What is it about?
A number of tools and programs are available to clinical researchers to analyze and visualize research data. This paper makes the case for using the R statistical computing environment because it's an open source platform and can help improve documentation and communication among research teams. The paper describes the platform and provides links to training and user resources.
Featured Image
Why is it important?
Clinical research increasingly utilizes large, complex datasets that necessitate the use of flexible platforms to conduct analyses. The R platform supports a large array of packages that help researchers conduct analyses or researchers can write their own custom analysis code. These are valuable features when analyzing multiple data types and/or "omic" data.
Perspectives
I hope that clinical researchers conducting research with "omics" based components will consider learning R and/or have their students learn how to use it early in their training. It's very useful for understanding conversion of raw data to usable analysis data. The ability to customize and save analysis steps with annotations is extremely helpful when you need to revisit analysis. The platform also enables researchers to produce a wide array of graphics for presentations and publications.
Michelle Lynn Wright
University of Texas at Austin
Read the Original
This page is a summary of: NuRsing Research in the 21st Century: R You Ready?, Biological Research For Nursing, November 2018, SAGE Publications,
DOI: 10.1177/1099800418810514.
You can read the full text:
Contributors
The following have contributed to this page
