Senate Votes Visualized

Grid Maps

It has been exactly one week since the Senate voted to start debate on Obamacare. There were three Obamacare repeal proposals that followed in the wake of the original vote. Each one failed, but in a different way. News outlets such as the NYTimes did a great job reporting how each Senator voted for all the proposals. I then used that data to geographically illustrate Senators’ votes for each Obamacare-related vote. See below for a timeline of this past week’s events and accompanying R-generated visuals.

Tuesday, July 25th, 2017

The senate votes to begin debate.

deb_final

This passes 51-50 with Pence casting the tie-breaking vote. The visual shows the number of (R) and (D) Senators in each state as well as how those Senators voted. We can easily identify Collins and Murkowski, the two Republicans who voted NO, by the purple halves of their states (Maine and Alaska, respectively). While Democrats vote as a bloc in this case and in the impending three proposal votes, it is the Republicans who switch between NO and YES over the course of the week of Obamacare votes. Look for the switches between red and purple.

Later that day…

The Senate votes on the Better Care Reconciliation Act.

rr_final

It fails 43-57 at the mercy of Democrats, Collins, Murkowski, and a more conservative bloc of Republicans.

Wednesday, July 26th, 2017

The Senate votes on the Obamacare Repeal and Reconciliation Act.

pr_final

It fails 45-55 at the mercy of Democrats, Collins, Murkowski, and a more moderate bloc of Republicans.

Friday, July 28th, 2017

The Senate votes on the Health Care Freedom Act.

sk_final

It fails 49-51 thanks to Democrats, Collins, Murkowski, and McCain. To hear the gasp behind the slice of purple in AZ, watch the video below.

Code

This was a great exercise in using a few R packages for the first time. Namely, geofacet and magick. The former is used for creating visuals for different geographical regions, and is how the visualization is structured to look like the U.S. The latter allows you to add images onto plots, and is how there’s a little zipper face emoji over DC (as DC has no Senators).

In terms of replication, my R notebook for generating included visuals is here. The github repo is here.


© Alexandra Albright and The Little Dataset That Could, 2017. Unauthorized use and/or duplication of this material without express and written permission from this blog’s author and/or owner is strictly prohibited. Excerpts, accompanying visuals, and links may be used, provided that full and clear credit is given to Alex Albright and The Little Dataset That Could with appropriate and specific direction to the original content.

A rising tide lifts all podcasts

Scatter Plots

A personal history of podcast listening

One afternoon of my junior year, I listened to a chunk of a Radiolab episode about “Sleep” as I myself heavily sank into unconsciousness. It was like guided meditation… supported, in part, by the Alfred P. Sloan Foundation. Jad and Robert’s forays on Radiolab quickly became my new bedtime stories. They helped me transition from days with my nose deep in books and, more accurately, my laptop to dreams that veered away from the geographic markers of one tiny college town in a valley of the Berkshires.

The Radiolab archives were a soundtrack to my last years of college and to my transition from “student” at a college to “staff member” at a university. A few months into my new place in the world, I found myself discussing Sarah Koenig’s Serial with my colleagues in neighboring cubicles. I also wasn’t a stranger to the virtual water cooler of /r/serialpodcast. I became so entrenched in the podcast’s following that I ended up being inspired to start blogging in order to document reddit opinion trends on the topic.

Faced with regular Caltrain rides from the crickets and “beam” store of Palo Alto to the ridiculous-elevation-changes of SF, I started listening to Gilmore Guys. You know, the show about two guys who talk about the Gilmore Girls. I did not think this would take (I mean, there were hundreds of episodes–who would listen to all that?!) but I was very wrong. The two hosts accompanied me throughout two full years of solo moments. Their banter bounced next to me during mornings biking with a smile caked across my face and palm trees to my left and right as well as days marked by fierce impostor syndrome. Their bits floated next to me in the aftermath of medical visits that frightened me and suburban grocery shopping endeavors (which also sometimes frightened me). Their words, light and harmless, sat with me during evenings of drinking beer on that third-of-a-leather-couch I bought on craigslist and silent moments of self-reflection.

