If you’re an R user, you may want to check out the 2-part series at R-bloggers on visualizing spatiotemporal datasets.
There are many visual methods used to identify patterns in space and time. I’ve discussed some in prior threads and will show a few others briefly here. One of the most difficult questions I often hear from others regarding markov type approaches, is how to identify states to be processed.
It’s a pretty technical article full of some great information. I’ve never used R myself, but I can tell I’m going to have to learn.
Jeffrey Breen has some nice one-liners for R that show how you can use the popular statistics package with the “Google Visualization API” to create some great interactive graphs, specifically the famous “Motion Chart” shown by Hans Rosling at TED2006.
First up: the googleVis package by Markus Gesmann and Diego de Castillo which makes it easy — often with just one-line of R — to harness the Google Visualization API. Annotated timelines, gauges, maps, org charts, tree maps, and more are suddenly at your command.
Lots of people wouldn’t really consider tools like ‘ggplot’ true visualization tools, but in some disciplines it’s exactly what’s called for: Simple visualization with no fuss. A talk given by Hadley Wickman, and available online, discusses its use along with the popular statistics package R.
Data analysis, the process of converting data into knowledge, insight and understanding, is a critical part of statistics, but there’s surprisingly little research on it. In this talk I’ll introduce some of my recent work, including a model of data analysis. I’m a passionate advocate of programming that data analysis should be carried out using a programming language, and I’ll justify this by discussing some of the requirement of good data analysis (reproducibility, automation and communication). With these in mind, I’ll introduce you to a powerful set of tools for better understanding data: the statistical programming language R, and the ggplot2 domain specific language (DSL) for visualisation.
Paul Butler got a lot of popularity from his beautiful Facebook Relationship graph, but surprised many people when he let it slip that he made the whole thing using R. Known as a statistics and analysis package, the thought that it could create graphs like that was unexpected. In a new blog post, he covers how he did it.
The solution was to manipulate the drawing order of the lines. I used a simple loop over my data to draw the lines, so it was easy to control which lines are drawn first using order(). I created an ordering based on the length of the lines, so that longer lines were drawn “behind” the shorter, more local lines. Then I used colorRampPalette() to generate a color palette from black to blue to white, and colored the lines according to order they were drawn.
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