Most Employed Nashville Residents Live Without Health Insurance

Data from the 2024 U.S. Census Bureau has shown that residents of Nashville ages 19 to 64 who are in the labor force on average don’t typically have health insurance coverage. The percentage or Nashvillians with coverage peaks at 30.4% in District 28, but dips as low as 1.3% in District 34.

This means at least 70-90% of employed adults in Nashville live without any insurance for if they get sick or injured.

Percentage of Adults, 19-64, Without Health Insurance in Nashville

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("b2b546278d54eb1ced62b21ec507abf728af01c2")

# ----------------------------------------------------------
# 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 = "RdYlGn", # 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
)