---
title: "S4123208 Assignment 3"
output:
  flexdashboard::flex_dashboard:
    storyboard: true
    theme: united
    source_code: embed
---


```{r setup, include=FALSE}
library(tidyverse)
library(flexdashboard)
library(readr)
library(stringr)
library(tidyverse)
library(janitor)
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
```

### Introduction
<div style="text-align:center; padding:40px;">

<h2 style="font-weight:800; color:#7B1FA2;">Beyond the Landslide</h2>
<h3 style="margin-top:-10px;">The Real Story of the 2025 Australian Federal Election</h3>

<p style="font-size:17px; max-width:800px; margin:auto;">
This data-driven dashboard explores the 2025 Federal Election using 
<b>open Australian Electoral Commission (AEC)</b> datasets.  
The media labelled the result a “Labor landslide”, but a closer look at the data 
reveals an uneven performance across states and many seats won by 
narrow margins.
</p>

<br>

<div style="text-align:left; display:inline-block; font-size:16px; color:#333;">
<b>Key Questions</b>
<ul>
<li>How did first-preference votes vary by party across Victoria?</li>
<li>Which divisions were truly marginal, and what does that mean for 2028?</li>
</ul>
</div>

<br><br>

<br><b>Assignment 3 – Data Visualisation</b>  
<br><b>Student:</b> Sai Nihanth Edara (S4123208)
</p>

</div>
###  About the Data
<div style="text-align:center; padding:30px;">

<h2 style="font-weight:700; color:#7B1FA2;">About the Dataset</h2>
<h3>Two Complementary Election Datasets from the AEC</h3>

<p style="font-size:17px; max-width:850px; margin:auto;">
The visualisations in this storyboard are powered by <b>Australian Electoral Commission (AEC)</b></a>.
Two official CSV files are used to explore voting behaviour and seat competitiveness in the
<b>2025 Federal Election</b>.
</p>

<br>

<div style="text-align:left; display:inline-block; font-size:16px; margin-top:10px;">
<ul>
<li><b>HouseTcpByCandidateByPollingPlaceDownload-31496.csv</b><br><br>
Provides <b>Two-Candidate-Preferred (TCP)</b> results for every polling place and division.<br>
It contains details such as state, division name, candidate, party, total votes,
and final TCP percentages used to identify <b>marginal seats</b>.</li><br>

<li><b>HouseStateFirstPrefsByPollingPlaceDownload-31496-VIC.csv</b><br><br>
Contains <b>First-Preference votes</b> for each candidate and party in Victoria.<br>
This reveals initial voter loyalties before preferences were distributed, allowing analysis of
<b>party vote share</b> across the state.</li>
</ul>
</div>

```{r}
tcp <- readr::read_csv("HouseTcpByCandidateByPollingPlaceDownload-31496.csv",
                       skip = 1) |>
  clean_names()
prefs_vic <- readr::read_csv("HouseStateFirstPrefsByPollingPlaceDownload-31496-VIC.csv",
                             skip = 1) |>
  clean_names()
```

### Data Cleaning & Processing
<div style="text-align:center; padding:30px;">

<h2 style="font-weight:700; color:#7B1FA2;">Data Cleaning & Processing</h2>
<h3>Preparing the AEC data for visualisation</h3>

<p style="font-size:17px; max-width:850px; margin:auto;">
Both AEC datasets were cleaned and transformed using <b>R</b> packages 
<code>tidyverse</code> and <code>janitor</code> to make the results suitable for visual analysis.
The goal was to create summary tables showing <b>party vote shares</b> and 
<b>margins of victory</b> at the division level.
</p>

