Introduction to R for Life Scientists
Instructors: Dr Michael Renton, Dr Martin Bader and Dr Etienne Laliberté
R is a free software environment for statistical computing and graphics. It is becoming more and more popular with biologists and ecologists as their statistical package of choice, due to the huge range of analyses available, the wide availability of on-line support forums, and the possibility of automating analyses through scripting (not to mention the fact that it’s absolutely free).
This course will be designed for people with little or no experience with R or any other particular software package, or for those wanting a quick refresher. It will not aim to cover statistical theory, but rather provide practical hands-on recipes and methods for data management and exploration; basic graphics; and routine statistical analyses such as t-tests, ANOVA, regression, and generalised linear models. We will also provide a list of resources for R for biologists and ecologists to guide participants towards further learning and R ‘packages’ most relevant to your requirements. We will slowly lead you through the basics of working with R at the beginning and progress on to practical exercises which students can work through at their own pace with full assistance from demonstrators. In the afternoon, we will continue with the self-paced exercises and also provide opportunities for people to present and ask questions about their own data and analysis requirements. We will also demonstrate and provide examples of some more advanced analyses and give a taste of the enormous power of scripting with R.
Nonlinear curve fitting techniques with R
Instructors: Dr Martin Bader, Dr Etienne Laliberté and Dr Michael Renton
Nature is nonlinear. As biologists we frequently encounter nonlinear phenomena ranging over a broad spectrum of issues such as (eco)physiological responses of organisms to the environment, growth and survival data, dose-response relationships or enzyme kinetics. The R platform provides a suite of powerful tools for modelling nonlinear data and performing group comparisons.
This one-day course will give an outline how to fit nonlinear regression models, assess model assumptions,obtain confidence intervals and make predictions. Moreover, we will discuss remedies for model violations and methods for model selection that will also allow you to test for differences between groups or treatments.
Publication-quality graphics with R
Instructors: Dr Etienne Laliberté, Dr Michael Renton and Dr Martin Bader
We all know that a picture is worth a thousand words. A good graph will display your key research results in a clear, elegant way. Being able to make publication-quality graphics efficiently is an essential skill for researchers, not the least because elegant graphics impress reviewers and give your publications that extra “edge” to make it in top journals. Surprisingly, this essential skill is seldom taught and most researchers rely on heavily manually-edited Excel graphs, which are time-consuming to make, limited in scope and often look bad. Specialised software packages exist, but these tend to be very expensive.
This one-day workshop will teach you how to make elegant, publication-quality graphics in the free, open-source R environment, using the powerful ggplot2 add-on graphics package (also free). In the morning, will cover the basics of the ggplot2 package, where you will learn how to make standard and more specialised statistical graphics. In the afternoon,we will show you how to polish graphs for publication, with lots of supervised time for you to work on your own plots with your own data.