The choice between using  SAS vs R is a raging debate in the data analysis community today. Industry surveys show quantitative professionals using R and SAS at similar rates with a small minority still relying on Python. Each tool has its advantages and disadvantages, and they serve very different markets. When it comes to SAS vs R – which one is better for you?

The Pros of SAS

SAS has been a dominant player in the commercial data analytics space. It has a good graphical user interface and a huge array of statistical functions as well as data analysis functions. Because of its large market share, there are many secondary tools that support SAS in addition to the good technical support SAS offers. There are also many other software applications interface with SAS.

You can earn certifications in SAS from various training institutes and SAS consulting is profitable because tens of thousands of large companies pay for experts to help them solve problems, create custom reports, or build interfaces.

Because SAS is so well entrenched in big businesses for whom price isn’t as much of an object, it is a good choice for those who want to work for airlines, financial services companies, CRM firms and logistics providers.

The Cons of SAS

SAS doesn’t update with new features as quickly as R, but its releases are thoroughly tested before being released. You may not get new features as soon as they show up in R, but you can be assured they work.

You can drag and drop data to create statistical models quickly. However, you cannot use the program to create complex graphical plots without a lot of effort.

The Pros of R

R is the open source alternative to SAS, and it wins on low cost. Academics and startups chose R for this reason. R has a disproportionate following in telecoms and tech companies.

R is widely used in academia and research, and this is why it has the better reputation for cutting edge data mining. If you want to use techniques like GLMET or ADABoost, you have to use R.

R has better graphical capabilities, especially if you want to customize graphs.

R offers functions that SAS cannot such as decision trees, association rule mining, and advanced data mining. This is why R is the tool to use for data mining.

The Cons of R

R has a steep learning curve and it requires you to understand both programming and data analysis. It is worse than learning C++.

Because R is a low-level programming language, coding something simple can take more time than a programming language like Python.

Data handling in R causes problems if you try to crunch large data sets on small computers. This isn’t an issue with SAS. Because of how R tries to load the entire data set before crunching the numbers, R will take longer to run on a PC than SAS, though the problem goes away if you’re working on a server.


If you’re a data scientist, you’re as likely to be using R as Python, and almost never using SAS. If you are data mining or need complex graphical outputs, R wins over SAS. However, by large businesses that cannot afford to have disruptions in their financial and marketing data analysis stalled and don’t care about the price tag.