RaukR, Advanced R for Bioinformatics

Description

In Life Sciences and Bioinformatics, the R programming language is pivotal for data transformation, statistical analyses, and crafting publication-ready visualizations. This workshop goes beyond the basics, offering participants a comprehensive understanding of the R ecosystem. We explore best coding practices, code profiling, delve into data wrangling intricacies, emphasize reproducible research and hands-on development of web apps using R.

Course materials

Course materials will be made available at the beginning of the workshop and will remain open and publicly accessible online for at least a year. You can check out the materials from previous years below.

Sessions

Our daily schedule begins with a morning session from 09:00 to 12:30, starting with breakfast from 08:30 to 09:00. There will be a 30-minute break at 10:30. Lunch is scheduled for 12:30 to 13:30, followed by the afternoon session from 13:30 to 17:00, which includes another 30-minute break at 15:00.

Please be aware that online guest lectures may take place after 17:00 due to differing time zones.

During most sessions, our instructors and teaching assistants will be available to support you with practical exercises and to answer any questions you may have.

Fee and selection

Once the registration period concludes, we will review your application and notify you of our decision. If accepted, you will have five working days to confirm your participation. Failure to do so would imply your inability to attend, and we will proceed accordingly. Once you confirmed, our course administrator will proceed with invoicing according to the .

For more info about the fee and selection process please visit the course website.

Topics covered

• Reproducible research in R (Quarto, Rmarkdown, Renv)
• Collaborative work using Git and GitHub
• R code style guide & best practices
• Code debugging, optimization and profiling
• Parallelization and vectorization in R
• Crafting your own functions
• Object oriented programming and R classes: S3, S4, R6 and RC
• Anatomy of an R package: Creating your own package from scratch
• Tidy data flow using tidyverse
• Using the language of graphics: ggplot2
• Developing web applications using Shiny
• R and Python integration using reticulate
• Team project work - developing data analyses workflow in R using acquired skills

Pre-requisites

This workshop is aimed at both Ph.D. students and researchers within life sciences who are already using R for bioinformatics data analyses and who would like to start using R at a more advanced level. In order for you and other participants to benefit the most, you should consider yourself eligible if you are comfortable with:

• Reading and transforming data,
• Installing and using third-party packages and
• Plotting using standard R graphics.

Level

intermediate