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Summary:  Someone had to say it.  In my opinion R is not the best way to learn data science and not the best way to practice it either.  More and more large employers agree.


Someone had to say it.  I know this will be controversial and I welcome your comments but in my opinion R is not the best way to learn data science and not the best way to practice it either.


Why Should We Care What Language You Use For Data Science

Here’s why this rises to the top of my thoughts.  Recently my local Meetup had a very well attended hackathon to walk people through the Titanic dataset using R.  The turnout was much higher than I expected which was gratifying.  The result, not so much.

Not everyone in the audience was a beginner and many were folks who had probably been exposed to R at some point but were just out of practice.  What struck me was how everyone was getting caught up in the syntax of each command, which is reasonably complex, and how many commands were necessary for example, to run the simplest decision tree.

Worse, it was as though they were learning a programming language and not the data science.  There was little or no conversation or questioning around cleansing, prep, transforms, feature engineering, feature selection, model selection, and absolutely none about about hyperparameter tuning.  In short, I am convinced that group left thinking that data science is about a programming language whose syntax they had to master, and not about the underlying major issues in preparing a worthwhile model.


Personal Experience

I have been a practicing data scientist with an emphasis on predictive modeling for about 16 years.  I know enough R to be dangerous but when I want to build a model I reach for my SAS Enterprise Miner (could just as easily be SPSS, Rapid Miner or one of the other complete platforms). 

The key issue is that I can clean, prep, transform, engineer features, select features, and run 10 or more model types simultaneously in less than 60 minutes (sometimes a lot less) and get back a nice display of the most accurate and robust model along with exportable code in my selection of languages.

The reason I can do that is because these advanced platforms now all have drag-and-drop visual workspaces into which I deploy and rapidly adjust each major element of the modeling process without ever touching a line of code.


Some Perspective

Through the 90’s (and actually well before) up through the early-2000’s if you studied data science in school you learned it on SAS or SPSS and in the base code for those packages that actually looks a lot like R.  R wasn’t around then and for decades, SAS and SPSS recognized that the way to earn market share was to basically give away your product to colleges which used it to train.  Those graduates would gravitate back to what they knew when they got out into the paying world.

In the mid-2000s these two platforms probably had at least an 80% market share and even today they have 36% and 17% respectively.  This doesn’t even begin to reflect how dominant they are among the largest companies which is probably double these numbers.

By 2000 both these providers were offering advanced drag-and-drop platforms deemphasizing code.  The major benefit, and it was huge, is that it let learners focus on the major elements of the process and understand what went on within each module or modeling technique without having to code it.

At the time, and still today, you will find SAS and SPSS purists who grew up coding who still maintain hand coding shops.  It’s what you learned on that you carry forward into commercial life.


Then Why Is R Now So Popular

It’s about the money.  R is open source and free.  Although SAS and SPSS provided very deep discounts to colleges and universities each instructor had to pay several thousand dollars for the teaching version and each student had to pay a few hundred dollars (eventually there were student web based versions that were free but the instructor still had to pay).

The first stable beta version of R was released in 2000.  If you look at the TIOBE index of software popularity you’ll see that R adoption had its first uptick when Hadoop became open source (2007) and the interest in data science began to blossom.  In 2014 it started a strong upward adoption curve along with the now exploding popularity of data science as a career and its wide ranging adoption as the teaching tool of choice.

This was an economic boon for colleges but a step back for learners who now had to drop back into code mode.  The argument is common that R’s syntax is at least easier than others but that begs the question that drag-and-drop is not only orders of magnitude easier but makes the modeling process much more logical and understandable.


Do Employers Care

Here you have to watch out for what appears to be a logical contradiction.  Among those who do the hiring, the requirement that you know R (or Python) is strong and almost a go/no go factor.  Why?  Because those doing the hiring were very likely to have been taught in R and their going-in assumption is if I had to know it then so do you.

Here’s the catch.  The largest employers, those with the most data scientists are rapidly reconsolidating on packages like SAS and SPSS with drag-and-drop.  Gartner says this trend is particularly strong in the mid-size and large companies.  You need to have at least 10 data scientists to break into this club and the average large company has more like 50. 

We’re talking about the largest banks, mortgage lenders, insurance companies, retailers, brokerages, telecoms, utilities, manufacturers, transportation, and largest B2C services companies.  Probably where you’d like to work unless you’re in Silicon Valley.

Once you have this many data scientists to manage you rapidly become concerned about efficiency and effectiveness.  That’s a huge investment in high priced talent that needs to show a good ROI.  Also, in this environment it’s likely that you have from several hundred to thousands of models that direct core business functions to develop and maintain. 

It’s easy to see that if everyone is freelancing in R (or Python) that managing for consistency of approach and quality of outcome, not to mention the ability for collaboration around a single project is almost impossible.  This is what’s driving the largest companies to literally force their data science staffs (I’m sure in a nice way) onto common platforms with drag-and-drop consistency and efficiency.


