Review: Kölner R Meeting 13 December 2013
Last week's Cologne R user group meeting was the best attended so far. Well, we had a great line up indeed. Matt Dowle came over from London to give an introduction to the
Matt's goal with the
I said early that
Thomas Rahlf talked about his forthcoming book Datendesign mit R (Data design with R). He shared with us his motivations and aims for the book. In his opinion there are many books that present beautiful charts and concepts (e.g. Tufte's books), but then don't show how they can be reproduced, as there are often done with software such as Adobe Illustrator. Or books explain the graphical functions of a software, yet fail to demonstrate how to create beautiful charts with them. Thus, Thomas' book will contain 100 examples demonstrating that desktop publishing quality charts can be produced with R and in some cases with the help of LaTeX. Indeed, all examples have about 40 lines of code and use the base R graphics system only and not
The book's accompanying web site gives you a taster already. The book itself will be published by Open Source Press next month.
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.
data.table package. He was joined by his collaborator Arun Srinivasan, who is based in Cologne. Their talk was followed by Thomas Rahlf on Datendesign mit R (Data design with R).data.table
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| Download slides |
Matt's goal with the
data.table package is to reduce times; time to write code and to execute code. His talk illustrated how the syntax of data.table, not unlike SQL, can produce shorter and more readable code that at the same time provides an efficient and fast way to analyse big in memory data sets with R. Arun presented on new developments in data.table 1.8.11, which not only fixes bugs but adds many new features such as melt/cast and further speed gains. I said early that
data.table rocks. For more details see the data.table home page.Data design with R
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| Thomas Rahlf: Datendesign mit R |
Thomas Rahlf talked about his forthcoming book Datendesign mit R (Data design with R). He shared with us his motivations and aims for the book. In his opinion there are many books that present beautiful charts and concepts (e.g. Tufte's books), but then don't show how they can be reproduced, as there are often done with software such as Adobe Illustrator. Or books explain the graphical functions of a software, yet fail to demonstrate how to create beautiful charts with them. Thus, Thomas' book will contain 100 examples demonstrating that desktop publishing quality charts can be produced with R and in some cases with the help of LaTeX. Indeed, all examples have about 40 lines of code and use the base R graphics system only and not
grid or any add-ons such as lattice or ggplot2. The book's accompanying web site gives you a taster already. The book itself will be published by Open Source Press next month.
The Schnitzel
Of course the evening ended with Schnitzel and Kölsch at the Lux. |
| The Luxus Schnitzel. Photo by Günter Faes |
Next Kölner R meeting
The next meeting is scheduled for 26 February 2013 (Wednesday before Altweiber), with two talks by Diego de Castillo (Connecting R with databases) and Kim Kuen Tang (R and kdb+).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.
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Next Kölner R User Meeting: 13 December 2013
Quick reminder: The next Cologne R user group meeting is scheduled for this Friday, 13 December 2013. We are delighted to welcome:
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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- Matt Dowle and Arun Srinivasan: Introduction to
data.table - Thomas Rahlf: Book presentation - Datendesign mit R
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.
View Larger Map
Kölner R Users
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R in Insurance Conference, London, 14 July 2014
The second conference on R in Insurance will be held on Monday 14 July 2014 at Cass Business School in London, UK.
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)
Details about the registration and abstract submission process will be published soon on www.RinInsurance.com.
You can contact us via rinsuranceconference at gmail dot com.
The organisers, Andreas Tsanakas and Markus Gesmann, gratefully acknowledge the sponsorship of Mango Solutions, RStudio, Cybaea and PwC.
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Not only verbs but also believes can be conjugated
Following on from last week, where I presented a simple example of a Bayesian network with discrete probabilities to predict the number of claims for a motor insurance customer, I will look at continuous probability distributions today. Here I follow example 16.17 in Loss Models: From Data to Decisions [1].
Suppose there is a class of risks that incurs random losses following an exponential distribution (density \(f(x) = \Theta {e}^{- \Theta x}\)) with mean \(1/\Theta\). Further, I believe that \(\Theta\) varies according to a gamma distribution (density \(f(x)= \frac{\beta^\alpha}{\Gamma(\alpha)} x^{\alpha \,-\, 1} e^{- \beta x } \)) with shape \(\alpha=4\) and rate \(\beta=1000\).
In the same way as I had good and bad driver in my previous post, here I have clients with different characteristics, reflected by the gamma distribution.
The textbook tells me that the unconditional mixed distribution of an exponential distribution with parameter \(\Theta\), whereby \(\Theta\) has a gamma distribution, is a Pareto II distribution (density \(f(x) = \frac{\alpha \beta^\alpha}{(x+\beta)^{\alpha+1}}\)) with parameters \(\alpha,\, \beta\). Its k-th moment is given in the general case by
\[
E[X^k] = \frac{\beta^k\Gamma(k+1)\Gamma(\alpha - k)}{\Gamma(\alpha)},\; -1 < k < \alpha. \] Thus, I can calculate the prior expected loss (\(k=1\)) as \(\frac{\beta}{\alpha-1}=\,\)333.33.
