This table shows the population growth rates among counties in the Nashville area and ranks counties by their percentage growth rate. This allows you to see the comparison of growth across the counties which are different sizes but also taking into consideration every countys starting population. Williamson County population growth was 25%, which is the fastest percentage growth, followed by Rutherford County at about 23%. Although, Davidson County does have the highest population, but the growth rate is much lower compared to Williamson and Rutherford County.

County	CurrentPop	EarlierPop	Region	Change	Percent_change
Williamson	260351	208242	Doughnut	52109	0.2502329
Rutherford	360646	292425	Doughnut	68221	0.2332940
Wilson	158805	129918	Doughnut	28887	0.2223479
Maury	107791	88738	Non-doughnut	19053	0.2147107
Davidson	715388	598184	Davidson	117204	0.1959330
Trousdale	11957	10131	Non-doughnut	1826	0.1802389
Sumner	204424	174773	Doughnut	29651	0.1696544
Macon	26240	23261	Non-doughnut	2979	0.1280684
Robertson	75539	67517	Doughnut	8022	0.1188145
Dickson	55983	51608	Non-doughnut	4375	0.0847737
Cheatham	41829	39087	Doughnut	2742	0.0701512
Cannon	14818	13958	Non-doughnut	860	0.0616134
Smith	20389	19389	Non-doughnut	1000	0.0515756
Hickman	25436	24561	Non-doughnut	875	0.0356256

Code:

Here is the R code, relied on data from U.S. Census Bureau’s American Community Survey.

# ==================================================
# 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: Display the Population_v2 data frame
# ==================================================
Population_v2
# ==================================================
# Step 25: Create and display a formatted table
# for the Population data frame
# ==================================================
Population_table <- kable(Population) %>%
  kable_styling(full_width = FALSE)

Population_table

# ==================================================
# Step 26: 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 27: Create and display a formatted table
# for the Doughnut data frame
# ==================================================
Doughnut_table <- kable(Doughnut) %>%
  kable_styling(full_width = FALSE)

Doughnut_table

# ==================================================
# Step 28: Create and display a formatted table
# for the Summary data frame
# ==================================================
Summary_table <- kable(Summary) %>%
  kable_styling(full_width = FALSE)

Summary_table


# ==================================================
# Step 29: 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

Report 2

Bryn Forchic

2026-09-02

Code: