What is most exciting about this package is that a lot of the steps one takes to make a complete graph have been split into individual functions. Thus, while one can make a scatterplot with wv.plot(), one can also use wv.axis() and wv.points() to do so as well. Each data visualization gets its own ID, or can be assigned one, so one can later start passing visualization (e.g. the points in the scatterplot itself) as arguments to other functions, thus allowing one to begin adding functions for interactivity.
A few examples of the visualizations are shown below, along with the necessary R code to get them to display. Note that these are embedded into the blog, I did so through the use of an inline frame.
The code below will generate a basic scatterplot.
x = rnorm(30)
y = rnorm(30)
wv.plot(x, y, "~/Desktop/scatterplot", height=300, width=300, xlim=c(-2.5,2.5), ylim=c(-2.5,2.5), xbreaks=c(0), ybreaks=c(0))
Plot with Multiple Data Types
Supposing you want to have a scatterplot with multiple point types and a line. You can build this manually with the following code.
x = rnorm(30); y = rnorm(30); z = runif(30);
wv.open("~/Desktop/plot3/", height=300, width=300);
wv.axis(c(-3.5, 3.5), c(-3.5, 3.5), xbreaks=-2:2, ybreaks=-2:2);
wv.points(x, y, xlim=c(-3.5, 3.5), ylim=c(-3.5, 3.5));
wv.lines(sort(x), z, col="red", xlim=c(-3.5, 3.5), ylim=c(-3.5, 3.5));
This is a new graph format.
x = c(2.5, 7, 11);
wv.bargraph(x, cats, "~/Desktop/barplot", ylim=c(0, 15), ybreaks=(1:5)*3);
As always, comments are welcome.