Now suppose I have three independent observations, namely losses of $100, $950 and $450 over the last 3 years. The mean loss is $500, which is higher than the $333.33 of my model.
Question: How should I update my belief about the client's risk profile to predict the expected loss cost for year 4 given those 3 observations?
Visually I can regard this scenario as a graph, with evidence set for years 1 to 3 that I want to propagate through to year 4.
Suppose there is a class of risks that incurs random losses following an exponential distribution (density \(f(x) = \Theta {e}^{- \Theta x}\)) with mean \(1/\Theta\). Further, I believe that \(\Theta\) varies according to a gamma distribution (density \(f(x)= \frac{\beta^\alpha}{\Gamma(\alpha)} x^{\alpha \,-\, 1} e^{- \beta x } \)) with shape \(\alpha=4\) and rate \(\beta=1000\).
In the same way as I had good and bad driver in my previous post, here I have clients with different characteristics, reflected by the gamma distribution.
The textbook tells me that the unconditional mixed distribution of an exponential distribution with parameter \(\Theta\), whereby \(\Theta\) has a gamma distribution, is a Pareto II distribution (density \(f(x) = \frac{\alpha \beta^\alpha}{(x+\beta)^{\alpha+1}}\)) with parameters \(\alpha,\, \beta\). Its k-th moment is given in the general case by
\[
E[X^k] = \frac{\beta^k\Gamma(k+1)\Gamma(\alpha - k)}{\Gamma(\alpha)},\; -1 < k < \alpha. \] Thus, I can calculate the prior expected loss (\(k=1\)) as \(\frac{\beta}{\alpha-1}=\,\)333.33.
Now suppose I have three independent observations, namely losses of $100, $950 and $450 over the last 3 years. The mean loss is $500, which is higher than the $333.33 of my model.
Question: How should I update my belief about the client's risk profile to predict the expected loss cost for year 4 given those 3 observations?
Visually I can regard this scenario as a graph, with evidence set for years 1 to 3 that I want to propagate through to year 4.
Predicting claims with a Bayesian network
Here is a little Bayesian Network to predict the claims for two different types of drivers over the next year, see also example 16.15 in [1].
Let's assume there are good and bad drivers. The probabilities that a good driver will have 0, 1 or 2 claims in any given year are set to 70%, 20% and 10%, while for bad drivers the probabilities are 50%, 30% and 20% respectively.
Further I assume that 75% of all drivers are good drivers and only 25% would be classified as bad drivers. Therefore the average number of claims per policyholder across the whole customer base would be:
To answer the above question I present the data here as a Bayesian Network using the
Next, I set the client's evidence (0 claims in year one and 1 claim in year two) and propagate these back through my network to estimate the probabilities that the customer is either a good (73.68%) or a bad (26.32%) driver. Knowing that a good driver has on overage 0.4 claims a year and a bad driver 0.7 claims I predict the number of claims for my customer with the given claims history as 0.4789.
Alternatively I could have added a third node for year 3 and queried the network for the probabilities of 0, 1 or 2 claims given that the customer had zero claims in year 1 and one claim in year 2. The sum product of the number of claims and probabilities gives me again an expected claims number of 0.4789.
[2] Søren Højsgaard (2012). Graphical Independence Networks with the gRain Package for R. Journal of Statistical Software, 46(10), 1-26. URL http://www.jstatsoft.org/v46/i10/
Let's assume there are good and bad drivers. The probabilities that a good driver will have 0, 1 or 2 claims in any given year are set to 70%, 20% and 10%, while for bad drivers the probabilities are 50%, 30% and 20% respectively.
Further I assume that 75% of all drivers are good drivers and only 25% would be classified as bad drivers. Therefore the average number of claims per policyholder across the whole customer base would be:
0.75*(0*0.7 + 1*0.2 + 2*0.1) + 0.25*(0*0.5 + 1*0.3 + 2*0.2) = 0.475
To answer the above question I present the data here as a Bayesian Network using the
gRain package [2]. I start with the contingency probability tables for the driver type and the conditional probabilities for 0, 1 and 2 claims in year 1 and 2. As I assume independence between the years I set the same probabilities. I can now review my model as a mosaic plot (above) and as a graph (below) as well.
Next, I set the client's evidence (0 claims in year one and 1 claim in year two) and propagate these back through my network to estimate the probabilities that the customer is either a good (73.68%) or a bad (26.32%) driver. Knowing that a good driver has on overage 0.4 claims a year and a bad driver 0.7 claims I predict the number of claims for my customer with the given claims history as 0.4789.
