Overview

The table below displays population, population growth, land area, population density, and density categories for selected Middle Tennessee counties. Davidson County emerged as the most densely populated county in the dataset, with at least 1,149 residents per square mile. Rutherford County ranked a distant second in density, and Williamson County ranked third. The results indicate that Davidson County remains the urban core of the Nashville Metropolitan Area. At the same time, surrounding counties such as Rutherford and Williamson continue to experience substantial population growth, but at much lower population densities than Davidson County.

Population Data with Density Measures
County CurrentPop EarlierPop Region Change Percent_change Square_Miles Density Density_Category
Davidson 715388 598184 Davidson 117204 0.1959 504 1419.4206 High Density
Rutherford 360646 292425 Doughnut 68221 0.2333 620 581.6871 High Density
Williamson 260351 208242 Doughnut 52109 0.2502 583 446.5712 Medium Density
Sumner 204424 174773 Doughnut 29651 0.1697 529 386.4348 Medium Density
Wilson 158805 129918 Doughnut 28887 0.2223 571 278.1173 Medium Density
Maury 107791 88738 Non-doughnut 19053 0.2147 613 175.8418 Medium Density
Robertson 75539 67517 Doughnut 8022 0.1188 476 158.6954 Medium Density
Cheatham 41829 39087 Doughnut 2742 0.0702 302 138.5066 Medium Density
Dickson 55983 51608 Non-doughnut 4375 0.0848 490 114.2510 Medium Density
Trousdale 11957 10131 Non-doughnut 1826 0.1802 114 104.8860 Medium Density
Macon 26240 23261 Non-doughnut 2979 0.1281 307 85.4723 Low Density
Smith 20389 19389 Non-doughnut 1000 0.0516 314 64.9331 Low Density
Cannon 14818 13958 Non-doughnut 860 0.0616 266 55.7068 Low Density
Hickman 25436 24561 Non-doughnut 875 0.0356 612 41.5621 Low Density

Code:

Here is the code that produced the analysis, which relied on information from the U.S. Census Bureau’s American Community Survey.

# ============================================================
# Step 1: Install and load required packages
# ============================================================
required_packages <- c(
  "tidyverse",
  "knitr",
  "kableExtra"
)

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

library(tidyverse)
library(knitr)
library(kableExtra)

# ============================================================
# Step 2: Create a vector containing the county names
# ============================================================
County <- c(
  "Cannon", "Cheatham", "Davidson", "Dickson", "Hickman", "Macon",
  "Maury", "Robertson", "Rutherford", "Smith", "Sumner", "Trousdale",
  "Williamson", "Wilson"
)

# ============================================================
# Step 3: Create a vector containing current population counts
# ============================================================
CurrentPop <- c(
  14818, 41829, 715388, 55983, 25436, 26240,
  107791, 75539, 360646, 20389, 204424, 11957,
  260351, 158805
)

# ============================================================
# Step 4: Create a vector containing earlier population counts
# ============================================================
EarlierPop <- c(
  13958, 39087, 598184, 51608, 24561, 23261,
  88738, 67517, 292425, 19389, 174773, 10131,
  208242, 129918
)

# ============================================================
# Step 5: Create a vector identifying each county's region
# ============================================================
Region <- c(
  "Non-doughnut", "Doughnut", "Davidson", "Non-doughnut",
  "Non-doughnut", "Non-doughnut", "Non-doughnut", "Doughnut",
  "Doughnut", "Non-doughnut", "Doughnut", "Non-doughnut",
  "Doughnut", "Doughnut"
)

# ============================================================
# Step 6: Combine the vectors into a population data frame
# ============================================================
Population <- data.frame(
  County,
  CurrentPop,
  EarlierPop,
  Region
)

# ============================================================
# Step 7: Add a Change variable showing population increase
# ============================================================
Population <- Population %>%
  mutate(Change = CurrentPop - EarlierPop)

# ============================================================
# Step 8: Sort the data frame by CurrentPop in descending order
# ============================================================
Population <- Population %>%
  arrange(desc(CurrentPop))

# ============================================================
# Step 9: Display the updated and sorted population data frame
# ============================================================
Population

# ============================================================
# Step 10: Create a data frame containing only County and Change
# ============================================================
Change_only <- Population %>%
  select(County, Change)

# ============================================================
# Step 11: Sort the Change_only data frame by Change descending
# ============================================================
Change_only <- Change_only %>%
  arrange(desc(Change))

# ============================================================
# Step 12: Display the Change_only data frame
# ============================================================
Change_only

# ============================================================
# Step 13: Create a Doughnut data frame containing only
#         Davidson and Doughnut-region counties
# ============================================================
Doughnut <- Population %>%
  filter(Region %in% c("Davidson", "Doughnut"))

# ============================================================
# Step 14: Sort the Doughnut data frame by Change descending
# ============================================================
Doughnut <- Doughnut %>%
  arrange(desc(Change))

# ============================================================
# Step 15: Display the Doughnut data frame
# ============================================================
Doughnut

# ============================================================
# Step 16: Create a Summary data frame containing totals by
#         Region
# ============================================================
Summary <- Population %>%
  group_by(Region) %>%
  summarise(
    TotalCurrentPop = sum(CurrentPop),
    TotalEarlierPop = sum(EarlierPop),
    TotalChange = sum(Change),
    .groups = "drop"
  )