That might sound like pretty heavy lifting for a podcast. But, (silly as it might sound) it was my security blanket throughout a few years of shifting priorities and support networks–tectonic plates grumbling under the surface of my loosely structured young adult life.

When it came time to move to Cambridge from Palo Alto, I bought a Leesa mattress thanks to Scott Aukerman’s 4am mattress store advert bit from Comedy Bang Bang. (Sorry, Casper.) Throughout my first doctoral academic year, I regularly listened to Two Dope Queens as I showered and made dinner after frisbee practices. Nowadays, like a good little liberal, I listen to the mix of political yammering, gossip, and calls to arms that makes up Pod Save America.

Podcasts seem to be an increasingly important dimension of our alone time. A mosaic of podcast suggestions is consistently part of entertainment recommendations across friends… which leads me to my question of interest: How are podcasts growing? Are there more created nowadays, or does it just feel like that since we discuss them more? 

Methodological motivation

In following the growth of the R-Ladies organization and the exciting work of associated women, I recently spotted a blog post by R-lady Lucy McGowan. In this post, Lucy looks at the growth of so-called ‘Drunk’ Podcasts. She finds a large growth in that “genre” (if you will) while making great usage of a beer emoji. Moreover, she expresses that:

While it is certainly true that the number of podcasts in general has absolutely increased over this time period, I would be surprised if the increase is as dramatic as the increase in the number of “drunk” podcasts.

I was super skeptical about this statement. I figured the increase in many podcast realms would be just as dramatic, if not more dramatic than that in the ‘drunk’ podcasts universe. So, I took this skepticism as an opportunity to build on Lucy’s code and emoji usage and look into release trends in other podcasting categories. Think of this all as one big excuse to try using emojis in my ggplot creations while talking about podcasts. (Thank you to the author of the emoGG package, a hero who also created Beyoncé color palettes for R.)

Plotting podcasts

I look into podcasting trends in the arenas of ‘sports’, ‘politics’, ‘comedy’ and ‘science.’ I figured these were general umbrella terms that many pods should fall under. However, you can easily adapt the code to look into different genres/search terms if you’re curious about other domains. (See my R notebook for reproducible work on this subject.) I, like Lucy, then choose emojis to use in my eventual scatterplot. Expressing a concept as complex as politics with a single emoji was challenging, but a fun exercise in using my millennial skillset.  (The ‘fist’ emoji was the best I could come up with for ‘politics’ though I couldn’t figure out how to modify the skin tone. I’m open to other suggestions on this front. You can browse through options with unicode here.)

In the end, I combine the plots for all four podcasting categories into one aggregated piece of evidence showing that many podcasts genres have seen dramatic increases in 2017. The growth in number of releases is staggering in all four arenas. (Thus, the title ‘A rising tide lifts all podcasts.’) So, this growth doesn’t seem to be unique to the ‘drunk’ podcast. In fact, these more general/conventional categories see much more substantive increases in releases.

pods

While the above deals with podcast releases, I would be very curious to see trends in podcast listening visualized. For instance, one could use the American Time Use Survey to break down people’s leisure consumption by type during the day. (It seems that the ATUS added “listening to podcast” in 2015.) I’d love to see some animated graphics on entertainment consumption over the hours reminiscent of Nathan Yau’s previous amazing work (“A Day in the Life of Americans”) with ATUS data.

Putting down the headphones

Regardless of the exact nature of the growth in podcasts over the past years, there is no doubt the medium has come to inhabit a unique space. Podcasts feel more steeped in solitude than other forms of entertainment like television or movies, which often are consumed in group settings. Podcasts have helped me re-learn how to be alone (but not without stories, ideas, and my imagination) and enjoy it. And, I am an only-child, so believe me… I used to be quite good at that.

The Little Dataset–despite this focus on podcasts–is brought to you by WordPress and not Squarespace. 🙂

Code

Check out this R Notebook for the code needed to reproduce the graphic. You can also see my relevant github repository.


© Alexandra Albright and The Little Dataset That Could, 2017. Unauthorized use and/or duplication of this material without express and written permission from this blog’s author and/or owner is strictly prohibited. Excerpts, accompanying visuals, and links may be used, provided that full and clear credit is given to Alex Albright and The Little Dataset That Could with appropriate and specific direction to the original content.