The need for R
There are so many statistical packages which can be used for solving problems in quantitative finance. But R is not a statistical package but it is a language. R is a flexible and powerful language for achieving high-quality analysis.
To use R, one does not need to be a programmer or computer-subject expert. The knowledge of basic programming definitely helps in learning R, but it is not a prerequisite for getting started with R.
One of the strengths of R is its package system. It is vast. If a statistical concept exists, chances are that there is already a package for it in R. There exist many functionalities that come built in for statistics / quantitative finance.
R is extendable and provides plenty of functionalities which encourage developers in quant finance to write their own tools or methods to solve their analytical problems.
The graphing and charting facilities present in R are unparalleled. R has a strong relationship with academia. As new research gets published, the likelihood is that a package for the new research gets added, due to its open source nature, which keeps R updated with the new concepts emerging in quant finance.
R was designed to deal with data, but when it came into existence, big data was nowhere in the picture. Additional challenges dealing with big data are the variety of data (text data, metric data, and so on), data security, memory, CPU I/O RSC requirements, multiple machines, and so on. Techniques such as map-reducing, in-memory processing, streaming data processing, down-sampling, chunking, and so on are being used to handle the challenges of big data in R.
Furthermore, R is free software. The development community is fantastic and easy to approach, and they are always interested in developing new packages for new concepts. There is a lot of documentation available on the Internet for different packages of R.
Thus, R is a cost-effective, easy-to-learn tool. It has very good data handling, graphical, and charting capabilities. It is a cutting-edge tool as, due to its open nature, new concepts in finance are generally accompanied by new R packages. It is demand of time for people pursuing a career in quantitative finance to learn R.