<br>

<div style="text-align:left; display:inline-block; font-size:16px;">
<ul>
<li><b>Remove metadata rows</b> – Skipped the first line in both CSVs (<code>skip = 1</code>).</li>
<li><b>Standardise column names</b> – Used <code>clean_names()</code> for consistent formatting.</li>
<li><b>Summarise by division & party</b> – Aggregated vote totals using <code>group_by()</code> and <code>summarise()</code>.</li>
<li><b>Compute vote shares (%)</b> – Added proportional calculations with <code>mutate()</code>.</li>
<li><b>Identify marginal seats</b> – Calculated the difference between the top two TCP candidates per division.</li>
</ul>
</div>

<br>

<p style="font-size:16px;">Example transformation (simplified):</p>

```{r}
library(dplyr)

# Summarise first preferences in Victoria
vic_summary_party <- prefs_vic %>%
  group_by(party_nm) %>%
  summarise(total_votes = sum(ordinary_votes, na.rm = TRUE)) %>%
  mutate(share = 100 * total_votes / sum(total_votes))
head(vic_summary_party)

```

### Victoria – First Preferences

<div style="text-align:center; padding:30px;">

<h2 style="font-weight:800; color:#7B1FA2; margin-bottom:5px;">Victoria – First Preference Votes by Party</h2>
<h3>Who did Victorians choose first?</h3>

<p style="font-size:17px; max-width:800px; margin:auto;">
This visualisation shows the share of <b>first-preference votes</b> across political parties in Victoria during the 
<b>2025 Federal Election</b>.  
It captures voter loyalty before preference redistribution in Australia’s preferential voting system.
</p>

```{r, fig.width=9, fig.height=5}
vic_summary_party <- prefs_vic %>%
  group_by(party_nm) %>%
  summarise(total_votes = sum(ordinary_votes, na.rm = TRUE), .groups = "drop") %>%
  mutate(share = 100 * total_votes / sum(total_votes)) %>%
  arrange(desc(share))

ggplot(vic_summary_party, aes(x = reorder(party_nm, share), y = share)) +
  geom_col(fill = "#E53935") +
  coord_flip() +
  geom_text(aes(label = sprintf("%.1f%%", share)), hjust = -0.2, size = 4) +
  labs(
    title = "First Preference by Party in Victoria, 2025",
    subtitle = "Labor leads Victoria’s first preferences",
    x = NULL, y = "Vote Share (%)"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold", color = "#333"),
    plot.subtitle = element_text(size = 11, color = "#555"),
    axis.text.y = element_text(size = 11),
    axis.text.x = element_text(size = 10)
  ) +
  ylim(0, max(vic_summary_party$share) + 5)
```

### Victoria – Marginal Divisions (TCP)

<div style="text-align:center; padding:30px;">

<h2 style="font-weight:800; color:#7B1FA2; margin-bottom:5px;">Victoria – Marginal Divisions (TCP)</h2>
<h3 margin-top:0;">Where the 2025 election was closest</h3>

