There are over twice as many occupied housing units as there are apartments.
What would be considered the outer ZIP codes of Rutherford county have lees occupied units, but they all have one thing in common. They all prefer renting a home instead of an apartment. The total of occupied rental apartments in Rutherford County ZIP codes is 45,448 which is dwarfed by a total of 127,752 rental homes in the area.
The table gives a clear view of all of the numbers related to housing in the Rutherford County. The table reflects no clear correlation between the pricing for 3-bedroom apartments and amount of rental apartment units.
| Data | |||||||||
| ZIP | Studio | BR1 | BR2 | BR3 | BR4 | Rentals | Rentals_MOE | Households | Households_MOE |
|---|---|---|---|---|---|---|---|---|---|
| 37037 | 1900 | 1990 | 2180 | 2790 | 3400 | 395 | 186 | 3128 | 372 |
| 37085 | 1320 | 1380 | 1520 | 1940 | 2360 | 113 | 86 | 1992 | 302 |
| 37086 | 1730 | 1820 | 1990 | 2540 | 3100 | 3434 | 488 | 12887 | 545 |
| 37118 | 1150 | 1170 | 1320 | 1660 | 2020 | 55 | 59 | 424 | 155 |
| 37127 | 1360 | 1420 | 1560 | 1990 | 2430 | 1650 | 330 | 7056 | 523 |
| 37128 | 1570 | 1640 | 1800 | 2300 | 2800 | 10523 | 1007 | 28968 | 1212 |
| 37129 | 1570 | 1640 | 1800 | 2300 | 2800 | 8241 | 990 | 23583 | 1187 |
| 37130 | 1280 | 1340 | 1470 | 1880 | 2290 | 11852 | 804 | 23624 | 927 |
| 37132 | 1280 | 1340 | 1470 | 1880 | 2290 | 0 | 14 | 0 | 14 |
| 37149 | 1150 | 1180 | 1320 | 1660 | 2020 | 81 | 69 | 937 | 198 |
| 37153 | 1670 | 1750 | 1920 | 2450 | 2990 | 281 | 175 | 1982 | 326 |
| 37167 | 1430 | 1500 | 1640 | 2100 | 2560 | 8823 | 720 | 23225 | 773 |
# ----------------------------------------------------------
# Step 1: Install & load required packages
# ----------------------------------------------------------
if (!require("tidyverse"))
install.packages("tidyverse")
if (!require("gt"))
install.packages("gt")
if (!require("leaflet"))
install.packages("leaflet")
if (!require("leafpop"))
install.packages("leafpop")
if (!require("sf"))
install.packages("sf")
if (!require("RColorBrewer"))
install.packages("RColorBrewer")
if (!require("classInt"))
install.packages("classInt")
if (!require("scales"))
install.packages("scales")
if (!require("htmlwidgets"))
install.packages("htmlwidgets")
if (!require("tidycensus"))
install.packages("tidycensus")
library(tidyverse)
library(gt)
library(sf)
library(leaflet)
library(leafpop)
library(RColorBrewer)
library(classInt)
library(scales)
library(htmlwidgets)
library(tidycensus)
# ----------------------------------------------------------
# Step 2: Area ZIP Codes
# ----------------------------------------------------------
ZIPList <- c(
"37127",
"37128",
"37129",
"37130",
"37132",
"37085",
"37118",
"37149",
"37037",
"37153",
"37167",
"37086"
)
# ----------------------------------------------------------
# Step 3: Download HUD SAFMR Excel file
# ----------------------------------------------------------
download.file(
"https://www.huduser.gov/portal/datasets/fmr/fmr2026/fy2026_safmrs.xlsx",
"rent.xlsx",
mode = "wb"
)
# ----------------------------------------------------------
# Step 4: Read Excel data
# ----------------------------------------------------------
FMR_Area <- readxl::read_xlsx(path = "rent.xlsx",
.name_repair = "universal")
# ----------------------------------------------------------
# Step 5: Filter FMR data for "ZIPList" ZIP codes,
# select and rename columns,
# and remove duplicates
# ----------------------------------------------------------
FMR_Area <- FMR_Area %>%
