Introduction

Republican Donald Trump easily won Tennessee in both the 2020 and 2024 presidential races. But this analysis suggests he won in a different way each time.

The following maps compare the most recent elections in terms of Republican and Democratic county-level gains and losses. Click on each county to see vote counts for each election. Zoom and pan the map to get a better look at each county.

It’s recommended to position the slider in the middle of a county so that you can observe its Democratic and Republican voter counts at the same time.

Voting shifts, 2016-2020

This map shows county-level vote shifts by party for the 2016 and 2020 elections. In 2016, Trump beat Democratic nominee Hillary Clinton. In 2020, Trump lost to Democratic nominee Joe Biden.

Voting shifts 2020-2024

This map shows county-level vote shifts by party for the 2020 and 2024 elections. In 2020, Trump lost to Democratic nominee Joe Biden. In 2024, Trump beat to Democratic nominee Kamala Harris.

Code

# Required packages

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("mapview"))
  install.packages("mapview")
if (!require("sf"))
  install.packages("sf")
if (!require("leaflet"))
  install.packages("leaflet")
if (!require("leaflet.extras2"))
  install.packages("leaflet.extras2")
if (!require("plotly"))
  install.packages("plotly")
if (!require("tidycensus"))
  install.packages("tidycensus")

library(tidyverse)
library(mapview)
library(sf)
library(leaflet)
library(leafpop)
library(readxl)
library(plotly)
library(tidycensus)

# Go ahead and transmit your Census API key
# so you don't forget to do it later when getting
# the map you will need:

census_api_key("PasteYourAPIKeyBetweenTheseQuoteMarks")

# Download and import election data
# from TN Secretary of State web site:
# https://sos.tn.gov/elections/results

# Get 2016 data

download.file(
  "https://sos-tn-gov-files.s3.amazonaws.com/StateGeneralbyPrecinctNov2016.xlsx",
  "RawElectionData2016.xlsx",
  quiet = TRUE,
  mode = "wb"
)

RawElectionData2016 <- read_xlsx("RawElectionData2016.xlsx")

# Filter, calculate, and select
# to get data of interest
# then store results in MyData dataframe

MyData2016 <- RawElectionData2016%>%
  filter(OFFICENAME == "United States President",
         CANDGROUP == "1") %>%
  mutate(
    Rep16 = PVTALLY1,
    Dem16 = PVTALLY2,
    Total16 = Rep16 + Dem16) %>%
  select(COUNTY, PRECINCT, OFFICENAME, Rep16, Dem16, Total16)

CountyData2016 <- MyData2016 %>% 
  select(COUNTY, Rep16, Dem16, Total16) %>% 
  group_by(COUNTY) %>% 
  summarize(across(everything(), sum)) 

# Get 2020 data

download.file(
  "https://sos-tn-gov-files.tnsosfiles.com/Nov2020PrecinctDetail.xlsx",
  "RawElectionData2020.xlsx",
  quiet = TRUE,
  mode = "wb"
)

RawElectionData2020 <- read_xlsx("RawElectionData2020.xlsx", sheet = "SOFFICEL")

# Filter, calculate, and select
# to get data of interest
# then store results in MyData dataframe

MyData2020 <- RawElectionData2020%>%
  filter(OFFICENAME == "United States President",
         CANDGROUP == "1") %>%
  mutate(
    Rep20 = PVTALLY1,
    Dem20 = PVTALLY2,
    Total20 = Rep20 + Dem20) %>%
  select(COUNTY, PRECINCT, OFFICENAME, Rep20, Dem20, Total20)

MyData2020 <- MyData2020 %>% 
  mutate(COUNTY = case_when(COUNTY == "Dekalb" ~ "DeKalb",
                            TRUE ~ COUNTY))

CountyData2020 <- MyData2020%>% 
  select(COUNTY, Rep20, Dem20, Total20) %>% 
  group_by(COUNTY) %>% 
  summarize(across(everything(), sum)) 

# Get 2024 data

CountyData2024 <- read_csv("https://raw.githubusercontent.com/drkblake/Data/refs/heads/main/CountyData2024.csv")

# Merge Data Files

AllData <- left_join(CountyData2016, CountyData2020, by = "COUNTY")
AllData <- left_join(AllData, CountyData2024, by = "COUNTY")

AllData <- AllData %>% 
  mutate(
    Rep16to20 = Rep20-Rep16,
    Dem16to20 = Dem20-Dem16,
    Rep20to24 = Rep24-Rep20,
    Dem20to24 = Dem24-Dem20,
    Rep20finish = case_when(
      Rep16to20 < 0 ~ "Loss",
      Rep16to20 > 0~ "Gain",
      TRUE ~ "No change"),
    Dem20finish = case_when(
      Dem16to20 < 0 ~ "Loss",
      Dem16to20 > 0~ "Gain",
      TRUE ~ "No change"),
    Rep24finish = case_when(
      Rep20to24 < 0 ~ "Loss",
      Rep20to24 > 0~ "Gain",
      TRUE ~ "No change"),
    Dem24finish = case_when(
      Dem20to24 < 0 ~ "Loss",
      Dem20to24 > 0~ "Gain",
      TRUE ~ "No change"))

# Get a county map

CountyMap <- get_acs(geography = "county",
                   state = "TN",
                   variables = c(Japanese_ = "DP05_0048"),
                   year = 2022,
                   survey = "acs5",
                   output = "wide",
                   geometry = TRUE)

CountyMap <- CountyMap %>%
  mutate(COUNTY = (str_remove(NAME," County, Tennessee"))) %>%
  left_join(AllData, CountyMap, by = "COUNTY") %>% 
  select(COUNTY,
         Rep16, Dem16, Total16,
         Rep20, Dem20, Total20,
         Rep24, Dem24, Total24,
         Rep16to20, Dem16to20,
         Rep20to24, Dem20to24,
         Rep20finish,Dem20finish,
         Rep24finish,Dem24finish,
         geometry)

# 2020 Map

Map16to20Rep <- mapview(
  CountyMap,
  zcol = "Rep20finish",
  col.regions = "red",
  layer.name = "Rep 2020",
  popup = popupTable(
    CountyMap,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c("COUNTY", "Rep16", "Rep20", "Rep16to20")
  )
)

mypalette = colorRampPalette(c('blue', 'lightblue'))

Map16to20Dem <- mapview(
  CountyMap,
  zcol = "Dem20finish",
  col.regions = mypalette,
  layer.name = "Dem 2020",
  popup = popupTable(
    CountyMap,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c("COUNTY", "Dem16", "Dem20", "Dem16to20")
  )
)

Map16to20Dem | Map16to20Rep

# 2024 Map

mypalette = colorRampPalette(c('red', 'pink'))

Map20to24Rep <- mapview(
  CountyMap,
  zcol = "Rep24finish",
  col.regions = mypalette,
  layer.name = "Rep 2024",
  popup = popupTable(
    CountyMap,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c("COUNTY", "Rep20", "Rep24", "Rep20to24")
  )
)

mypalette = colorRampPalette(c('blue', 'lightblue'))

Map20to24Dem <- mapview(
  CountyMap,
  zcol = "Dem24finish",
  col.regions = mypalette,
  layer.name = "Dem 2024",
  popup = popupTable(
    CountyMap,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c("COUNTY", "Dem20", "Dem24", "Dem20to24")
  )
)

Map20to24Dem | Map20to24Rep