In the fall 2024 semester, I took an election analytics class that used data analytics in the coding language R to produce various maps, charts and graphs. Because of the nature of the semester, we focused on numbers and statistics specifically centering around the election. This website serves as a portfolio for all my work done in the class. Some graphs, charts or maps are from examples rather than correct numbers gotten from a website.

GRAPI by Tennessee House District

GRAPI stands for “Gross Rent As a Percentage of Income,” a statistic used to show the affordability of rent in a given area. The map shows the proportion of renters overspending on housing within each Tennessee House of Representatives District in Rutherford County. Financial experts recommend spending no more than 30 percent of your income on rent. This map shows everywhere in Rutherford County that is “overspending” that at 35 percent or greater. Numbers are listed below the map in a chart, depicting each Tennessee House of Representatives District in Rutherford County.

Data come from the 2022 five-year American Community Survey, published by the U.S. Census Bureau.

Estimate by district
District Estimate Estimate_MOE From To
State House District 13 (2022), Tennessee 42.9 7.7 35.2 50.6
State House District 37 (2022), Tennessee 40.3 5.5 34.8 45.8
State House District 34 (2022), Tennessee 35.9 4.8 31.1 40.7
State House District 49 (2022), Tennessee 35.7 5.6 30.1 41.3
State House District 48 (2022), Tennessee 34.4 5.9 28.5 40.3
 
# Installing and loading required packages

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("tidycensus"))
  install.packages("tidycensus")
if (!require("sf"))
  install.packages("sf")
if (!require("mapview"))
  install.packages("mapview")
if (!require("gtExtras"))
  install.packages("gtExtras")

library(tidyverse)
library(tidycensus)
library(sf)
library(mapview)
library(gtExtras)

# Transmitting API key

census_api_key("e96c46601fca77f6f3fcb0f72b673a75aed0ff2a")

# Fetching ACS codebooks

DetailedTables <- load_variables(2022, "acs5", cache = TRUE)
SubjectTables <- load_variables(2022, "acs5/subject", cache = TRUE)
ProfileTables <- load_variables(2022, "acs5/profile", cache = TRUE)
Codebook <- DetailedTables %>% 
  select(name, label, concept)
Codebook <- bind_rows(Codebook,SubjectTables)
Codebook <- bind_rows(Codebook,ProfileTables)
Codebook <- Codebook %>% 
  distinct(label, .keep_all = TRUE)
rm(DetailedTables,
   SubjectTables,
   ProfileTables)

# Filtering the codebook

MyVars <- Codebook %>% 
  filter(grepl("GRAPI", label) &
           grepl("Percent!!", label))

# Making a table of the filtered variables

MyVarsTable <- gt(MyVars) %>%
  tab_header("Variables") %>%
  cols_align(align = "left") %>%
  gt_theme_538

# Displaying the table

MyVarsTable

# Defining the variable to retrieve

VariableList = 
  c(Estimate_ = "DP04_0142P")

# Fetching data

AllData <- get_acs(
  geography = "state legislative district (lower chamber)",
  state = "TN",
  variables = VariableList,
  year = 2022,
  survey = "acs5",
  output = "wide",
  geometry = TRUE
)

# Mutating, selecting and sorting the data

AllData <- AllData %>%
  mutate(
    District = NAME,
    Estimate = Estimate_E,
    Estimate_MOE = Estimate_M,
    From = round(Estimate - Estimate_MOE, 2),
    To = round(Estimate + Estimate_MOE, 2)
  ) %>%
  select(District, Estimate, Estimate_MOE, From, To, geometry) %>%
  arrange(desc(Estimate))

# Filtering for Rutherford County districts

MyData <- AllData %>%
  filter(
    District == "State House District 13 (2022), Tennessee" |
      District == "State House District 37 (2022), Tennessee" |
      District == "State House District 49 (2022), Tennessee" |
      District == "State House District 48 (2022), Tennessee" |
      District == "State House District 34 (2022), Tennessee"
  )

# Producing a map

MapData <- st_as_sf(MyData)

MyMap <- mapview(MapData,
        zcol = "Estimate",
        layer.name = "Estimate",
        popup = TRUE)
#Displaying the map

MyMap

# Producing a table

TableData <- st_drop_geometry(MapData)
MyTable <- gt(TableData) %>%
  tab_header("Estimate by district") %>%
  cols_align(align = "left") %>%
  gt_theme_538

# Displaying the table

MyTable

Precinct-level Rutherford election results from 2022

This map depicts data from the gubernatorial race in 2022 with Democrat Jason Martin versus Republican incumbent Bill Lee. The redder the district, the higher percentage of Republican votes. The purpler the district, the higher percentage of Democrat votes.

