# Why should you use R?

There exists several reasons why one should start using R. During the last decade R has become the leading tool for statistics, data analysis, and machine learning. By now, R represents a viable alternative to traditional statistical programs such as Stata, SPSS, SAS, and Matlab. The reasons for R’s success are manifold.

First, R is an open-source language and computing environment than runs on many plattforms including Windows, Macintosh and Linux. The software is published under the Free Software Foundation‘s GNU General Public License, that allows every user to freely distribute, study, change, and improve the software.

Second, R is far more than a statistical package: it is a proper programming language. This is, one can create its own objects, functions, and packages. Technically, R is a free implementation of the S programming language. However, most code written in S will still run successfully in R. R provides a large variety of basic to advanced statistical and graphical techniques that come at little to no cost to the user.

Finally, R is becoming the standard. The advantages named above encourage the growing use of R in cutting edge social science research. R is well maintained by an active and highly talented community. Every user can develop and publish its own R package and further contribute to R’s success. Packages represent extensions to the base R and can easily be installed and used in R. Many packages are submitted by prominent members of their respective fields. Currently, there are over 10,000 packages available on the official repository for R packages, named CRAN. In order to give you an indication of R’s success, at the beginning of the decade CRAN only hosted little more than 2,000 packages. Some version of R is likely to remain popular for the indefinite future.

Hence, one can easily conclude that, as the emerging standard for statistical programming, it is likely to be a highly rewarding process to learn how to use R. The following post outlines the most Pros and Cons of R.

In physics departments it looks like Python is becoming the de-facto standard for statistical analyses

Yes, Python is a very good software too.