R is Your Friend
Preface
This text was created for STAT 1810: Introduction to Statistical Computing in R at Cal Poly - San Luis Obispo by Dr. Kelly Bodwin, Dr. Allison Theobold, Dr. Charlotte Mann, Dr. Zoe Rehnberg, Dr. Emily Robinson, and Dr. Julia Schedler.
This text is not meant to be a complete course or textbook by itself; rather, think of it as “long-form” class slides. We will summarize the main concepts in each chapter, show you examples, point you to more in-depth readings from outside sources, and ask you to try out short tasks in R as you go.
How to Use This Text
Watch out sections contain things you may want to look out for - common errors, etc.
Example sections contain code and other information. Don’t skip them!
Note sections contain clarification points (anywhere I would normally say “note that ….). Make sure to read them to avoid any common pitfalls or misconceptions.
Consider these sections to be required readings. This is where we will direct you to existing materials to explain or introduce a concept.
Similarly, consider these sections to be required viewing for the course material.
Check-in sections contain small tasks that you need to do throughout the reading, to practice or prepare. Although they are not graded, please treat them as required!
Each chapter will have a longer practice exercise to complete and turn in. These are intended to be done with help from instructors and peers.
Consider these to be optional readings/viewings. The world of python programming has so many interesting tidbits, we can’t possibly teach them all - but we want to share them with you nonetheless!
We will usually make these “click to expand” so that they don’t distract from your reading.
These are personal opinion comments from the authors. Take them with a grain of salt; we aren’t the only R programmers worth listening to, we are just sharing what has worked for us.
We will usually make these “click to expand” so that they don’t distract from your reading.
Additional Resources
References or additional readings may come from the following texts:
- Statistical Computing Using R and Python (https://srvanderplas.github.io/stat-computing-r-python/)
- R for Data Science (https://r4ds.hadley.nz/)
You can find additional help for Coding in R from the following resources:
Part of this course will be dedicated to conducting familiar statistical analyses using R code. You can find a refresher on statistical concepts from the following resources: