Assignment3_Haoming
HQ
2023-09-27
#Q1#
library(tidycensus)
library(sf)## Linking to GEOS 3.11.2, GDAL 3.6.2, PROJ 9.2.0; sf_use_s2() is TRUElibrary(tmap)## The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
## which was just loaded, will retire in October 2023.
## Please refer to R-spatial evolution reports for details, especially
## https://r-spatial.org/r/2023/05/15/evolution4.html.
## It may be desirable to make the sf package available;
## package maintainers should consider adding sf to Suggests:.
## The sp package is now running under evolution status 2
## (status 2 uses the sf package in place of rgdal)## Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
## remotes::install_github('r-tmap/tmap')#Q2#
#'B03002_012E Estimate!!Total:!!Hispanic or Latino: HISPANIC OR LATINO ORIGIN BY RACE'
#'B03002_003E Estimate!!Total:!!Not Hispanic or Latino:!!White alone HISPANIC OR LATINO ORIGIN BY RACE'
#'B03002_004E Estimate!!Total:!!Not Hispanic or Latino:!!Black or African American alone HISPANIC OR LATINO ORIGIN BY RACE'
#'B03002_006E Estimate!!Total:!!Not Hispanic or Latino:!!Asian alone HISPANIC OR LATINO ORIGIN BY RACE'
#'B19013_001E household income
var<- c('B03002_012E','B03002_003E','B03002_004E','B01001_001E','B19013_001E')
Bexar_segregation <- get_acs(geography = "tract", variables = var, county = "Bexar",
state = "TX",output="wide", geometry = TRUE)## Getting data from the 2017-2021 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.##
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|======================================================================| 100%#Q3#
names(Bexar_segregation)[3]<- 'Hispanic'
names(Bexar_segregation)[5]<- 'White'
names(Bexar_segregation)[7]<- 'Black'
names(Bexar_segregation)[9]<- 'Total'
names(Bexar_segregation)[11]<- 'HHincome'
#Q4#
Bexar_segregation$B03002_012M<- NULL
Bexar_segregation$B03002_003M<- NULL
Bexar_segregation$B03002_004M<- NULL
Bexar_segregation$B01001_001M<- NULL
Bexar_segregation$B19013_001M<- NULL
#Q4#
Bexar_segregation$pct_Hisp <- 100*Bexar_segregation$Hispanic/Bexar_segregation$Total
Bexar_segregation$pct_White <- 100*Bexar_segregation$White/Bexar_segregation$Total
Bexar_segregation$pct_Black <- 100*Bexar_segregation$Black/Bexar_segregation$Total
#Q5#
write.csv(Bexar_segregation,"C:/Users/haomi/Desktop/Urban Planning Methods I URP-5363-901-Fall 2023/Sep 26 - Week 6/bexar_seg.csv")
#Q6#
tm_shape(Bexar_segregation) +tm_fill(col = "pct_Hisp")+ tm_layout(title = "Hispanic Percent")
tm_shape(Bexar_segregation) +tm_fill(col = "pct_Black")+ tm_layout(title = "Black or African American Percent")
tm_shape(Bexar_segregation) +tm_fill(col = "pct_White")+ tm_layout(title = "White Percent")
#Q7#
library(ggplot2)
ggplot(data = Bexar_segregation, aes(x = pct_Hisp, y = HHincome)) + geom_point()## Warning: Removed 4 rows containing missing values (`geom_point()`).
#Q8#