Gartner Won’t Even Rate You Unless You Have Drag-and-Drop

Gartner’s ‘Magic Quadrant for Advanced Analytic Platforms’ and Forrester’s report on ‘Enterprise Insight Platform Suites’ are both well regarded ratings of comprehensive data science platforms.  The difference is that Gartner won’t even include you in their ranking unless you have a Visual Composition Framework (drag-and-drop). 

As a result Tibco, which ranks second in the 2016 Forrester chart was not even considered by Gartner because it lacks this particular feature.  Tibco users must work directly in code.  Salford Systems was also rejected by Gartner for the same reason.

Gartner is very explicit that working in code is incompatible with the large organization need for quality, consistency, collaboration, speed, and ease of use.  Large groups of data scientists freelancing in R and Python are very difficult to manage for these characteristics and that’s no longer acceptable.

Yes essentially all of these platforms do allow highly skilled data scientists to insert their own R or Python code into the modeling process.  The fact is however that the need for algorithms not already embedded in the platform is rapidly declining.  If you absolutely need something as exotic as XGboost you can import it.  But only if that level of effort is warranted by a need for an unusually high level of accuracy.  It’s now about efficiency and productivity.


Should You Be Giving Up R

If you are an established data scientist who learned in R then my hat’s off to you, don’t change a thing.  If you’re in a smaller company with only a few colleagues you may be able to continue that way.  If you move up into a larger company that wants you to use a standardized platform you shouldn’t have any trouble picking it up.

If you’re an early learner you are probably destined to use whatever tools your instructor demands.  Increasingly that’s R.  It’s not likely that you have a choice.  It’s just that in the commercial world the need to actually code models in R is diminishing and your road map to a deep understanding of predictive modeling is probably more complex than it needs to be.


A Quick Note About Deep Learning

A quick note about deep learning.  Most programming in Tensorflow is occuring in Python and if you know R you shouldn’t have a problem picking it up.  Right now, to the best of my knowledge, there are no drag-and-drops for deep learning.  For one thing, deep learning is still expensive to execute in terms of manpower, computing resource, and data acquisition.  The need for those skills here in 2017 are still pretty limited, albeit likely to grow rapidly.  Like core predictive modeling, when things are difficult I’m sure there’s someone out there focusing on making it easier and I bet that drag-and-drop is not far behind.



About the author:  Bill Vorhies is Editorial Director for Data Science Central and has practiced as a data scientist and commercial predictive modeler since 2001.  He can be reached at:

[email protected]


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Comment by Tony Lange on May 18, 2017 at 9:37pm

I like what Shen Wang said, plus a few others.

Congrats to William Vorhies for having the guts to start such a controversial discussion, his opinion which I totally disagree with anyway. But I think to remain courteous and debate this like adults is the most important thing. Nothing is totally wrong or right with the pros and cons, and everything has uncertainties and a tiny extra bit of food for thought is the best outcome here. 

Comment by Zhen Wang on May 18, 2017 at 5:13pm

No coding language is bad. Some python modules are missing their counterpart in R, and some R packages are missing in python. In my opinion, a good data scientist should be able to hack their way through any problem, using any tools/languages that are best for the job at hand. 

Comment by Vincent Granville on May 18, 2017 at 2:53pm

One thing not to forget, most job ads (for data scientists) require R. At least it helps to know R to land a job.

Comment by Paul E. Johnson on May 18, 2017 at 10:23am
This is factually incorrect on some levels. One, SPSS is not given to universities as a free sample. My place pays more than $100,000 year for the luxury. If we are getting a discount compared to big companies, that is good news to me. The other thing that seems factually wrong is that new hires who need to do data science will be more easily managed if they use SAS or SPSS. SAS and SPSS are both coding languages and it create new routines, code will have to be created. The big question is whether it is easier to manage coders writing SPSS or SAS than R. I don't think the jury is out on that one. R wins hands down.
Comment by Laura E. Wood Squier on May 18, 2017 at 7:46am

Well said!

One side point benefit on standardizing on a drag and drop workbench is also the ease of transferring the modeling efforts to other team members.  I have worked with too many clients over the years that have lost work effort of elegantly written code when a team member has left.  I'm also trying to better understand what people are doing with Predictive/Advanced Analyics and am running a survey on the topic.  My goal is to get real feedback on what people are doing, versus industry hype:

Comment by Bob Muenchen on May 18, 2017 at 3:24am

A number of people have commented that drag-and-drop interfaces do not yield reproducible analyses. The "visual composition framework" that Gartner emphasizes is nothing like the menu-and-dialog-box interface that many people are familiar with from SPSS, Statistica, Stata, or R Commander. KNIME, RapidMiner, SAS Enterprise Miner, and SPSS Modeler all provide a workflow interface (Gartner's VCF) that is amenable to extremely complex analyses that are completely reproducible. Of those, only KNIME is open source. RapidMiner is free, but only under 10,000 cases. KNIME and RapidMiner also have hooks into other systems such as Spark and H20, as well as R, Python, etc. Personally, I use R more than any other tool, but the KNIME interface is quick and fun to use, and researchers who are not full-time data scientists are likely to be far more productive with it than with any programming language. These aren't folks who develop new methods of analysis, they simply apply those developed by others. 