Data reflect official results downloaded automatically from the Tennessee Secretary of State’s website.

# 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")

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

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

download.file(
  "https://sos-prod.tnsosgovfiles.com/s3fs-public/document/20221108AllbyPrecinct.xlsx",
  "RawElectionData.xlsx",
  quiet = TRUE,
  mode = "wb"
)

RawElectionData <- read_xlsx("RawElectionData.xlsx", sheet = "SOFFICELso")

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

MyData <- RawElectionData %>%
  filter(COUNTY == "Rutherford", CANDGROUP == "1") %>%
  mutate(
    Lee = PVTALLY1,
    Martin = PVTALLY2,
    Total = PVTALLY1 + PVTALLY2,
    Lee_Pct = round(PVTALLY1 / (PVTALLY1 + PVTALLY2), 2),
    Martin_Pct = round(PVTALLY2 / (PVTALLY1 + PVTALLY2), 2),
    Winner = case_when(
      PVTALLY1 > PVTALLY2 ~ "Lee (R)",
      PVTALLY2 > PVTALLY1 ~ "Martin (D)",
      .default = "Tie"
    )
  ) %>%
  select(COUNTY, PRECINCT, Total, Lee, Martin, Lee_Pct, Martin_Pct, Winner)

# Download and unzip a precinct map to pair with the vote data

download.file("https://github.com/drkblake/Data/raw/main/Voting_Precincts_5_31_24.zip","TNVotingPrecincts.zip")

unzip("TNVotingPrecincts.zip")

All_Precincts <- read_sf("Voting_Precincts_5_31_24.shp")

# Filter for particular county precincts

County_Precincts <- All_Precincts %>%
  filter(COUNTY == 149) %>%
  rename(PRECINCT = NEWVOTINGP)

# Merge election data and map file

MergeFile <- merge(MyData, County_Precincts, by = "PRECINCT", all.x = TRUE)

# Drop unneeded columns from MergeFile

MergeFile <- MergeFile %>%
  select(PRECINCT,
         Total,
         Lee,
         Martin,
         Lee_Pct,
         Martin_Pct,
         Winner,
         geometry)

# Format MergeFile as a map, and
# call the map MyMap

MyMapFile <- st_as_sf(MergeFile)

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

MyMap <- mapview(
  MyMapFile,
  zcol = "Lee_Pct",
  col.regions = mypalette, at = seq(0, 1, .2),
  map.types = ("OpenStreetMap"),
  layer.name = "Pct. for Lee",
  popup = popupTable(
    MyMapFile,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c(
      "PRECINCT",
      "Lee",
      "Martin",
      "Total",
      "Lee_Pct",
      "Martin_Pct",
      "Winner"
    )
  )
)

# Showing the map

MyMap

Early voting in Rutherford County

Early voting opened on Oct. 16 in across Tennessee. Any registered voter in Rutherford County could vote early until Oct. 31 at any of the nine locations.

Each orange dot marks an early voting location. Operating hours and the exact location address can be seen by clicking the orange dot.

For reference for students and faculty, the blue dot indicates Middle Tennessee State University.

For more information, see the Early Voting page on Rutherford County’s website.

