HW2 - Highest Education Attainment by Region-2017
Abigail Strick
Reseach Question:
Do people who live in blue voting regions have higher education levels than people who live in red voting regions? In other words, which regions have the most highly educated Americans (according to the map on https://blogs.cfainstitute.org/investor/2018/03/13/red-states-blue-states-two-economies-one-nation/)?
library(readr)
library(dplyr)
library(ggplot2)
library(tidyverse)
library(magrittr)
Education <- read_csv("C:/Users/abbys/Downloads/R12019574_SL020.csv", col_names = TRUE)
print(Education)## # A tibble: 4 x 63
## Geo_FIPS Geo_GEOID Geo_NAME Geo_QName Geo_STUSAB Geo_SUMLEV Geo_GEOCOMP
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1 02000US1 Northea~ Northeas~ us 020 00
## 2 2 02000US2 Midwest~ Midwest ~ us 020 00
## 3 3 02000US3 South R~ South Re~ us 020 00
## 4 4 02000US4 West Re~ West Reg~ us 020 00
## # ... with 56 more variables: Geo_FILEID <chr>, Geo_LOGRECNO <chr>,
## # Geo_US <lgl>, Geo_REGION <dbl>, Geo_DIVISION <lgl>, Geo_STATECE <lgl>,
## # Geo_STATE <lgl>, Geo_COUNTY <lgl>, Geo_COUSUB <lgl>, Geo_PLACE <lgl>,
## # Geo_PLACESE <lgl>, Geo_TRACT <lgl>, Geo_BLKGRP <lgl>,
## # Geo_CONCIT <lgl>, Geo_AIANHH <lgl>, Geo_AIANHHFP <lgl>,
## # Geo_AIHHTLI <lgl>, Geo_AITSCE <lgl>, Geo_AITS <lgl>, Geo_ANRC <lgl>,
## # Geo_CBSA <lgl>, Geo_CSA <lgl>, Geo_METDIV <lgl>, Geo_MACC <lgl>,
## # Geo_MEMI <lgl>, Geo_NECTA <lgl>, Geo_CNECTA <lgl>, Geo_NECTADIV <lgl>,
## # Geo_UA <lgl>, Geo_UACP <lgl>, Geo_CDCURR <lgl>, Geo_SLDU <lgl>,
## # Geo_SLDL <lgl>, Geo_VTD <lgl>, Geo_ZCTA3 <lgl>, Geo_ZCTA5 <lgl>,
## # Geo_SUBMCD <lgl>, Geo_SDELM <lgl>, Geo_SDSEC <lgl>, Geo_SDUNI <lgl>,
## # Geo_UR <lgl>, Geo_PCI <lgl>, Geo_TAZ <lgl>, Geo_UGA <lgl>,
## # Geo_BTTR <lgl>, Geo_BTBG <lgl>, Geo_PUMA5 <lgl>, Geo_PUMA1 <lgl>,
## # SE_T150_001 <dbl>, SE_T150_002 <dbl>, SE_T150_003 <dbl>,
## # SE_T150_004 <dbl>, SE_T150_005 <dbl>, SE_T150_006 <dbl>,
## # SE_T150_007 <dbl>, SE_T150_008 <dbl>Renaming the Variables:
Education1<-rename(Education, "US_Region"=Geo_NAME,
"Total_Pop_25_Years_and_Over"=SE_T150_001,
"Less_than_High_School"=SE_T150_002,
"Atleast_High_School_Graduate"=SE_T150_003,
"Atleast_Some_College"=SE_T150_004,
"BA_Or_Above"=SE_T150_005,
"MA_Or_Above"=SE_T150_006,
"Professional_Degree_or_More"=SE_T150_007,
