library(readr)
library(dplyr)
library(knitr)
library(kableExtra)
library(ggplot2)
VoterData<-read_csv("/Users/juliushunte/Downloads/VOTER_Survey_July17_Release1-csv copy.csv")
urbanreligon <-VoterData%>%
mutate(Demographics = ifelse(urbancity_baseline==1,"City",
ifelse(urbancity_baseline==2,"Suburb",
ifelse(urbancity_baseline==3,"Town",
ifelse(urbancity_baseline==4,"Rural Area",
ifelse(urbancity_baseline==5,"Other",NA))))),
imminaturalize = ifelse(immi_naturalize_baseline==1,"Favor",
ifelse(immi_naturalize_baseline==2,"Oppose",NA)),
ft_muslims = ifelse(ft_muslim_2017==997,NA,ft_muslim_2017),
ImmiMakeDiff = ifelse(immi_makedifficult_baseline==1,"Much easier",
ifelse(immi_makedifficult_baseline==2,"Slightly easier",
ifelse(immi_makedifficult_baseline==3,"No change",
ifelse(immi_makedifficult_baseline==4,"Slightly harder",
ifelse(immi_makedifficult_baseline==5,"Much harder",
ifelse(immi_makedifficult_baseline==8,"Not Sure",NA)))))))%>%
select(Demographics,imminaturalize,ft_muslims,ImmiMakeDiff)
gridExtra::grid.arrange(
urbanreligon%>%
filter(imminaturalize=="Favor")%>%
ggplot()+
geom_histogram(aes(ft_muslims),fill="blue")+
geom_vline(aes(xintercept=mean(urbanreligon$ft_muslims,na.rm=TRUE)))+
ggtitle("Average Feelings towards Muslims for those who favor"),
urbanreligon%>%
filter(imminaturalize=="Oppose")%>%
ggplot()+
geom_histogram(aes(ft_muslims),fill="red")+
geom_vline(aes(xintercept=mean(urbanreligon$ft_muslims,na.rm=TRUE)))+
ggtitle("Average Feelings towards Muslims for those who oppose"),
nrow=1)
urbanreligon%>%
group_by(Demographics,imminaturalize)%>%
summarize(n=n())%>%
mutate(percent = n/sum(n))%>%
filter(imminaturalize=="Favor")%>%
kable()%>%
kable_styling(latex_options = c("striped", "scale_down"), font_size = 11)
|
Demographics
|
imminaturalize
|
n
|
percent
|
|
City
|
Favor
|
1084
|
0.4798583
|
|
Other
|
Favor
|
33
|
0.6470588
|
|
Rural Area
|
Favor
|
482
|
0.3250169
|
|
Suburb
|
Favor
|
1264
|
0.4210526
|
|
Town
|
Favor
|
442
|
0.3873795
|
|
NA
|
Favor
|
24
|
0.3750000
|
urbanreligon%>%
group_by(Demographics,imminaturalize)%>%
summarize(n=n())%>%
mutate(percent = n/sum(n))%>%
filter(imminaturalize=="Oppose")%>%
kable()%>%
kable_styling(latex_options = c("striped", "scale_down"), font_size = 11)
|
Demographics
|
imminaturalize
|
n
|
percent
|
|
City
|
Oppose
|
734
|
0.3249225
|
|
Other
|
Oppose
|
7
|
0.1372549
|
|
Rural Area
|
Oppose
|
691
|
0.4659474
|
|
Suburb
|
Oppose
|
1145
|
0.3814124
|
|
Town
|
Oppose
|
453
|
0.3970202
|
|
NA
|
Oppose
|
21
|
0.3281250
|
urbanreligon%>%
filter(Demographics %in% c("City","Rural Area"))
## # A tibble: 3,742 x 4
## Demographics imminaturalize ft_muslims ImmiMakeDiff
## <chr> <chr> <int> <chr>
## 1 Rural Area <NA> 61 No change
## 2 City Favor 49 Much easier
## 3 City Favor NA Much easier
## 4 City Favor 100 Slightly easier
## 5 City Favor 69 Slightly easier
## 6 Rural Area Favor 71 Slightly easier
## 7 Rural Area Favor 73 No change
## 8 Rural Area <NA> 71 No change
## 9 Rural Area Favor 90 Much easier
## 10 City Favor NA Slightly easier
## # ... with 3,732 more rows
prop.table(table(urbanreligon$Demographics))%>%
kable()
|
Var1
|
Freq
|
|
City
|
0.2846522
|
|
Other
|
0.0064264
|
|
Rural Area
|
0.1868700
|
|
Suburb
|
0.3782762
|
|
Town
|
0.1437752
|
prop.table(table(urbanreligon$imminaturalize))%>%
kable()
|
Var1
|
Freq
|
|
Favor
|
0.5217868
|
|
Oppose
|
0.4782132
|
chisq.test(urbanreligon$Demographics, urbanreligon$imminaturalize)
##
## Pearson's Chi-squared test
##
## data: urbanreligon$Demographics and urbanreligon$imminaturalize
## X-squared = 115.81, df = 4, p-value < 2.2e-16