# Required packages

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

library(tidyverse)
library(sf)
library(mapview)
library(leaflet)
library(leaflet.extras2)
library(leafpop)

mapviewOptions(basemaps.color.shuffle = FALSE)

# Load the address and lat/long data

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

# Add MTSU

long <- -86.361861
lat <- 35.848997

Addresses_gc <- Addresses_gc %>% 
  add_row(Location = "MTSU",
          long = long,
          lat = lat) %>% 
  mutate(Point = case_when(Location == "MTSU" ~ "MTSU",
                           TRUE ~ "Early vote here"))

MapData <- st_as_sf(Addresses_gc,
                    coords = c("long", "lat"),
                    crs = 4326)

# Make the map

MyMap <- mapview(MapData,
                 zcol = "Point",
                 layer.name = "Point",
                 col.regions = c("orange", "blue"),
                 map.types = c("OpenStreetMap","Esri.WorldImagery"),
                 popup = popupTable(
                   MapData,
                   feature.id = FALSE,
                   row.numbers = FALSE,
                   zcol = c("Location",
                            "Address",
                            "Week",
                            "Weekend")))
# Show the map

MyMap

Early voting tracker in Rutherford County 2024

So far, 60,000 people have early voted in Rutherford County. That is about 27% of all registered voters for the Nov. 5 election. Here are the vote totals per day as of the most recent full day of early voting, according to data from the Rutherford County Election Commission.

Precinct-level voter turnout ranges from 15%-37%. Click a precinct to see details.

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("foreign"))
  install.packages("foreign")
if (!require("sf"))
  install.packages("sf")
if (!require("scales"))
  install.packages("scales")
if (!require("mapview"))
  install.packages("mapview")
if (!require("leaflet"))
  install.packages("leaflet")
if (!require("leaflet.extras2"))
  install.packages("leaflet.extras2")

library(tidyverse)
library(foreign)
library(sf)
library(scales)
library(mapview)
library(leaflet)
library(leafpop)

# Get the daily data files

download.file("https://github.com/drkblake/Data/raw/refs/heads/main/DailyEVFiles.zip",
              "DailyEVFiles.zip")

unzip("DailyEVFiles.zip")

# Combine the daily data files

AddData <- read.dbf("10162024.dbf")
AllData <- AddData

# Add each day's  file name to this list, then run

datafiles <- c("10172024.dbf",
               "10182024.dbf",
               "10192024.dbf",
               "10212024.dbf",
               "10222024.dbf",
               "10232024.dbf",
               "10242024.dbf",
               "10252024.dbf",
               "10262024.dbf",
               "10282024.dbf",
               "10292024.dbf",
               "10302024.dbf",
               "10312024.dbf")

for (x in datafiles) {
  AddData <- read.dbf(x, as.is = FALSE)
  AllData <- rbind(AllData, AddData)
}

# Save AllData file as .csv
write_csv(AllData,"EarlyVoterData2024.csv")

# Get total votes so far

TotalVotes <- nrow(AllData)
PctVotes <- round((TotalVotes / 224746)*100, digits = 0)

VotesByDay <- AllData %>%
  group_by(VOTEDDATE) %>%
  summarize(Votes = n()) %>%
  rename(Date = VOTEDDATE) %>%
  mutate(Date = (str_remove(Date,"2024-")))

# "#2C7865" is a green shade

chart = ggplot(data = VotesByDay,
               aes(x = Date,
                   y = Votes))+
  geom_bar(stat="identity", fill = "#41B3A2") +
  geom_text(aes(label=comma(Votes)),
            vjust=1.6,
            color="black",
            size=3.5)+
  theme(
    axis.title.x = element_blank(),
    axis.ticks.y = element_blank(),
    axis.title.y = element_blank(),
    axis.text.y = element_blank(),
    panel.background = element_blank())

chart

### Precinct-level map of early voting turnout ***

# Aggregate early voting data by precinct

PrecinctData <- AllData %>%
  group_by(PCT_NBR) %>%
  summarize(Votes = n()) %>%
  rename(Precinct = PCT_NBR)

# Download and unzip a precinct map to pair with the vote data

download.file("https://github.com/drkblake/Data/raw/main/Voting_Precincts_5_31_24.zip","TNVotingPrecincts.zip")

unzip("TNVotingPrecincts.zip")

All_Precincts <- read_sf("Voting_Precincts_5_31_24.shp")

# Filter for RuCo precincts and
# strip dash from precinct numbers

County_Precincts <- All_Precincts %>%
  filter(COUNTY == 149) %>%
  rename(Precinct = NEWVOTINGP) %>%
  mutate(Precinct = (str_remove(Precinct,"-")))

