This table shows the population, land area, population density, and density category for each Tennessee county. The table allows you to compare how large the population is among counties. The findings show that counties in the High Density category have substantially more residents than counties in the Medium Density or Low Density categories. According to the data, Tennessee’s population is not evenly distributed since a few counties have significantly higher population densities than the majority of the state’s counties.

The data shown on this table came from the U.S. Census Bureau’s American Community Survey. For more information on this topic, please see the ACS Website.

County	CurrentPop	EarlierPop	Region	Change	Percent_change	Square_Miles	Density	Density_Category
Davidson	715388	598184	Davidson	117204	0.1959330	504	1419.42	High Density
Rutherford	360646	292425	Doughnut	68221	0.2332940	620	581.69	High Density
Williamson	260351	208242	Doughnut	52109	0.2502329	583	446.57	Medium Density
Sumner	204424	174773	Doughnut	29651	0.1696544	529	386.43	Medium Density
Wilson	158805	129918	Doughnut	28887	0.2223479	571	278.12	Medium Density
Maury	107791	88738	Non-doughnut	19053	0.2147107	613	175.84	Medium Density
Robertson	75539	67517	Doughnut	8022	0.1188145	476	158.70	Medium Density
Cheatham	41829	39087	Doughnut	2742	0.0701512	302	138.51	Medium Density
Dickson	55983	51608	Non-doughnut	4375	0.0847737	490	114.25	Medium Density
Trousdale	11957	10131	Non-doughnut	1826	0.1802389	114	104.89	Medium Density
Macon	26240	23261	Non-doughnut	2979	0.1280684	307	85.47	Low Density
Smith	20389	19389	Non-doughnut	1000	0.0515756	314	64.93	Low Density
Cannon	14818	13958	Non-doughnut	860	0.0616134	266	55.71	Low Density
Hickman	25436	24561	Non-doughnut	875	0.0356256	612	41.56	Low Density

Code:

# ==================================================
# Step 1: Install (if necessary) and load the tidyverse package
# ==================================================

if (!requireNamespace("tidyverse", quietly = TRUE)) {
  install.packages("tidyverse")
  library(tidyverse)
}

if (!requireNamespace("knitr", quietly = TRUE)) {
  install.packages("knitr")
}

if (!requireNamespace("kableExtra", quietly = TRUE)) {
  install.packages("kableExtra")
  
}

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

# ==================================================
# Step 2: Create a vector containing 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 values
# ==================================================
CurrentPop <- c(14818, 41829, 715388, 55983, 25436, 26240, 107791, 75539,
                360646, 20389, 204424, 11957, 260351, 158805)

# ==================================================
# Step 4: Create a vector containing earlier population values
# ==================================================
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 category
# ==================================================
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 all vectors into a data frame
# ==================================================
Population <- data.frame(
  County,
  CurrentPop,
  EarlierPop,
  Region
)

# ==================================================
# Step 7: Display the original Population data frame
# ==================================================
Population

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

# ==================================================
# Step 9: Display the sorted Population data frame
# ==================================================
Population

# ==================================================
# Step 10: Add a Change variable showing population growth
# ==================================================
Population <- Population %>%
  mutate(Change = CurrentPop - EarlierPop)

# ==================================================
# Step 11: Display the updated Population data frame
# ==================================================
Population

# ==================================================
# Step 12: Create a data frame containing only
# the County and Change variables
# ==================================================
Change_only <- Population %>%
  select(County, Change)

# ==================================================
# Step 13: Sort the Change_only data frame by
# Change in descending order
# ==================================================
Change_only <- Change_only %>%
  arrange(desc(Change))

# ==================================================
# Step 14: Display the Change_only data frame
# ==================================================
Change_only

# ==================================================
# Step 15: Create a Doughnut data frame containing
# only Davidson and Doughnut region counties
# ==================================================
Doughnut <- Population %>%
  filter(Region == "Davidson" | Region == "Doughnut")

# ==================================================
# Step 16: Sort the Doughnut data frame by Change
# in descending order
# ==================================================
Doughnut <- Doughnut %>%
  arrange(desc(Change))

# ==================================================
# Step 17: Display the Doughnut data frame
# ==================================================
Doughnut
# ==================================================
# Step 18: Create a Summary data frame that totals
# population values by Region
# ==================================================
Summary <- Population %>%
  group_by(Region) %>%
  summarize(
    CurrentPop = sum(CurrentPop),
    EarlierPop = sum(EarlierPop),
    Change = sum(Change)
  )

# ==================================================
# Step 19: Display the Summary data frame
# ==================================================
Summary
# ==================================================
# Step 20: Copy the Population data frame into
# a new data frame called Population_v2
# ==================================================
Population_v2 <- Population

# ==================================================
# Step 21: Create a Percent_change variable
# ==================================================
Population_v2 <- Population_v2 %>%
  mutate(Percent_change = Change / EarlierPop)

# ==================================================
# Step 22: Sort Population_v2 by Percent_change
# in descending order
# ==================================================
Population_v2 <- Population_v2 %>%
  arrange(desc(Percent_change))

# ============================================================
# Step 23: Download Tennessee County Gazetteer File
# ============================================================

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 24: Create Land_Area Data Frame
# ============================================================

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

# ============================================================
# Step 25: Add Square_Miles to Population_v2
# ============================================================

Population_v2 <- Population_v2 %>%
  left_join(Land_Area, by = "County")

# ============================================================
# Step 26: Create Density Variable
# ============================================================

Population_v2 <- Population_v2 %>%
  mutate(Density = round(CurrentPop / Square_Miles, 2))

# ============================================================
# Step 27: Create Density_Category Variable
# ============================================================

Population_v2 <- Population_v2 %>%
  mutate(
    Density_Category = case_when(
      Density >= 500 ~ "High Density",
      Density >= 100 & Density < 500 ~ "Medium Density",
      TRUE ~ "Low Density"
    )
  )

# ============================================================
# Step 28: Sort by Density
# ============================================================

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

# ==================================================
# Step 29: Display the Population_v2 data frame
# ==================================================
Population_v2

# ==================================================
# Step 30: Create and display a formatted table
# for the Population_v2 data frame
# ==================================================

Population_v2_table <- kable(Population_v2) %>%
  kable_styling(full_width = FALSE)

Population_v2_table

# ==================================================
# Step 31: Create and display a formatted table
# for the Change_only data frame
# ==================================================
Change_only_table <- kable(Change_only) %>%
  kable_styling(full_width = FALSE)

Change_only_table

# ==================================================
# Step 32: Create and display a formatted table
# for the Doughnut data frame
# ==================================================
Doughnut_table <- kable(Doughnut) %>%
  kable_styling(full_width = FALSE)

Doughnut_table

# ==================================================
# Step 33: Create and display a formatted table
# for the Summary data frame
# ==================================================
Summary_table <- kable(Summary) %>%
  kable_styling(full_width = FALSE)

Summary_table


# ============================================================
# Step 34: Create and display a formatted table
# for the Population_v2 data frame
# ============================================================

Population_v2_table <- kable(Population_v2) %>%
  kable_styling(full_width = FALSE)

Population_v2_table

# ============================================================
# 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

Report 3

Bryn Forchic

2026-09-08

Code: