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Updated on 16th Nov, 22 8119 Views

A Short Description of Both Software Suites

SAS and R are important data analytics tools used in today’s tech world. Both tools are extensively used by Data Scientists and Data Analysts. Making a choice between SAS and R has been a longstanding debate in the world of Data Science.

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Statistical Analysis System (SAS) is a programming language that is used to read data from spreadsheets and databases and output the results of statistical analysis in tables and graphs and as RTF, HTML, and PDF docs. SAS is commonly used for financial analytics capabilities. SAS programming language has a wide range of statistical functions and a huge amount of graphical user interfaces (GUIs) for deploying in data analytics applications. SAS is easy to learn, and it offers great technical support. It can be considered as an expensive alternative to R; nonetheless, SAS is a proprietary tool.

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R is mostly used by the research community, professors and researchers, among other faculties. Since R is an open-source tool, we can get the latest version as soon as it is released. R is mainly used for statistical analysis, graphical representations, and reporting.

Here, we take a simplified yet concise look at the various features, functions, and strengths and weaknesses of each of these tools.

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Features of SAS and R

A market leader in commercial analytics space An effective data handling and storage facility
Provides a graphical point-and-click user interface A comprehensive and integrated collection of intermediate tools for data analysis
Data can be published in HTML, PDF, Excel, and other formats using the Output Delivery System Offers graphical facilities for data analysis and the display can be done in either soft or hard copy
Retrieves data from various sources and performs statistical analysis Includes conditionals, loops, user-defined recursive functions, and input and output facilities

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SAS R Comparison

Parameters of Comparison

Ease of Learning

SAS is very good when it comes to picking a new tool to learn without any prior programming language experience. We can analyze SQL code, integrate it with macros, and so on. Learning SAS can be an excellent experience for beginners.

R is a bit tougher to learn as compared to SAS. Before learning R, we must have a basic knowledge of programming. R is not a high-level programming language. Due to its low-level programming nature, even a small mistake can turn out to be a huge problem. Thus, when it comes to picking the first programming language to learn, R is not suggestible.

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Managing Data

In terms of handling and managing data, SAS is in a better position since the data is increasing at a huge pace day by day and SAS is better at handling it. Furthermore, R works only on RAM, and increasing the RAM as and when the data increases is not a feasible option. This is where R uses packages of plyr and dplyr.


Graphics is a very important aspect of any Data Science or Data Analytics capabilities. The ability to visualize and analyze data is a crucial part. R is the winner in this area, thanks to the availability of various packages like ggplot, Latice, and RGIS.

SAS is not great at graphical capabilities. Though Base SAS offers improvisation of some graphical capabilities, these capabilities are not widely known, and so R gets a clear lead in this aspect.

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Working with Big Data

While working with Big Data, R has some very good features which can be utilized by Big Data, Data Science, and Data Analytics communities. R has very good integration with Hadoop, and it also has a parallelization capability. If we are looking for deploying analytics at scale for Machine Learning capabilities, then R is the language to choose. Of late, SAS is taking fast strides to execute analytics within Hadoop without the need to move cluster data. But still, SAS lags behind R when it comes to integrating successfully with Big Data tools like Hadoop and others.

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Industry Deployment

Since R is an open-source programming language, it can be used by anybody and, as R is freely available to everyone, it finds widespread usage among small and medium enterprises. R is also easily scalable, thanks to its various packages and libraries which can be used for any application and function. SAS, on the other hand, is extremely useful for large organizations. SAS is mostly used for end-to-end infrastructure deployment, data warehousing, data quality, data analytics, and reporting capabilities.

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R has the most advanced graphical capabilities as compared to SAS. There are numerous packages which provide advanced graphical capabilities. R incorporates the latest features quickly as the packages get added on by programmers across the world. Currently, R is in popular demand. Although SAS has been the market leader in corporate jobs, it is very expensive for start-ups. However, gaps in the market share are vanishing fast.

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Service Support

R has the biggest online community but without customer service support, which makes it difficult for people to tackle any technical issue. Whereas, SAS has dedicated customer service, along with its community. Hence, installation and other technical challenges get easily sorted.

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Recommendation: Which one is better?

SAS programming syntax is a high-level language, easy to learn, and designed as an SQL DML (Data Manipulation Language). It has decent functional graphical capabilities, but customization on plots is very difficult. SAS updates its capabilities only in new version rollouts; it releases updates in a controlled environment which are well tested.

  • Companies currently employing SAS: Bing, Ford, Uber, and Foursquare
  • Companies currently employing R: Barclays, Nestlé, HSBC, and Volvo

SAS vs R

As said earlier, SAS is the prevalent tool in the market analytics space, but it is an expensive option and is not always updated with the latest statistical functions. R is an open-source alternative and is typically used for academic and research purposes. Due to its open-source nature, the latest functions are added quickly.

The hoice between SAS and R always depends on organizational requirements. Large-scale organizations usually opt for SAS over R. Further, for a beginner wishing to learn a programming language, R is not the correct option. Because, it is lengthy and complex to learn. The learner must also consider his/her career phase and financial stability while making a choice between SAS and R.

