Rescheduled and new online format: Data Analysis (with R)

This workshop was postponed due to the corona crisis.

A new online course on Data Analysis with R by Rick Scavetta (Scavetta Academy) will be launched in Mai 2020.

The workshop will be co-hosted by the IRTG and IMPRS-LS

Sign up with Elizabeth irtg1064@bmc.med.lmu.de

Workshop description:

The online workshop will consist of three components:

• An online, independent eCourse, including videos eBook and exercises.
• Two 2-hour sessions spread over two days for group discussions and exercises
• Individual 30 minute consultations for each student spread over 2 days

The dates are:

• 29. May - Access to online content begins, i.e. Kick-off
• 3. & 4. June 9:30 - 11:30 - 2 2-hour live online learning sessions with the whole group
• 5. & 8. June 9:00 - 17:00 - 12 30-minute 1:1 training sessions with each student

For all the live sessions, there is a concurrent session planned in the afternoon. That means for the group sessions (3. & 4. June), participants may join in the afternoon session with the other school if they are not available in the morning. For the 1:1 sessions, it may be possible to arrange afternoon sessions, or choose a different day altogether.

R is an open-source cross-platform software tool that combines data manipulation, statistical modelling and visualisation.
The Data Analysis workshop enables laboratory-based life scientists to use the R statistical programming environment to analyse their own data. This workshop focuses on data manipulation and biostatistics modelling using relevant examples from the life sciences.

Using plenty of hands-on exercises, participants will learn about:

• The most common data structures and functions in R,
• How to manage and ask specific questions of their data, and
• How to use the results of statistical tests.

Packages (e.g. the tidyverse) and paradigms (vectorization) that make R well-suited to data manipulation, as well as common beginner pit-falls, will be introduced.

Basic visualisations will be covered, but will be treated in more depth in the separate Data Visualization workshop.

Methods for dealing with missing data will be broached at various parts in the workshop.

Approximately one third of class time is dedicated to having the students work on their own data-sets under the supervision of the instructor. The goal is to develop data analysis solutions as part of the workshop.

Extra material is provided in the reference book for specific problems, e.g. pattern matching with regular expressions, and control structures (e.g. loops and conditional statements). Participants will be provided with all data-sets and access to the book after the workshop to continue working on these case studies.