Conclusions

In particular for universities, but also for enterprises, the often crucial point is that R can be used free of charge, in contrast to commercial solutions for data analysis. It also supports all common operating systems (Linux, Windows, macOS). The range of free help resources, including manuals, tutorials, and blogs [10], is very extensive. Community support is also very good, along with additional fee-based support from third-party providers and numerous training offerings. The large developer community ensures that the project is actively developed in the medium and long term.

For beginners without any programming experience, the barriers to getting started with R are fairly low; in fact, you can get started quite quickly and learn the basics required for statistical analysis within a few days. Reading a dataset and computing a regression model takes just two lines of code, and you do not need to deal with concepts such as object-oriented programming.

To sum things up, R is suitable for any application scenario in which statistics software is used regularly. The main arguments – besides the elimination of licensing costs for the software itself – are, above all, the breadth of supported methods, the enormous flexibility, and good automation. The active community offers support and guarantees the long-term development of the software.

Although data analysis can in principle be performed with any programming language, R was developed specifically for this purpose, and many common methods are already implemented. Only if very large datasets have to be analyzed in a limited time on comparatively weak hardware, if users use statistics software only sporadically, or if you only need very special methods is the use of R questionable.

If you want to keep track of the latest developments or want to chat with a member of the Core Team, you can visit an R conference like useR! [11] or the European R Users Meeting (eRum) [12]. Many larger cities have regular R meetups [13].

The Author

Mira Céline Klein works as a data scientist at INWT Statistics in Berlin, performing data analysis for customers in R and Python. Her work focuses on predictive analytics, data quality, automated reporting, and programming best practices. She spends her free time visiting museums, cooking, and watching Star Trek.