R is the leading tool for statistics, data analysis, and machine learning. It is more than a statistical package; it’s a programming language, so you can create your own objects, functions, and packages.
Speaking of packages, there are over 2,000 cutting-edge, To get an idea of what packages are out there, just take a look at these Task Views. Many packages are submitted prominent members of their respective fields.
Like all programs, R programs explicitly document the steps of your analysis and make it easy to reproduce and/or update analysis, which means you can quickly try many ideas and/or correct issues.
You can easily use it anywhere. It's platform-independent, so you can use it on any operating system. And it's free, so you can use it at any employer without having to persuade your boss to purchase a license.
Not only is R free, but it's also open-source. That means anyone can examine the source code to see exactly what it’s doing. This also means that you, or anyone, can fix bugs and/or add features, rather than waiting for the vendor to find/fix the bug and/or add the feature--at their discretion--in a future release.
R allows you to integrate with other languages and enables you to interact with many data sources and other statistical packages (psych).
Explicit parallelism is straightforward in R: several packages allow you to take advantage of multiple cores, either on a single machine or across a network.
R has a large, active, and growing community of users. The mailing lists provide access to many users and package authors who are experts in their respective fields. Additionally, there are several R conferences every year.
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