• Robust data analysis: an introduction to R
  • Preface
    • License
  • 1 Data Analysis
    • 1.1 Data analysis in reality
    • 1.2 Role of Excel
    • 1.3 Requirements to data analysis software
    • 1.4 More
  • 2 Introduction to R
    • 2.1 Features of R
    • 2.2 Functions, objects, packages and scripts
    • 2.3 R vs. RStudio
    • 2.4 Programming language vs natural language
    • 2.5 More
  • 3 Workshop
    • 3.1 RStudio Cloud
    • 3.2 Data visualisation
    • 3.3 Import data
    • 3.4 Tidy + Transform Data
    • 3.5 Report with RMarkdown
  • 4 Beyond the Basics
    • 4.1 Git & R
    • 4.2 Examples of R in the wild
    • 4.3 Neat packages & functions
      • Data import
      • Data exploration
      • Data manipulation
      • Data visualisation
      • ggpubr
      • holepunch
      • Best practices
  • 5 Learning R
    • 5.1 Learning strategies
      • Learn with isolated & digestible examples
      • Look for a steady stream of data or exercises
      • Watch recordings
      • Read blog posts
    • 5.2 Embrace imperfection
      • Exploit imperfection
    • 5.3 How to ask for help
      • Look online
      • Create a reproducible example
      • Ask on your question online
    • 5.4 Become part of the community
      • R-Ladies
      • Join an online community
      • rOpenSci
      • Join an R meetup
      • Write blog posts
      • Engage on Twitter
  • Appendix
    • More R learning resources
  • Published with bookdown

Robust data analysis: an introduction to R

Appendix

More R learning resources

  • (Data wrangling) blog by Suzan Baert.

  • Working with R book by Steph Locke.

  • Nick Tierney’s R Resources.

  • Online Tutorial Teacups, Giraffes, & Statistics by Hasse Walum & Desirée De Leon.

  • R for Data Science book by Garrett Grolemund and Hadley Wickham.

  • Online learning material Learning Statistics with R by Danielle Navarro.

  • A Guide to R for Excel Users by David Keyes.

  • RStudio Cheatsheets.