transmute(
ZIP = ZIP.Code,
Studio = SAFMR.0BR,
BR1 = SAFMR.1BR,
BR2 = SAFMR.2BR,
BR3 = SAFMR.3BR,
BR4 = SAFMR.4BR
) %>%
filter(ZIP %in% ZIPList) %>%
distinct()
# ----------------------------------------------------------
# Step 6: Download and unzip the ZCTA shapefile
# ----------------------------------------------------------
download.file(
"https://www2.census.gov/geo/tiger/GENZ2020/shp/cb_2020_us_zcta520_500k.zip",
"ZCTAs2020.zip",
mode = "wb"
)
unzip("ZCTAs2020.zip")
# ----------------------------------------------------------
# Step 7: Load ZCTA shapefile into R
# ----------------------------------------------------------
ZCTAMap <- read_sf("cb_2020_us_zcta520_500k.shp")
# ----------------------------------------------------------
# Step 8: Prepare ZIP column for joining
# ----------------------------------------------------------
FMR_Area$ZIP <- as.character(FMR_Area$ZIP)
# ----------------------------------------------------------
# Step 9: Join the FMR data to the ZCTA polygons
# ----------------------------------------------------------
FMR_Area_Map <- left_join(FMR_Area, ZCTAMap, by = c("ZIP" = "ZCTA5CE20"))
# ----------------------------------------------------------
# Step 10: Drop unneeded Census columns
# ----------------------------------------------------------
FMR_Area_Map <- FMR_Area_Map %>%
select(-c(AFFGEOID20, GEOID20, NAME20, LSAD20, ALAND20, AWATER20))
# ----------------------------------------------------------
# Step 11: Convert to sf object and reproject to WGS84 (EPSG:4326),
# then filter to valid geometries (fix for hover/popup)
# ----------------------------------------------------------
FMR_Area_Map <- st_as_sf(FMR_Area_Map)
if (!is.na(sf::st_crs(FMR_Area_Map))) {
FMR_Area_Map <- st_transform(FMR_Area_Map, 4326)
}
# >>> Fix: keep only rows with valid geometry so popups/labels match features <<<
FMR_Area_Map <- FMR_Area_Map %>%
dplyr::filter(!sf::st_is_empty(geometry) & !is.na(sf::st_geometry_type(geometry)))
# (Optional) See which ZIPs didn't have polygons
# missing_zips <- setdiff(ZIPList, ZCTAMap$ZCTA5CE20)
# if (length(missing_zips)) {
# message("ZIPs without ZCTA polygons (not mapped): ", paste(missing_zips, collapse = ", "))
# }
# ----------------------------------------------------------
# Step 12: Fetch ACS data
# ----------------------------------------------------------
census_api_key("904ea9b9c6b5f8f7d6b1d4846a59ee8cc3b48d1f") # <- Add your API key
Census_Data <- get_acs(
geography = "zcta",
variables = c("DP04_0047", "DP04_0045"),
year = 2024,
survey = "acs5",
output = "wide",
geometry = FALSE
)
Census_Data <- Census_Data %>%
transmute(
ZIP = GEOID,
Rentals = DP04_0047E,
Rentals_MOE = DP04_0047M,
Households = DP04_0045E,
Households_MOE = DP04_0045M
)
# ----------------------------------------------------------
# Step 13: Filter ACS data for "ZIPList" ZIP codes
# ----------------------------------------------------------
Census_Data <- Census_Data %>%
filter(ZIP %in% ZIPList)
# Left-join ACS counts & MOEs into the map data by ZIP
FMR_Area_Map <- FMR_Area_Map %>%
left_join(Census_Data, by = "ZIP")
# ==========================================================
# Step 14: EDIT MAP CONTROL POINTS AS NEEDED
# ==========================================================
ShadeBy <- "BR3" # Which numeric column to shade by: "Studio","BR1","BR2","BR3","BR4"
PaletteName <- "Blues" # Any sequential/diverging RColorBrewer palette (e.g., "Blues","OrRd","PuBuGn","GnBu")
legend_classes <- 7 # Number of legend classes/bins (typical: 4–7)
