SAS vs R and why do we prefer SAS over R

76 views Comments last seen 2018/09/24 05:53:44am News & Updates
It’s a race of Humans Vs Humans. May the better survive has taken the shape from May the better team win. Tomorrow it's going to be Humans Vs Machines. So better be prepared for those who evolve will last. In the era of today where the technology is changing at a rapid pace, we have to learn and keep yourself updated and dance to the tunes of techno era. Data is everywhere and Big Organizations have started leveraging the data while a few are still in the investment phase. Data is becoming large, Technology keeps on changing at a pace hard to imagine and customers are becoming smart. To tap the smart customers in a smarter way people are moving leaps and bounds and adapting to computer networks, Skills Languages and hardware. Everyone is moving towards a digital platform and embracing the same.   From the perspectives of Data Science Machine Learning   Artificial intelligence, some languages were born while the others evolved. The most common languages or the infra in data industry are SAS R Python Hadoop Hive Impala Spark Pyspark and HBase. In today's article, we would be comparing the brand leader SAS with the cutting edge R   First of all I would not say you follow my preference. Though this my preference some would be of the same opinion while others may feel a difference.                                   
  • SAS is a database tool while R is more of programming tool . If we take an example of running a logistic regression, both tools are able to do it but SAS takes less coding and less steps to do so. That's why SAS is preferred more in analytics industry. SAS is more structured than the R.
 
  • Due to License version SAS has good support services while R is freeware. So big companies prefer SAS over R. Thought the R community is fast in giving resolutions but there are some who would not want to put a stake on the open community
 
  • SAS is not just a language but an umbrella of complete analytics tools that are used in industry from ETL extraction to Data Visualisation.
 
  • SAS it a complete end to end solution with a suite of ' SAS Intelligence Platform', it is a framework of comprehensive sets of business solutions, technologies, and services. All of the functionality is available from one vendor and through one framework.
 
  • Due to cost factor Small and mid cap companies opt R but R is not able to handle big volumes of data(there are technique used to break the data and feed it into R).However In SAS there is no such restrictions and its much used.
 
  • SAS has a database of its own and can connect to different databases which does not give a tough time with integrations while R faces a challenge
 
  • R does not have its own database format. In R, you can save your session. Data can be exported to flat files/SAS/Excel and many more file formats, but none inherent to R only.
 
  • SAS has the capability of extensive data handling and connecting to different servers with ease ( so you can pull data from different sources easily)
 
  • SAS offers different tools like IML, E-Miner ( so you can replicate R code easily)
 
  • SAS has all packages installed ( so you dont need to remember the names of different packages to be installed like you do in R )
 
  • R can give you a tough time Because R can't handle large data
 
  • R is a language not an entire analytics environment
 
  • SAS support is excellent and it offers amazing GUI based functionality
 
  • SAS can be held responsible in the court of law
 
  • Scare of coding -SAS lets you build models without coding while R says sorry
 
  • With R you are probably on your own while with SAS community you are with the world and You are the World!!
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