MapData <-  left_join(PrecinctData, County_Precincts, by = "Precinct")

RegData <- read_csv("RegVotersRuCo.csv") %>%
  mutate(Precinct = as.character(Precinct))

MapData <- left_join(MapData, RegData, by = "Precinct")

MapData <- MapData %>%
  mutate(Percent = round((Votes/RegVoters)*100), digits = 0) %>%
  rename(Voters = RegVoters) %>%
  select(Precinct, Votes, Voters, Percent, geometry)

MapData_sf <- st_as_sf(MapData)

Map <- mapview(
  MapData_sf,
  zcol = "Percent",
  layer.name = "Pct. early voted",
  popup = popupTable(
    MapData_sf,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c(
      "Precinct",
      "Votes",
      "Voters",
      "Percent"
    )
  )
)

# Display map

Map

# Calcualte turnout statistics

MinTurnout <- min(MapData$Percent)
MaxTurnout <- max(MapData$Percent)
MedianTurnout <- median(MapData$Percent)
MeanTurnout <- mean(MapData$Percent)

Cable news tracker segment 2024

This interactive chart compares the amount of cable news coverage mentioning “Donald Trump,” “Joe Biden,” and “Kamala Harris” between late April and early October, the most recent period for which data are available.

This chart includes all coverage from MSNBC, CNN and Fox.

The charts show that, overall, less coverage is given for Kamala Harris and Joe Biden, regardless of whether the news source leans liberal or conservative.

MSNBC

This chart includes data from only MSNBC.

CNN

This chart includes data from only CNN.

Fox

This chart includes data from only Fox.

#Fox News

FoxNews <- AllData %>%
  filter(Series == "FOXNEWS")
figFox <- plot_ly(
  FoxNews,
  x = ~ WeekOf,
  y = ~ Trump,
  name = 'Trump',
  type = 'scatter',
  mode = 'none',
  stackgroup = 'one',
  fillcolor = '#B8001F')
figFox <- figFox %>% add_trace(y = ~ Harris,
                               name = 'Biden',
                               fillcolor = '#507687')
figFox <- figFox %>% add_trace(y = ~ Harris,
                               name = 'Harris',
                               fillcolor = '#384B70')
figFox <- figFox %>% layout(
  title = 'Segment counts, Fox News, by topic and week',
  xaxis = list(title = "Week of", showgrid = FALSE),
  yaxis = list(title = "Count", showgrid = TRUE)
)

figFox
# CNN

CNN <- AllData %>%
  filter(Series == "CNN")
figCNN <- plot_ly(
  CNN,
  x = ~ WeekOf,
  y = ~ Trump,
  name = 'Trump',
  type = 'scatter',
  mode = 'none',
  stackgroup = 'one',
  fillcolor = '#B8001F')
figCNN <- figCNN %>% add_trace(y = ~ Harris,
                               name = 'Biden',
                               fillcolor = '#507687')
figCNN <- figCNN %>% add_trace(y = ~ Harris,
                               name = 'Harris',
                               fillcolor = '#384B70')
figCNN <- figCNN %>% layout(
  title = 'Segment counts, CNN, by topic and week',
  xaxis = list(title = "Week of", showgrid = FALSE),
  yaxis = list(title = "Count", showgrid = TRUE)
)

figCNN
# MSNBC

MSNBC <- AllData %>%
  filter(Series == "MSNBC")
figMSNBC <- plot_ly(
  MSNBC,
  x = ~ WeekOf,
  y = ~ Trump,
  name = 'Trump',
  type = 'scatter',
  mode = 'none',
  stackgroup = 'one',
  fillcolor = '#B8001F')
figMSNBC <- figMSNBC %>% add_trace(y = ~ Harris,
                                   name = 'Biden',
                                   fillcolor = '#507687')
figMSNBC <- figMSNBC %>% add_trace(y = ~ Harris,
                                   name = 'Harris',
                                   fillcolor = '#384B70')
figMSNBC <- figMSNBC %>% layout(
  title = 'Segment counts, MSNBC, by topic and week',
  xaxis = list(title = "Week of", showgrid = FALSE),
  yaxis = list(title = "Count", showgrid = TRUE)
)

figMSNBC
if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("plotly"))
  install.packages("plotly")
library(tidyverse)
library(plotly)

