Introduction. Software have increased the efficiency of data processing, including statistical processing. Software are varied in purpose, proposed processing methods, and method of interaction with the user. According to the results of our research, the most commonly used software tools for statistical data processing are the Microsoft Excel word processor, SPSS, and Statistica. Numerous scientific articles and books are devoted to the use of the R language for processing biological and medical data. The R programming language was specifically designed for statistical data processing, but is unfairly neglected. Purpose of the investigation. Assessing the capabilities of the R programming language as a scientifically based tool for obtaining derived statistical data. Materials and methods. The R programming language describes data processing scenarios and provides an extensible platform. The advantages of this language include free use, extensibility through packages, advanced graphic capabilities, input and output of data in various formats, presentation of data processing in the form of a script, implementation for various operating systems. The main disadvantages of the R language are the difficulty of learning the program and the relative slowness of data processing. The R language is a popular programming language at the moment. Results and discussion. We have given two examples of data processing using the R language. The first example examined the significance of the difference between the sample mean and the established norm. The study used a hypothesis test about the numerical value of the mathematical expectation of a normal distribution with unknown variance. In the second example, data was clustered using the k-means method. For both examples, a script was developed in R and commented line by line. Examples of data processing demonstrate that scripts in the R language are simple and do not require programming knowledge, which refutes the corresponding drawback. When writing scripts, you can take help from the R language and numerous published works and websites. Conclusion. The R language is a powerful tool and will help the researcher in obtaining derived data. We hope that readers will use this language more often in their work.