Monday morning:

• Introduction to R ( lecture slides; and a script file for the R code used)
• Lab session 0 (worksheet; dataset duncan.txt)

Monday afternoon:

• Organising data and code ( lecture slides; datasets cyclist.xlsx and compsci.csv; and a script file for the R code used)
• Lab session 1 (worksheet; dataset infant.xlsx; and some solutions (for when you need them!))

Tuesday morning:

• Literate programming in R Markdown ( lecture slides; markdown example;  code for lecture slides)
• Lab session 2 (worksheet; Rmd solution and  output)

Tuesday afternoon:

• Data visualisation ( lecture slides)
• Lab session 3 (worksheet; and R scripts)

Wednesday morning:

• Statistical models in R (the lecture material; and as background, some slides from a previous course — an overview for social scientists, but most of it is generic)
• Lab session 4 (worksheet; dataset berkeley.txt)

Wednesday afternoon:

• More on 'automated' analysis and reporting (lecture slides [updated!])
• Lab session 5 (worksheet; data and script files)

Thursday morning:

• Participant-led discussion (slides from participants)
• Using knitr (lecture slides [updated!]; demo .Rnw; demo .Rmd, header.tex)

Thursday afternoon:

• Multivariate Statistics ( lecture slides) (PCA & MDS; R code for talk figures: lecture.r)
• Lab session 6 (Download worksheet lab.r and dataset cereal.dat; warning, this may download as "cereal.dat.txt"; rename to remove the ".txt" or edit "lab.r" to add ".txt")

Friday morning:

• Reproducibility in science ( lecture slides)
• Some further information sources etc on R