#install.packages(readr)
#install.packages(dplyr)
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
VoterData <- read_csv("~/Tsukasa/NY/CUNY/Class/Spring 2019/Programming for Social Research/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 '~/Tsukasa/NY/CUNY/Class/Spring 2019/Programming for Social Research/VoterData2017(1).csv'
## 1531 child_age7_1_baseline 1/0/T/F/TRUE/FALSE 6 '~/Tsukasa/NY/CUNY/Class/Spring 2019/Programming for Social Research/VoterData2017(1).csv'
## 1531 child_age8_1_baseline 1/0/T/F/TRUE/FALSE 4 '~/Tsukasa/NY/CUNY/Class/Spring 2019/Programming for Social Research/VoterData2017(1).csv'
## 1531 child_age9_1_baseline 1/0/T/F/TRUE/FALSE 2 '~/Tsukasa/NY/CUNY/Class/Spring 2019/Programming for Social Research/VoterData2017(1).csv'
## 2947 religpew_muslim_baseline 1/0/T/F/TRUE/FALSE 2 '~/Tsukasa/NY/CUNY/Class/Spring 2019/Programming for Social Research/VoterData2017(1).csv'
## .... ........................ .................. ...... ..........................................................................................
## See problems(...) for more details.
NewVoterData <- VoterData %>%
select(ft_rep_2017, ft_altright_2017, ft_immig_2017) %>%
rename("Feelings toward Republicans"=ft_rep_2017, "Feelings toward the alt right"
=ft_altright_2017, "Feelings toward immigrants"=ft_immig_2017) %>%
mutate(`Feelings toward Republicans`=ifelse(`Feelings toward Republicans`>100,NA,`Feelings toward Republicans`),
`Feelings toward the alt right`=ifelse(`Feelings toward the alt right`>100, NA,`Feelings toward the alt right`),
`Feelings toward immigrants`=ifelse(`Feelings toward immigrants`>100, NA,`Feelings toward immigrants`))
NewVoterData %>%
filter(!is.na(`Feelings toward Republicans`)) %>%
group_by(`Feelings toward Republicans`) %>%
summarize(`Feelings toward the alt right` = mean(`Feelings toward the alt right`, na.rm = TRUE), `Feelings toward immigrants` =
mean(`Feelings toward immigrants`, na.rm = TRUE))
## # A tibble: 101 x 3
## `Feelings toward Republ~ `Feelings toward the al~ `Feelings toward immi~
## <dbl> <dbl> <dbl>
## 1 0 4.46 70.7
## 2 1 8.34 69.0
## 3 2 9.04 74.6
## 4 3 7.03 73.1
## 5 4 12.1 71.6
## 6 5 11.7 68.5
## 7 6 12.9 72.5
## 8 7 17.7 72.2
## 9 8 16.0 67.4
## 10 9 12.2 68.2
## # ... with 91 more rows
NewVoterData %>%
filter(!is.na(`Feelings toward the alt right`)) %>%
group_by(`Feelings toward the alt right`) %>%
summarize(`Feelings toward Republicans` = mean(`Feelings toward Republicans`, na.rm = TRUE), `Feelings toward immigrants` =
mean(`Feelings toward immigrants`, na.rm = TRUE))
## # A tibble: 101 x 3
## `Feelings toward the alt~ `Feelings toward Repub~ `Feelings toward immi~
## <dbl> <dbl> <dbl>
## 1 0 14.9 73.6
## 2 1 18.5 71.3
## 3 2 21.1 70.8
## 4 3 20.3 67.3
## 5 4 26.1 67.5
## 6 5 30.7 66.7
## 7 6 31.1 69.8
## 8 7 30.4 66.8
## 9 8 43.3 64.1
## 10 9 36 60.0
## # ... with 91 more rows
NewVoterData %>%
filter(!is.na(`Feelings toward immigrants`)) %>%
group_by(`Feelings toward immigrants`) %>%
summarize(`Feelings toward Republicans` = mean(`Feelings toward Republicans`, na.rm = TRUE), `Feelings toward the alt right` =
mean(`Feelings toward the alt right`, na.rm = TRUE))
## # A tibble: 101 x 3
## `Feelings toward immig~ `Feelings toward Repub~ `Feelings toward the al~
## <dbl> <dbl> <dbl>
## 1 0 47.1 40.7
## 2 1 52.1 45.0
## 3 2 43.2 30.7
## 4 3 63.9 43.2
## 5 4 51.7 33.3
## 6 5 62.6 57
## 7 6 55.4 39.2
## 8 7 50.6 35.2
## 9 8 57.5 39.2
## 10 9 52.5 46.3
## # ... with 91 more rows