These are some resources you can take a look at to brush up on your R or statistical modeling, that I have found helpful. I am continually adding to this list; please check back regularly for updates!

I’ve divided this resources page into three sections: resources about learning how to code in R, resources about mixed models, and resources about Bayesian statistics.

This is a book on using the **tidyverse** family of R packages to work with data in R. It is available in ebook and print form. It covers how to clean and manipulate data, and how to make simple plots. It 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!

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.

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).

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

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.

Richard McElreath has created an amazing Bayesian course called Statistical Rethinking. It has a print book and ebook version, as well as a set of free video lectures. Solomon Kurz translated all the code examples in the book to **brms**, **ggplot2**, and **tidyverse** code: Statistical Rethinking Recoded. Both are highly recommended.

This is another useful book-length course. It’s based on the **rstan** interface.

Statistical Modeling with R: a dual frequentist and Bayesian approach for life scientists

The title of this book by Pablo Inchausti says it all. It is a well-written and engaging introduction to statistical analysis. It’s one of the rare books that shows the frequentist and Bayesian approaches to the same problem side-by-side.

Here are some more links to tutorials and lessons for learning basic Bayesian statistics with **brms**:

- Building a multilevel model in brms by Rens van de Schoot
- An introduction to Bayesian multilevel models using brms by Ladislas Nalborczyk
- Andrew Heiss’ blog has lots of tutorials using
**brms**

Here is a tutorial that goes through all the cool plots and tables you can make from a **brms** model with Matthew Kay’s **tidybayes** package.