The debate about the ever-changing marketshare of these various tools is best settled with data. I collect and discuss such data regularly here: and the two most popular surveys of data science software use have just opened up, so we're all free to provide more data to see where the data science market stands today:

Comment by R. Lanham on May 18, 2017 at 1:51am

I'll take R.  First, you aren't dependent on a firm to keep interfaces and tools up to date.  Second, knowing syntax allows you to integrate other code and optimise algorithms easily.  Sure, if you do basic stats on small data, SPSS is fine.  So is Excel.  But most of us do a lot more. SAS allows that and always has--but it is vast, clunky and arcane in my opinion.  You get nowhere without classes, etc.  I dispute the idea that their user base is growing--in my experience, especially in Europe, it is in cash cow mode--used only at old and relatively low innovation shops like food companies.  But that is a fact that can be checked.  I doubt anyone quite knows.  One thing is certain.  R is free and has thousands of brilliant people building free things for it every day.  Over my 30+ years in data, I have found a lot of people just don't like free.  So be it.  Free tends to win, and it always will.  BTW, R is not enough, you need Python too, but if R scares you for syntax, Python will give you nightmares.  That said, I have seen several 9 year olds who code it just fine.  I am happy SAS exists--sometimes they innovate.  Within weeks the equivalent is available for free in R.  Long may that continue.

Comment by Tony Lange on May 18, 2017 at 12:40am

I think everyone is entitled to their opinion. But in essence I totally disagree with William. The purpose of R is not to teach data analytics, neither is any language for that matter. IF one uses IMSL one of the oldest sources of numerical processing algorithms written in FORTRAN, and goes through the documentation, one can start to get an idea of how complex much of these "analytical tools" are and how dangerous using any analytics can be. 

I personally hate the hyped up term "data analytics" etc, its actually maths, stats, and numerical methods. How do you teach these topics, with a toolset ? Absolutely not. Teaching can only be done by going through the pure and applied maths step by step , the equations, the assumptions, etc with a pen or chalk. Computerisation is just final small step.

You would be surprised how many people don't really know what a mean is, that gaussians are the biggest underlying assumptions, and simply totally at odds with reality. Never mind an the s-domain, which begat the FFT, and most theories and methods which simply do not fit with reality. Who understands the huge error issues in numerical techniques, significant digits, and that computer ALU's randomly make mistakes ?  

Yes graphical tools are awesome, easier to use, but in fact most users of these tools have no clue about what the blocks do, or how they work. In fact they worsen the situation where people apply methods or algorithms willy-nilly and take their results as gospel.

Today, with all these tools, every man and his dog can become a "data analyst", and "therein lies the rub". 

Comment by Mari Tietze on May 17, 2017 at 11:41pm

Thank YOU!!!  Teaching health informatics, including SPSS for analytics, I have often critiqued the value of R . . . this put it in perspective.  

Comment by Srikanth KS on May 17, 2017 at 9:32pm


The article looks more like a propaganda, than a criticism.

With Data Science boom, we have people who want to learn quickly with languages like R, Python, SAS without a deep understanding of the language, its design choices and memory management. Most workshops/hackathons are done in haste, leaving learners in dire straits.

For those who are willing to spend time and effort to understand the language itself, choose to read the right books (Hadley's Advanced R, Matloff's Art of R programming) instead of doing a few MOOCs.

Quoting JJ Allaire: " ... Again, you should understand R as a user interface, or a language that’s capable of providing very good user interfaces. Developers don’t implement core algorithms or core distributed computing primitives in R. They use R as a way of interacting with those things.Once people understand this, and they see how good R is, how they can use dplyr to interact with a Spark cluster ... "

R provides convenient statistical modeling  and machine learning capabilites (orchestrated by caret, mlr (inspired by scikit-learn)), ability to pipeline cleaning, building models, hyper parameter tuning, ensembling and building API endpoints to be used in production.

There are few GUIs built on R(a lot of commenters mentioned them). There is even radiant built on shiny. Those who wish drag and drop kind of stuff, choose them. But, once you are adept at programming, you are most likely not to use them. Note that visualization is a different behemoth altogether.

I believe that this is neither the time or place to talk more about R or Python. I have spent years doing data analysis in R and Python. I have built production models in R, Python, H2o etc. I have written R packages, some are on CRAN.

For those who want to choose a language/tool for data analysis in an unbiased manner, please get hold of R, Python, Julia, SAS and SPSS (if your pockets permit) and spend some time with them. People are paid to write against R and open-source.

Best Wishes,
Srikanth KS

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