Preface

Howdy,

This website is more-or-less the living result of a collaborative project between us. We’re not trying to be an exhaustive resource for all environmental chemists. Rather, we’re focusing on developing broadly applicable data science course content (tutorials and recipes) based in R chemistry courses and research.

This book is broken up into five parts:

  • Part 1: Getting Started in R is a general guide for the complete novice that will help you install, setup, and run R code.
  • Part 2: How to Code in R introduces the basics of R programming as well as a usual R workflow, and how to use R markdown to communicate your code with others.
  • Part 3: Data Analysis in R introduces data analysis workflows and showcases how you can use R and the tidyverse to import and clean up your data into a consistent format to tackle the vast majority of the data science/analysis problems you’ll encounter in undergraduate environmental chemistry courses.
  • Part 4: Visualization in R goes deeper in techniques used for visualizing different forms of data.
  • Part 5: Modelling in R introduces different types of linear and non-linear models that you can use to understand and analyse data.

We recommend that you read through Parts 1, 2, and 3 in sequential order. These provide the foundation for the more advanced workflows used in Parts 4 and 5.

Providing Feedback

If you notice an error, mistake or if you have suggestions for adding features or improving the book, please reach out to us or flag an issue on GitHub.

Acknowlegements

Additionally, we would like to thank Jeremy Gauthier, Andrew Folkerson, Mark Panas, and Stephanie Schneider for all of their comments, suggestions, and hard work integrating the concepts of this book into the CHM410 Laboratory curriculum.