`prefio`

Working with preferential data in R

Floyd Everest, Heather Turner and Damjan Vukcevic

What is preferential data?

A sample ballot for ranking Australian house of representatives candidates. — A sample HoR ballot, courtesy of the AEC

One entry = a ranking among a common set of items.
e.g., ballots for the house of reps!
“computational social choice, recommender systems, data mining, machine learning, combinatorial optimization, to name just a few” — PrefLib
Other kinds of preferences can be incomplete or even include ties

What does tabular preferential data look like?

Wangaratta: (Charles, Beatriz, Allie), Geelong: (Allie, Beatriz)

"Lucky" long format.
ID	VoterLocation	Candidate	Rank
2	Wangaratta	Allie	3
2	Wangaratta	Beatriz	2
2	Wangaratta	Charles	1
3	Geelong	Allie	2
3	Geelong	Beatriz	1

"Unlucky" wide format.
ID	VoterLocation	Allie	Beatriz	Charles
2	Wangaratta	3	2	1
3	Geelong	2	1	NA

What does tabular preferential data look like?

Wide format data from the most recent Federal election (2025 Senators for ACT)
Long format data from the most recent NSW legislative assembly election (2023 member for Albury)

Let’s collect some votes!

Rank these café orders from most to least preferred

Latte
Iced latte
Flat white
Black coffee
Cold brew
Matcha
Tea (coffee has too much caffeine)

vote.floydeverest.com

The history of `prefio`

I wanted to make NSW elections accessible
Integrating with the PrefLib database
“stole” Heather’s code from PlackettLuce for loading PrefLib data and handling preferences
Adapted it to mesh nicely with the tidyverse ecosystem

vote.floydeverest.com

A “quick” demo

Let’s take a quick look at the 2023 New South Wales legislative assembly election.

list.files("nswla_data/")

 [1] "Albury.zip"              "Auburn.zip"             
 [3] "Badgerys Creek.zip"      "Ballina.zip"            
 [5] "Balmain.zip"             "Bankstown.zip"          
 [7] "Barwon.zip"              "Bathurst.zip"           
 [9] "Bega.zip"                "Blacktown.zip"          
[11] "Blue Mountains.zip"      "Cabramatta.zip"         
[13] "Camden.zip"              "Campbelltown.zip"       
[15] "candidates.xlsx"         "Canterbury.zip"         
[17] "Castle Hill.zip"         "Cessnock.zip"           
[19] "Charlestown.zip"         "Clarence.zip"           
[21] "Coffs Harbour.zip"       "Coogee.zip"             
[23] "Cootamundra.zip"         "Cronulla.zip"           
[25] "Davidson.zip"            "Drummoyne.zip"          
[27] "Dubbo.zip"               "East Hills.zip"         
[29] "Epping.zip"              "Fairfield.zip"          
[31] "Gosford.zip"             "Goulburn.zip"           
[33] "Granville.zip"           "Hawkesbury.zip"         
[35] "Heathcote.zip"           "Heffron.zip"            
[37] "Holsworthy.zip"          "Hornsby.zip"            
[39] "Keira.zip"               "Kellyville.zip"         
[41] "Kiama.zip"               "Kogarah.zip"            
[43] "Lake Macquarie.zip"      "Lane Cove.zip"          
[45] "Leppington.zip"          "Lismore.zip"            
[47] "Liverpool.zip"           "Londonderry.zip"        
[49] "Macquarie Fields.zip"    "Maitland.zip"           
[51] "Manly.zip"               "Maroubra.zip"           
[53] "Miranda.zip"             "Monaro.zip"             
[55] "Mount Druitt.zip"        "Murray.zip"             
[57] "Myall Lakes.zip"         "Newcastle.zip"          
[59] "Newtown.zip"             "North Shore.zip"        
[61] "Northern Tablelands.zip" "Oatley.zip"             
[63] "Orange.zip"              "Oxley.zip"              
[65] "Parramatta.zip"          "Penrith.zip"            
[67] "Pittwater.zip"           "Port Macquarie.zip"     
[69] "Port Stephens.zip"       "Prospect.zip"           
[71] "Riverstone.zip"          "Rockdale.zip"           
[73] "Ryde.zip"                "Shellharbour.zip"       
[75] "South Coast.zip"         "Strathfield.zip"        
[77] "Summer Hill.zip"         "Swansea.zip"            
[79] "Sydney.zip"              "Tamworth.zip"           
[81] "Terrigal.zip"            "The Entrance.zip"       
[83] "Tweed.zip"               "Upper Hunter.zip"       
[85] "Vaucluse.zip"            "Wagga Wagga.zip"        
[87] "Wahroonga.zip"           "Wakehurst.zip"          
[89] "Wallsend.zip"            "Willoughby.zip"         
[91] "Winston Hills.zip"       "Wollondilly.zip"        
[93] "Wollongong.zip"          "Wyong.zip"

