Feeling frustrated with the limitations of current data analysis programs, a growing number of laboratorians have turned to an open-source software that requires some programing skills. You may have heard of the statistical programing language R. If you are ready to learn more, you should not miss this afternoon’s session “Storytelling with R.” Expert users of the program will showcase tools that they have developed and implemented to enhance data analytics in their daily practice.
Clinical laboratorians have debuted custom R applications at prior AACC Annual Scientific Meetings. But this year, the moderator and presenters decided to focus on the reporting and visualization capabilities of R. Besides being a statistical programing language, R is also noted for its extensive graphing and reporting capabilities. The functionality of R is immense, and clinical laboratorians have multiple opportunities to grow their knowledge about it through a large user community that shares extensions or packages allowing for dynamic and interactive graphics.
Today’s session will feature some of these possibilities. Stephen Master, MD, Janet Simons, PhD, Shannon Haymond, PhD, and Patrick Mathias, MD, PhD, will demonstrate how to use R to create dynamic reports or dashboards of laboratory operations and quality indicators. Dustin Bunch, PhD, will demonstrate an interactive web tool for comparing methods for reference intervals using an R package called Shiny. And Daniel Holmes, MD, will demonstrate how to prepare a manuscript using R and the Bookdown package.
The speakers will also explain how R can help clinical laboratorians tackle several challenges in laboratory data analytics, such as limited access to data, manual and repetitive analysis, analyses that are not reproducible, and tools that are too limited for advanced analytics and visualization. They’ll show how R can help with each of these areas and how R workflows enable reproducible data analysis and the capacity for a broad and evolving set of statistical analyses and data visualizations.
Laboratorians who currently analyze data using Excel or EP Evaluator, or who are making graphs for presentations or publications, can benefit from learning R. Though there is a steep learning curve, there are several beginner courses designed specifically for laboratory medicine. In fact, AACC university offered two introductory courses on R this past Sunday. Attendees taking these courses had the opportunity to learn about the basics, including the relevant packages needed for common analyses such as reference intervals, linear regression, method comparison, and other cases.
Today, the speakers will aim to help attendees learn to adapt these tools for their own use and get inspired by the vast opportunities R offers the lab.