The bSpace site is the main site for the bootcamp. It has information on software installation, a zip file with the materials for the modules (under Resources), and the Chat Room for online questions. See http://dlab.berkeley.edu/training/r-bootcamp for accessing the bSpace site and note that you need to follow the instructions there when you access the site for the first time.
The Github site (https://github.com/berkeley-scf/r-bootcamp-2013) is updated more regularly and is the master repository for materials for the modules; you can also get the individual files from Github. Instructions for accessing the materials on Github are available in one of the later slides in this document and on the bSpace site.
If you have an administrative question, email r-bootcamp@lists.berkeley.edu.
If you need Airbears access as a guest (i.e., you don't have a CalNet ID), try:
Note to BB: Remember to start recording.
This isn't really all that related to R, programming or statistics, but check out the 'binary solo' in this Flight of the Conchords song.
The bootcamp will be organized in modules, each of which will be a combination of lecture/demo presentation concluded by a breakout session in which you'll work on a problem or two. Solutions to the breakout problems will be presented before the start of the next module.
Counselors will be available to help answer any questions you have (just wave to one of them), or feel free to just ask the presenter during the presentation. The counselors will also be monitoring the bSpace chat room, so feel free to type in a question there and one of the counselors will answer the question. Or if you know the answer, help us out by answering it yourself or adding your thoughts.
Your counseloRs are: Jacob, Laura, Chris K., Jarrod, Tessa, Fu, Chao, Marla, and Christine.
I encourage you to:
This is a bootcamp. So there may be some pain involved! If you find yourself not following everything, that's ok. You may miss some details, but try to follow the basics and the big picture.
We'll present most of the material from within RStudio, using R Markdown documents with embedded R code. R Markdown is an extension to the Markdown markup language which makes it easy to write HTML in a simple plain text format. This allows us to both run the R code directly as well as compile on-the-fly to an HTML file that can be used for presentation. All files will be available on bspace, under Resources and Github. Note that Github is likely to have the most up-to-date documents while bSpace will only be updated periodically.
Note: The files named moduleX_blah_slides.html have individual slides, while the files named moduleX_blah.html have the same content but all on one page.
Warning: in some cases the processing of the R code in the R Markdown is screwy and the slides have error messages that do not occur if you just run the code directly in R or RStudio.
To download the files from Github, you can do the following.
Within RStudio go to Project->New Project->Version Control->Git and enter:
Then to update from the repository to get any changes we've made, you can select (from within RStudio): Tools->Version Control->Pull Branches
or from the Workspace/History/Git window: Git->More->Pull Branches
Be warned that you probably do not want to make your own notes or changes to the files we are providing. Because if you do, and you then do a “Git Pull” to update the materials, you'll have to deal with the conflict between your local version and our version. You probably will want to make a personal copy of such files in another directory or by making copies of files with new names.
Then to update from the repository to get any changes we've made:
The pieces of an R session include:
RStudio provides an integrated development environment in which all of these pieces are in a single application and tightly integrated, with a built-in editor for your code/scripts.
Other software is better than R at various tasks
E.g., Python is very good for text manipulation, interacting with the operating system, and as a glue for tying together various applications/software in a workflow
R can be much slower than compiled languages (but is often quite fast with good coding practices!)
R's packages are only as good as the person who wrote them; no explicit quality control
R is a sprawling and unstandardized
The building blocks of scientific computing:
