#install.packages("readr")
library(readr)
library(knitr)
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, unionNewVoterData<-read_csv("/Users/meiminshan/Desktop/Voter Data 2018.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## weight_latino = col_logical(),
## weight_18_24 = col_logical(),
## town_open_2018 = col_character(),
## redovote2016_t_2017 = col_character(),
## job_title_t_2017 = 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()
## # ... with 132 more columns
## )## See spec(...) for full column specifications.## Warning: 1319 parsing failures.
## row col expected actual file
## 1424 religpew_muslim_baseline 1/0/T/F/TRUE/FALSE 90 '/Users/meiminshan/Desktop/Voter Data 2018.csv'
## 1537 child_age7_1_baseline 1/0/T/F/TRUE/FALSE 6 '/Users/meiminshan/Desktop/Voter Data 2018.csv'
## 1537 child_age8_1_baseline 1/0/T/F/TRUE/FALSE 4 '/Users/meiminshan/Desktop/Voter Data 2018.csv'
## 1537 child_age9_1_baseline 1/0/T/F/TRUE/FALSE 2 '/Users/meiminshan/Desktop/Voter Data 2018.csv'
## 2958 religpew_muslim_baseline 1/0/T/F/TRUE/FALSE 2 '/Users/meiminshan/Desktop/Voter Data 2018.csv'
## .... ........................ .................. ...... ...............................................
## See problems(...) for more details.head(NewVoterData)## # A tibble: 6 x 1,074
## case_identifier caseid weight_panel weight_latino weight_18_24
## <dbl> <dbl> <dbl> <lgl> <lgl>
## 1 779 3.82e8 0.503 NA NA
## 2 2108 3.82e8 0.389 NA NA
## 3 2597 3.82e8 0.684 NA NA
## 4 4148 NA NA NA NA
## 5 4460 3.82e8 0.322 NA NA
## 6 5225 3.82e8 0.594 NA NA
## # … with 1,069 more variables: weight_overall <dbl>, cassfullcd <dbl>,
## # add_confirm_2018 <dbl>, inputzip_2018 <dbl>, votereg_2018 <dbl>,
## # votereg_f_2018 <dbl>, regzip_2018 <dbl>, inputstate2_2018 <dbl>,
## # vote18_2018 <dbl>, vote18_other_2018 <dbl>, trumpapp_2018 <dbl>,
## # trumpfeel_2018 <dbl>, fav_trump_2018 <dbl>, fav_ryan_2018 <dbl>,
## # fav_obama_2018 <dbl>, fav_hrc_2018 <dbl>, fav_sanders_2018 <dbl>,
## # fav_putin_2018 <dbl>, fav_schumer_2018 <dbl>, fav_pelosi_2018 <dbl>,
## # fav_comey_2018 <dbl>, fav_mueller_2018 <dbl>,
## # fav_mcconnell_2018 <dbl>, systems_leader_2018 <dbl>,
## # systems_army_2018 <dbl>, systems_democ_2018 <dbl>,
## # governed_2018 <dbl>, view1_2018 <dbl>, satisf_dem_2018 <dbl>,
## # view2_2018 <dbl>, inst_court_2018 <dbl>, inst_media_2018 <dbl>,
## # inst_congress_2018 <dbl>, inst_justice_2018 <dbl>,
## # inst_FBI_2018 <dbl>, inst_military_2018 <dbl>, inst_church_2018 <dbl>,
## # inst_business_2018 <dbl>, track_2018 <dbl>, persfinretro_2018 <dbl>,
## # econtrend_2018 <dbl>, life_2018 <dbl>, stranger_2018 <dbl>,
## # trustgovt_2018 <dbl>, immi_contribution_2018 <dbl>,
## # immi_naturalize_2018 <dbl>, immi_makedifficult_2018 <dbl>,
## # immi_muslim_2018 <dbl>, immi_stay_2018 <dbl>, immi_num_2018 <dbl>,
## # immi_region_eur_2018 <dbl>, immi_region_latin_2018 <dbl>,
## # immi_region_mid_2018 <dbl>, immi_region_india_2018 <dbl>,
## # immi_region_china_2018 <dbl>, immi_region_afr_2018 <dbl>,
## # immi_legal_2018 <dbl>, tax_2018 <dbl>, tax_class_you_2018 <dbl>,
## # tax_class_economy_2018 <dbl>, tax_class_poor_2018 <dbl>,
## # tax_class_wealthy_2018 <dbl>, tax_class_middle_2018 <dbl>,
## # tax_self_2018 <dbl>, tax_goal_growth_2018 <dbl>,
## # tax_goal_corpor_2018 <dbl>, tax_goal_wealthy_2018 <dbl>,
## # tax_goal_middle_2018 <dbl>, tax_goal_poor_2018 <dbl>,
## # tax_goal_federal_2018 <dbl>, tax_goal_you_2018 <dbl>,
## # tax_corp_2018 <dbl>, tax_rich_2018 <dbl>, taxdoug_2018 <dbl>,
## # tax_sys_2018 <dbl>, regula_2018 <dbl>, marij_2018 <dbl>,
## # nkorea_2018 <dbl>, speech_2018 <dbl>, amendment_2018 <dbl>,
## # anthem_2018 <dbl>, CR_touch_2018 <dbl>, CR_careA_2018 <dbl>,
## # CR_careB_2018 <dbl>, CR_careC_2018 <dbl>, CR_careD_2018 <dbl>,
## # CR_careE_2018 <dbl>, CR_careF_2018 <dbl>, CR_careG_2018 <dbl>,
## # CR_careH_2018 <dbl>, parties_2018 <dbl>, represent_dem_2018 <dbl>,
## # represent_rep_2018 <dbl>, prio_dem_2018 <dbl>, prio_rep_2018 <dbl>,
## # third_econ_2018 <dbl>, third_soc_2018 <dbl>, third_immi_2018 <dbl>,
## # democracy_2018 <dbl>, elect_2018 <dbl>, …Research Question: How do different regions think that immigration should be increased, decreased or kept the same?
