As new college students, or any person trying to rent, rent in Rutherford County has steadily gone up over the years. Below is the code for the map and table that shows different sized rental units, and their average price range for every ZIP code in Rutherford County. As you click on the map, you’ll see the average prices, and the availabilty of the different sized units. Hope you find the best area to rent in Rutherford County.
## # A tibble: 1,392 × 3
## name label concept
## <chr> <chr> <chr>
## 1 DP02PR_0001 Estimate!!HOUSEHOLDS BY TYPE!!Total households Select…
## 2 DP02PR_0001P Percent!!HOUSEHOLDS BY TYPE!!Total households Select…
## 3 DP02PR_0002 Estimate!!HOUSEHOLDS BY TYPE!!Total households!!Married… Select…
## 4 DP02PR_0002P Percent!!HOUSEHOLDS BY TYPE!!Total households!!Married-… Select…
## 5 DP02PR_0003 Estimate!!HOUSEHOLDS BY TYPE!!Total households!!Married… Select…
## 6 DP02PR_0003P Percent!!HOUSEHOLDS BY TYPE!!Total households!!Married-… Select…
## 7 DP02PR_0004 Estimate!!HOUSEHOLDS BY TYPE!!Total households!!Cohabit… Select…
## 8 DP02PR_0004P Percent!!HOUSEHOLDS BY TYPE!!Total households!!Cohabiti… Select…
## 9 DP02PR_0005 Estimate!!HOUSEHOLDS BY TYPE!!Total households!!Cohabit… Select…
## 10 DP02PR_0005P Percent!!HOUSEHOLDS BY TYPE!!Total households!!Cohabiti… Select…
## # ℹ 1,382 more rows| 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("494018a70f114d4f76b10537730ccc9c7dbfe36b") # <- 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
ProfileTables <- load_variables(2024, "acs5/profile", cache = TRUE)```