R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.
History of R Programming
The history of R goes back about 20-30 years ago. R was developed by Ross lhaka and Robert Gentleman in the University of Auckland, New Zealand, and the R Development Core Team currently develops it.This programming language name is taken from the name of both the developers. The first project was considered in 1992. The initial version was released in 1995, and in 2000, a stable beta version was released.
Features of R
R is a domain-specific programming language which aims to do data analysis. It has some unique features which make it very powerful. The most important arguably being the notation of vectors. These vectors allow us to perform a complex operation on a set of values in a single command.
There are the following features of R programming:
- It is data analysis software.
- It is a well-designed, easy, and effective language which has the concepts of user-defined, looping, conditional, and various I/O facilities.
- It provides effective data handling and storage facility.
- It is an open-source, powerful, and highly extensible software.
- It provides highly extensible graphical techniques.
- It allows us to perform multiple calculations using vectors.
- R is an interpreted language
What is R used for?
- Statistical inference
- Data analysis
- Machine learning algorithm
Why Learn R Programming?
- R is one of the most popular statistical programming languages for data scientists. It is heavily used in the field of machine learning, scientific computing, and statistical analysis.
- Since R is an interpreted programming language, you can run your code without any compiler. This makes development easier.
- R can be used to perform vector calculations. It is a vector language and can be used to add functions to a single vector.
