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ONLINE COURSE – Introduction to R for ecologists and evolutionary biologists (IRFB03) This course will be delivered live
20 May 2020 - 21 May 2020£250.00
This course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE – Western European Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email email@example.com for full details or to discuss how we can accommodate you).
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.
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 2 ECT’s
Language – English
Other payment options are available please email firstname.lastname@example.org
PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact email@example.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
Dr. Andrew Jackson
A mixture of lectures and hands-on practical’s. Data sets for computer practicals will be provided by the instructors.
Assumed quantitative knowledge
No quantitative understanding of statistics is required.
Assumed computer background
No experience in the software R is required but some computer experience is preferred.
Equipment and software requirements
A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links.
It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK firstname.lastname@example.org
Wednesday 20thDay 1 – Morning 09:30 – 12:30
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 analysesDay 1 – Afternoon 13:15-17:00
Objects in R and their different classes: data is not just data.
Importing basic datasets
Basic data visualisation using ggplot
Customising ggplotsThursday 21stDay 2 – Morning 09:30 – 12:30
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 graphsDay 2 – Afternoon 13:15-17:00
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 ModelsFriday 22ndHalf day catch-up for other time zones
The instructors were excellent and clearly were the reasons for my previous comments. They both combined a deep understanding of statistics and ecology at the same level.Any questions or queries I’ve had, were thus first answered with an ecological point of view and then translated into statistical consideration thereby making much more sense on both side.In addition the course was very well organised, the course director and the two instructors were very friendly as well as professional. On the top of learning many useful things, I’ve also had a very good time during the week there.” Clement Garcia,
Spatial ecologist, Centre For Environment, Fisheries & Aquaculture Science (CEFAS), England
(Attended ADVR course)