Election Analytics Final Project Portfolio

GRAPI by Tennessee House District

This map shows that over one third of Rutherford County renters cannot afford their leases. This problem is especially true for residents on the western side of the county, closest to Davidson County.

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

Code:

# 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("PutYourAPIKeyHere")

# 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

Nearly every Rutherford County voting precinct favored incumbent Republican Bill Lee over Democratic challenger Jason Martin in the 2022 race for Tennessee governor.

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.

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")

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 Locations in Rutherford County

Do you want to avoid the lines at the polls this election day? Try these early voting locations between October 16-31 to cast your early vote.

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")

# 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

Precinct-level 2024 Early Voting Turnout Percentages in Rutherford County

Roughly 51 percent of Rutherford County votes voted early this year. Check out the voting totals for each day:

Code:

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)


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 tracker graphic comparing cable news coverage volume for Trump, Harris and Biden 

Not all press is good press, but no press is always bad. This interactive chart compares the amount of stories mentioned Donald Trump, Joe Biden, and Kamala Harris on major cable news networks.

Code:

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

State-level U.S. Electoral Vote Map and Graphic

This map shows which electoral votes went to each state during the 2024 Presidential election.

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("PasteYourAPIKeyBetweenTheseQuoteMarks")

# 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

County-level Vote Total Shifts for Democrats and Republicans from 2016 to 2020 and from 2020 to 2024

These maps show voting shifts between the 2016-2020 elections and the 2020-2024 elections. Use the slider to compare voting gains and losses across parties.

2016-2024

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