Big data is one of the hottest topics in medicine today, including clinical laboratories. Yet commercial spreadsheet packages are rarely sufficient for many of the data analysis tasks required for routine laboratory work or to develop publications in this field. These applications typically are slow, make it difficult to manipulate large data files, and have limited statistical functionality, as well as poor plotting utilities.
That’s where the program R comes in. This package enables clinical laboratory professionals to build the necessary tools from scratch or use freely available code to perform myriad tasks in data analysis/management, statistical analysis, and graphics. It also produces customizable, publication-quality figures.
In short, R is a lifeline for any laboratory that needs to better understand its data. You just have to learn to use it.
That’s exactly what Stephen Master, MD, PhD, of Weill Cornell Medical College, and Daniel Holmes, MD, of St Paul's Hospital, will teach you during their 2-part Short Course at the 68th AACC Annual Scientific Meeting & Clinical Lab Expo on Wednesday, August 2. Part one kicks off at 10:30 a.m. and runs until 12 p.m.; part two occurs between 2:30 p.m. and 5 p.m.
The sessions provide an introduction to R, focusing on statistical analysis relevant to clinical chemistry. You will learn how to perform common clinical chemistry-related computational tasks and produce high-quality, publication-ready figures, among other applications.
By the time both sessions end, you’ll be able to import data into R from a spreadsheet program to clean, subset, and manipulate numerical and non-numerical data; describe and create R data types; perform routine statistical tasks, such as calculating median, quantiles, t-tests, and Wilcoxon signed rank; and perform regression analysis.
The best part of the training? You don’t need any programming experience! “Being a nerd is not a prerequisite,” say the presenters. “But it is advantageous.”