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Plot concept and intersection sizes

Source: R/plot-intersections.R
plot_scopus_intersections.Rd

Draws the counts from scopus_intersections() as a lollipop chart on a log-scale axis, so a niche of a dozen records stays legible beside a parent literature of many thousands. Rows are ordered by size, with the largest at the top, and one or more rows can be shown in an accent colour, typically a study's own niche. The axis range and the gap between each point and its count label are derived from the data, so the chart reads the same whether the counts span one order of magnitude or six.

Usage

plot_scopus_intersections(x, highlight = NULL, highlight_label = NULL, ...)

# S3 method for class 'scopus_intersections'
autoplot(object, ...)

Arguments

x

A scopus_intersections object from scopus_intersections().

highlight

Optional character vector of row labels to draw in an accent colour, for example the intersection that defines a study's niche.

highlight_label

Legend label for the highlighted rows. The default, NULL, derives the label from what is highlighted: "Focal intersection" when every highlighted row is an intersection, "Focal concept" when every one is a concept, and "Focal set" for a mixture. Supply a string to use that instead.

...

Currently unused, present for S3 consistency.

object

A scopus_intersections object (for the autoplot() method).

Value

A ggplot2::ggplot object. Needs the suggested package ggplot2.

Details

A count of zero cannot be placed on a log axis, so rows whose count is zero or NA are dropped with a warning, which the caption also notes. An empty intersection is itself a finding; the printed object keeps the zero even though the chart cannot.

See also

scopus_intersections()

Examples

sets <- tibble::tibble(
  label = c("semantic priming", "mental simulation",
            "semantic priming \u00d7 mental simulation"),
  query = "q",
  n = c(6600, 2100, 15),
  type = c("concept", "concept", "intersection"),
  size = c(1L, 1L, 2L),
  members = c("semantic priming", "mental simulation",
              "semantic priming; mental simulation")
)
class(sets) <- c("scopus_intersections", class(sets))
plot_scopus_intersections(sets)

plot_scopus_intersections(sets, highlight = sets$label[3])