Why Clinical Laboratorians Should Embrace the R Programming Language

R is heavily utilized for its graphic and reporting capabilities, including the ability to render publication-quality figures with interactivity and to generate web-based dashboards and other reports in a variety of formats.As a statistical programming language, R allows laboratorians and others to transform and analyze data and communicate results. R uses text-based commands to process data, and as such it functions as a full-fledged programming language for the advanced user.Unlike Excel and many other graphical user interface (GUI)-based programs, R’s reliance on text-based structure makes it straightforward to review at any time the commands used in a data processing pipeline to ensure that the correct steps were taken. The ability to use superior, more accurate statistical methods by taking advantage of the massive repository of available add-on packages is a significant advantage for laboratorians.Importantly, the R community is recognized for its purposeful inclusivity, both in welcoming diversity among members and in fostering new members’ ability to learn the language.Integrated Tools for Sharing ResultsR provides a number of convenient tools for sharing and communicating results with dynamic reporting and the potential for interactivity. There are several methods for creating graphics interfaces in R, including Shiny, a package for creating general web-based interfaces to R programs.Among other things, these methods make it possible for a laboratorian to develop interactive business intelligence-style dashboards for operational management that would otherwise require commercial software, such as Tableau or QlikView. Thus, learning R provides a foundation for creating a wide variety of tools that can be scaled anywhere from an individual user to system-wide clinical deployment of a complete data science pipeline.Laboratorians have developed R packages to perform many of the routine tasks of assay validation without using commercial software. In addition, R is ideal for many of the calculations and data processing steps that are repeatedly performed in a clinical laboratory.Suppose, for example, that administrators require that a laboratory report its annual test volumes year-by-year. Given the importance of predictive analytics for the future of laboratory medicine, R provides an ideal tool for clinical laboratorians to learn about or experiment with these new analytic techniques.The aforementioned user and contributor base has embraced the open source movement. That said, R is one of the fastest growing programming languages and is experiencing a surge of interest within pathology and laboratory medicine.Learning R requires working with data and writing and executing code. These resources encompass, for example, comprehensive curricula that teach the basics for using R to wrangle, analyze, and visualize data; modules with targeted instruction for performing a specific analysis in R (e.g., build and validate time series forecast models); and other, more focused tutorials on how to use a particular R package or function (e.g., convert datetime formats).Help with R is not hard to find. R provides an excellent format for learning about and, ultimately, implementing the types of computational tools required in a new era of laboratory medicine.