3 min read

A course in statistical programming

Graduate students in statistics often take (or at least have the opportunity to take) a statistical computing course, but often such courses are focused on methods (like numerical linear algebra, the EM algorithm, and MCMC) and not on actual coding.

For example, here’s a course in “advanced statistical computing” that I taught at Johns Hopkins back in 2001.

Many (perhaps most) good programmers learned to code outside of formal courses. But many statisticians are terrible programmers and would benefit by a formal course.

Moreover, applied statisticians spend the vast majority of their time interacting with a computer and would likely benefit from more formal presentations of how to do it well. And I think this sort of training is particularly important for ensuring that research is reproducible.

One really learns to code in private, struggling over problems, but I benefited enormously from a statistical computing course I took from Phil Spector at Berkeley.

Brian Caffo, Ingo Ruczinski, Roger Peng, Rafael Irizarry, and I developed a statistical programming course at Hopkins that (I think) really did the job.

I would like to develop a similar such course at Wisconsin: on statistical programming, in the most general sense.

I have in mind several basic principles:

  • be self-sufficient

  • get the right answer

  • document what you did (so that you will understand what you did 6 months later)

  • if primary data change, be able to re-run the analysis without a lot of work

  • are your simulation results reproducible?

  • reuse of code (others’ and your own) rather than starting from scratch every time

  • make methods accessible to (and used by) others

Here are my current thoughts about the topics to include in such a course. The key aim would be to make students aware of the basic principles and issues: to give them a good base from which to learn on their own. Homework would include interesting and realistic programming assignments plus create a Sweave-type document and an R package.

  • Basic unix tools (find; df; top; ps ux; grep); unix on Mac and windows

  • Emacs/vim/other editors (rstudio/eclipse)

  • Latex (for papers; for presentations)

  • slides for talks; posters; figures/tables

  • Advanced R (fancy data structures; functions; object-oriented stuff)

  • Advanced R graphics

  • R packages

  • Sweave/asciidoc/knitr

  • minimal Perl (or Python or Ruby); example of data manipulation

  • Minimal C (or C++); examples of speed-up

  • version control (eg git or mercurial); backups

  • reproducible research ideas

  • data management

  • managing projects: data, analyses, results, papers

  • programming style (readable, modular); general but not too general

  • debugging/profiling/testing

  • high-throughput computing; parallel computing; managing big jobs

  • finding answers to questions: man pages; documentation; web

  • more on visualization; dynamic graphics

  • making a web page; html & css; simple cgi-type web forms?

  • writing and managing email

  • managing references to journal articles