T-test

library(readr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(knitr)
library(kableExtra)
library(ggplot2)

VoterData<-read_csv("/Users/juliushunte/Downloads/VOTER_Survey_July17_Release1-csv copy.csv")
urbanreligon <-VoterData%>%
 mutate(Demographical = ifelse(urbancity_baseline==1,"City",
                        ifelse(urbancity_baseline==4,"Rural Area",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(Demographical,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(imminaturalize)%>%
      summarize(ft_muslims = mean(ft_muslims, na.rm=TRUE))%>%
      kable()

imminaturalize	ft_muslims
Favor	64.27866
Oppose	33.67957
NA	50.15795

urbanreligon%>%
  group_by(Demographical)%>%
  summarize(ft_muslims = mean(ft_muslims,na.rm=TRUE))%>%
  kable()

Demographical	ft_muslims
City	54.29596
Rural Area	43.26636
NA	49.99255

t.test(ft_muslims~Demographical, data = urbanreligon)

## 
##  Welch Two Sample t-test
## 
## data:  ft_muslims by Demographical
## t = 8.2702, df = 1828.4, p-value = 2.542e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   8.413957 13.645261
## sample estimates:
##       mean in group City mean in group Rural Area 
##                 54.29596                 43.26636