Nashville area gas heat usage, by county
Jason Noble
2024-03-22
Nashville area gas heat usage, by county
Attached is the code used for the Week 10 Lab of Dr. Ken Blake’s JOUR 3841 course. Running the code in its entirety will only leave you with the last map, but running the code by each individual line will display both maps in separate instances.
# Installing and loading 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")
library(tidyverse)
library(tidycensus)
library(sf)
library(mapview)
if (!require("leaflet")) install.packages("leaflet")
if (!require("leaflet.extras2")) install.packages("leaflet.extras2")
library(leaflet)
library(leaflet.extras2)
library(leafpop)
# Transmitting API key
census_api_key("71f42f990c13d3e5c61a6c6f723de11b70e679a0")
# Fetching ACS codebooks
DetailedTables <- load_variables(2022, "acs5", cache = TRUE)
SubjectTables <- load_variables(2022, "acs5/subject", cache = TRUE)
ProfileTables <- load_variables(2022, "acs5/profile", cache = TRUE)
# Double checking target variables
ChosenVars <- filter(ProfileTables,name == "DP04_0063P"|
name == "DP02_0001")
print(ChosenVars$name)## [1] "DP02_0001" "DP04_0063P"print(ChosenVars$label)## [1] "Estimate!!HOUSEHOLDS BY TYPE!!Total households"
## [2] "Percent!!HOUSE HEATING FUEL!!Occupied housing units!!Utility gas"print(ChosenVars$concept)## [1] "Selected Social Characteristics in the United States"
## [2] "Selected Housing Characteristics"# Specifying target variables
VariableList =
c(GasHeat_ = "DP04_0063P",
Households_ = "DP02_0001")
# Fetching data
mydata <- get_acs(
geography = "county",
state = "TN",
variables = VariableList,
year = 2022,
survey = "acs5",
output = "wide",
geometry = TRUE)##
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|======================================================================| 100%# Reformatting data
mydata <-
separate_wider_delim(mydata,
NAME,
delim = " County, ",
names = c("County", "State"))
# Filtering data
filtereddata <- mydata %>%
filter(County == "Davidson"|
County == "Rutherford"|
County == "Williamson"|
County == "Cheatham"|
County == "Robertson"|
County == "Sumner"|
County == "Wilson")
# Plotting data
ggplot(filtereddata, aes(x = GasHeat_E, y = reorder(County, GasHeat_E))) +
geom_errorbarh(aes(xmin = GasHeat_E - GasHeat_M, xmax = GasHeat_E + GasHeat_M)) +
geom_point(size = 3, color = "darkblue") +
theme_minimal(base_size = 12.5) +
labs(title = "Pct. households with gas heat",
subtitle = "Nashville-area counties. Brackets show error margins.",
x = "2018-2022 ACS estimate",
y = "")
# Mapping data
mapdata <- filtereddata %>%
rename(GasHeat = GasHeat_E,
Households = Households_E)
mapdata <- st_as_sf(mapdata)
mapviewOptions(basemaps.color.shuffle = FALSE)
mapview(mapdata, zcol = "GasHeat",
layer.name = "Pct. with gas heat",
popup = TRUE)# Exporting data in .csv format
CSVdata <- st_drop_geometry(mapdata)
write.csv(CSVdata, "mydata.csv", row.names = FALSE)
# SECOND MAP - Orange Gradient
# Defining a custome color palette for the map
mypalette = colorRampPalette(c('lightgray', 'orange'))
# Generating the map
mapview(mapdata, zcol = "GasHeat",
col.regions = mypalette, at = seq(10, 60, 10),
layer.name = "Pct. gas heat",
popup = popupTable(mapdata,
feature.id = FALSE,
row.numbers = FALSE,
zcol = c("County",
"State",
"GasHeat",
"Households")))