Why to choose R Programming Language?
- 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, user-contributed packages available on CRAN (not to mention Bioconductor and Omegahat). To get an idea of what packages are out there, just take a look at these Task Views. Many packages are submitted by 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 (C/C++, Java, Python) and enables you to interact with many data sources: ODBC-compliant databases (Excel, Access) and other statistical packages (SAS, Stata, SPSS, Minitab).
- Explicit parallelism is straightforward in R (see the High Performance Computing Task View): several packages allow you to take advantage of multiple cores, either on a single machine or across a network. You can also build R with custom BLAS.
- 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. The most prominent and general is useR. Finance-related conferences include Rmetrics Workshop on Computational Finance and Financial Engineering in Meielisalp, Switzerland and R/Finance: Applied Finance with R in Chicago, USA.
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