Voting Patterns Across Australian States – 2025 Federal Election

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

# Load data
data <- read_csv("HouseVotes_State.csv")

ggplot(data, aes(x = reorder(StateNm, TotalVotes), y = TotalVotes, fill = StateNm)) +
  geom_col(show.legend = FALSE) +
  geom_text(aes(label = scales::comma(TotalVotes)), hjust = -0.1, size = 5) +
  coord_flip() +
  labs(title = "Total Votes Counted by State in 2025",
       subtitle = "Data sourced from Australian Electoral Commission",
       x = "State", y = "Total Votes") +
  scale_fill_brewer(palette = "Set3") +
  scale_y_continuous(labels = scales::label_comma(), expand = expansion(mult = c(0, 0.1))) +
  theme_minimal(base_size = 18) +
  theme(plot.title = element_text(face = "bold"),
        plot.subtitle = element_text(size = 14, margin = margin(b = 15)),
        axis.title = element_text(face = "bold"))

vote_types <- data %>%
  select(StateNm, PostalVotes, PrePollVotes, AbsentVotes, OrdinaryVotes) %>%
  pivot_longer(cols = -StateNm, names_to = "VoteType", values_to = "Count")
# Create ggplot
p <- ggplot(vote_types, aes(x = reorder(StateNm, Count), y = Count, fill = VoteType)) +
  geom_col(position = "stack") + coord_flip() +labs(title = "Vote Types by State and Territory",subtitle = "Comparing Postal, PrePoll, Absent, and Ordinary Votes",x = "State", y = "Number of Votes", fill = "Vote Type" ) +scale_fill_brewer(palette = "Pastel1") + scale_y_continuous(labels = label_comma()) + theme_minimal(base_size = 16) + theme(plot.title = element_text(face = "bold"),plot.subtitle = element_text(size = 10, margin = margin(b = 13)),axis.title = element_text(face = "bold") )
# Make it interactive
ggplotly(p)

donut_data <- data %>%
  mutate(Share = TotalVotes / sum(TotalVotes) * 100)
plot_ly(donut_data, labels = ~StateNm, values = ~TotalVotes, type = 'pie',textinfo = 'label+percent', hole = 0.5) %>%
  layout(title = "Share of National Votes by State – 2025", showlegend = FALSE)