I would like to thank the generosity of the SAS Institute and its employees to provide SAS On Demand for Academics for free without whom this book would not exist. In addition, I also want to thank the baristas from Starbucks Gurgaon. These are the people who downvote my questions on Stackoverflow. You inspire me guys.

SAS for R users is aimed at entry‐level data scientists. It is not aimed at researchers in academia nor is it aimed at high‐ end data scientists working on Big Data, deep learning, or machine learning. In short, it is merely aimed at human learning business analytics (or data science as it is now called).

Both SAS and R are widely used languages and yet both are very different. SAS is a programming language that was designed in the 1960s which is broadly divided into Data Steps and a wide variety of Procedure or PROC steps, while R is an object oriented, mostly functional, language designed in the 1990s.

There are many, many books covering either but only very few books covering both.

Why then write the book? After all, I have written two books on R, and one on Python for R. SAS language remains the most widely used language in enterprises, contributing directly to the brand name, and profitability of one of the largest private software companies that invests hugely in its own research instead of borrowing research in the name of open source. A statistics student knowing Python (esp Machine Learning ML), R, SAS, Big Data (esp Spark ML), Data Visualization (using Tableau) is a mythical unicorn unavailable to recruiters who often have to settle for a few of these skills and then train them in house.

As a teacher, I want my students to have jobs – there is no ideological tilt to open source or any company here. The probability of students getting jobs from campus greatly increases if they know BOTH SAS and R not just one of them. That is why this book has been written.