Analysis Using R Software: A Big Opportunity for Epidemiology and Public Health Data Analysis
DOI:
https://doi.org/10.62404/jhse.v1i1.9Keywords:
R Software, Data Analysis, Epidemiology, Public HealthAbstract
R is a programming language, open-source, developed by various of the world's most active statisticians with powerful function and visualization for data analysis from simple to complex data such as machine learning and artificial intelligence. Data visualization technologies have the ability to assist public health professionals with decision-making. Visualization appears to help decision making by increasing the quantity of information communicated and reducing the cognitive and intellectual strain of processing information. There are numerous commercially available statistical software packages that are widely utilized by epidemiologists worldwide. For industrialized nations, the price of software is not a significant issue. However, for underdeveloped nations, the true expenses are frequently excessive. Some academics in developing nations rely on software that has been illegally copied a copy of the software program. There are several benefits to using R, including the possibility of using software packages for free (open source) and the volume and availability of software packages. It is simple to retain and repeat commands on the same data analysis with multiple data frames, facilitating the work of health monitoring officers who frequently analyze data with similar variables but at different times.
References
Giorgi, F. M., Ceraolo, C., & Mercatelli, D. (2022). The R Language: An Engine for Bioinformatics and Data Science. Life, 12(5), 648. https://doi.org/10.3390/life12050648
GNU. (2023). What is R? https://www.r-project.org/about.html
Greenacre, M. (2022). R for Health Data Science. Journal of the Royal Statistical Society Series A: Statistics in Society. https://doi.org/10.1111/rssa.12851
Harrison, E., & Riinu, P. (2020). R for Health Data Science. In R for Health Data Science. CRC press. https://doi.org/10.1201/9780367855420
Khan, A. (2013). R-software: A newer tool in epidemiological data analysis. Indian Journal of Community Medicine, 38(1), 56. https://doi.org/10.4103/0970-0218.106630
Park, S., Bekemeier, B., Flaxman, A., & Schultz, M. (2022). Impact of data visualization on decision-making and its implications for public health practice: a systematic literature review. Informatics for Health and Social Care, 47(2), 175–193. https://doi.org/10.1080/17538157.2021.1982949
Wickham, H., & Grolemund, G. (2017). R for Data Science: Visualize, Model, Transform, Tidy, and Import Data. In O’Reilly Media.
Downloads
Published
Versions
- 2023-07-21 (2)
- 2023-04-29 (1)
How to Cite
Issue
Section
License
Copyright (c) 2023 Journal of Health Sciences and Epidemiology
This work is licensed under a Creative Commons Attribution 4.0 International License.