Data science tutorials with learnr and gradethis

This post was contributed by Lee Suddaby and Zeno Kujawa, second year students at the University of Edinburgh majoring in Mathematics and Data Science, respectively.

Over the university summer break, we (Zeno and Lee) were busy making preparations for moving more of our Introduction to Data Science course from being human-graded to computer-graded. We both took this course in the Fall of 2019, as part of our first-year studies at the University of Edinburgh, and this is where we first learned R.

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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.

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Shiny for JSM 2019

It took me all of 30 minutes from starting this mini-project to writing this post. This is not meant to be a brag, but instead an ode to reproducibility. Last year for JSM 2018 I made a Shiny app to browse the conference schedule. I personally found that app really useful, and I know a few others did as well. And I saved my code in a GitHub repo. Now that JSM 2019 is almost here, I thought I’d try my code again.

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I have been meaning to try out the gt package for a while now, but didn’t really have a great use case for it. However over the last few days I have been looking over the useR 2019 schedule and felt like I would have an easier time picking talks yo attend if the schedule was formatted in wide format (talks occurring at the same time in different rooms listed next to each other) as opposed to the long format.

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Much has been written in statistics and data science education literature about pedagogical tools and approaches to provide a practical computational foundation for students. However a common friction point for getting students (and faculty) started with computing is installation and setup. If you’ve heard me talk about teaching R, you’ve probably heard me mention the following day one dilemma: Option 1 😰 Option 2 😎 1.

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Time zones are hard

Citizen Statistician is back from a hiatus! I hope to post more regularly in the coming weeks, including writing a post on converting from WordPress to blogdown. I have recently been dealing with time zone changes. I’ll say a bit more about it shortly. But first, here is a picture of my 2 year old “dealing” with time zone changes. His schedule is completely thrown off, he doesn’t know what to do with himself, so he keeps moving around in his room in his sleep.

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Citizen Statistician

Learning to swim in the data deluge