1. About

This workshop is designed to provide an accessible introduction to R statistical software by getting participants up and running with practical data analysis exercises. Along the way, you will learn how to transform, visualise, and analyse data in R. After the course, you will be familiar with the tools available in R to perform commonly used data analyses.

2. Curriculum

Foundations

Learn the absolute essentials of R and its ecosystems.

  • Brief R background, why R

  • RStudio basics, panes, utilities

  • Running R scripts, objects, functions

  • R packages, CRAN, Bioconductor

  • Getting help

  • R community

Data Essentials

Learn the most important data types and data structures in R.

  • Data import: readr

  • Data structure basics: vectors, matrices, data frames

  • Factor basics

  • Data exploration basics

Data Manipulation

Learn to explore and properly prepare data for analysis.

  • Data manipulation basics, dplyr’s sing table verbs

  • Relational data, dplyr’s two table verbs

  • Tidy data

  • Pivoting data, tidyr’s gathering and spreading

Data Visualization

Learn to produce meaningful and beautiful data visualisation.

  • Graphics systems: grid, base, ggplot2, lattice

  • Grammar of graphics - ggplot2

  • Visualisation basics (1)

  • Exporting graphics

Data Modelling

Learn to conduct simple summary statistics and linear regression models.

  • Descriptive analysis with continuous and categorical variables

  • Statistical tests

  • Visualisation basics (2)

  • Regression analysis, hypothesis testing

Data Practices

Learn to adopt ‘good enough’ practices when using R.

  • Data project directory

  • Coding style

  • Documentation

  • Sharing data