Reproducible Research in R: An introductory course on modern data analyses and workflows for PhD students & Postdocs | Danish Diabetes and Endocrine Academy
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Reproducible Research in R: An introductory course on modern data analyses and workflows for PhD students & Postdocs

Reproducible Research in R: An introductory course on modern data analyses and workflows for PhD students & Postdocs -
Event info

Event date: 

25/04/2023 - 09:30 to 27/04/2023 - 16:45

Registration deadline: 

25/03/2023 - 23:30

Reproducibility and open scientific practices are increasingly demanded of scientists and researchers. Training on how to apply these practices in data analysis has not kept up with demand. With this course, we hope to begin meeting that demand.

By the end of the course, participants will have a basic level of proficiency in using the R statistical computing language, enabling them to improve their data and code literacy, and to conduct a modern and reproducible data analysis. The course will place particular emphasis on research in diabetes and metabolism; it will be taught by instructors working in this field and it will use relevant examples where possible.


Date: 25-27 April 2023
Place: DGI Huset Aarhus, Denmark 


To participate in the course, you will have to be a PhD Student or Postdoc working in the fields of diabetes or metabolism. Priority is given to participants employed at Danish research and health institutions or in the life science industry.

To help manage expectations and develop the material for this course, we make a few assumptions about who you are as a participant in the course:

  • You currently or will soon do some quantitative data analysis
  • You
    • know nothing or little about R (or computing in general);
    • haven’t used code-based programs for doing data analysis (e.g. have used SPSS);
    • have used coding programs before (e.g. used SAS or Stata), but not R;
    • or know how to use R, but haven’t used the tidyverse or RStudio.

While we have these assumptions to help 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! We welcome everyone, that is until the course capacity is reached.

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.

In addition to the assumptions, we also have a fairly focused scope for teaching and expectations for learning. So this may also help you decide if this course is for you.

  • We do teach how to use R, starting from the very basics and targeted to beginners.
  • We do not teach statistics (these are already covered by most university curriculums).
  • We do teach from a team science, reproducible research, and open scientific perspective (i.e. by including a collaborative group project that uses a transparent and reproducible analysis workflow).
  • We do teach using practical, applied, and hands-on lessons and exercises, with a few short lectures that introduce a topic.


The course is designed as a series of participatory live-coding lessons, where the instructor and learner code together, along with hands-on exercises interspersed throughout the course and a final group assignment to do a simple data analysis project. In the course we will:

  1. Explain what an open and reproducible data analysis workflow is, what it looks like, and why it is important.
  2. Explain and demonstrate why R is rapidly becoming the standard program of choice for doing modern data analysis in science.
  3. Demonstrate and apply collaborative tools and techniques when working in team settings (including working with your future self).
  4. Show and apply the fundamental tools and skills for conducting a reproducible and modern analysis for a research project.
  5. Show where to go to get help and to continue learning modern data analysis skills.
We’ll be addressing the following questions
  • What is R, why should I use it, and how do I use it?
  • What does a modern data analysis setup and workflow look like?
  • What is reproducibility and how is it different from replicability?
  • How can I ensure my data analysis project is reproducible?
  • How can I import and work with my data in R?
  • How can I visualize my data and make publication-quality figures?
  • Why should I and how can I keep track of changes to my analysis files?
  • How can I write reports to document, describe, and present analyses in a reproducible way?


See the programme here

  • Luke Johnston, Steno Diabetes Center Aarhus (DK) 


Luke Johnston, Team Leader
Steno Diabetes Center Aarhus


Registration deadline

25 March 2023


Pre-workshop instructions

To attend the course, you will have to complete the pre-course tasks no later than 20 April, 2023. You find the pre-course tasks here:

Bring your own laptop

Make sure to bring your own laptop, since the course includes hands-on learning.


The DDEA offers accommodation in Aarhus to participants living outside the Aarhus area. Please, state if you need accommodation 24, 25, or 26 April when you register. 

Networking Dinner

The DDEA organizes Networking Dinners on 25 and 26 April. Please, state if you want to participate in one or both Networking Dinners when you register.


A course certificate will be sent to all participants on request at the end of the course. Full participation is required to attain ECTS points. The course equals 2.7 ECTS points.

No-show fee

Please note that it is free of charge to participate in the course. However, the DDEA will charge a no-show fee of 1000 DKK if you do not show up and have not unregistered from the course prior to its start.