csv files, sometimes tens of thousands of them, in order to combine them into a single analytical dataset I can use. When it’s only a few dozen, using fread(), read_csv, or the like can be fine, but nothing is quite as fast as using awk or cat.Here’s a snippet of code that allows one to use bash in R to concatenate csv files in a directory. People in the lab have found it helpful so maybe others will as well.
I recently came across this paper by Michal Brzezinski about (the lack of) power laws in citation distributions. It made me a little curious about the citations of my own articles so I threw together a little script using James Keirstead’s Scholar package for R. In the plot above, every line represents a single article with time on the x-axis and (cumulative) number of citations on the y-axis.It’s not super informative, so we can break it down a few ways to graphically explore the data.
Until then, I’m just going to throw up some random code snippets that resulted from the summer.
Way back when I was first learning R, I ran across an old listserv post that talked about how the colon (:) operator was the fastest way to generate a sequence. I never really thought about it, but I got in the habit of always using it whenever I needed a sequence.
As a Valentine’s Day (gag) gift to one of my friends, I created a 
Shiny app1 that will calculate the number of people in the United States who meet specified sex, age, marital status, race/ethnicity, educational attainment, employment status, and annual income requirements.
While taking a disease dynamics course, I thought it would be a good opportunity to learn how to use the Shiny package in R and create an interactive interface for some of my problem sets. After a few trial runs with smaller, simpler setups, I have wrapped up the side project (for now). You can see it in action here 1 and you can view the final code on my Git.