Comparative Analysis of Tool Functionality in Descriptive Statistics of Language R
https://doi.org/10.21686/2413-2829-2024-4-25-35
Abstract
The article provides methods and procedures of getting numerical statistical characteristics of qualitative and quantitative features by means of language R in the process of preliminary data analysis. The most expedient, in view of simplicity of use and information value of result, specification of functions of language descriptive statistics was shown. The author studied comparative advantages of tools of descriptive statistics of the language with the open software code R in comparison with similar units Python and Ms Excel. A conclusion was drawn about the convenience and appeal of using language R for estimating descriptive statistics for beginners, who have not got specific knowledge in programming. For example, it was shown that in order to get min 12 characteristics of descriptive data statistics grouped by categories only one command will be enough. As for estimating one statistical characteristic for segment of data produced by several grouping features, again only one command is necessary. Findings of the research can be useful for investigators of different fields, both beginners and experts, who work with methods of statistical data processing in academic and practical spheres.
About the Authors
T. G. Apal’kova
Financial University under the Government of the Russian Federation
Russian Federation
Russian Federation
Tamara G. Apal’kova - PhD, Associate Professor of the Department for Mathematics of the Faculty of Information Technology and Big Data Analysis
49/2 Leningradskiy Avenue, Moscow, 125167
K. G. Levchenko
Financial University under the Government of the Russian Federation
Russian Federation
Russian Federation
Kirill G. Levchenko - PhD, Associate Professor of the Department for Mathematics of the Faculty of Information Technology and Big Data Analysis
49/2 Leningradskiy Avenue, Moscow, 125167
References
1. Bavrina A. P. Sovremennye pravila ispolzovaniya metodov opisatelnoy statistiki v mediko-biologicheskikh issledovaniyakh [Modern Rules for the Use of Descriptive Statistics Methods in Biomedical Research]. Meditsinskiy almanakh [Medical Almanac], 2020, No. 2 (63), pp. 95–104. (In Russ.).
2. Voronkova N. Kh., Sennikova A. E. Informatsionnaya diagnostika sotsialnykh obektov i protsessov s pomoshchyu opisatelnoy statistiki [Information Diagnostics of Social Objects and Processes Using Descriptive Statistics Methods]. Vestnik Altayskoy akademii ekonomiki i prava [Bulletin of the Altai Academy of Economics and Law], 2021, No. 11 (part 2), pp. 161–164. (In Russ.).
3. Kudryavtsev O. E., Tamrazyan S. E. Primenenie metodov opisatelnoy statistiki v analize tamozhennykh riskov [Application of Descriptive Statistics Methods in Customs Risk Analysis]. Vestnik Finansovogo universiteta [Bulletin of the Financial University], 2017, No. 21 (1), pp. 117–124. (In Russ.).
4. Nabor dannykh «2023 Data Scientists Salary» [Data set "2023 Data Scientists Salary"]. (In Russ.). Available at: https://www.kaggle.com/da-tasets/henryshan/2023-data-scientistssalary (accessed 15.03.2024).
5. Revelle W. An Introduction to Psychometric Theory with Applications in R. Available at: https://personality-project.org/r/book/ (accessed 15.03.2024).
Review
For citations:
Apal’kova T.G., Levchenko K.G. Comparative Analysis of Tool Functionality in Descriptive Statistics of Language R. Vestnik of the Plekhanov Russian University of Economics. 2024;(4):25-35. (In Russ.) https://doi.org/10.21686/2413-2829-2024-4-25-35
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