Category: 2019

library(tidyverse)
library(sf)
library(ggmap)
source("../../admin_scripts/util.R")

sf_council <- st_read("../../data/gis/city_council/Council_Districts_2016.shp")sf_divs <- st_read("../../data/gis/2019/Political_Divisions.shp")df <- readRDS("../../data/processed_data/df_all_2019_09_05.Rds")
df <- df %>%
  group_by(year, election, office, district, candidate, party, warddiv19) %>%
  summarise(votes=sum(votes)) %>%
  ungroup() %>%
  mutate(party = ifelse(party == "DEMOCRAT", "DEMOCRATIC", party))

bb <- st_bbox(sf_council %>% filter(DISTRICT == "10"))

expand_bb <- function(x, factor){
  mean_x <- mean(x)
  return(mean_x + factor * (x - mean_x))
}

bb[c(1,3)] <- expand_bb(bb[c(1,3)], 1.2)
bb[c(2,4)] <- expand_bb(bb[c(2,4)], 1.2)

names(bb) <- c("left", "bottom", "right", "top")
basemap <- get_map(location=bb, maptype="toner-lite")


get_labpt_df <- function(sf){
  df <- st_centroid(sf) %>%
    mutate(
      x=sapply(geometry, "[", 1),
      y=sapply(geometry, "[", 2)
    ) %>%
    as.data.frame() %>%
    select(-geometry)
  return(df)
}

phila_whole <- st_union(sf_council)

ggplot(sf_council) +
  geom_sf(data=phila_whole, fill="white", color=NA) +
  geom_sf(
    fill=strong_green, color = "white", size = 1, alpha=0.5
  ) +
  geom_sf(
    data=sf_council %>% filter(DISTRICT == "10"),
    fill=strong_green, color = "white", size = 1
  ) +
  geom_text(
    data = get_labpt_df(sf_council),
    aes(x=x,y=y,label=DISTRICT)
  ) +
  ggtitle("Philadelphia Council Districts") +
  theme_map_sixtysix()

district_map <- ggmap(
  basemap, 
  extent="device",
  base_layer = ggplot(sf_council),
  maprange = FALSE
) +
  theme_map_sixtysix()

sf_divs$council_district <- sf_divs %>%
  st_centroid() %>%
  st_covered_by(sf_council) %>%
  (function(x){sf_council$DISTRICT[unlist(x)]})

district_map +
  geom_sf(
    aes(alpha = (DISTRICT == "10")),
    fill="black",
    color = "grey50",
    size=2
  ) +
  scale_alpha_manual(values = c(`TRUE` = 0.2, `FALSE` = 0), guide = FALSE) +
  ggtitle(sprintf("Council District %s", 10))

races <- tribble(
  ~election, ~office, ~district, ~short_office, 
  "general", "PRESIDENT OF THE UNITED STATES", NA, "President",
  "general", "GOVERNOR", NA, "Governor",
  "general", "REPRESENTATIVE IN CONGRESS", "2", "US Congress District 2",
  "general", "MAYOR", NA, "Mayor"
  # "2018", "general", "SENATOR IN THE GENERAL ASSEMBLY-2ND DISTRICT", "PA Senate"
)

candidate_votes <- df %>% 
  inner_join(races) %>%
  group_by(warddiv19, short_office, year, election) %>%
  mutate(
    total_votes = sum(votes),
    pvote = votes / sum(votes)
  ) %>% 
  group_by()

candidates_overall <- candidate_votes %>%
  group_by(short_office, year, election, candidate, party) %>%
  summarise(votes = sum(votes)) %>%
  group_by(short_office, year, election) %>%
  mutate(
    pvote = votes/sum(votes),
    use = pvote > 0.05
  ) %>%
  filter(use)

candidate_votes <- candidate_votes %>% 
  inner_join(candidates_overall %>% select(year, election, short_office, candidate))