"Doctorate"=SE_T150_008)
names(Education1)## [1] "Geo_FIPS" "Geo_GEOID"
## [3] "US_Region" "Geo_QName"
## [5] "Geo_STUSAB" "Geo_SUMLEV"
## [7] "Geo_GEOCOMP" "Geo_FILEID"
## [9] "Geo_LOGRECNO" "Geo_US"
## [11] "Geo_REGION" "Geo_DIVISION"
## [13] "Geo_STATECE" "Geo_STATE"
## [15] "Geo_COUNTY" "Geo_COUSUB"
## [17] "Geo_PLACE" "Geo_PLACESE"
## [19] "Geo_TRACT" "Geo_BLKGRP"
## [21] "Geo_CONCIT" "Geo_AIANHH"
## [23] "Geo_AIANHHFP" "Geo_AIHHTLI"
## [25] "Geo_AITSCE" "Geo_AITS"
## [27] "Geo_ANRC" "Geo_CBSA"
## [29] "Geo_CSA" "Geo_METDIV"
## [31] "Geo_MACC" "Geo_MEMI"
## [33] "Geo_NECTA" "Geo_CNECTA"
## [35] "Geo_NECTADIV" "Geo_UA"
## [37] "Geo_UACP" "Geo_CDCURR"
## [39] "Geo_SLDU" "Geo_SLDL"
## [41] "Geo_VTD" "Geo_ZCTA3"
## [43] "Geo_ZCTA5" "Geo_SUBMCD"
## [45] "Geo_SDELM" "Geo_SDSEC"
## [47] "Geo_SDUNI" "Geo_UR"
## [49] "Geo_PCI" "Geo_TAZ"
## [51] "Geo_UGA" "Geo_BTTR"
## [53] "Geo_BTBG" "Geo_PUMA5"
## [55] "Geo_PUMA1" "Total_Pop_25_Years_and_Over"
## [57] "Less_than_High_School" "Atleast_High_School_Graduate"
## [59] "Atleast_Some_College" "BA_Or_Above"
## [61] "MA_Or_Above" "Professional_Degree_or_More"
## [63] "Doctorate"Recoding MA_Or_Above, Professional_Degree_or_More, Doctorate as 1 variable called DegreesAboveBA
Education2=Education1%>%
mutate(DegreesAboveBA=(MA_Or_Above+Professional_Degree_or_More+Doctorate))
head(Education2)Selecting the variables
Education3<-select(Education2,US_Region,Total_Pop_25_Years_and_Over,Less_than_High_School,Atleast_High_School_Graduate,Atleast_Some_College,BA_Or_Above,DegreesAboveBA)
print(Education3)## # A tibble: 4 x 7
## US_Region Total_Pop_25_Ye~ Less_than_High_~ Atleast_High_Sc~
## <chr> <dbl> <dbl> <dbl>
## 1 Northeas~ 38965012 4430933 34534079
## 2 Midwest ~ 45584874 4543533 41041341
## 3 South Re~ 81181926 11332322 69849604
## 4 West Reg~ 50539832 7130326 43409506
## # ... with 3 more variables: Atleast_Some_College <dbl>,
## # BA_Or_Above <dbl>, DegreesAboveBA <dbl>dim(Education3)## [1] 4 7I’m summarizing here that there are more people who attain at least a High School diploma (or equivalent) than people who attain a Bachelor’s degree, within the general population. This is a filtering point in the education system as the same boolean holds true when applied to a Bachelor’s degree and degrees attained beyond a Bachelor’s.
