R is a highly extensible language and environment for statistical computing and graphics. It provides a wide variety of statistical, such as linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering and graphical techniques. 

As described by Inside-R, R is a number of things:

R is data analysis software: data scientists, statisticians, data journalists, analysts and others who need to make sense of data use R for statistical analysis and data visualisation.

R is a programming language: you can do data analysis in R by writing scripts and functions in the R programming language. Because R is a complete, interactive, object-oriented language: designed by statisticians, for statisticians.

R is a great tool for data visualization. One of the design principles of R was that visual exploration of data through charts is an essential part of the data analysis process. As a result, the R package includes excellent tools for creating graphics, such as bar charts, scatterplots or multi-panel Lattice charts. You can use R to generate new visualisations based on your needs. R's graphical system is heavily influenced by thought leaders in data visualization like Bill Cleveland and Edward Tufte, and it's used by data visualisation stars like the New York Times, the Economist and FlowingData.

Finally, R is an open-source project: you can freely download the R package and start running experiments with it. Its robust and vibrant community of users constantly discuss and suggest new uses of the R project and share their source codes.