Interactive presentation with slidify and googleVis

Last week I was invited to give an introduction to googleVis at Lancaster University. This time I decided to use the R package slidify for my talk. Slidify, like knitr, is built on Markdown and makes it very easy to create beautiful HTML5 presentations.

Introduction to googleVis

Separating content from layout is always a good idea. Markup languages such as TeX/LaTeX or HTML are built on this principle. Ramnath Vaidyanathan has done a fantastic job with slidify, as it is very straightforward to create presentations with R. There are a couple of advantages compared to traditional presentation software packages:
  • RMarkdown helps me to focus on the content
  • Integration of R code is build in
  • HTML5 allows me to embed interactive content, such as
    • Videos
    • googleVis and other interactive charts
    • shiny apps (more on this next week)
In the past I have used knitr in combination with pandoc to generate a slidy presentation. However, with slidfiy I can do all this in R directly. And better, Ramnath provides me with a choice of different layout frameworks and syntax highlighting options. Finally to top it all, publishing the slides on Github was only one more R statement: publish('mages', 'Introduction_to_googleVis').

I will give a half-day tutorial on googleVis with Diego de Castillo at useR2013! in Albacete on 9 July 2013. I hope to see some of you there.


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Don't be misguided by the beauty of mathematics, if the data tells you otherwise

I was trained as a mathematician and it was only last year, when I attended the Royal Statistical Society conference and met many statisticians that I understood how different the two groups are.

In mathematics you often start with some axioms, things you assume to be true, and these axioms are then the basis from which new theory is derived. In statistics or more general in science you start with a theory, or better a hypothesis and try to disprove it. And if you can't disprove it, you accept it until you have other evidence. Or to phrase it like Karl R. Popper: you can only be proven wrong.

Now, why do I mention this? I have met many mathematicians who talk about the beauty of mathematics and I agree, a mathematical concept, theorem or proof can indeed be beautiful. However, when you work in applied mathematics and particular when you use mathematics to build models, there is a danger that you stick to the beautiful idea and ignore reality. Remember the financial crisis?

For example, it might be handy to assume that your data follow a normal distribution, e.g. to make the calculations easier. However, if the data tells you otherwise then be bold and ruthless and change your model. As strange as it might sound, it is has to be your aim to prove a model doesn't work in order to use it successfully.

Remember Pythagoras? He believed in beautiful integers and the realisation that the square root of two was not a fraction of two integers caused a big crisis.

I would argue that we need mathematics to do statistics and statistics to do science. The developments over the last 350 years really demonstrate the success the scientific method. Of course some ideas had to go: the earth can no longer be regarded as the centre our solar system - instead it appears more like a little pale blue dot.

Diggle and Chetwynd, from Lancaster University, published a nice little book that gives a good introduction into statistics and of the scientific method. Two quotes of the book stuck in my mind (pages 1&2):

A scientific theory cannot be proved in the rigours sense of a mathematical theorem. But it can be falsified, meaning that we can conceive of an experimental or observational study that would show the theory to be false.
The American physicist Richard Feynman memorable said that 'theory' was just a fancy name for a guess. If observation is inconsistent with theory then the theory, however elegant, has to go. Nature cannot be fooled.


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Claims Inflation - a known unknown

Over the last year I worked with two colleagues of mine on the subject of inflation and claims inflation in particular. I didn't expect it to be such a challenging topic, but we ended up with more questions than answers. The key question and biggest challenge is to define what inflation, or indeed claims inflation actually is and how to measure it. We published a summary of our thoughts and findings in this month's issue of The Actuary.

Last year's discussion about the differences between the retail price index (RPI) and consumer price index (CPI) in the UK only exemplified the challenge. The economist Tim Harford illustrated the differences between the RPI and CPI with a simple example of price changes for a shirt and blouse in his Radio 4 programme More or Less. The radio podcast is still available from the BBC. Start listening after about 18 minutes into the show.


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R in Insurance: Programme and Abstracts published


I am delighted to announce that the programme and abstracts for the first R in Insurance conference at Cass Business School in London, 15 July 2013, have been published.

The conference committee received strong abstracts from academia and the industry, covering:
  • Pricing
  • Reserving
  • Data mining
  • Capital modelling
  • Automate reporting
  • Catastrophe modelling
  • High-performance computing
  • Software development management
Register by the end of May to get the early bird booking fee.

We gratefully acknowledge the sponsorship of Mango Solutions and CYBAEA, without whom the event wouldn't be possible.

Programme and Abstracts

Register by the end of May to get the early bird booking fee.


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