- Course announcement page
- Short URL to this repo: http://bit.ly/20170403advr
Computational analysis using free, cross-platform scripting languages such as R is becoming an increasingly common part of biological research. For a scientist, knowledge of advanced programming techniques can be extremely useful. Such skills are vital for writing fast, reliable, maintainable programs, and using defensive programming techniques can help to avoid the introduction of errors into code and reduce the amount of time spent finding and fixing these errors. Additionally, earning good practice in writing code can help to ensure that scripts published and/or shared with the community are robust and easy to maintain/develop.
This two-day course, delivered by experts in programming for data analysis, will teach participants advanced techniques in writing reliable, robust code in R. The material will provide the opportunity to gain experience and understanding of object-oriented programming, packaging your code for distribution, advanced approaches for data visualisation, unit testing, and debugging. Sessions will be driven by many practical exercises.
Morning and afternoon coffee breaks: 30 minutes
| Time | Material |
|---|---|
| 9:30 - 10:30 | Introduction |
| 10:30 - 11:00 | Package development |
| 11:00 - 13:00 | Testing and unit testing |
| 13:00 - 14:00 | Lunch |
| 14:00 - 17:00 | OO programming |
| 17:00 - 17:30 | Discussion, wrap-up |
Social dinner in Heiderberg.
| Time | Material |
|---|---|
| 9:30 - 10:30 | Debugging |
| 10:30 - 11:00 | Profiling |
| 11:00 - 13:00 | Functional programming |
| 13:00 - 14:00 | Lunch |
| 14:00 - 17:30 | Tidyverse, ggplot, shiny |
| 17:00 - 17:30 | General good practice discussion and wrap-up |
A list of all required packages and how to install them is proved here.
This material, unless otherwise stated, has been adapted from our general teaching material resource and is is made available under the Creative Commons Attribution license.
You are free to:
- Share - copy and redistribute the material in any medium or format
- Adapt - remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution - You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Some content from the tidyverse and ggplot2 sections comes from the Data Carpentry lessons (see references in the respective sections), which are licensed under CC-BY.
The shiny example apps and the ggivs material comes from the RStudio tutorials and package documentations.