Abstract
The purpose of the article is to study the possibilities of using the RStudio environment to solve problems in mathematics, physics and economics. In particular, the emphasis is on the integration of statistical analysis tools, the use of a set of mathematical functions and operations that ensure efficiency and accuracy in calculations. Research methods. An analysis of scientific papers, methodological recommendations and examples of RStudio application in various fields of knowledge related to mathematics, physics and economics was conducted, the functionality of the RStudio environment was tested on practical tasks, and the results obtained using RStudio were compared with the results obtained using other methods to assess the effectiveness of the environment. The scientific novelty lies in the use of R functionality, which allows developing and implementing new methods and approaches to data analysis and solving scientific problems. Conclusions. The RStudio environment demonstrates its effectiveness for solving problems in mathematics, physics and economics due to a wide range of functions for numerical modeling, statistical analysis and data visualization. Using RStudio allows you to significantly simplify the process of solving complex mathematical problems, in particular, it contributes to the integration of mathematical methods in the study of physical phenomena and economic processes, which allows you to form new approaches to data analysis and modeling. RStudio can be used as an effective tool in education, contributing to the formation of analytical thinking and programming skills in students engaged in mathematical, physical and economic problems. In particular, the use of the Tukey criterion and the functions of the RStudio environment for comparing data in large arrays, such as the results of experiments or measurements. The results of the study confirm the feasibility of implementing RStudio in the practice of teaching and scientific research, and also indicate the need for further research into the capabilities of the environment in solving problems in scientific fields.
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