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Create a ternable object

Source: R/ternable.R
as_ternable.Rd

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
#> # A tibble: 976 × 8
#>    DivisionNm CountNumber ElectedParty   ALP   GRN   LNP     Other    IND
#>    <chr>            <dbl> <chr>        <dbl> <dbl> <dbl>     <dbl>  <dbl>
#>  1 Adelaide             0 ALP          0.465 0.190 0.242 0.104     0     
#>  2 Adelaide             1 ALP          0.467 0.194 0.242 0.0968    0     
#>  3 Adelaide             2 ALP          0.476 0.205 0.244 0.0745    0     
#>  4 Adelaide             3 ALP          0.483 0.210 0.249 0.0578    0     
#>  5 Adelaide             4 ALP          0.493 0.222 0.285 0.0001000 0     
#>  6 Adelaide             5 ALP          0.691 0     0.309 0         0     
#>  7 Aston                0 ALP          0.373 0     0.377 0.209     0.0414
#>  8 Aston                1 ALP          0.373 0     0.378 0.206     0.0434
#>  9 Aston                2 ALP          0.376 0     0.380 0.210     0.0338
#> 10 Aston                3 ALP          0.378 0     0.384 0.202     0.0358
#> # ℹ 966 more rows

# Create the ternable object
tern <- as_ternable(prefviz::aecdop25_transformed, items = ALP:IND)
#> Warning: Not all rows sum to 1. Normalizing items automatically.
tern
#> Ternable object
#> ----------------
#> Items: ALP, GRN, LNP, Other, IND 
#> Vertices: 5 
#> Edges: 20