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. Using a very practical approach bases mostly on code-along sessions (instructor and learner coding together), the course will:
We’ll be addressing the following questions:
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.
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:
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 don’t turn anyone away if we can.
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.
To further develop your R skills and knowledge, we are having an advanced R course from September 8-9, 2020 that will build off of this course (though it isn’t dependent on this course). Keep on eye on DDA announcements to register and learn more about it!
The workshop is structured as a series of participatory live-coding sessions (instructor and learner coding together) interspersed with hands-on exercises and group work, using either a practice dataset or some other real-world dataset. There are some lectures given, mainly at the start and end of the workshop. The official DDA program can be found in this PDF.
|Date and time||Session topic||Type||Instructor|
|9:30||Arrival; coffee and snacks|
|10:00||Introduction to the course||Lecture||Luke|
|10:30||Management of R projects (with short coffee break)||Code-along||Luke|
|13:30||Data management and wrangling||Code-along||Bettina|
|14:30||Coffee break and hotel check-in|
|15:00||Finding and obtaining open datasets||Lecture||Daniel|
|15:30||Data management and wrangling (with short break)||Code-along||Bettina|
|17:30||End of day short survey|
|20:30||Social activity in the basement hotel bar|
|8:30||Collaboration and teamwork in research||Lecture||Daniel|
|9:00||Version control and collaborative practices||Code-along||Luke|
|10:15||Coffee break and snacks|
|10:30||Version control and collaborative practices||Code-along||Luke|
|14:45||Coffee break and snacks|
|17:00||End of day short survey|
|20:30||Drinks and chats around a bonfire|
|7:00-8:30||Breakfast and checkout|
|8:30||Research in the era of (ir)reproducibility and open science||Lecture||Luke|
|9:15||Creating reproducible documents||Code-along||Luke|
|10:15||Coffee break and snacks|
|10:30||Creating reproducible documents||Code-along||Luke|
|13:15||Group work: Presentation of projects, and discussions|
|15:15||Closing remarks and short survey|
The course material is found as an online book at r-cubed.rostools.org.
The first version of this course, called Reproducible Quantitative Methods: Data analysis workflow using R, was taught in March, 2019 in Middelfart, Denmark.
The original R Markdown teaching material can be found in the GitLab repository. Anyone is free to use, re-use, modify, and so on (as per the license) as long as you properly attribute the work. Please see the “How to cite” section of the README in the repository.