Presidential Elections Popular Vote Swing States [USA]

.chart_usa_presidential_elections_swing_states.r

library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)

sf <- 2
width <- 600 * sf
height <- 335 * sf
options(vsc.dev.args = list(width = width, height = height, res = 72 * sf))

df <- read_csv(paste0(
  "https://raw.githubusercontent.com/fivethirtyeight/election-results/",
  "refs/heads/main/election_results_presidential.csv"
))

swing_states <- c("AZ", "GA", "MI", "NC", "NV", "PA", "WI")

votes_2020 <- df |>
  filter(
    office_name == "U.S. President",
    state_abbrev %in% swing_states,
    ballot_party %in% c("DEM", "REP"),
    stage == "general"
  ) |>
  select(cycle, state_abbrev, ballot_party, votes) |>
  setNames(c("year", "state", "party", "votes"))

# https://election.lab.ufl.edu/2024-general-election-turnout/
votes_2024 <- tibble(
  state = swing_states,
  ballots = c(3428000, 5297258, 5710000, 5725000, 1487887, 7050000, 3425000),
  rep_proj_share = c(.52, .503, .495, .509, .506, .501, .496),
  dem_proj_share = c(.469, .488, .485, .479, .475, .489, .489)
) |>
  mutate(
    REP = ballots * rep_proj_share,
    DEM = ballots * dem_proj_share
  ) |>
  pivot_longer(
    cols = c(REP, DEM),
    names_to = "party",
    values_to = "votes"
  ) |>
  mutate(year = 2024) |>
  select(year, state, party, votes) |>
  setNames(c("year", "state", "party", "votes"))


# Combine and arrange data
combined_votes <- rbind(votes_2020, votes_2024) |>
  group_by(year, party) |>
  summarize(votes = sum(votes))

# Define party colors
party_colors <- c(
  "DEM" = "#1f78b4", # blue
  "REP" = "#e31a1c" # red
)


# Generate plot
chart <-
  ggplot(combined_votes, aes(x = party, y = votes, fill = party)) +
  geom_bar(
    stat = "identity",
    position = position_stack(reverse = TRUE)
  ) +
  geom_text(
    aes(label = comma(round(votes))),
    position = position_stack(vjust = 0.5),
    size = 2,
    color = "white",
    fontface = "bold"
  ) +
  scale_fill_manual(values = party_colors) +
  facet_wrap(~year, nrow = 1) +
  labs(
    title =
      "2024 Swing State's Historical Popular Vote in US Presidential Elections",
    subtitle = paste(
      paste(swing_states, collapse = ", "),
      "· @USMortality · Updated: 11/19/24 12:52pm"
    ),
    caption = "Source: decisiondeskhq.com, election.lab.ufl.edu",
    x = "",
    y = "Votes",
    fill = "Party"
  ) +
  scale_y_continuous(labels = comma) +
  theme_bw() +
  theme(
    legend.position = "top",
    axis.text.x = element_blank(),
    axis.ticks.x = element_line(color = "black")
  )

ggsave(
  filename = "chart1.png", plot = chart, width = width, height = height,
  units = "px", dpi = 72 * sf, device = grDevices::png, type = "cairo"
)


# Combine and calculate margins
margins <- bind_rows(votes_2020, votes_2024) |>
  pivot_wider(names_from = party, values_from = votes) |>
  mutate(margin = DEM - REP)

# Define colors for margins
# Correctly define colors for TRUE/FALSE
margin_colors <- c(`TRUE` = "#1f78b4", `FALSE` = "#e31a1c")

# Generate bar chart with margins
chart <-
  ggplot(margins, aes(x = state, y = margin, fill = margin > 0)) +
  geom_bar(stat = "identity", width = 0.7) +
  scale_fill_manual(
    values = margin_colors,
    labels = c("Negative (REP)", "Positive (DEM)")
  ) +
  facet_wrap(~year, nrow = 1) +
  labs(
    title = "Swing State Margins in U.S. Presidential Elections",
    subtitle = paste(
      paste(swing_states, collapse = ", "),
      "· @USMortality · Updated: 11/19/24 12:52pm"
    ),
    caption = "Source: decisiondeskhq.com, election.lab.ufl.edu",
    x = "",
    y = "Margin (DEM - REP)",
    fill = "Margin Direction"
  ) +
  scale_y_continuous(labels = comma) +
  theme_bw() +
  theme(
    legend.position = "top",
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.major.y = element_line(color = "gray90"),
    panel.grid.minor.y = element_blank()
  )

# Save the chart
ggsave(
  filename = "chart2.png", plot = chart, width = width, height = height,
  units = "px", dpi = 72 * sf, device = grDevices::png, type = "cairo"
)


library(readr)

swing_states <- c("AZ", "GA", "MI", "NC", "NV", "PA", "WI")

df1 <- read_csv("https://election.lab.ufl.edu/data-downloads/turnoutdata/Turnout_1980_2022_v1.1.csv") |>
  filter(YEAR %in% c(2016, 2020, 2024)) |>
  select(STATE_ABV, YEAR, VEP, TOTAL_BALLOTS_COUNTED) |>
  setNames(c("state", "year", "eligible_population", "votes"))

df2 <- read_csv("https://election.lab.ufl.edu/data-downloads/turnoutdata/Turnout_2024G_v0.3.csv") |>
  mutate(YEAR = 2024) |>
  select(STATE_ABV, YEAR, VEP, TOTAL_BALLOTS_COUNTED) |>
  setNames(c("state", "year", "eligible_population", "votes"))

df <- rbind(df1, df2) |>
  filter(!is.na(state)) |>
  mutate(is_swing = state %in% swing_states) |>
  group_by(year, is_swing) |>
  summarize(
    eligible_population = sum(eligible_population),
    votes = sum(votes),
    turnout = votes / eligible_population
  ) |>
  select(year, is_swing, turnout)

# Generate plot
chart <-
  ggplot(df, aes(x = is_swing, y = turnout, fill = is_swing)) +
  geom_bar(
    stat = "identity",
    position = position_stack(reverse = TRUE)
  ) +
  geom_text(
    aes(label = scales::percent(turnout, accuracy = 0.1)),
    position = position_stack(vjust = 0.5),
    size = 4,
    color = "white",
    fontface = "bold"
  ) +
  scale_fill_manual(values = c("TRUE" = "darkgreen", "FALSE" = "black")) +
  facet_wrap(~year, nrow = 1) +
  labs(
    title = "2024 Swing State's Historical Popular Vote in US Presidential Elections",
    subtitle = paste(
      paste(swing_states, collapse = ", "),
      "· @USMortality · Updated: 11/19/24 12:52pm"
    ),
    caption = "Source: decisiondeskhq.com, election.lab.ufl.edu",
    x = "Swing State",
    y = "Turnout",
    fill = "Swing State"
  ) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  theme_bw() +
  theme(
    legend.position = "top",
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.ticks.x = element_line(color = "black")
  )

ggsave(
  filename = "chart3.png", plot = chart, width = width, height = height,
  units = "px", dpi = 72 * sf, device = grDevices::png, type = "cairo"
)