Workflow of statistical data analysis

  1. Introduction, replication and robustness (Exercise on Mon., 16.8., 12:30)
    • Motivation
    • Is workflow obvious? Consistency. Reproducibility.
    • Replicability. Structure of a paper.
    • Aims of statistical data analysis. Making the analysis reproducible. Interaction with coauthors.
  2. Documentation I (Exercise on Tue., 17.8., 12:30)
    • Literate programming, Weaving and tangling
    • An example with Rnw, R and LaTeX.
    • Why markup languages? An example with R-Markdown.
  3. Documentation II (Exercise on Wed., 18.8., 12:30)
    • Chunks. How to include results.
    • Practical issues. Tables. Alternatives.
    • Version control (Motivation)
  4. Documentation III (Exercise on Thu., 19.8., 12:30)
    • Version control with git. Non-linear work.
    • Concurrent edits. Limitations.
    • Version control - practicalities.
  5. Organising work (Exercise on Fri., 20.8., 12:30)
    • Scripting. Robustness.
    • Functions. Calcuations that take a lot of time. Randomness. Exploiting structure.
    • Human readable scripts. Debugging. Structure in Models. Results of functions.
Please choose a chapter:

Chapter: