As the COVID-19 pandemic unfolds, many of us have started to see most of our academic gatherings move to online. No doubt, this shift has caused a lot of stress and anxiety, especially for PhD students with upcoming thesis defenses scheduled. There’s been a lot of uncertainty around whether universities and departments will allow defenses to proceed as scheduled, whether they would require that they be held remotely, or (worst case) they would ask that defenses be rescheduled altogether.
🌈HAPPY PRIDE MONTH!! Fun fact: my rainbow Twitter banner (which I keep up year-round) was actually made using R! Given that pride season is officially upon us, I thought it’d be fun to share the code I wrote to generate a quick, fun ggplot rainbow flag.
To make the flag, I wrote a function, rainbow_flag(), which depends on one parameter, n. This will control how many colors of the rainbow I want to use (see examples below).
Yesterday, I was trying to put some finishing touches on a figure I made in ggplot2 that visualizes some simulation results. The plot features several panels using facet_grid(), and uses colors to distinguish between different regression models that were fit to the simulated data. I wanted to label certain axes and panel names using the Greek letters I had used as parameter notation, and I also wanted the labels in the color legend to correspond to the different regression models I had fit.
This is my first time doing 🎉Tidy Tuesday🎉 ! The data for this week came from a FiveThirtyEight blogpost, which breaks down post-college salaries by discipline. The documentation and data for this week can be found in this GitHub repo.
One thing I found really interesting in the data was the variable College_jobs, which counted the number of people per major with jobs that required a college degree. I wanted to use this information to look at each major’s median income by percent of recent grads employed in positions requiring/not requiring college degrees.
About three years ago, I received a letter in the mail from Nielsen inviting me to participate in one of their panels. After spending a while on the phone with a representative to determine that it wasn’t a scam, I figured I’d give it a go. I tend to take great interest in knowing where data come from (especially when reporters and media sources try to use statistics to make a point), and as an avid tv watcher, it was cool to learn more about how Nielsen generates ratings and estimates program viewership.