What we're about

R is an open source programming language for statistical computing, data analysis, and graphical visualization. R has an estimated one million users worldwide, and its user base is growing. It is commonly used within academia, in fields like computational biology and applied statistics, and in commercial areas such as quantitative finance and business intelligence.

Among R's strengths as a language are its powerful built-in tools for inferential statistics, its compact modeling syntax, its data visualization capabilities, and its ease of connectivity with persistent data stores (from databases to flatfiles).

In addition, R's open source nature and its extensibility via add-on "packages" has allowed it to keep up with the leading edge in academic research.

For all its strengths, though, R has an admittedly steep learning curve; the first steps towards learning and using R can be challenging.

To this end, the Berlin R Users Group is dedicated to bringing together practitioners of R to exchange knowledge, inspire new users, and spur the adoption of R for innovative research and commercial applications.

We want to be a diverse and inclusive group. Please come, no matter your race, gender and sexual orientation. We support the http://berlincodeofconduct.org (http://berlincodeofconduct.org/).

(inspired by the Bay Area R Users Group's introduction)

The logo text is written in ARIAL BLACK, the powerpoint file (https://github.com/brry/misc/raw/master/BRUG_full.pptx) is online.

Upcoming events (2)

#34 INLA-package: Geostatistical analysis of health insurance claims

Dr. Boris Kauhl | A practical overview how R and the INLA-package facilitate the geostatistical analysis of large, spatially referenced health insurance claims Health insurance claims provide a rich set of information at fine spatial scales. This is both, a great opportunity and a challenge, as such large, complex spatial data require particular methods to deal with location information. The main focus of this presentation is the application of spatial Bayesian modelling within the INLA package. Two applications will be explored: Visualizing spatial patterns of chronic diseases and examining risk factors at the individual and aggregated level. Of particular interest is explicitly spatial modelling as it enables us to see, which persons are where at major risk for chronic diseases. We will have an in depth discussion of current methodological and computational challenges and solutions to deal with large spatial data sets. Dr. Boris Kauhl works for the AOK Nordost health insurance where he focuses on spatial analyses to enhance planning and allocation of healthcare. He did his PhD about Geographic Information Systems (GIS) in public health at Maastricht University, the Netherlands. --- Short contributions and announcements from the audience are always welcome. We want to be a diverse and inclusive group, please come, bring your friends. We're hoping for a nice crowd enjoying interesting talks and chats. Our generous host Europace AG will prepare drinks and snacks for us! See you!

Needs a date and time

Needs a location

This is not an event per se, but a place to share topic wishes and offers. Please send me a message ([masked]) if you want to present. Here are the results from the spring 2017 survey (n=34) as an inspiration, with the number of people selecting each topic at the start of the line: 20 Time series analysis 19 Advanced usage of ggplot 18 Out of memory handling of large datasets 17 Computationally efficient programming, code optimization 17 Interactive graphics, dashboarding (shiny) 16 Deep learning / Machine Learning 15 Decision trees, random forests 15 Web scraping with R 14 Linear Models (Regression, Anova) 14 Mixed Linear Models 14 R for Data Science (general intro) 14 R project workflow (directories, intermediate data/graphs) 14 Usage of data.table 13 Data visualization, Vis literacy 13 Spatial statistics with R (maps, shapefiles, kriging ...) 12 R and Docker (container) 11 Natural language processing 11 Simulation and Bayesian optimization, bayesian stats with rstan (or rstanarm) 10 Interactive maps (leaflet) 10 Intro to MCMC 10 knitr, rmarkdown, bookdown, how to build reports, R notebooks 9 Code parallelization 9 Jupyter Notebook, feather exchange format 9 R internals - How the interpreter works, C-API 8 Intro to the TidyVerse 8 Optimization, parameter estimation 8 Rccp 8 Unit testing (for package development) 7 Collaborative coding (github) 7 Installation of Rstudio Server 7 Regular expressions (character string management) 7 Tutorial: write an R package with Rstudio and github 5 (choice based) conjoint analysis 4 Intro to ggplot

Past events (35)

#33 Touring the tidyverse: tidy models


Photos (26)