qdap (version 2.3.0)

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, ...)

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.

Value

Returns a Venn plot by grouping variable(s).

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).

See Also

venneuler

Examples

# NOT RUN {
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"))
# }