Competition and Representation in New Mexico’s State Legislature

# Combine margins data with unopposed data and winner party
# Get winner party from margins data directly
house_competitive_summary <- house_margins |>
  select(election_date, district_name, candidate_party_name, competitive_5, competitive_10) |>
  rename(winner_party = candidate_party_name) |>
  left_join(
    unopposed_analysis_house |>
      select(election_date, district_name, dem_unopposed, rep_unopposed),
    by = c("election_date", "district_name")
  ) |>
  mutate(
    chamber = "House",
    unopposed = (winner_party == "Democratic" & dem_unopposed) | 
                (winner_party == "Republican" & rep_unopposed)
  )

senate_competitive_summary <- senate_margins |>
  select(election_date, district_name, candidate_party_name, competitive_5, competitive_10) |>
  rename(winner_party = candidate_party_name) |>
  left_join(
    unopposed_analysis_senate |>
      select(election_date, district_name, dem_unopposed, rep_unopposed),
    by = c("election_date", "district_name")
  ) |>
  mutate(
    chamber = "Senate",
    unopposed = (winner_party == "Democratic" & dem_unopposed) | 
                (winner_party == "Republican" & rep_unopposed)
  )

# Combine and summarize
competitiveness_by_party <- bind_rows(house_competitive_summary, senate_competitive_summary) |>
  mutate(
    non_competitive = !competitive_10 & !unopposed
  ) |>
  group_by(election_date, chamber, winner_party) |>
  summarise(
    competitive = sum(competitive_10, na.rm = TRUE),
    very_competitive = sum(competitive_5, na.rm = TRUE),
    unopposed = sum(unopposed, na.rm = TRUE),
    non_competitive = sum(non_competitive, na.rm = TRUE),
    total = n(),
    .groups = "drop"
  ) |>
  filter(!is.na(winner_party), total >= 2) |>
  mutate(
    party = ifelse(winner_party == "Democratic", "D", "R")
  ) |>
  select(election_date, chamber, party, competitive, very_competitive, non_competitive, unopposed, total) |>
  arrange(chamber, election_date, party)

# Create DT table
competitiveness_by_party |>
  DT::datatable(
    colnames = c("Year", "Chamber", "Party", 
                 "Comp", "Very Comp", 
                 "Non Comp", "Unopposed", "Total"),
    caption = "Competitiveness Metrics by Year, Chamber, and Winning Party",
    options = list(
      pageLength = 10,
      dom = 'frtip',
      scrollX = TRUE
    ),
    rownames = FALSE
  )

# Load district boundaries using tigris
# Get New Mexico state legislative districts (suppress download messages)
options(tigris_use_cache = TRUE)
invisible(capture.output({
  suppressMessages({
    suppressWarnings({
      house_districts_raw <- tigris::state_legislative_districts(state = "NM", house = "lower", year = 2021)
      senate_districts_raw <- tigris::state_legislative_districts(state = "NM", house = "upper", year = 2021)
    })
  })
}))

# Extract district number from available columns
# tigris uses SLDLST for lower house and SLDUST for upper house
# Need to handle potential leading zeros and format matching
if("SLDLST" %in% names(house_districts_raw)) {
  house_districts <- house_districts_raw |>
    mutate(district_num = as.character(as.numeric(SLDLST))) |>
    select(district_num, geometry)
} else if("NAME" %in% names(house_districts_raw)) {
  house_districts <- house_districts_raw |>
    mutate(
      district_num = gsub(".*District (\\d+).*", "\\1", NAME),
      district_num = as.character(as.numeric(district_num))
    ) |>
    select(district_num, geometry)
} else {
  house_districts <- house_districts_raw |>
    mutate(district_num = as.character(row_number())) |>
    select(district_num, geometry)
}

if("SLDUST" %in% names(senate_districts_raw)) {
  senate_districts <- senate_districts_raw |>
    mutate(district_num = as.character(as.numeric(SLDUST))) |>
    select(district_num, geometry)
} else if("NAME" %in% names(senate_districts_raw)) {
  senate_districts <- senate_districts_raw |>
    mutate(
      district_num = gsub(".*District (\\d+).*", "\\1", NAME),
      district_num = as.character(as.numeric(district_num))
    ) |>
    select(district_num, geometry)
} else {
  senate_districts <- senate_districts_raw |>
    mutate(district_num = as.character(row_number())) |>
    select(district_num, geometry)
}

