My collaboration network for 2010 to 2020 (+ other plots)

n what has become a bit of an annual tradition, here is my collaboration network for 2010 to 2020. This year was rough. Of the two first-author papers published this year, one was pre-pandemic. I think it’s fair to say this wasn’t the level of productivity I was expecting of myself. Hopefully, a few projects still in the pipeline will come out early next year. All that said, I’m thankful for a strong network of kind collaborators who picked up my slack when necessary, checked in on me even when we didn’t have an active project, and understood when childcare …

Applying an intro-level networks concept to deleting tweets

here are a few services out there that will delete your old tweets for you, but I wanted to delete tweets with a bit more control. For example, there are some tweets I need to keep up for whatever reason (e.g., I need it for verification) or a few jokes I’m proud of and don’t want to delete. If you just want the R code to delete some tweets based on age and likes, here it is (noting that it is based on Chris Albon’s Python script). In this post, I go over a bit of code about what I …

Our new paper about opioid prescribing patterns in the US

ome notes about a new (open access) paper with Keith Humphreys, Mark Cullen, and Sanjay Basu — “Opioid prescribing patterns among medical providers in the United States, 2003-17: retrospective, observational study” — just published in BMJ. 

Collaboration network from 2010 to 2019

have been trying to wrap my head around working with temporal networks — not just simple edge activation that changes over time but also evolving node attributes and nodes that may appear and disappear at random. What better way than to work with a small concrete example I’m already very familiar with?

A visual tour of my publications

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.