GIS HW 2

Map of Median Household Income in Bexar County, TX

#load packages
library(tidycensus)
library(tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0      ✔ purrr   0.3.4 
✔ tibble  3.1.8      ✔ dplyr   1.0.10
✔ tidyr   1.2.0      ✔ stringr 1.4.1 
✔ readr   2.1.2      ✔ forcats 0.5.2 
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(sf)

Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUE

library(ggplot2)
library(classInt)

#save API key to working directory

 tidycensus::census_api_key(key = "f23f22ab8d214cd5ce4152926f7fc9f7289da68b", install = T, overwrite=TRUE)

Your original .Renviron will be backed up and stored in your R HOME directory if needed.

Your API key has been stored in your .Renviron and can be accessed by Sys.getenv("CENSUS_API_KEY"). 
To use now, restart R or run `readRenviron("~/.Renviron")`

[1] "f23f22ab8d214cd5ce4152926f7fc9f7289da68b"

v15_Profile <- load_variables(year = 2019 ,
dataset = "acs5/profile",
cache = TRUE)
 View(v15_Profile)
 
 v15_Profile%>%
filter(grepl(pattern = "INCOME", x = label))%>%
select(name, label)

# A tibble: 174 × 2
   name       label                                                             
   <chr>      <chr>                                                             
 1 DP03_0051  Estimate!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS…
 2 DP03_0051P Percent!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)…
 3 DP03_0052  Estimate!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS…
 4 DP03_0052P Percent!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)…
 5 DP03_0053  Estimate!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS…
 6 DP03_0053P Percent!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)…
 7 DP03_0054  Estimate!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS…
 8 DP03_0054P Percent!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)…
 9 DP03_0055  Estimate!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS…
10 DP03_0055P Percent!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)…
# … with 164 more rows

#pull population data and income data

sa_acs<-get_acs(geography = "tract",
state="TX",
county = "Bexar",
year = 2019,
variables=c("DP05_0001E", "DP03_0062E") ,
geometry = T,
output = "wide")

Getting data from the 2015-2019 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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# create a county FIPS code - 5 digit
sa_acs$county<-substr(sa_acs$GEOID, 1, 5)
# rename variables and filter missing cases
sa_acs2<-sa_acs%>%
  mutate(totpop= DP05_0001E,
         medincome=DP03_0062E) %>%
  st_transform(crs = 2919)%>%
  na.omit()

library(tmap)
library(tmaptools)
tm_shape(sa_acs2)+
  tm_polygons("medincome",
              title="Median Household Income (in dollars)",
              palette="Greens",
              style="pretty", n=5 )+
  tm_format("World",
            title="San Antonio median household Estimates - Pretty Breaks",
            legend.outside=T)+
  tm_scale_bar()+
  tm_compass()