SAS vs. R has likely been the largest argument analytics business might have seen.

Here's a Short Description of These Two Ecosystems:


SAS has become the undisputed market leader in commercial analytics space. The program offers an enormous variety of statistical functions that have a great GUI (Enterprise Guide & Miner) for individuals to learn fast and supplies wonderful technical support. Nevertheless, it ends up being the highest-priced alternative and isn't enriched continuously with the latest statistical functions.

Read more: Understanding SAS Interface


R is the Open source counterpart of SAS, which has been applied in researchers and professors. Due to its open-source nature, the latest techniques get released immediately. There's plenty of documentation accessible over the web, and it's a very cost-effective alternative.

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Sas Vs. R

1. Availability/Price

SAS is a commercial software. It's not cheap and still beyond reach for the majority of the professionals (in individual ability). Nevertheless, it holds the largest market share in Private Organizations. Therefore, unless you're in an organization that has invested in SAS and until, it may not be easy to reach one.

Or on the other hand, R is free and may be downloaded by anyone.

2. Ease of Learning

SAS is not difficult to learn and supplies simple choice (PROC SQL) for individuals who already understand SQL. Otherwise, it's a great secure GUI interface in its repository. About resources, there are tutorials available on sites of numerous universities, and SAS has a complete instruction manual. People come at a price, although there are certifications from SAS training institutes.

R has the abrupt learning curve among the two languages recorded here. It needs you to comprehend and to learn to program. R is a programming language (low level), and therefore, straightforward processes can require more extended codes.

3. Progress in the Application

All two ecosystems have all the fundamental and most needed functions accessible. This attribute matters if you happen to be working on algorithms and the latest technologies.

As a result of their open nature, R gets the latest features fast (R compared to Python). SAS, on the other hand, upgrades its abilities in new variation rollouts. The development of new techniques is quick since R has been used extensively in professors in the past.

SAS release upgrades in managing the environment; thus, they're well analyzed. R on the flip side, there are opportunities for mistakes in the latest developments and has opened contributions.

4. What Is the Argument?

It is not that easy, Linux may do everything Windows and more, but windows still rules. Among the most significant reasons for continued Windows dominance are more comfortable user experience and impetus. In spite of all of the advantages Linux offers (better security, no viruses, and similar user experience particularly in the Ubuntu forms), the common man favors Windows, not to say Linux does not have a lively support community and its die-hard following.

5. Statistical Capacity

Other SAS programs and SAS Stat pack an active force and cover virtually the entire gamut of techniques and statistical evaluation. Yet since R is open source and individuals can submit their particular programs/libraries, the most recent cutting edge techniques are always released in R. To date, R has got nearly 15,000 programs in the CRAN (Comprehensive R Archive Network - The website which keeps the R job) repository.

A number of the most modern techniques like GLMET, AdaBoost RF, are accessible to be used in R but not in SAS. Many experimental programs are also obtainable in R., in most Kaggle competitors (which needs a site post of its own), the victor (who are amongst the world's best data miners) have nearly always used R to construct their models.

Yet a word does have to be put in about SAS since SAS is a paid application with support, any new invention, or new statistical technique must be checked and accepted in this facet R is the hands-down winner. SAS is used in several mission-critical duties where only experimental techniques are unable to be permitted to creep in. While this is essential for the environment, SAS works in; also, it means it will keep playing catch up with R in terms of the latest inventions. On the other hand, since anybody can upload a program in R, user beware!

6. Customer Care Support & Community

R has the largest online community, but no customer care support. If you have a problem, you're by yourself. You are going to get plenty of help.

SAS, on the other hand, has dedicated customer service in addition to the community. Therefore, in case you have difficulties in some other technical challenges or setup, you can reach out to them.


Certainly, there's no victor in this race. It is going to be premature to place bets on what's going to predominate, given the dynamic character of business. Determined by your conditions (career phase, Financials, etc.) you can add your weights and come up with what might be appropriate for you. Here are some special scenarios:

  • If you're a fresher entering the analytics sector, I will urge you to learn basics of Data Science with R as your first language. It holds the greatest job market share and is easy to learn.
  • If you're someone who has spent time in business, your expertise should try and diversify be learning a brand new tool.
  • For professionals and specialists in business, individuals ought to know at least 2 of these. That will add lots of flexibility for the future and open up new chances.
  • If you're in a startup/freelancing, R is less useless.

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