R, March 2014 U Washington Software Carpentry Bootcamp, HUB 145, 4001 Stevens Way NE

http://tiny.cc/ruw-syllabus points to this page

http://tiny.cc/ruw-githubb points to the Github repository the instructors will use during the bootcamp <-- this seems to point to the repository that underpins this website; still potentially useful!

CORRECTION (?): the actual Github repository instructors will work with during the bootcamp: https://github.com/LJWilliams/UW-R

http://tiny.cc/gapminder points to the Gapminder data we'll use

Etherpad link

Slides for git intro

We will write R code together "live" in the sessions, but below I link to written content on most topics, for your future reference.

Session 1.1

• Basics of R/RStudio
• Workspace and working directory
• RStudio projects
• Working via scripts (vs. direct typing/pasting in Console)
• Sourcing and compiling an HTML "notebook" from a script
• See
  • R basics, workspace and working directory, RStudio projects -- source

Session 1.2

• Care and feeding of R objects
• See
  • Basic care and feeding of data in R -- source
  • R objects (beyond data.frames) and indexing -- source

Session 1.3

• Version control with Git
• Local usage of Git; RStudio as a Git client; Git commands in the shell.

Session 1.4

• Making figures using ggplot2.
• See
  • http://docs.ggplot2.org/
  • Overview of the R graphics landscape
  • Taking control of qualitative colors in ggplot2
  • Head-to-head comparisons of ggplot2 and lattice
  • Data Visualization with R & ggplot2 by Karthik Ram (first few slides make a good introduction)

Session 2.1

• File organization.
• R functions and the debugger.

Session 2.2

• The shell, especially file management.
• R native equivalents for same operations.
• Version control with remote/hosted repositories. Github.

Session 2.3

• Data aggregation with R, including the plyr package.
• See
  • plyr webpage
  • Data aggregation -- source

Session 2.4

• Automation and pipelines.
• File I/O, writing figures to file.
• Scripts and commands that run scripts in RStudio, R, Make.
• See
  • Getting data out of R -- source
  • Writing figures to file -- source
  • An introduction to Make by Karl Broman, aimed at stats / data science types