Geom for drawing diamond plots of lattice structures

Create a custom ggplot2 geom for drawing diamond plots, which are used to visualize lattice structures. This is particularly useful for representing association rules and their ancestor–descendant relationships in a concise graphical form.

Usage

geom_diamond(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  na.rm = FALSE,
  linetype = "solid",
  nudge_x = 0,
  nudge_y = 0.125,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

mapping: Aesthetic mappings, usually created with ggplot2::aes().
data: A data frame containing the lattice structure to be plotted.
stat: The statistical transformation to apply, default is "identity".
position: Position adjustment for the geom, default is "identity".
na.rm: Logical; if TRUE, missing values are silently removed.
linetype: Line type used for edges. Defaults to "solid".
nudge_x: Horizontal nudge applied to label positions.
nudge_y: Vertical nudge applied to label positions.
show.legend: Logical; should a legend be drawn? Defaults to FALSE.
inherit.aes: Logical; if TRUE, inherit default aesthetics from the plot. Defaults to TRUE.
...: Additional arguments passed on to ggplot2::layer().

Value

A ggplot2 layer object representing a diamond plot. This layer can be added to an existing ggplot object.

Details

In a diamond plot, nodes (diamonds) represent items or conditions in the lattice, while edges represent inclusion (subset) relationships between them. The geom combines node and edge rendering with flexible aesthetic options for labels and positioning.

Examples

if (FALSE) { # \dontrun{
library(ggplot2)
data("iris")
rules <- dig_associations(part)

# select some rule to visualize the ancestors
rule <- rules[1000, , drop = FALSE]

# prepare data for visualization of rule ancestors
ante <- parse_condition(rule$antecedent)[[1]]
cons <- parse_condition(rule$consequent)[[1]]
res <- dig_associations(part,
                       antecedent = all_of(ante),
                       consequent = all_of(cons),
                       min_length = 0,
                       max_length = Inf,
                       min_coverage = 0,
                       min_support = 0,
                       min_confidence = 0,
                       measures = c("lift", "conviction"),
                       max_results = Inf)

# convert all columns into dummy logical variables
part <- partition(iris, .breaks = 3)

# find all antecedents with Sepal for rules with consequent Species=setosa
rules <- dig_associations(part,
                         antecedent = starts_with("Sepal"),
                         consequent = `Species=setosa`,
                         min_length = 0,
                         max_length = Inf,
                         min_coverage = 0,
                         min_support = 0,
                         min_confidence = 0,
                         measures = c("lift", "conviction"),
                         max_results = Inf)

# add abbreviated condition for labeling
rules$abbrev <- shorten_condition(rules$antecedent)

# plot the lattice of rules
ggplot(rules) +
    aes(condition = antecedent, fill = confidence,
       linewidth = confidence, size = coverage,
       label = abbrev) +
   geom_diamond()
} # }