feel.close.to, data.eng$consciousness.dependeble,  
         show.col.prc = T)

feel.close.to	I0303 - Trustworthy, disciplined - Do you agree that Recep Tayyip ErdoÄŸan has this feature?						Total
	Completely disagree	Somewhat disagree	Neither agree nor disagree	Somewhat agree	Completely agree	Do not know/No answer
AKP	10 6.1 %	16 9.9 %	40 20.4 %	170 64.6 %	351 84.2 %	0 0 %	587 48.8 %
CHP	85 51.8 %	91 56.2 %	60 30.6 %	18 6.8 %	6 1.4 %	0 0 %	260 21.6 %
HDP	41 25 %	24 14.8 %	20 10.2 %	6 2.3 %	3 0.7 %	0 0 %	94 7.8 %
IYI	10 6.1 %	12 7.4 %	13 6.6 %	3 1.1 %	3 0.7 %	0 0 %	41 3.4 %
MHP	1 0.6 %	6 3.7 %	15 7.7 %	30 11.4 %	27 6.5 %	0 0 %	79 6.6 %
Others	17 10.4 %	13 8 %	48 24.5 %	36 13.7 %	27 6.5 %	0 0 %	141 11.7 %
Total	164 100 %	162 100 %	196 100 %	263 100 %	417 100 %	0 100 %	1202 100 %
χ2=736.347 · df=20 · Cramer’s V=0.391 · p=0.000

sjt.xtab(data.eng$feel.close.to, data.eng$emotional.anxious,  
         show.col.prc = T)

feel.close.to	I0304 - Gets angry easily, anxious - Do you agree that Recep Tayyip ErdoÄŸan has this feature?						Total
	Completely disagree	Somewhat disagree	Neither agree nor disagree	Somewhat agree	Completely agree	Do not know/No answer
AKP	283 81.6 %	138 64.2 %	90 46.6 %	36 17.6 %	33 14.9 %	0 0 %	580 49.1 %
CHP	11 3.2 %	20 9.3 %	41 21.2 %	77 37.6 %	108 48.6 %	0 0 %	257 21.7 %
HDP	6 1.7 %	10 4.7 %	12 6.2 %	24 11.7 %	40 18 %	0 0 %	92 7.8 %
IYI	3 0.9 %	4 1.9 %	11 5.7 %	18 8.8 %	5 2.3 %	0 0 %	41 3.5 %
MHP	26 7.5 %	16 7.4 %	12 6.2 %	16 7.8 %	9 4.1 %	0 0 %	79 6.7 %
Others	18 5.2 %	27 12.6 %	27 14 %	34 16.6 %	27 12.2 %	0 0 %	133 11.3 %
Total	347 100 %	215 100 %	193 100 %	205 100 %	222 100 %	0 100 %	1182 100 %
χ2=451.108 · df=20 · Cramer’s V=0.309 · p=0.000

sjt.xtab(data.eng$feel.close.to, data.eng$emotional.calm,  
         show.col.prc = T)

feel.close.to	I0308 - Calm and emotionally balanced - Do you agree that Recep Tayyip ErdoÄŸan has this feature?						Total
	Completely disagree	Somewhat disagree	Neither agree nor disagree	Somewhat agree	Completely agree	Do not know/No answer
AKP	26 11.2 %	24 15.4 %	85 41.1 %	160 66.1 %	290 83.3 %	0 0 %	585 49.4 %
CHP	119 51.3 %	70 44.9 %	42 20.3 %	16 6.6 %	8 2.3 %	0 0 %	255 21.5 %
HDP	41 17.7 %	23 14.7 %	18 8.7 %	7 2.9 %	1 0.3 %	0 0 %	90 7.6 %
IYI	10 4.3 %	9 5.8 %	15 7.2 %	4 1.7 %	2 0.6 %	0 0 %	40 3.4 %
MHP	10 4.3 %	5 3.2 %	16 7.7 %	24 9.9 %	24 6.9 %	0 0 %	79 6.7 %
Others	26 11.2 %	25 16 %	31 15 %	31 12.8 %	23 6.6 %	0 0 %	136 11.5 %
Total	232 100 %	156 100 %	207 100 %	242 100 %	348 100 %	0 100 %	1185 100 %
χ2=540.443 · df=20 · Cramer’s V=0.338 · p=0.000

sjt.xtab(data.eng$feel.close.to, data.eng$openness.newexpriences,  
         show.col.prc = T)

feel.close.to	I0305 - Innovative, open - Do you agree that Recep Tayyip ErdoÄŸan has this feature?						Total
	Completely disagree	Somewhat disagree	Neither agree nor disagree	Somewhat agree	Completely agree	Do not know/No answer
AKP	11 5.8 %	17 11.3 %	48 27.7 %	156 59.8 %	353 84 %	0 0 %	585 49 %
CHP	106 55.8 %	75 49.7 %	48 27.7 %	24 9.2 %	6 1.4 %	0 0 %	259 21.7 %
HDP	39 20.5 %	16 10.6 %	21 12.1 %	14 5.4 %	3 0.7 %	0 0 %	93 7.8 %
IYI	8 4.2 %	14 9.3 %	12 6.9 %	4 1.5 %	3 0.7 %	0 0 %	41 3.4 %
MHP	4 2.1 %	5 3.3 %	13 7.5 %	28 10.7 %	28 6.7 %	0 0 %	78 6.5 %
Others	22 11.6 %	24 15.9 %	31 17.9 %	35 13.4 %	27 6.4 %	0 0 %	139 11.6 %
Total	190 100 %	151 100 %	173 100 %	261 100 %	420 100 %	0 100 %	1195 100 %
χ2=643.475 · df=20 · Cramer’s V=0.367 · p=0.000

#Models


akp.model <- glm( akp ~ extraversion + agreeableness + consciousness + emotional.stability + openness,
                  family = "binomial",
                  data = data.eng)


chp.model <- glm( chp ~ extraversion + agreeableness + consciousness + emotional.stability + openness, 
                  family = "binomial",
                  data = data.eng)

mhp.model <- glm( mhp ~ extraversion + agreeableness + consciousness + emotional.stability + openness, 
                  family = "binomial",
                  data = data.eng)

hdp.model <- glm( hdp ~ extraversion + agreeableness + consciousness + emotional.stability + openness, 
                  family = "binomial",
                  data = data.eng)


plot_models(akp.model, chp.model, mhp.model, hdp.model,
            show.p = TRUE,
            legend.title = "Voted for...",
            axis.lim = c(0.5, 2.2),
            show.values = TRUE)

#NAI DATA


#use leader names as row name
lead <- lead %>% 
  remove_rownames %>% 
  column_to_rownames(var="Leader") 

## Warning: Setting row names on a tibble is deprecated.

#remove country name
lead <- within(lead, rm(Country))



# Big5 Correspondance Analysis

ca.data.big5 <- lead %>% 
  select(Extraversion, Agreeableness, Conscientiousness, Emotional_stability,  Openness )

ca.big5 <- CA(ca.data.big5, graph = F)
fviz_ca_biplot(ca.big5,
               title = "Big5",
               repel = TRUE)

 # Dark Correspondence Analysis
ca.data.dark <- lead %>% 
  select(Narcissism, Psychopathy, Machiavellianism)

ca.dark <- CA(ca.data.dark, graph = F)
fviz_ca_biplot(ca.dark,
               title = "Dark",
               repel = TRUE)

# Total Correspondence Analysis
ca.lead <- CA(lead, col.sup = 6:8, graph = F)
fviz_ca_biplot(ca.lead,
               title = "Big5 & Dark",
               repel = TRUE)