Renjin will allow R scripts to transparently interact with data wherever it’s stored, whether that’s on disk, in a remote database, or in the cloud.
Renjin offers performance improvements in executing R code using techniques such as deferred computation, implicit paralellism, and just-in-time compilation.
Renjin enables R developers to deploy their code to Platform-as-a-Service providers like Google Appengine, Amazon Beanstalk or Heroku.
Built on the JVM, Renjin allows R code to interact directly with JVM libraries and data structures, without the need for expensive data transfer or brittle inter-process communication.
More productive development
With Renjin, analysts can move directly from prototype to production. There is no need to port R code to C++ or Java for production use.
The new packages.renjin.org
Renjin's new packages.renjin.org offers continuous builds of CRAN packages for Renjin users, but is also part of a larger strategy to improve package management for R users running Renjin
Alex Bertram presents Renjin at the RIOT Workshop in Prague [video]
Follow along as Alex Bertram explains some of the optimizations that have been implemented in Renjin as part of our collaboration with Hannes Mühleisen from CWI.
GenBench: real-world genomics benchmarks for R
R is a popular language in the field of bioinformatics and the Bioconductor project has become the main repository for extension packages and data formats. In this post, Ieuan describes 'GenBench', a suite of benchmarks to test the performance of GNU R and Renjin for genomics.
CRAN packages are automatically built and tested against Renjin. Many of them can be loaded directly into Renjin without the need to install them first.
Renjin is a project initiated by BeDataDriven, a company providing consulting in analytics and decision support systems. We also provide commercial support for Renjin.