Variable 1. Europe, immi_region_eur_2018
unique(NewVoterData$immi_region_eur_2018)## [1] 2 1 NA 3library(dplyr)
NewVoterData<-NewVoterData %>%
mutate(immi = ifelse(immi_region_eur_2018==1, "Increase",
ifelse(immi_region_eur_2018==2, "Keep about the same",
ifelse(immi_region_eur_2018==3, "Decrease", NA))))
unique(NewVoterData$immi)## [1] "Keep about the same" "Increase" NA
## [4] "Decrease"table(NewVoterData$immi_region_eur_2018)##
## 1 2 3
## 953 3929 1041Variable 2. Mexico, immi_region_latin_2018
unique(NewVoterData$immi_region_latin_2018)## [1] 2 NA 3 1library(dplyr)
NewVoterData<-NewVoterData %>%
mutate(immi = ifelse(immi_region_latin_2018==1, "Increase",
ifelse(immi_region_latin_2018==2, "Keep about the same",
ifelse(immi_region_latin_2018==3, "Decrease", NA))))
unique(NewVoterData$immi)## [1] "Keep about the same" NA "Decrease"
## [4] "Increase"table(NewVoterData$immi_region_latin_2018)##
## 1 2 3
## 808 2986 2128Variable 3. Middle East, immi_region_mid_2018
unique(NewVoterData$immi_region_mid_2018)## [1] 2 3 NA 1library(dplyr)
NewVoterData<-NewVoterData %>%
mutate(immi = ifelse(immi_region_mid_2018==1, "Increase",
ifelse(immi_region_mid_2018==2, "Keep about the same",
ifelse(immi_region_mid_2018==3, "Decrease", NA))))
unique(NewVoterData$immi)## [1] "Keep about the same" "Decrease" NA
## [4] "Increase"table(NewVoterData$immi_region_mid_2018)##
## 1 2 3
## 590 2635 2702Variable 4. India, immi_region_india_2018
unique(NewVoterData$immi_region_india_2018)## [1] 2 1 NA 3library(dplyr)
NewVoterData<-NewVoterData %>%
mutate(immi = ifelse(immi_region_india_2018==1, "Increase",
ifelse(immi_region_india_2018==2, "Keep about the same",
ifelse(immi_region_india_2018==3, "Decrease", NA))))
unique(NewVoterData$immi)## [1] "Keep about the same" "Increase" NA
## [4] "Decrease"table(NewVoterData$immi_region_india_2018)##
## 1 2 3
## 695 3601 1611Variable 5. China, immi_region_china_2018
unique(NewVoterData$immi_region_china_2018)## [1] 2 NA 3 1library(dplyr)
NewVoterData<-NewVoterData %>%
mutate(immi = ifelse(immi_region_china_2018==1, "Increase",
ifelse(immi_region_china_2018==2, "Keep about the same",
ifelse(immi_region_china_2018==3, "Decrease", NA))))
unique(NewVoterData$immi)## [1] "Keep about the same" NA "Decrease"
## [4] "Increase"table(NewVoterData$immi_region_china_2018)##
## 1 2 3
## 619 3443 1867Variable 6. Africa, immi_region_afr_2018
unique(NewVoterData$immi_region_afr_2018)## [1] 2 NA 3 1library(dplyr)
NewVoterData<-NewVoterData %>%
mutate(immi = ifelse(immi_region_afr_2018==1, "Increase",
ifelse(immi_region_afr_2018==2, "Keep about the same",
ifelse(immi_region_afr_2018==3, "Decrease", NA))))
unique(NewVoterData$immi)## [1] "Keep about the same" NA "Decrease"
## [4] "Increase"table(NewVoterData$immi_region_afr_2018)##
## 1 2 3
## 748 3385 1788