Thomas Dinsmore's Blog

In a previous post, I summarized some myths about SAS and R — arguments offered by proponents of one or the other that deserve to be dismissed.

In this post, I will review some arguments that do make sense — things to consider if you are an aspiring analyst or if you are an executive making decisions about software for your organization.

(1) Every analysis technique available in SAS is available in R — plus many more

It’s fair to say that any analysis you can do in SAS you can also do in R.  The reverse, however, is not true — there are many techniques available in R that are not available in SAS.

As an open source platform, R is open to innovation, and offers few barriers to entry for new techniques.  An analyst who develops a new technique can quickly publish it in R, even if the technique has only niche appeal; it’s a great example of the long tail effect in action.

Commercial software providers like SAS, on the other hand, use product management calculus to balance the benefits of introducing a new technique against the cost to develop and support it.  The marginal revenue from adding a feature is hard to measure, while the costs are known, so conservative companies like SAS tend to lag well behind the cutting edge.  SAS also tends to bundle popular new capabilities into new products rather than enhancing the existing product, forcing customers to add more SAS software licenses to the stack if they want the capability.

Random Forests is a case in point.  Breiman and Cutler published their seminal article describing the technique in October, 2001; the following year, they published the randomForest package in R.  In December, 2012, SAS released an “experimental” version of what it calls “HP Forests” in SAS High Performance Analytics, and in 2013 included the PROC in SAS Enterprise Miner 13.1.

Ten years is a long time to wait.

(2) SAS is easier to learn and use than R

R mavens dispute this point, but they are wrong.  R is significantly harder to learn and use than SAS, at several levels, and for a number of reasons.

Bob Muenchen recently published an excellent catalogue of Things That Make R Hard to Learn.  Bob should know; he makes a living helping users cross the chasm from SAS to R.  Here is a brief except, but you should definitely read the whole thing:

R has a reputation of being hard to learn. Some of that is due to the fact that it is radically different from other analytics software. Some is an unavoidable byproduct of its extreme power and flexibility. And, as with any software, some is due to design decisions that, in hindsight, could have been better.

There are two main reasons SAS is easier to user than R.  First, as a commercial product every element of SAS is governed by a common design that unifies the SAS programming language, user interfaces and documentation.  As a result, SAS programming syntax and documentation is generally consistent across procedures; statements generally mean the same thing whether you are working in PROC ACCESS or PROC XML.

Developers who contribute R packages, on the other hand, operate independently and without a comparable design.  While each individual package may be well or poorly written, there is no governing principle that ensures packages are consistent with one another.  While R aficionados celebrate its diversity, to the outsider it just seems messy.

SAS’ strong development tools add significant value for the user.  SAS Enterprise Guide, for example, included with Analytics Pro at no extra charge, offers a workflow interface and the ability to generate SAS or SQL code behind the scenes.  There is no equivalent code-generating tool available for R today.

(3) SAS offers an “enterprise-grade” solution

Individual analysts surveyed by Rexer last year said that cost and ease of use are the most important factors they consider when choosing analytic tools.  For enterprises, however, the selection criteria are more complex.

Technical support is a key concern for most organizations; some go so far as to adopt blanket policies banning the use of unsupported software.  SAS invests heavily in its Support organization; unlike many large software vendors, Technical Support is a career track at SAS, with low employee turnover.  With locations located in multiple countries, SAS is able to support customers globally and at enterprise scale.

When SAS licenses its software, it warrants that the software is materially free of defects.  This warranty is backed up by a contractual commitment to fix defects that surface.  Hence, SAS offers the customer a “single throat to choke” — customers know when they license SAS that a single organization is responsible for development, distribution, implementation and support of the software, and accountability is clear.

Open source R, of course, has no organic technical support.  Organizations such as Revolution Analytics offer technical support either for open source R or Revolution’s own commercial R distribution.  Third party service providers like Revolution can be highly knowledgeable and effective; however, if there is a software defect in an R package, the support provider can only notify the developer and request resolution.

