mages' blog

Next Kölner R User Meeting: 26 February 2014

The next Cologne R user group meeting is scheduled for tomorrow, 26 February 2014. We are delighted to welcome:

• Diego de Castillo: R and databases
• Kim Kuen Tang: Hands on using R and kdb+ together
• Frank Celler: ArangoDB (Lightning Talk)

Further details and the agenda are available on our KölnRUG Meetup site.

Please sign up if you would like to come along. Notes from past meetings are available here.


The organisers, Bernd Weiß and Markus Gesmann, gratefully acknowledge the sponsorship of Revolution Analytics, who support the Cologne R user group as part of their vector programme.


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R in Insurance 2014 Conference Poster

Here is the poster for the 2nd R in Insurance conference on Monday 14 July 2014 at Cass Business School in London:

R in Insurance 2014 conference poster. Download PDF version

Important dead lines to keep in mind:

• Abstract submissions: 28 March 2014
• Early bird booking: 30 May 2014
• R in Insurance Conference: 14 July 2014

For all further information see: www.rininsurance.com.

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

Adding labels within lattice panels by group

The other day I had data that showed the development of many products over time. I grouped the products into categories and visualised the data as line graphs in lattice. But instead of adding an extensive legend to the plot I wanted to add labels to each line's latest point. How do you do that? It turns out that panel.groups is there to help again.

Here is my solution:

R code

Registration for the 2014 'R in Insurance' conference has opened

The registration for the second conference on R in Insurance on Monday 14 July 2014 at Cass Business School in London has opened.

This one-day conference will focus again on applications in insurance and actuarial science that use R, the lingua franca for statistical computation. Topics covered may include actuarial statistics, capital modelling, pricing, reserving, reinsurance and extreme events, portfolio allocation, advanced risk tools, high-performance computing, econometrics and more. All topics will be discussed within the context of using R as a primary tool for insurance risk management, analysis and modelling.

The intended audience of the conference includes both academics and practitioners who are active or interested in the applications of R in insurance.

Invited talks will be given by:

• Arthur Charpentier, Département de mathématiques Université du Québec à Montréal
• Montserrat Guillen, Dept. Econometrics University of Barcelona together with Leo Guelman, Royal Bank of Canada (RBC Insurance division)

Attendance of the whole conference is the equivalent of 6.5 hours of CPD for members of the Actuarial Profession.

We invite you to submit a one-page abstract for consideration. Both academic and practitioner proposals related to R are encouraged. The submission deadline for abstracts is 28 March 2014.

Details about the registration and abstract submission are given on the dedicated R in Insurance page at Cass Business School.

Sponsors

The organisers, Andreas Tsanakas and Markus Gesmann, gratefully acknowledge the sponsorship of Mango Solutions, Cybaea, PwC and RStudio.



Last year's programme, abstracts and talks are available online.

Control an LED with the Raspberry Pi and via the web

What a great little device the Raspberry Pi is! After my initial setup it is time to play around with the input and output pins. The first example has to be to switch on an LED. This can also be done remotely via a web interface and better, I cannot only switch the LED off, but the whole Raspberry Pi as well.


Thanks to the work of many others, it is straightforward to control the GPIO (General-purpose input/output) pins. Gordon's Project site shows how this can be done with his wiringPi library. For testing purposes I use an LED and a small resistor on pin 17. To control the LED it is as simple as:

gpio mode 0 out
gpio write 0 1
gpio write 0 0

I can also use a browser to control the pins. The webiopi projects provides an out-of-the-box solution for this. That means, I can control the pins from any other device in the network, such as my mobile phone!


Even better, I can switch the Pi off from my phone as well. Daniel Pecos presents a little script and layout that shuts the Pi down via a physical button on pin 4. But, I can also use my phone via webiopi; with Daniel's script running in the background, I can set pin 4 to input, tap it and the Raspberry Pi will shutdown.

Does sexual activity change with age?

Recently the Guardian's Data Blog reported about the results from the third National Survey of Sexual Attitudes and Lifestyles in the UK. One of the questions asked in the survey was if the participants had sex in the last four weeks. The results - a summary is available in this info graphic - show that the British have their most sexual active period when they are in their 20s - 40s.

The article ended with the Guardian asking its readers to answer the same question over the course of a week. Last Friday they published some high level numbers in a follow-up post. Of course there are many things you may criticise about their survey, e.g. it isn't randomised. However, the data provide a nice little example to get familiar with the prop.test function in R, to test if the proportions (probabilities of success) in several groups are the same, or that they equal certain given values.

Here are the data and a first plot:



Although the numbers of responses vary a lot between age groups, the proportion of those who answered with 'Yes' look more similar:


The function prop.test allows me to test, if the proportions of those who said 'Yes' are the same between different age groups.

Running the test across all age groups shows that if the hypothesis were true, then it would be very unlikely to observe the data by chance; the p-value is less than 2.2e-16. Unlike when I compare the age groups of 25-34 and 35-44 years old. Here the p-value is 11.4% and hence I might accept that the behaviours of the two groups are similar (61% vs. 59%). Adding the next age group on the other hand suggests that the three groups are less likely to have the same proportion (p-value is 0.2%). Still, when I compare the groups of 35-44 and 45-54 years old, then I might accept again that they have a similar sexual activity (p-value 8%).


What do I make of this? Well, most will not notice a change of their sexual activity on a day to day basis. Only when they look back over the decades they will notice a significant change. No surprise there, ageing is a slow process.

Session Info

R version 3.0.2 (2013-09-25)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats  graphics  grDevices  utils  datasets  methods   base     

other attached packages:
[1] latticeExtra_0.6-26 lattice_0.20-24  RColorBrewer_1.0-5 

loaded via a namespace (and not attached):
[1] grid_3.0.2  tools_3.0.2