Quilt plots. Like heat maps, only…heat maps

Stephen tweets:

Quilt Plots: A Simple Tool for the #Visualisation of Large Epidemiological Data http://t.co/mjYCo0nRTv

— Stephen Rudd (@SAGRudd) January 15, 2014

A “quilt plot”

Quilt plots. Sounds interesting. The link points to a short article in PLoS ONE, containing a table and a figure. Here is Figure 1.

If you looked at that and thought “Hey, that’s a heat map!”, you are correct. That is a heat map. Let’s be quite clear about that. It’s a heat map.

So, how do the authors justify publishing a method for drawing heat maps and then calling them “quilt plots”?

A brief introduction to “apply” in R

At any R Q&A site, you’ll frequently see an exchange like this one:

Q: How can I use a loop to […insert task here…] ?
A: Don’t. Use one of the apply functions.

So, what are these wondrous apply functions and how do they work? I think the best way to figure out anything in R is to learn by experimentation, using embarrassingly trivial data and functions.

R has a JSON package

Named rjson, appropriately. It’s quite basic just now, but contains methods for interconversion between R objects and JSON. Something like this:

library(rjson)
data <- list(a=1,b=2,c=3)
json <- toJSON(data)
json
[1] "{\"a\":1,\"b\":2,\"c\":3}"
cat(json, file="data.json")

Use cases? I wonder if RApache could be used to build an API that serves R data in JSON format?