Analysis of large amount of data and its visualization in R

Orlov Evgeniy
PhD of Physics and Mathematical Science, Associate Professor of the Department of Statistics and Mathematical Methods in Economics, Odessa National Economic University, Odessa, E-mail:orlov_ev@onu.edu.ua, ORCID ID: https://orcid.org/0000-0002-9212-9973

Krivosheina Elіzaveta
student of Faculty of Economics and Enterprise Management, Odesa National Economic University, Odessa, E-mail:yelyzavetakryvosheina@gmail.com, ORCID ID:

Sirenko Anna
student of Faculty of Economics and Enterprise Management, Odessa National Economic University, Odesa, E-mail:sirenko.anna06@gmail.com, ORCID ID:

Cite this article:

Orlov E., Krivosheina E., Sirenko A. (2023) Analysis of large amount of data and its visualization in R. Ed.: V.V. Kovalenko (ed.-in-ch.) and others [Analiz velykykh obiemiv danykh ta yikh vizualizatsiia v R; za red.: V.V. Kovalenko (gol. red.)], Scientific Bulletin of the Odessa National Economic University (ISSN 2313-4569), Odessa National Economics University, Odessa, No. 9 (310), pp. 37-43.

Abstract

The article is devoted to the study of methods and tools for analysis and visualization of large volumes of data in the R programming environment. One of the unsolved problems in the analysis of large volumes of data and their visualization in R is the need to use specialized tools for working with large volumes of data, which can be quite difficult to master. With large volumes of data, it is important to have effective visualization tools to identify patterns and dependencies. However, automating this process remains a challenge for many developers. There is also the issue of visualizing the results, as visualizing large amounts of data can be a daunting task. Also, some graphs and charts can be too difficult to interpret and understand. Various approaches to handling large data sets in R are described, such as the use of big data packages, visualization tools, and data analysis tools. Examples of using the R programming environment for complex data analysis of large companies are given. An example of analyzing large volumes of data in R is analyzing social media data. Social media platforms such as Twitter, Facebook and Instagram generate large amounts of data that can be used to analyze user sentiment, research trends and determine reactions to news and events. To analyze social media data, you can use the R package "twitteR", which allows you to retrieve and process data from the Twitter API. For example, you can use this package to collect and analyze tweets from specific hashtags or accounts to determine user sentiment and trends. By understanding the characteristics of shopping center visitors, Uber and Twitter users, owners and management companies can make decisions about optimizing the center's operation and improving the level of service.

Keywords

large volumes of data, R programming environment, statistical analysis, data visualization.

JEL classification: C810, C820, C870; DOI: 10.32680/2409-9260-2023-9-310-37-43

UD classification: 311.21; 303.72

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