# Pre-workshop learning materials

These are some resources you can take a look at to brush up on your R or statistical modeling before the workshop. *These are all optional!* No pressure to look at any of these, but some of you might find them helpful.

I’ve divided this resources page into two sections: one with resources about learning how to code in R and one with resources about mixed models but there is a lot of overlap.

## Learning R

R for Data Science

This is a book on using the “tidyverse” family of R packages to work with data in R. We will be using some of the tidyverse packages in the workshop. It is available in ebook and print form. It is a great introduction to the kinds of data we will be working with: how to make simple plots, clean and manipulate the data, and even gets into the very basics of statistical modeling.

USDA SciNet’s list of free online R training courses

This is a list of free online tutorials, many available through AgLearn, on R. Many of them cover statistical models including linear mixed models. Shout-out to my fellow ARS statisticians Sara and Kathy, who compiled this list of resources!

SESYNC Cyberhelp lessons

These are tutorials on data science for researchers including a lot of lessons on R and a few on statistics. I helped develop many of these lessons in my previous job at SESYNC. I would highly recommend the “Basic R”, “Tidy Data in R”, “Plots in R”, “Model Formulas”, and “Advanced Tidyverse” as the most relevant ones for this workshop.

UCLA Stats Consulting’s page of R resources

The stats consultants at UCLA do a great job putting together tutorials, lessons, and examples on statistics using R. (For SAS users, they also have a nice page of SAS lessons and examples).

QCBS R Workshop series

Another excellent set of R lessons provided by the Québec Centre for Biodiversity Science.

## Learning mixed models

Animated visualization of mixed models

This simple visualization does an amazing job explaining what mixed models are.

Basic tutorial on mixed models in R

A brief and accessible introduction.

Mixed models for agriculture in R

This is a lot more technical in terms of the statistics compared with other resources I’ve posted here, but it is very relevant to agricultural data analysis.

Towards Data Science blog post on mixed models

This tutorial by a statistician has some nice demonstrations of the “partial pooling” aspect of mixed models, and goes on to advocate a Bayesian approach to fitting mixed models.