Before we can begin to apply rigorous statistical tools to research data, we often need to approach our data intuitively, and look for meaningful associations, surprising patterns, or irregularities, to formulate hypotheses. This is Exploratory Data Analysis (EDA). This workshop introduces the essential tools and strategies that are available for EDA through the free statistical workbench R. Steps covered in this workshop are broadly relevant for many areas of modern, quantitative biology such as flow cytometry, expression profile analysis, function prediction and more.

Participants will gain practical experience and skills to be able to:

Use R and its analysis tools, read and modify code, and explore protocols that can be adapted for their own research tasks.
Write R functions and analysis scripts.
Plot and visualize data using the elementary built-in routines via their (sometimes bewildering) array of parameters to sophisticated, publication-ready presentations.