| Week | Location | Date | Responsible | Room | Address |
|---|---|---|---|---|---|
| 8 | AU-PH | Feb 20, 11.00-12.00 | Simon, Jonas | Lunch room, 1261-118 | Bartholins Alle 2, DK-8000 Aarhus C |
| 10 | SDCA | Mar 5, 11.00-12.00 | Luke, Anders | Coffee area, level 4, Forum, Aarhus University Hospital | Palle Juul-Jensens Boulevard 11, Indgang A, 8200 Aarhus N |
| 12 | AU-PH | Mar 18, 13.00-14.00 | Stefan, Jie | Lunch room, 1261-118 | Bartholins Alle 2, DK-8000 Aarhus C |
| 14 | SDCA | Apr 4, 10.00-11.00 | Daniel, Omar | Coffee area, level 4, Forum, Aarhus University Hospital | Palle Juul-Jensens Boulevard 11, Indgang A, 8200 Aarhus N |
| 16 | AU-PH | Apr 19, 12:30-13:30 | Nuno, Simon | Lunch room, 1261-118 | Bartholins Alle 2, DK-8000 Aarhus C |
| 18 | SDCA | Apr 30, 11.00-12.00 | Jonas, Anders | Coffee area, level 4, Forum, Aarhus University Hospital | Palle Juul-Jensens Boulevard 11, Indgang A, 8200 Aarhus N |
| 20 | AU-PH | May 17, 10.00-11.00 | Stefan, Daniel | Meeting room, 1264-220 | Bartholins Alle 2, DK-8000 Aarhus C |
| 22 | SDCA | May 27, 13:30-14:30 | Luke, Omar | Coffee area, level 4, Forum, Aarhus University Hospital | Palle Juul-Jensens Boulevard 11, Indgang A, 8200 Aarhus N |
| 24 | AU-PH | Jun 10, 13.00-14.00 | Nuno, Jie | Lunch room, 1261-118 | Bartholins Alle 2, DK-8000 Aarhus C |
Coding Café
Steno Diabetes Center Aarhus and Aarhus University
Welcome to the Coding Café at the Steno Diabetes Center Aarhus (SDCA) and the Department of Public Health at Aarhus University (AU-PH).
The Coding Café is a bi-weekly open “café” for anyone working at SDCA, Aarhus University Hospital, Department of Public Health, Department of Clinical Medicine and Department of Biomedicine, Aarhus University to go and get assistance with coding in R. The only prerequisite so far is that you have had some type of introduction to R, preferably through a course such as this offered by the Danish Diabetes and Endocrinology Academy or similar introductory courses.
The sessions are open and anyone can join in and out at anytime during the allotted time. There will be two instructors with experience working in R. We also highly encourage the participants to interact with each other - maybe there is someone in the group that has the solution to your problem.
During the sessions there will be coffee and tea - or whatever. If you want to get a calendar invitation to also receive updates if rooms or times are changed, please send an email to Daniel Ibsen, dbi@ph.au.dk. Otherwise, the latest schedule will always be on this website.
Upcoming schedule
Practical information
Things to do before asking for help
Being a good coder in R does not necessarily mean that one just sits down and writes a lot of code like writing a piece of text. Even experienced R users search online for code. The great thing about R is that there is often multiple solutions to a coding problem. What often separates an experienced user from a new user is that the experienced user know what to search for, where to search and understand the replies.
We suggest that you try to search on either Google or directly in Stackoverflow. As a next step, it can be useful to use AI-tools like ChatGPT-3 but beware that it can make up functions and that it does not always work.
These are suggestions to try before asking a question.
Asking a clear coding question
Some issues are simple such as I cannot download this package. Other times what you want to do is more complex and needs a bit of background for a person outside your project to understand what you want to do. Therefore, you need to prepare and provide the context to the person you are asking for help. This could be in the form of showing the structure of your dataset, which functions you used, and what you have tried so far.
Note about sensitive data
Many of us work with very sensitive data - especially those working in Denmark’s Statistics (DST). If you are working on DST, or something similar where you are not allowed to show the data to anyone else, you need to prepare your question a bit more. We recommend that you find an example dataset that looks similar to the dataset you work with and make a coding example.