Andrew's R Course for DNEP June 2018
Andrew.Stewart@manchester.ac.uk http://personalpages.manchester.ac.uk/staff/andrew.j.stewart/Site/Andrew_Stewart.html
Coupland 1 Turing seminar room 1000-1200, PC cluster 1300-1500
If you’re bringing your laptop to the R workshops (recommended), you’ll want to install R (the language) and RStudio (the interface that helps us interact with R) in advance - each is available for OSX, Windows, and various flavours of Unix. You can install R from here:
https://www.stats.bris.ac.uk/R/
And RStudio from here:
https://www.rstudio.com/products/rstudio/download/#download
The course material is available in this repository. You’ll find folders for Day One and Day Two. Within each of these, you’ll find separate Morning and Afternoon folders. Each Morning folder contains the slides for the seminar, and the R code and data I use in the slides. The Afternoon folders contain the afternoon workshops (each written as an .html file), and the data needed for each workshop. Additionally, you’ll also find a Cheat Sheets and Handy Guides folder which contains a number of helpful reference resources, and a Quick Start guide to begin working with R.
On Day One we will cover the basics of R, data wrangling, data visualisation, and classical analysis techniques such as AN(C)OVA and regression. On Day Two we will focus on (generalised) linear mixed models (aka mixed models, hierarchical linear models, random effects models) for the modelling of normal, non-normal, binomial, and ordinal data.
Below are some helpful R resources - it would be useful to look at the first one before the initial workshop.
This is a very clear and focused introduction to R, RStudio, and R Markdown. You probably want to read the first four chapters sooner rather than later…
This is the online interactive version of the book of the same name. It focuses more on the data science side of things than on statistics per se, and is very useful (especially in terms of data wrangling).
The following cookbook contains lots of useful examples of graphing using the ggplot2 package in R. A new version of the book is due to be released during Summer 2018.