Researchers from psychology and other disciplines are increasingly relying on computational analyses of large data sets to draw conclusions about human behaviour. This kind of research requires skills that are not often taught as part of the psychology curriculum, and the Melbourne School of Psychological Sciences is therefore pleased to announce the first annual Complex Human Data summer school. This page covers the resources for Day 2 of the summer school.

Day 2

The goal in Day 2 is to cover topics relevant to data analysis with R. The coverage is somewhat eclectic: there are sections that discuss tools that will help you with your workflow (e.g., git, R Markdown, etc), sections that discuss how to manipulate and visualise data (e.g., tidyverse), and a section that discusses statistical modelling in R (e.g., linear models, mixed models). Given the breadth of coverage, we won’t go into much detail on any topic. You should think of each section as an initial introduction to something that is itself a much larger topic.

Within each content section, there is a written tutorial linked to below and as well as the HTML slides that I’ll be presenting from. Eventually the intention is to have the tutorials go into more detail on topics, but at the moment they’re a bit hit and miss!

The interactive components to each section:

Other information

Assumed knowledge:

Additionally, it may be handy to have the Day 0 introduction to R resources easily accessible so here they are:

R packages used in the tutorials: tidyverse, skimr, here, lme4, lsr, BayesFactor


One goal we had when putting all this together was to try to provide you with a sense of the extent to which the tools in this Summer School can be adapted to many different purposes. One hint to that is to look at how the resources for Day 2 have been constructed.

Once you start feeling comfortable in R, it’s remarkable what fun things you can find to do with it!