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.
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 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