Assignment3
Gabby Rodriguez
2023-10-02
# 1. Import the census data library (tidycensus) and GIS libraries (sf)(tmap). (1’)
library(tidycensus)
library(sf)## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; 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')# 2. Using your census API key, capture the population of Hispanic, Non-Hispanic White, Non-Hispanic Black or African American, total population, and median income for all census tracts in Bexar County. (1’)
var=c('B03002_012E','B03002_003E','B03002_004E','B01001_001E', 'B19013_001E')
#'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'
#B01001_001E Total Population
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%# 3. Rename the columns for estimates and remove the columns for MOE (1’)
names(Bexar_segregation)[3] <- 'Hispanic'
names(Bexar_segregation)[5] <- 'White'
names(Bexar_segregation)[7] <- 'Black or African American'
names(Bexar_segregation)[9] <- 'TotalPop'
names(Bexar_segregation)[11] <- 'MedianIncome'
Bexar_segregation$B03002_012M <- NULL
Bexar_segregation$B03002_003M <- NULL
Bexar_segregation$B03002_004M <- NULL
Bexar_segregation$B01001_001M <- NULL
Bexar_segregation$B19013_001M <- NULL
# 4. Calculate the percentage of Hispanic, Non-Hispanic White, Non-Hispanic Black or African American. (1’)
Bexar_segregation$pct_Hisp <- 100*Bexar_segregation$Hispanic/Bexar_segregation$TotalPop
Bexar_segregation$pct_White <- 100*Bexar_segregation$White/Bexar_segregation$TotalPop
Bexar_segregation$pct_BAA <- 100*Bexar_segregation$'Black or African American'/Bexar_segregation$TotalPop
# 5. Save the data to csv file. (1’)
write.csv(Bexar_segregation, "/Users/gabbyrodriguez/Documents/UTSA MSURP/Urban Planning Methods/Week 6/Bexar_seg.csv")
# 6. Map the percentage of Hispanic, Non-Hispanic White, Non-Hispanic Black or African American. (1’)
tm_shape(Bexar_segregation) +tm_fill(col = "pct_Hisp")+ tm_layout(title = "Hispanic Percent")
tm_shape(Bexar_segregation) +tm_fill(col = "pct_White")+ tm_layout(title = "White Percent")
tm_shape(Bexar_segregation) +tm_fill(col = "pct_BAA")+ tm_layout(title = "Black or African American Percent")
# 7. Import library(ggplot2), plot the scatter plot between median income and percentage of Hispanic. (1’)
library(ggplot2)
ggplot(data=Bexar_segregation, aes(x = MedianIncome, y = pct_Hisp)) + geom_point()## Warning: Removed 4 rows containing missing values (`geom_point()`).
# 8. Move your code to R Markdown and publish the output to Rpubs. (1’)