August 7, 2015 Simon Raper

Glasseye: bringing together markdown, d3 and the Tufte layout

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Glasseye is a package I’m developing to present the results of statistical analysis in an attractive and hopefully interesting way. It brings together three great things that I use a lot:

  1. The markdown markup language.
  2. The Tufte wide margin layout
  3. Visualisation using d3.js

See a full demo of what it can do here and the you can visit the github repository here

Here is what it looks like when transformed into html.


Screen Shot 2015-08-07 at 13.48.35


The idea is to be able to write up work in markdown and have the results transformed into something like a Tufte layoutof which more below. For the Tufte layout I took the excellent tufte.css style sheet developed by Dave Liepmann and co and made a few changes to suit my purposes. Finally I’ve added some d3 charts (just a small selection at the moment but this will grow) that can easily invoked from within the markdown.

It’s all very very beta at the moment. I’m not claiming it’s ready to go. I would like to add lots more charts, redesign the d3 code and improve it’s overall usability (in particular replace the tags approach with something more in the spirit of markdown) however I thought I’d share it as it is. Hope you find it interesting

About the Author

Simon Raper I am an RSS accredited statistician with over 15 years’ experience working in data mining and analytics and many more in coding and software development. My specialities include machine learning, time series forecasting, Bayesian modelling, market simulation and data visualisation. I am the founder of Coppelia an analytics startup that uses agile methods to bring machine learning and other cutting edge statistical techniques to businesses that are looking to extract value from their data. My current interests are in scalable machine learning (Mahout, spark, Hadoop), interactive visualisatons (D3 and similar) and applying the methods of agile software development to analytics. I have worked for Channel 4, Mindshare, News International, Credit Suisse and AOL. I am co-author with Mark Bulling of Drunks and Lampposts - a blog on computational statistics, machine learning, data visualisation, R, python and cloud computing. It has had over 310 K visits and appeared in the online editions of The New York Times and The New Yorker. I am a regular speaker at conferences and events.

Machine Learning and Analytics based in London, UK