Reproducible Research in R: An advanced workshop on creating collaborative and automated analysis pipelines
Reproducibility and open scientific practices are increasingly in demand and needed by scientists and researchers in modern research environments. Our work often require or involve a high level of hands-on collaboration on scientific projects throughout all stages of the research lifecycle. We are faced with obstacles that we have no training for nor knowledge on how to address. Obstacles, that can be as simple as not having a shared way of writing code or managing files, can impede effective collaboration. Or they can be complex, like documenting software dependencies or steps in an analysis pipeline in a way that makes it easy to resolve issues and get the most recent set of results after collaborators have worked on a project. Aside from the impact on collaboration, these barriers can even affect projects with just one primary researcher. Ultimately, this can have consequences on the reliability and reproducibility of scientific results, especially considering that the measures taken to address these barriers are often not explicitly shared in classical science output (like a publication).
With this course, we aim to begin addressing this gap. By using a highly practical, hands-on approach that revolves around code-along sessions (instructor and learner coding together), reading activities, and hands-on exercises, our overarching learning outcome is that at the end of the course, participants will be able to: Describe what an open, collaborator-friendly, and nearly-automated reproducible data analysis pipeline and workflow looks like, and then create a project that follows these concepts by using R.
- Time & place
- Who can attend?
- Detailed description
- Programme
- Organisers
- Registration
- Additional information
TIME & PLACE
Dates: 7-9 December 2022
Place: MBK, Pilestræde 61, 1112 Copenhagen K, Denmark
WHO CAN ATTEND?
This course is designed in a specific way and is ideal for you if:
- You are a researcher, preferably working in the biomedical field (ranging from experimental to epidemiological). Specifically, this course targets those working on topics in diabetes and metabolism.
- You currently do quantitative data analysis.
- You preferably:
- have taken the intermediate Reproducible Research in R course, as this course is a natural extension to that one;
- know a moderate or more amount of R (or computing in general);
- know how to use R and are fairly familiar with the tidyverse, rmarkdown, RStudio, Git, and GitHub.
Considering that this is a natural extension of the introductory and intermediate r-cubed courses, this course builds on the knowledge and skills learned during those courses, including Git, RStudio R Projects, functions, functional programming, and R Markdown. If you do not have familiarity with these tools, you will need to go over the material from the introductory and intermediate courses beforehand (more details about pre-course tasks will be sent out a couple of weeks before the course).
While having these assumptions help to focus the content of the course, if you have an interest in learning R but don’t fit any of the above assumptions, you are still welcome to attend the course!
Priority is given to participants employed at Danish institutions and in the Danish life science industry. If the event is overbooked, the DDA reserves its right to select participants based on the defined requirements and country of employment.
Please note, that you are not guaranteed a seat if you do not meet the target group requirements. IF the event is overbooked, the DDA reserves its right to reject participants based on the defined requirements and country of employment.
DETAILED DESCRIPTION
Our specific learning outcomes are to:
- Identify potential actions to streamline collaboration on a data analysis project and create projects that apply many of these actions using R.
- Describe and define the distinct steps involved in a pipeline that goes from raw data to final results, and to use R to build this pipeline in an automated and explicit way.
- Apply functional programming concepts to run statistical analyses that fit within the conceptual framework of automated pipelines and that can be used regardless of what statistical method is used.
- Explain the benefits of why scientific findings (and the pipelines) should be easily accessible and demonstrate these principles by building a website in R to display results, using a reproducible and automated pipeline.
And we will not learn:
- Any details on or about specific statistical methods or models (these are already covered by most university curriculum). We cover how to run statistical methods, but not which statistical methods to use for your data or project.
- Making figures or plots (data visualization could be a whole course on its own).
Because learning and coding is ultimately not just a solo activity, during this course we also aim to provide opportunities to chat with fellow participants, learn about their work and how they do analyses, and to build networks of support and collaboration.
The specific software and technologies we will cover in this course are R, RStudio, Git, GitHub, and Quarto, while the specific R packages are {renv}, {precommit}, {styler}, {lintr}, {targets}, and several of the {tidymodels} packages.
PROGRAMME
The programme will be announced later.
ORGANISERS
Luke Johnston, Postdoc, Steno Diabetes Center Aarhus (DK)
REGISTRATION
Registration deadline:
3 November 2022
ADDITIONAL INFORMATION
Pre-course tasks
Participants will have to reserve time in their calendar to do pre-course tasks. Course material is available online at https://r-cubed-advanced.rostools.org/
Considering that this is a natural extension of the introductory r-cubed course, this course incorporates tools learned during that course, including basic Git usage as well as use of RStudio R projects. If you do not have familiarity with these tools, you will need to go over the material from the introduction course beforehand.
Bring your own laptop
Make sure to bring your own laptop, since the course includes hands-on learning.
Accommodation 6 December
The DDA offers accommodation in Copenhagen to participants living outside of Copenhagen area. Please, state if you need accommodation 6 December when you register.
Accommodation 7 & 8 December
The DDA offers accommodation in Copenhagen to all participants living outside Copenhagen area for the nights of 7 & 8 December. Please state if you need accommodation when you register.
Networking Dinner 7 & 8 December
The DDA organizes a Networking Dinner both 7 & 8 December. Please, state if you would like to join these when you register. And if you have any special diet requirements.
Certification
A course certificate will be given to all attending participants at the end of the course. Full participation is required to attain 2.1 ECTS points.
No-show fee
Please note that it is free of charge to participate in the course however the DDA will charge a no-show fee of 500 DKK if you do not show up and have not unregistered from the course prior to its start.