Chapter 2 Overview
In this workshop you will learn hands-on how to begin to interoperate between Excel and R. But this workshop is not only about learning R; we will learn R using additional software: RStudio and GitHub. These tools will help us develop good habits for working in a reproducible and collaborative way — critical attributes of the modern analyst.
It’s going to be fun and empowering!
Accompanying slides: Google Slides
2.1.1 You are all welcome here
This is going to be a fun workshop.
This workshop will give you hands-on experience and confidence with R, and how to interoperate between Excel and R — it is not about wholesale replacing everything you do in Excel into R.
We will learn technical skills that you can incrementally incorporate into your existing workflows. But a big part of interfacing between Excel and R is not only skillsets, it is mindsets. It is the mindset about how we think about data. How we shape data and organize data and analyze data. And how what we do now can make our analytical life better in the future.
A modern R user has a workflow framed around collaboration, and uses an ecosystem of tools and practices. We will be learning three main things all at the same time:
- coding with best practices (R/RStudio/tidyverse)
- collaborative bookkeeping (Git/GitHub)
- reporting and publishing (RMarkdown/GitHub)
We are going to go through a lot in these two days and it’s less important that you remember it all. More importantly, you’ll have experience with it and confidence that you can do it. The main thing to take away is that there are good ways to work between R and Excel; we will teach you to expect that so you can find what you need and use it! A theme throughout is that tools exist and are being developed by real, and extraordinarily nice, people to meet you where you are and help you do what you need to do.
You are all welcome here, please be respectful of one another. Everyone in this workshop is coming from a different place with different experiences and expectations. But everyone will learn something new here, because there is so much innovation in the data science world. Instructors and helpers learn something new every time, from each other and from your questions. If you are already familiar with some of this material, focus on how we teach, and how you might teach it to others. Use these workshop materials not only as a reference in the future but also for talking points so you can communicate the importance of these tools to your communities. A big part of this training is not only for you to learn these skills, but for you to also teach others and increase the value and practice of open data science in science as a whole.
2.2 Guiding principles / recurring themes
“Keep the raw data raw” — A hard line separating raw data and analyses. In R, we have data in one file and written computational commands saved as a separate file.
Think ahead for Future You, Future Us. Help make lives easier — first and foremost your own. Create breadcrumbs for yourselves and others: document and share your work.