I used Tableau to generate something like 20 candidate data
visualizations for the paper. It sounds like a lot (and it was), but it was
relatively fast to do, and Tableau integrates some basic statistics that
can help uncover what I like to think of as "visual clues" to where and
how to focus the models later on. The scatterplot matrix below is a
good example of how Tableau improves on a simple, but powerful
visualization. Scatterplot matrices can be used to quickly and
efficiently evaluate a range of bivariate relationships all at once.
However, where there are close to 30,000 observations, the graph can
be a little "blobby," as below. Tableau includes a trendline function,
though, and it's easier to apply, remove, or modify than it would be in
R, in my opinion (prettier, too). Moreover, you can pan over it and get
an R-square value that tells you how good the trend line actually is.
Tableau also makes it easy to investigate different types of
relationships: linear, logarithmic, exponential, polynomial or power.
In the chart below, I started out with a linear trend line, and it was
okay, but not great. R values between 0.8 and 0.9 or so. When I
switched to a polynomial (factor 3) trendline, it matched far more
closely, had fewer points outside the confidence bands, and, most
importantly, delivered a useful insight. You can see from the gold
points and line that unemployed people who lived in Medicaid
expansion jurisdictions were able to pretty quickly get their coverage.
In particular, the data suggest that public coverage rates rise most
sharply in the first four or five years of expansion. This makes sense
because there is probably a natural saturation point, particularly when
the unemployment level is relatively stable, so the proportion of the
population that is unemployed is not very different from year to year.
