Networks of voting similarity for U.S. House and Senate for each session of Congress
(Pictured above are the 70th (left) and 110th (right) Senate.)
This analysis was originally perfomed in:
Andris C, Lee D, Hamilton MJ, Martino M, Gunning CE, et al. (2015). The Rise of Partisanship and Super-Cooperators in the U.S. House of Representatives. PLOS ONE 10(4): e0123507.
The data comes from voteview.com:
Lewis, Jeffrey B., Keith Poole, Howard Rosenthal, Adam Boche, Aaron Rudkin, and Luke Sonnet (2020). Voteview: Congressional Roll-Call Votes Database. https://voteview.com/
First, you need to acquire four files from voteview:
- Member Ideology (House, All Congresses, CSV),
Hall_votes.csv - Members' Votes (House, All Congresses, CSV),
Hall_members.csv - Member Ideology (Senate, All Congresses, CSV),
Sall_votes.csv - Members' Votes (Senate, All Congresses, CSV),
Sall_members.csv
Update agreements.py to reference the votes CSV that you wish to tally. This will produce a tab-delimited file that shows the number of agreements (if >0) between any pair of congressmen across every congressional session. The House takes longer to run than the Senate and benefits from running over multiple cores.
Next, to find the vote thresholds, use thresholds.R to combine the pairwise data from agreements.tsv with the member ideology data you've downloaded. Again, update file paths as necessary for House vs. Senate.
Finally, we have the data we need to construct networks. Use networkize.R to finish the job, updating paths as appropriate.
What's with all the hardcoded paths? This code was intended to be run in an ad hoc/one-off fashion for the House of Representatives. Later I decided to do the Senate as well. Unfortunately, it was never really intended to be pretty.
- Python (tested on 3.6)
- R (tested on 4.0)
networktidyverse