Senate-non-major-party-votes

library(magrittr)
library(data.table)
library(dplyr)
library(dtplyr)
library(broom)
library(grid)
library(gridExtra)
library(gtable)
library(ggplot2)
library(ggthemes)
library(viridis)
library(grattan)
library(scales)
library(eechidna)  # 2013 boundaries


`%notin%` <- function(x, y) !x %in% y

year = "2016"

switch(year,
       "2016" = {
         senate <- fread("http://vtr.aec.gov.au/Downloads/SenateFirstPrefsByDivisionByVoteTypeDownload-20499.csv")
       },
       "2013" = {
         senate <- fread("http://results.aec.gov.au/17496/website/Downloads/SenateFirstPrefsByDivisionByVoteTypeDownload-17496.csv")
       })
       

votes_by_majorness <- 
  senate[,
  Other := !grepl(paste0("(Labor)|(Liberal National)|(National Party)|", 
                         "(Greens)|",
                         "(Country Liberals)|(Liberal.The National)|", 
                         "(Liberal\\s.\\sNationals)|(^(The Nationals)$)|(^(Liberal)$)"), 
                  PartyName)
] %>%
  group_by(DivisionNm, Other) %>%
  summarise(n_votes = sum(TotalVotes)) %>%
  ungroup %>%
  group_by(DivisionNm) %>%
  mutate(prop_votes = n_votes / sum(n_votes)) %>%
  mutate(ELECT_DIV2 = gsub("[^A-Z]", "", toupper(DivisionNm))) %>%
  filter(Other)

switch(year, 
       "2016" = {
         seat_coding <- 
           fread("hexagons/2016SeatGeoCoding.csv") %>%
           rename(id = SeatCd) %>%
           mutate(ELECT_DIV2 = toupper(gsub("[^A-Z]", "", Seat))) %>%
           mutate(ELECT_DIV2 = ifelse(ELECT_DIV2 == "THROSBY",
                                      "WHITLAM",
                                      ifelse(ELECT_DIV2 == "FRASER",
                                             "FENNER",
                                             ELECT_DIV2)))
         "2013" = {
           seat_coding <- 
             fread("hexagons/SeatGeoCoding.csv") %>%
             rename(id = SeatCd) %>%
             mutate(ELECT_DIV2 = toupper(gsub("[^A-Z]", "", Seat)))
         }
         
       })
switch(
  year, 
  
  "2016" = {
    elect_2016 <- readRDS("elect_2016_simple.rds")
    elect_2016@data$id <- seq_along(nrow(elect_2016@data))
    Electoral_divisions_by_id <- readRDS("elect_div_by_id.rds")
    elect_2016_swept <- tidy(elect_2016)
    elect_2016_swept <- merge(elect_2016_swept, Electoral_divisions_by_id, by = "id") %>%
      as.data.table
  }, 
  
  "2013" = {
    elect_2016_swept <- 
      nat_map %>% # eechidna::
      mutate(ELECT_DIV2 = gsub("[^A-Z]", "", toupper(ELECT_DIV))) %>%
      as.data.table
  }
)
  
joined <- 
  elect_2016_swept %>%
  merge(merge(seat_coding, votes_by_majorness, by = "ELECT_DIV2"), by = "ELECT_DIV2") %>%
  # limit to 40%
  mutate(prop_votes_factor = factor(percent(pmin(floor(prop_votes * 20) / 20, 0.4)), 
                                    levels = percent(seq(0.1, 0.4, by = 0.05)))) %>%
  as.data.table



metros <- 
  joined %>% 
  filter(`Metro/Regional` == "Metro") %>%
  melt.data.table(measure.vars = c("Sydney", "Melbourne", "Brisbane", "Adelaide", "Perth"), 
                  variable.name = "City", 
                  value.name = "is_in") %>%
  filter(as.logical(is_in))

