R for Scientists

Learning objectives: This course will provide a comprehensive foundation in programming methodology for quantitative biology applications that can be readily applied to any programming language. It is recommended for students interested in establishing or expanding their computational biology skillset. After completing this course, students should be able to: 1. Understand and explain the role of numerical and statistical methods in biology 2. Execute numerical computations using a widely-used programming language 3. Recognize common programming motifs that can be readily applied to other widely used languages 4. Design and troubleshoot algorithms to analyze diverse biological data and implement them using functions and scripts 5. Apply statistical programming techniques to model biological systems 6. Generate and interpret diverse plots based on biological datasets Course overview: Once a small subfield of biology, computational biology has evolved into a massive field of its own, with computational methods fast becoming a vital toolkit leveraged by biologists across the discipline. As the size and complexity of biological datasets grows, computational methods allow scientists to make sense of these data, scaling quantitative methods to extract meaningful insights that help us better understand ourselves and the living world around us. In this course, we will learn the basics of computer programming in R, a powerful programming language with wide use in the biological sciences. Topics will include a basic introduction to R and the RStudio environment, data types and control structures, reading and writing files in R, data processing and visualization, manipulating common biological datasets; and statistical testing and modeling in R