Title: Visualizes Preference Data via Ternary Plots in Two and Higher Dimensions
Version: 0.1.1
Description: Visualises preference and ranking data by extending traditional ternary plots to support high-dimensional simplexes. The package provides methods to transform compositional data into coordinates suitable for 2D and high-dimensional ternary plots (see Cook & Laa (2024) https://dicook.github.io/mulgar_book/). Compatibility with interactive visualization packages such as 'plotly' or 'detourr' allows users to explore high-dimensional preference structures dynamically.
License: GPL (≥ 3)
Encoding: UTF-8
RoxygenNote: 7.3.3
Imports: dplyr, prefio, tibble, tidyr, rlang, ggplot2, tidyselect, geozoo
Suggests: knitr, rmarkdown, tourr, kableExtra, ggthemes, testthat (≥ 3.0.0)
Config/testthat/edition: 3
Depends: R (≥ 4.1.0)
LazyData: true
VignetteBuilder: knitr
URL: https://numbats.github.io/prefviz/
BugReports: https://github.com/numbats/prefviz/issues
NeedsCompilation: no
Packaged: 2026-04-07 12:57:15 UTC; ADMIN
Author: Linh Ngo [aut, cre], Dianne Cook ORCID iD [aut], Damjan Vukcevic ORCID iD [aut]
Maintainer: Linh Ngo <linhngo66.work@gmail.com>
Repository: CRAN
Date/Publication: 2026-04-13 11:50:02 UTC

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

Description

Visualises preference and ranking data by extending traditional ternary plots to support high-dimensional simplexes. The package provides methods to transform compositional data into coordinates suitable for 2D and high-dimensional ternary plots (see Cook & Laa (2024) https://dicook.github.io/mulgar_book/). Compatibility with interactive visualization packages such as 'plotly' or 'detourr' allows users to explore high-dimensional preference structures dynamically.

Author(s)

Maintainer: Linh Ngo Linh.Ngo2@monash.edu

Authors:

See Also

Useful links:

Add data edges

Description

Internal helper to create paths/edges between observations in a ternary plot. Used by new_ternable() to create the data_edges component.

Usage

add_data_edges(data, group_col_chr)

Arguments

data

A data frame input from new_ternable()

group_col_chr

Character vector of group column names

Draw the 2D ternary simplex

Description

Draws the boundary of a 2D ternary simplex as an equilateral triangle

Usage

add_ternary_base(...)

Arguments

...

Arguments passed to ggplot2::geom_polygon(), such as colour, fill, linewidth, etc.

Value

A ggplot object

Examples

library(ggplot2)

# Basic simplex
ggplot() + add_ternary_base()

# Customize appearance
ggplot() + add_ternary_base(colour = "blue", linewidth = 1.5)

Add vertex labels to ternary plot

Description

Adds text labels at the vertices of a ternary simplex with automatic positioning adjustments.

Usage

add_vertex_labels(
  vertex_labels_df,
  nudge_x = c(-0.02, 0.02, 0),
  nudge_y = c(-0.05, -0.05, 0.05),
  ...
)

Arguments

vertex_labels_df

A data frame containing vertex coordinates and labels. Should have columns x1, x2, and labels. Can be specified manually or obtained from a ternable object: ternable_object$simplex_vertices.

nudge_x

Numeric vector of length 3 specifying horizontal nudges for each vertex label.

nudge_y

Numeric vector of length 3 specifying vertical nudges for each vertex label.

...

Arguments passed to ggplot2::geom_text(), such as size, colour, fontface, etc.

Value

A ggplot object

Examples

library(ggplot2)

# Create a ternable object
tern <- as_ternable(prefviz::aecdop22_transformed, ALP:Other)

ggplot() +
  add_ternary_base() +
  add_vertex_labels(tern$simplex_vertices, size = 5, fontface = "bold")

Distribution of preferences by candidate by division in the Australian Federal Election (2022 and 2025)

Description

Provides details on how votes are distributed and transferred among candidates in all count stages of the preferential voting system. All electoral divisions in the Australian Federal Election are included.

