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