Resources

Resources for each day of the summer school are available here.

Day 0

Content: R Bootcamp

The goal in Day 0 is to provide an “R Bootcamp”, which provides a basic introduction to programming in R. The content of the bootcamp covers the core toolkit section of Danielle Navarro’s R for Psychological Science. Other resources for the day:

• Before the session. Install R and RStudio (instructions for Windows and Mac )

• Slides: part1, part2, part3

• Data : toydata.csv, toydata.RData

Day 1

Content: GitHub, Project Organisation and Workflow, Google App Engine

The morning of Day 1 covers Git, GitHub, and project organisation. The goal is to help you develop a workflow that saves time for yourself and allows others to easily understand and reproduce your work. The afternoon shows you how to host an experiment as a web application using Google App Engine.

• Before the session. Install Python 2.7 and the Google App Engine SDK

• Tutorials: git, project organisation, webpages, Google app engine

• Slides: git, project organisation

Day 2

Content: Online Experiments

Day 2 aims to teach you how to develop and run an online experiment.

• Slides: 1) experiment motivation, 2) programming basics, 3) template experiment, 4) more complex experiments

Day 3

Content: Introduction to Tidyverse, Data wrangling, Exploratory data analysis and visualization

Day 3 focuses on using the Tidyverse to explore, visualize, and understand your data.

• Slides: dplyr, ggplot

Day 4

Content: Statistics, Probabilistic models

Day 4 focuses on running statistical analyses (both frequentist and Bayesian) in R, and Bayesian cognitive models.

• Before the session. Please install the lme4, brms, tidybayes and BayesFactor packages in R, along with JAGS.
  
• Slides: statistics, probabilistic models
  
• Tutorial: statistics
  
• Exercises: coughing patient, mixed effects models, food web
• Notebooks for samplingframes project : analysis, models

Day 5

Content: Big Data approaches to psychological processes

• Slides: big data and psychology

Other

Some online resources that you may find useful

• A guide to reproducible code in ecology and evolution contains a lot of useful advice about folder organization, version control and other topics.
• Happy git and github for the useR provides a good overview of how to use Git and GitHub in conjunction with an R-based workflow.
• R for psychological science covers the contents of the bootcamp and a few other topics
• R for data science provides a deeper coverage of data analysis using R, aimed primarily at data scientists
• Machine learning: A probabilistic perspective is intended primarily for computer scientists, but many ideas it covers are relevant for probabilistic modeling in cognitive science