(4) SAS costs more than R

“Duh!” you say; “R is free!”  True enough.  R is open source software, distributed with a free license to use; for a single analyst, the incremental TCO to download, install and use R on an existing machine is zero.   This is also true for other key components of the R ecosystem, such as RStudio, the popular development environment.   Low cost of entry is a key driver behind R’s growing popularity.

SAS, on the other hand, charges a subscription fee which consists of a term license to use the software plus technical support and maintenance into a subscription fee.  Entry costs to license the most basic package (SAS Analytics Pro) costs $8,700 (first year fee) at the SAS online store; this package includes Base SAS, SAS/STAT and SAS/Graph.  SAS renewal fees generally run 25-30% of the first year fee.  SAS bundles its analytic features into a number of separate packages, such as SAS/ETS for time series, SAS/OR for optimization and SAS/IML for matrix manipulation; if you require these capabilities, you must pay extra.  SAS also offers access engines for an assortment of data sources, each of which can be licensed individually for $3,000 each.

The version of SAS sold through the online store is for single Windows machines only.  SAS sells its software for servers through its sales force, and pricing is negotiated; “list” price depends on the computing power of the server, measured by cores or sockets.  Server pricing for Analytics Pro starts in the low six figures.

SAS offers a virtualized “University Edition” which is free but not open source.  See here for a review.

Bottom line — for the analyst

Aspiring analysts ask “should I learn SAS or R?”   I’m tempted to answer “why not both?” but that begs the question of which to learn first.

If SAS is the primary tool at your organization or university, learn it and use it.  There are still more jobs available for SAS users than R users (though the gap is narrowing); and even prospective employers who do not currently use SAS treat it as a proxy for analytics know-how.

If your organization or university supports both SAS and R, look for trends in usage.  Is the R community growing rapidly?  Are the “best and brightest” people using SAS or R?  Is your management putting out subtle (or not-so-subtle) messages promoting use of one or the other?   Take the pulse of your organization and make your choice.

If your organization or university does not already license SAS, if you aspire to free-lance consulting or you are simply unemployed, learn R.  Doing so costs you nothing, and there are plenty of low-cost options for training and self-directed learning.

Bottom line — for the enterprise

If you are making decisions about software for an analytics team or an entire organization, the calculus is more complex.

R has more analytic techniques than SAS, but what techniques to you actually need?  Take note of your team’s actual current and future analytic needs, and act accordingly.  If you are using SAS today, the chances are very good that a handful of PROCs account for 95% of current usage; the same is true for R.

SAS is easier to learn than R, but if all or most of your analysts already know R, what difference does it make?  Many younger analysts entering the workforce already know how to use R, and it is a waste of time and money to force them to learn SAS.  On the other hand, if your analysts rely on SAS, you can expect to invest considerable time and money for retraining.

Do you need an enterprise solution?  If you organization spans multiple countries, if you support more than twenty users, the chances are that the answer is “yes”.  For a larger organization, it’s hard to beat SAS’ ability to mobilize support, training and consulting resources around the world.  This is likely to change in the future, as organizations like Revolution Analytics build scale and credibility.

SAS costs more than R, but R is not free.  If you are concerned about SAS costs, carefully evaluate your spending and take note of the value offered by SAS.  Keep in mind that software licensing costs are only one component of Total Cost of Ownership (TCO); third-party support for R is not free, and neither is training and conversion.  Do the math.

In general, SAS works well for organizations that are in the middle of Tom Davenport’s maturity cycle pictured above.   These organizations have the basic data infrastructure and business cases for analytics, combined with a need for rapid scale and consistency across locations.  As organizations mature, they become less dependent on a single vendor for analytics and more willing to develop a “best-in-breed” approach; they are more interested in innovation and “cutting-edge” techniques, and the analysts they hire have the will and skill to learn R.  These organizations are adopting R at an increasing rate.

Adoption of R is most pervasive among analytic service providers, such as consultants, system integrators and marketing service providers.  These organizations are sensitive to software costs and tend to hire most highly skilled analysts, for whom R’s learning curve is not a serious issue.  Costs aside, SAS restrictions on use — designed to prevent cannibalization– are highly problematic for service providers.