Resources

This page is a work in progress, on which I’m going to list and categorize resources I’ve found useful in my journey to use R.

Starting out

I began my R journey in June 2020, by working through the RStudio primers and the wonderful RYouWithMe course created by R-Ladies Sydney. These gave me a solid foundation in the basics and confidence to begin exploring on my own.

Reference

Here are some books I have found useful in continuing my journey:

• R for data science by Hadley Wickham & Garrett Grolemund
• Data science in education using R by Ryan Estrellado, Emily Bovee, Jesse Mostipak, Joshua Rosenberg and Isabella Velásquez

Data visualization

Resources I’ve found useful specifically on the topic of data visualization:

• Chapters 3 and 7 of R for data science by Hadley Wickham & Garrett Grolemund
• Data visualization: A practical introduction by Kieran Healy
• Fundamentals of Data Visualization by Claus Wilke
• R Graphics Cookbook by Winston Chang, a practical recipe guide for visualizing data using R, and primarily ggplot2
• ggplot2: elegant graphics for data analysis by Hadley Wickham, Danielle Navarro and Thomas Lin Pedersen, which focuses more on explaining the Grammar of Graphis used by ggplot2, and helping the reader to get better at customizing plots by understanding more about the theory behind how ggplot2 works
• R Studio Cloud’s primers on how to Visualize data

Creating summary tables in R

Here are some useful blogposts and other resources I’ve found for creating tables in R for summarizing data. Packages I’ve been getting to know so far include arsenal and gt.

• David Keyes' summary of How to make beautiful tables in R offers short reviews and demonstrations of gt, kable and kableExtra, formattable, DT, reactable, flextable, huxtable, rhandsontable, and pixiedust

• Pascal Schmidt’s How to easily create descriptive summary statistics tables in R studio - By group reviews and demonstrates packages that are particularly useful for summary statistics tables that compare groups, including two of my favorites so far for this purpose, arsenal and tableone

• 10+ guidelines for better tables in R, in which Thomas Mock adapts for R (using gt) tables used as examples in Jon Schwabish’s Ten guidelines for better tables

Multilevel modelling

I’ve begun working through the Centre for Multilevel Modelling’s LEMMA course (Learning Environment for Multilevel Methodology and Applications). The Centre for Multilevel Modelling is based at the University of Bristol in the UK, and this fabulous resource is free to use with registration. The course begins with a refresher in introductory statistics and multiple regression, and progresses to worked examples of increasingly complex multilevel models, with worked examples in R (and other packages) and quizzes to test understanding as you progress.