Poverty Rates Among Races Across U.S. Regions (2013-2017)

For this assignment, I will be looking at the poverty rates among races across the United States Region using the American Community Surveys 2013-2017. More specfically, I will be looking at the poverty rates for White, Black, and Hispanic or Latino across the regions of Northeast, Midwest, South, and West.

This was my first time playing around using R Notebook, so I chose a simple dataset I was comfortable with and did basic operations using dylyr magic.

Importing the data

library(readr)
acs2017<-read_csv("C:/Users/wroni/OneDrive/Documents/QC MADASR/SOC 712/R12021804_SL020.csv", col_names = TRUE)
## Parsed with column specification:
## cols(
##   .default = col_logical(),
##   Geo_FIPS = col_double(),
##   Geo_GEOID = col_character(),
##   Geo_NAME = col_character(),
##   Geo_QName = col_character(),
##   Geo_STUSAB = col_character(),
##   Geo_SUMLEV = col_character(),
##   Geo_GEOCOMP = col_character(),
##   Geo_FILEID = col_character(),
##   Geo_LOGRECNO = col_character(),
##   Geo_REGION = col_double(),
##   SE_T119_001 = col_double(),
##   SE_T119_002 = col_double(),
##   SE_T119_003 = col_double(),
##   SE_T120_001 = col_double(),
##   SE_T120_002 = col_double(),
##   SE_T120_003 = col_double(),
##   SE_T126_001 = col_double(),
##   SE_T126_002 = col_double(),
##   SE_T126_003 = col_double()
## )
## See spec(...) for full column specifications.
head(acs2017)

Renaming the variables

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
acs2017_1<-rename(acs2017, Region=Geo_NAME, 
                   White=SE_T119_002,
                   Black=SE_T120_002,
                   Hispanic=SE_T126_002)
names(acs2017_1)
##  [1] "Geo_FIPS"     "Geo_GEOID"    "Region"       "Geo_QName"   
##  [5] "Geo_STUSAB"   "Geo_SUMLEV"   "Geo_GEOCOMP"  "Geo_FILEID"  
##  [9] "Geo_LOGRECNO" "Geo_US"       "Geo_REGION"   "Geo_DIVISION"
## [13] "Geo_STATECE"  "Geo_STATE"    "Geo_COUNTY"   "Geo_COUSUB"  
## [17] "Geo_PLACE"    "Geo_PLACESE"  "Geo_TRACT"    "Geo_BLKGRP"  
## [21] "Geo_CONCIT"   "Geo_AIANHH"   "Geo_AIANHHFP" "Geo_AIHHTLI" 
## [25] "Geo_AITSCE"   "Geo_AITS"     "Geo_ANRC"     "Geo_CBSA"    
## [29] "Geo_CSA"      "Geo_METDIV"   "Geo_MACC"     "Geo_MEMI"    
## [33] "Geo_NECTA"    "Geo_CNECTA"   "Geo_NECTADIV" "Geo_UA"      
## [37] "Geo_UACP"     "Geo_CDCURR"   "Geo_SLDU"     "Geo_SLDL"    
## [41] "Geo_VTD"      "Geo_ZCTA3"    "Geo_ZCTA5"    "Geo_SUBMCD"  
## [45] "Geo_SDELM"    "Geo_SDSEC"    "Geo_SDUNI"    "Geo_UR"      
## [49] "Geo_PCI"      "Geo_TAZ"      "Geo_UGA"      "Geo_BTTR"    
## [53] "Geo_BTBG"     "Geo_PUMA5"    "Geo_PUMA1"    "SE_T119_001" 
## [57] "White"        "SE_T119_003"  "SE_T120_001"  "Black"       
## [61] "SE_T120_003"  "SE_T126_001"  "Hispanic"     "SE_T126_003"

Selecting the variables I need

library(dplyr)
acs2017_2<-select(acs2017_1, Region, White, Black, Hispanic)
dim(acs2017_2)
## [1] 4 4
names(acs2017_2)
## [1] "Region"   "White"    "Black"    "Hispanic"

Summary of variables

head(acs2017_2)

Results

Based on the results, Whites have the higest poverty numbers in all four regions of the United States compared to the other two major races. However, this may be due to higher population of Whites in those regions compared to Blacks and Hispanics. Furthermore, South region has the highest poverty rate for both Whites and Blacks compared to the other three major regions. Hispanics has the higest poverty rate in the West region but it is similar to the South region.