Transferring, transforming, visualizing, and storing data are important skills that are not routinely taught or learned in curricula for scientists and laboratorians. The accessibility of modern programming languages such as R provide a rich yet approachable toolkit to handle important data centric tasks. This session will include 3 sessions describing practical laboratory data challenges and the solutions devised by alumni of the MSACL Data Science courses. These sessions will cover capabilities for (1) extracting large volumes of data from multiple files, (2) assembling and visualizing method validation data in a structured report, and (3) developing dashboards with quality metrics.
Take Home Pearls:
1. R is a statistical programming language that provides rich functionality for data science activities yet is accessible for learners without a computer science background.
2. R-based workflows enable automation and reproducibility for commonly encountered data analysis tasks in clinical laboratories.