Venue

University of Lausanne, Room TBD

Scientific results are mostly conveyed through graphics and tables, and representing data graphically in a clear way is an important task for any scientist.

Creating such graphs is not a trivial task though and choosing the right representation depends on many factors: among the most important the data itself, the message that the researcher wants to get across, and the way in which the data is presented (a figure in an article is completely different from a figure in slides).

During this course, we will present different ways for representing data, how to choose among them, why you should avoid using error bars, how to design efficient graphs, which tools to use (and which tools to avoid!), how to design graphs for specific media, good practices for plotting data, and common mistakes to avoid.

This course will also include practicals using the R statistical software. During these practicals, we will discuss and introduce the differents models for creating graphics in R (including base R and ggplot2).

At the end of the course, the participants are expected to:
- apply data visualisation methods to represent their data and get their message across;
- choose the right method to represent a dataset graphically
- & use the R software (base R and ggplot2) to produce data visualization

Registration is CLOSE

Deadline for Registration: TBA