mages' blog

The programme and the presentation files of the first R in Insurance conference have been published on GitHub.

Front slides of the conference presentations

Additionally to the slides many presenters have made their R code available as well:

• Alexander McNeil shared the examples of the CreditRisk+ model he presented.
• Lola Miranda made a Windows version of the double chain-ladder package DCL available via the Cass knowledge web site.
• Alessandro Carrato's 1-year re-reserving code is hosted on the ChainLadder project web site.
• Giorgio Spedicato's life contingencies package is on CRAN already.
  
• Simon Brickman and Adam Rich's HTML presentation and underlying R code for Automated Reporting is included in the GitHub repository.
  
• Stefan Eppert pointed out that KatRisk published an illustrative catastrophe model in R.
• Hugh Shanahan's code to integrate R with Azure for high-throughput analysis is on GitHub.

Thanks again to all the presenters who have helped to make the event a success.

Hopefully, we see you again next year!

Despite the hot weather and the beginning of the school holiday season in North Rhine Westphalia the Cologne R user group met yet again for two fascinating talks and beer and schnitzel afterwards.

Analysing Twitter data to evaluate the US Dollar / Euro exchange rates

Dietmar Janetzko presented ideas to forecast US Dollar / Euro exchange rate movements for the following day.


To forecast exchange rate movements, Dietmar distinguishes two school of thoughts. The first one is based on the analysis of fundamental analysis, e.g. figures of GDP, debt, unemployment, etc. and the other one is based on news, e.g. announcements from central banks, e.g. from Ben Bernanke and other industry experts.

While the data for the fundamental analysis is usually updated slowly, e.g. annually or quarterly, news can be of higher frequency and less regular. As a result the forecasting horizon in a very liquid market, as the forex market, can vary from one minute or less to next year or decade.

Dietmar's aim was to forecast the exchange rate for the following day and to outperform the forecast of a random walk. For his experiment he used daily exchange rates from Quandl, which has a nice R interface, and Twitter data from topsy, which gives him access to Twitter's 'firehose'.

For his analysis Dietmar focused on the number of tweets of the terms Euro + Crisis + <concept>, whereby he used a dictionary of nearly 600 different concept words.

His training algorithms used functions of the following packages: forecast, caret and car, looking for predictors that have a smaller error than a random error.

It goes without saying that Dietmar hasn't made millions from his algorithms yet, but the discussion and the end of his presentation will hopefully have given him a few pointers to do just that.

Graphs in R

Afshin Sadeghi, who has a background in Steiner tree methods for Protein-Protein interaction networks, gave an overview of the various graph packages in R. He started his talk with a little overview of the graph terminology of nodes, edges, trees, directed and undirected graphs.


Afshin then gave a brief overview of the various graph packages in R and the different visualisation options. The most popular package seems to be igraph, maintained by Gabor Csardi. Although different packages use sometimes different graph objects, there are often conversation tools available, e.g. igraph.to.graphNEL, allowing users to use the best algorithms from all packages.

You can access Afshin's slides via our Meetup site.

Next Kölner R meeting

The next meeting has been scheduled for 18 October 2013.

Please get in touch if you would like to present and share your experience, or indeed if you have a request for a topic you would like to hear more about. For more details see also our Meetup page.

Thanks again to Bernd Weiß for hosting the event and Revolution Analytics for their sponsorship.

Yesterday the first R in Insurance conference took place at Cass Business School in London.

I think the event went really well, but as a member of the organising committee my view is probably skewed. Still, we had a variety of talks, a full house, a great conference dinner and to top it all, the Tower Bridge opened while we had our drinks at the end of the evening.

I will post a more complete review in the future with links to the files of the presentations and R code, once we had a chance to collate all the information.

Many thanks again to all who helped to make this event a success, particularly Andreas Tsanakas at Cass and to our sponsors Mango Solutions and CYBAEA.

Today Diego and I will give our googleVis tutorial at useR!2013 in Albacete, Spain.

googleVis Tutorial at useR! 2013

We will cover:

• Introduction and motivation
• Google Chart Tools
• R package googleVis
  
  • Concepts of googleVis
  • Case studies
• googleVis on shiny

The useR!2013 conference in Albacete, Spain, will commence next Wednesday, 10 July, and on the day before Diego and I will give a googleVis tutorial.

The following Monday, 15 July, the first R in Insurance event will take place at Cass Business School and I am absolutely delighted with the programme and the fact that we are sold out.

On Tuesday, 16 July, the LondonR user group meets in the City, awaiting presentations by Andrie de Vries (Revolution Analytics), Rich Pugh (Mango Solutions) and Hadley Wickham (RStudio).

Finally on Friday, 19 July, the next Cologne R user group meeting is scheduled with two talks: Predicting the Euro/Dollar exchange rates with Twitter (Dietmar Janetzko) and Networks in R using igraph (Afshin Sadeghi).

Last week I had the honour to give the opening keynote talk at the Talking Data South West
conference, organised by the Exeter Initiative for Statistics and its Applications. The event was chaired by Steve Brooks and brought together over 100 people to discuss all aspects of data: from collection and analysis through to visualisation and communication.

Building interactive relationships with data and colleagues

The programme was very good with a variety of talks such as How data collection from smart phones can improve agronomic decision making in potato crops by Robert Allen or Spatial data and analysis in the improvement of aquatic ecosystem health and drinking water quality by Nick Palling. I also liked Richard Everson's presentation on Visualising and understanding multi-criterion league tables, which showed new ideas to create rankings.

However, my highlight was Alan Smith's talk on Information for the Masses: Using Visualisation to Engage the Public. Alan heads up the Data Visualisation Unit at the Office for National Statistics and they created some fantastic online visualisation tools. He presented some interactive examples of the Census 2011 data set. Alan mentioned the hilarious story of a young lady, who had moved from Leeds to Elmbridge and used the Census data to find out why her new home was so dull compared to her old.

Screen shot of the 2011 Census comparator

The screen shot shows the age distribution of Leeds (left) and Elmbridge (right). Focus on the 20-24 year old age group and you'll get the joke.

The Google Charts Tools provide two kinds of heat map charts for geographical data, the Flash based Geomap and the HTML5/SVG based Geochart.

I prefer the Geochart as it doesn't require Flash, but so far there have been two shortcomings with it: I couldn't add additional tooltip information and the default Mercator projection shows Greenland the size of Africa. Both of those issues seemed to have been resolved by Google. Although the features aren't officially documented and released yet, Mitchell Foley from the Google Chart Tools team presented the new developments at the Google I/O 2013 conference in May already.



With version 0.4.3 of googleVis, and thanks to John Muschelli, gvisGeoChart gained a new argument hovervar allowing users to add further information to the tooltip. Additionally, following the examples in Mitchell's presentation I can change the projection as well. The official release from Google shouldn't be too far away.

So, here are again the heat maps of countries' credit ratings from three American and one Chinese rating agency, sourced from Wikipedia. However, this time I use gvisGeoChart, setting the projection to Kavrayskiy VII and the tooltip to the actual rating letter(s), see the R code below.