Introduction

During my 2024 Fall Semester, I took a professional development course at MTSU which is designed to analyze, graph and interpret election data so that maps and hard to understand statistical information using R and Rstudio. Below, you can find a small collection of my work.

---

-Pre-election analyses-

GRAPI by Tennessee House District map

This is my GRAPI by Tennessee House District map, which shows the percentage of Homeowners who are overspending on rent in Rutherford County categorized by Tennessee House Districts. The percentages that can be seen is the average income amount spent on rent for each housing district, with there being a notable shift in how much people spend on rent around Rutherford county, with it varying from 35% to 42% across the county. Overall, as it is recommended that rent should not be more than 30% of your income according to financial expert.This is the map which depicts the estimate at which people pay their rent out of their own income alongside the estimate by district.

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

This is the code that makes this map happen, with several different processes that help to produce this map and table as a tidy and informative package of info on the ongoing housing crisis across the Middle Tennessee Area.

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("9fbd73ce43871afbb3837dd9a865f064940a6a53")

# 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

---

Gubernatorial Voting in Rutherford County, by precinct

Precinct-level Rutherford election results from 2022:

This is a Gubernatorial map for the most recent race for Tennessee Governor, showing precinct level results in Rutherford County. Incumbent Governor Bill Lee was shown to be favored by a majority of Rutherford County voting precincts over Democratic Challenger Jason Martin in the 2022 race for the governorship.

According to official results from the Tennessee secretary of state, Martin polled best in precincts around the Middle Tennessee State University campus in central Murfreesboro and in precincts along the county’s northwest border with Davidson County.

R Code

For reference, here is the code that produced the map:

# 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 Location Demo

This demo shows the projected amount of early voters in Rutherford County, with 60,000 people voting already. This graph helps to represent vote totals of the most recent full day of early voting.

This is an interact-able map that allows you to see the times and locations for possible early voting in Rutherford County for the 2024 Presidential Election, showing times for interested individuals to get their voting done early.

For interested individuals looking to vote early in Rutherford County, you can find more information on this here. For individuals interested in how this works, this is the corresponding code.

Code

# 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"
)

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")))

---

Early Voting Tracker Demo

This demo shows the projected amount of early voters in Rutherford County, with 60,000 people voting already. This graph helps to represent vote totals of the most recent full day of early voting.

This map shows the percentage of early voters specific to precincts in Rutherford county. You can click on a precinct to see specific details for each.

This is the corresponding code block that makes this work.

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)

# Fetch and unzip the early voting files

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

# Read the first daily voting file

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("https://raw.githubusercontent.com/drkblake/Data/refs/heads/main/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"
    )
  )
)

Map

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

---

Cable news coverage tracker:

This is a representation of the overall coverage of Kamala Harris and Donald Trump respectively, showing the overall media coverage as a whole. Whenever identifying key aspects of a candidate’s success in a political campaign, media visibility is an important factor to be made aware of. This can be seen with both candidates, with Kamala Harris receiving an increased amount of coverage in the time that Joe Biden Dropped out of the race as the Democratic Candidate, succeeding him and running against Donald Trump. This can also be seen with Donald Trump, with him receiving more coverage than both candidates combined on left leaning news organizations due to a combination of factors-shock value in messaging, opposing policies/values of Kamala Harris, and the overall notability of Donald Trump as a former TV Star and a former Government official make it a more reliable and profitable messaging/advertising for the aforementioned candidate as it provides them an opportunity to advertise their policy at no cost.

---

---

This is a representation of MSNBC, a left leaning news organizations coverage of the Presidential Candidates of 2024. Donald Trump was covered by MSNBC, moreso than both Kamala Harris and Joe Biden combined. This can be attributed several factors, one of them being that Kamala Harris entered the Presidential race late and didn’t differentiate herself from her predecessor Joe Biden.

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

# Defining date range

startdate <- "20240429"
enddate <- "20241112"

### 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

### Results for MSNBC, CNN, and Fox News, separately

# 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 = ~ Biden,
                                   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

# 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 = ~ Biden,
                               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

#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 = ~ Biden,
                               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

---

National Election Map and Electoral Vote Graphic:

This is a National Election Map showing the results of the 2024 Presidential Election, specifically the electoral college results. Donald Trump won the 2024 Presidential Election, winning 312 Electoral votes whilst Kamala Harris won 226 Electoral votes. You can select each state individually to see what respective candidate won each state and by what margin.

---

This is the code that makes the Map and Corresponding Electoral graph work.

R Code:

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("9fbd73ce43871afbb3837dd9a865f064940a6a53")

# 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

---

Presidential TN County Voting Shifts Map

This is an interact-able vote tracker that shows the number of people that voted in the Presidential Election of 2020 and 2024 respectively in the state of Tennessee. The map shows where Democrats and Republicans respectively gained more support in specific counties, with red and blue showing a gain for Republicans or Democrats respectively whilst a degrading color represents a loss. In 2020, although not acting as a majority in the state or a deciding factor in the election, Tennessee had a large bump in Democratic Voters, with this being a remarkable shift from 2024, where only half a dozen counties experienced remarkable or noticeable growth in Democrat voters, with it being noticeably smaller than Republican turnouts and points of growth in the 2024 election. Although some holdouts remained across the State, the sweeping Republican victory marks a shift in voter turnout, due to an overall decrease in voter turnout 2024, general election fatigue, and a general feeling of apathy from voters whenever attempting to vote for either respective candidate. To see the gains and losses for Democrats or Republicans respectively, you can click and drag on the small bubble that allows you to drag the map back and forth to interact with it.

This is the corresponding code which makes this possible.

R 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("9fbd73ce43871afbb3837dd9a865f064940a6a53")

# 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)