Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand were the creators of R, which is now a widely used language for Machine learning. They created it for the application of S programming language in 1993 and the open source project was set up in 1997.
These two men started it as an experiment to bring into play a statistical test bed in Lisp using programming language providing in S. They eventually realized that they had created something that
exceeded S. R turned out to be the best technology for statistical programming and applied machine
While going through this post you will learn:-
- What is R?
- It is featured.
- Benefits of R.
- Difficulties using R.
- Who is using R?
What is R?
- It is a computer language used to write a program and it is a variant of Lisp.
- You can consider it as an open source environment for statistical programming and visualization.
- R can disintegrate and run scripts (R program) that are types/loaded from the .R extension.
- Preparing models that can be queried and updates are also performed by it.
- It creates and displays graphics on the screen.
Features of R
- R is used to analyze, plot and build a statistical model for data and is considered an ultimate
program for one-off analyses prototyping and academic work.
- On the other hand, it is not well suited for building models to be arranged in scalable or
Benefits of R
- R is an open and a free source for all. Anyone can download it and use it anytime.
- It charges you nothing.
- It is easy to use.
- The user can read the source code and learn from it.
- The user can modify it as per his/her use.
- The attractive feature of R is its package.
- It has very powerful algorithms put into service.
- The users have direct access to it, making them it user-friendly.
- As read above, R is inspired by propriety language S, hence it is much better for working in
vectors, matrices and data frame.
Difficulties using R.
- The main difficulties faced using R are-
- The algorithm implemented in R has its own naming conventions and parameters. Some make
predictions but even the result of the standard names can vary due to the complexity.
- This requires the thorough reading of the documentation in each package that the user uses.
- The built-in help is not so quite built in and requires help from the web.
- It is not built for use with streaming or big data or working across multiple machines.
Who is Using R?
Despite these flaws, one cannot ignore the strong benefits that R holds.
- Many renowned companies such as Oracle, IBM, Mathematica, MATLAB, SPSS, SAS, and others
provide integration with R and their platforms.
- Commercial companies too support R.
- R has been considered as the most competent platform for a successful practicing data scientist.
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