Electoral College in Canada

stateInfo <- read.csv(params$STATES_PATH) %>% as_tibble()

addedSenateSeats <- params$SENATE_SEATS
perCapita <- sum(stateInfo$Population) / params$TARGET_VOTES

reps <- stateInfo %>% 
  mutate(
    Number = ceiling(Population / perCapita) + 
           case_when(Type == "Territory" ~ 0, TRUE ~ 2)
    ) %>%
  select(State, Population, Number)

kable(reps, col.names = c("Province", "Population", "Alotted Seats"))

ps <- ggplot(reps) +
  geom_parliament(aes(seats = Number, fill = State)) +
  coord_fixed() + 
  theme_void() +
  theme(legend.position = "bottom", plot.title = element_text(hjust = 0.5, face = "bold"), plot.subtitle = element_text(hjust = 0.5)) +
  guides(color = guide_legend(nrow = 2, byrow = TRUE)) +
  labs(
    legend = NULL, 
    title = "Electoral College in Canada", 
    caption = paste0("Total ", sum(reps$Number), " votes from ", length(stateInfo$State), " provinces and territories")
  ) +
  scale_fill_discrete(labels = reps$State)
ps

options(scipen = 999)

plotData <- reps %>% 
  arrange(desc(Population)) %>% 
  mutate(PerCapita = Population / Number)
hPlot <- ggplot(plotData) +
  geom_bar(
    aes(x = reorder(State, -PerCapita), y = PerCapita), 
    stat = "identity"
  ) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 5)) +
  xlab("Province") +
  ylab("Voters per Rep")
ggplotly(hPlot)

preAllianceData <- data %>%
  group_by(Party, State) %>%
  summarise(TotalVotes = sum(Vote), .groups = "rowwise") %>%
  filter(!is.na(Party))
alliances <- read.csv(params$ALLIANCES_PATH) %>% as_tibble() %>% filter(Sc == 1)

alliancePreData <- tribble(~ Party, ~ State, ~ TotalVotes, ~ OriginalParty)
for (i in 1:length(preAllianceData$Party)) {
  pad <- preAllianceData[i,]
  ali <- which(alliances$Party == pad$Party)
  if (!identical(ali, integer(0))) {
    allianceEl <- alliances[ali,]
    alliancePreData <- alliancePreData %>% 
      add_row(Party = allianceEl$Alliance, State = pad$State, TotalVotes = pad$TotalVotes, OriginalParty = pad$Party)
  } else {
    alliancePreData <- alliancePreData %>% 
      add_row(Party = "Other", State = pad$State, TotalVotes = pad$TotalVotes, OriginalParty = pad$Party)
  }
}

stateWiseData <- alliancePreData %>%
  group_by(Party, State) %>%
  summarise(TotalVotes = sum(TotalVotes), .groups = "rowwise")

stateWinners <- tribble(~ Party, ~ State, ~ Votes)
for (state in unique(stateWiseData$State)) {
  stateData <- stateWiseData %>% filter(State == state)
  partyInfo <- stateData[which.max(stateData$TotalVotes),]
  stateWinners <- stateWinners %>% add_row(Party = partyInfo$Party, State = partyInfo$State, Votes = partyInfo$TotalVotes)
}


# Votes Calculation
preRes <- tribble(~ Party, ~ Votes)
for (state in unique(reps$State)) {
  iReps <- reps[which(reps$State == state),]
  iRes <- stateWinners[which(stateWinners$State == state),]
  preRes <- preRes %>% add_row(Party = iRes$Party, Votes = iReps$Number)
}

Results <- preRes %>% 
  group_by(Party) %>% 
  summarise(Votes = sum(Votes)) %>% 
  mutate(Percent = sprintf((Votes / sum(Votes)) * 100, fmt = '%#.1f')) %>%
  arrange(desc(Votes)) %>% 
  add_column(Colour = c("red", "blue"))
#add_column(Colour = c("orange", "green", "lightblue", "red", "cyan", "darkgreen", "pink", "black", "grey", "purple", "white"))

p <- ggplot(Results) +
  geom_parliament(aes(seats = Votes, fill = Party)) +
  coord_fixed() + 
  theme_void() +
  theme(legend.position = "bottom", plot.title = element_text(hjust = 0.5, face = "bold"), plot.subtitle = element_text(hjust = 0.5)) +
  guides(color = guide_legend(nrow = 2, byrow = TRUE)) +
  labs(
    legend = NULL, 
    title = "Electoral College in Canada", 
    caption = paste0("Total ", sum(Results$Votes), " votes from ", length(stateInfo$State), " provinces and territories"),
    subtitle = paste0("", Results[1,]$Party, " wins with ", Results[1,]$Votes, " votes")
  ) +
  scale_fill_manual(values = Results$Colour, labels = Results$Party)
p

kable(Results %>% select(Party, Votes, Percent), col.names = c("Alliance", "Votes in the College", "Votes %"))

detailed <- tribble(~ Province, ~ Winner, ~ WinnerVotes, ~ Opposition, ~ OppositionVotes)
for (state in unique(stateWiseData$State)) {
  sorted <- stateWiseData %>% filter(State == state) %>% arrange(desc(TotalVotes))
  winner <- sorted[1,]
  op <- sorted[2,]
  detailed <- detailed %>% add_row(Province = state, Winner = winner$Party, WinnerVotes = winner$TotalVotes, Opposition = op$Party, OppositionVotes = op$TotalVotes)
}
  
kable(detailed, col.names = c("Province", "Winner", "Vote %", "Runner Up", "Vote %"))