R tips

This repository contains a series of R programming tips covering topics across data cleaning, data visualisation, machine learning, statistical theory and data productionisation.

Many kudos to Dr Chuanxin Liu, my former PhD student and code editor, for teaching me how to code in R in my past life as an immunologist.

Content summary

Legend	Category
📚	Data cleaning
🎨	Data visualisation
🔮	Machine learning
🔨	Productionisation
🔢	Statistics

Tutorials

🎨 Data visualisation

• An introduction to ggplot2 using volcano plots
• Using DiagrammeR to draw flow charts

📚 Data cleaning

• Data cleaning using data.table and tidyverse
• Manipulating character strings using regular expressions

🔨 Data report productionisation

• Creating SQL <> R workflows
• Automating R Markdown report generation - Part 1
• Automating R Markdown report generation - Part 2

🔮 Machine learning in R

• Working with dummy variables and factors

🔢 Statistical theory in R

• Introduction to expectation and variance
• Beyond expectations: centrality measures in statistics
• Introduction to the normal distribution
• Introduction to the Chi-squared and F distribution
• Introduction to the T distribution (TBC)
• Introduction to binomial distributions
• Introduction to hypergeometric, geometric, negative binomial and multinomial distributions

Citations

• Hadley Wickham (2017). tidyverse: Easily Install and Load the 'Tidyverse'. R package version 1.2.1. https://CRAN.R-project.org/package=tidyverse

• Matt Dowle and Arun Srinivasan (2019). data.table: Extension of data.frame. R package version 1.12.6. https://CRAN.R-project.org/package=data.table

• Hadley Wickham (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.4.0. https://CRAN.R-project.org/package=stringr

• Max Kuhn. (2019). caret: Classification and Regression Training. R package version 6.0-84. https://CRAN.R-project.org/package=caret

  • Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, Brenton Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, Andrew Ziem, Luca Scrucca, Yuan Tang, Can Candan and Tyler Hunt.

• Jacob Kaplan (2020). fastDummies: Fast Creation of Dummy (Binary) Columns and Rows from Categorical Variables. R package version 1.6.1. https://CRAN.R-project.org/package=fastDummies

• Kirill Müller (2017). here: A Simpler Way to Find Your Files. R package version 0.1. https://CRAN.R-project.org/package=here

• Paul Murrell (2015). compare: Comparing Objects for Differences. R package version 0.2-6. https://CRAN.R-project.org/package=compare

• A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18--22.

• Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang, Hyunsu Cho, Kailong Chen, Rory Mitchell, Ignacio Cano, Tianyi Zhou, Mu Li, Junyuan Xie, Min Lin, Yifeng Geng and Yutian Li (2020). xgboost: Extreme Gradient Boosting. R package version 1.0.0.2. https://CRAN.R-project.org/package=xgboost

• Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9), 1-20. URL http://www.jstatsoft.org/v11/i09/

• Microsoft Corporation and Steve Weston (2019). doParallel: Foreach Parallel Adaptor for the parallel Package. R package version 1.0.15. https://CRAN.R-project.org/package=doParallel

• Richard Iannone (2020). DiagrammeR: Graph/Network Visualization. R package version 1.0.6.1. https://CRAN.R-project.org/package=DiagrammeR