Christ almighty, why not just use SAS at that point. Ross Ihaka was so disappointed by Revolution's value-subtracted offerings that he disowned the entire R project.
Isn't SAS better than R?
1. Usage: R has seen continual growth whereas SAS's market share has been on the decline for years. This is based on number of scholarly citations, Google Trends, number of books and blog posts with the software's name in the title, surveys, online forum references, sales volume, use in Kaggle competitions, and some other measures [0] [1]. This is consistent with my anecdotal observations in academia that R tends to be much more popular among young professors and grad students whereas SAS is mostly used by the old guard. Now, this doesn't directly speak to which is "better", but more researchers believe--and this belief is increasing--that R is better suited for their research.
2. Number of packages: While R usage uptake seems to be approximately constant, package growth appears exponential. Across all packages, R has approximately 150 times as many functions as SAS procs and in 2014 alone added more functions than the total number of SAS procs [2].
3. Package distribution: R has CRAN. I'm not aware of any centralized repository or standards for distributing packages developed by the SAS community.
4. Reproducibility: R is free, SAS is expensive and the license has to be renewed. The reproducibility crisis in the medical, biological, and social sciences is exacerbated by proprietary software that locks out other labs without the software from replicating an analysis. This may not be a conern for business, but the cost should be.
5. Scalability and ease of use: R has its quirks and warts as a programming language, but trying to write anything but a small one-off script in SAS is really something else. This is just pure opinion (which I imagine is shared by many others, but I don't have any data to back it up), but try writing a simple FizzBuzz in both and then come back and tell me I'm wrong (I was going to just post examples of both but couldn't even find one for SAS in this massive list! [3]).
6. Data visualization: I also don't have any data for a comparison here, but data visualization is frequently touted as one of the strong points of R. The native plotting is easy and powerful, and then there's the legendary ggplot2.
[0] http://r4stats.com/articles/popularity/
[1] http://blog.revolutionanalytics.com/2014/01/in-data-scientis...
[2] http://www.statsblogs.com/2015/05/22/r-now-contains-150-time...