Assigment 5: Wrangling Data with dplyr
Variables used were the same as in Assignment # 4. They are:-
Family Income (faminc_2016)
Type of Area (urbancity_baseline)
Education level (educ_baseline)
Political Ideology (post_ideo5_2012)
2016 Voter Election (presvote16post_2016)
Immigration reform (immi_makedifficult_baseline)
Question 1:
Does the type of area you live affect family income?
Responses that did not meet the conditions for this study were marked as NA which stands for not applicable
The table below displays cross tabulations with the proportions displayed for the observations
knitr::opts_chunk$set(
echo = FALSE)
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#read_csv("/Users/safiesaf/Downloads/VOTER_Survey_July17_Release1-csv.csv")
VoterData<-read_csv("/Users/safiesaf/Downloads/VOTER_Survey_July17_Release1-csv.csv")## Parsed with column specification:
## cols(
## .default = col_integer(),
## weight_2017 = col_double(),
## redovote2016_t_2017 = col_character(),
## job_title_t_2017 = col_character(),
## weight_2016 = col_double(),
## 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()
## # ... with 123 more columns
## )## See spec(...) for full column specifications.NewVoterData<-rename(VoterData,"TypeofArea"=urbancity_baseline,
"FamilyIncome"=faminc_2016,
"EducationLevel"=educ_baseline,
"PoliticalIdeology"=post_ideo5_2012,
"VoterElection"=presvote16post_2016,
"ImmigrationReform"=immi_makedifficult_baseline)
NewVoterData<-select(NewVoterData,
TypeofArea,
FamilyIncome,
EducationLevel,
PoliticalIdeology,
VoterElection,
ImmigrationReform)
head(NewVoterData)## # A tibble: 6 x 6
## TypeofArea FamilyIncome EducationLevel PoliticalIdeolo… VoterElection
## <int> <int> <int> <int> <int>
## 1 2 97 5 3 1
## 2 4 7 3 4 2
## 3 1 6 2 3 1
## 4 1 8 3 3 3
## 5 2 5 5 4 2
## 6 2 4 4 1 1
## # ... with 1 more variable: ImmigrationReform <int>NewVoterData<-mutate(NewVoterData,
TypeofArea=ifelse(TypeofArea==1,"City",
ifelse(TypeofArea==2,"Suburb",
ifelse(TypeofArea==3,"Town",
ifelse(TypeofArea==4,"Rural Area",
ifelse(TypeofArea==5,"Other",NA))))))
head(NewVoterData$TypeofArea)## [1] "Suburb" "Rural Area" "City" "City" "Suburb"
## [6] "Suburb"NewVoterData<-mutate(NewVoterData,
FamilyIncome=ifelse(FamilyIncome>=1 & FamilyIncome<=5,"$0-49,000",
ifelse(FamilyIncome>=6 & FamilyIncome<=9,"$50,000:99,999",
ifelse(FamilyIncome>=10 & FamilyIncome<=13,"$100,000:249,999",
ifelse(FamilyIncome>=14 & FamilyIncome<=31,"$250,000",
ifelse(FamilyIncome>=32 & FamilyIncome<=97,"Other",NA))))))
head(NewVoterData$FamilyIncome)## [1] "Other" "$50,000:99,999" "$50,000:99,999" "$50,000:99,999"
## [5] "$0-49,000" "$0-49,000"table(NewVoterData$TypeofArea,NewVoterData$FamilyIncome)##
## $0-49,000 $100,000:249,999 $250,000 $50,000:99,999 Other
## City 872 388 49 649 301
## Other 15 11 1 13 11
## Rural Area 613 200 18 467 185
## Suburb 841 739 82 944 396
## Town 410 187 10 367 167NewVoterFamIncTable<-table(NewVoterData$TypeofArea,NewVoterData$FamilyIncome)
prop.table(NewVoterFamIncTable)##
## $0-49,000 $100,000:249,999 $250,000 $50,000:99,999
## City 0.1098790323 0.0488911290 0.0061743952 0.0817792339
## Other 0.0018901210 0.0013860887 0.0001260081 0.0016381048
## Rural Area 0.0772429435 0.0252016129 0.0022681452 0.0588457661
## Suburb 0.1059727823 0.0931199597 0.0103326613 0.1189516129
## Town 0.0516633065 0.0235635081 0.0012600806 0.0462449597
##
## Other
## City 0.0379284274
## Other 0.0013860887
## Rural Area 0.0233114919
## Suburb 0.0498991935
## Town 0.0210433468Question 2:
Does Education level have an impact on political ideology?
