R Course: Lesson 0

Guest post by Page Piccinini

After telling you about my R imposter syndrome two weeks ago, I will start sharing my R course material with you here, beginning with the first lesson (well, Lesson 0) today.

This lesson is meant to set up your R working environment and get you familiar with RStudio, Git, and Bitbucket. This lesson does not include any substantial coding in R, but will situate you for productive R coding, including creating separate working environments for different projects and version control for any scripts. With these practices in place you will be a more productive R programmer in the long run, whether you are working alone or with other researchers.

On the Power of Power (and of Saying Stop)

“Power” – I remember this expression making me and my classmates giggle in our undergrad introductory stats course, much as other expressions had made us giggle in high school sex ed. But I am not here for giggling. Instead, I want to share two things about power that I have understood (which actually work astonishingly well not only for statistics, but also for real life).

An ARdent R UseR’s StoRy

Guest post from Page Piccinini (postdoc at École Normale Supérieure, Paris)

I’ve been an R user for about 8 years (with the occasional break, including that year I took off from science and worked as a real estate agent’s personal assistant). Recently I’ve started teaching an informal R course in our department*. How I went from being self-taught to teaching a course was the prompt for this blog post. Honestly though, it feels weird to say that I’m an R programmer, I guess because I feel like it’s been ingrained in me that I’m not really an R programmer. I don’t do the most complicated things. There are so many people who know so much more than me. I’ve spent a lot of time thinking about this disconnect, the fact that people come to me to ask for help in R and I (almost) always have an answer, yet I’ll still have those moments of panic where I think I’m doing everything wrong. I’ve decided my impostor syndrome can be reduced to a couple key issues in how coding is taught and treated inside and outside of academia. First is gender discrimination. This isn’t a new idea, plenty of women have discussed gender discrimination they’ve experienced in science and the tech industry. Second is how people outside of my field (Linguistics) view my field. And third, and probably the most unfortunate of the three, is how people within my field (and potentially science at large) treat each other when it comes to coding and statistics.