Keith Baggerly and Karl Broman
July 17-19, 2017

This module is part of the Summer Institute in Statistics for Big Data!

Taught by

Keith A. Baggerly
[email protected]

and

Karl Broman
[email protected]

• pdf
• html
• MS Word
• Rmd Source

• pdf
• html
• MS Word
• Rmd Source

Karl's Software Carpentry Course

These are from RStudio's list

• Rmarkdown; there's also a reference guide
• Package Development/Devtools

There are many other sheets there (including some for user contributions and translations), so check it out!

These are from GitHub

• Git/GitHub
• Git
• Links to Translations
• More Resources

Lecture 0, Basic Intro, Keith, 5-10 min pdf, printable pdf
Introduction to the course, administration, course goals
Definitions - reproduction vs replication

Lecture 1, Intro and Common Problems, Karl, 40 min pdf, printable pdf
An introduction to reproducible research by way of commonly encountered problems, plus discussion of the organization of project files

Lecture 2, A Train Wreck, Keith, 40 min pdf, printable pdf
A case study describing just how bad things can get, with clinical implications

Lecture 3, R Markdown and Literate Programming, Karl, 45 min RMarkdown notes, Rmd example
An introduction to R markdown, RStudio, and Literate Programming, with examples illustrating how to produce reproducible reports

Homework part 1, participants, 45 min
Set up the analysis folder, write the preprocessing script in R markdown, compile to html / pdf / word

Lecture 4, R Packages, Keith, 45-60 min (much live demo) pdf, printable pdf
How to write R packages quickly and easily with devtools, roxygen2, rmarkdown, and knitr - overhead, code, data, vignettes, clean code, and templates

Homework part 2, participants, 30 min
writing a basic package

Lecture 5, EDA, Big Jobs, and Automation, Karl, 75 min (includes some short activities) pdf, printable pdf, activity 1 spin example, activity 2 caching example
Capturing exploratory data analysis, handling the challenges arising when data or jobs are big enough to make rerunning unpleasant or infeasible, and a brief introduction to automation with GNU Make

Lecture 6, Vitamin D, Keith, 10-15 min pdf, printable pdf
Discussion of how recommendations are set, and reconstruction of analyses obscured by lack of code and misapplied terminology. Linked to course homeworks.

R package sisbid3, with a vignette on adding data to R packages
just the vignette
report fitting logistic regression to Priemel et al

Lecture 7, Problems with Replication, Keith, 40 min pdf, printable pdf
A review of several factors which can make results harder to replicate (be seen again with new samples) vs hard to reproduce (starting from the same raw data)

Lecture 8, Git on your Computer, Keith, 50 min, mostly live pdf, printable pdf
Using git to track files and versions; init, status, add, commit, branch, checkout, merge

Lecture 9, Git with GitHub, Keith, 45 min pdf, printable pdf
Introducing GitHub, perhaps the "killer app" for git; working with remote repositories, bare repos, cloning, pull, push

Homework, participants, 45 min
Establishing a repo at GitHub
Post your package to GitHub

This session will be a mixture of lecture and live demo.

Lecture 10, Collaborating with Git, Keith, 45 min pdf, printable pdf
Working with others, making comments, providing feedback, fixing errors

Homework, participants, 45 min
Working with your neighbor's repos

Homework, participants, 45 min
Add comments and vignettes to your package on GitHub

Lecture 11, Implementing RR at MDACC, Keith, 45 min pdf, printable pdf
A review of ongoing efforts within the biostat department at MD Anderson to produce reproducible reports, and how we took a report written a few years ago using a mix of R and Stata and revamped it in R/rmarkdown to emulate not just the results but also the "look and feel" of the initial MS word output. Hits on tables and figures in rmarkdown, references, reformatting headers.

Lecture 12, Writing Good Reports, Keith, 45 min pdf, printable pdf
The "non-codeable" parts of reproducibility - trying to increase the odds your collaborators will understand what it is you're trying to do.

Homework, participants, 45 min
Automating common tasks with templates - report structures, directory structures, and look and feel

Lecture 13, Summary and Wrapup, Karl, 45 min pdf, printable pdf
Maintaining the Mindset, summary of the course, sharing your work, some links to resources, and brief discussions of topics that we didn't cover

Final Class Discussion

Evals, participants, 5 min

• Christopher Gandrud, Reproducible Research with R and Rstudio, 2e (2015)
• Hadley Wickham, R Packages (2015)
• Yihui Xie, Dynamic Documents with R and knitr, 2e (2015)
• Karl Broman's Tools for RR Course
• Other links: resources.md