Chapter 4 R

Preamble: thought of as just for statistics…Why do you want to learn R?

R is a programming language with roots in statistics but also for powerful for data science and reproducible research.

4.1 Get a sense of R

• R best practices - Krista DeStasio

• How to Clean Messy Data in R - David Keyes

• Data Wrangling Part 1: Basic to Advanced Ways to Select Columns - Suzan Baert

4.2 Intro to R with RStudio/tidyverse

Many modern R users interact with R through RStudio. RStudio provides a software interface (called the IDE: Integrated Development Environment), concepts of tidy data, analytical packages (including tidyverse and tidymodels), reproducible reporting and publishing (RMarkdown, bookdown, blogdown), and interactive dashboards (Shiny), and a powerful user and developer community (#rstats).

• R for Excel Users - Julia Lowndes & Allison Horst

  • This is for anyone, not just Excel users! Assumes no previous coding experience, and teaches R as a workflow with GitHub and RMarkdown, with a focus on collaboration & reproducibility.

• R for Data Science - Hadley Wickham & Garrett Grolemund

  • THE go-to resource for learning R with the tidyverse.
  • learn with the R4DS community…11.1.1

• R Education for Beginners - RStudio Education

4.3 Code style guide

• Tidyverse style guide - Hadley Wickham
  • “Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread.”
  • Describes style for the tidyverse and Google’s current R Style Guide

4.4 Statistics with R

• Statistics and R - Rafael Irizarry & Michael Love

  • Check out the follow-up courses from this series

• Modern Statistics for Modern Biology - Susan Holmes & Wolfgang Huber
  

• Introduction to (environmental) data analysis & stats in R - Allison Horst

  • Assumes no previous experience with R or stats. Also teaches GitHub

• Advanced methods for (environmental) data analysis in R - Allison Horst

  • Assumes you’ve taken the previous course above

• Learning Statistics with R - Danielle Navaro ((???/)(https://twitter.com/djnavarro/))

  • an introductory textbook on statistics with R pitched at beginners focusing on base R rather than tidyverse tools.

• Data science with R - Danielle Navaro ((???/)(https://twitter.com/djnavarro/))

  • A tidyverse focused course on “robust data science tools” covering data manipulation, data visualisation, an introduction to github and blogdown, as well as programming tutorials that incidentally teach you the basics of making generative artwork!
  • watch on Danielle’s YouTube Channel

4.5 RMarkdown

This What is RMarkdown 1-minute video describes how RMarkdown powerfully combines simple text formatting with executable R code (also called literate programming), fueling efficient, reproducible research. R Markdown is also powerful for publishing.

• RStudio’s RMarkdown Website - RStudio

  • Assumes no previous experience with RMarkdown, and has a showcase Gallery

• RMarkdown: The Definitive Guide - Yihui Xie, J.J. Allaire, Garrett Grolemund

• RMarkdown Cookbook - Yihui Xie, Christophe Dervieux, Emily Reiderer

• RMarkdown for Scientists - Nick Tierney

  • 3-hour lesson book

• RMarkdown Driven Development - Emily Riederer

  • Assumes familiarity with RMarkdown and describes “the progression of stages between a single ad-hoc RMarkdown script and more advanced and reusable data products like R projects and packages.”

• Making free websites with RStudio’s R Markdown - Julie Lowndes

  • 1-hour tutorial to make and publish a free website, no R required
    

• How I teach RMarkdown - Alison Hill

4.6 Shiny

• Mastering Shiny - Hadley Wickham

• Shiny tips & tricks for improving your apps and solving common problems - Dean Attali

4.7 Machine Learning

• tidymodels by RStudio Education

• Supervised Machine Learning for Text Analysis in R by Emil Hvitfeldt and Julia Silge

4.8 Full Courses

• QCBS
• Reproducible Quantitative Methods - Christie Bahlai