DEM 7093 GIS HW2
Daniel Mamani, MS
1/28/2021
Packages
library(tidycensus)
library(tidyverse)## -- Attaching packages ----------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.3
## v tidyr 1.1.1 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts -------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(sf)## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1library(ggplot2)
library(classInt)
library(patchwork)
library(dplyr)
library(ggsn)## Loading required package: gridExtract ACS summary file data
Here I get data profile variables from 2017 for Bexar County, TX Census Tracts
The data profile tables are very useful because they contain lots of pre-calculated variables.
Here is a query where we extract several variables from tehe 2017 ACS for Bexar County, Texas. We can also get the spatial data by requesting geometry=TRUE. Useing output=“wide” will put each variable in a column of the data set, with each row being a census tract.
v15_Profile <- load_variables(2017 , "acs5/profile", cache = TRUE) #demographic profile tables
#Search for my variable of interest
v15_Profile[grep(x = v15_Profile$label, "Median"), c("name", "label")]## # A tibble: 24 x 2
## name label
## <chr> <chr>
## 1 DP03_0062 Estimate!!INCOME AND BENEFITS (IN 2017 INFLATION-ADJUSTED DOLLARS~
## 2 DP03_0062P Percent Estimate!!INCOME AND BENEFITS (IN 2017 INFLATION-ADJUSTED~
## 3 DP03_0086 Estimate!!INCOME AND BENEFITS (IN 2017 INFLATION-ADJUSTED DOLLARS~
## 4 DP03_0086P Percent Estimate!!INCOME AND BENEFITS (IN 2017 INFLATION-ADJUSTED~
## 5 DP03_0090 Estimate!!INCOME AND BENEFITS (IN 2017 INFLATION-ADJUSTED DOLLARS~
## 6 DP03_0090P Percent Estimate!!INCOME AND BENEFITS (IN 2017 INFLATION-ADJUSTED~
## 7 DP03_0092 Estimate!!INCOME AND BENEFITS (IN 2017 INFLATION-ADJUSTED DOLLARS~
## 8 DP03_0092P Percent Estimate!!INCOME AND BENEFITS (IN 2017 INFLATION-ADJUSTED~
## 9 DP03_0093 Estimate!!INCOME AND BENEFITS (IN 2017 INFLATION-ADJUSTED DOLLARS~
## 10 DP03_0093P Percent Estimate!!INCOME AND BENEFITS (IN 2017 INFLATION-ADJUSTED~
## # ... with 14 more rowssa_acs<-get_acs(geography = "tract",
state="TX",
county = c("Bexar"),
year = 2017,
variables=c( "DP05_0001E", # Total Population
"DP03_0062E") ,# Median Household Income
geometry = T, output = "wide")## Getting data from the 2013-2017 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.## Using the ACS Data Profile##
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|======================================================================| 100%Renaming variables and filtering data
#create a county FIPS code - 5 digit
sa_acs$county<-substr(sa_acs$GEOID, 1, 5)
#rename variables and filter missing cases
sa_acs3<-sa_acs%>%
mutate(totpop= DP05_0001E, mhi=DP03_0062E) %>%
# st_transform(crs = 102740)%>%
na.omit()Creating my map using tmap.
Quantile Breaks vs Pretty Breaks
library(tmap)
library(tmaptools)
tm_shape(sa_acs3)+
tm_polygons("mhi", title="Median Household Income", palette="Blues", style="quantile", n=5 ,legend.hist=T)+
tm_format("World", title="San Antonio Median Household Income - Quantile Breaks", legend.outside=T)+
tm_scale_bar()+
tm_compass()
tm_shape(sa_acs3)+
tm_polygons("mhi", title="Median Household Income", palette="Blues", style="pretty", n=5,legend.hist=T )+
tm_format("World", title="San Antonio Median Household Income - Pretty Breaks", legend.outside=T)+
tm_scale_bar()+
tm_compass()