4 Introduction to course
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4.1 📖 Reading task: The Big Picture
Time: ~10 minutes
While you’ve read the syllabus, listened to the presentation, and likely have an overall idea of why we are teaching this course, repetition is a critical component to learning, so here is more emphasis on some common considerations or thoughts you might have about the course.
Why learn this now?
We are in a special time in research. We are facing several large scale technological and societal changes:
- We researchers are experiencing higher demands from funding agencies, universities, and peers for transparency and rigor in our research.
- Our work is getting more and more complex, which requires higher degrees of (potentially highly distributed and virtual) team-based science.
- There is a higher public attention on research, with mass participation and attention through the Internet and social media.
- Our access to powerful computing resources and massive datasets is leading to an increasing rise in more complex analytics and data processing, such as through machine learning and AI.
- Increasingly your research output is someone else’s research input, such as with meta-research1 or meta-analysis.
1 Evidence-based evaluation and development of research methods.
What will we do?
In this course, our ultimate goal is to:
- Create a project that has a report (in HTML or Word) where you reproducibly import some data, process it a bit, and create some figures and tables, all done in a way that makes it easier for you and others to collaborate together.
In the end, we’re aiming for:
- Having a self-contained project (within a single folder).
- Having a record of changes made to the files.
- Making it easier for others to collaborate.
- Making it simpler to connect the project with a scientific output like a paper.
- Structuring analyses to be more reproducible (or at least more easily inspectable).
Why R?
Why are we using R and why learn it? There are many many reasons, some of which are listed below:
- It is open source and free. Which means that you can take the knowledge and skills you gain for using R anywhere you go in your career.
- There is a very large, fairly friendly online community.
- So many learning resources, support, and help!
- There is a massive selection of packages. Need to do something? There’s probably already a package to do it for you.
- Latest statistical methods.
- Productivity tools.
- Report writing.
- Visualization.
- Many many more.
- Strong push by major R developers, for instance from Posit who makes RStudio and contributes heavily to R packages, to improve beginner-friendliness and usability of R, for example with the tidyverse and Quarto.
- R has one of the best data visualization tools available with ggplot2.
- So many more powerful capabilities when it comes to:
- Big Data
- Programming
- Reproducibility
Why Git and Quarto?
You probably came to this course to learn R. So why are we also going to go over Git and Quarto. Well, the main learning outcome for the course is reproducible research. And that involves more than just R.
Why do we take time to teach setting up a project folder and file structure? Because that’s the very first step in making your work reproducible and also more organized.
Why do we teach Git? Because without sharing code, your research cannot be independently reproducible. Using Git is the most common way to share code.
Why do we teach Quarto? Because simply writing R code will not be enough to ensure it is also reproducible. Quarto is a tool designed to run in a reproducible way. So it forces you to write code in a way that is reproducible.
Why are we going slow/fast?
We always get feedback that for some it is too fast and for others it is too slow, especially during this introductory course. Ideally, everyone would say it was the perfect speed. But that is impossible to achieve. So instead, we aim as much as possible to have fewer people say it was too fast than people who say it was too slow. This is an introduction course, so we’re trying to assume as little to no knowledge on many of these concepts. So, for those with some knowledge, it will feel slow at times! You can always help your neighbour out to help pass the time 😁 🎉
Any questions?
Throughout the many times we’ve taught this course and others, we get asked a lot of questions. We have a Frequently Asked Questions page for keeping track of some of these questions. Check out this page, maybe your question has already been answered!
If you want to get help virtually or after the course, you can join the Discord channel where you can ask questions in the questions-or-advice text channel.
4.2 Quality of life settings
Before ending, we’re going to set some RStudio options that will help you out a lot. Go to Tools -> Global Options... and do these tasks:
- In “General”, under the “Basic” tab, uncheck all boxes under “R Session”, “Workspaces”, and “History”, as well as changing the “Save workspace to .RData on exit” to “Never”.
- In “Code”, under the “Editing” tab, change the “Tab width” to 2. The tidyverse style guide as well as styler both use 2 spaces for tabs, and since we are using the package, we can set this option here to save us editing issues.
- In “Code”, under the “Saving” tab, check all the boxes under “General” and “Auto-save”. This last one, the “Auto-save”, will help out a lot, since one of the biggest “troubleshooting issues” we encounter when helping during the version control session is that people forget to save. This solves that problem.