div_winners <- candidate_votes %>%
  group_by(year, election, short_office, warddiv19) %>%
  summarise(
    winner = candidate[which.max(votes)],
    winner_party = party[which.max(votes)],
    winner_pvote = pvote[which.max(votes)],
    twoparty_pvote = pvote[which.max(votes)] / 
      sum(pvote[party %in% c("DEMOCRATIC", "REPUBLICAN")], na.rm=TRUE),
    total_votes = total_votes[1]
  )
  
sf_divs <- sf_divs %>% rename(warddiv19 = DIVISION_N)

sf_divs$in_bbox <- sapply(
  sf_divs$geometry,
  function(g) {
    coords <- as.matrix(g)
    any(
      coords[,1] > bb["left"] &
      coords[,1] < bb["right"] &
      coords[,2] > bb["bottom"] &
      coords[,2] < bb["top"] 
    )
  }
)

ggraces <- sf_divs %>% 
      filter(in_bbox) %>%
      left_join(div_winners) %>% 
      mutate(
        winner_party = format_name(substr(winner_party,1,3)),
        race=sprintf("%s (%s)", short_office, year)
      ) %>%
      mutate(race=factor(race, levels=unique(race[order(year)])))

district_map + 
  geom_sf(data=phila_whole, fill="white", color=NA) +
  geom_sf(
    data=ggraces %>% filter(year %in% c(2015, 2016, 2018)),
    aes(fill = winner_party, alpha=100 * 2 * (twoparty_pvote - 0.5)),
    color=NA
  ) +
  geom_sf(
    data=sf_council %>% filter(DISTRICT == "10"),
    fill=NA,
    color="white",
    size=1
  )+
  facet_wrap(~race) +
  scale_fill_manual(NULL, values=c("Dem"=strong_blue, "Rep"=strong_red)) +
  theme_map_sixtysix() %+replace% 
  theme(legend.position = "bottom", legend.direction = "horizontal") +
  scale_alpha_continuous("Win Margin (%)") +
  guides(alpha = guide_legend(override.aes = list(fill = strong_blue)))+
  ggtitle("District 10's recent elections")

sth_re <- "REPRESENTATIVE IN THE GENERAL ASSEMBLY"
sth_candidates <- df %>% 
  filter(
    year == "2018", election=="general", 
    office == sth_re
  ) %>%
  mutate(
    party=format_name(substr(party,1,3))
  ) %>%
  group_by(warddiv19, district, year, election) %>%
  mutate(
    total_votes = sum(votes),
    pvote = votes / sum(votes)
  ) %>% 
  group_by()

sth_div_winners <- sth_candidates %>%
  filter(party %in% c("Dem", "Rep")) %>%
  group_by(year, election, district, warddiv19) %>%
  summarise(
    winner = candidate[which.max(votes)],
    winner_party = party[which.max(votes)],
    winner_pvote = pvote[which.max(votes)],
    twoparty_pvote = pvote[which.max(votes)] / sum(pvote)
  )

sf_sth <- st_read("../../data/gis/state_house/PaHouse2017_01.shp")sth_labpts <- sf_divs %>%
  filter(in_bbox) %>%
  left_join(sth_div_winners) %>%
  group_by(district) %>% 
  dplyr::do(data.frame(geometry = st_union(.$geometry))) %>%
  ungroup() %>%
  mutate(geometry = st_sfc(geometry)) %>%
  st_as_sf() %>%
  get_labpt_df()

sth_labpts[sth_labpts$district=="170", c('x','y')] <- c(-75.02, 40.111)
sth_labpts[sth_labpts$district=="172", c('x','y')] <- c(-75.07, 40.079)
sth_labpts[sth_labpts$district=="173", c('x','y')] <- c(-75.002, 40.096)

