An opportunity to teach, an opportunity to give back…
If you’ve seen one of my data science education talks or attended one of my workshops in the last few years, you’ve probably heard me talk about the unvotes package in R.
This package provides the voting history of countries in the United Nations General Assembly, along with information such as date, description, and topics for each vote.
I love using data from this package in my teaching, especially on day one of class, because the data are rich while being accessible.
Last weekend Maria Tackett and I gave an introduction to R workshop as part of the 2021 ENAR Fostering Diversity in Biostatistics Workshop for high school and undergraduate students. Our goal was to give them a taster for exploring and visualizing data with R and, hopefully, leave them wanting to learn more.
We only had 75 minutes for the workshop and a totally beginner crowd. We knew that they would be a mix of undergraduate and high school students, but didn’t know much else about them as we prepared for the workshop.
Over the past few years I’ve been working on moving from a mindset of end-of-semester project to semester-long project. Inevitably students end up doing lots of work as the deadline approaches at the end of the semester (and I can’t blame them, that’s how I work around deadlines too, and how just about anyone I know works), but creating opportunities for them to get started on their projects earlier in the semester is very important.
If you’ve ever been to an R workshop I gave, you probably heard me say “if the only thing you get out of this workshop is that RStudio projects are awesome and you should use them, this workshop was worth your time”. And I stand by this statement, they are awesome!1
But sometimes you just want a project-less RStudio! When, you ask? Imagine you have an RStudio project open where you’re writing course slides, or a blog post, or a package… And then imagine a student asks a coding question and you want to run their code quickly but don’t want to populate your environment with the objects that code creates.
I attended useR! 2012 this past summer and one of the highlights of the conference was a presentation by Yihui Xie and JJ Allaire on knitr. As an often frustrated user of Sweave, I was very impressed with how they streamlined the process of integrating R with LaTeX and other document types, and I was excited to take advantage of the tools. It also occurred to me that these tools, especially the simpler markdown language, could be useful to the students in my introductory statistics course.