Usage

aecdop_2022

aecdop_2025

Format

A tibble of 14 columns:

StateAb

State or territory abbreviation (e.g., "ACT", "NSW", "VIC")

DivisionID

Numeric identifier for the electoral division

DivisionNm

Name of the electoral division (e.g., "Bean", "Canberra")

CountNumber

Round in the counting procedure, starting from 0 (first preference)

BallotPosition

Position of the candidate on the ballot paper

CandidateID

Unique numeric identifier for the candidate

Surname

Candidate's surname

GivenNm

Candidate's given name(s)

PartyAb

Party abbreviation (e.g., "UAPP", "ALP", "LP")

PartyNm

Full party name (e.g., "United Australia Party", "Australian Labor Party")

Elected

Whether the candidate was elected: "Y" (yes) or "N" (no)

HistoricElected

Whether the candidate was elected in the previous election: "Y" (yes) or "N" (no)

CalculationType

Type of calculation:

Preference Count

Number of votes received

Preference Percent

Percentage of votes received

Transfer Count

Number of votes transferred from other candidates

Transfer Percent

Percentage of votes transferred from other candidates

CalculationValue

Numeric value for the calculation type (votes or percentage)

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 35096 rows and 14 columns.

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 30888 rows and 14 columns.

Details

Two datasets are provided:

Source

Australian Electoral Commission (AEC) Distribution of Preferences 2022 Distribution of Preferences 2025

Examples

# Load the datasets
data(aecdop_2022)
data(aecdop_2025)

# First preferences for Bean division in 2022
aecdop_2022 |> 
  dplyr::filter(DivisionNm == "Bean",
         CountNumber == 0,
         CalculationType == "Preference Count")

Distribution of preferences in wide form for selected parties (2022 and 2025)

Description

Wide-form versions of the Australian Federal Election distribution-of- preferences data, aggregated to selected parties within each electoral division and couting round. Each row gives the vote share for a set of parties and an "Other" category at a given count stage in a given division. These datasets are derived from aecdop_2022 and aecdop_2025 for ease of analysis and visualisation.

Usage

aecdop22_transformed

aecdop25_transformed

Format

For aecdop22_transformed, a tibble with 1,052 rows and 6 columns:

DivisionNm

Name of the electoral division (e.g., "Adelaide").

CountNumber

Round in the counting procedure, starting from 0 (first preference).

ElectedParty

Party abbreviation of the candidate ultimately elected in the division (e.g., "ALP", "LNP").

ALP

Proportion of votes for the Australian Labor Party at this count, between 0 and 1.

LNP

Proportion of votes for the Coalition grouping at this count, between 0 and 1.

Other

Proportion of votes for all other parties and candidates combined at this count, between 0 and 1.

For aecdop25_transformed, a tibble with 976 rows and 8 columns:

DivisionNm

Name of the electoral division (e.g., "Adelaide").

CountNumber

Round in the counting procedure, starting from 0 (first preference).

ElectedParty

Party abbreviation of the candidate ultimately elected in the division (e.g., "ALP", "GRN", "LNP", "IND").

ALP

Proportion of votes for the Australian Labor Party at this count, between 0 and 1.

GRN

Proportion of votes for the Australian Greens at this count, between 0 and 1.

LNP

Proportion of votes for the Coalition grouping at this count, between 0 and 1.

Other

Proportion of votes for all other parties and candidates combined at this count, between 0 and 1.

IND

Proportion of votes for independent candidates at this count, between 0 and 1.

An object of class tbl_df (inherits from tbl, data.frame) with 1052 rows and 6 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 976 rows and 8 columns.

Details

Two datasets are provided:

Within each row, the party columns represent proportions that sum to 1 (up to rounding), giving a compositional view of the distribution of preferences at each count stage. These structures are designed for use in simplex-based visualisations and related methods.

See Also

aecdop_2022, aecdop_2025

Examples

data(aecdop22_transformed)
data(aecdop25_transformed)

# Proportions for Adelaide over the count in 2022
aecdop22_transformed |>
  dplyr::filter(DivisionNm == "Adelaide")

Create a ternable object

Description

Creates a ternable object, which contains observation coordinates, simplex vertices, and edges necessary for building a ternary plot in both two and higher dimensions.

Usage

as_ternable(
  data,
  items = dplyr::everything(),
  group = NULL,
  order_by = NULL,
  decreasing = FALSE,
  na_method = c("drop_na", "drop_group"),
  ...
)

Arguments

data

A data frame containing the item (alternative) columns used to construct the ternary plot.

items

<tidy-select> Columns representing the items to be plotted as vertices of the simplex. Default is tidyselect::everything(), which selects all columns. Must select at least 3 columns. All columns must be non-negative and sum to 1.

group

Optional column name indicating the grouping variable. If specified, the data will be grouped by this variable. This is useful for creating paths between observations within each group.

order_by

Optional column name indicating the order variable. If specified, the data will be ordered by this variable. This is useful for creating paths between observations within each group.

decreasing

Logical. If TRUE, paths are ordered in decreasing order of order_by. If FALSE (default), ordering is increasing.

na_method

Character string specifying how to handle missing values in order_by. One of:

  • "drop_na" (default): drop only rows where order_by is NA;

  • "drop_group": drop entire groups that contain any NA in order_by.

