rm(list=ls(all=TRUE)) 

#Data prep
setwd("D:/emre/SkyDrive/makale/FactCheck")
library(lessR)
## 
## lessR 3.5.0      feedback: gerbing@pdx.edu      web: lessRstats.com
## -------------------------------------------------------------------
## 1. Read a text, Excel, SPSS, SAS or R data file from your computer
##    into the mydata data table, enter:  mydata <- Read()  
## 2. For a list of help topics and functions, enter:  Help()
## 3. Use theme function for global settings, Ex: theme(colors="gray")
library(foreign)
library(nnet)
library(effects)
library(car)
## 
## Attaching package: 'car'
## The following object is masked from 'package:effects':
## 
##     Prestige
## The following objects are masked from 'package:lessR':
## 
##     bc, Recode, sp
library(reshape)
library(sjPlot)
library(pander)


mydata <- read.csv("combinedR.csv")
mydata$Tone <- NULL

#Sorting the variables
mydata$Party[mydata$Party == 1] <- "AKP"
mydata$Party[mydata$Party == 2] <- "CHP"
mydata$Party[mydata$Party == 3] <- "MHP"
mydata$Party[mydata$Party == 4] <- "HDP"
mydata$Party[mydata$Party == 5] <- "Others"
mydata$f.party <- as.factor(mydata$Party)

mydata$Scale3[mydata$Scale3==1] <- "True"
mydata$Scale3[mydata$Scale3==2] <- "Partially True"
mydata$Scale3[mydata$Scale3==3] <- "False"
mydata$fact.level <- as.factor(mydata$Scale3)


mydata$MessageMedium[mydata$MessageMedium==1] <- "Twitter"
mydata$MessageMedium[mydata$MessageMedium == 2] <- 0
mydata$MessageMedium[mydata$MessageMedium==0] <- "News"
mydata$MessageMedium<-recode(mydata$MessageMedium,"3=NA")
mydata$medium <- as.factor(mydata$MessageMedium)

mydata$Topic.f<- NA
mydata$Topic.f[mydata$Topic=="Ekonomi"] <- 1
mydata$Topic.f[mydata$Topic=="Mali Politikalar"] <- 1
mydata$Topic.f[mydata$Topic=="Sanayi, Ticaret ve Finans"] <- 1
mydata$Topic.f<-recode(mydata$Topic.f,"NA=0")
mydata$Topic.f[mydata$Topic.f==1] <- "Economy"
mydata$Topic.f[mydata$Topic.f==0] <- "Other Issues"
mydata$Topic.f <- as.factor(mydata$Topic.f)


sjp.xtab(mydata$f.party, mydata$fact.level, margin = "row", bar.pos = "stack", coord.flip = TRUE, 
         show.n = FALSE, show.prc = TRUE,  axis.titles = "Message Sender", geom.colors = c("red", "green", "lightblue"))

sjp.xtab(mydata$f.party, mydata$medium, margin = "row", bar.pos = "stack", coord.flip = TRUE, 
         show.n = FALSE, show.prc = TRUE,  axis.titles = "Message Medium", geom.colors = c("red", "green"))

sjp.xtab(mydata$f.party, mydata$Topic.f, margin = "row", bar.pos = "stack", coord.flip = TRUE, 
         show.n = FALSE, show.prc = TRUE,  axis.titles = "Message Medium", geom.colors = c("red", "green"))

mydata$fact.level <- relevel(mydata$fact.level, ref = "Partially True")
mydata$f.party <- relevel(mydata$f.party, ref = "AKP")

ct1 <- sjt.frq(mydata$fact.level)
pander(ct1)
## Warning in pander.default(ct1): No pander.method for "sjTable", reverting
## to default.No pander.method for "sjtfrq", reverting to default.
ct2 <- sjt.xtab(mydata$fact.level, mydata$medium)
pander(ct2)
## Warning in pander.default(ct2): No pander.method for "sjTable", reverting
## to default.No pander.method for "sjtxtab", reverting to default.
ct3 <- sjt.xtab(mydata$fact.level, mydata$f.party)
pander(ct3)
## Warning in pander.default(ct3): No pander.method for "sjTable", reverting
## to default.No pander.method for "sjtxtab", reverting to default.
ct4 <- sjt.xtab(mydata$fact.level, mydata$Topic.f)
pander(ct4)
## Warning in pander.default(ct4): No pander.method for "sjTable", reverting
## to default.No pander.method for "sjtxtab", reverting to default.
#Multinominal Model
model1 <- multinom(fact.level ~ f.party + Topic.f,  data=mydata)
## # weights:  21 (12 variable)
## initial  value 951.398242 
## iter  10 value 833.632163
## final  value 832.880993 
## converged
summary(model1)
## Call:
## multinom(formula = fact.level ~ f.party + Topic.f, data = mydata)
## 
## Coefficients:
##       (Intercept) f.partyCHP  f.partyHDP f.partyMHP f.partyOthers
## False -0.09136019  0.1323051 -1.13659372  0.1808132     0.6620887
## True   0.18226651  0.6280537  0.08746294  0.8444776     0.4219144
##       Topic.fOther Issues
## False          -0.1154050
## True            0.4961434
## 
## Std. Errors:
##       (Intercept) f.partyCHP f.partyHDP f.partyMHP f.partyOthers
## False   0.2448914  0.2608881  0.5806088  0.3470032     0.3807328
## True    0.2185409  0.2116489  0.3392891  0.2769016     0.3458620
##       Topic.fOther Issues
## False           0.2526225
## True            0.2208873
## 
## Residual Deviance: 1665.762 
## AIC: 1689.762
exp(coef(model1)); exp(confint(model1))
##       (Intercept) f.partyCHP f.partyHDP f.partyMHP f.partyOthers
## False   0.9126889   1.141457  0.3209103   1.198191      1.938838
## True    1.1999339   1.873960  1.0914018   2.326762      1.524878
##       Topic.fOther Issues
## False           0.8910052
## True            1.6423750
## , , False
## 
##                         2.5 %   97.5 %
## (Intercept)         0.5647690 1.474941
## f.partyCHP          0.6845277 1.903390
## f.partyHDP          0.1028415 1.001380
## f.partyMHP          0.6069551 2.365352
## f.partyOthers       0.9193082 4.089044
## Topic.fOther Issues 0.5430597 1.461884
## 
## , , True
## 
##                         2.5 %   97.5 %
## (Intercept)         0.7818708 1.841534
## f.partyCHP          1.2376687 2.837371
## f.partyHDP          0.5612822 2.122209
## f.partyMHP          1.3522372 4.003603
## f.partyOthers       0.7741708 3.003540
## Topic.fOther Issues 1.0652528 2.532165