General Information

Software Carpentry's mission is to help people become more productive by teaching them basic computing skills like program design, version control, data management, and task automation. This workshop will be a comprehensive introduction to the R language. It will cover the fundamentals of the language, data structures and manipulation, data visualization, reproducible research, package building, and interactive documents. The goal will be to expose researchers familiar with programming and statistics to the extended capabilities of R as a programming language, and how it can integrate into their own research workflows.

Note that the workshop is open to everyone, not just PyCon attendees, but does assume prior programming experience in some language.

Instructors: Ramnath Vaidyanathan

Helpers: Denis Haine, John Blischak

Where: Room 525, Palais de Congres, Montreal, Quebec. Get directions with OpenStreetMap or Google Maps.

Contact: Please mail admin@software-carpentry.org for more information.

Etherpad for discussion

We can discuss the lessons in real-time using this Etherpad.

If the link to the Etherpad is no longer available, you can find the transcript here (unfortunately it does not have the highlighting to indicate different users).

Topics Covered

1. Basics of R
2. Data Structures
3. Functions and Control Structures
4. Data Manipulation
5. Data Visualization
6. Reproducible Research
7. Package Building
8. Interactive Documents

The lessons taught can be found here.

Requirements

All participants are requested to install the following software on their laptops prior to the workshop. It is very important that you install the versions recommended below (or a later one).

1. R 3.0.2
2. RStudio 0.98.501

The instructor will also be providing notes and exercises for the workshop as IPython Notebooks. If you want to take advantage of that, I would recommend that you install IPython and its dependencies. You may use Conda by Continuum Analytics to manage installation of Python packages.

References

While there are several resources available online to learn R, there are very few that approach the topic from a programming perspective. Since many of you already have a programming/statistical background, I will recommend the following references:

1. Advanced R Programming by Hadley Wickham.
2. Art of R Programming by Norman Matloff.

Sponsors

Our thanks to Enthought for their generous sponsorship of this bootcamp.