Advancing Nursing Research Through Interactive Data Visualization With R Shiny. Issue 1 (January 2023)

• Record Type: Journal Article
• Authors: Heinsberg, Lacey W.; Koleck, Theresa A.; Ray, Mitali; Weeks, Daniel E.; Conley, Yvette P.
• Is Part Of: Biological research for nursing. Volume 25:Issue 1(2023)
• Page Start: 107
• Page End: 116
• Publication Date: 2023-01
• Subjects: genomics -- omics -- nurse scientists -- data science -- visual analytics -- information visualization; Clinical biochemistry -- Periodicals; Physiology, Pathological -- Periodicals; Nursing -- Periodicals; Nursing -- Research -- Periodicals; 610.73
• DOI: 10.1177/10998004221121109
• Languages: English
• ISSNs: 1099-8004

Abstract

Scientific data visualization is a critical aspect of fully understanding data patterns and trends. To date, the majority of data visualizations in nursing research – as with other biomedical fields – have been static. The availability of electronic scientific journal articles (which are quickly becoming the norm) has created new opportunities for dynamic and interactive data visualization which carry added cognitive benefits and support the ability to understand data more fully. Therefore, here we highlight the benefits of R, an open-source programming language, for scientific data visualization, with a specific focus on creating dynamic, interactive figures using the R shiny package. For R users, we have included a tutorial with example code to create three increasingly complex shiny applications. For individuals more interested in understanding the potential of R shiny as an innovative tool to interact with research data, we have included links to online versions of the examples that do not require any programming or R experience. We believe that widespread adoption of dynamic and interactive scientific data visualization will further support nurse scientists' higher-level mission of advancing our understanding of health and wellness of individuals and communities.