This is a class at Colorado State University taught by Matt Ross, Caitlin Mothes, and Katie Willi.

M-W-F 10:00am-11:50am, WCNR Building Room 243

The broad goal of this course is to make your graduate or undergraduate research easier, more fun, less frustrating, more collaborative, and more supported, while also preparing you for a career that will include data science skills. The more specific goals of this course are to teach students to:

1. Describe the basic code, file, and data management skills required to efficiently and effectively analyze environmental datasets of a range of sizes.

2. Apply linear regression, basic machine learning, and trend analysis techniques to reveal patterns in environmental data relevant to their field of study.

3. Actively distinguish and select appropriate levels of analysis and code complexity to answer the questions they have about their data.

4. Publicly share a code portfolio and participate in collaborative coding efforts.

This class is very fun to teach, but the hardest part is that students come to it with wildly different backgrounds. We are hoping that we can level the playing field through our teaching, making it so that everyone leaves the class with significant skills in R. However, we know that many of you have zero experience coding in R and others have many years of experience. This class will move extremely quickly and if you have little to no experience in R, we strongly encourage you to spend some time reviewing the additional resources listed below. If you can't review this material or don't already have a general understanding of some of it, this course could move too quickly to stay afloat.

• You must install all necesarry software to the computer you will be using in class before the course begins. From the online book Happy Git and GitHub for the useR, complete all of Lessons 4 - 12 (skipping lesson 10, use HTTPS tokens and not SSH), and lessons 13 and/or 14 for troubleshooting if you have any issues. This book is a phenomenal resource for learning git, GitHub, and RStudio integration.

• Even if you already have R/RStudio installed, please make sure to install the newest versions. At MINIMUM you must have R version 4.1 or greater

• RStudio Primers - Interactive online coding. Really excellent base material and the most useful way to prepare for the course.

  • Please complete lessons 1-4 (Basics, Work with Data, Visualize Data, Tidy Your Data).
  • Lessons 5-7 are extra or can be resources as you learn the same ideas in class.

• "Placement" Quiz - This quiz will not be graded, however it is still required to take before class begins. This quiz should give you an idea of your preparedness for the course, and may highlight potential weak areas that require further review.

• Stat 158 - Vectors, data frames, installing R, etc...

• RStudio Materials - A series of videos, books, and more to get you started in R.

• R for Data Science - Covers all of the basic intro material, from a tidyverse perspective. As discussed, this is one way to find solutions in R, it happens to be my preferred way, but there are lots of Base R ways that work just fine! This is a big book, and should be thought of as a reference!

• Stat 159 - A CSU specific course for an intro to the tidyverse

• R Markdown - The primary book for learning more about R Markdown and all of its quirks.

• Cheatsheets - Short clear documents that cover so much material from dplyr to shiny apps. Great for quick references.

This class is flipped, meaning all lectures are delivered on our YouTube channel and you will be expected to watch them before class. To encourage this we will have weekly quizzes on lecture content. These quizzes will be helpful for us to see what concepts are easier/harder to grasp and how we can best help in class.

So without lectures in class what do we do together? We code! This class has almost 6 hours of contact time per week, and we fully expect you to finish your homework in the class period alloted. The flipped class allows for deeper discussion about the common pitfals of coding.

Generally we will do a quick live-code review of concepts from the videos, but more than 1.5 hours per day will be dedicated to you coding in class.

As such, coming to class is a vital part of how you can be successful and we fully expect you to be there everyday, except for the inevitable mitigating circumstances that we all know will arise.

We also will actively encourage a collaborative coding environment where students help each other, discuss the best approach to solving various coding problems, and deeply engage in online coding help including AI code assistants like GitHub CoPilot or ChatGPT-3. We also hope that outside of class, you will use our Teams channel to discuss code issues!

Each week we will cover new topics, you will submit your code through GitHub, and your peers will grade your code based on a key we share. A full syllabus that we may occasionally update is here with a schedule below.

We will always send announcements with assigments, video links, and other updates through canvas.

A final note. The data science field changes extremely quickly. We will teach you the best way we know how to do things, but these areas change very quickly and we may teach you things that are outdated. For example, over the long term, Quarto is replacing RMarkdown because it is more natively multi-lingual (able to use Python, Julia, etc...), but we are using RMarkdown. Why? Because Rmarkdown is currently more stable, but likely the 2024 version of this class will use Quarto. The differences between these technologies is small, but not zero. A bigger shift we will discuss is how to code with AI assistants, another place where we are hoping to expose you to the cutting-edge resources so you start your career using the best tools available.