Makeover Monday Week 8 – Potatoes in the EU

I’ll say this first – I don’t eat potatoes.  Although potatoes are super tasty, I refuse to have them as part of my diet.  So I was less than thrilled about approaching a week that was pure potato (especially coming off the joy of Valentine’s Day).  Nonetheless – it presented itself with a perfect opportunity for growth and skill testing.  Essentially, if I could make a viz I loved about a vegetable I hate – that would speak to my ability to interpret varying data sets and build out displays.

I’m very pleased with the end results.  I think it has a very Stephen Few-esque approach.  Several small multiples with high and low denoted, color playing throughout as a dual encoder.  And there’s even visual interest in how the data was sorted for data shape.

So how did I arrive there?  It started with the bar chart of annual yield.  I had an idea on color scheme and knew that I wanted to make it more than gray.

This gave perfect opportunity to highlight the minimum and maximum yields.  To see what years different countries production was affected by things like weather and climate.  It’s actually very interesting to see that not too many of the dark bars (max value) are in more recent times.  Seems like agricultural innovation is keeping pace with climate issues.

After that I was hooked on this idea of sets of 3.  So I knew I wanted to replicate a small multiple in a different way using the same sort order.  That’s where Total Yield came in.  I’ve been pondering this one in the shower on the legitimacy of adding up annual ratios for an overall yield.  My brain says it’s fine because the size of the country doesn’t change.  But my vulnerable brain part says that someone may take issue with it.  I’d love for a potato farming expert to come along and tell me if that’s a silly thing to add up.  I see the value in doing a straight total comparison of the years.  Because although there’s fluctuation in the yield annually, we have a normalized way to show how much each countries produces irrespective of total land size.

Next was the dot plot of the current year.  This actually started out its life as a KPI indicator of up or down from previous, but it was too much for the visual.  I felt the idea of the dot plot of current year would do more justice to “right now” understanding.  Especially because you can do some additional visual comparison to its flanks and see more insight.

And then rinse/repeat for the right side.  This is really where things get super interesting.  The amount of variability in pricing for each country, both by average and current year.  Also – 2013 was a great year for potatoes.

Funnel Plots

As I continue to read through Stephen Few’s “Signal: Understanding What Matters in a World of Noise” there have been some new charts or techniques I’ve come across.

In an attempt to understand their purpose on a deeper level (and implement them in my professional life), I’m on a mission to recreate them in Tableau.

First up is a funnel plot. Stephen explains that funnel plots are good when we may need to adjust something before an accurate comparison can be made. In the example video, I adjust how we’re looking at the average profit per item on a given order compared to all of the orders.

What’s interesting is that in tandem with this exercise, I’m working on an quantitative analysis class for my MBA, so there was quite a bit of intersection. I actually quickly pulled the confidence interval calculation (in particular the standard error equation) from the coursework.

I find that overall statistical jargon is really sub-par in explaining what is going on, and all the resources I used left me oscillating between “oh I totally get this” and “I have no idea what this means.” To that end, I’m open to any comments or feedback to the verbiage used in the video or expert knowledge you’d like to share.

Link to full workbook on Tableau public for calculated fields:

Dot Plots

Today I was reading Stephen Few’s Information Dashboard Design aloud while Josh was doing some fall PC clean up and was on the chapter “An Ideal Library of Graphs.” As Stephen describes it there are several charts or graphs that should make their way onto dashboards and he goes into detail on the reason behind each and how to properly apply them.

We stopped to discuss the dot plot, because it is one that both of us under utilize.  From my perspective there’s a lot of under utilized space below each dot.  As we were exploring it deeper, I also felt uncomfortable with dots above a certain threshold, because at that point the actual data point was lost.  We decided to open up Tableau and start playing around.  In my mind I was thinking that using a Gantt bar as the point would better represent the data.  (Mind you, Stephen says that you use dot plots when you may not go to zero, otherwise stick to bar graphs).  I thought Gantt bars presented the perfect “happy medium” between the dot plot and the bar chart without causing the end data reader to incorrectly visualize length.

Below are the four different representations we came up.  All things equal, I think I might be most partial to the ‘Plus Plot.’  I think the general shape tends to draw your eye directly to the center point, and even if they were larger you’d still know where the center was.  I can see how this might get cluttered quickly, or if more encoding were done things could go awry.

I also found that I liked the Gantt bar, but needed to shorten the bar significantly to eliminate mentally drawing drop lines and visualizing as bars.