Biological Data Science with R
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Biological Data Science with R

About the Book

This book is a companion to a series of courses introducing the essentials of biological data science with R. While this book was written with the accompanying live instruction in mind, this book can be used as a self-contained self study guide for quickly learning the essentials need to get started with R. The BDSR book and accompanying course introduces methods, tools, and software for reproducibly managing, manipulating, analyzing, and visualizing large-scale biological data using the R statistical computing environment. This book also covers essential statistical analysis, and advanced topics including survival analysis, predictive modeling, forecasting, and text mining.

About the Author

Stephen Turner
Stephen Turner

I am a principal scientist specializing in bioinformatics engineering in support of species restoration and conservation using synthetic biology and genome engineering. I have a M.S. in statistics and Ph.D. in genetics, and did my postdoc in epidemiology. I was a professor in Public Health at the University of Virginia School of Medicine and founding director of UVA's Bioinformatics Core from 2011-2019. I taught graduate courses in data science and bioinformatics, and wrote the Biological Data Science with R textbook to accompany the course. I left academia for consulting 2019-2023 to build bioinformatics and data science solutions for the US Government in public health, defense, and national security. I've been working in de-extinction and conservation genomics ever since. I love talking about genomics, bioinformatics, R, Docker, de-extinction, trail running, and homebrewing.

Subscribe to my newsletter/blog Paired Ends (https://blog.stephenturner.us/) for regular updates on new and interesting research in bioinformatics, computational biology, and data science, with the occasional post on programming.

Table of Contents

  • Preface
  • Core Lessons
    • 1. Basics
    • 2. Tibbles
    • 3. Data Manipulation
    • 4. Data Visualization
    • 5. Tidy EDA
    • 6. RMarkdown
  • Electives
    • 1. Essential Statistics
    • 2. Survival Analysis
    • 3. Predictive Modeling
    • 4. Probabilistic Forecasting
    • 5. Text Mining
    • 6. Phylogenetic Trees
    • 6. RNA-seq
  • Summary
  • References
  • Appendices
    • A. Setup
    • B. Additional Resources

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