R Materials Collection

A few weeks ago, a friend of mine on Twitter asked about materials and lectures for R beginners. I replied with a few resources, but then I realized that the books and materials that I know of are scattered across different places. To make things more organized and easy to access, I decided to compile a collection of R learning mateirals. Some of them I have studied, while others are still on my to-read list.

Recommeded materials are marked with a star (☆). I have also included Korean resources, but most of them are in English.

R General, Grammar and Principles

• ☆ R for Data Science (2e) By Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund
  • Great book! I learned a lot from it when I first started using R.

• Online R learning for applied statistics By Chenxin Li

• Introduction to R for Biologists By Maria Doyle, Jessica Chung, Vicky Perreau

• Must Learning with R By DoublekPark, 훈지
  • Written in Korean
• R을 이용한 데이터 처리 & 분석 실무 By 서민구
  • Written in Korean

• R Programming for Data Science By Roger D. Peng

• rstudio3edu By Desirée De Leon, Alison Hill

• ☆ Advanced R By Hadley Wickham
  • Advanced R grammar and principles.
• The R Inferno
  • Interesting topics. It’s about “trouble spots, oddities, traps, glitches” in R.

Data Visualization

• ☆ Fundamentals of Dava Visualization {:target=’_blank’} By Claus O. Wilke
  • Useful for data-viz.
• ☆ ggplot2: Elegant Graphics for Data Analysis (3e) By Hadley Wickham
  • Manual of ggplot2 written by its developer.

• R Graphics Cookbook (2e) By Winston Chang

• Spatial Statistics for Data Science By Paula Moraga

• Best Practices for Dava Visualization By Andreas Krause, Nicola Rennie, Brian Tarran

• Interactive Web-Based Data Visualization with R, plotly and shiny By Carson Sievert

• Mastering Shiny By Hadley Wickham

• JavaScript for R By John Coene

• R Graph Gallery

R Development

• Building reproducible analytical pipelines with R By Bruno Rodrigues

• Applied HPC with R By George G. Vega Yon

• Mastering Software Development in R By Roger D. Peng, Sean Kross, and Brooke Anderson

• R Packages (2e) By Hadley Wickham, Jennifer Bryan

R for Specific Analysis

• Causal Inference in R By Malcolm Barrett, Lucy D’Agostino McGowan, Travis Gerke

• Survival Analysis in R By Emily C. Zabor

• RNAseq in R By Maria Doyle, Belinda Phipson, Matt Ritchie, Anna Trigos, Harriet Dashnow, Charity Law

• a Little Book of R for Bioinformatics By Avril Coghlan

R Markdown and Quarto

• R Markdown Cookbook By Yihui Xie, Christophe Dervieux, Emily Riederer

Useful Packages

• Getting Started with Seurat v4
  • One of key packages for genomic analysis

• Scientific Journal and Sci-Fi Themed Color Palettes for ggplot2

• ggpubr: ‘ggplot2’ Based Publication Ready Plots

• GraphPad-like figure in R By Artur Matysik

• Document of ggstatsplot package

• tidyplot package
  • Publication-ready plots for scientific papers.

Useful Blogs

• Julia Silge’s Blog (Data scientist and software engineer at Posit PBC)
• Nicola Rennie’s Blog
• Plotly로 시작하는 인터랙티브 데이터 시각화
  • Written in Korean

Articles about R and Viz General

• Eglen, S. J. (2009). A Quick Guide to Teaching R Programming to Computational Biology Students. PLoS Computational Biology, 5(8), e1000482. https://doi.org/10.1371/journal.pcbi.1000482

• Krzywinski, M., & Altman, N. (2013). Error bars. Nature Methods, 10(10), 921-922. https://doi.org/10.1038/nmeth.2659

• Lord, S. J., Velle, K. B., Mullins, R. D., & Fritz-Laylin, L. K. (2020). SuperPlots: Communicating reproducibility and variability in cell biology. Journal of Cell Biology, 219(6). https://doi.org/10.1083/jcb.202001064

• Midway, S. R. (2020). Principles of Effective Data Visualization. Patterns, 1(9), 100141. https://doi.org/10.1016/j.patter.2020.100141

• Streit, M., & Gehlenborg, N. (2014). Bar charts and box plots. Nature Methods, 11(2), 117-117. https://doi.org/10.1038/nmeth.2807