# Defining date range

startdate <- "20240429"
enddate <- "20241028"

### Trump

# Defining query

# Note:
# In queries, use %20 to indicate a space
# Example: "Donald%20Trump" is "Donald Trump"
# Use parentheses and %20OR%20 for "either/or" queries
# Example: "(Harris%20OR%20Walz)" is "(Harris OR Walz)"

query <- "Donald%20Trump"

# Building the volume dataframe

vp1 <- "https://api.gdeltproject.org/api/v2/tv/tv?query="
vp2 <- "%20market:%22National%22&mode=timelinevol&format=csv&datanorm=raw&startdatetime="
vp3 <- "000000&enddatetime="
vp4 <- "000000"
text_v_url <- paste0(vp1, query, vp2, startdate, vp3, enddate, vp4)
v_url <- URLencode(text_v_url)
v_url
Trump <- read_csv(v_url)
Trump <- Trump %>%
  rename(Date = 1, Trump = 3)

### Biden

# Defining query

query <- "Joe%20Biden"

# Building the volume dataframe

vp1 <- "https://api.gdeltproject.org/api/v2/tv/tv?query="
vp2 <- "%20market:%22National%22&mode=timelinevol&format=csv&datanorm=raw&startdatetime="
vp3 <- "000000&enddatetime="
vp4 <- "000000"
text_v_url <- paste0(vp1, query, vp2, startdate, vp3, enddate, vp4)
v_url <- URLencode(text_v_url)
v_url
Biden <- read_csv(v_url)
Biden <- Biden %>%
  rename(Date = 1, Biden = 3)

AllData <- left_join(Trump, Biden)

### Harris

# Defining query

query <- "Kamala%20Harris"

# Building the volume dataframe

vp1 <- "https://api.gdeltproject.org/api/v2/tv/tv?query="
vp2 <- "%20market:%22National%22&mode=timelinevol&format=csv&datanorm=raw&startdatetime="
vp3 <- "000000&enddatetime="
vp4 <- "000000"
text_v_url <- paste0(vp1, query, vp2, startdate, vp3, enddate, vp4)
v_url <- URLencode(text_v_url)
v_url
Harris <- read_csv(v_url)
Harris <- Harris %>%
  rename(Date = 1, Harris = 3)

AllData <- left_join(AllData, Harris)

### Graphic

AllData <- AllData %>%
  arrange(Date)

# Add "WeekOf" variable to the data frame

if (!require("lubridate"))
  install.packages("lubridate")
library(lubridate)

AllData$WeekOf <- round_date(AllData$Date,
                             unit = "week",
                             week_start = getOption("lubridate.week.start", 1))

CombinedCoverage <- AllData %>%
  group_by(WeekOf) %>%
  summarize(
    Trump = sum(Trump, na.rm = TRUE),
    Biden = sum(Biden, na.rm = TRUE),
    Harris = sum(Harris, na.rm = TRUE)
  )

fig <- plot_ly(
  CombinedCoverage,
  x = ~ WeekOf,
  y = ~ Trump,
  name = 'Trump',
  type = 'scatter',
  mode = 'none',
  stackgroup = 'one',
  fillcolor = '#B8001F')
fig <- fig %>% add_trace(y = ~ Biden,
                         name = 'Biden',
                         fillcolor = '#507687')

fig <- fig %>% add_trace(y = ~ Harris,
                         name = 'Harris',
                         fillcolor = '#384B70')
fig <- fig %>% layout(
  title = 'Segment counts, by topic and week',
  xaxis = list(title = "Week of", showgrid = FALSE),
  yaxis = list(title = "Count", showgrid = TRUE)
)

fig

2024 Presidential Election Electoral Votes

The 2024 Presidential Election results highlight federal-level outcomes for electoral votes. The map shows which state electoral votes went red: Republican, or blue: Democrat. The chart shows a numeric breakdown of Democrat versus Republican electoral votes.

