I released an R package over 9 months ago called geofacet, and have long promised a blog post about the approach. This is the first post in what I plan to be a series of two or three posts. In this post I’ll introduce what the package does and compare it to some other approaches for visualizing geographic data.
I’m always looking for ways to spark my kid’s interest in computers, data, etc. This has proven to be more difficult than I thought it would be (kids these days…). I suspect this may have something to do with the ubiquity of electronic devices that “just work”, making them less novel and less interesting to tinker with, but speculation on this is a post for another time…
Anyway, all of my kids are into Pokemon so when I came across some Pokemon data the other day that leant itself very nicely to a Trelliscope display, I thought I might have a chance to engage them. And then I thought why not write a post about it. You can find the resulting display and code to recreate it in this post. Hope you enjoy!
In response to a user’s request and after a short conversation with Carson Sievert (creator / maintainer of the plotly R package), I recently made a small tweak to TrelliscopeJS to make it very easy to create Trelliscope displays with interactive Plotly panels. A simple example is shown in this post.
This post shows a simple example of creating an interactive display that allows you to navigate thousands of instagram posts with just a few lines of code using TrelliscopeJS. The example comes from a hackathon in the DARPA XDATA program earlier this year.
There are many map plotting features in rbokeh that I haven’t been able to cover in detail in the documentation. This post will go into a few of those, including google map types, custom map styles, and using different layer functions to plot on top of a map.
The rbokeh package version 0.5.0 was recently released. For those not familiar with rbokeh, it is a plotting library based on BokehJS with the goal of making it easy to flexibly create declarative interactive web-based visualizations in R. To get an overview of what you can do with it, please see here.
A little-known feature in rbokeh is a function that will save an htmlwidget (including rbokeh figures of course) to a github gist and share it, along with its source code, through services like bl.ocks.org or allow it to be embedded in a web-based document or presentation.
A common issue when dealing with more than a few thousand data points is how to effectively make scatterplots. There is a lot of research on this topic that I won’t go into in any detail, but in this post I’ll just point out a few features that come with rbokeh that allow you to work with larger scatterplots, including level of detail thresholds, WebGL, and hexbins, and finally faceting.