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Summary Chart

Here is a summary chart of the Nashviile rent rates

Two-bedroom stats, by rent category
Rent_Category	Count	Minimum	Average	Maximum
Above average	28	1930	2309.286	2740
Below average	52	1460	1713.462	1920

CODE:

# Getting and loading required packages

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

# Reading data from:
# https://www.huduser.gov/portal/datasets/fmr/fmr2025/fy2025_safmrs.xlsx
# Note that you are downloading the 2025 data. We have been working with 2024 data.
# The data frame should have 51,899 observations of 18 variables

download.file("https://www.huduser.gov/portal/datasets/fmr/fmr2025/fy2025_safmrs.xlsx", "rent.xlsx", mode = "wb")

FMR <- read_xlsx(path = "rent.xlsx", .name_repair = "universal")

# Making a list of Nashville-area ZIP codes

ZIPList <- c(
  "37135",
  "37215",
  "37064",
  "37060",
  "37014",
  "37122",
  "37027",
  "37046",
  "37221",
  "37153",
  "37210",
  "37202",
  "37024",
  "37218",
  "37062",
  "37179",
  "37025",
  "37206",
  "37065",
  "37214",
  "37067",
  "37246",
  "37068",
  "37167",
  "37069",
  "37189",
  "37070",
  "37204",
  "37072",
  "37208",
  "37076",
  "37212",
  "37080",
  "37216",
  "37085",
  "37020",
  "37086",
  "38476",
  "37089",
  "37160",
  "37090",
  "37174",
  "37115",
  "37180",
  "37116",
  "37201",
  "37118",
  "37203",
  "37015",
  "37205",
  "37127",
  "37207",
  "37128",
  "37209",
  "37129",
  "37211",
  "37130",
  "37213",
  "37220",
  "37037",
  "37222",
  "37217",
  "37228",
  "37219",
  "37232",
  "37013",
  "37131",
  "37224",
  "37132",
  "37229",
  "37133",
  "37236",
  "37238",
  "37240",
  "37243",
  "37138",
  "38401",
  "37143",
  "37011",
  "37149"
)

# Filtering for Nashville-area ZIP codes and
# selecting columns of interest
# FMR_Nash data frame should have 80 observations of six variables

FMR_Nash <- FMR %>%
  filter(ZIP.Code %in% ZIPList) %>%
  select(ZIP.Code, SAFMR.0BR, SAFMR.1BR, SAFMR.2BR, SAFMR.3BR, SAFMR.4BR) %>% 
  distinct()

# Renaming the columns

colnames(FMR_Nash) <- c("ZIP", "Studio", "BR1", "BR2", "BR3", "BR4")

# Averaging estimates

FMR_Nash <- FMR_Nash %>%
  mutate(ZIP_Average = (Studio + BR1 + BR2 + BR3 + BR4) / 5)

# Sorting in descending order by ZIP_Average

FMR_Nash <- FMR_Nash %>%
  arrange(desc(ZIP_Average))

# Finding the average of the ZIP_Average values

Average_ZIP_Average <- mean(FMR_Nash$ZIP_Average)

Average_ZIP_Average

# Recoding 

FMR_Nash <- FMR_Nash %>%
  mutate(
    Rent_Category = case_when(
      ZIP_Average > Average_ZIP_Average ~ "Above average",
      ZIP_Average == Average_ZIP_Average ~ "Average",
      ZIP_Average < Average_ZIP_Average ~ "Below average",
      .default = "Error"))

# Showing the data as a table

FMR_Nash_table <- gt(FMR_Nash) %>% 
  tab_header("Nashville-area FMR, by size and ZIP") %>%
  cols_align(align = "left") %>%
  gt_theme_538

FMR_Nash_table

# Summarize the data for BR2 (two-bedroom stats)
Summary_BR2 <- FMR_Nash %>%
  group_by(Rent_Category) %>%
  summarise(
    Count = n(),
    Minimum = min(BR2, na.rm = TRUE),
    Average = mean(BR2, na.rm = TRUE),
    Maximum = max(BR2, na.rm = TRUE)
  ) %>%
  arrange(Rent_Category)

# Check the summary table
print(Summary_BR2)

# Show the summary table using gt for formatting
Summary_Nash <- gt(Summary_BR2) %>% 
  tab_header("Two-bedroom stats, by rent category") %>%
  cols_align(align = "left") %>%
  gt_theme_538()

# Show the table
Summary_Nash