Get full distribution of preferences in each instant runoff voting round as percentage

Compute the preference in each round of instant runoff voting from input data, transforming the results into a tidy format for visualization. Each row represents one round, with columns for each candidate's preference percentage and the election winner.

Usage

dop_irv(x, value_type = c("percentage", "count"), ...)

Arguments

x

Input data. Accepts the same formats as prefio::pref_irv():

• A preference vector where each element represents one ballot

• A data frame with a column for preference

value_type

Character string specifying the output format. Either:

• "percentage" (default): Returns vote shares as proportions (0-1)

• "count": Returns raw vote counts

...

Additional arguments passed to prefio::pref_irv(), including:

• preferences_col: Column name containing preference orderings

• frequency_col: Column name containing vote frequencies

Value

A tibble with the following structure:

• round: Integer, the round number (1 to n)

• One column per candidate: Numeric, the percentage of votes (0-1) that candidate received in that round. NA values are replaced with 0 for eliminated candidates.

• winner: Character, the name of the eventual IRV winner (same for all rows)

Examples

# Example 1: From preference vector
votes <- prefio::preferences(c("A > B > C", "B > A > C", "C > B > A", "A > B > C"))
dop_irv(votes, value_type = "count")
#> # A tibble: 1 × 5
#>   round     A     B     C winner
#>   <int> <int> <int> <int> <chr> 
#> 1     1     2     1     1 A     

# Example 2: From data frame with custom column names
vote_data <- tibble::tibble(
  prefs = prefio::preferences(c("A > B > C", "B > C > A", "C > A > B")),
  counts = c(100, 75, 25)
)
dop_irv(vote_data, value_type = "percentage",
        preferences_col = prefs,
        frequency_col = counts)
#> # A tibble: 2 × 5
#>   round     A     B     C winner
#>   <int> <dbl> <dbl> <dbl> <chr> 
#> 1     1 0.5   0.375 0.125 A     
#> 2     2 0.625 0.375 0     A