# Customizable popup labels:
# Keys MUST match columns we will pass (ZIP and the *_fmt columns created below).
# Values are the human-friendly headers shown in the popup. Reorder to change popup order.
popup_labels <- c(
ZIP = "ZIP",
Studio_fmt = "Studio",
BR1_fmt = "1-Bed",
BR2_fmt = "2-Bed",
BR3_fmt = "3-Bed",
BR4_fmt = "4-Bed",
# --- NEW FIELDS (comma-formatted strings defined below) ---
Rentals_fmt = "Renter-occupied units",
Rentals_MOE_fmt = "Renter MOE (±)",
Households_fmt = "Occupied housing units",
Households_MOE_fmt = "Occupied MOE (±)"
)
# ==========================================================
# Friendly labels for legend title (optional)
friendly_names <- c(
Studio = "Studio",
BR1 = "1-Bed",
BR2 = "2-Bed",
BR3 = "3-Bed",
BR4 = "4-Bed"
)
# Legend title shows ONLY the selected ShadeBy field (friendly name if available)
legend_title <- if (ShadeBy %in% names(friendly_names)) {
friendly_names[[ShadeBy]]
} else {
ShadeBy
}
# ----------------------------------------------------------
# Step 15: Helper: Build a Brewer palette safely for a requested size
# - Uses up to the palette's native max colors
# - Interpolates if you ask for more than the palette provides
# ----------------------------------------------------------
build_brewer_colors <- function(name, k) {
info <- RColorBrewer::brewer.pal.info
if (!name %in% rownames(info)) {
stop(sprintf("Palette '%s' not found in RColorBrewer.", name))
}
max_n <- info[name, "maxcolors"]
base <- RColorBrewer::brewer.pal(min(max_n, max(3, k)), name)
if (k <= length(base)) {
base[seq_len(k)]
} else {
grDevices::colorRampPalette(base)(k)
}
}
# ----------------------------------------------------------
# Step 16: Jenks breaks + robust fallback (prevents non-unique breaks)
# ----------------------------------------------------------
vals <- FMR_Area_Map[[ShadeBy]]
vals <- vals[!is.na(vals)]
# 1) Try Jenks
ci <- classInt::classIntervals(vals, n = legend_classes, style = "jenks")
breaks <- sort(unique(ci$brks))
# 2) If Jenks couldn't produce enough unique breaks, fall back to quantiles, then pretty
if (length(breaks) < 3) {
qbreaks <- quantile(
vals,
probs = seq(0, 1, length.out = legend_classes + 1),
na.rm = TRUE,
type = 7
)
qbreaks <- sort(unique(as.numeric(qbreaks)))
if (length(qbreaks) >= 3) {
breaks <- qbreaks
} else {
pbreaks <- pretty(range(vals, na.rm = TRUE), n = legend_classes)
pbreaks <- sort(unique(as.numeric(pbreaks)))
if (length(pbreaks) >= 3) {
breaks <- pbreaks
} else {
# As a last resort, ensure at least two unique break points around a single value
rng <- range(vals, na.rm = TRUE)
if (rng[1] == rng[2]) {
b0 <- rng[1]
eps <- if (abs(b0) < 1) 1e-9 else abs(b0) * 1e-9
breaks <- c(b0 - eps, b0 + eps)
} else {
breaks <- rng
}
}
}
}
# 3) Final guard: ensure strictly increasing, unique breaks (and at least two)
breaks <- sort(unique(breaks))
if (length(breaks) < 2) {
b0 <- vals[1]
eps <- if (abs(b0) < 1) 1e-9 else abs(b0) * 1e-9
breaks <- c(b0 - eps, b0 + eps)
}
# Palette length must match # of bins (breaks - 1)
n_bins <- max(1, length(breaks) - 1)
pal_colors <- build_brewer_colors(PaletteName, n_bins)
pal_bin <- colorBin(
palette = pal_colors,
domain = FMR_Area_Map[[ShadeBy]],
bins = breaks,
na.color = "#cccccc",
right = FALSE
)
# ----------------------------------------------------------
# Step 17: Precompute FillColor (avoid hard-coding a field name in leaflet)
# ----------------------------------------------------------
FMR_Area_Map$FillColor <- pal_bin(FMR_Area_Map[[ShadeBy]])
# Build a clean hover label with comma formatting (no dollar signs)
FMR_Area_Map$HoverLabel <- sprintf(
"ZIP %s: %s = %s",
FMR_Area_Map$ZIP,
legend_title,
ifelse(is.na(FMR_Area_Map[[ShadeBy]]), "NA", scales::comma(FMR_Area_Map[[ShadeBy]]))
)
# ----------------------------------------------------------
# Step 18: Popup table with comma formatting (no dollar signs)
# Create a formatted copy for display; keep numerics unchanged in FMR_Area_Map
# ----------------------------------------------------------
popup_data <- FMR_Area_Map %>%
mutate(
Studio_fmt = ifelse(is.na(Studio), NA, scales::comma(Studio)),
BR1_fmt = ifelse(is.na(BR1), NA, scales::comma(BR1)),
BR2_fmt = ifelse(is.na(BR2), NA, scales::comma(BR2)),
BR3_fmt = ifelse(is.na(BR3), NA, scales::comma(BR3)),
BR4_fmt = ifelse(is.na(BR4), NA, scales::comma(BR4)),
# --- NEW: formatted ACS fields for the popup ---
Rentals_fmt = ifelse(is.na(Rentals), NA, scales::comma(Rentals)),
Rentals_MOE_fmt = ifelse(is.na(Rentals_MOE), NA, scales::comma(Rentals_MOE)),
Households_fmt = ifelse(is.na(Households), NA, scales::comma(Households)),
Households_MOE_fmt = ifelse(is.na(Households_MOE), NA, scales::comma(Households_MOE))
)
