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Combine topics and distribution legend into an overview figure (experimental)

Source: R/4_1_topicsPlot.R
topicsPlotOverview.Rd

Assembles a single composite figure from the individual topic word clouds returned by `topicsPlot()`. The shape of the composition depends on `overview_plot_type`:

  • `"topics_grid"` / `"textTopics"`: a grid of the most prevalent topics (no test), controlled by `overview_n_topics`.

  • `"one_dimension_topics"`: three columns (negative / non-significant / positive), controlled by `scatter_legend_n` (length 3).

  • `"two_dimension_topics"`: a 3x3 quadrant layout with the distribution legend in the centre, controlled by `scatter_legend_n` (length 9).

Usage

topicsPlotOverview(
  plot_list,
  overview_plot_type,
  overview_n_topics = 9,
  scatter_legend_n = NULL,
  title_font = "sans"
)

Arguments

plot_list

(list) Output from the `topicsPlot()` function.

overview_plot_type

(character) One of `"topics_grid"`, `"textTopics"`, `"one_dimension_topics"`, or `"two_dimension_topics"`.

overview_n_topics

(integer) For the `"topics_grid"` / `"textTopics"` overview only: the number of topic plots to include. Plots are arranged in 3 columns (the default of 9 produces a 3x3 grid). Ignored for the test-based 1D and 2D overviews, which instead use `scatter_legend_n`. Default: 9.

scatter_legend_n

(numeric vector) For the test-based 1D and 2D overviews: the per-category counts that determine how many topic plots appear in the overview. Length 3 for 1D (negative, non-significant, positive) and length 9 for 2D (one value per quadrant). A value of 0 omits that category. Ignored for the prevalent-topics overview.

title_font

Font family used for all non-word text elements in the plots (e.g., titles, axis labels, tick labels, legend text, annotations). Default: `"sans"`.

Value

A patchwork composition that can be printed, saved with `ggsave()`, or further modified with patchwork operators.

Details

This function is intended as a convenience for quickly inspecting results. For publication figures, users typically save the individual topic plots (returned separately by `topicsPlot()`) and assemble them in external software such as PowerPoint, Google Drawings, or Illustrator.

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