Get a Census developer API Key

Obtain one at the Census Developer website

Save your API key to your working directory

use tidycensus::census_api_key(key = "yourkeyhere", install = T)

One time to install your key for use in tidycensus

library(tidycensus)
library(tidyverse)
library(sf)
library(ggplot2)
library(classInt)
library(dplyr)

Examine data profile tables

The load_variables() function will load all available variables in the ACS for a given year

v15_Profile <- load_variables(year = 2019 ,
                              dataset =  "acs5/profile",
                              cache = TRUE)

Calling View(v15_Profile) will let you interactively browse and filter the ACS variables, this is one way to search for what you’re looking for.

Search for variables by keywords in the label

v15_Profile%>%
  filter(grepl(pattern = "POVERTY", x = label))%>%
  select(name, label)
## # A tibble: 38 × 2
##    name       label                                                             
##    <chr>      <chr>                                                             
##  1 DP03_0119  Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
##  2 DP03_0119P Percent!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PA…
##  3 DP03_0120  Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
##  4 DP03_0120P Percent!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PA…
##  5 DP03_0121  Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
##  6 DP03_0121P Percent!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PA…
##  7 DP03_0122  Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
##  8 DP03_0122P Percent!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PA…
##  9 DP03_0123  Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
## 10 DP03_0123P Percent!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PA…
## # … with 28 more rows
v15_Profile%>%
  filter(grepl(pattern = "Built 2000 to 2009", x = label))%>%
  select(name, label)
## # A tibble: 2 × 2
##   name       label                                                              
##   <chr>      <chr>                                                              
## 1 DP04_0019  Estimate!!YEAR STRUCTURE BUILT!!Total housing units!!Built 2000 to…
## 2 DP04_0019P Percent!!YEAR STRUCTURE BUILT!!Total housing units!!Built 2000 to …

Extract from ACS summary file data

The tidycensus package has a function get_acs() that will download data from the Census API for you automatically assuming you’ve installed your key from above

Here I get data profile variables from 2017 for Bexar County, TX Census Tracts

Here is a query where we extract several variables from the 2017 ACS for Bexar County, Texas. We can also get the spatial data by requesting geometry=TRUE.

Using output="wide" will put each variable in a column of the data set, with each row being a census tract.

sa_acs<-get_acs(geography = "tract",
                state="TX",
                county = "Bexar",
                year = 2019,
                variables=c("DP05_0001E", "DP03_0119PE") ,
                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
head(sa_acs)

Here, I create some other variables that we may need later

# 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,
         ppov=DP03_0119PE) %>%
  st_transform(crs = 2919)%>%
  na.omit()

Write data out to shapefile

You may need to create or modify some data in R and then use it in the desktop GIS (QGIS), we can write any data from R into a variety of data formats using the sf::st_write() function.

#eval=F means eval=FALSE which means this code is not running, it is here for you to view though
st_geometry(sa_acs2)<-NULL
foreign::write.dbf(as.data.frame(sa_acs2), file="C:/Users/xee291/OneDrive - University of Texas at San Antonio/Documents/UTSA 2022-2023/GIS Class/Lab 1/Lab 1_V2/acs_lab1.dbf")
#change the directory for your computer
sf::st_write(sa_acs2,
             dsn="C:/Users/xee291/OneDrive - University of Texas at San Antonio/Documents/UTSA 2022-2023/GIS Class/Lab 1/Lab 1_V2/sa_tr_dp03.gpkg",
             layer="sa_tr_dp03",
             driver="GPKG") ## Save as geopackage format - QGIS likes this
mydat<- st_read("C:/Users/xee291/OneDrive - University of Texas at San Antonio/Documents/UTSA 2022-2023/GIS Class/Lab 1/Lab 1_V2/sa_tr_dp03.gpkg")
names(mydat)

Some basic mapping of variables

Here I generate a quantile break for % black in census tracts and compare it to a Jenks break. Note in ggplot, the Jenks break is harder to do

library(patchwork)
library(ggsn)
library(xplorerr)
source("https://raw.githubusercontent.com/coreysparks/Rcode/master/mutate_map_funs.R")
ppov_map<-sa_acs2 %>%
  mutate_map_brks(ppov, n=6, style="quantile")%>%
           mutate_map_brks(ppov, n=6, style="jenks")
p1<-ggplot(ppov_map, aes(fill = ppov_qbrks)) + 
  geom_sf(color="grey50") + 
  ggtitle("Proportion in poverty", 
          subtitle = "Bexar County Texas, 2017 - Quantile Breaks")+
    scale_fill_brewer(palette = "Reds") + 
  scale_color_brewer(palette = "Reds")+
  theme(axis.text.x = element_blank(),
        axis.text.y = element_blank())+
  north(ppov_map)+
  scalebar(ppov_map, location="bottomleft",
           dist=5, transform = T,
           dist_unit = "km",
           model="WGS84",
           st.size =2 )
p1

p2<-ggplot(ppov_map, aes(fill = ppov_jbrks)) + 
  geom_sf(color="grey50") + 
  ggtitle("Proportion in Poverty", 
          subtitle = "Bexar County Texas, 2017 - Jenks Breaks")+
  scale_fill_brewer(palette = "Reds") + 
  scale_color_brewer(palette = "Reds")+
    theme(axis.text.x = element_blank(),
          axis.text.y = element_blank())+
  north(ppov_map)+
  scalebar(ppov_map, 
           location="bottomleft",
           dist=5,
           transform = T,
           dist_unit = "km",
           model="WGS84",
           st.size =2)
p2

p1 + p2

You can save the image from above to your computer by using ggsave()

ggsave(filename="C:/Users/xee291/OneDrive - University of Texas at San Antonio/Documents/UTSA 2022-2023/GIS Class/Lab 1/Lab 1_V2/lab1map1.png",
       dpi = "print")
## Saving 7 x 5 in image

Slightly easier way using tmap

The tmap package is an attractive alternative to using ggplot() when making maps, and makes basic cartographic principles easier.

Note: style="fisher" is equivalent to style="jenks" and scales better to larger data.

Note: View this website for more style and other tmap tricks: https://bookdown.org/nicohahn/making_maps_with_r5/docs/tmap.html

library(tmap)
library(tmaptools)
tm_shape(sa_acs2)+
  tm_polygons("ppov",
              title="% in Poverty",
              palette="Blues",
              style="quantile", n=5 )+
  tm_format("World",
            title="San Antonio Poverty Estimates - Quantile Breaks",
            legend.outside=T)+
  tm_scale_bar()+
  tm_compass()

tm_shape(sa_acs2)+
  tm_polygons("ppov",
              title="% in Poverty",
              palette="Blues",
              style="fisher",
              n=5,
              legend.hist=T )+
  tm_format("World",
            title="San Antonio Poverty Estimates - Jenks Breaks",
            legend.outside=T)+
  tm_scale_bar()+
  tm_compass()

Interactive map with mapview

(Not necessary for Homework 2, but here to view)

library(mapview)
library(RColorBrewer)
ppov_map$ppov_jbrks<-relevel(ppov_map$ppov_jbrks,ref = "0-5.5" )
pal <- colorRampPalette(brewer.pal(7, "Blues")) #set colors
mapview(ppov_map,
        zcol="ppov_jbrks",
        legend=T,
        map.types="OpenStreetMap",
        layer.name="% in Poverty")