The 2 day course will consist of a series of modules designed to build required R skills and statistical understanding to develop yourself or move on to more advanced courses. At its conclusion, participants will have acquired basic skills in coding with R, and will be able to perform and interpret simple analyses, and critically evaluate similar analyses from the scientific literature and technical reports. All example datasets used for practical’s will have an ecological and evolutionary theme
The course is aimed at biologists with no experience using the software R and no understanding or exposure to statistics.
Last Up-Dated – 21:05:2020
Duration – Approx. 14 hours
ECT’s – Equal to 1.5 ECT’s
Language – English
A mixture of lectures and hands-on practical’s. Data sets for computer practicals will be provided by the instructors.
No quantitative understanding of statistics is required.
No experience in the software R is required but some computer experience is preferred.
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.
All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience
PLEASE READ – CANCELLATION POLICY
Cancellations/refunds are accepted as long as the course materials have not been accessed,.
There is a 20% cancellation fee to cover administration and possible bank fess.
If you need to discuss cancelling please contact email@example.com.
If you are unsure about course suitability, please get in touch by email to find out more firstname.lastname@example.org
Day 1 – approx. 7 hours
What is R, why is it perceived as difficult and why its role in reproducible science makes it ideal.
How R works: making life easier with Rstudio; how to install packages; and how to set up your workspace to your liking.
Rnotebooks as the analogue of field and lab notebooks.
Setting up a basic workflow as a template for all your analyses
Objects in R and their different classes: data is not just data.
Importing basic datasets
Basic data visualisation using ggplot
Day 2 – approx. 7 hours
Basic statistics in R: summaries, tables, correlations, t-tests and ANOVA
Simple linear regression as the foundations for nearly all statistical models
Models are just lines on a page: adding our models to our graphs
Understanding linear model statistical outputs
Using these outputs to gain insights about the data
General linear models: fitting more than one line on a page
Introduction to non-gaussian error models: Generalized Linear Models