dop_transform: Transform AEC distribution of preferences from long to wide...

In prefviz: Visualizes Preference Data via Ternary Plots in Two and Higher Dimensions

Transform AEC distribution of preferences from long to wide format

Description

Transform AEC distribution of preferences from long to wide format, with optional scaling and normalization. This function is useful for converting all distribution of preference data with similar format into format ready for ternary plots.

Usage

dop_transform(
  data,
  key_cols,
  value_col,
  item_col,
  normalize = TRUE,
  scale = 1,
  fill_value = 0,
  winner_col = NULL,
  winner_identifier = "Y"
)

Arguments

data	A data frame containing preference or vote distribution data, with format similar to AEC Distribution of Preferences 2022
key_cols	Columns that identify unique observations, e.g., DivisionNm, CountNumber
value_col	Numeric and non-negative. Column containing the numeric values to aggregate, e.g., CalculationValue, Votes.
item_col	Column name containing the items (candidates/parties) of the election, e.g., Party, Candidate. This column will become column names in the output wide format.
normalize	Logical. If TRUE (default), normalizes values within each group to sum to 1. If FALSE, returns raw aggregated values.
scale	Numeric. If normalize = FALSE, divides all values by this scale factor. Default is 1 (no scaling).
fill_value	Numeric. Value to use for missing combinations after pivoting. Default is 0.
winner_col	Optional character string specifying a column that indicates the winner/elected party. If provided, this column will be joined back to the output based on key columns. Useful for preserving election outcome information. Default is NULL.
winner_identifier	Optional character string specifying the value in winner_col that identifies winning candidates (e.g., "Y", "Elected"). Only used if winner_col is specified. Default is "Y".

Value

A data frame in wide format with:

• Key columns identifying each observation

• Columns for each item (candidate/party) containing aggregated/normalized values

• Winner column (if winner_col was specified)

Examples

library(dplyr)
# Convert AEC 2025 Distribution of Preference data to wide format
data(aecdop_2025)

# We are interested in the preferences of Labor, Coalition, Greens and Independent. 
# The rest of the parties are aggregated as Other.
aecdop_2025 <- aecdop_2025 |>
 filter(CalculationType == "Preference Percent") |> 
 mutate(Party = case_when(
    !(PartyAb %in% c("LP", "ALP", "NP", "LNP", "LNQ")) ~ "Other",
    PartyAb %in% c("LP", "NP", "LNP", "LNQ") ~ "LNP",
   TRUE ~ PartyAb))

dop_transform(
  data = aecdop_2025,
  key_cols = c(DivisionNm, CountNumber),
  value_col = CalculationValue,
  item_col = Party,
  winner_col = Elected
)

prefviz documentation built on April 13, 2026, 5:07 p.m.