<p style="font-size:17px; max-width:800px; margin:auto;">
This chart shows the <b>most competitive Victorian seats</b> based on the
<b>Two-Candidate-Preferred (TCP)</b> results in the AEC data.  
Seats with the smallest margins are the ones most likely to change hands in the next election.
</p>


```{r, fig.width=9, fig.height=5}
# 1.aggregate TCP votes per division+party, but only for VIC
tcp_vic <- tcp %>%
  filter(state_ab == "VIC") %>% 
  group_by(division_nm, party_nm) %>%
  summarise(total_votes = sum(ordinary_votes, na.rm = TRUE), .groups = "drop")

# 2. for each division, sort parties by votes and keep top 2
tcp_vic_top2 <- tcp_vic %>%
  group_by(division_nm) %>%
  arrange(desc(total_votes), .by_group = TRUE) %>%
  slice_head(n = 2) %>%       
  ungroup()

# 3. turn those 2 rows into wide form so we can compute margin safely
tcp_vic_wide <- tcp_vic_top2 %>%
  group_by(division_nm) %>%
  mutate(div_total = sum(total_votes, na.rm = TRUE)) %>%
  mutate(share = 100 * total_votes / div_total) %>%
  arrange(desc(share), .by_group = TRUE) %>%
  # now we have exactly 2 rows per division (if there were 2 candidates)
  summarise(
    top_share = first(share),
    second_share = nth(share, 2),
    .groups = "drop"
  ) %>%
  # keep only divisions that actually had 2 candidates
  filter(!is.na(second_share)) %>%
  mutate(margin = top_share - second_share)

# 4. pick the 10 smallest positive margins
marginals_vic <- tcp_vic_wide %>%
  arrange(margin) %>%
  slice_head(n = 10)

ggplot(marginals_vic, aes(x = reorder(division_nm, margin), y = margin)) +
  geom_col(fill = "#1E88E5") +
  coord_flip() +
  geom_text(aes(label = sprintf("%.1f%%", margin)), hjust = -0.1, size = 4) +
  labs(
    title = "Most Marginal Divisions in Victoria (2025)",
    subtitle = "Ten Victorian divisions with the smallest TCP winning margins.",
    x = NULL,
    y = "TCP margin (percentage points)"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold", color = "#333"),
    plot.subtitle = element_text(size = 11, color = "#555"),
    axis.text.y = element_text(size = 11)
  ) +
  ylim(0, max(marginals_vic$margin) + 2)
```

### Interpretation & Insights
<div style="text-align:center; padding:30px;">

<h2 style="font-weight:800; color:#7B1FA2;">Interpretation & Insights</h2>
<h3 margin-top:0;">Understanding the story behind the numbers</h3>


<p style="font-size:17px; max-width:850px; margin:auto;">
The 2025 Australian Federal Election is often described as a <i>“Labor landslide.”</i>  
Yet, a closer examination of the <b>Victorian results</b> reveals a more complex political landscape.
</p>

<br>

<div style="text-align:left; display:inline-block; font-size:16px; max-width:900px;">
<ul>
<li><b> Labor’s strong base:</b> Labor dominated Victoria’s first preferences, particularly in metropolitan areas such as Melbourne and Ballarat.</li>

<li><b> Growing diversity in votes:</b> Greens and Independents continued to strengthen their share, reflecting voter interest in local and environmental issues.</li>

<li><b> Marginal seats remain decisive:</b> Despite Labor’s overall success, many divisions were won by <b>less than 2 %</b>.  
This underlines that even a “landslide” can rest on razor-thin margins in key battlegrounds.</li>

<li><b> Outlook for 2028:</b> These marginal divisions, especially those in outer metropolitan and regional areas will be critical in shaping future campaigns and party strategies.</li>
</ul>
</div>

<br>

<p style="font-size:16px; max-width:850px; margin:auto;">
<b>Take-home message:</b>  
While aggregate results tell a story of dominance, local-level data shows that electoral competitiveness and voter diversity remain strong across Victoria. This demonstrates how <b>data storytelling</b> can move beyond headlines to uncover deeper truths in political behaviour.
</p>

</div>


### References
<div style="text-align:center; padding:30px;">

<h2 style="font-weight:800; color:#7B1FA2;">References</h2>

<div style="text-align:left; max-width:850px; font-size:16px; line-height:1.6;">

<p><b>Data Sources:</b></p>

<ul>
<li>Australian Electoral Commission (AEC). (2025). <em>House of Representatives – Two Candidate Preferred by Polling Place (2025 Federal Election)</em>.  
Retrieved from <a href="https://results.aec.gov.au/31496/Website/HouseDownloadsMenu-31496-Csv.htm">results.aec.gov.au</a></li>

<li>Australian Electoral Commission (AEC). (2025). <em>House of Representatives – State First Preferences by Polling Place (Victoria)</em>.  
Retrieved from <a href="https://results.aec.gov.au/31496/Website/HouseDownloadsMenu-31496-Csv.htm">results.aec.gov.au</a></li>
</ul>