Tutorials:

On 2003-03-19 there will be several half-day tutorials organized jointly by the R Core Development Team, the R Foundation for Statistical Computing and the Austrian Association for Statistical Computing (AASC):

Fees for each tutorial are EUR 50 (academic), EUR 250 (non-academic) and EUR 25 (students).

Participants are encouraged to check this webpage before the course begins and download the files, images and further material here.
If possible having a laptop with the released version of R (1.6.2) and the released version of Bioconductor (1.1) (for the Bioconductor tutorials) will be helpful.

presented by Robert Gentleman

A brief introduction to genome science, microarray technology, what you are measuring and why.
Using R and Bioconductor packages.
What packages are there and what they do.
Finding documentation, using vignettes

Biobase:

• data structures and operations on data structures

Annotate:

• the general approach to dealing with annotation
• getting annotation for your experiment
• creating hypertext reports for colleagues

genefilter:

• setting specific filtering criteria
• finding genes with interesting patterns

geneplotter:

• creating heat maps
• plotting chromosomal location

presented by Robert Gentleman

In this 1/2 day tutorial we will assume a working knowledge of the bioconductor packages listed above.

We will consider how to write extensions to these packages. Look at more advanced plotting methods and interactions.

Application of machine learning methods to genomic data. Topics will include knn, randomForests, edd.

Applications of graphs and graph theory (not graphics!) to various computational problems.

presented by Thomas Lumley and Douglas Bates

The tutorial will cover the techniques of extending the R statistical computing environment by creating packages of your own R functions, data sets, classes and methods and by incorporating compiled code written in C, C++ or Fortran. We will emphasize the R packaging system that provides convenient tools for development of R extensions. These extensions can be as simple as a private or in-house collection of data sets or they can be much, much more complex.

We will cover the newly-available "S4" classes and methods from the methods package (as described in John Chambers' book "Programming with Data", Springer, 1998) and the .Call interface to C code from R.

Basic documentation (in the Rd format) of all functions, methods, and data sets sets in the package should be included with the package. We will describe how to generate and edit such documentation. We will also describe more sophisticated documentation in the form of "vignettes" that blend R code and its documentation using Sweave.

For Windows users: Windows users should ensure that they have ActivePerl installed. Also, Windows users should be careful to select the option to install tools for making packages when they install R. This option is not selected by default.

presented by PaulMurrell

This course is designed to teach intermediate-level R users how to create customised statistical graphics (plots). We will cover the following topics:

1. Arranging multiple graphics
2. Defining coordinate systems
3. Controlling the appearance of graphics (colour, etc)
4. Graphics which are not plots (e.g., legends)

There will be a focus on using the new graphics system for R (provided by the add-on package grid).

Attendees should be comfortable with writing their own R functions. A familiarity with the fundamentals of the base graphics system is not required in order to be able to understand the new graphics system.

The course will consist of two parts:

1. A brief review of the fundamentals of R's base graphics system. We will look at the following R functions:
   plot.new(), plot.window(), par(), layout(), axis(), lines(), points(), text()
2. An introduction to R's new graphics system (the add-on package grid). We will look at the following R functions:
   viewport(), push.viewport(), pop.viewport(), grid.layout(), unit(), gpar(), grid.xaxis(), grid.yaxis(), grid.lines(), grid.points(), grid.text().