I think so, at least for my use, but I'm at a large company and I don't see the licensing costs (nor do they directly come out of my budget). It's not to say R is bad, it just takes (me) longer to do similar work.
Of course, we're also a Teradata shop and being able to run R in-database is awfully tempting - but I doubt we'd save any money at that point (even if its not my budget, still have to justify it), and we'd have a ton of stuff to migrate. And we're not really performance bottlenecked right now.
There's a cost tradeoff there, but R has really been awesome in getting really solid statistical and data manipulation software in the hands of individuals and small businesses for free. For instance, I don't have SAS at home - my basement hacking uses R.
What I can say is that SAS allows you to get quicker at visualisations and analysis without actually knowing what you are doing. R requires you to think before and ask the questions you would like to get from the data.
Sas isn't better than R, the two softwares just target different ideal use cases IMO.
R is far more flexible, but interpreted and has some really annoying properties that make production work/reliability really hard.
SAS is a quirky model of computation that limits it in some/many areas, but makes standard operations on rectangular data and data munging/etl on such a breeze. It's compiled. Macros are both a blessing and a curse.
I still think it's better at that use than RevoR, and some of the data munging in R is not great.
Plus, I admit when I saw that RevoR has an rxdatastep or whatever, I admit I was a bit "lolwut?
This is the best answer I've seen, I wish I'd seen it before I wrote mine. Great explanations.
R/S was really meant as "glue" for FORTRAN and C linear algebra libraries (ask Chambers if you don't believe me, and look at how glmnet works). The other stuff is just to get data into a form that can be fed to LAPACK & friends.
You can burn at the stake for that kind of question.
Jokes aside, it's not even close for actual modeling building. SAS has vertical integrations which make it worthwhile for corporations. Things like portfolio risk management, marketing optimization software, intranets, BI, etc., but R is unparalleled for model building.
Naturally, for anything interesting (eg fitting a lasso penalized GLM) SAS (and others) encourage you to simply call out to R. (I will now wait for someone to amuse me by bringing up the misleadingly named PROC in SAS that does L1 regularization... to linear models.)
I can use either, personally I prefer R. Or Python. Or scala. Or really anything that isn't SAS. I feel dirty after using it.
Again, it's sort of like COBOL: it's dying, but until it's dead, there's a lot of money to be made maintaining old macro libraries etc for dinosaur companies.
If anyone brings up the FDA... Just don't do that ok?
You may be waiting for a while. The thing that R has going for it is documentation. The thing it has going against it is the monstrosity of the language implementation.
Julia is nice if you're coming from Matlab. And it sure as hell is more efficient than R or Python. But the libraries just aren't there yet. I went to implement dropout regularization a while back (a year or two ago?) and there were just so many things that I take for granted in R and Python that were completely missing. I mean, yeah it's fun to write your own SGD implementation... Once... Per lifetime...
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.