report3

In this table below, it summarizes population, population change, and population density for fourteen Tennessee counties. Davidson County has the largest current population at 715,388 residents and also has the highest population density at approximately 1,419 people per square mile, placing it in the High Density category.

The data reveal important differences between population size and population density. For example, Williamson County has the third-largest population (260,351) but is classified as Medium Density because its population is spread across a relatively large land area. In contrast, counties such as Hickman, Cannon, and Smith have much smaller populations and lower densities, resulting in Low Density classifications.

Population Data with Percent Change, 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

Here is the code that produced the analysis. The analysis relied on data from U.S Census Bureau American Community Survey.

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 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 data
#=========================================================
CurrentPop <- c(14818, 41829, 715388, 55983, 25436, 26240, 107791, 75539,
                360646, 20389, 204424, 11957, 260351, 158805)

#=========================================================
# Step 4: Create a vector containing earlier population data
#=========================================================
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 vectors into a population 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 current
# population in descending order
#=========================================================
Population <- Population %>%
  arrange(desc(CurrentPop))

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

#=========================================================
# Step 10: Add a Change variable showing the difference
# between current and earlier population values
#=========================================================
Population <- Population %>%
  mutate(Change = CurrentPop - EarlierPop)

#=========================================================
# Step 11: Display the updated population data frame
# with the new Change variable
#=========================================================
Population

#=========================================================
# Step 12: Create a data frame containing only County
# and Change, sorted by Change in descending order
#=========================================================
Change_only <- Population %>%
  select(County, Change) %>%
  arrange(desc(Change))

#=========================================================
# Step 13: Display the Change_only data frame
#=========================================================
Change_only

#=========================================================
# Step 14: Create a Doughnut data frame containing only
# Davidson and Doughnut region counties, sorted by
# Change in descending order
#=========================================================
Doughnut <- Population %>%
  filter(Region %in% c("Davidson", "Doughnut")) %>%
  arrange(desc(Change))

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

#=========================================================
# Step 16: 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 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 to
# Population_v2
#=========================================================
Population_v2 <- Population_v2 %>%
  mutate(Percent_change = Change / EarlierPop)

#=========================================================
# Step 20: Sort Population_v2 by Percent_change
# in descending order
#=========================================================
Population_v2 <- Population_v2 %>%
  arrange(desc(CurrentPop))

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

#=========================================================
# Step 22: Create and display a formatted table for
# Population
#=========================================================
Population_tbl <- Population %>%
  kbl(caption = "Population Data") %>%
  kable_styling(full_width = FALSE)

Population_tbl

#=========================================================
# Step 23: Create and display a formatted table for
# Change_only
#=========================================================
Change_only_tbl <- Change_only %>%
  kbl(caption = "Population Change by County") %>%
  kable_styling(full_width = FALSE)

Change_only_tbl

#=========================================================
# Step 24: Create and display a formatted table for
# Doughnut
#=========================================================
Doughnut_tbl <- Doughnut %>%
  kbl(caption = "Davidson and Doughnut Counties") %>%
  kable_styling(full_width = FALSE)

Doughnut_tbl

#=========================================================
# Step 25: Create and display a formatted table for
# Summary
#=========================================================
Summary_tbl <- Summary %>%
  kbl(caption = "Regional Population Summary") %>%
  kable_styling(full_width = FALSE)

Summary_tbl

#=========================================================
# Step 26: Create and display a formatted table for
# Population_v2
#=========================================================
Population_v2_tbl <- Population_v2 %>%
  kbl(caption = "Population Data with Percent Change") %>%
  kable_styling(full_width = FALSE)

Population_v2_tbl
# ============================================================
# 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
# ==========================================================
# Step 27: Add Square_Miles from Land_Area to Population_v2
# ==========================================================

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

# Display the updated data frame
Population_v2
#=========================================================
# Step 28: Add a Density variable to Population_v2
#=========================================================
Population_v2 <- Population_v2 %>%
  mutate(Density = CurrentPop / Square_Miles)

#=========================================================
# Step 29: Display the updated Population_v2 data frame
# with the new Density variable
#=========================================================
Population_v2

#=========================================================
# Step 30: Create a Density_Category variable based on
# population density thresholds
#=========================================================
Population_v2 <- Population_v2 %>%
  mutate(
    Density_Category = case_when(
      Density >= 500 ~ "High Density",
      Density >= 100 & Density < 500 ~ "Medium Density",
      Density < 100 ~ "Low Density"
    )
  )

#=========================================================
# Step 31: Sort Population_v2 by Density in descending
# order (highest density to lowest density)
#=========================================================
Population_v2 <- Population_v2 %>%
  arrange(desc(Density))

#=========================================================
# Step 32: Create and display the final formatted table
#=========================================================
Population_v2_tbl <- Population_v2 %>%
  kbl(
    caption = "Population Data with Percent Change, Density, and Density Category"
  ) %>%
  kable_styling(full_width = FALSE)

Population_v2_tbl
#=========================================================
# Step 33: Export the sorted data frame to CSV
#=========================================================
write_csv(Population_v2, "Population_v2.csv")

Population_v2 <- read.csv("Population_v2.csv")

#=========================================================
# Step 34: Add Density and Density_Category variables
# to Population_v2
#=========================================================
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"
    )
  )