(This article was first published on

**Realizations in Biostatistics**, and kindly contributed to R-bloggers)I work in an environment dominated by SAS, and I am looking to integrate R into our environment.

Why would I want to do such a thing? First, I do not want to get rid of SAS. That would not only take away most of our investment in SAS training and hiring good quality SAS programmers, but it would also remove the advantages of SAS from our environment. These advantages include the following:

- Many years of collective experience in pharmaceutical data management, analysis, and reporting
- Workflow that is second to none (with the exception of reproducible research, where R excels)
- Reporting tools based on ODS that are second to none
- SAS has much better validation tools than R, unless you get a commercial version of R (which makes IT folks happy)
- SAS automatically does parallel processing for several common functions

So, if SAS is so great, why do I want R?

- SAS’s pricing model makes it so that if I get a package that does everything I want, I pay thousands of dollars per year more than the basic package and end up with a system that does way more than I need. For example, if I want to do a CART analysis, I have to buy Enterprise Miner, which does way more than I would need.
- R is more agile and flexible than SAS
- R more easily integrates with Fortran and C++ than SAS (I’ve tried the SAS integration with DLLs, and it’s doable, but hard)
- R is better at custom algorithms than SAS, unless you delve into the world of IML (which is sometimes a good solution).

I’m still looking at ways to do it, although the integration with IML/IML studio is promising.

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