Alternatively I could have added a third node for year 3 and queried the network for the probabilities of 0, 1 or 2 claims given that the customer had zero claims in year 1 and one claim in year 2. The sum product of the number of claims and probabilities gives me again an expected claims number of 0.4789.
References
[1] Klugman, S. A., Panjer, H. H. & Willmot, G. E. (2004), Loss Models: From Data to Decisions, Wiley Series in Proability and Statistics.[2] Søren Højsgaard (2012). Graphical Independence Networks with the gRain Package for R. Journal of Statistical Software, 46(10), 1-26. URL http://www.jstatsoft.org/v46/i10/
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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] Rgraphviz_2.6.0 gRain_1.2-2 gRbase_1.6-12 graph_1.40.0
loaded via a namespace (and not attached):
[1] BiocGenerics_0.8.0 igraph_0.6.6 lattice_0.20-24 Matrix_1.1-0
[5] parallel_3.0.2 RBGL_1.38.0 stats4_3.0.2 tools_3.0.2googleVis 0.4.7 with RStudio integration on CRAN
In my previous post, I presented a preview version of googleVis that provided an integration with RStudio's Viewer pane (introduced with version 0.98.441).
Over 80% in my little survey favoured the new default output mechanism of googleVis within RStudio. Hence, I uploaded googleVis 0.4.7 on CRAN over the weekend.
However, there were also some thoughtful comments, which suggested that the RStudio Viewer pane is not always the best option. Indeed, Flash charts and gvisMerge output will still be displayed in your default browser, but also if you work on larger charts and with smaller screen, then the browser might still be the better option compared to the Viewer pane - of course you can launch the browser from the Viewer pane as well.
Hence, googleVis gained a new option
The screen shot below shows a geo chart within the RStudio Viewer pane of the
devastating typhoon track of Haiyan that hit Southeast Asia last week.
Over 80% in my little survey favoured the new default output mechanism of googleVis within RStudio. Hence, I uploaded googleVis 0.4.7 on CRAN over the weekend.
However, there were also some thoughtful comments, which suggested that the RStudio Viewer pane is not always the best option. Indeed, Flash charts and gvisMerge output will still be displayed in your default browser, but also if you work on larger charts and with smaller screen, then the browser might still be the better option compared to the Viewer pane - of course you can launch the browser from the Viewer pane as well.
Hence, googleVis gained a new option
'googleVis.viewer' that controls the default output of the googleVis plot method. On package load it is set to getOption("viewer") and if you use RStudio, then its viewer pane will be used for displaying non-Flash and un-merged charts. You can set options("googleVis.viewer" = NULL) and the googleVis plot function will open all output in the default browser again. Thanks to J.J. from RStudio for the tip. The screen shot below shows a geo chart within the RStudio Viewer pane of the
devastating typhoon track of Haiyan that hit Southeast Asia last week.
Session Info
RStudio v0.98.456 and 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
[7] base
other attached packages:
[1] googleVis_0.4.7 XML_3.95-0.2
loaded via a namespace (and not attached):
[1] RJSONIO_1.0-3 tools_3.0.2
googleVis
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gvisGeoChart
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News
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R
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RStudio
Display googleVis charts within RStudio
The preview version 0.98.441 of RStudio introduced a new viewer pane to render local web content and with that it allows me to display googleVis charts within RStudio rather than in a separate browser window.
I think this is a rather nice feature and hence I have updated the plot method in googleVis to use the RStudio viewer pane as the default output. If you use another editor, or if the plot is using one of the Flash based charts, then the browser is still the default display.
The behaviour can also be controlled via the option
Of course shiny apps can also run in the viewer pane. Here is the example of the
For the time being you can get the next version 0.4.6 of googleVis from our project site only. Please get in touch if you find any issues or bugs with this version, or add them to our issues list.
Is this a step in the right direction? Please use the voting buttons below.
I think this is a rather nice feature and hence I have updated the plot method in googleVis to use the RStudio viewer pane as the default output. If you use another editor, or if the plot is using one of the Flash based charts, then the browser is still the default display.
The behaviour can also be controlled via the option
viewer. Set options("viewer"=NULL) and googleVis will plot all output in the browser again.Of course shiny apps can also run in the viewer pane. Here is the example of the
renderGvis help page of googleVis. For more information about the new viewer pane see the online RStudio documentation. 
For the time being you can get the next version 0.4.6 of googleVis from our project site only. Please get in touch if you find any issues or bugs with this version, or add them to our issues list.
Is this a step in the right direction? Please use the voting buttons below.
Session Info
R Under development (unstable) (2013-10-25 r64109)
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] googleVis_0.4.6
loaded via a namespace (and not attached):
[1] RJSONIO_1.0-3 tools_3.1.0 