HS<-summarize(Education3,Atleast_High_School_Graduate
=sum(as.numeric(Atleast_High_School_Graduate),na.rm = TRUE))
print(HS)## # A tibble: 1 x 1
## Atleast_High_School_Graduate
## <dbl>
## 1 188834530BA<-summarize(Education3,BA_Or_Above=sum(as.numeric(BA_Or_Above),na.rm = TRUE))
print(BA)## # A tibble: 1 x 1
## BA_Or_Above
## <dbl>
## 1 66887603MA_PHD_PROFESSIONAL<-summarize(Education3,DegreesAboveBA=sum(as.numeric(DegreesAboveBA),na.rm = TRUE))
print(MA_PHD_PROFESSIONAL)## # A tibble: 1 x 1
## DegreesAboveBA
## <dbl>
## 1 35828661HS>BA## Atleast_High_School_Graduate
## [1,] TRUEBA>MA_PHD_PROFESSIONAL## BA_Or_Above
## [1,] TRUEHS>BA+MA_PHD_PROFESSIONAL## Atleast_High_School_Graduate
## [1,] TRUESelecting, renaming, and recoding using Magrittr:
Edu <-Education %>%
select(Geo_NAME,SE_T150_001,SE_T150_002,SE_T150_003,SE_T150_004,SE_T150_005,SE_T150_006,SE_T150_007,SE_T150_008) %>%
rename(US_Region=Geo_NAME,
Total_Pop_25_Years_and_Over=SE_T150_001,
Less_than_High_School=SE_T150_002,
Atleast_High_School_Graduate=SE_T150_003,
Atleast_Some_College=SE_T150_004,
BA_Or_Above=SE_T150_005,
MA_Or_Above=SE_T150_006,
Professional_Degree_or_More=SE_T150_007,
Doctorate=SE_T150_008
) %>%
mutate(DegreesBeyondBA = MA_Or_Above + Professional_Degree_or_More+Doctorate)%>%
select(US_Region,Total_Pop_25_Years_and_Over,Less_than_High_School,Atleast_High_School_Graduate,Atleast_Some_College,BA_Or_Above,DegreesBeyondBA)%>%
print(Edu)## # A tibble: 4 x 7
## US_Region Total_Pop_25_Ye~ Less_than_High_~ Atleast_High_Sc~
## <chr> <dbl> <dbl> <dbl>
## 1 Northeas~ 38965012 4430933 34534079
## 2 Midwest ~ 45584874 4543533 41041341
## 3 South Re~ 81181926 11332322 69849604
## 4 West Reg~ 50539832 7130326 43409506
## # ... with 3 more variables: Atleast_Some_College <dbl>,
## # BA_Or_Above <dbl>, DegreesBeyondBA <dbl>Results:
One can see that the highest levels of education attained can be found in the Northeast, which is known to be a blue voting region. The lowest levels of education attained (Less_than_High_School) are the South (red voting region) and West regions (blue voting region). However, the West region is also shown to have the second highest attainment of education beyond a Bachelor’s degree.
GGplots of Education By Region
ggplot(data = Edu) + geom_col(aes(x=US_Region, y= Less_than_High_School/Total_Pop_25_Years_and_Over), fill = "pink") + labs(x="Regions",y="Less_than_High_School", title="Highest Education Attainment by Region")
ggplot(data = Edu) + geom_col(aes(x=US_Region,y=Atleast_High_School_Graduate/Total_Pop_25_Years_and_Over), fill = "pink") + labs(x="Regions",y="Atleast_High_School_Graduate", title="Highest Education Attainment by Region")
ggplot(data = Edu) + geom_col(aes(x=US_Region, y= Atleast_Some_College/Total_Pop_25_Years_and_Over), fill = "pink") + labs(x="Regions",y="Atleast_Some_College", title="Highest Education Attainment by Region")
ggplot(data = Edu) + geom_col(aes(x=US_Region, y= Atleast_High_School_Graduate/Total_Pop_25_Years_and_Over), fill = "pink") + labs(x="Regions",y="Atleast_High_School_Graduate", title="Highest Education Attainment by Region")
ggplot(data = Edu) + geom_col(aes(x=US_Region, y=BA_Or_Above/Total_Pop_25_Years_and_Over), fill = "pink") + labs(x="Regions",y="BA_Or_Above", title="Highest Education Attainment by Region")
ggplot(data = Edu) + geom_col(aes(x=US_Region, y= DegreesBeyondBA/Total_Pop_25_Years_and_Over), fill = "pink") + labs(x="Regions",y="DegreesBeyondBA (MA,PHD,Professional_Degree", title="Highest Education Attainment by Region")