Case study: Richmond electorate (2001

Richmond is a federal electorate in northern New South Wales, covering the Northern Rivers region including Ballina, Lismore, Byron Bay, and Tweed Heads. Historically a Labor–National marginal, it has been contested by the Nationals, Labor, and (more recently) teal/independent candidates.

This vignette walks through a complete single-electorate analysis using readaec.

1. Available elections

Start by confirming which elections we have data for.

list_elections()
#>   year event_id       date               type has_downloads
#> 1 2001    10822 2001-11-10            general         FALSE
#> 2 2004    12246 2004-10-09            general         FALSE
#> 3 2007    13745 2007-11-24            general          TRUE
#> 4 2010    15508 2010-08-21            general          TRUE
#> 5 2013    17496 2013-09-07            general          TRUE
#> 6 2016    20499 2016-07-02 double_dissolution          TRUE
#> 7 2019    24310 2019-05-18            general          TRUE
#> 8 2022    27966 2022-05-21            general          TRUE
#> 9 2025    31496 2025-05-03            general          TRUE

2. Two-party preferred trend

Pull the TPP result for Richmond in every election since 2001. The get_tpp() function returns one row per division; we filter to Richmond and stack the years.

# AEC CSV downloads are available from 2007 onwards
years <- list_elections()$year[list_elections()$has_downloads]

tpp_richmond <- map_dfr(years, function(yr) {
  get_tpp(yr) |>
    filter(tolower(division) == "richmond") |>
    select(division, division_id, state, alp_pct, lnp_pct, total_votes, year)
})

tpp_richmond
#> # A tibble: 7 × 7
#>   division division_id state alp_pct lnp_pct total_votes  year
#>   <chr>          <dbl> <chr>   <dbl>   <dbl>       <dbl> <dbl>
#> 1 Richmond         145 NSW      58.9    41.1       81486  2007
#> 2 Richmond         145 NSW      57.0    43.0       80835  2010
#> 3 Richmond         145 NSW      53.0    47.0       85278  2013
#> 4 Richmond         145 NSW      54.0    46.0       98398  2016
#> 5 Richmond         145 NSW      54.1    45.9      100320  2019
#> 6 Richmond         145 NSW      58.2    41.8       99784  2022
#> 7 Richmond         145 NSW      60      40        104952  2025

ggplot(tpp_richmond, aes(x = year)) +
  geom_line(aes(y = alp_pct, colour = "ALP"), linewidth = 1.2) +
  geom_point(aes(y = alp_pct, colour = "ALP"), size = 3) +
  geom_line(aes(y = lnp_pct, colour = "LNP/Nat"), linewidth = 1.2) +
  geom_point(aes(y = lnp_pct, colour = "LNP/Nat"), size = 3) +
  geom_hline(yintercept = 50, linetype = "dashed", colour = "grey60") +
  annotate("text", x = min(years) + 0.3, y = 51,
           label = "50% — majority", size = 3, colour = "grey50", hjust = 0) +
  scale_colour_manual(values = c("ALP" = "#E4281B", "LNP/Nat" = "#1C4F9C")) +
  scale_x_continuous(breaks = years) +
  scale_y_continuous(limits = c(30, 70),
                     labels = function(x) paste0(x, "%")) +
  labs(
    title    = "Richmond (NSW): two-party preferred vote, 2001–2025",
    subtitle = "ALP vs LNP/National Coalition",
    x        = NULL,
    y        = "TPP vote share",
    colour   = NULL,
    caption  = "Source: Australian Electoral Commission via readaec"
  ) +
  theme_minimal(base_size = 13) +
  theme(legend.position = "bottom",
        panel.grid.minor = element_blank())

3. Who won each election?

get_members_elected() returns the elected member for every division. We filter to Richmond to build a candidate-by-year summary.

