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2 Syllabus

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 based mostly on code-along sessions (instructor and learner coding together), the course 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.

In this course, 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?

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.

2.1 Is this course for you?

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 are a researcher, likely working in the biomedical field (ranging from experimental to epidemiology).
  • 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.

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.

2.2 General schedule

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 general schedule outline is shown in the below table. This is not a fixed schedule of the timings of each session, some may be shorter and others may be longer, and is instead meant to be an approximate guide and overview.

Table 2.1: General schedule for the 3 days of the course.
Date and time Session topic Type
Day 1
Arrival; Coffee, tea, and snacks
10:00 Introduction to the course Lecture
10:30 Management of R projects (with short break) Code-along
12:30 Lunch
13:30 Collaboration and teamwork in research Lecture
14:00 Version control and collaborative practices Code-along
14:30 Break with coffee, tea, and snacks
15:30 Version control and collaborative practices Code-along
17:30 End of day short survey
Day 2
Breakfast
9:00 Data management and wrangling Code-along
10:15 Break with coffee, tea, and snacks
10:30 Data management and wrangling (with short break) Code-along
12:15 Lunch
13:15 Research in the era of (ir)reproducibility and open science Lecture
14:00 Creating reproducible documents Code-along
14:45 Break with coffee, tea, and snacks
15:00 Creating reproducible documents Code-along
17:00 End of day short survey
Day 3
Breakfast and checkout
9:00 Data visualizations Code-along
10:15 Break with coffee, tea, and snacks
11:00 Data visualizations (with short break) Code-along
12:15 Lunch
13:15 Group work
15:30 Presentation of projects, and discussions
16:45 Closing remarks and short survey
17:00 Farewell