By: Karthik Janar Printer Friendly Format
In the world of data science, two popular programming languages are used today. R and Python. A good comparison of R and Python is dicussed in this tutorial. Personally I prefer R over Python because of its rich set of freely available packages and its visualization capabilities. R is based on an older language named S. S was originally developed at Bell Labs by John Chambers in 1976 as an internal statistical analysis environment using fortran libraries. In 1988 it was rewritten in C.
The philosophy behind the S language was to develop an interactive environment. In 1991 Ross Ihaka and Robert Gentleman created R. In 1995 R was released as GNU license. The syntax of R is very similar to S. The R group manages the code and actively release annual releases and bug fixes. R's graphics capabilities are very sophisticated and better than most stat packages. R-help and R-devel are two mailing lists which are very active and also the StackTrace thread.
With R you are granted to:
- The freedom to run the program, for any purpose (freedom 0)
- The freedom to study how the program works and adapt to your needs(freedom 1)
- The freedom to redistribute copies so you can help your neighbour (freedom 2)
- The freedom to improve the program, and release your improvements to the public (freedom 3)
Drawbacks of R
- As R is based on S, it means that it is based on a 40 year old concept.
- Little builtin support for dynamic and 3D graphics (although recently it is getting better)
- Objects have to be stored in physical memory (there have been some advancements in this)
Design of the R System
The R System is divided into two parts.
- The "base" R system that you can downloand from CRAN
- Everything else
The base R system contains all fundamental functions and packages such as utils, stats, datasets, graphics, grDevices, grid, methods, tools, parallel, compiler, splines, tcltk, stats4.
There are also "Recommend" packages such as boot, class, cluster, codetools, foreign, KernSmooth, lattice, mgcv, nime, rpart, survival, MASS, spatial, nnet, Matrix.
There are thousands of other packages that you can download from CRAN, BioConductor and GitHub.
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