The 2025 Australian Federal Election marked a decisive victory
for the Labor Party.
This story explores how and
where that triumph unfolded — visualising state-wise swings,
flipped divisions, and the overall national trends that
shaped the result.
Data is sourced directly from the Australian Electoral Commission (AEC) official open datasets for the 2025 Federal Election.
Key terms:
These results are visualised to explain the story behind the numbers.
The following datasets are loaded from the official Australian Electoral Commission (AEC) portal.
we can see one of the data set below:
## # A tibble: 6 × 11
## Party NSW VIC QLD WA SA TAS ACT NT National LastElection
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Austral… 28 27 12 11 7 4 3 2 94 77
## 2 Liberal 6 6 0 4 2 0 0 0 18 27
## 3 Liberal… 0 0 16 0 0 0 0 0 16 21
## 4 The Nat… 6 3 0 0 0 0 0 0 9 10
## 5 Country… 0 0 0 0 0 0 0 0 0 0
## 6 The Gre… 0 0 1 0 0 0 0 0 1 4
All five CSVs were downloaded from the AEC’s open data portal and cleaned for analysis. They include party-level results, state swings, and division-level preferences, forming the foundation of this visual storytelling project.
This chart shows total seats won by each party in the 2025 election. Labor dominates nationally, securing a strong majority. Liberals and Nationals trail behind, while Greens and Independents maintain smaller but meaningful representation.
This chart highlights all divisions that changed party control in 2025. Most flips favoured Labor, especially across Victoria and New South Wales. Urban areas drove much of the swing, while regional seats mostly remained stable.
Labor’s two-party-preferred (TPP) vote share was highest in Victoria and New South Wales, confirming strong urban support. Queensland remained more competitive, and Western Australia showed moderate improvement for Labor.
Labor received the highest share of first-preference votes, ahead of the Liberals. Minor parties such as the Greens and Independents held steady, showing consistent voter bases. Preference flows later amplified Labor’s margin in the final TPP count.
These were the most closely contested divisions in the 2025 election. Many tight races were between Labor and Liberal candidates. Winning these marginal seats played a key role in Labor’s overall victory.
Metro momentum: Labor’s success came from strong swings in metropolitan seats and steady results in regional areas.
Rural resilience: The Coalition held many rural divisions but could not offset losses in the cities.
Long-run signal: The 2025 outcome extends a clear trend toward city-based progressive voting patterns, with margins tightening in a handful of competitive outer-metro seats.
| Seat gains from flips — 2025 Australian Federal Election | |
| Number of divisions that changed hands to each party | |
| PartyNm | Flipped_Seats_Gained |
|---|---|
| Australian Labor Party | 17 |
| Independent | 2 |
| Liberal | 1 |
Interpretation: This table shows the number of divisions that changed hands and the party that gained them. It complements the national picture and marginal-seat analysis by highlighting where momentum shifted in 2025.
Data Sources
Australian Electoral Commission. (2025). Federal Election 2025
results datasets.
https://results.aec.gov.au/
Packages Used
Wickham, H., François, R., Henry, L., & Müller, K. (2023). dplyr:
A grammar of data manipulation (Version 1.1.4) [R package].
https://CRAN.R-project.org/package=dplyr
Wickham, H. (2016). ggplot2: Elegant graphics for data analysis.
Springer-Verlag New York.
https://ggplot2.tidyverse.org
Xie, Y. (2023). knitr: A general-purpose package for dynamic report
generation in R (Version 1.46) [R package].
https://CRAN.R-project.org/package=knitr
Allaire, J. J., & Xie, Y. (2024). rmarkdown: Dynamic documents
for R (Version 2.27) [R package].
https://CRAN.R-project.org/package=rmarkdown
Chang, W. (2024). shiny: Web application framework for R (Version
1.9.1) [R package].
https://CRAN.R-project.org/package=shiny
Acknowledgement
Data visualisation and storytelling techniques applied in this work are based on best practices from the MATH2237/MATH2270 – Data Visualisation and Communication course at RMIT University.