Install the necessary software (R, RStudio and some associated packages). The software is compatible with Windows, MAC OS X, and Linux. The button below links to the installation guide.
You should use your own computers since this will ensure that you continue working on the applications learned and benefit fully from the workshop. We will not do any programming or computer work on the first day, so you don’t need to bring in your computers until the second day.
In the link below, you will find a handout outlining the expectations for student proficiency in R. This acts as a self-assessment. Please go through the code and be sure that you are comfortable with R before the workshop.
Previously, students proficient in MatLab, Perl and/or Python, but with little experience in R, have also benefited from the workshop. However, if you are not comfortable with any programming or scripting language, you may find the second day difficult. If you can at least follow what is being written you are in good shape, students often comment that we place too much emphasis on previous R knowledge, but we’d rather be overly cautious than not give any warning at all. We will not be using the commands in the handout during the workshop tutorial – we will focus on more flexible and reproducible code.
The workshop’s first day will focus on principles of data visualisation, with a focus on design principles and plotting quantitative data (i.e. statistical graphs). This will involve an interactive discussion of your own examples of data visualisation.
For this purpose, you can submit either one of your own plots of quantitative data, or a figure from a published research article. In either case, please include a couple of sentences describing the data/plot (very useful for understanding what is going on) and why you find it to be a particularly good or bad example of Data Visualisation. Your input will help to develop relevant examples for the workshop.
In addition, please bring in 4 print-outs of your example. This will be for in-class exercises.
Finally, you should also prepare some of your own data to work with so that you can make figures on the second day.
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