Lab 6

Fair Market Rent in Rutherford Co.

Rutherford FMR, by size and ZIP
ZIP	Studio	BR1	BR2	BR3	BR4	ZIP_Average	Rent_Category
37037	1660	1710	1920	2410	2940	2128	Above average
37086	1580	1620	1820	2290	2790	2020	Above average
37128	1510	1550	1740	2190	2670	1932	Above average
37129	1420	1460	1640	2060	2510	1818	Above average
37153	1410	1450	1630	2040	2490	1804	Above average
37167	1290	1330	1490	1870	2280	1652	Below average
37085	1260	1290	1450	1820	2210	1606	Below average
37127	1240	1270	1430	1800	2190	1586	Below average
37130	1180	1210	1360	1710	2080	1508	Below average
37132	1180	1210	1360	1710	2080	1508	Below average
37118	1100	1130	1270	1590	1960	1410	Below average
37149	1100	1130	1270	1590	1960	1410	Below average

Code:

# Getting 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")

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

# 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)

# Making the map

Rent_Category_Map <- mapview(
  FMR_RuCo_Map,
  zcol = "BR3",
  col.regions = brewer.pal(9,"BuGn"),
  layer.name = "Three-bedroom rent",
  popup = popupTable(
    FMR_RuCo_Map,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c("ZIP", "Studio", "BR1", "BR2", "BR3", "BR4")))

# Showing the map

Rent_Category_Map

# 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)

census_api_key("93315bc9312a299da799361cc702a29db484d7cd")
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)

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

ZIP_Map <- mapview(
  FMR_RuCo_Map,
  zcol = "ZIP_Average",
  col.regions = brewer.pal(9, "Blues"),
  layer.name = "Average 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

ZIP_Map