Use this R script to specify a U.S. county and two mutually exclusive five-year time periods, then extract and map county-division-level variable estimates and error margins for the specified county and time periods from the U.S. Census Bureau API. The script will produce a “slider map” that allows a user to compare data from both time periods by dragging a horizontal slider bar across the map. Want to skip the explanations, copy the script in one chunk, and go? Scroll down to “The complete script.”
Several packages are required, including TidyCensus and mapview. This code checks to see whether the required packages are installed already, installs them if they aren’t, and loads them into memory.
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("tidycensus")) install.packages("tidycensus")
if (!require("sf")) install.packages("sf")
if (!require("mapview")) install.packages("mapview")
if (!require("leaflet.extras2")) install.packages("leaflet.extras2")
if (!require("scales")) install.packages("scales")
library(tidycensus)
library(tidyverse)
library(sf)
library(mapview)
library(leaflet.extras2)
library(scales)
library(leaflet)
library(leafpop)
options(tigris_use_cache = TRUE)The U.S. Census Bureau’s API requires an API key. You can get one for
free at https://api.census.gov/data/key_signup.html. Once you
get yours, replace PastYourAPIKeyBetwenTheseQuoteMarks with
your API key. Also, delete the # from the front of the
# census_api_key code.
# NOTE: Obtain a Census API key from
# https://api.census.gov/data/key_signup.html
# and replace the census_api_key code line's
# PastYourAPIKeyBetwenTheseQuoteMarks text with your
# API key. Also, delete the "#" from the front of the
# census_api_key code.
#census_api_key("PasteYourAPIKeyBetwenTheseQuoteMarks")Just about any variable available from the Census API can be plugged into the script. The code retrieves variable name catalogs for the Detailed, Subject, and Profile tables variables for a specified five-year American Community Survey dataset. You can search the tables to find variables of interest to you. The default year, 2021, will retrieve variables for the 2017-2021 five-year ACS dataset. You can change the year, if preferred.
DetailedTables <- load_variables(2021, "acs5", cache = TRUE)
SubjectTables <- load_variables(2021, "acs5/subject", cache = TRUE)
ProfileTables <- load_variables(2021, "acs5/profile", cache = TRUE)This code retrieves specified data for both of two specified years.
The default years are 2016 and 2021. You may specify other years, if
preferred. The median rent (B25064_001) estimates have been specified by
default. You may edit these variable codes to specify other variables,
if preferred. Two datasets are created - one for the earlier period
(MapData1), and one for the later period (MapData2). Lines marked
#Customizable can be customized to fetch data for other
counties in other states and during other years.
MapData1 <- get_acs(geography = "county subdivision",
state = "TN", #Customizable
county = "Rutherford", #Customizable
variables = c(Variable1 = "B25064_001"), #Customizable
year = 2016, #Customizable
survey = "acs5",
output = "wide",
geometry = TRUE)## Getting data from the 2012-2016 5-year ACSMapData2 <- get_acs(geography = "county subdivision",
state = "TN", #Customizable
county = "Rutherford", #Customizable
variables = c(Variable2 = "B25064_001"), #Customizable
year = 2021,
survey = "acs5",
output = "wide",
geometry = TRUE)## Getting data from the 2017-2021 5-year ACSmapviewOptions(basemaps.color.shuffle = FALSE)This code merges the MapData1 and MapData2 datasets, then calculates the specified variable’s change from the first period to the second, and the significance of the change.
MapData1_nogeo <- st_drop_geometry(MapData1)
ChangeData <- merge(MapData2, MapData1_nogeo, by = "GEOID")
ChangeData$County <- ChangeData$NAME.x
ChangeData$Change <- ChangeData$Variable2E - ChangeData$Variable1E
ChangeData$Sig <- significance(ChangeData$Variable1E,
ChangeData$Variable2E,
ChangeData$Variable1M,
ChangeData$Variable2M,
clevel = 0.90)
ChangeData$Significance <- ifelse(ChangeData$"Sig" == "TRUE",
"Significant",
"Nonsignificant")This code produces two maps, one showing levels of the specified
variable during the most recent time period, and a second showing the
specified variables levels of change from the earlier time period.