A “quick” demo

# Load the entire election with readr + purrr
election <- list.files("nswla_data/", pattern = "\\.zip$", full.names = TRUE) |>
  map(function(x) {
    read_delim(x,
               delim = "\t",
               show_col_types = FALSE,
               col_select = c(District, BPNumber, CandidateName, PrefCounted))
  }) |>
  list_rbind() |>
  print(n = 4)

# A tibble: 12,203,709 × 4
  District BPNumber CandidateName PrefCounted
  <chr>    <chr>    <chr>               <dbl>
1 Albury   9996     CLANCY Justin           1
2 Albury   9997     CLANCY Justin           1
3 Albury   9998     CLANCY Justin           1
4 Albury   9999     CLANCY Justin           1
# ℹ 12,203,705 more rows

A “quick” demo

# Format the preferences with prefio
election <- election |>
  long_preferences(vote,
                   id_cols = c(District, BPNumber),
                   item_col = CandidateName,
                   rank_col = PrefCounted) |>
  print(n = 5)

# A tibble: 4,701,930 × 3
  District BPNumber            vote
  <chr>    <chr>         <prefrncs>
1 Albury   1        [CLANCY Justin]
2 Albury   10       [CLANCY Justin]
3 Albury   100      [CLANCY Justin]
4 Albury   1000     [CLANCY Justin]
5 Albury   10000    [CLANCY Justin]
# ℹ 4,701,925 more rows

A “quick” demo

election |>
  group_by(vote) |>
  summarise(frequency = n()) |>
  sample_n(5) |>
  knitr::kable(
    format = "html",
    table.attr = 'style="font-size: 0.6em; width: 80%;"'
  )

vote	frequency
[DIXON Jamie > DAVIS Kenneth > WILLMOTT Mia > HOMER Chris > WATSON Anna > BARNES Mikayla > GRANATA Rita]	5
[GREGORY Desiree > BETTS Peta > BULJUBASIC Mirsad (Max) > CARLE Adrian > FARRELL Kevin > KING Amelia > ADAMSON Greg > LANDINI David > DALTON Helen]	1
[DUROUX Brett > NOVAK Debrah > ANKERSMIT Leon > WILLIAMSON Richie > LEVI Nicki > KELLER George]	1
[WHAN Steven > THALER Andrew > HOLGATE James > GOLDIE Jenny > TANSON Josie > PRYOR Chris]	1
[BRUCE Sophie-Anne > CONDIE Jenna > PALMER Michelle > MARSCHALL Richard > KEIGHTLEY Greg > DOYLE Trish]	12

A “quick” demo

Compare our computed results with the official results (much faster than I would have previously thought possible).

# Format the preferences with prefio
election |>
  summarise(
    Winner = pref_irv(vote)$winner,
    .by = District
  )

# A tibble: 93 × 2
   District       Winner         
   <chr>          <chr>          
 1 Albury         CLANCY Justin  
 2 Auburn         VOLTZ Lynda    
 3 Badgerys Creek DAVIES Tanya   
 4 Ballina        SMITH Tamara   
 5 Balmain        SHETTY Kobi    
 6 Bankstown      DIB Jihad      
 7 Barwon         BUTLER Roy     
 8 Bathurst       TOOLE Paul     
 9 Bega           HOLLAND Michael
10 Blacktown      BALI Stephen   
# ℹ 83 more rows

Where to next?

I’d like to integrate this back into PlackettLuce, where it was born.
It would be great to directly produce visualisations for preferences in the future.
There are likely many features needed to make this useful to people other than me.
If you work with preferential data, give it a go and let me know of any features which may be useful to add!

The results

Democratically confirmed best coffee order

votes <- read_csv("voter_app/data/responses.csv") |>
  long_preferences(vote,
                   id_cols = c(device_hash, timestamp),
                   rank_col = rank,
                   item_col = item) |>
  pull(vote)

pref_irv(votes)$winner

[1] "Latte"

Distribution of preferences

Candidate	Round 1	Round 2	Round 3	Round 4	Round 5
Black coffee	10	10	10
Flat white	16	16	19	24	29
Iced latte	7	8
Latte	12	12	15	18	39
Cold brew	2
Tea (coffee has too much caffeine)	11	11	12	13
Matcha	10	11	12	13

prefio

Working with preferential data in R

What is preferential data?

What does tabular preferential data look like?

What does tabular preferential data look like?

Let’s collect some votes!

The history of prefio

A “quick” demo

A “quick” demo

A “quick” demo

A “quick” demo

A “quick” demo

Where to next?

The results

Democratically confirmed best coffee order

Distribution of preferences

🎉 Thanks for listening!

`prefio`

The history of `prefio`