Lab 7

Many Davidson County residents have no health insurance despite employment

Seven districts within Davidson County have 20% or more of their residents ages 19 to 65 living with no health insurance, despite the fact that they hold jobs. The map below displays the percentage of residents ages 19 to 65 who are employed but have no health insurance; click on a district to view percentages.

Code:

# ----------------------------------------------------------
# Step 1: Install required packages (if missing)
# ----------------------------------------------------------

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("tidycensus"))
  install.packages("tidycensus")
if (!require("sf"))
  install.packages("sf")
if (!require("leaflet"))
  install.packages("leaflet")
if (!require("htmlwidgets"))
  install.packages("htmlwidgets")
if (!require("plotly"))
  install.packages("plotly")   # For the interactive dot plot

# ----------------------------------------------------------
# Step 2: Load libraries
# ----------------------------------------------------------

library(tidyverse)
library(tidycensus)
library(sf)
library(leaflet)
library(htmlwidgets)
library(plotly)

# ----------------------------------------------------------
# Step 3: Transmit Census API key (uncomment and paste yours)
# ----------------------------------------------------------

census_api_key("e96c46601fca77f6f3fcb0f72b673a75aed0ff2a")

# ----------------------------------------------------------
# Step 4: Fetch ACS codebooks (for variable lookup if needed)
# ----------------------------------------------------------

DetailedTables <- load_variables(2024, "acs5", cache = TRUE)
SubjectTables  <- load_variables(2024, "acs5/subject", cache = TRUE)
ProfileTables  <- load_variables(2024, "acs5/profile", cache = TRUE)

# ----------------------------------------------------------
# Step 5: Specify target variable(s)
# ----------------------------------------------------------

VariableList <- c(Estimate_ = "DP03_0108P")

# ----------------------------------------------------------
# Step 6: Fetch ACS data (county subdivision, Tennessee)
# ----------------------------------------------------------

mydata <- get_acs(
  geography = "county subdivision",
  state = "TN",
  variables = VariableList,
  year = 2024,
  survey = "acs5",
  output = "wide",
  geometry = TRUE
)

# ----------------------------------------------------------
# Step 7: Reformat the NAME field into Area / County / State
# ----------------------------------------------------------

mydata <- separate_wider_delim(
  mydata,
  NAME,
  delim = ", ",
  names = c("Area", "County", "State")
)

# ----------------------------------------------------------
# Step 8: Filter to Rutherford County
# ----------------------------------------------------------

filtereddata <- mydata %>%
  filter(County %in% c("Davidson County"))

# ----------------------------------------------------------
# Step 9: Prepare data for mapping (rename, as sf, CRS)
# ----------------------------------------------------------

mapdata <- filtereddata %>%
  rename(
    Estimate = Estimate_E,
    Range = Estimate_M
  ) %>%
  st_as_sf()

# Ensure CRS is WGS84 for Leaflet
mapdata <- st_transform(mapdata, 4326)

# ----------------------------------------------------------
# Step 10: Build color palette with quantile-based breaks
# ----------------------------------------------------------

qs <- quantile(mapdata$Estimate, probs = seq(0, 1, length.out = 6), na.rm = TRUE)

pal <- colorBin(
  palette = "Reds", # Can specify other palettes here
  domain = mapdata$Estimate,
  bins = qs,
  pretty = FALSE
)

# ----------------------------------------------------------
# Step 11: Build the plotly dot plot with error bars
# ----------------------------------------------------------

# Add point color from the same Leaflet palette and ordered y factor
filtereddata <- filtereddata %>%
  mutate(
    point_color = pal(Estimate_E),
    y_ordered   = reorder(Area, Estimate_E),
    hover_text  = dplyr::if_else(
      !is.na(Area),
      paste0("Area: ", Area),
      Area
    )
  )

# Create the plotly scatter with horizontal error bars and thin gray borders
mygraph <- plot_ly(
  data = filtereddata,
  x = ~Estimate_E,
  y = ~y_ordered,
  type = "scatter",
  mode = "markers",
  marker = list(
    color = ~point_color,
    size  = 8,
    line  = list(
      color = "rgba(120,120,120,0.9)",  # thin gray border for contrast
      width = 0.5
    )
  ),
  error_x = list(
    type       = "data",
    array      = ~Estimate_M,      # + side
    arrayminus = ~Estimate_M,      # - side
    color      = "rgba(0,0,0,0.65)",
    thickness  = 1
  ),
  text = ~hover_text,
  # Show District (from hover_text) and the X value with thousands separators
  hovertemplate = "%{text}<br>%{x:,}<extra></extra>"
) %>%
  layout(
    title = list(text = "Estimates by area<br><sup>County subdivisions. Brackets show error margins.</sup>"),
    xaxis = list(title = "ACS estimate"),
    yaxis = list(title = "")
  )

# display the plot
mygraph

# ----------------------------------------------------------
# Step 12: Create popup content for the map
# ----------------------------------------------------------

mapdata$popup <- paste0(
  "<strong>", mapdata$Area, "</strong><br/>",
  "<hr>",
  "Estimate: ", format(mapdata$Estimate, big.mark = ","), "<br/>",
  "Plus/Minus: ", format(mapdata$Range, big.mark = ",")
)

# ----------------------------------------------------------
# Step 13: Build the Leaflet map
# ----------------------------------------------------------

DivisionMap <- leaflet(mapdata) %>%
  # Choose one basemap:
  addProviderTiles(providers$CartoDB.Positron) %>%
  # addProviderTiles(providers$Esri.WorldStreetMap, group = "Streets (Esri World Street Map)") %>%
  # addProviderTiles(providers$Esri.WorldImagery,   group = "Satellite (Esri World Imagery)") %>%
  addPolygons(
    fillColor   = ~pal(Estimate),
    fillOpacity = 0.5, 
    color       = "black",
    weight      = 1,
    popup       = ~popup
  ) %>%
  addLegend(
    pal    = pal,
    values = ~Estimate,
    title  = "Estimate",
    labFormat = labelFormat(big.mark = ",")
  )

DivisionMap

# ----------------------------------------------------------
# Step 14: Export graph as a standalone HTML file
# ----------------------------------------------------------
# This creates a fully self-contained HTML file for the dot plot.

saveWidget(
  widget = as_widget(mygraph),
  file = "ACSGraph.html",
  selfcontained = TRUE
)

# ----------------------------------------------------------
# Step 15: Export map as a standalone HTML file
# ----------------------------------------------------------
# This creates a fully self-contained HTML you can open or share.
# Adjust the path/filename as you like.

saveWidget(
  widget = DivisionMap,
  file = "ACSMap.html",
  selfcontained = TRUE
)