Of the many different counties in mid-state, the highest population density belongs to Davidson County with Rutherford at less than half of that density total in second. Those two are the only counties designated at high densities while the next eight counties are at medium density. The remaining four are at low density. Rutherford has the largest square mileage total at 620 while Maury and Hickman counties are right behind at 613 and 612 respectively. This shows that Rutherford has a lot of space to work with, but a lot of people still around to take up all the space. This data all came from the U.S. Census Bureau’s American Community Survey, and is taken from the most recent data available.

The table below demonstrates these findings.

Population_v2 with Density and Density_Category
County CurrentPop EarlierPop Region Change Percent_change Square_Miles Density Density_Category
Davidson 715388 598184 Davidson 117204 0.1959330 504 1419.42063 High Density
Rutherford 360646 292425 Doughnut 68221 0.2332940 620 581.68710 High Density
Williamson 260351 208242 Doughnut 52109 0.2502329 583 446.57118 Medium Density
Sumner 204424 174773 Doughnut 29651 0.1696544 529 386.43478 Medium Density
Wilson 158805 129918 Doughnut 28887 0.2223479 571 278.11734 Medium Density
Maury 107791 88738 Non-doughnut 19053 0.2147107 613 175.84176 Medium Density
Robertson 75539 67517 Doughnut 8022 0.1188145 476 158.69538 Medium Density
Cheatham 41829 39087 Doughnut 2742 0.0701512 302 138.50662 Medium Density
Dickson 55983 51608 Non-doughnut 4375 0.0847737 490 114.25102 Medium Density
Trousdale 11957 10131 Non-doughnut 1826 0.1802389 114 104.88596 Medium Density
Macon 26240 23261 Non-doughnut 2979 0.1280684 307 85.47231 Low Density
Smith 20389 19389 Non-doughnut 1000 0.0515756 314 64.93312 Low Density
Cannon 14818 13958 Non-doughnut 860 0.0616134 266 55.70677 Low Density
Hickman 25436 24561 Non-doughnut 875 0.0356256 612 41.56209 Low Density

Code

Here is the code for these findings and results on square miles and density in the mid-state area.

# 1. Install tidyverse, knitr, and kableExtra if not already installed; then load them
if (!require(tidyverse)) {
  install.packages("tidyverse")
}
if (!require(knitr)) {
  install.packages("knitr")
}
if (!require(kableExtra)) {
  install.packages("kableExtra")
}

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

# 2. Define county names
County <- c("Cannon", "Cheatham", "Davidson", "Dickson", "Hickman", "Macon",
            "Maury", "Robertson", "Rutherford", "Smith", "Sumner", "Trousdale",
            "Williamson", "Wilson")

# 3. Define current population values
CurrentPop <- c(14818, 41829, 715388, 55983, 25436, 26240, 107791, 75539, 360646,
                20389, 204424, 11957, 260351, 158805)

# 4. Define earlier population values
EarlierPop <- c(13958, 39087, 598184, 51608, 24561, 23261, 88738, 67517, 292425,
                19389, 174773, 10131, 208242, 129918)

# 5. Define region classification for each county
Region <- c("Non-doughnut", "Doughnut", "Davidson", "Non-doughnut", "Non-doughnut",
            "Non-doughnut", "Non-doughnut", "Doughnut", "Doughnut", "Non-doughnut",
            "Doughnut", "Non-doughnut", "Doughnut", "Doughnut")

# 6. Combine all vectors into a single data frame
Population <- data.frame(
  County,
  CurrentPop,
  EarlierPop,
  Region
)

# 7. Sort Population by CurrentPop in descending order
Population <- Population %>% arrange(desc(CurrentPop))

# 8. Add Change variable showing population growth
Population <- Population %>% mutate(Change = CurrentPop - EarlierPop)

# 9. Display the updated Population data frame
Population

# 10. Create Change_only data frame with only County and Change
Change_only <- Population %>% select(County, Change)

# 11. Sort Change_only by Change in descending order
Change_only <- Change_only %>% arrange(desc(Change))

# 12. Display the Change_only data frame
Change_only

# 13. Create Doughnut data frame containing only Davidson or Doughnut rows
Doughnut <- Population %>% filter(Region %in% c("Davidson", "Doughnut"))

# 14. Sort Doughnut data frame by Change in descending order
Doughnut <- Doughnut %>% arrange(desc(Change))

# 15. Display the Doughnut data frame
Doughnut

# 16. Create Summary data frame that totals CurrentPop, EarlierPop, and Change by Region
Summary <- Population %>%
  group_by(Region) %>%
  summarize(
    Total_CurrentPop = sum(CurrentPop),
    Total_EarlierPop = sum(EarlierPop),
    Total_Change = sum(Change)
  )

# 17. Display the Summary data frame
Summary

# 18. Copy Population data into Population_v2
Population_v2 <- Population

# 19. Add Percent_change variable using mutate()
Population_v2 <- Population_v2 %>% mutate(Percent_change = Change / EarlierPop)

# 20. Sort Population_v2 by Percent_change in descending order
Population_v2 <- Population_v2 %>% arrange(desc(Percent_change))

# 21. Display the Population_v2 data frame
Population_v2

# 22. Create kableExtra-formatted tables for all data frames
Population_kable <- Population %>% 
  kable("html", caption = "Population Data Frame") %>% 
  kable_styling(full_width = FALSE)

Change_only_kable <- Change_only %>% 
  kable("html", caption = "Change Only Data Frame") %>% 
  kable_styling(full_width = FALSE)

Doughnut_kable <- Doughnut %>% 
  kable("html", caption = "Doughnut Region Data Frame") %>% 
  kable_styling(full_width = FALSE)

Summary_kable <- Summary %>% 
  kable("html", caption = "Summary by Region") %>% 
  kable_styling(full_width = FALSE)

Population_v2_kable <- Population_v2 %>% 
  kable("html", caption = "Population_v2 with Percent Change") %>% 
  kable_styling(full_width = FALSE)

# ============================================================
# 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
)

# ============================================================
# 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)
  )

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

Land_Area

# Add Square_Miles from Land_Area to Population_v2 using left_join()
Population_v2 <- Population_v2 %>%
  left_join(Land_Area, by = "County")

# Add Density variable to Population_v2
Population_v2 <- Population_v2 %>%
  mutate(
    Density = CurrentPop / Square_Miles
  )
# Create Density_Category using case_when()
Population_v2 <- Population_v2 %>%
  mutate(
    Density_Category = case_when(
      Density >= 500 ~ "High Density",
      Density >= 100 & Density <= 499 ~ "Medium Density",
      Density < 100 ~ "Low Density"
    )
  )

Population_v2 <- Population_v2 %>%
  arrange(desc(Density))

Population_v2_table <- Population_v2 %>%
  kable("html", caption = "Population_v2 with Density and Density_Category") %>%
  kable_styling(full_width = FALSE)

Population_v2_table