# Prepare unopposed data for mapping (focus on 2024)
# Extract district numbers and normalize (remove leading zeros)
unopposed_map_house <- unopposed_analysis_house |>
  filter(election_date == 2024) |>
  mutate(
    district_num = gsub(".*District (\\d+).*", "\\1", district_name),
    district_num = as.character(as.numeric(district_num)),  # Normalize to remove leading zeros
    unopposed_status = case_when(
      dem_unopposed ~ "Democrat Unopposed",
      rep_unopposed ~ "Republican Unopposed",
      TRUE ~ "Contested"
    )
  )

unopposed_map_senate <- unopposed_analysis_senate |>
  filter(election_date == 2024) |>
  mutate(
    district_num = gsub(".*District (\\d+).*", "\\1", district_name),
    district_num = as.character(as.numeric(district_num)),  # Normalize to remove leading zeros
    unopposed_status = case_when(
      dem_unopposed ~ "Democrat Unopposed",
      rep_unopposed ~ "Republican Unopposed",
      TRUE ~ "Contested"
    )
  )

# Join with district boundaries
house_map_data <- house_districts |>
  left_join(unopposed_map_house, by = "district_num") |>
  mutate(
    unopposed_status = ifelse(is.na(unopposed_status), "Contested", unopposed_status)
  )

senate_map_data <- senate_districts |>
  left_join(unopposed_map_senate, by = "district_num") |>
  mutate(
    unopposed_status = ifelse(is.na(unopposed_status), "Contested", unopposed_status)
  )



# State House map
p1 <- ggplot(house_map_data) +
  geom_sf(aes(fill = unopposed_status), color = "white", linewidth = 0.3) +
  scale_fill_manual(
    values = c("Democrat Unopposed" = "#1f77b4", 
               "Republican Unopposed" = "#d62728",
               "Contested" = "grey90"),
    name = "Status"
  ) +
  labs(
    title = "2024 State House: Unopposed Districts"
  ) +
  theme_void() +
  theme(
    plot.title = element_text(size = 14),
    legend.position = "bottom"
  )

# State Senate map
p2 <- ggplot(senate_map_data) +
  geom_sf(aes(fill = unopposed_status), color = "white", linewidth = 0.3) +
  scale_fill_manual(
    values = c("Democrat Unopposed" = "#1f77b4", 
               "Republican Unopposed" = "#d62728",
               "Contested" = "grey90"),
    name = "Status"
  ) +
  labs(
    title = "2024 State Senate: Unopposed Districts"
  ) +
  theme_void() +
  theme(
    plot.title = element_text(size = 14),
    legend.position = "bottom"
  )

p1

p2

# Calculate margins for 2024 House
house_2024 <- house_results |>
  filter(election_date == 2024) |>
  group_by(district_name) |>
  mutate(
    total_votes = sum(votes, na.rm = TRUE),
    dem_votes = sum(votes[candidate_party_name == "Democratic"], na.rm = TRUE),
    rep_votes = sum(votes[candidate_party_name == "Republican"], na.rm = TRUE),
    dem_pct = (dem_votes / total_votes) * 100,
    rep_pct = (rep_votes / total_votes) * 100,
    dem_margin = dem_pct - rep_pct
  ) |>
  slice(1) |>
  ungroup() |>
  arrange(desc(dem_margin)) |>
  mutate(
    rank = row_number(),
    winner = ifelse(dem_margin > 0, "Democratic", "Republican"),
    district_num = gsub(".*District (\\d+).*", "\\1", district_name)
  )

