Data Analysis Using Visualization Techniques
Data visualization, a rapidly emerging trend, is widely adopted in various sectors. Enterprises and academic institutions leverage the potency of visual representations, such as graphs and charts, to translate intricate data into easily understandable formats. Data visualization simplifies the interpretation and communication of complex data, aiding decision-making processes. It also fosters a deeper understanding of the underlying patterns and trends, offering a bridge between raw data and actionable insights. Data visualization is an indispensable tool in strategic planning and operational efficiency, facilitating quick and precise decision-making. This research paper provides an extensive overview of data visualization techniques utilizing the R programming language. The paper discusses the data collection process, stressing its importance for reliable visualizations. Then, it explores the benefits of using R for visualization, emphasizing its versatility and rich package ecosystem. Additionally, the paper covers various visualization techniques in R, such as Pie Diagrams, Bar Graphs, Histograms, Box Plots, Line Plots, Mosaic Plots, Bubble Charts, Violin Plots, and Scatter Diagrams. Each technique is illustrated and offers insights into its strengths and applications. The goal is to equip researchers and practitioners with the knowledge and tools to leverage R for effective data visualization, highlighting the novel aspects of combining these techniques for enhanced data comprehension.
Authors
- Sreoshy Saha (ORCID: https://orcid.org/0000-0001-6745-9770)
- Tohedul Islam (ORCID: https://orcid.org/0000-0001-6528-1147)
- Abdus Salam (ORCID: https://orcid.org/0000-0002-6251-9575)
- Khondoker Md. Sabit Hasan
- Mirza Sadman Mehrab
- Nipa
Institutions
- American International University-Bangladesh (BD)
Publication Details
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1109/ecce64574.2025.11013513
- Citations
- 1
- Primary Topic
- Data Mining Algorithms and Applications
- Type
- article
- Field-Weighted Citation Impact
- 3.25