Resources for any project

Here you'll find a list of external resources we've found helpful across a range of topics. If you have a favourite site/tutorial/package that we haven't covered here, please let us know so we can share it with the world!

Basics of using R for ecology

R for Data Science - An excellent introduction on the basics of data management and plotting in R. Thorough, clear, and not condescending, this book is a MUST for anyone new to R or wanting to improve their workflow. Check out the Explore and Wrangle sections if you don't have time to go through the whole book!
Data Wrangling in R - Allison Horst is the queen of stats illustrations and making coding less intimidating. Check out her tutorials on data wrangling with dplyr here to make cleaning your data a less daunting task.
R-sig-mixed-models - You can join this mailing list online and ask your stats questions out to the greater world of very smart stats folks!

Making beautiful figures

R for Data Science Chapter3 - Hadley Wickham's book is our top pick for understanding how to make figures in ggplot, which should come as no surprise - he created the package!
gganimate Vignette - This is a fabulous resource if you're interested in making animated plots - great for showing trends over time.
Fishualize Documentation - We might be biased because we're both marine ecologists, but the fishualize package has some beautiful colour palettes based on different species of FISH! Don't see your favourite fish (because we know you all have one)? You can contribute palettes as well!
Patchwork Documentation - patchwork is probably the most intuitive package to create multi-panel ggplot objects without having to trial-and-error different values for your plot margins.
The Complete Guide to ggplot Scales - If you're sick of googling how to change the colours or other aesthetics of different scales in ggplot every time you try to make a figure pretty, this is a great resource to help you out.

Linear models (including LMMs, GLMs, and GLMMs)

An Introduction to Hierarchical Modeling - Feeling lost when people talk about random effects or GLMMs? Check out this fantastic visualization and explanation of what mixed effect modeling really means! Note that this resource doesn't get into some really important parts of mixed effect modeling (like shrinkage/partial pooling - see the link below for more info on that), but it's an incredible introduction.
Partial Pooling in Mixed Effects Models - This tutorial provides some excellent visualizations to help explain the concept of partial pooling in mixed models - or why using a random effect in your model is a better idea than modeling each group separately. There are some great animations showing this concept here as well.
Sean Anderson's GLMM Course - The holy grail for GLMMs. This course, designed by Sean Anderson, is the most comprehensive (free) course that's out there, with thorough explanations and examples with different link functions. Whether you're a newbie to GLMMs or a seasoned veteran, you're bound to learn something from this course!
Crossed vs. nested random effects - If you've ever gotten confused about how to specify the types of random effects you have in your model, this surprisingly detailed Stack Exchange answer will help sort you out!

Non-linear models (including GAMs)

Noam Ross's GAM Course - A fabulous tutorial to help make entering the world of Generalized Additive Models a little less intimidating. Designed by Noam Ross, this course helps you get started with using GAMs to model non-linear relationships and using the mgcv package in R.

Bayesian!

Statistical Rethinking - This is a great intro to both the conceptual background behind Bayesian analyses and how to code them. McElreath's book is fantastic, but if you can't get your hands on a copy, he has his course materials here!
Statistical Rethinking in brms - If you liked Statistical Rethinking but wish you could see the examples coded in the more commonly used brms package, you're in luck! A. Solomon Kurz goes through the entire book in this freely accessible Bookdown.
Tidybayes Documentation - tidybayes is a great package with awesome documentation to help you figure out how to extract and plot outputs from a brms model.
Prior Choice Recommendations - Should you use uninformative or informative priors? Should they be weak or strong? Here is a great page to walk you through different priors and the pros/cons of each choice!

Spatial stats in R

Introduction to working with spatial data in R - Here is a wonderful tutorial from the Hong Kong University Cetacean Ecology Lab, which gives a thorough introduction to using spatial data in R using the sf package.

Miscellaneous coding help

Git and GitHub with R - This is a great, comprehensive tutorial on how to install git, set-up GitHub, and get it all talking to RStudio. From beginning to end, you'll be pushing and pulling in no time!
R Markdown Cookbook - Want to contribute, but you've never used RMarkdown before? Here is a comprehensive "Cookbook" on all the cool things you can do in RMarkdown. TLDR? Then checkout this cheatsheet instead.
Guide to reproducible code - Here's a nice guide by the British Ecological Society on tips and tricks to make your code reproducible (and essentially bullet-proof).
Equatiomatic Documentation - This is a super cool package for turning your models into the mathematical equations they actually represent!