ggplot(house_2024, aes(x = rank, y = dem_margin, fill = winner)) +
  geom_col() +
  geom_hline(yintercept = 0, color = "black", linewidth = 0.5) +
  geom_hline(yintercept = c(-5, 5), linetype = "dashed", color = "grey50", alpha = 0.7) +
  scale_fill_manual(values = c("Democratic" = "#1f77b4", "Republican" = "#d62728")) +
  labs(
    title = "2024 State House Districts Ranked by Democratic Margin",
    x = "District Rank (by Democratic Margin)",
    y = "Democratic Margin (percentage points)",
    fill = "Winner"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16),
    legend.position = "bottom",
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank()
  ) +
  scale_y_continuous(breaks = seq(-60, 80, by = 20))

# Calculate margins for 2024 Senate
senate_2024 <- senate_results |>
  filter(election_date == 2024) |>
  group_by(district_name) |>
  mutate(
    total_votes = sum(votes, na.rm = TRUE),
    dem_votes = sum(votes[candidate_party_name == "Democratic"], na.rm = TRUE),
    rep_votes = sum(votes[candidate_party_name == "Republican"], na.rm = TRUE),
    dem_pct = (dem_votes / total_votes) * 100,
    rep_pct = (rep_votes / total_votes) * 100,
    dem_margin = dem_pct - rep_pct
  ) |>
  slice(1) |>
  ungroup() |>
  arrange(desc(dem_margin)) |>
  mutate(
    rank = row_number(),
    winner = ifelse(dem_margin > 0, "Democratic", "Republican"),
    district_num = gsub(".*District (\\d+).*", "\\1", district_name)
  )

ggplot(senate_2024, aes(x = rank, y = dem_margin, fill = winner)) +
  geom_col() +
  geom_hline(yintercept = 0, color = "black", linewidth = 0.5) +
  geom_hline(yintercept = c(-5, 5), linetype = "dashed", color = "grey50", alpha = 0.7) +
  scale_fill_manual(values = c("Democratic" = "#1f77b4", "Republican" = "#d62728")) +
  labs(
    title = "2024 State Senate Districts Ranked by Democratic Margin",
    x = "District Rank (by Democratic Margin)",
    y = "Democratic Margin (percentage points)",
    fill = "Winner"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16),
    legend.position = "bottom",
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank()
  ) +
  scale_y_continuous(breaks = seq(-60, 80, by = 20))

seats_votes_table <- seats_votes |>
  mutate(
    dem_vote_share = round(dem_vote_share, 1),
    dem_seat_share = round(dem_seat_share, 1),
    seat_vote_diff = round(dem_seat_share - dem_vote_share, 1)
  ) |>
  select(chamber, election_date, dem_vote_share, dem_seat_share, seat_vote_diff, dem_seats, rep_seats) |>
  arrange(chamber, election_date)

max_abs_diff <- max(abs(seats_votes_table$seat_vote_diff), na.rm = TRUE)
color_func <- scales::col_numeric(
  palette = "RdBu",
  domain = c(-max_abs_diff, max_abs_diff),
  reverse = FALSE
)

seats_votes_table |>
  select(chamber, election_date, dem_vote_share, dem_seat_share, seat_vote_diff, dem_seats, rep_seats) |>
  DT::datatable(
    colnames = c("Chamber", "Year", "Dem Vote Share (%)", 
                 "Dem Seat Share (%)", "Seat-Vote Diff", "Dem Seats", "Rep Seats"),
    caption = "Seats-Votes Relationship: All Election Years by Chamber",
    options = list(pageLength = 25, dom = 't')
  ) |>
  DT::formatStyle(
    "seat_vote_diff",
    backgroundColor = DT::styleInterval(
      cuts = seq(-max_abs_diff, max_abs_diff, length.out = 20),
      values = color_func(seq(-max_abs_diff, max_abs_diff, length.out = 21))
    )
  ) |>
  DT::formatRound(columns = c("dem_vote_share", "dem_seat_share", "seat_vote_diff"), digits = 1)