district_map + 
  geom_sf(data=phila_whole, fill="white", color=NA) +
  geom_sf(
    data=sf_divs %>% 
      filter(in_bbox) %>%
      left_join(sth_div_winners),
    aes(fill = winner_party, alpha=100 * 2 * (twoparty_pvote - 0.5)),
    color=NA
  ) +
  geom_sf(
    data=sf_sth, fill=NA, color="black"
  ) +
  geom_sf(
    data=sf_council %>% filter(DISTRICT == "10"),
    fill=NA,
    color=rgb(1,1,1,0.8),
    size=2
  )+
  geom_text(
    data=sth_labpts,
    aes(x=x,y=y, label=district)
  )+
  scale_fill_manual(NULL, values=c("Dem"=strong_blue, "Rep"=strong_red)) +
  theme_map_sixtysix() %+replace% 
  theme(legend.position = "bottom", legend.direction = "horizontal") +
  scale_alpha_continuous("Win Margin (%)") +
  guides(alpha = guide_legend(override.aes = list(fill = strong_blue)))+
  ggtitle("District 10's 2018 PA House elections")

d10_scatter <- div_winners %>% 
  inner_join(sf_divs %>% filter(council_district == "10")) %>%
  unite("office", short_office, year) %>%
  mutate(
    pdem_twoparty = ifelse(winner_party == "DEMOCRATIC", twoparty_pvote, 1-twoparty_pvote),
    win_gap = 2 * 100 * (pdem_twoparty-0.5)
  ) %>%
  select(office, warddiv19, win_gap, total_votes) %>%
  gather("key", "value", win_gap, total_votes) %>%
  unite(key, office, key) %>%
  spread(key, value) 

gap_labeller <- function(x){
  sprintf(
    "%s%s%%%s",
    ifelse(x==0, "", "+"),
    abs(x),
    ifelse(x>0, " D", ifelse(x<0, " R", ""))
  )
}

plot_scatter <- function(col, office, title){
  col <- enquo(col)
  ggplot(
    d10_scatter,
    aes(x=President_2016_win_gap, y=!!col)
  ) +
    geom_abline(slope=1, intercept=0, linetype="dashed") +
    geom_hline(yintercept=0, color="black") +
    geom_vline(xintercept=0, color="black") +
    geom_point(aes(size = President_2016_total_votes), alpha = 0.3, pch = 16) +
    coord_fixed() +
    theme_sixtysix() %+replace%
    theme(legend.position = "right", legend.direction="vertical") +
    scale_size_area("Votes in 2016") +
    scale_x_continuous(
      "Win Gap for President (2016)", 
      labels=gap_labeller  
    )+
    scale_y_continuous(
      sprintf("Win Gap for %s", office), 
      labels=gap_labeller  
    ) +
    ggtitle(title, "Division-level results") 
}

plot_scatter(Mayor_2015_win_gap, "Mayor (2015)", "Kenney was popular even in Trump's Divisions")

plot_scatter(`US Congress District 2_2018_win_gap`, "US Congress District 2 (2018)", "Boyle won a nationalized race")

council <- df %>%
  filter(election == "general") %>%
  filter(office == "DISTRICT COUNCIL") 

council_totals <- council %>% 
  filter(candidate != "Write In") %>%
  filter(party %in% c("DEMOCRATIC", "REPUBLICAN")) %>%
  group_by(year, candidate, party, district) %>% 
  summarise(votes=sum(votes)) %>%
  mutate(is_competitive = votes > 100) %>%
  group_by(year, district) %>%
  summarise(
    n_contested = sum(is_competitive),
    winner = candidate[which.max(votes)],
    winner_pct = votes[which.max(votes)] / sum(votes),
    winner_party = party[which.max(votes)]
  )

## council_totals %>% filter(winner_pct == 1)

oneill_df <- council_totals %>%
  filter(district == 10) %>%
  mutate(office = "Council", short_office="Council District 10") %>%
  rename(prep = winner_pct) %>%
  select(year, prep, short_office) %>%
  bind_rows(
    df %>% 
      filter(
        office %in% c("MAYOR", "PRESIDENT OF THE UNITED STATES", "GOVERNOR"),
        election == "general",
        party %in% c("DEMOCRATIC", "REPUBLICAN")
      ) %>%
      mutate(short_office = format_name(office)) %>%
      inner_join(sf_divs %>% as.data.frame() %>% filter(council_district == 10) %>% select(warddiv19)) %>%
      group_by(year, party, short_office) %>%
      summarise(votes = sum(votes)) %>%
      group_by(year, short_office) %>%
      summarise(
        prep = votes[party == "REPUBLICAN"] / sum(votes)
      )
  )

