Early Undergraduate Biostatistics & Data Science Introduction Using R, R Studio & the Tidyverse

Abstract

Increasingly, students training in the biological sciences depend on a proper grounding in biological statistics, data science and experimental design. As biological datasets increase in size and complexity, transparent data management and analytical methods are essential skills for undergraduate biologists. We propose that using the software R and RStudio are effective tools to train first- and second-year undergraduate students in data visualization and foundational statistical analyses. Here, we present the redesigned laboratory curriculum for our Experimental Design and Statistics course, a required course for all first- or second-year biology majors at Lawrence University, a small liberal arts institution in northeast Wisconsin. We include an example 10-week syllabus and eight laboratory exercises (as supplementary materials) for undergraduate institutions that aim to introduce and guide students through acquiring a basic understanding of biostatistical analyses and skills using R and RStudio. We also provide a flexible framework and examples that are easily modifiable and cover the essential biostatistics and data science skills needed for biology undergraduates. Finally, we discuss the potential pitfalls and obstacles as well as the intrinsic benefits and expected outcomes of our laboratories.