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

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

# Getting a U.S.map shapefile

# Note: Provide your Census API key in the line below

census_api_key("e96c46601fca77f6f3fcb0f72b673a75aed0ff2a")

# U.S. Map

omit <- c("Alaska", "Puerto Rico", "Hawaii")
USMap <- get_acs(
  geography = "state",
  variables = "DP02_0154P",
  year = 2022,
  survey = "acs5",
  output = "wide",
  geometry = TRUE) %>%
  filter(!(NAME %in% omit)) %>%
  mutate(Full = NAME) %>%
  select(GEOID, Full, geometry)
st_write(USMap,"USMap.shp", append = FALSE)

# Data file

USData <- read_csv("https://raw.githubusercontent.com/drkblake/Data/refs/heads/main/ElectoralVotesByState2024.csv")

# Edit / update election data

USData <- data_edit(USData)
write_csv(USData,"ElectoralVotesByState2024.csv")
write_csv(USData,"ElectoralVotesByState2024_latest.csv")

# Merge election and map data

USWinners <- merge(USMap,USData) %>% 
  mutate(Winner = (case_when(
    Harris > Trump ~ "Harris",
    Trump > Harris ~ "Trump",
    .default = "Counting"))) %>%
  mutate(Votes = Votes.to.allocate) %>% 
  select(State, Votes, Harris, Trump, Winner, geometry)

# Make the election map

USpalette = colorRampPalette(c("darkblue","darkred"))

BigMap <- mapview(USWinners, zcol = "Winner",
                  col.regions = USpalette,
                  alpha.regions = .8,
                  layer.name = "Winner",
                  popup = popupTable(
                    USWinners,
                    feature.id = FALSE,
                    row.numbers = FALSE,
                    zcol = c(
                      "State",
                      "Votes",
                      "Harris",
                      "Trump",
                      "Winner")))

# Showing the map

BigMap

# Make the electoral vote tracker

# Loading the data from a local .csv file

AllData <- read.csv("ElectoralVotesByState2024.csv")
AllData <- AllData %>%
  arrange(State)

# Formatting and transforming the data for plotting

MyData <- AllData %>%
  select(State, Votes.to.allocate,
         Unallocated, Harris, Trump) %>% 
  arrange(State)

MyData <- MyData %>%
  pivot_longer(cols=c(-State),names_to="Candidate")%>%
  pivot_wider(names_from=c(State)) %>%
  filter(Candidate == "Harris" |
           Candidate == "Trump" |
           Candidate == "Unallocated") %>%
  arrange(Candidate)

MyData <- MyData %>% 
  mutate(total = rowSums(.[2:52]))

# Formatting a horizontal line for the plot

hline <- function(y = 0, color = "darkgray") {
  list(
    type = "line",
    x0 = 0,
    x1 = 1,
    xref = "paper",
    y0 = y,
    y1 = y,
    line = list(color = color)
  )
}

