Analyzing Big Data in real life poses challenges with respect to performance, methodology and reusability. R is well known for its succinct syntax for analytic tasks as well as plethora of tools for data analysis and visualization, but it is not always associated with scalability. In this talk we will present a scalable environment that allows the use of R (and other languages) in a collaborative setting, enabling sharing, reusability and reproducibility. In addition, it opens new possibilities for visualization and interactive graphics by providing seamless integration of JavaScript and R. Finally, the distributed nature of the design allows us to provide R tools that allow out-of-core data processing interfacing different back-ends including Hadoop without sacrificing the ease of use of R. We will also show a flexible framework for developing distributed models in R while re-using as much of existing work as possible. As part of this talk we will illustrate the use of those tools on real data sets, including interactive visualization and distributed computing.