members_richmond <- map_dfr(years, function(yr) {
  tryCatch(
    get_members_elected(yr) |>
      filter(tolower(divisionnm) == "richmond") |>
      select(divisionnm, surname, givennm, partyab, stateab, year),
    error = function(e) NULL
  )
})

members_richmond |>
  select(year, given_name = givennm, surname, party = partyab) |>
  arrange(year)
#> # A tibble: 7 × 4
#>    year given_name surname party
#>   <dbl> <chr>      <chr>   <chr>
#> 1  2007 Justine    ELLIOT  ALP  
#> 2  2010 Justine    ELLIOT  ALP  
#> 3  2013 Justine    ELLIOT  ALP  
#> 4  2016 Justine    ELLIOT  ALP  
#> 5  2019 Justine    ELLIOT  ALP  
#> 6  2022 Justine    ELLIOT  ALP  
#> 7  2025 Justine    ELLIOT  ALP

4. First preference vote breakdown

First preferences show how votes were distributed across all candidates before preferences were distributed. This reveals the full competitive landscape beyond just the two-party contest.

fp_richmond <- map_dfr(years, function(yr) {
  get_fp(yr) |>
    filter(tolower(division) == "richmond") |>
    select(year, division, surname, given_name, party, party_name, total_votes)
})

# Total formal votes per year (for calculating shares)
fp_totals <- fp_richmond |>
  group_by(year) |>
  summarise(total_formal = sum(total_votes, na.rm = TRUE))

# Major party shares
fp_major <- fp_richmond |>
  filter(party %in% c("ALP", "NAT", "NP", "LIB", "GRN")) |>
  left_join(fp_totals, by = "year") |>
  mutate(
    pct = round(total_votes / total_formal * 100, 1),
    party_label = case_when(
      party %in% c("NAT", "NP") ~ "Nationals",
      party == "ALP"             ~ "ALP",
      party == "LIB"             ~ "Liberal",
      party == "GRN"             ~ "Greens",
      TRUE                       ~ party
    )
  )

ggplot(fp_major, aes(x = year, y = pct, colour = party_label)) +
  geom_line(linewidth = 1.1) +
  geom_point(size = 2.5) +
  scale_colour_manual(values = c(
    "ALP"       = "#E4281B",
    "Nationals" = "#006644",
    "Liberal"   = "#1C4F9C",
    "Greens"    = "#10C25B"
  )) +
  scale_x_continuous(breaks = years) +
  scale_y_continuous(labels = function(x) paste0(x, "%")) +
  labs(
    title   = "Richmond (NSW): first preference vote shares",
    x       = NULL,
    y       = "First preference share",
    colour  = NULL,
    caption = "Source: AEC via readaec"
  ) +
  theme_minimal(base_size = 13) +
  theme(legend.position = "bottom",
        panel.grid.minor = element_blank())

5. How did Richmond swing relative to NSW?

Compare Richmond’s election-to-election swing against all other NSW divisions to see whether it moved with or against the state trend.

# Build consecutive election pairs from years with downloads
election_pairs <- Map(c, head(years, -1), tail(years, -1))

nsw_swings <- map_dfr(election_pairs, function(pair) {
  get_swing(pair[1], pair[2], state = "NSW") |>
    filter(!redistribution_flag) |>
    mutate(period = paste0(pair[1], "–", pair[2]))
})

richmond_swing <- nsw_swings |>
  filter(tolower(division) == "richmond") |>
  select(period, division, alp_swing, year_from, year_to)

nsw_avg_swing <- nsw_swings |>
  group_by(period, year_from, year_to) |>
  summarise(avg_alp_swing = mean(alp_swing, na.rm = TRUE), .groups = "drop")

swing_comparison <- richmond_swing |>
  left_join(nsw_avg_swing, by = c("period", "year_from", "year_to")) |>
  mutate(relative_swing = alp_swing - avg_alp_swing)

swing_comparison |>
  select(period, richmond_swing = alp_swing, nsw_avg = avg_alp_swing,
         relative_swing)
#> # A tibble: 6 × 4
#>   period    richmond_swing nsw_avg relative_swing
#>   <chr>              <dbl>   <dbl>          <dbl>
#> 1 2007–2010          -1.88   -4.56          2.68 
#> 2 2010–2013          -4.01   -3.27         -0.739
#> 3 2013–2016           0.98    4.11         -3.13 
#> 4 2016–2019           0.12   -1.31          1.43 
#> 5 2019–2022           4.15    3.14          1.01 
#> 6 2022–2025           1.77    3.97         -2.20

