**Revolutions**, and kindly contributed to R-bloggers)

As part of all the news from yesterday, we also announced our vision and roadmap for the Revolution R product line for 2010. You can see a short summary of our vision in this two-minute video, or see more details in the roadmap whitepaper available for download. But here’s a quick overview of our plans:

First, we intend to make it possible to use the R language to perform statistical analyses on very large datasets, without being constrained by the amount of RAM available, and by being able to draw on the power of a number of machines in a cluster or in the cloud to tackle these large problems. We’ve got quite a bit of experience in this area: our ParallelR libraries have been available for more than 2 years now. But now we’re taking it to the next level, so that as an R programmer you don’t have to parallelize the R computations yourself: instead, we’re writing algorithms for data manipulation and statistical models that automatically run in parallel and take advantage of the CPUs and machines available. (I talked about some of the details of this project at the R/Finance 2010 conference.)

Second, we’re working on a Web Services layer for R, to make it easier for application developers to build applications — especially Web-based applications — that take advantage of computations done in R. At the back-end of this layer sits one or more R servers (again, in a cluster or in the cloud) to support the demands of high-performance applications.

Third, we’re building a graphical user interface for R. It’s being built as a thin-client application on top of the web-services layer described above. It’s being designed so that even a casual user who needs to analyze data can open a web browser and point and click to do "standard" statistical analyses … but you always have access to the R code both as a learning tool for the language, and so you can extend the GUI for new applications.

Finally, we plan to make it easier to migrate to Revolution R if you’re currently using other statistical tools, to help you translate legacy data and code into the R environment.

It’s a big plan, but it’s one believe in: we strongly feel that R is the best environment out there for doing statistical analysis, and we’re doing everything we can to translate R’s success in the academic world to the commercial environment. Read more in the document linked below.

Revolution Analytics: Executive Roadmap: The R Revolution

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**Revolutions**.

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