Finally, it combines the two maps into a single slider map. Lines marked
#Customizable can be edited to customize the titles on the
map’s keys.
CurrentMap <- mapview(MapData2, zcol = "Variable2E",
col.regions = RColorBrewer::brewer.pal(9, "Blues"), alpha.regions = .5,
layer.name = "Median rent, 2017-21",#Customizable
popup = FALSE
)## Warning: Found less unique colors (9) than unique zcol values (21)!
## Interpolating color vector to match number of zcol values.ChangeMap <- mapview(ChangeData,
zcol = "Change",
col.regions = RColorBrewer::brewer.pal(9, "Greys"), alpha.regions = .5,
layer.name = "Change from 2012-16",#Customizable
popup = popupTable(ChangeData,
zcol = c("Change",
"Significance")))## Warning: Found less unique colors (9) than unique zcol values (21)!
## Interpolating color vector to match number of zcol values.CurrentMap | ChangeMapJust here for the script? You’ll find it below, all in one chunk, ready to be copied and pasted.
# Installing required packages
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("tidycensus")) install.packages("tidycensus")
if (!require("sf")) install.packages("sf")
if (!require("mapview")) install.packages("mapview")
if (!require("leaflet.extras2")) install.packages("leaflet.extras2")
if (!require("scales")) install.packages("scales")
library(tidycensus)
library(tidyverse)
library(sf)
library(mapview)
library(leaflet.extras2)
library(scales)
library(leaflet)
library(leafpop)
options(tigris_use_cache = TRUE)
# NOTE: Obtain a Census API key from
# https://api.census.gov/data/key_signup.html
# and replace the census_api_key code line's
# PastYourAPIKeyBetwenTheseQuoteMarks text with your
# API key. Also, delete the "#" from the front of the
# census_api_key code.
#census_api_key("PasteYourAPIKeyBetwenTheseQuoteMarks")
# Getting ACS variable info
# Change "2021" to your preferred year.
DetailedTables <- load_variables(2021, "acs5", cache = TRUE)
SubjectTables <- load_variables(2021, "acs5/subject", cache = TRUE)
ProfileTables <- load_variables(2021, "acs5/profile", cache = TRUE)
# Getting the map data
MapData1 <- get_acs(geography = "county subdivision",
state = "TN", #Customizable
county = "Rutherford", #Customizable
variables = c(Variable1 = "B25064_001"), #Customizable
year = 2016, #Customizable
survey = "acs5",
output = "wide",
geometry = TRUE)
MapData2 <- get_acs(geography = "county subdivision",
state = "TN", #Customizable
county = "Rutherford", #Customizable
variables = c(Variable2 = "B25064_001"), #Customizable
year = 2021,
survey = "acs5",
output = "wide",
geometry = TRUE)
mapviewOptions(basemaps.color.shuffle = FALSE)
# Calculating change
MapData1_nogeo <- st_drop_geometry(MapData1)
ChangeData <- merge(MapData2, MapData1_nogeo, by = "GEOID")
ChangeData$County <- ChangeData$NAME.x
ChangeData$Change <- ChangeData$Variable2E - ChangeData$Variable1E
ChangeData$Sig <- significance(ChangeData$Variable1E,
ChangeData$Variable2E,
ChangeData$Variable1M,
ChangeData$Variable2M,
clevel = 0.90)
ChangeData$Significance <- ifelse(ChangeData$"Sig" == "TRUE",
"Significant",
"Nonsignificant")
CurrentMap <- mapview(MapData2, zcol = "Variable2E",
col.regions = RColorBrewer::brewer.pal(9, "Blues"), alpha.regions = .5,
layer.name = "Median rent, 2017-21",#Customizable
popup = FALSE
)
ChangeMap <- mapview(ChangeData,
zcol = "Change",
col.regions = RColorBrewer::brewer.pal(9, "Greys"), alpha.regions = .5,
layer.name = "Change from 2012-16",#Customizable
popup = popupTable(ChangeData,
zcol = c("Change",
"Significance")))
CurrentMap | ChangeMap