── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0 ✔ purrr 1.0.0
✔ tibble 3.1.8 ✔ dplyr 1.0.10
✔ tidyr 1.2.1 ✔ stringr 1.5.0
✔ readr 2.1.3 ✔ forcats 0.5.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUEHW_2_Bexar County
DEM 7093: Basic Maps Using Tidycensus + Saving Dataframe as Shapefile for QGIS
Examine data profile tables
load_variables() function loads all the variables in the ACS for a given year
Search for variables by keywords in the label
# A tibble: 24 × 2
name label
<chr> <chr>
1 DP03_0062 Estimate!!INCOME AND BENEFITS (IN 2021 INFLATION-ADJUSTED DOLLARS…
2 DP03_0062P Percent!!INCOME AND BENEFITS (IN 2021 INFLATION-ADJUSTED DOLLARS)…
3 DP03_0063 Estimate!!INCOME AND BENEFITS (IN 2021 INFLATION-ADJUSTED DOLLARS…
4 DP03_0063P Percent!!INCOME AND BENEFITS (IN 2021 INFLATION-ADJUSTED DOLLARS)…
5 DP03_0067 Estimate!!INCOME AND BENEFITS (IN 2021 INFLATION-ADJUSTED DOLLARS…
6 DP03_0067P Percent!!INCOME AND BENEFITS (IN 2021 INFLATION-ADJUSTED DOLLARS)…
7 DP03_0068 Estimate!!INCOME AND BENEFITS (IN 2021 INFLATION-ADJUSTED DOLLARS…
8 DP03_0068P Percent!!INCOME AND BENEFITS (IN 2021 INFLATION-ADJUSTED DOLLARS)…
9 DP03_0069 Estimate!!INCOME AND BENEFITS (IN 2021 INFLATION-ADJUSTED DOLLARS…
10 DP03_0069P Percent!!INCOME AND BENEFITS (IN 2021 INFLATION-ADJUSTED DOLLARS)…
# … with 14 more rows# v15_Profile%>%
# filter(grepl(pattern = "Built 2000 to 2009", x = label))%>%
# select(name, label)Extract from ACS summary file data
Getting data profile for Bexar County from ACS 5-Year dataset. I am getting total population and median income. The function geometry=TRUE gives me spatial data and output=“wide” puts each variable in a column of the dataset and each row becomes a census tract.
#|warning: false
#|message: false
#|echo:fenced
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 ACSWarning: • You have not set a Census API key. Users without a key are limited to 500
queries per day and may experience performance limitations.
ℹ For best results, get a Census API key at
http://api.census.gov/data/key_signup.html and then supply the key to the
`census_api_key()` function to use it throughout your tidycensus session.
This warning is displayed once per session.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%head(sa_acs)Simple feature collection with 6 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -98.56606 ymin: 29.36662 xmax: -98.44832 ymax: 29.54044
Geodetic CRS: NAD83
GEOID NAME DP05_0001E DP05_0001M
1 48029190601 Census Tract 1906.01, Bexar County, Texas 4514 788
2 48029181100 Census Tract 1811, Bexar County, Texas 6359 523
3 48029140700 Census Tract 1407, Bexar County, Texas 4838 303
4 48029180602 Census Tract 1806.02, Bexar County, Texas 3751 536
5 48029140800 Census Tract 1408, Bexar County, Texas 5124 453
6 48029170200 Census Tract 1702, Bexar County, Texas 5599 617
DP03_0062E DP03_0062M geometry
1 46818 14436 MULTIPOLYGON (((-98.52602 2...
2 64930 6521 MULTIPOLYGON (((-98.56225 2...
3 36222 6656 MULTIPOLYGON (((-98.47348 2...
4 38389 7038 MULTIPOLYGON (((-98.56606 2...
5 43299 3656 MULTIPOLYGON (((-98.4778 29...
6 25755 7141 MULTIPOLYGON (((-98.5283 29...Create a county FIPS code - 5 digit
# 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,
medinc=DP03_0062E) %>%
st_transform(crs = 2919)%>%
na.omit()Optional* Write data out to a shapefile
Mapping Variables with GGplot
#|code-fold: true
library(patchwork)
library(ggsn)Loading required package: grid#library(XploreR)
source("https://raw.githubusercontent.com/coreysparks/Rcode/master/mutate_map_funs.R")
income_map<-sa_acs2 %>%
mutate_map_brks(medinc, n=6, style="quantile")%>%
mutate_map_brks(medinc, n=6, style="jenks")
p1<-ggplot(income_map, aes(fill = medinc_qbrks)) +
geom_sf(color="grey50") +
ggtitle("Median Household Income",
subtitle = "Bexar County Texas, 2019 - Quantile Breaks")+
scale_fill_brewer(palette = "Reds") +
scale_color_brewer(palette = "Reds")+
theme(axis.text.x = element_blank(),
axis.text.y = element_blank())+
north(income_map)+
scalebar(income_map, location="bottomleft",
dist=5, transform = T,
dist_unit = "km",
model="WGS84",
st.size =2 )
p1
p2<-ggplot(income_map, aes(fill = medinc_jbrks)) +
geom_sf(color="grey50") +
ggtitle("Median Household Income",
subtitle = "Bexar County Texas, 2019 - Jenks Breaks")+
scale_fill_brewer(palette = "Reds") +
scale_color_brewer(palette = "Reds")+
theme(axis.text.x = element_blank(),
axis.text.y = element_blank())+
north(income_map)+
scalebar(income_map,
location="bottomleft",
dist=5,
transform = T,
dist_unit = "km",
model="WGS84",
st.size =2)
p2
library(tmap)
library(tmaptools)
tm_shape(sa_acs2)+
tm_polygons("medinc",
title="Median Income",
palette="Greens",
style="pretty", n=5 )+
tm_format("World",
title="Author:Coda Rayo-Garza",
legend.outside=T)+
tm_layout(main.title = "Bexar County Median Income Estimates:Pretty Breaks",
main.title.position = "center")+
tm_scale_bar()+
tm_compass()
tm_shape(sa_acs2)+
tm_polygons("medinc",
title="Median Income",
palette="Greens",
style="fisher",
n=5,
legend.hist=T )+
tm_format("World",
title="Author:Coda Rayo-Garza",
legend.outside=T)+
tm_layout(main.title = "Bexar County Median Income Estimates:Jenks Breaks",
main.title.position = "center")+
tm_scale_bar()+
tm_compass()
Making An Interactive Map Using Mapview
library(mapview)
library(RColorBrewer)
income_map$medinc_jbrks<-relevel(income_map$medinc_jbrks,ref = "0-10463" )
##ppov_map$ppov_jbrks was from the ggplot section
pal <- colorRampPalette(brewer.pal(7, "Blues")) #set colors
# to change the pivot table that populates when you click on a geo element on the map:
# sub <- ppov_map %>%
# select(Poverty_Rate = ppov_jbrks, ppov, Name = NAME)
mapview(income_map,
zcol="medinc_jbrks",
legend=T,
map.types="OpenStreetMap",
layer.name="Household Median Income")