Are Scots more left-wing?

Henderson replication.R

library(foreign)
library(car)
library(broom)
library(reshape)
library(ggplot2)

dat <- read.spss("BES2015_W2_Panel_v2.0.sav",to.data.frame = TRUE)

### First, left-right values
### Here, positive values -> more left wing
for(i in 1:5){
 the.var <- paste0("lr", i,"W1")
 the.newvar <- paste0("lr", i, "score")
 dat[,the.newvar] <- car::recode(dat[,the.var],
	"'Strongly disagree'=1;
	'Disagree'=2;
	'Neither agree nor disagree'=3;
	'Agree'=4;
	'Strongly agree'=5;else=NA")
	dat[,the.newvar] <- as.numeric(dat[,the.newvar])
}

# check correlations
cor(dat[,c("lr1score", "lr2score", "lr3score", "lr4score", "lr5score")], use = "pairwise.complete.obs")
dat$lrindex <- rowMeans(dat[,c("lr1score", "lr2score", "lr3score", "lr4score", "lr5score")], na.rm = TRUE)

dat$country <- car:::recode(dat$gor, 
	"'Scotland'='Scotland';'Wales'='Wales';else='England'")

summary(lrmod <- lm(lrindex ~ 0 + country, dat))

### Now, authoritarian-libertarian values
### Here, positive values -> more authoritarian
for(i in 1:5){
 the.var <- paste0("al", i,"W1")
 the.newvar <- paste0("al", i, "score")
 dat[,the.newvar] <- car::recode(dat[,the.var],
	"'Strongly disagree'=1;
	'Disagree'=2;
	'Neither agree nor disagree'=3;
	'Agree'=4;
	'Strongly agree'=5;else=NA")
 dat[,the.newvar] <- as.numeric(dat[,the.newvar])
}
# check correlations
cor(dat[,c("al1score", "al2score", "al3score", "al4score", "al5score")], use = "pairwise.complete.obs")

dat$alindex <- rowMeans(dat[,c("al1score", "al2score", "al3score", "al4score", "al5score")], na.rm = TRUE)

dat$country <- car:::recode(dat$gor, 
	"'Scotland'='Scotland';'Wales'='Wales';else='England'")

summary(almod <- lm(alindex ~ 0 +  country, dat))

### Now for welfarism
### Here, negative values -> greater support for welfare

welfare.qs <- c("reasonForUnemploymentW1",
"immigrantsWelfareStateW1",
"govtHandoutsW1",
"polForTheRichW1",
"businessBonusW1")

for (q in welfare.qs) {
 the.var <- q
 the.newvar <- paste0("wf", which(welfare.qs==q),"score")
 dat[,the.newvar] <- car::recode(dat[,the.var],
	"'Strongly disagree'=1;
	'Disagree'=2;
	'Neither agree nor disagree'=3;
	'Agree'=4;
	'Strongly agree'=5;else=NA")
	dat[,the.newvar] <- as.numeric(dat[,the.newvar])
}

### Some of these need to be reversed, since agreeing is the pro-welfare response
dat$wf1score <- 6 + dat$wf1score * - 1
dat$wf4score <- 6 + dat$wf4score * - 1

cor(dat[,c("wf1score", "wf2score", "wf3score", "wf4score", "wf5score")], use = "pairwise.complete.obs")

dat$wfindex <- rowMeans(dat[,c("wf1score", "wf2score", "wf3score", "wf4score", "wf5score")], na.rm = TRUE)

summary(wfmod <- lm(wfindex ~0 +  country, dat))


lrmod.df <- tidy(lrmod)
wfmod.df <- tidy(wfmod)
almod.df <- tidy(almod)

lrmod.df$outcome <- "Leftism"
wfmod.df$outcome <- "Concern over welfare scams"
almod.df$outcome <- "Authoritarianism"

plot.df <- merge_recurse(list(lrmod.df,wfmod.df,almod.df))

plot.df$estimate.hi <- plot.df$estimate + plot.df$stderror * 1.96
plot.df$estimate.lo <- plot.df$estimate - plot.df$stderror * 1.96

plot.df$Country <- gsub("country","",plot.df$term)

speccie.replication <- ggplot(plot.df, aes(x = outcome, y= estimate, ymin = estimate.lo, ymax = estimate.hi, fill = Country)) + 
	geom_bar(stat="identity",position="dodge") + 
	geom_errorbar(position="dodge") + 
	scale_fill_manual(values = c("red","blue4","springgreen4")) + 
	scale_x_discrete("Scale") + 
	scale_y_continuous("Score") + 
	theme_bw() 

pdf(file="speccie_replication.pdf", width = 10, height= 8)
print(speccie.replication)
dev.off()