There is no question that Davidson County has grown rapidly over the past five years. However, according to the latest Census data, it is not the fastest growing county in the Nashville-Metropolitan Statistical Area. In fact, it is only the 5th highest county in terms of growth per 100 residents in the area despite having the highest number of new residents in that time period. The top three growing counties in the last five years are Williamson, Rutherford, and Wilson, which all border Davidson County. Compared to Davidson County’s growth rate of about 20 percent, doughnut counties have an average growth rate of around 17 percent, whereas non-doughnut counties have an average growth rate of around 9 percent.
Below is a table that represents the data for each county from the U.S. Census Bureau’s American Community Survey. It is sorted by the change in growth per 100 residents, starting with Williamson County at a 25 percent growth.
## County CurrentPop EarlierPop Region Change Percent_change
## 1 Williamson 260351 208242 Doughnut 52109 0.25023290
## 2 Rutherford 360646 292425 Doughnut 68221 0.23329401
## 3 Wilson 158805 129918 Doughnut 28887 0.22234794
## 4 Maury 107791 88738 Non-doughnut 19053 0.21471072
## 5 Davidson 715388 598184 Davidson 117204 0.19593302
## 6 Trousdale 11957 10131 Non-doughnut 1826 0.18023887
## 7 Sumner 204424 174773 Doughnut 29651 0.16965435
## 8 Macon 26240 23261 Non-doughnut 2979 0.12806844
## 9 Robertson 75539 67517 Doughnut 8022 0.11881452
## 10 Dickson 55983 51608 Non-doughnut 4375 0.08477368
## 11 Cheatham 41829 39087 Doughnut 2742 0.07015120
## 12 Cannon 14818 13958 Non-doughnut 860 0.06161341
## 13 Smith 20389 19389 Non-doughnut 1000 0.05157564
## 14 Hickman 25436 24561 Non-doughnut 875 0.03562559Below is the R code used to produce the analysis.
# ============================================================
# Step 1: Install and load required packages
# ============================================================
if (!require("tidyverse")) {
install.packages("tidyverse")
}
library(tidyverse)
if (!require("knitr")) {
install.packages("knitr")
}
library(knitr)
if (!require("kableExtra")) {
install.packages("kableExtra")
}
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
# ============================================================
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 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
# (CurrentPop minus EarlierPop)
# ============================================================
Population <- Population %>%
mutate(Change = CurrentPop - EarlierPop)
# ============================================================
# Step 11: Display the updated data frame with Change
# ============================================================
Population
# ============================================================
# Step 12: Create a Change_only data frame containing
# County and Change, sorted by Change descending
# ============================================================
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 descending
# ============================================================
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
# CurrentPop, EarlierPop, and Change 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: Copy the Population data frame to Population_v2
# ============================================================
Population_v2 <- Population
# ============================================================
# Step 19: Add a Percent_change variable to Population_v2
# (Change divided by EarlierPop)
# ============================================================
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(Percent_change))
# ============================================================
# Step 21: Display the Population_v2 data frame
# ============================================================
Population_v2
# ============================================================
# Step 22: Create and display a formatted table for
# the Population data frame
# ============================================================
Population_tbl <- Population %>%
kbl(caption = "Population Data") %>%
kable_styling(full_width = FALSE)
Population_tbl
# ============================================================
# Step 23: Create and display a formatted table for
# the Change_only data frame
# ============================================================
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
# the Doughnut data frame
# ============================================================
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
# the Summary data frame
# ============================================================
Summary_tbl <- Summary %>%
kbl(caption = "Regional Population Summary") %>%
kable_styling(full_width = FALSE)
Summary_tbl
# ============================================================
# Step 26: Create and display a formatted table for
# the Population_v2 data frame
# ============================================================
Population_v2_tbl <- Population_v2 %>%
kbl(caption = "Population Growth Rates") %>%
kable_styling(full_width = FALSE)
Population_v2_tbl