Today I’m going to write about four viz types that have solved a lot of problems for me but that I don’t see out in the wild very often. I really like each of these approaches as they are analytically sharp and pack an emotional wallop.
Note: a collection of dashboards illustrating each of these types may be found at the end of this post.
1) Highlight Table
This one always resonates well with students in my Tableau classes. Consider this Superstore sales cross-tab that shows profit broken down by sub-category and region. Can you easily discern which combination of sub-category and region are performing well and which are performing poorly?
Now let’s see what happens if we take the same data but present it in a highlight table.
With this you can tell “from the back of the room” that Office Machines in the South are doing great and that Tables in the East or doing poorly. Indeed, Tables are doing poorly across all four regions.
In addition to its clarity I like this approach because it’s so easy to create in Tableau and that is serves as a type of “gateway” visualization for people that are shackled to spreadsheets. That is, people can still have their raw numbers, but they can really see what’s working and what isn’t.
Why Not Use a Heat Map?
I’m the first to recognize that the highlight table just shows one measure (in this case, profit) across multiple dimensions. A heat map would allow us use size for sales magnitude and color for profitability.
Which chart type you use depends on what you want to relate but I think the heat map dilutes the message that is so viscerally clear with the highlight table.
2) Jitterplot (aka, “Strip Plot”)
I only recently discovered and blogged about the the joys of this viz type, and I’m still over the moon with just how many problems this approach solves for me (and I suspect for others).
Consider this profit distribution plot where we place Order ID on the level of detail.
One can surmise from the standard deviation bars that most of the marks cluster in the center, but this visualization really doesn’t “sing”. Contrast this with the visualization below where we leverage the x-axis and “jitter” the marks left and right of center.
I first came across this viz type six years ago when I was using Tableau 2.0. I was trying to compare two measures against each other and was miffed that I could not control the spacing between the major category groupings; i.e., the clustering between 2012 and 2011 within one category is the same as the spacing between bars across categories, as shown below.
This makes for a tough read. I complained to Tableau’s “Yoda”, Marc Reuter, and he suggested I create a bar-in-bar chart, like the one shown here.
The chart is clear, easy-to-read, and it takes up a little less screen real estate. We do, however, need to display a legend.
4) Divergent (or Staggered) Stacked Bar Chart
I’ve written about this viz type quite a bit (see this post and that post) and while this was initially conceived as something that would elegantly handle Likert scale survey questions it is proving to be a great way to see how sentiment / performance skew positive or negative across multiple dimensions.
Going back to our Superstore Sales data, suppose we wanted to see the percentage of orders that took a certain number of days to ship (0 days, 1 day, 2 days … up to 5 or more days). A traditional stacked bar chart is easy to create, but very difficult to grok.
Compare the chart above with a divergent stacked bar chart where 0 to 2 days is considered good and 3 or more days is considered poor, and where the goal is to have 80% of orders shipped in two or fewer days.
I’ve also included an option to just show good / bad in a more binary form.
I’ve long suspected that survey folks who ask to just see the percentage of respondents that chose the “top two boxes” request this because nobody had presented them with a divergent stacked bar approach. With this type of chart you can see both the big picture and the details.