Data Visualisation with R

Overview

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

However, creating graphs is far from a straightforward task, and selecting an appropriate visualization can prove to be quite intricate. The process of designing an effective graph hinges on numerous factors, including but not limited to the nature of the data, the researcher's intended message, the intended context for the graph (such as whether it will appear in a scholarly article or presentation slides), the target audience, and various other elements that need to be considered.

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, common mistakes to avoid, and advanced techniques for showing complex data during presentations. Along the way, we will consider many examples, both good and bad, including those proposed by the participants.

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

Audience

This course is addressed to scientists who need to produce data visualisation and who have already used the R software before.

Learning outcomes

At the end of the course, the participants are expected to be able 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 visualisations

Prerequisites

Knowledge / competencies

This course is designed for scientists who know at least the basics of R programming, for instance those who have attended any of the SIB courses on First Steps in R. Participants should be able to read and import data files, as well as select subsets of data, but do not need to know how to create plots in R. .

Technical

You are required to bring your own laptop with a wifi connection, and the following software installed BEFORE the course: R and R Studio. A list of packages will be provided in due time.

Application

Registration fees are 200 CHF for academics and 1000 CHF for for-profit companies. While participants are registered on a first come, first served basis, exceptions may be made to ensure diversity and equity, which may increase the time before your registration is confirmed.

You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 days.

Applications close as soon as the course is full. Deadline for free-of-charge cancellation is set to 25/11/2024. Cancellation after this date will not be reimbursed. Please note that participation to SIB courses is subject to our general conditions.

Venue and Time

The course will take place in Lausanne.

The course will start at 9:00 and end around 17:00.

More information will be provided to the registered participants in due time.

Additional information

Coordination: Valeria Di Cola, SIB Training.

We will recommend 0.5 ECTS credits for this course (given a passed exam at the end of the course).

You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

Please note that participation in SIB courses is subject to our general conditions.

SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.

For more information, please contact training@sib.swiss.