ggplot(
  oneill_df ,
  aes(x=asnum(year), y=2*100*(prep-0.5), color=short_office)
) +
  geom_hline(yintercept = 00, color="grey20") +
  geom_point(
    size=3
  ) +
  geom_line(
    aes(group=short_office),
    size=1
  ) +
  theme_sixtysix() +
  ylab("Percent for Republican") +
  geom_text(
    data=data.frame(
      year=2015.2, prep=c(0.30, 0.95, 0.55, 0.4), label=c("Mayor", "O'Neill", "President", "Governor"), 
      short_office=c("Mayor", "Council District 10", "President of the United States", "Governor")
    ),
    hjust=0, fontface="bold",
    aes(label=label)
  ) +
  scale_color_manual(
    guide=FALSE,
    values=c(
      "Mayor"=light_blue, 
      "Council District 10"=light_red, 
      "President of the United States"=light_purple, 
      "Governor"=light_green
    )
  )+
  xlab(NULL) +
  scale_x_continuous(breaks = seq(2003, 2015, 4)) +
  scale_y_continuous(labels=function(x) gap_labeller(-x)) +
  ggtitle("O'Neill runs far ahead of other Republicans")

turnout <- df %>% filter(
  election=="general",
  year %in% 2015:2018,
  office %in% c("MAYOR", "GOVERNOR", "PRESIDENT OF THE UNITED STATES", "DISTRICT ATTORNEY")
) %>%
  group_by(year, warddiv19) %>%
  summarise(turnout=sum(votes))

turnout <- sf_divs %>% 
  as.data.frame() %>%
  select(warddiv19, Shape__Are) %>%
  left_join(turnout) %>%
  group_by(year) %>%
  mutate(
    turnout_per_area = turnout / Shape__Are,
    turnout_per_area_std = (
      turnout_per_area - mean(turnout_per_area)
    ) / sd(turnout_per_area)
  )

turnout_wide <- turnout %>% 
  select(-turnout_per_area_std) %>%
  gather("key", "value", turnout, turnout_per_area) %>%
  unite("key", key, year) %>%
  spread(key, value) %>%
  inner_join(sf_divs %>% as.data.frame() %>% filter(council_district == "10") %>% select(warddiv19))

turnout_scatter <- function(year, title){
  turnout_col <- sym(paste0("turnout_", year))
  ggplot(
    turnout_wide %>% left_join(d10_scatter),
    aes(
      x=President_2016_win_gap,
      y=100*!!turnout_col / turnout_2016
    )
  ) + 
    geom_point(
      aes(size=President_2016_total_votes),
      alpha = 0.5, pch=16
    ) +
    theme_sixtysix() +
    geom_smooth(
      aes(weight=President_2016_total_votes),
      color = "black", method=lm
    ) +
    scale_y_continuous(
      sprintf("Votes in %s / Votes in 2016 (%%)", year),
      breaks=seq(0,100,10)
    )+
    scale_x_continuous(
      "Win Gap for President (2016)",
      labels=gap_labeller
    ) +
    scale_size_area("Votes in 2016") +
    expand_limits(y=0) +
    ggtitle(title)
}

turnout_scatter(2015, "Republican Divisions voted disproportionately\n in 2015")

turnout_scatter(2018, "Republican Divisions had less turnout advantage\n in 2018")

turnout_wide %>% 
  left_join(d10_scatter) %>%
  summarise(
    gap_2015=weighted.mean(President_2016_win_gap, w=turnout_2015),
    gap_2016=weighted.mean(President_2016_win_gap, w=turnout_2016),
    gap_2018=weighted.mean(President_2016_win_gap, w=turnout_2018)
  )