Introduction

I’ve written some “tutorial”-like content recently—see
here,
here, and
here—but
I’ve been lacking on ideas for “original” content since then. With that said,
I thought it would to try to re-create something with R. (Not too long ago
I saw that
Andrew Heiss did something akin to this with
Charles Minard’s well-known visualization of Napoleon’s 1812.)

The focus of my re-creation here is the price contour map shown on the
front page of the website for the Electric Reliability Council of Texas,
the independent system operator of electric power flow
for about 90 percent of Texas residents, as well as the employer of yours truly).

Without going too much into the details of what kind of information
and insight the map is supposed to provide ¹, it’s sufficient for the
reader to simply understand the following:

• “cooler” colors (i.e. blue and green)
  generally indicate regions of electric energy stability—where electric power
  generation is sufficiently meeting electric demand at a given point
  in time;
• and “warmer” colors (i.e. orange and red) generally represent the
  opposite—regions where there is some “stress” at a given point in time
  due to either the existence or threat of power imbalance.

Anyways, aside from my familiarity with the map (just from visiting
the website so often), I thought that it would
be interesting to try to re-create it because it presents some “challenges”
that I had not really tackled before (even in my previous endeavors
with {ggplot2} and maps)—namely,
(1) manipulating KML data
and (2) creating Voronoi
shapes. ²

Re-creation Process

Unfortunately, the code needed to re-produce the map is not exactly trivial,
so I’m going to leave details-hungry readers
a link to the repository
to explore the techniques that I used on their own time. ³ To summarize the process,
here’s what I did:

1. I downloaded the price data CSVs for a specific settlement
   interval—7:15 AM interval on November 15, 2018—from public links
   on ERCOT’s website. ⁴

   • The “Settlement Point Prices at Resource Nodes, Hubs and Load Zones” link
     in the “Real-Time Prices Reports” section at http://www.ercot.com/mktinfo.
   • The “DAM Settlement Point Prices” link in the “DAM Results Reports” section
     at http://www.ercot.com/mktinfo.

   Additionally, I downloaded the “static” KML data upon which the map is built.

   • http://www.ercot.com/content/cdr/static/texas_counties.kml
   • http://www.ercot.com/content/cdr/static/ercot_boundary.kml

   Finally, I downloaded “dynamic” KML data representing the price points
   themselves (for both the Real-Time Market (RTM) and the Day-Ahead Market (DAM).

   • http://www.ercot.com/content/cdr/contours/rtmSppPoints.kml
   • http://www.ercot.com/content/cdr/contours/damSpp7Points.kml

   I should Note that with this step and the others that follow,
   I repeated the actions for both data sets—those representing the
   RTM and DAM prices and points.

2. With the KML files for the price points, I extracted the
   longitude and latitude value pairs from the points defined in the KML
   using the {tidykml} package.
   Additionally, I converted the legend (embedded in the KML) into a vector
   to be used for the final recreated plot(s) themselves. ⁵

3. With the CSVs with the price data, I did a bit of “data munging” to convert
   the data for the two markets into an equivalent, easy-to-work-with format, and
   I joined this with the coordinate data from the previous step.

4. With the KML files representing the map’s static data, I created
   a SpatialPolygonsDataFrame encompassing both the Texas state and county boundaries,
   as well as the ERCOT region boundaries (which is separate).

   This was probably the most difficult part for me to figure out. Nonetheless,
   with the help of the R community’s packages geared towards working with spatial
   data—most notably, the {sp}
   and {sf} packages—as well as some great
   documentation and tutorials—I found
   this one
   particularly helpful, although one may consider its techniques “outdated” since it doesn’t
   utilize {sf} for interpolation. ⁶

5. Next was the “heart” of the process—the interpolation to create the Voronoi
   shapes. ⁷ This may be the part that is most interesting to the reader,
   since a similar process could be applied to one’s own project. ⁸

6. Finally, I used {ggplot2} to create the visualizations themselves.
   Notably, one must be careful with the order of calls to
   geom_polygon(), geom_sf(), and coord_sf() in order to get
   the ordering of layers correct.

As for other details about the implementation, this process provided a perfect
example of how/why functions can be so useful—it was really nice to just call
a custom function twice to repeat each step for
the two data sets, which certainly saved me some time and effort.

The Maps

So what does the final product look like? Below, I’ve placed my re-creations
and the actual image side by side (with the help of the
{magick} package).

Not bad, right? OK, so the sizing isn’t perfect, but I would say it’s close enough.

To take things one step further, I tried “subtracting” the values between the two
re-created maps. There is some value to doing this—to visualize the discrepancies
between prices in the two markets (which, hypothetically, should be equal).

Because this map displays information that is inherently different than those
of the stand-alone RTM and DAM maps, I used a different color
palette—specifically, the “E” palette that comes with the
awesome {viridis} package,
which is conveniently made available in
{ggplot2} ever since the v3 release.

Final Thoughts

I really enjoyed this challenge to myself. I can certainly see myself doing something
similar in the future, perhaps with a focus with which I’m less familiar (to give
myself an even greater challenge). ⁹