make_plot <- function(.data, guide = FALSE, the_city = NULL){
  if (guide) guide <- "legend"
  if (!is.null(the_city)){
      arrange(.data, group, order) %>%
      {
        x_range <- max(.$long) - min(.$long)
        y_range <- max(.$lat) - min(.$lat)
        ggplot(., aes(x = long, y = lat, group = group, order = order)) + 
          geom_polygon(aes(fill = prop_votes_factor), color = "black") + 
          theme_map(base_size = 20) +
          # scale_fill_viridis("Proportion votes to non-major parties\nexceeded:", 
          #                    discrete = TRUE, drop = FALSE, guide = guide) + 
          scale_fill_discrete_grattan("Proportion votes to non-major parties\nexceeded:", 
                                      drop = FALSE, guide = guide) + 
          coord_map() +
          theme(legend.position = c(0, 1), 
                legend.justification = c(0, 1), 
                legend.background = element_rect(fill = NA)) + 
          # annotate("text", 
          #          x = min(.$long) + x_range * 1.05, 
          #          y = min(.$lat) + y_range * 0.5,
          #          hjust = 0.75,
          #          color = "black",
          #          fontface = "bold", 
          #          size = 14/(14/5),
          #          label = the_city)
          ggtitle(the_city) + 
          theme(plot.title = element_text(size = rel(1), margin = margin(0,0,0,0), vjust = 1))
      }
  } else {
    arrange(.data, group, order) %>%
      ggplot(aes(x = long, y = lat, group = group, order = order)) + 
      geom_polygon(aes(fill = prop_votes_factor), color = "black", size = 0.1) + 
      theme_map(base_size = 20) +
      # scale_fill_viridis("Proportion votes to non-major parties\nexceeded:", 
      #                    discrete = TRUE, drop = FALSE, guide = guide) + 
      scale_fill_discrete_grattan("Senate 1st pref. votes to\nnon-major parties\nexceeded:", 
                                  drop = FALSE, guide = guide) +  
      coord_map() + 
      theme(legend.position = c(0, 1), 
            legend.justification = c(0, 1), 
            legend.background = element_rect(fill = NA))
  }
}

plot_city <- function(city){
  if (city == "Sydney"){
    metros %>%
      filter(City == "Sydney") %>%
      # Avoid Lord Howe Island
      filter(between(lat, -35, -33)) %>%
      make_plot(the_city = city)
  } else {
   metros %>%
      filter(City == city) %>%
      make_plot(the_city = city)
  }
}

joined %>% 
  filter(`Metro/Regional` != "Metro") %>%
  filter(long > 113) %>%
  make_plot(guide = TRUE)

vplayout <- function(x, y) viewport(layout.pos.col = x, layout.pos.row = y)
grid.newpage()
# Prepare the plotting area.  9 x 8 cells.
pushViewport(viewport(layout = grid.layout(8, 9)))  # reversed
print({
  # Australia
  joined %>% 
    filter(long > 113) %>% 
    mutate(prop_votes_factor = factor(ifelse(`Metro/Regional` == "Metro", 
                                             NA_character_, 
                                             as.character(prop_votes_factor)), 
                                      levels = percent(seq(0.1, 0.4, by = 0.05)))) %>% 
    make_plot(guide = TRUE)
}, 
# push this plot to cover the cells with x-coordinates 1-9 and y-coordinates 1-6.
vp = vplayout(1:9, 1:6))
print(plot_city("Perth"), vp = vplayout(1:2, 3:4 + 1))
print(plot_city("Adelaide"), vp = vplayout(4:5, 5:6))
print(plot_city("Melbourne"), vp = vplayout(6:8, 5:7 + 1))
print(plot_city("Sydney"), vp = vplayout(7:9, 3:5 + 1))
print(plot_city("Brisbane"), vp = vplayout(7:9, 1:3))

# Copy to a pdf 22 inches by 14 inches.
dev.copy2pdf(file = paste0(year, "-Protest-senate-votes-by-Division.pdf"), width = 22, height = 14)