I will be speaking at the Bay Area User Group meeting tonight about Communicating Risk. Anthony Goldbloom from Kaggle and Karim Chine from ElasticR will be there as well. The meeting will be at Microsoft in Mountain View.
Later this week I will give a similar presentation at the R in Finance conference in Chicago. Please get in touch if you are around and would like to share a coffee with me.
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.
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.
Building R packages is not particular hard, but it can be a bit of a daunting endeavour at the beginning, particularly if you are more of a statistician than a computer scientist or programmer. Some concepts may appear foreign or like red tape, yet many of them evolved over time for a reason. They help to stay organise, collaborate more effectively with others and write better code. So, here are my slides of the R package development workshop at Lancaster University.
Following on from last week’s post, here are my slides on using googleVis on shiny from the Advanced R workshop at Lancaster University, 21 May 2013.
googleVis on shiny
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.
Every year the UK’s general insurance actuarial community organises a big conference, which they call GIRO, short for General Insurance Research Organising committee.
This year’s conference is in Brussels from 18 - 21 September 2012. Despite the fact that Brussels is actually in Belgium the UK actuaries will travel all the way to enjoy good beer and great talks. On Wednesday morning I will run a session on Using R in insurance.
Today I feel very lucky, as I have been invited to the Royal Statistical Society conference to give a tutorial on interactive web graphs with R and googleVis.
I prepared my slides with RStudio, knitr, pandoc and slidy, similar to my Cambridge R talk. You can access the RSS slides online here and you find the original R-Markdown file on github. You will notice some HTML code in the file, which I had to use to overcome my knowledge gaps of Markdown or its limitations.
This evening I will talk about Dynamical systems in R with simecol at the LondonR meeting.
Thanks to the work by Thomas Petzoldt, Karsten Rinke, Karline Soetaert and R. Woodrow Setzer it is really straight forward to model and analyse dynamical systems in R with their deSolve and simecol packages.
I will give a brief overview of the functionality using a predator-prey model as an example.
This is of course a repeat of my presentation given at the Köln R user group meeting in March.
Tonight I will give a talk at the Cambridge R user group about googleVis. Following my good experience with knitr and RStudio to create interactive reports, I thought that I should try to create the slides in the same way as well.
The first Kölner R user meeting was great fun. About 20 useRs had turned up to exchange their ideas, questions and experience with R. Three talks about R & Excel, ggplot2 & XeLaTeX and Dynamical systems with R & simecol had kicked off the evening, with Kölsch (beer) losing our tongues further.
Thankfully a lot of people had brought along their laptops, as unfortunately we lacked a cable to connect any of the computers to the installed projector.
The London R user group met again last Wednesday at the Shooting Star pub. And it was busy. More than 80 people had turned up. Was it the free beer and food, sponsored by Mango, which attracted the folks or the speakers? Or the venue? James Long, who organises the Chicago R user group meetings and who gave gave the first talk that night, noted that to his knowledge only the London and Chicago R users would meet in a pub.
Data analysis is often an iterative and interactive process. However, when I present about this subject, I feel often limited by the presentation software I use. It doesn’t matter if I use LaTeX/PDF, PowerPoint or Keynote. In all cases it is either very difficult or impossible to include interactive charts, such as Flash or SVG charts. As a result I have to switch between various applications during the talk. This can be fun, but quite often it is not.
On 7 September 2011 I attended the London R user group meeting. It was a very good turn out with about 50 attendees at the Shooting Star, a pub close to Liverpool Street Station. The session started at 18:00 with four presentations, followed by drinks sponsored by Mango Solutions. The slides of the presentation are available on londonr.org.
The first presentation was given by Lisa Wainer from UCL Department of Security and Crime Science about crime data analysis using R.