Visualizing US Presidential Elections

Scatter plot of popular votes by state in 1900I am a curious cat by nature, and a visual one at that, so back in 2012 I made a little scatter plot of the popular votes in the recent presidential election…

{ “tl;dr” : “https://github.com/ology/Election-Viz/1900 to 2012 elections” }

It was mildly interesting.  It showed which states favored which candidate and by how much.

A couple weekends ago, I noticed the project sitting incomplete on my shelf (A.K.A. ~/devel/R/) and decided to resurrect it.  I wanted to see the same type of charts for multiple years and with reference lines.  Besides the popular vote, there are other interesting columns too: percentage votes and electoral votes.

Also, I desired to harvest and integrate other state facts for those years, like population, voter turnout, etc.

So I had my marching orders:

  1. Harvest the election data
  2. Process (sanitize) the election data
  3. Plot the election data

Simple!

For #1 I initially had just copy-pasted the popular vote data for 1012, by hand, to a file.  But being a programmer, this type of manual drudgery is abhorrent.  I’m far too lazy for that.

So first I looked for available, friendly comma-separated-value files to just download and skip to step #2…  No luck.  Then I decided that I trusted the wikipedia sources and their relatively consistent tabular format.

Because it seemed like an interesting challenge, I tried to parse the raw wikipedia markup but nearly went (more) insane doing it.  Fortunately, I have used the handy Perl module HTML::TableExtractor to great success.  So a bit of LWP plus some caching logic and I was harvesting!

#2: Because wikipedia entries have varying ways to list the candidates, the table headers and the actual data itself, I had to (possibly) sanitize the row cells.  Perl to the rescue again!  Its regular expression engine makes text manipulation a breeze.  (And yes, I know all about string operations, and even interweb fetching in R, but 1. I was already harvesting the table with Perl and 2. I wanted to keep the division of labor as independent as possible.)

For your inner geek, check out my wikipedia-scrape code at github.

#3: For plotting and stats in general, I use R.  I love R.  It is insanely powerful.

As such, a big part of this project was to learn more about R!  And indeed I did: Fetching list.files(), using a custom axis() and pretty(), using ifelse() to toggle argument values, using identify() to allow mouse interaction… Also I made friends with paste() the cousin of Perls join() and learned about how to manhandle legend().

Anyway, when I added a 45° slope=1 line, it made the division between states obvious.  This is a reference line, not the result of linear regression, btw.  When I graphed the vote percentage columns, the state division was even more obvious.  Then reference line serves to show how many other candidates received votes besides the two being plotted.

Scatter plot of percentage votes by state in 1900

For your inner geek, check out my presidential-candidates.R code at github.

Here are all the charts for the 1900 to 2012 elections.

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