...

Additional arguments (currently unused, reserved for future extensions).

Value

A ternable object (S3 class) containing:

data

: The validated and normalized data frame

data_coord

: Transformed coordinates for all observations

data_edges

: Edge connections for drawing paths between observations

simplex_vertices

: Vertex coordinates and labels for the simplex

simplex_edges

: Edge connections for drawing the simplex boundary

vertex_labels

: Labels of the vertices, same as names of the selected item columns

Examples


# Load and transform the dataset
prefviz::aecdop25_transformed

# Create the ternable object
tern <- as_ternable(prefviz::aecdop25_transformed, items = ALP:IND)
tern

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

Description

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:

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")

# 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)

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:

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
)

Centroids of electoral divisions in the 2025 Australian Federal Election

Description

Provides the centroids of all electorates in the 2025 Australian Federal Election. The dataset is computed from 2025 Electoral Boundaries data.

Usage

elb_centroid

Format

A tibble of 5 columns:

id

Unique identifier for electorate

elect_div

Electoral division name

area_sqkm

Area of the electorate in square kilometres

long

Longitude of the electoratecentroid

lat

Latitude of the electorate centroid

Source

Australian Electoral Commission (AEC) https://www.aec.gov.au/electorates/maps.htm

Examples

library(ggplot2)
library(ggthemes)

# Load the dataset
data(elb_centroid)

# Plot the centroids on top of the electoral boundaries
ggplot(elb_map) + 
  geom_polygon(
    aes(x = long, y = lat, group = group),
    fill = "grey90", color = "white") +
  geom_point(
    data = elb_centroid,
    aes(x = long, y = lat),
    size = 1, alpha = 0.8
  ) +
  theme_map()

Electoral boundaries map for the 2025 Australian Federal Election

Description

Provides the points that make up the boundaries of each electoral division in the 2025 Australian Federal Election.

Usage

elb_map

Format

A tibble of 8 columns:

long

Longitude of point in polygon

lat

Latitude of point in polygon

hole

Whether the polygon has a hole

piece

Polygon piece number

group

Polygon group number

order

Order of polygon within group

id

Unique identifier for polygon

elect_div

Electoral division name

Source

Australian Electoral Commission (AEC) https://www.aec.gov.au/electorates/maps.htm

Examples

library(ggplot2)
library(ggthemes)

# Load the dataset
data(elb_map)

# Plot the map
ggplot(elb_map) + 
  geom_polygon(
    aes(x = long, y = lat, group = group),
    fill = "grey90", color = "white") +
  theme_map()

Create polygonal regions in a ternary plot based on a reference point

Description

geom_ternary_region() and stat_ternary_region() divide the ternary triangle into three polygonal regions centered around a specific reference point.

Geometrically, lines are drawn from the reference point perpendicular to the three edges of the triangle. These lines partition the simplex into three zones, where each zone is associated with the closest vertex (item). This is often used to visualize "winning regions" or catchment areas for each item.

Usage

geom_ternary_region(
  mapping = NULL,
  position = "identity",
  show.legend = NA,
  inherit.aes = FALSE,
  x1 = 1/3,
  x2 = 1/3,
  x3 = 1/3,
  vertex_labels = NULL,
  ...
)

stat_ternary_region(
  mapping = NULL,
  data = NULL,
  geom = "polygon",
  position = "identity",
  show.legend = NA,
  inherit.aes = FALSE,
  x1 = 1/3,
  x2 = 1/3,
  x3 = 1/3,
  vertex_labels = NULL,
  ...
)

StatTernaryRegion

Arguments

mapping

Set of aesthetic mappings created by ggplot2::aes(). To map aesthetics to the computed region labels, use ggplot2::after_stat(), e.g., aes(fill = after_stat(vertex_labels)).

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

  • The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.

  • A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".

