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, ...)
NULL generates
one word list for all text. Also takes a single grouping variable or a list
of 1 or more grouping variables.TRUE removes the duplicated words
from the analysis (only single usage is considered).TRUE adds a title corresponding to the
grouping.var.NULL and
NA are equivalent to 1.0TRUE uses the names from the
target.words
list corresponding to cloud.colors.NULL and
NA are equivalent to 1.0."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.TRUE, set the legend horizontally
rather than vertically.Returns a Venn plot by grouping variable(s).
Produce a Venn diagram by grouping variable.
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).
## <strong>Not run</strong>: # 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")) # ## <strong>End(Not run)</strong>
venneuler