Monday morning:

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

Monday afternoon:

• Literate programming in R Markdown ( lecture slides; Markdown example; R Markdown example)
• Lab session 1 (worksheet; model solution;)

Tuesday morning:

• Data handling ( datahandling.pdf [updated!]; a script file [updated!] of the R code used and the compsci dataset)
• Lab session 2 (worksheet; dataset infant.xlsx;solutions)

Tuesday afternoon:

• Data visualisation ( lecture slides)
• Lab session 3 (R script files; and for notes on further resources, see the 2016 worksheet)

Wednesday morning:

• Exploratory analysis for multivariate data (lecture slides)
• Lab session 4 (worksheet; dataset cereal.dat)

Wednesday afternoon:

• 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 5 (worksheet, and the dataset berkeley.txt)

Thursday morning:

• Publication-quality tables and graphs ( publication.pdf; and a script file for the R code used)
• Lab session 6 (worksheet; files aspirin.csv, template.docx, references.bib, epl.csl, epl2.cls, latex_example.Rmd, header.tex; solutions for main practical)

Thursday afternoon:

• Reproducibility in science ( lecture slides)
• Lab session 7 (additional session for catch-up, questions etc.; no new materials)

Friday morning:

• Collaboration and outreach (worksheet, example repo, final exercise)
• Some further information sources etc on R (to appear here)