Here is the solution code for the Week 10 Lab assignment:
# 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)
# Transmitting API key
# census_api_key("PasteYourAPIKeyBetweenTheseQuoteMarks")
# 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$name)
print(ChosenVars$label)
print(ChosenVars$concept)
# Specifying target variables
VariableList =
c(PctGasHeat_ = "DP04_0063P",
Households_ = "DP02_0001")
# Fetching data
mydata <- get_acs(
geography = "county",
state = "TN",
variables = VariableList,
year = 2022,
survey = "acs5",
output = "wide",
geometry = TRUE)
# 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 = PctGasHeat_E, y = reorder(County, PctGasHeat_E))) +
geom_errorbarh(aes(xmin = PctGasHeat_E - PctGasHeat_M, xmax = PctGasHeat_E + PctGasHeat_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(PctGasHeat = PctGasHeat_E,
Households = Households_E)
mapdata <- st_as_sf(mapdata)
mapviewOptions(basemaps.color.shuffle = FALSE)
mapview(mapdata, zcol = "PctGasHeat",
layer.name = "Pct. with gas heat",
popup = TRUE)
# Better map
# Installing and activating some additional packages
if (!require("leaflet")) install.packages("leaflet")
if (!require("leaflet.extras2")) install.packages("leaflet.extras2")
library(leaflet)
library(leaflet.extras2)
library(leafpop)
# Defining a custome color palette for the map
mypalette = colorRampPalette(c('lightgray', 'darkorange'))
# Generating the map
mapview(mapdata, zcol = "PctGasHeat",
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",
"PctGasHeat",
"Households")))
# Exporting data in .csv format
CSVdata <- st_drop_geometry(mapdata)
write.csv(CSVdata, "mydata.csv", row.names = FALSE)And here is the code plus its output:
# 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)
# Transmitting API key
# census_api_key("PasteYourAPIKeyBetweenTheseQuoteMarks")
# 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(PctGasHeat_ = "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 = PctGasHeat_E, y = reorder(County, PctGasHeat_E))) +
geom_errorbarh(aes(xmin = PctGasHeat_E - PctGasHeat_M, xmax = PctGasHeat_E + PctGasHeat_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(PctGasHeat = PctGasHeat_E,
Households = Households_E)
mapdata <- st_as_sf(mapdata)
mapviewOptions(basemaps.color.shuffle = FALSE)
mapview(mapdata, zcol = "PctGasHeat",
layer.name = "Pct. with gas heat",
popup = TRUE)# Better map
# Installing and activating some additional packages
if (!require("leaflet")) install.packages("leaflet")
if (!require("leaflet.extras2")) install.packages("leaflet.extras2")
library(leaflet)
library(leaflet.extras2)
library(leafpop)
# Defining a custome color palette for the map
mypalette = colorRampPalette(c('lightgray', 'darkorange'))
# Generating the map
mapview(mapdata, zcol = "PctGasHeat",
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",
"PctGasHeat",
"Households")))# Exporting data in .csv format
CSVdata <- st_drop_geometry(mapdata)
write.csv(CSVdata, "mydata.csv", row.names = FALSE)