trans_venn: Venn Diagram by Grouping Variable

Description

Produce a Venn diagram by grouping variable.

Usage

trans_venn(
  text.var,
  grouping.var,
  stopwords = NULL,
  rm.duplicates = TRUE,
  title = TRUE,
  title.font = NULL,
  title.color = "black",
  title.cex = NULL,
  title.name = NULL,
  legend = TRUE,
  legend.cex = 0.8,
  legend.location = "bottomleft",
  legend.text.col = "black",
  legend.horiz = FALSE,
  ...
)

Value

Returns a Venn plot by grouping variable(s).

Arguments

text.var: The text variable.
grouping.var: The grouping variables. Default NULL generates one word list for all text. Also takes a single grouping variable or a list of 1 or more grouping variables.
stopwords: Words to exclude from the analysis.
rm.duplicates: logical. If TRUE removes the duplicated words from the analysis (only single usage is considered).
title: logical. IF TRUE adds a title corresponding to the grouping.var.
title.font: The font family of the cloud title.
title.color: A character vector of length one corresponding to the color of the title.
title.cex: Character expansion factor for the title. NULL and NA are equivalent to 1.0
title.name: A title for the plot.
legend: logical. If TRUE uses the names from the target.words list corresponding to cloud.colors.
legend.cex: Character expansion factor for the legend. NULL and NA are equivalent to 1.0.
legend.location: The x and y co-ordinates to be used to position the legend. The location may also be specified by setting x to a single keyword from the list "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center". This places the legend on the inside of the plot frame at the given location.
legend.text.col: The color used for the legend text.
legend.horiz: logical; if TRUE, set the legend horizontally rather than vertically.
...: Other arguments passed to plot.

Warning

The algorithm used to overlap the Venn circles becomes increasingly overburdened and less accurate with increased grouping variables. An alternative is to use a network plot with {codeDissimilarity measures labeling the edges between nodes (grouping variables) or a heat map (qheat).

Examples

Run this code

if (FALSE) {
with(DATA , trans_venn(state, person, legend.location = "topright"))
#the plot below will take a considerable amount of time to plot
with(raj.act.1 , trans_venn(dialogue, person, legend.location = "topleft"))
}