  • For more information and other ways to specify the position, see the layer position documentation.

show.legend

Logical. Should this layer be included in the legends? NA (default) includes it if aesthetics are mapped. FALSE never includes it; TRUE always includes it.

inherit.aes

If FALSE, overrides the default aesthetics rather than combining with them.

x1, x2, x3

Numeric values defining the reference point in ternary coordinates (proportions). Must sum to 1 (or will be normalized). Default is c(1/3, 1/3, 1/3) (the centroid), which divides the space into three equal regions.

vertex_labels

Character vector of length 3 providing names for the regions. The order must correspond to the three vertices of the ternary plot. If NULL, regions are labeled "Region 1", "Region 2", and "Region 3", starting from the rightmost vertex and moving clockwise.

...

Other arguments passed on to layer()'s params argument. These arguments broadly fall into one of 4 categories below. Notably, further arguments to the position argument, or aesthetics that are required can not be passed through .... Unknown arguments that are not part of the 4 categories below are ignored.

  • Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.

  • When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.

  • Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.

  • The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

geom

The geometric object to use to display the data. Default is "polygon".

Format

An object of class StatTernaryRegion (inherits from Stat, ggproto, gg) of length 3.

Value

A ggplot object

Computed variables

stat_ternary_region() calculates the following variables which can be accessed with after_stat():

x, y

Cartesian coordinates defining the polygon shapes.

id

Numeric identifier for the specific geometric points used to build the polygons:

  • 1-3: The main vertices of the ternary triangle.

  • 4: The reference point (center).

  • 5-7: The projection points on the edges.

group

Integer (1, 2, or 3) identifying which region the polygon belongs to.

vertex_labels

The label assigned to the region (derived from the vertex_labels parameter).

Examples

library(ggplot2)
# Get ternable
tern22 <- as_ternable(prefviz::aecdop22_transformed, ALP:Other)

# Draw the ternary plot
ggplot(get_tern_data(tern22, plot_type = "2D"), aes(x = x1, y = x2)) +
  add_ternary_base() +
  geom_ternary_region(
    vertex_labels = tern22$vertex_labels,
    aes(fill = after_stat(vertex_labels)), 
    alpha = 0.3, color = "grey50",
    show.legend = FALSE
  )

Transform compositional data using Helmert matrix

Description

Transform n-dimension compositional data (all values sum to 1) into an (n-1)-dimensional Euclidean space using the Helmert matrix. This dimension reduction is the geometric basis for plotting points within the simplex.

Usage

helmert_transform(data, items = dplyr::everything(), append = FALSE)

Arguments

data

A data frame or matrix containing the compositional data.

items

<tidy-select> Columns representing the components of the composition. Default is tidyselect::everything(), which selects all columns. Must select at least 3 columns.

append

(Optional) A logical value indicating whether the transformed data should be appended to the original data frame. Default is FALSE.

Value

A data frame containing the Helmert-transformed coordinates, named x1, x2, ..., x(n-1), where n is the number of items. If append = TRUE, these columns are added to the input data.

Examples

# Example 1: Transform a matrix (all columns)
comp_mat <- matrix(c(0.5, 0.3, 0.2,
                     0.4, 0.4, 0.2,
                     0.6, 0.2, 0.2),
                   ncol = 3, byrow = TRUE)
helmert_transform(comp_mat)

# Example 2: Transform specific columns in a data frame
df <- data.frame(
  electorate = c("A", "B", "C"),
  ALP = c(0.5, 0.4, 0.6),
  LNP = c(0.3, 0.4, 0.2),
  Other = c(0.2, 0.2, 0.2)
)
helmert_transform(df, items = c(ALP, LNP, Other))

Validate and order path data

Description

Internal helper to validate and reorder data within each group according to the order_by aesthetic. Used by stat_ordered_path().

Usage

ordered_path_df(
  data,
  group,
  order_by,
  decreasing = FALSE,
  na_method = c("drop_na", "drop_group")
)

Arguments

data

A data frame containing at least an order_by column (and optionally a group column created by ggplot2).

decreasing

Logical. If TRUE, sort order_by in decreasing order; otherwise in increasing order.

na_method

Character string indicating how to handle missing values in order_by. One of "drop_na" or "drop_group".

Value

A data frame with the same columns as data, but potentially fewer rows (after dropping rows or groups) and with rows reordered within each group.

Print method for ternable objects

Description

Print method for ternable objects

Usage

## S3 method for class 'ternable'
print(x, ...)

Arguments

x

A ternable object

...

Additional arguments passed to print methods

Value

The object, invisibly

Reorder observations for a path geom

Description

stat_ordered_path() reorders observations along each path using a user-supplied ordering aesthetic (order_by) before drawing the path. The statistic can be used to ensure that paths are drawn in a consistent order even when the input data are not pre-sorted. This is equivalent to reordering the data before passing it to geom_path().

