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Why you should learn R over SAS?

Comparing things has always been in the trend- be it Linux or Windows, Intel or AMD. Its always been a favourite geeky discussion. Linkedin groups like Advanced Business Analytics, Data Mining and Predictive Modelling has been witness to heated debate regarding the pros and cons of open source technology R and SAS. It has been noted down that R can be out up as an equivalent to SAS. Infact, detailed comparisons would conclude that R is pretty much cooler in terms of functions than SAS.

The Debate: R VS SAS

  • One might argue that there are technologies like SPSS, Statistica and others in face of R, but unfortunately all of these come with a price tag. On the other hand, R is free and open-source. Initially started in New Zealand, today R is termed to be the best statistical tool available.
  • SAS Stat and other SAS packages combine and compress almost whole of statistical analytics and techniques. Still R is deemed to be the first place where every latest techniques are released. It is open source and allows people to submit their packages or libraries. Techniques like GLMET, ADABoost are housed in R but not in SAS. SAS, on the other hand is a paid software where any new technique needs to be vetted and then accepted.
  • It has been argued that R is mainly for academics while SAS is for business. Such notions gets nullified with the recent evidences. Companies like Google and Pfizer are prominently known to use R actively in their business. The tendency of companies to move from SAS to R is now becoming common as R emerges to become the leading statistical computing technology.
  • SAS proponents have always maintained that it vetting and testing of new methods before being implemented takes time. However, in business anything that proceeds at a tortoise’s pace is mostly discarded once there is a better option. This is what is happening with SAS and R. While SAS comes with a lag time for implementation of new methods, R provides access to both the latest as well as already tried and successful techniques. This happens to be a good enough reason why a conversion to R from SAS is going to be the future.
  • R being a programming language entitles you with more freedom while using in analytics. This is because R comes with great extensions for larger data sets
  • R comes with larger support communities since it is free and open-source.
  • The absence of GUI lags R. Although programming has more advantages over GUI, seldom will a person put in the required time to learn programming. Conversely, programmers will take less effort in mastering GUI completely. Whatever be the issue, this can be one factor that will put a bar on R’s long term growth.
  • R is slowly catching up with SAS from the job perspective.

Demand for R jobs on the rise  while SAS jobs decline
Image: Revolution Analytics Blog

The scholarly use of SPSS, SAS, R and Stata through 2013 to 2017 shows a steady rise for R and Stata. It is predicted that in three years time, R will reach 25,000 scholarly articles by 2015. In a time span of just one year, r has added more features to it than SAS did in all these years. The rapid increase in add-on packages coupled with R’s monopoly on latest analytics methods has become instrumental in driving R’s growth. Add to that, its free price and powerful language acts as an catalyst for growth as well.

Forecast Update  Will 2014 be the Beginning of the End for SAS and SPSS    r4stats.com
Image: r4stats.com

It is evident that R will come off as a sure winner in the near future. All evidences point at R’s undeterred growth. It won’t be long enough when R will surpass SAS, the beginning of which has already begun. Thus, the ideal choice will be to learn R. SAS may be easy to learn but it’s always said, good things are hard to gain and once achieved, you become the ruler.

Editor’s Note: Understanding what lies in the future of analytics, Venturesity offers online course on R. Learn everything about analytics and give a kick start to your analytics career.

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