A Primer for Computational Biology aims to provide life scientists and students the skills necessary for research in a data-rich world. The text covers accessing and using remote servers via the command-line, writing programs and pipelines for data analysis, and provides useful vocabulary for interdisciplinary work. The book is broken into three parts:

- Introduction to Unix/Linux: The command-line is the “natural environment” of scientific computing, and this part covers a wide range of topics, including logging in, working with files and directories, installing programs and writing scripts, and the powerful “pipe” operator for file and data manipulation.

- Programming in Python: Python is both a premier language for learning and a common choice in scientific software development. This part covers the basic concepts in programming (data types, if-statements and loops, functions) via examples of DNA-sequence analysis. This part also covers more complex subjects in software development such as objects and classes, modules, and APIs.

- Programming in R: The R language specializes in statistical data analysis, and is also quite useful for visualizing large datasets. This third part covers the basics of R as a programming language (data types, if-statements, functions, loops and when to use them) as well as techniques for large-scale, multi-test analyses. Other topics include S3 classes and data visualization with ggplot2.

The R Project for statistical computing (R) is a programming language and environment for statistics and graphing. Another commonly used programming language for statistics and data mining is Python. Both Python and R are easy to learn. If the primary purpose is statistical analysis, then R is usually preferred.Why learn/teach R? One of the major reasons why R is becoming more popular (TIOBE,2018) is that it is an open-source (i.e. free) software. Also, when dealing with a large number of variables, multiple datasets, and large samples, R is also a more efficient tool than traditional drop-down menu software such as SPSS. Finally, R programming is now very easy to use with the development of helpful packages.This open text will introduce R packages and step-by-step codes for conducting common statistical analyses in psychological research and classrooms. Funding acknowledgment: The author would like to thank the Society for the Teaching of Psychology (STP), American Psychological Association Division 2 Instructional Resource Award for their generous support of this project. This resource is licensed under the Creative Commons Attribution-ShareAlike4.0 International license (CC BY-SA4.0)