# Build the popup data with user-defined labels as actual column names.
# IMPORTANT: We pass THIS object to popupTable and use its own colnames in zcol.
popup_labels <- popup_labels # keep as defined above
popup_keys <- intersect(names(popup_labels), names(popup_data)) # columns available to show
popup_display <- popup_data %>%
sf::st_drop_geometry() %>%
select(all_of(popup_keys))
colnames(popup_display) <- unname(popup_labels[popup_keys])
# ----------------------------------------------------------
# Step 19: Build the Leaflet interactive map with base layer options
# - Default: CartoDB.Positron (your original)
# - Options: Esri.WorldStreetMap and Esri.WorldImagery
# ----------------------------------------------------------
Rent_Category_Map <- leaflet(FMR_Area_Map, options = leafletOptions(preferCanvas = TRUE)) %>%
# Default/base layer (original view)
addProviderTiles(providers$CartoDB.Positron, group = "Streets (CartoDB Positron)") %>%
# Additional selectable base layers
addProviderTiles(providers$Esri.WorldStreetMap, group = "Streets (Esri World Street Map)") %>%
addProviderTiles(providers$Esri.WorldImagery, group = "Satellite (Esri World Imagery)") %>%
# Your data layer (overlay group)
addPolygons(
fillColor = ~ FillColor,
color = "#444444",
weight = 1,
opacity = 1,
fillOpacity = 0.7,
label = ~ HoverLabel,
labelOptions = labelOptions(
style = list("font-weight" = "bold"),
textsize = "12px",
direction = "auto"
),
popup = leafpop::popupTable(
popup_display,
feature.id = FALSE,
row.numbers = FALSE,
zcol = colnames(popup_display)
),
highlight = highlightOptions(
weight = 2,
color = "#000000",
fillOpacity = 0.8,
bringToFront = TRUE
),
group = "FMR by ZIP"
) %>%
# Legend
addLegend(
position = "bottomright",
pal = pal_bin,
values = FMR_Area_Map[[ShadeBy]],
title = legend_title,
opacity = 0.7,
labFormat = labelFormat(
big.mark = ",",
digits = 0,
between = " – "
)
) %>%
# Layer control to switch basemaps ONLY (overlay toggle removed)
addLayersControl(
baseGroups = c(
"Streets (CartoDB Positron)",
"Streets (Esri World Street Map)",
"Satellite (Esri World Imagery)"
),
options = layersControlOptions(collapsed = FALSE)
)
# Note: No overlayGroups parameter, so "FMR by ZIP" can't be toggled off.
# ----------------------------------------------------------
# Step 20: Display the map
# ----------------------------------------------------------
Rent_Category_Map
# ----------------------------------------------------------
# Step 21: (Optional) Save the map as an HTML file
# ----------------------------------------------------------
outfile <- paste0("FMR_",
ShadeBy,
"_",
PaletteName,
"_",
legend_classes,
"Classes.html")
htmlwidgets::saveWidget(widget = Rent_Category_Map,
file = outfile,
selfcontained = TRUE)
message("Saved map to: ", normalizePath(outfile))
# ----------------------------------------------------------
# Step 22: Downshifting map file to a data file &
# showing contents as a table
# ----------------------------------------------------------
Data_From_Map <- st_drop_geometry(FMR_Area_Map) %>%
select(-c(FillColor,HoverLabel))
Data_From_Map_Table <- gt(Data_From_Map) %>%
tab_header(title = "Data") %>%
cols_align(align = "left")
Data_From_Map_Table