One of the first projects I posted on this site discussed how to scrape headlines from Memeorandum.com, a news aggregation site with an easy-to-use archival feature that makes it possible to browse news stories all the way back to 2004. In that post, I commented on how Memeorandum, which uses an algorithm to find and cluster American news from across the political spectrum, can be thought of as a rich and accessible source for text data.
A little overdue, but I finally took the time to dig into some local primary results after Washington voted on March 10th. While working on the Shaun Scott campaign I learned that Seattle election results can swing wildly after election night thanks to King County’s vote-by-mail system. Last fall, late ballots swung decisively toward the progressive candidates for city council in both the primary and general elections, and while our campaign came a few points short in D4, we closed the gap considerably after all votes were counted.
Back when I was in the DCL, some of my favorite assignments involved recreating graphics from The Upshot at the The New York Times. I was working on some post-primary analysis for the Shaun Scott campaign recently when I recalled a graphic from 2016 that compared county-level election results between the 2016 and 2012 presidential races. I like this style of electoral map because it draws the viewer’s attention to the change taking place on election night, and it indicates which direction the country is headed at a glance.
About a year ago, I started recording my sleep schedule in a Google sheet. Something in the back of my unemployed mind told me that this would be good content for my new website, which I was building mainly in hopes of finding a job – after all, what potential employer wouldn’t want to see the kind of healthy bedtimes I was managing? I didn’t end up using the data because – surprisingly enough – it would be a long time before I had enough to amount to anything interesting.
A draft of my recent work on leaked emails and political knowledge is now available for comment. Below is a copy of the abstract, for reference.
In scholarly and popular discourse, leaks have long been associated with transparency, but their potential as a source of disinformation has gone largely unexamined. The turmoil of the 2016 U.S. presidential election cast doubt on leaks’ association with the truth by demonstrating the potentially disorienting impact of leaked emails on political knowledge and discourse.
Before I enrolled in the Data Challenge Lab at Stanford, my approach to data science in R was almost entirely ad hoc. Beyond the very fundamentals of base R, I relied on a combination of Stack Overflow and code copied from textbooks and lecture slides to get anything done. Naturally, I couldn’t do much. The DCL changed all of that by introducing me to the Tidyverse, which not only gave me a unified set of tools to explore, visualize, and customize my data, but also gave me a much more intuitive sense for how data works and feels.
While it’s hard to beat the ease and expressiveness of ggplot2 for most of my visualization needs, I haven’t always been terribly happy with how it’s default settings look. The process of creating a presentation quality plot usually involves toying around with the 80+ arguments of theme() and trying to remember what they control and what kind of element_ they are. It’s easy enough to remember how to change an axis.
Some surprising news for those who spent the past year listening to me complain about writing my honors thesis: it’s time for round two!
Thanks to the initiative and support of Professor Francis Fukuyama, I’ll be spending some time in the coming year revisiting the research I pursued as an honors student with the Center on Democracy, Development, and the Rule of Law. Funding comes courtesy of the Knight Foundation, and my findings will eventually be made available in a report written for Stanford’s Project on Democracy and the Internet.
Welcome to my personal site. If you’re reading this, you’re probably a) my mother, or b) someone who’s interested in my background and experience as a data scientist. In either case, you’re in the right place!
I plan to add content to this site in the form of either blog posts or project pages. Blog posts (like this one) will usually be quick and explanatory, and might include my thoughts on things I’m learning, tricky problems I’m facing, or maybe some bite-sized analyses.