If you would like to learn more about R, check out this R Tutorial.

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13 thoughts on “SAS Versus R”

  1. Interesting Blog. Globally, SAS is still the market leader in available corporate jobs but ‘R’ has also been reported to increase over last few years. What do you think about it ?

  2. I would bet money that every company that you have listed as SAS users also have R users on staff, and every single company you have listed as R users has SAS users.

  3. I have used both SAS and R for many years.
    SAS has great data handling capabilities. If you have complicated transaction data and need to heavily process it then SAS works well and is much more flexible than standard SQL. If the new Hadoop version of SAS works well (my company is testing it out), I think SAS will remain a strong power in corporate processing.
    However SAS has some weaknesses.
    1. It is very expensive.
    2. Graphics are complicated and fiddly. I think the JMP program from SAS has better capabilities but I have never used it.
    3. SAS’s machine learning algorithms are mediocre and are about 10 years behind the state of the art, I think SAS needs to buy a machine learning company. For heavy machine learning algorithms my company uses Salford Systems tool which are easy to use and not super expensive.

    There are many things I like about R. The algorithms are powerful and usually easy to use even for an inexperienced R user. Second it is open source, R has some weaknesses though especially in a corporate environment.
    1. IT security hates open source software especially on a server that has sensitive data. Therefore a company will very likely have to purchase Microsoft’s (Revolution) R which is still less expensive than SAS. This also means that you won’t have access to state of the art algorithms until Microsoft implements them.
    2. R’s data handling is complicated and difficult for complex data. Usually most users will do most of the data crunching in Hive or some other SQL like program and then read the processed file into R. The way I use R is to embed it in a SAS program and then use the X command and SAS file pipes to control the R processing

  4. I have used SAS 10+ years and R 5+ years.

    Main Pros of SAS :
    – Good GUI (SAS Enterprise Miner), however You have to be SAS and R expert in order to use it fully (SAS coding, R coding etc)
    – Special solutions like Credit Scoring and Survival Data Mining. Free R and Microsoft R currently lacking such good solutions (there are some R packages like discSurv, but it takes time to learn them).
    – Very good support by SAS (technical support, good SAS courses and books, SAS Global Forum etc)

    Main Cons of SAS:
    – SAS programming language is somewhat inflexible and dated (especially SAS macro language, DATA step)

    Main Pros of R:
    – Good frameworks for automated data mining (R packages mlr, caret)
    – Very good and flexible language for programming
    – State-of-arte algorithms ready for use immediately after publication (> 10 000 packages!)
    – Reusage of R solutions / functions within SAS, Vertica etc

    Main Cons of R:
    – No good GUI like SAS Enterprise Miner
    – Lack of special solutions like credit scoring, survival data mining

  5. I think it important to note when looking at the above comparison that each of the companies listed as employing R are also heavy SAS users.

    Analytics does not have to exist in an SAS OR R world, the market leading organisations are embracing SAS AND R. Using silo’d technology is no different than having silos of data that you can’t get the most out of.

  6. The statement that SAS doesn’t have great graphical capabilities is wrong. With SAS Visual Analytics and gplots everything is possible.

  7. Because R is open source, it has now been integrated in nearly every major analytic tool (and several not so major ones) on the planet. I have personally programmed R from within SAS, Matlab, Alteryx, Tableau, Anaconda Python, and Spark and there are many more. The entire question of R versus SAS – or even R versus nearly *anything* – is broken. Analytics and data science to means R And SAS And Python And Java And web pages And And And And And….

    Lots of good commnts here on relative strengths. From my personnal, narrow perspective I would echo SAS for big data, smooth commercial operation, and specialized tools for different industries – financial, medical, and many others. SAS’s great strength in regulatory environments hasn’t been mentioned until now but it’s a huge factor in those industries where models get sued. R for open source, latest developments, and, well, integration with many other tools. That’s really the point of this post. Build R into whatever proprietary system you have and take advantage of its strengths. R integrates so widely and so well that debates on R *versus* anything should be a thing of the past.

  8. This comparison only scratches the surface. Very, very gently. So many things are missing, don’t even know where to start… Look at Quora, where comprehensive comparisons exist. SAS and R are highly comparable in terms of statistical methods implemented. They compete strongly. SAS graphing module is very powerful if one knows how to use it. Price? In most cases – nobody cares, really. If a company uses SAS, in 99.999% its income is much greater than the cost of SAS licence. which becomes negligible. And I say this as a big fan of R, using R for the last 15 years. It’s not mentioned, that SAS is validated while R not – this makes problems in controlled environments, like clinical research. Luckily it’s not a big challenge to build a fully validated environment based on R and many companies do that. It’s a myth, that R cannot be used for that purpose. Many clinical research companies, including the top most ones employ R in their processes. Other thing – R is much more flexible than SAS. One can embed R even on simplified computers, like BananaPi or Odroid. Don’t try this with SAS. This way one can make a fully independent computing unit with own user interface. One more thing: Bing, Ford and Uber belong to the group of the most well known R users, for years.

  9. I have been using SAS for about 12 years and I love it. With that said, I still prefer to create my visualizations in Tableau.

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