Mapping GRAPI
Dr. Ken Blake
2024-02-11
Gross Rent as a Percentage of Income, or GRAPI, is a standard measure of housing affordability. This script will map Rutherford County’s subdivisions, then shade them according to the percentage of renters in each division who are paying 35 percent of more of their total household income on rent and utilities. The estimates come from the 2022 five-year American Community Survey.
Note that, to use the script, you must obtain a Census API
key and paste it into the code in place of
PasteYourAPIKeyBetweenTheseQuoteMarks, and delete the
# character at the start of that line of code.
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("tidycensus")) install.packages("tidycensus")
if (!require("mapview")) install.packages("mapview")
if (!require("sf")) install.packages("sf")
library(tidyverse)
library(tidycensus)
library(mapview)
library(sf)
census_api_key("PasteYourAPIKeyBetweenTheseQuoteMarks")
DetailedTables <- load_variables(2022, "acs5", cache = TRUE)
SubjectTables <- load_variables(2022, "acs5/subject", cache = TRUE)
ProfileTables <- load_variables(2022, "acs5/profile", cache = TRUE)
mydata <- get_acs(
geography = "county subdivision",
state = "TN",
variables = c(MyVar_ = "DP04_0142P"),
year = 2022,
survey = "acs5",
output = "wide",
geometry = TRUE)
mydata <-
separate_wider_delim(mydata,
NAME,
delim = ", ",
names = c("District", "County", "State"))
mydata <- filter(
mydata,
County == "Rutherford County")
mydata <- arrange(mydata, County, desc(MyVar_E))
write.csv(mydata, "mydata.csv", row.names = FALSE)
mydata_sf <- st_as_sf(mydata)
mydata_sf %>%
ggplot(aes(fill = MyVar_E)) +
geom_sf(color = NA) +
scale_fill_viridis_c(option = "magma")
mydata_mapview <- mapview(mydata_sf, zcol = "MyVar_E",
col.regions = RColorBrewer::brewer.pal(9, "Blues"), alpha.regions = .5,
layer.name = "Pct. spending >34% on rent",#Customizable
popup = TRUE)
mydata_mapviewHere’s the script in action, with the output it produces:
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("tidycensus")) install.packages("tidycensus")
if (!require("mapview")) install.packages("mapview")
if (!require("sf")) install.packages("sf")
library(tidyverse)
library(tidycensus)
library(mapview)
library(sf)
#census_api_key("PasteYourAPIKeyBetweenTheseQuoteMarks")
DetailedTables <- load_variables(2022, "acs5", cache = TRUE)
SubjectTables <- load_variables(2022, "acs5/subject", cache = TRUE)
ProfileTables <- load_variables(2022, "acs5/profile", cache = TRUE)
mydata <- get_acs(
geography = "county subdivision",
state = "TN",
variables = c(MyVar_ = "DP04_0142P"),
year = 2022,
survey = "acs5",
output = "wide",
geometry = TRUE)##
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|======================================================================| 100%mydata <-
separate_wider_delim(mydata,
NAME,
delim = ", ",
names = c("District", "County", "State"))
mydata <- filter(
mydata,
County == "Rutherford County")
mydata <- arrange(mydata, County, desc(MyVar_E))
write.csv(mydata, "mydata.csv", row.names = FALSE)
mydata_sf <- st_as_sf(mydata)
mydata_sf %>%
ggplot(aes(fill = MyVar_E)) +
geom_sf(color = NA) +
scale_fill_viridis_c(option = "magma") 
mydata_mapview <- mapview(mydata_sf, zcol = "MyVar_E",
col.regions = RColorBrewer::brewer.pal(9, "Blues"), alpha.regions = .5,
layer.name = "Pct. spending >34% on rent",#Customizable
popup = TRUE)
mydata_mapview
