The Role of R Programming in Health Care

The Role of R Programming in Health Care

R programming has evolved as a powerful statistical computing and graphics tool in health care, delivering significant insights through data analytics and assisting in decision-making processes. The use of R programming in health care has various advantages, including improved analytical capabilities and decision-making processes. R allows academics and healthcare professionals to extract relevant insights from complicated datasets by facilitating sophisticated statistical studies. R, for example, can be used in epidemiological studies to model disease trends, analyze risk factors, and predict prospective epidemics, assisting in the creation of focused public health treatments. Furthermore, R’s data visualization power is demonstrated by utilities like ggplot2, which enable healthcare practitioners to construct visually appealing representations of patient outcomes or demographic trends (1). R’s adaptability and flexibility make it a great tool in healthcare, where robust statistical analysis and intuitive data presentation are critical.

Ggplot2 and Shiny are two notable data visualization alternatives in the R programming language, each with their own set of benefits and disadvantages. The ggplot2 software is praised for its flexibility, which allows users to build visually pleasing and useful plots (2). In healthcare, for example, a hospital administrator may use ggplot2 to create customized graphs illustrating patient satisfaction scores over time, allowing for the detection of trends and areas for development. However, the higher learning curve of the software may be difficult for beginners. On the other hand, Shiny excels at creating interactive dashboards that provide a dynamic method to interact with health data (3). A public health department, for example, may use Shiny to construct a dashboard displaying the progress of an infectious disease, letting users interact with real-time data. However, using Shiny may involve more resources for hosting and maintenance, and developing sophisticated dashboards requires careful thought to minimize information overload and provide user-friendly experiences.

Sources

Vaishnavi Dutt Mishra. 2023. Principle & Practices of Data Analytics. Academic Guru Publishing House. https://books.google.com/books?hl=en&lr=&id=nt_gEAAAQBAJ&oi=fnd&pg=PA1&dq=R+PROGRAMMING+data+visualization+power+is+demonstrated+by+utilities+like+ggplot2

Micah Allen, Davide Poggiali, Kirstie Whitaker, Tom Rhys Marshall, and Rogier A. Kievit. 2019. Raincloud Plots: A Multi-Platform Tool for Robust Data Visualization. p. 63. https://doi.org/10.12688/wellcomeopenres.15191.1

Mark A. Perkins and Jonathan W. Carrier. 2023. Using R Shiny to Develop a Dashboard Using IPEDS, U.S. Census, and Bureau of Labor Statistics Data. e0278573. https://doi.org/10.1371/journal.pone.0278573

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Overview
In this learning activity, you will explore the use of R programming in health care. R is an open-source free software used for statistical computing and graphics. You will write an essay to examine how R is used for data analytics and decision-making in health care.

Instructions
Write a 1-2 page essay in which you:

1. Examine the benefits of using R programming in health care. Support with examples.
2. Critique two data visualization options in R programming language supported with an example of a health care application for each of the selected visualization options.
3. Use at least two sources to support your writing. Choose sources that are credible, relevant, and appropriate. Cite each source listed on your source page at least one time within your assignment. For help with research, writing, and citation, access the library or review library guides.
This course requires the use of Strayer Writing Standards (SWS). The library is your home for SWS assistance, including citations and formatting. Please refer to the Library site for all support. Check with your professor for any additional instructions.

Resources
The R Project for Statistical ComputingLinks to an external site..
Learning RLinks to an external site..
Using Visual Analytics, Big Data Dashboards for Healthcare InsightsLinks to an external site..