Reproducibility and open scientific practices are increasingly demanded of, and needed by, scientists and researchers in our modern research environments. We increasingly produce larger and more complex amounts of data that often need to be heavily cleaned, reorganized, and processed before it can be analyzed. This data processing phase often consumes the majority of the time spent coding and doing data analysis. Training for this aspect of research has sadly not kept pace with the demand.
We hope to begin addressing this gap in training with this course. Throughout the course we will be using a highly practical approach that revolves around code-along sessions (instructor and learner coding together), hands-on exercises, and group work.
By the end of the course, participants will: have improved their competency in processing and wrangling datasets; have improved their proficiency in using the R statistical computing language; know how to write re-usable and well-documented code; and know how to make modern and reproducible data analysis projects.
While the data processing often consumes the majority of the time spent doing coding, there is little to no training and support provided for it. This has led to minimal attention, scrutiny, and rigour in describing, detailing, and reviewing these procedures in studies, and contributes to the systemic lack of code sharing among researchers. This aspect of research is often completely hidden and may likely be the source of many unintentional irreproducible results. With this course we aim to begin addressing this gap in training.
The learning objectives of the course will be to:
The course will enable participants to answer questions such as:
During the course, we will:
And we will not learn:
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 (more details about pre-course tasks will be sent out a couple of weeks before the course).
Luke Johnston, Team Leader
Steno Diabetes Center Aarhus
This course is designed a specific way and is ideal for you if:
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! We welcome everyone, that is until the course capacity is reached.
Priority is given to participants employed at Danish institutions and in the Danish life science industry. If the event is overbooked, the DDEA 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 DDEA reserves its right to reject participants based on the defined requirements and country of employment.
Participants will have to reserve time in their calendar to do pre-course tasks. The course material is available online.
Deadline for completing pre-course tasks: 1 June 2023
Considering that this course 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.
Make sure to bring your own laptop since the course includes hands-on learning.
The DDEA offers accommodation in Copenhagen to participants living outside of the Greater Copenhagen Area. Please state if you need accommodation 5, 6, or 7 June when you register.
The DDEA organizes Networking Dinners on 6 and 7 June. Please state if you would like to join one or both dinners when you register.
A course certificate will be given to all attending participants on request at the end of the course. Full participation is required to attain 2,3 ECTS points.
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.