[This article was first published on mages' blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Last Friday the Cologne R user group came together for the 14th time, and for the first time we met at Startplatz, a start-up incubator venue. The venue was excellent, not only did they provide us with a much larger room, but also with the whole infrastructure, including table-football and drinks. Many thanks to Kirill for organising all of this!Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Photo: Günter Faes |
We had two excellent advanced talks. Both were very informative and well presented.
Data Science at the Command Line
Kirill Pomogajko showed us how he uses various command line tools to pre-process log-files for further analysis with R.Photo: Günter Faes |
The columns appear to be separated by a blank at first glance, but the second column has strings such as “Air Force”. Furthermore, other columns have missing data and another uses speech-marks. Thus, it’s messy and difficult to read into R.
To solve the problem Kirill developed a Makefile that uses tools such as
scp
, sed
and awk
to download and clean the server files. Kirill’s tutorial files are available via GitHub.
An Introduction to RStan and the Stan Modelling Language
Paul Viefers gave an great introduction to Stan and RStan, with a focus on explaining the differences to other MCMC packages such as JAGS.
Photo: Günter Faes |
Stan is a probabilistic programming language for Bayesian inference. One of the major challenges in Bayesian analysis is that often there is no analytical solution for the posterior distribution. Hence, the posterior distribution is approximated via simulations, such as Gibbs sampling in JAGS. Stan, on the other hand, uses Hamiltonian Monte Carlo (HMC), an algorithm that is more subtle in proposing jumps, using more structure by translation into Hamiltonian mechanics framework.
Paul ended his talk by walking us through the various building blocks of a Stan script, using a hierarchical logistic regression example.
You can access Paul’s slides on Dropbox.
Drinks and Networking
No Cologne R user group meeting is complete without Kölsch and networking. In the end some of us ended up in a fancy burger place.Next Kölner R meeting
The next meeting will be scheduled in September. Details will be published on our Meetup site. Thanks again to Revolution Analytics for their sponsorship.
This post was originally published on mages’ blog.
To leave a comment for the author, please follow the link and comment on their blog: mages' blog.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.