What is R?

R is a number of things, simultaneously. Depending on who you ask, R is:

  • A software package
  • A programming language
  • A toolkit for developing statistical and analytical tools
  • An extensive library of statistical and mathematical software and algorithms
  • A scripting language
  • A complete data science environment
  • …

Why use R?

  • R is cross-platform and runs on Windows, Mac, and Linux (as well as more obscure systems).
  • R provides a vast number of useful statistical tools, many of which have been painstakingly tested.
  • R produces publication-quality graphics in a variety of formats.
  • R plays well with FORTRAN, C, and scripts in many languages.
  • R scales, making it useful for small and large projects. It is NOT Excel.
  • R does not have a meaningfully useful graphical user interface (GUI).

Why not use R?

  • R cannot do everything.
  • R is not always the “best” tool for the job.
  • R will not hold your hand. Often, it will slap your hand instead.
  • The documentation can be opaque (but there is documentation).
  • R can drive you crazy (on a good day) or age you prematurely (on a bad one).
  • Finding the right package to do the job you want to do can be challenging; worse, some contributed packages are unreliable.
  • R does not have a meaningfully useful graphical user interface (GUI).

R License and the Open Source Ideal

R is free (yes, totally free!) and distributed under GNU license. In particular, this license allows one to:

  • Download the source code
  • Modify the source code to your heart’s content
  • Distribute the modified source code and even charge money for it, but you must distribute the modified source code under the original GNU license]{}

This license means that R will always be available, will always be open source, and can grow organically without constraint.

R and Data Science

Data Science is both a field of study and a process. All of us practice data science when using R.

The data science process.

R has capabilities and shines in all aspects of the data science process.