Williamson and Rutherford counties grew faster than Davidson County over the past five years, even though Davidson picked up more people, the latest Census data show. In terms of growth per 100 residents among counties in the Nashville Metropolitan Statistical Area, Davidson County ranked fifth, behind Williamson, Rutherford, Wilson, and Maury counties. Davidson did lead the region in total growth, however, logging 117,204 new residents during the period. Rutherford came in second by that measure, with 68,221 new residents, and Williamson placed third with 52,109.
This table shows details for each county, with the data sorted by the changed per 100 residents. Williamson, for example, grew by 25 percent, or 25 new residents for every 100 original residents. Davidson, by contrast, grew about 20 percent.
## 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 0.03562559# ==============================================================
# Step 1: Install (if needed) and load the tidyverse, knitr, and
# kableExtra packages
# ==============================================================
if (!requireNamespace("tidyverse", quietly = TRUE)) {
install.packages("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 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 figures
# (one value per county, in the same order as County)
# ==============================================================
CurrentPop <- c(14818, 41829, 715388, 55983, 25436, 26240, 107791, 75539, 360646,
20389, 204424, 11957, 260351, 158805)
# ==============================================================
# Step 4: Create a vector of earlier population figures
# (one value per county, in the same order as County)
# ==============================================================
EarlierPop <- c(13958, 39087, 598184, 51608, 24561, 23261, 88738, 67517, 292425,
19389, 174773, 10131, 208242, 129918)
# ==============================================================
# Step 5: Create a vector classifying each county's region
# (Doughnut, Non-doughnut, or Davidson)
# ==============================================================
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 the vectors into a single data frame
# ==============================================================
Population <- data.frame(
County,
CurrentPop,
EarlierPop,
Region
)
# ==============================================================
# Step 7: Display the resulting data frame
# ==============================================================
Population
# ==============================================================
# Step 8: Sort the Population data frame by CurrentPop,
# in descending order
# ==============================================================
Population <- Population %>%
arrange(desc(CurrentPop))
# ==============================================================
# Step 9: Redisplay the Population data frame (now sorted)
# ==============================================================
Population
# ==============================================================
# Step 10: Use mutate() to add a "Change" variable, calculated
# as CurrentPop minus EarlierPop
# ==============================================================
Population <- Population %>%
mutate(Change = CurrentPop - EarlierPop)
# ==============================================================
# Step 11: Display the updated data frame
# ==============================================================
Population
# ==============================================================
# Step 12: Use select() to create a new data frame, Change_only,
# containing just County and Change, then use arrange()
# to sort it 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: Use filter() to create a new data frame, Doughnut,
# keeping only rows where Region is "Davidson" or
# "Doughnut", then use arrange() to sort it 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: Use group_by() and summarize() to create a Summary
# data frame that totals CurrentPop, EarlierPop, and
# Change from the Population data frame, grouped 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 Population into a new data frame, Population_v2,
# use mutate() to add a Percent_change variable
# (Change divided by EarlierPop), and sort the result
# by Percent_change in descending order
# ==============================================================
Population_v2 <- Population %>%
mutate(Percent_change = Change / EarlierPop) %>%
arrange(desc(Percent_change))
# ==============================================================
# Step 19: Display the Population_v2 data frame
# ==============================================================
Population_v2
# ==============================================================
# Step 20: Create a kableExtra-formatted table for the
# Population data frame, then display it
# ==============================================================
Population_table <- Population %>%
kbl(caption = "Population") %>%
kable_styling()
Population_table
# ==============================================================
# Step 21: Create a kableExtra-formatted table for the
# Change_only data frame, then display it
# ==============================================================
Change_only_table <- Change_only %>%
kbl(caption = "Change_only") %>%
kable_styling()
Change_only_table
# ==============================================================
# Step 22: Create a kableExtra-formatted table for the
# Doughnut data frame, then display it
# ==============================================================
Doughnut_table <- Doughnut %>%
kbl(caption = "Doughnut") %>%
kable_styling()
Doughnut_table
# ==============================================================
# Step 23: Create a kableExtra-formatted table for the
# Summary data frame, then display it
# ==============================================================
Summary_table <- Summary %>%
kbl(caption = "Summary") %>%
kable_styling()
Summary_table
# ==============================================================
# Step 24: Create a kableExtra-formatted table for the
# Population_v2 data frame, then display it
# ==============================================================
Population_v2_table <- Population_v2 %>%
kbl(caption = "Population_v2") %>%
kable_styling()
Population_v2_table