This book is written for biologists, from undergraduate students to seasoned professionals, who wish to develop coding skills for the management, visualization and analysis of biological data. Coding and quantitative skills are critical for all areas of biological inquiry, yet many biology programs do not emphasize training in math, statistics, and computer science. This book tries to demystify coding in R, using a hands-on tutorial style without too much technical jargon. As such, it should be useful to current students, recent graduates, and working professionals who have not received formal training in mathematics and computer science at the university level. After completing this book you will know the fundamentals of R programming, flow control (if statements and for loops), and you will be able to create reproducible reports that feature your own, embedded custom R functions, and high-quality, professional visualizations. You'll also learn how to work with relational data using regular expressions and dplyr pipes -- two powerful tools for data management. This is the first book in a series that includes R STATS Crash Course for Biologists (2013) and R Machine Learning Crash Course for Biologists (2014).
This book is written for biologists, from undergraduate students to seasoned professionals, who wish to develop coding skills for the management, visualization and analysis of biological data. Coding and quantitative skills are critical for all areas of biological inquiry, yet many biology programs do not emphasize training in math, statistics, and computer science. This book tries to demystify coding in R, using a hands-on tutorial style without too much technical jargon. As such, it should be useful to current students, recent graduates, and working professionals who have not received formal training in mathematics and computer science at the university level. After completing this book you will know the fundamentals of R programming, flow control (if statements and for loops), and you will be able to create reproducible reports that feature your own, embedded custom R functions, and high-quality, professional visualizations. You'll also learn how to work with relational data using regular expressions and dplyr pipes -- two powerful tools for data management. This is the first book in a series that includes R STATS Crash Course for Biologists (2013) and R Machine Learning Crash Course for Biologists (2014).
