🌈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.
There are many great reasons to create a personal academic website as a graduate student. I will be walking students through this very helpful tutorial by Alison Hill on building a personal website using R Studio and Blogdown, and try to show students how easily they can create and update a website to highlight their research achievements and upcoming talks, and begin to form their online academic identity!
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.
A Shiny app to visualize potential impacts of the 'grad student' tax
The generalize R package is designed for researchers to implement post-hoc statistical methods for assessing and improving upon the generalizability of RCT findings to a well-defined target population. See the Github repo’s webpage for more details on installation and usage.