ONLINE COURSE – Reproducible and collaborative data analysis with R (RACR01) This course will be delivered live
5th - 7th September
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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.
CET – however all sessions will be recorded and made available allowing attendees from different time zones to follow. Please email email@example.com for full details or to discuss how we can accommodate you).
ABOUT THIS COURSE
The computational part of a research is considered reproducible when other scientists (including ourselves in the future) can obtain identical results using the same code, data, workflow and software. Research results are often based on complex statistical analyses which make use of various software. In this context, it becomes rather difficult to guarantee the reproducibility of the research, which is increasingly considered a requirement to assess the validity of scientific claims. Moreover, reproducibility is not only important for findings published in academic journals. It also becomes relevant for sharing analyses within a team, with external collaborators and with one’s supervisor. During this three-day course, the participants will be introduced to a suite of tools they can use in combination with R to make reproducible the computational part of their own research. A strong emphasis is given to collaboration, and participants will learn how to set up a project to work with other people in an efficient way.
On day 1 the participants learn about the most important aspects that make research reproducible, which go beyond simply sharing R code. This includes problems arising from the use of different packages versions, R versions, and operating systems. The concept of research compendium is introduced and proposed as general framework to organise any research project. Day 2 is dedicated to version control with Git and GitHub which are fundamental tools for keeping track of code changes and for collaborating with other people on the same project. We will cover both, basic and more advanced features, like tagging, branching, and merging. On day 3 the participants are introduced to literate programming using RMarkdown with the focus on writing a scientific article. The aim is to bind the outputs of the R analysis (i.e. results, tables, and figures) together with the text of the article. Participants will also learn how to use templates to fulfil requirements of different journals.
Please email firstname.lastname@example.org with any questions.
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Species Distribution Modelling With Bayesian Statistics Using R (SDMB04)
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Introduction to generalised linear models using R and Rstudio (IGLM05)
Bayesian Data Analysis (BADA02)
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