# Producing the plot

fig <- plot_ly(
  MyData,
  x = ~ Candidate,
  y = ~ AK,
  legend = FALSE,
  marker = list(color = c("384B70", "B8001F", "gray")),
  type = 'bar',
  name = 'AK'
) %>% 
  add_annotations(
    visible = "legendonly",
    x = ~ Candidate,
    y = ~ (total + 20),
    text = ~ total,
    showarrow = FALSE,
    textfont = list(size = 50)
  ) 
fig <- fig %>% add_trace(y = ~ DE, name = 'DE')
fig <- fig %>% add_trace(y = ~ DC, name = 'DC')
fig <- fig %>% add_trace(y = ~ MT, name = 'MT')
fig <- fig %>% add_trace(y = ~ ND, name = 'ND')
fig <- fig %>% add_trace(y = ~ SD, name = 'SD')
fig <- fig %>% add_trace(y = ~ VT, name = 'VT')
fig <- fig %>% add_trace(y = ~ WY, name = 'WY')
fig <- fig %>% add_trace(y = ~ HI, name = 'HI')
fig <- fig %>% add_trace(y = ~ ID, name = 'ID')
fig <- fig %>% add_trace(y = ~ ME, name = 'ME')
fig <- fig %>% add_trace(y = ~ NH, name = 'NH')
fig <- fig %>% add_trace(y = ~ RI, name = 'RI')
fig <- fig %>% add_trace(y = ~ NE, name = 'NE')
fig <- fig %>% add_trace(y = ~ NM, name = 'NM')
fig <- fig %>% add_trace(y = ~ WV, name = 'WV')
fig <- fig %>% add_trace(y = ~ AR, name = 'AR')
fig <- fig %>% add_trace(y = ~ IA, name = 'IA')
fig <- fig %>% add_trace(y = ~ KS, name = 'KS')
fig <- fig %>% add_trace(y = ~ MS, name = 'MS')
fig <- fig %>% add_trace(y = ~ NV, name = 'NV')
fig <- fig %>% add_trace(y = ~ UT, name = 'UT')
fig <- fig %>% add_trace(y = ~ CT, name = 'CT')
fig <- fig %>% add_trace(y = ~ OK, name = 'OK')
fig <- fig %>% add_trace(y = ~ OR, name = 'OR')
fig <- fig %>% add_trace(y = ~ KY, name = 'KY')
fig <- fig %>% add_trace(y = ~ LA, name = 'LA')
fig <- fig %>% add_trace(y = ~ AL, name = 'AL')
fig <- fig %>% add_trace(y = ~ CO, name = 'CO')
fig <- fig %>% add_trace(y = ~ SC, name = 'SC')
fig <- fig %>% add_trace(y = ~ MD, name = 'MD')
fig <- fig %>% add_trace(y = ~ MN, name = 'MN')
fig <- fig %>% add_trace(y = ~ MO, name = 'MO')
fig <- fig %>% add_trace(y = ~ WI, name = 'WI')
fig <- fig %>% add_trace(y = ~ AZ, name = 'AZ')
fig <- fig %>% add_trace(y = ~ IN, name = 'IN')
fig <- fig %>% add_trace(y = ~ MA, name = 'MA')
fig <- fig %>% add_trace(y = ~ TN, name = 'TN')
fig <- fig %>% add_trace(y = ~ WA, name = 'WA')
fig <- fig %>% add_trace(y = ~ VA, name = 'VA')
fig <- fig %>% add_trace(y = ~ NJ, name = 'NJ')
fig <- fig %>% add_trace(y = ~ NC, name = 'NC')
fig <- fig %>% add_trace(y = ~ GA, name = 'GA')
fig <- fig %>% add_trace(y = ~ MI, name = 'MI')
fig <- fig %>% add_trace(y = ~ OH, name = 'OH')
fig <- fig %>% add_trace(y = ~ IL, name = 'IL')
fig <- fig %>% add_trace(y = ~ PA, name = 'PA')
fig <- fig %>% add_trace(y = ~ FL, name = 'FL')
fig <- fig %>% add_trace(y = ~ NY, name = 'NY')
fig <- fig %>% add_trace(y = ~ TX, name = 'TX')
fig <- fig %>% add_trace(y = ~ CA, name = 'CA')
fig <- fig %>% layout(yaxis = list(title = 'Electoral votes'),
                      barmode = 'stack',
                      showlegend = FALSE,
                      shapes = list(hline(270)))
# Showing the plot

fig

TN Presidential County Shift

President-elect Donald Trump easily won Tennessee during the 2020 and the 2024 presidential elections. 

The maps below show the amount of Democratic and Republican votes gained or lost during the election. While there was a rise of voting for both parties during the 2020 election, there was a loss for both parties during the 2024 election. 

Use the interactive maps below to compare Republican and Democratic gains and losses at the county level between the two elections, relative to the previous presidential race. Here’s how to explore them:

Voting shifts, 2016-2020

The map highlights county-level voting shifts for each party in the 2016 and 2020 elections. In 2016, Donald Trump defeated Democratic nominee Hillary Clinton. In 2020, Trump was defeated by Democratic nominee Joe Biden.

Voting shifts, 2020-2024

The map illustrates county-level voting shifts for each party in the 2020 and 2024 elections. In 2020, Donald Trump was defeated by Democratic nominee Joe Biden. In 2024, Trump defeated Democratic nominee Kamala Harris.

# 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("e96c46601fca77f6f3fcb0f72b673a75aed0ff2a")

# 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