The table below shows Nashville-area counties sorted in descending order by percent population change between the earlier and current population estimates. These data, obtained from the U.S. Census Bureau’s American Community Survey (ACS), provide a clear picture of how population growth has varied across counties in the Nashville region. Counties with the highest percent change experienced the fastest rates of growth, suggesting increased residential development and migration into those areas. In contrast, counties with lower percent change values grew more slowly during the same period. More information can be found on the ACS Website.

Population Data
County	CurrentPop	EarlierPop	Region	Change
Cannon	14818	13958	Non-doughnut	860
Cheatham	41829	39087	Doughnut	2742
Davidson	715388	598184	Davidson	117204
Dickson	55983	51608	Non-doughnut	4375
Hickman	25436	24561	Non-doughnut	875
Macon	26240	23261	Non-doughnut	2979
Maury	107791	88738	Non-doughnut	19053
Robertson	75539	67517	Doughnut	8022
Rutherford	360646	292425	Doughnut	68221
Smith	20389	19389	Non-doughnut	1000
Sumner	204424	174773	Doughnut	29651
Trousdale	11957	10131	Non-doughnut	1826
Williamson	260351	208242	Doughnut	52109
Wilson	158805	129918	Doughnut	28887

County Change Data
County	Change
Davidson	117204
Rutherford	68221
Williamson	52109
Sumner	29651
Wilson	28887
Maury	19053
Robertson	8022
Dickson	4375
Macon	2979
Cheatham	2742
Trousdale	1826
Smith	1000
Hickman	875
Cannon	860

Davidson and Doughnut Counties
County	CurrentPop	EarlierPop	Region	Change
Davidson	715388	598184	Davidson	117204
Rutherford	360646	292425	Doughnut	68221
Williamson	260351	208242	Doughnut	52109
Sumner	204424	174773	Doughnut	29651
Wilson	158805	129918	Doughnut	28887
Robertson	75539	67517	Doughnut	8022
Cheatham	41829	39087	Doughnut	2742

Regional Population Summary
Region	CurrentPop	EarlierPop	Change
Davidson	715388	598184	117204
Doughnut	1101594	911962	189632
Non-doughnut	262614	231646	30968

Population Data with Percent Change
County	CurrentPop	EarlierPop	Region	Change	Percent_change
Williamson	260351	208242	Doughnut	52109	0.250
Rutherford	360646	292425	Doughnut	68221	0.233
Wilson	158805	129918	Doughnut	28887	0.222
Maury	107791	88738	Non-doughnut	19053	0.215
Davidson	715388	598184	Davidson	117204	0.196
Trousdale	11957	10131	Non-doughnut	1826	0.180
Sumner	204424	174773	Doughnut	29651	0.170
Macon	26240	23261	Non-doughnut	2979	0.128
Robertson	75539	67517	Doughnut	8022	0.119
Dickson	55983	51608	Non-doughnut	4375	0.085
Cheatham	41829	39087	Doughnut	2742	0.070
Cannon	14818	13958	Non-doughnut	860	0.062
Smith	20389	19389	Non-doughnut	1000	0.052
Hickman	25436	24561	Non-doughnut	875	0.036

Code:

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

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

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

# ==================================================
# Step 5: Create a vector of region classifications
# ==================================================
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 vectors into a Population data frame
# ==================================================
Population <- data.frame(
  County,
  CurrentPop,
  EarlierPop,
  Region
)

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

# ==================================================
# Step 8: Display the updated Population data frame
# ==================================================
Population

# ==================================================
# Step 9: Create a Change_only data frame containing
#         only the County and Change variables
# ==================================================
Change_only <- Population %>%
  select(County, Change)

# ==================================================
# Step 10: Sort Change_only by Change in descending
#          order
# ==================================================
Change_only <- Change_only %>%
  arrange(desc(Change))

# ==================================================
# Step 11: Display the Change_only data frame
# ==================================================
Change_only

# ==================================================
# Step 12: Create a Doughnut data frame containing
#          only Davidson and Doughnut regions
# ==================================================
Doughnut <- Population %>%
  filter(Region %in% c("Davidson", "Doughnut"))

# ==================================================
# Step 13: Sort Doughnut by Change in descending
#          order
# ==================================================
Doughnut <- Doughnut %>%
  arrange(desc(Change))

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

# ==================================================
# Step 15: Create a Summary data frame containing
#          population totals by Region
# ==================================================
Summary <- Population %>%
  group_by(Region) %>%
  summarize(
    CurrentPop = sum(CurrentPop),
    EarlierPop = sum(EarlierPop),
    Change = sum(Change),
    .groups = "drop"
  )

# ==================================================
# Step 16: Display the Summary data frame
# ==================================================
Summary

# ==================================================
# Step 17: Copy Population into a new data frame
#          called Population_v2
# ==================================================
Population_v2 <- Population

# ==================================================
# Step 18: Create a Percent_change variable
# ==================================================
Population_v2 <- Population_v2 %>%
  mutate(Percent_change = Change / EarlierPop)

# ==================================================
# Step 19: Sort Population_v2 by Percent_change
#          in descending order
# ==================================================
Population_v2 <- Population_v2 %>%
  arrange(desc(Percent_change))

# ==================================================
# Step 20: Display the Population_v2 data frame
# ==================================================
Population_v2

# ==================================================
# Step 21: Create a kableExtra-formatted table for
#          Population
# ==================================================
Population_tbl <- Population %>%
  kable(
    caption = "Population Data",
    digits = 3
  ) %>%
  kable_styling(full_width = FALSE)

# ==================================================
# Step 22: Create a kableExtra-formatted table for
#          Change_only
# ==================================================
Change_only_tbl <- Change_only %>%
  kable(
    caption = "County Change Data",
    digits = 3
  ) %>%
  kable_styling(full_width = FALSE)

# ==================================================
# Step 23: Create a kableExtra-formatted table for
#          Doughnut
# ==================================================
Doughnut_tbl <- Doughnut %>%
  kable(
    caption = "Davidson and Doughnut Counties",
    digits = 3
  ) %>%
  kable_styling(full_width = FALSE)

# ==================================================
# Step 24: Create a kableExtra-formatted table for
#          Summary
# ==================================================
Summary_tbl <- Summary %>%
  kable(
    caption = "Regional Population Summary",
    digits = 3
  ) %>%
  kable_styling(full_width = FALSE)

# ==================================================
# Step 25: Create a kableExtra-formatted table for
#          Population_v2
# ==================================================
Population_v2_tbl <- Population_v2 %>%
  kable(
    caption = "Population Data with Percent Change",
    digits = 3
  ) %>%
  kable_styling(full_width = FALSE)

# ==================================================
# Step 26: Display all formatted tables
# ==================================================
Population_tbl
Change_only_tbl
Doughnut_tbl
Summary_tbl
Population_v2_tbl

Report 2: Nashville Area Population Change Report

Megan Carroll

2026-09-05

Code: