Rent Map Data

Rents in Rutherford County

Below is an interactive map detailing average rent estimates by ZIP code. The map is interactive, and upon investigating the shaded areas details the estimated rent by unit size, estimated number of rental units, total households, as well as error margins for each.

Code:

# Installing and loading required packages

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("gtExtras"))
  install.packages("gtExtras")
if (!require("leafpop"))
  install.packages("leafpop")
if (!require("sf"))
  install.packages("sf")
if (!require("mapview"))
  install.packages("mapview")
if (!require("RColorBrewer"))
  install.packages("RColorBrewer")
if (!require("tidycensus"))
  install.packages("tidycensus")

library(tidyverse)
library(gtExtras)
library(sf)
library(mapview)
library(leafpop)
library(RColorBrewer)
library(tidycensus)

# Reloading the rent data

FMR_RuCo <- read_csv("https://raw.githubusercontent.com/drkblake/Data/refs/heads/main/FMR_RuCo.csv")

# Showing the rent data

FMR_RuCo_table <- gt(FMR_RuCo) %>%
  tab_header("Rutherford FMR, by size and ZIP") %>%
  cols_align(align = "left") %>%
  gt_theme_538
FMR_RuCo_table

# Downloading the ZIP code map file

download.file(
  "https://www2.census.gov/geo/tiger/GENZ2020/shp/cb_2020_us_zcta520_500k.zip",
  "ZCTAs2020.zip"
)

# Unzipping the ZIP code map file

unzip("ZCTAs2020.zip")

# Loading the ZIP code file into R as "ZCTAMap"

ZCTAMap <- read_sf("cb_2020_us_zcta520_500k.shp")

# Making ZIP a character variable

FMR_RuCo$ZIP <- as.character(FMR_RuCo$ZIP)

# Joining the files

FMR_RuCo_Map <- left_join(FMR_RuCo, ZCTAMap, by = c("ZIP" = "ZCTA5CE20"))

# Dropping unneeded columns

FMR_RuCo_Map <- FMR_RuCo_Map %>%
  select(-c(AFFGEOID20, GEOID20, NAME20, LSAD20, ALAND20, AWATER20))

# Converting FMR_RuCo_Map

FMR_RuCo_Map <- st_as_sf(FMR_RuCo_Map)

census_api_key("32a6d157cbd9ca98ee8b0dcaf65290d150813e2b")

Census_Data <- get_acs(
  geography = "zcta",
  variables = c("DP04_0047", "DP04_0045"),
  year = 2023,
  survey = "acs5",
  output = "wide",
  geometry = FALSE
)

Census_Data <- Census_Data %>% 
  rename(c("Rentals" = "DP04_0047E",
           "Rentals_MOE" = "DP04_0047M",
           "Households" = "DP04_0045E",
           "Households_MOE" = "DP04_0045M"))

glimpse(Census_Data)

# Merging FMR_RuCo_Map and Census_Data

FMR_RuCo_Map <- left_join(FMR_RuCo_Map, Census_Data, by = c("ZIP" = "GEOID"))

# Mapping by ZIP_Average

BR3_Map <- mapview(
  FMR_RuCo_Map,
  zcol = "BR3",
  col.regions = brewer.pal(9, "YlOrRd"),
  layer.name = "Three Bedroom Rent",
  popup = popupTable(
    FMR_RuCo_Map,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c("ZIP", "Studio", "BR1", "BR2", "BR3", "BR4","Rentals", "Rentals_MOE", "Households", "Households_MOE")))

# Showing the map

BR3_Map