Usage

stat_ordered_path(
  mapping = NULL,
  data = NULL,
  geom = "path",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  decreasing = TRUE,
  na_method = c("drop_na", "drop_group"),
  ...
)

StatOrderedPath

Arguments

mapping

Set of aesthetic mappings created by ggplot2::aes(). Must at least supply x, y, and order_by. The group aesthetic can be used to define separate paths.

data

Data frame to be used for this layer. If NULL, the default, the data is inherited from the plot.

geom

The geometric object to use to draw the paths. Defaults to "path".

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

  • The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.

  • A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".

  • For more information and other ways to specify the position, see the layer position documentation.

show.legend

Logical or NA. Should this layer be included in the legends? NA, the default, includes the layer if any aesthetics are mapped.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them.

decreasing

Logical. If TRUE, paths are ordered in decreasing order of order_by. If FALSE (default), ordering is increasing.

na_method

Character string specifying how to handle missing values in order_by. One of:

  • "drop_na" (default): drop only rows where order_by is NA;

  • "drop_group": drop entire groups that contain any NA in order_by.

...

Additional parameters passed on to the underlying geom.

Format

An object of class StatOrdered (inherits from Stat, ggproto, gg) of length 4.

Details

The statistic expects an order_by aesthetic that supplies the variable used to order observations within each group.

Missing values order_by are handled according to na_method. If na_method is "drop_na", missing values are dropped from the data, and the path is still drawn, but might skipping steps due to missing values. If na_method is "drop_group", the entire group whose missing values belong is dropped from the data, and the path is not drawn.

Ties in order_by are allowed but trigger a warning and preserve the original row order for tied values.

Duplicates in order_by are dropped and a warning is issued.

Grouping is controlled via the usual group aesthetic. If a group column is present in the data, reordering is performed independently within each group; otherwise, the entire data is treated as a single path.

Value

A ggplot object

A ggplot2 layer that can be added to a plot object.

Aesthetics

stat_ordered_path() understands the following aesthetics (required are in bold).

Examples

library(ggplot2)
library(dplyr)
# Data prep
input_df <- prefviz::aecdop22_transformed |> 
   filter(DivisionNm %in% c("Higgins", "Monash"))
tern22 <- as_ternable(input_df, ALP:Other)

# Base plot
p <- get_tern_data(tern22, plot_type = "2D") |> 
  ggplot(aes(x = x1, y = x2)) +
  add_ternary_base() +
  geom_ternary_region(
    aes(fill = after_stat(vertex_labels)),
    vertex_labels = tern22$vertex_labels,
    alpha = 0.3, color = "grey50",
    show.legend = FALSE
  ) +
  geom_point(aes(color = ElectedParty)) +
  add_vertex_labels(tern22$simplex_vertices) +
  scale_color_manual(
    values = c("ALP" = "red", "LNP" = "blue", "Other" = "grey70"),
    aesthetics = c("fill", "colour")
  )

# Add ordered paths
p + 
  stat_ordered_path(
    aes(group = DivisionNm, order_by = CountNumber, color = ElectedParty))

Getter functions to extract components from ternable object for ternary plots

Description

Performs additional transformations on ternable object components, making it ready for both 2D ternary plot with ggplot2 and high-dimensional ternary plots with tourr.

Usage

get_tern_data(ternable, plot_type = c("2D", "HD"))

get_tern_edges(ternable, include_data = FALSE)

get_tern_labels(ternable)

Arguments

ternable

A ternable object created by as_ternable().

plot_type

Only in get_tern_data(). Character string specifying the type of plot to be drawn. Either "2D" for a 2D ternary plot or "HD" for a high-dimensional ternary plot.

include_data

Logical. Only in get_tern_edges(). If TRUE, return data edges, along with simplex edges. If FALSE, only return simplex edges.

Details

These functions are designed to work together for creating animated tours of high-dimensional ternary data:

Value

See Also

as_ternable() for creating ternable objects

Examples

library(ggplot2)
# Create a ternable object
tern <- as_ternable(prefviz::aecdop22_transformed, ALP:Other)

# Use with tourr (example)
tourr::animate_xy(
 get_tern_data(tern, plot_type = "HD"),
 edges = get_tern_edges(tern),
 obs_labels  = get_tern_labels(tern),
 axes = "bottomleft")

# Use with ggplot2 (example)
ggplot(get_tern_data(tern, plot_type = "2D"), aes(x = x1, y = x2)) +
  add_ternary_base() +
  geom_point(aes(color = ElectedParty))