Responses that did not meet the conditions for this study were marked as NA which stands for not applicable
The table below displays cross tabulations with the proportions displayed for the observations
## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00## [1] "4 Year" "Some College" "HS Graduate" "Some College"
## [5] "4 Year" "2 Year"## [1] "Moderate" "Conservative" "Moderate" "Moderate"
## [5] "Conservative" "Very Liberal"##
## Conservative Liberal Moderate Very Conservative
## 2 Year 222 123 299 75
## 4 Year 495 376 690 182
## HS Graduate 599 263 650 241
## No HS 48 19 45 23
## Post Grad 276 269 422 100
## Some College 492 336 661 183
##
## Very Liberal
## 2 Year 38
## 4 Year 187
## HS Graduate 94
## No HS 7
## Post Grad 116
## Some College 133##
## Conservative Liberal Moderate Very Conservative
## 2 Year 0.0289665971 0.0160490605 0.0390135699 0.0097860125
## 4 Year 0.0645876827 0.0490605428 0.0900313152 0.0237473904
## HS Graduate 0.0781576200 0.0343162839 0.0848121086 0.0314457203
## No HS 0.0062630480 0.0024791232 0.0058716075 0.0030010438
## Post Grad 0.0360125261 0.0350991649 0.0550626305 0.0130480167
## Some College 0.0641962422 0.0438413361 0.0862473904 0.0238778706
##
## Very Liberal
## 2 Year 0.0049582463
## 4 Year 0.0243997912
## HS Graduate 0.0122651357
## No HS 0.0009133612
## Post Grad 0.0151356994
## Some College 0.0173538622Question 3:
Does who you vote for in the 2016 election have an impact on immgration reform?
Responses that did not meet the conditions for this study were marked as NA which stands for not applicable
The table below displays cross tabulations with the proportions displayed for the observations
## [1] "Hillary Clinton" "Donald Trump" "Hillary Clinton" "Gary Johnson"
## [5] "Donald Trump" "Hillary Clinton"## [1] "Slightly easier" "No change" "Much easier" "Much easier"
## [5] "Slightly easier" "Slightly harder"##
## Much easier Much harder No change Slightly easier
## Donald Trump 203 1091 879 501
## Evan McMullin 5 2 5 8
## Gary Johnson 37 45 42 55
## Hillary Clinton 406 546 847 766
## Jill Stein 19 17 21 25
##
## Slightly harder
## Donald Trump 640
## Evan McMullin 2
## Gary Johnson 40
## Hillary Clinton 640
## Jill Stein 17##
## Much easier Much harder No change Slightly easier
## Donald Trump 0.0295961510 0.1590610876 0.1281527920 0.0730427176
## Evan McMullin 0.0007289692 0.0002915877 0.0007289692 0.0011663508
## Gary Johnson 0.0053943724 0.0065607231 0.0061233416 0.0080186616
## Hillary Clinton 0.0591923021 0.0796034407 0.1234873888 0.1116780872
## Jill Stein 0.0027700831 0.0024784954 0.0030616708 0.0036448462
##
## Slightly harder
## Donald Trump 0.0933080624
## Evan McMullin 0.0002915877
## Gary Johnson 0.0058317539
## Hillary Clinton 0.0933080624
## Jill Stein 0.0024784954