# ============================================================
# Step 17: Display the Summary data frame
# ============================================================
Summary

# ============================================================
# Step 18: Create a copy of the Population data frame
# ============================================================
Population_v2 <- Population

# ============================================================
# Step 19: Add a Percent_change variable
# ============================================================
Population_v2 <- Population_v2 %>%
  mutate(Percent_change = Change / EarlierPop)

# ============================================================
# Step 20: Sort the data frame by Percent_change descending
# ============================================================
Population_v2 <- Population_v2 %>%
  arrange(desc(Percent_change))

# ============================================================
# Step 21: Display the Population_v2 data frame
# ============================================================
Population_v2

# ============================================================
# Step 22: Create a formatted table for Population
# ============================================================
Population_tbl <- Population %>%
  kbl(
    caption = "Population Data",
    digits = 4
  ) %>%
  kable_styling(
    bootstrap_options = c(
      "striped",
      "hover",
      "condensed"
    ),
    full_width = FALSE
  )

# ============================================================
# Step 23: Display the Population formatted table
# ============================================================
Population_tbl

# ============================================================
# Step 24: Create a formatted table for Change_only
# ============================================================
Change_only_tbl <- Change_only %>%
  kbl(
    caption = "County Population Change",
    digits = 4
  ) %>%
  kable_styling(
    bootstrap_options = c(
      "striped",
      "hover",
      "condensed"
    ),
    full_width = FALSE
  )

# ============================================================
# Step 25: Display the Change_only formatted table
# ============================================================
Change_only_tbl

# ============================================================
# Step 26: Create a formatted table for Doughnut
# ============================================================
Doughnut_tbl <- Doughnut %>%
  kbl(
    caption = "Davidson and Doughnut Counties",
    digits = 4
  ) %>%
  kable_styling(
    bootstrap_options = c(
      "striped",
      "hover",
      "condensed"
    ),
    full_width = FALSE
  )

# ============================================================
# Step 27: Display the Doughnut formatted table
# ============================================================
Doughnut_tbl

# ============================================================
# Step 28: Create a formatted table for Summary
# ============================================================
Summary_tbl <- Summary %>%
  kbl(
    caption = "Regional Population Summary",
    digits = 4
  ) %>%
  kable_styling(
    bootstrap_options = c(
      "striped",
      "hover",
      "condensed"
    ),
    full_width = FALSE
  )

# ============================================================
# Step 29: Display the Summary formatted table
# ============================================================
Summary_tbl

# ============================================================
# Step 30: Create a formatted table for Population_v2
# ============================================================
Population_v2_tbl <- Population_v2 %>%
  kbl(
    caption = "Population Data with Percent Change",
    digits = 4
  ) %>%
  kable_styling(
    bootstrap_options = c(
      "striped",
      "hover",
      "condensed"
    ),
    full_width = FALSE
  )

# ============================================================
# Step 31: Display the Population_v2 formatted table
# ============================================================
Population_v2_tbl

# ============================================================
# Step 32: Download Tennessee County Gazetteer File
# ============================================================
# Import county land-area data from the U.S. Census Bureau.

TN_Counties <- read_delim(
  "https://www2.census.gov/geo/docs/maps-data/data/gazetteer/2025_Gazetteer/2025_gaz_counties_47.txt",
  delim = "|",
  show_col_types = FALSE
)

# ============================================================
# Step 33: Create Land_Area Data Frame
# ============================================================
# Keep county name and land area in square miles.
# Remove " County" from county names.
# Round land area to the nearest whole square mile.

Land_Area <- TN_Counties %>%
  transmute(
    County = str_remove(NAME, " County"),
    Square_Miles = round(ALAND_SQMI)
  )

# ============================================================
# Step 34: Display Results
# ============================================================
# View the completed land-area data frame.

Land_Area

# ============================================================
# Step 35: Add Square_Miles from Land_Area using a left join
# ============================================================
Population_v2 <- Population_v2 %>%
  left_join(
    Land_Area %>%
      select(County, Square_Miles),
    by = "County"
  )

# ============================================================
# Step 36: Display the updated Population_v2 data frame
# ============================================================
Population_v2

# ============================================================
# Step 37: Add Density and Density_Category variables
# ============================================================
Population_v2 <- Population_v2 %>%
  mutate(
    Density = CurrentPop / Square_Miles,
    Density_Category = case_when(
      Density >= 500 ~ "High Density",
      Density >= 100 & Density < 500 ~ "Medium Density",
      Density < 100 ~ "Low Density"
    )
  )

# ============================================================
# Step 38: Sort the Population_v2 data frame by Density
#         in descending order
# ============================================================
Population_v2 <- Population_v2 %>%
  arrange(desc(Density))

# ============================================================
# Step 39: Display the updated Population_v2 data frame
# ============================================================
Population_v2

# ============================================================
# Step 40: Create a formatted table for Population_v2
# ============================================================
Population_v2_tbl <- Population_v2 %>%
  kbl(
    caption = "Population Data with Density Measures",
    digits = 4
  ) %>%
  kable_styling(
    bootstrap_options = c(
      "striped",
      "hover",
      "condensed"
    ),
    full_width = FALSE
  )

# ============================================================
# Step 41: Display the Population_v2 formatted table
# ============================================================
Population_v2_tbl