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.
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.
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.
A little over a year ago, we decided to propose a data visualization course at the first-year level. We had been thinking about this for awhile, but never had the time to teach it given the scheduling constraints we had. When one of the other departments on campus was shut down and the faculty merged in with other departments, we felt that the time was ripe to make this proposal.
One of the many nice things about summer is the time and space it allows for blogging. And, after a very stimulating SRTL conference (Statistics Reasoning, Teaching and Learning) in Rotorua, New Zealand, there’s lots to blog about. Let’s begin with a provocative posting by fellow SRTL-er Tim Erickson at his excellent blog A Best Case Scenario. I’ve known Tim for quite awhile, and have enjoyed many interesting and challenging discussions.
Part of the reason why we have been somewhat silent at Citizen Statistician is that it’s DataFest season, and that means a few weeks (months?) of all consuming organization followed by a weekend of super fun data immersion and exhaustion… Each year that I organize DataFest I tell myself “next year, I’ll do [blah] to make my life easier”. This year I finally did it! Read about how I’ve been streamlining the process of registrations, registration confirmations, and dissemination of information prior to the event on my post titled “Organizing DataFest the tidy way” on the R Views blog.
Ten years after Ioannidis alleged that most scientific findings are false, reproducibility – or lack thereof – has become a full-blown crisis in science. Flagship journals like Nature and Science have published hand-wringing editorials and revised their policies in the hopes of heightening standards of reproducibility. In the statistical and data sciences, the barriers towards reproducibility are far lower, given that our analysis can usually be digitally encoded (e.g., scripts, algorithms, data files, etc.
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