swing_comparison |>
  select(period, richmond_swing = alp_swing, nsw_avg = avg_alp_swing) |>
  tidyr::pivot_longer(c(richmond_swing, nsw_avg),
                      names_to = "series", values_to = "swing") |>
  mutate(series = if_else(series == "richmond_swing", "Richmond", "NSW average")) |>
  ggplot(aes(x = period, y = swing, fill = series)) +
  geom_col(position = "dodge") +
  geom_hline(yintercept = 0, colour = "grey40") +
  scale_fill_manual(values = c("Richmond" = "#E4281B", "NSW average" = "grey70")) +
  scale_y_continuous(labels = function(x) paste0(ifelse(x > 0, "+", ""), x, "pp")) +
  labs(
    title   = "Richmond ALP swing vs NSW average",
    x       = NULL,
    y       = "ALP swing (percentage points)",
    fill    = NULL,
    caption = "Source: AEC via readaec"
  ) +
  theme_minimal(base_size = 13) +
  theme(legend.position = "bottom",
        axis.text.x = element_text(angle = 30, hjust = 1),
        panel.grid.minor = element_blank())

6. Enrolment and turnout over time

Is the electorate growing? Is turnout holding up?

turnout_richmond <- map_dfr(years, function(yr) {
  get_turnout(yr) |>
    filter(tolower(divisionnm) == "richmond") |>
    select(divisionnm, year, everything())
})

turnout_richmond
#> # A tibble: 7 × 8
#>   divisionnm  year divisionid stateab enrolment turnout turnoutpercentage
#>   <chr>      <dbl>      <dbl> <chr>       <dbl>   <dbl>             <dbl>
#> 1 Richmond    2007        145 NSW         90103   85133              94.5
#> 2 Richmond    2010        145 NSW         92391   85587              92.6
#> 3 Richmond    2013        145 NSW         97421   89681              92.1
#> 4 Richmond    2016        145 NSW        112715  102146              90.6
#> 5 Richmond    2019        145 NSW        119332  108381              90.8
#> 6 Richmond    2022        145 NSW        118638  107208              90.4
#> 7 Richmond    2025        145 NSW        126814  113552              89.5
#> # ℹ 1 more variable: turnoutswing <dbl>

enrolment_richmond <- map_dfr(years, function(yr) {
  get_enrolment(yr) |>
    filter(tolower(divisionnm) == "richmond") |>
    select(divisionnm, year, everything())
})

enrolment_richmond
#> # A tibble: 7 × 12
#>   divisionnm  year divisionid stateab closeofrollsenrolment
#>   <chr>      <dbl>      <dbl> <chr>                   <dbl>
#> 1 Richmond    2007        145 NSW                     90018
#> 2 Richmond    2010        145 NSW                     92384
#> 3 Richmond    2013        145 NSW                     97338
#> 4 Richmond    2016        145 NSW                    112820
#> 5 Richmond    2019        145 NSW                    119446
#> 6 Richmond    2022        145 NSW                    118652
#> 7 Richmond    2025        145 NSW                    126908
#> # ℹ 7 more variables: notebookrolladditions <dbl>, notebookrolldeletions <dbl>,
#> #   reinstatementspostal <dbl>, reinstatementsprepoll <dbl>,
#> #   reinstatementsabsent <dbl>, reinstatementsprovisional <dbl>,
#> #   enrolment <dbl>

Summary

This case study shows how a few lines of readaec code can reconstruct the full electoral history of any Australian federal division:

Function	What it gives you
`get_tpp(year)`	ALP vs LNP two-party preferred by division
`get_fp(year)`	First preferences by candidate
`get_members_elected(year)`	Who won each seat
`get_swing(from, to)`	Election-to-election TPP change
`get_enrolment(year)`	Enrolled voters by division
`get_turnout(year)`	Turnout metrics by division
`get_fp_by_booth(year, state)`	First preferences at booth level

For spatial analysis, combine get_fp_by_booth() with get_polling_places() to map results at the polling-place level.