ggplot2 Time Series Heatmaps

How do you easily get beautiful calendar heatmaps of time series in ggplot2? E.g:

From MarginTale

I was impressed by the lattice-based  implementation from Paul Bleicher of Humedica, which you can find referenced in http://blog.revolutionanalytics.com/2009/11/charting-time-series-as-calendar-heat-maps-in-r.html. Then, when other blogs like http://timelyportfolio.blogspot.com/2012/04/piggybacking-and-hopefully-publicizing.html picked up the topic, I decided to try a ggplot2 implementation. In a comment to the above Revolution Analytics post, Hadley already presented a quick ggplot rendition, upon which I build here.

How do you attack the problem? Looking at the example output above:

1. We facet_grid by "months" and "years" 
2. The data itself is plotted by "week of month" and "day of week" and coloured according to the value of interest

So, given a time series we just have to fiddle with time indexes to create a data.frame containing the time series as well as per observation the corresponding "month", "year", "week of month", "day of week". The rest is then a one-liner of code with Hadley's wonderful ggplot2 system.

The following code contains step by step comments:

It should be easy to wrap into a function and I hope its useful.

Boxplots and Day of Week Effects

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After following some R-related quant finance blogs like Timely Portfolio, Systematic Investor or Quantitative thoughts-  to name some of my favourites - I decided to start my own. I'll first focus on R snippets which come in handy, and will potentially expand to quant trading and backtesting as time allows.

I'll start with a simple graphical boxplot analysis of "days of the week effects" with two R snippet/tidbits regarding:

1. How do you adapt the ggplot2 plotting of boxplots to a mundane 50%-box 95%-line 5%-dots view?
2. How do you subdivide your days in weekdays easily and robustly? 

Lets jump directly into the code which can be downloaded at https://gist.github.com/1974563:


Running the code, we get following output:

From MarginTale

These boxplots now show 50% of the observations in the box, the vertical lines cover 95% and the dots 2.5%. I find this easier to communicate than the standard definition. This is implemented in the functions myBoxPlotSummary and myBoxPlotOutliers which are in turn called from stat_summary in ggplot.

A second issue I tripped over is the sorting of days in the above boxplot. If one uses the obvious way and just defines a factor as "weekdays(index(...))" then the plot function will alphabetically sort the days - not exactly what you want. If you then try to order the factors, your solution will depend on how locale (the language you use) specifies the abbreviations of the weekdays. A robust solution shown  in the code is to use the function .indexwday from the package xts.