VoterData <- read_csv("VoterData2017(1).csv")

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   redovote2016_t_2017 = col_character(),
##   job_title_t_2017 = col_character(),
##   izip_2016 = col_character(),
##   presvote16post_t_2016 = col_character(),
##   second_chance_t_2016 = col_character(),
##   race_other_2016 = col_character(),
##   healthcov_t_2016 = col_character(),
##   employ_t_2016 = col_character(),
##   pid3_t_2016 = col_character(),
##   religpew_t_2016 = col_character(),
##   votemeth16_rnd_2016 = col_character(),
##   presvote16post_rnd_2016 = col_character(),
##   vote2016_cand2_rnd_2016 = col_character(),
##   Clinton_Rubio_rnd_2016 = col_character(),
##   Clinton_Cruz_rnd_2016 = col_character(),
##   Sanders_Trump_rnd_2016 = col_character(),
##   Sanders_Rubio_rnd_2016 = col_character(),
##   second_chance_rnd_2016 = col_character(),
##   obamaapp_rnd_2016 = col_character(),
##   fav_grid_row_rnd_2016 = col_character()
##   # ... with 121 more columns
## )

## See spec(...) for full column specifications.

## Warning: 13 parsing failures.
##  row                      col           expected actual                   file
## 1418 religpew_muslim_baseline 1/0/T/F/TRUE/FALSE     90 'VoterData2017(1).csv'
## 1531 child_age7_1_baseline    1/0/T/F/TRUE/FALSE     6  'VoterData2017(1).csv'
## 1531 child_age8_1_baseline    1/0/T/F/TRUE/FALSE     4  'VoterData2017(1).csv'
## 1531 child_age9_1_baseline    1/0/T/F/TRUE/FALSE     2  'VoterData2017(1).csv'
## 2947 religpew_muslim_baseline 1/0/T/F/TRUE/FALSE     2  'VoterData2017(1).csv'
## .... ........................ .................. ...... ......................
## See problems(...) for more details.

NewVoterData<- VoterData%>%
   select(ft_muslim_2017, ft_black_2017, ft_hisp_2017, ideo5_2017)

head(NewVoterData)

## # A tibble: 6 x 4
##   ft_muslim_2017 ft_black_2017 ft_hisp_2017 ideo5_2017
##            <dbl>         <dbl>        <dbl>      <dbl>
## 1             50            51           79          3
## 2             61            98           95          4
## 3             49            87           91          3
## 4             NA            NA           NA         NA
## 5             80            90           90          4
## 6            100           100          100          1

Variables Selected and Renamed

NewVoterData <-NewVoterData%>%
  rename("Ideology" = ideo5_2017,"FeelingsTowardsMuslims" = ft_muslim_2017, "FeelingsTowardsBlacks" = ft_black_2017, "FeelingsTowardsHispanics" = ft_hisp_2017, "FeelingsTowardsMuslims" = ft_muslim_2017)
head(NewVoterData)

## # A tibble: 6 x 4
##   FeelingsTowardsMusli… FeelingsTowardsBlac… FeelingsTowardsHispa… Ideology
##                   <dbl>                <dbl>                 <dbl>    <dbl>
## 1                    50                   51                    79        3
## 2                    61                   98                    95        4
## 3                    49                   87                    91        3
## 4                    NA                   NA                    NA       NA
## 5                    80                   90                    90        4
## 6                   100                  100                   100        1

NewVoterData <- NewVoterData%>%
  mutate(Ideology= ifelse(Ideology==1, "Liberal",
                    ifelse(Ideology==2, "Liberal", 
                    ifelse(Ideology==3, "Moderate", 
                    ifelse(Ideology==4, "Conservative", 
                    ifelse(Ideology==5, "Conservative",
                    ifelse(Ideology==6, "Not Sure", NA)))))),
        FeelingsTowardsMuslims= ifelse(FeelingsTowardsMuslims>100, NA, FeelingsTowardsMuslims), 
        FeelingsTowardsBlacks= ifelse(FeelingsTowardsBlacks>100, NA, FeelingsTowardsBlacks), FeelingsTowardsHispanics= ifelse(FeelingsTowardsHispanics>100, NA, FeelingsTowardsHispanics))
 
NewVoterData <- NewVoterData%>%
  filter(!is.na(Ideology))%>%
  group_by(Ideology )%>%
  summarize(AvgFeelingsTowardsMuslims = mean(FeelingsTowardsMuslims, na.rm = TRUE),
            SDFeelingsTowardsMuslims= sd(FeelingsTowardsMuslims, na.rm= TRUE),
            AvgFeelingsTowardsBlacks = mean(FeelingsTowardsBlacks, na.rm = TRUE),
            SDFeelingsTowardsBlacks= sd(FeelingsTowardsBlacks, na.rm = TRUE),
            AvgFeelingsTowardsHispanics = mean(FeelingsTowardsHispanics, na.rm = TRUE),
            SDFeellingsTowardsHispanics= sd(FeelingsTowardsHispanics, na.rm=TRUE))

head(NewVoterData)

## # A tibble: 4 x 7
##   Ideology AvgFeelingsTowa… SDFeelingsTowar… AvgFeelingsTowa…
##   <chr>               <dbl>            <dbl>            <dbl>
## 1 Conserv…             32.9             28.0             65.6
## 2 Liberal              69.2             24.5             80.1
## 3 Moderate             53.2             28.4             71.2
## 4 Not Sure             45.2             30.0             65.8
## # … with 3 more variables: SDFeelingsTowardsBlacks <dbl>,
## #   AvgFeelingsTowardsHispanics <dbl>, SDFeellingsTowardsHispanics <dbl>

Most striking results are the vast difference between the Avg Conservative and Liberal FeelingsTowardsMuslims with SD taken into consideration.

ggplot(NewVoterData)+
  geom_bar(aes(x=Ideology, y=AvgFeelingsTowardsMuslims, fill=Ideology), stat="Identity")+
  coord_flip()

Those who identified their Ideology as Liberal are shown to have the highest rating for their average FeelingsTowardsMuslims.

ggplot(NewVoterData)+
  geom_histogram(aes(x=AvgFeelingsTowardsMuslims, fill=Ideology), binwidth=10)

Assignment 7

Variables Selected and Renamed

Most striking results are the vast difference between the Avg Conservative and Liberal FeelingsTowardsMuslims with SD taken into consideration.

Those who identified their Ideology as Liberal are shown to have the highest rating for their average FeelingsTowardsMuslims.

On this graph, those who identified their Ideology as Moderate and/or Not Sure are shown to have similar average FeelingsTowardsMuslims.