I recently got back from Strata West 2017 (where I ran a very well received workshop on
Spark). One thing that really stood out for me at the exhibition hall was
datashader from Continuum Analytics.
I had the privilege of having Peter Wang himself demonstrate
datashader for me and answer a few of my questions.
I am so excited about
datashader capabilities I literally will not wait for the functionality to be exposed in
rbokeh. I am going to leave my usual
rmarkdown world and dust off
Jupyter Notebook just to use
datashader plotting. This is worth trying, even for diehard
R users. Continue reading Datashader is a big deal
Nina Zumel and I have been working on packaging our favorite graphing techniques in a more reusable way that emphasizes the analysis task at hand over the steps needed to produce a good visualization. The idea is: we sacrifice some of the flexibility and composability inherent to ggplot2 in R for a menu of prescribed presentation solutions (which we are sharing on Github).
For example the plot below showing both an observed discrete empirical distribution (as stems) and a matching theoretical distribution (as bars) is a built in “one liner.”
Please read on for some of the ideas and how to use this package. Continue reading WVPlots: example plots in R using ggplot2
I suspect I am not unique in not being able to remember how to control the point shapes in R. Part of this is a documentation problem: no package ever seems to write the shapes down. All packages just use the “usual set” that derives from S-Plus and was carried through base-graphics, to grid, lattice and ggplot2. The quickest way out of this is to know how to generate an example plot of the shapes quickly. We show how to do this in ggplot2. This is trivial- but you get tired of not having it immediately available. Continue reading How to remember point shape codes in R