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Tableau KISS – Revisualising To Bee or not to Bee

Back in February I posted an article regarding what I called “Tableau KISS” on my personal blog. The feedback from the community was great and I received lots of positive comments and messages, but since then I haven’t posted much in the way of follow up. Let’s change that….

Tableau KISS

The idea behind Tableau KISS was to show that Tableau shouldn’t have to be complex – Keep It Simple Stupid! – and that interesting, engaging visualisations doesn’t necessarily need to use edge-cases, complex visualisations or use “wow factors” to tell interesting stories. With that in mind I promised I would be revisiting some visualisations on Tableau Public to show how similar stories can be told through simple techniques, focussing on those techniques a user might learn through the Tableau Fundamentals course. Furthermore I also wanted to focus on simple story telling that anyone can understand, rather than use visualisations that require a lot of understanding.

Revisiting Iron Viz Foodfight

With that in mind I have decided to revisit each of the recent Iron Viz Foodfight entries and visualise each dataset in turn using only the Tableau KISS principles. Clearly each of the Iron Viz entries were done with the competition in mind, the most prestigious and most technically challenging event in the Tableau community, and so almost all contain complexities beyond Fundamentals level, therefore it will be a fun and interesting challenge to see what can be achieved without them.

Clearly there can be no comparison between my TableauKISS visualisation and the original, I am not attempting to produce a “better” version and am in no way suggesting the original visualisations were wrong to use complex techniques (almost a must in Iron Viz!). Instead I want the focus of these articles to be on creating a new visualisation using the same data, and how I got there.

To Bee or not to Bee

To start on this journey I’m going to look at my Colleague Carl Allchin’s visualisation To Bee or not to Bee.

Full view

I loved this visualisation, the story of bees was interesting and the depth of data just enough to provide a good level of detail at State level. From a difficulty perspective it certainly had nothing uber-complex, but using parameters for the highlight and also blending with the Hex Map certainly provided a level of complexity beyond a beginner.

Creating the KISS version

Revisualising this data with simple techniques was actually a lot harder than I expected, the state level data provided a challenge to get onto a chart without cluttering and providing too much information. I circled round and round before settling on telling the story at a national level and then allowing the user to filter to the state of their choice.

Initially I had opted to tell the story via Story Points but instead opted for a vertical scrolling “infographic” style visualisation, click on the image below to go to the interactive version.

Bee Tableau Kiss

How To

Let’s look at some of the challenges I faced in putting this together.

Index Charts in Tableau

The four charts in this visualisation are all Index Charts – looking at the percentage change in a given measure since 2006. Creating them in Tableau is simple and simply requires the use of the Quick Table Calculation.

  1. Start with a simple line chart with Year on Columns and the Measure on Rows.

 

Line Chart

2. Using the Quick Table Calculation option set up a percentage difference chart…

 Table Calculation

 

3. Set the percentage difference to be calculated relative to the first year.

 

Relative to

 

Weighted Averages

Another complexity of this visualisation was that the measures I was comparing were in some cases already State-level averages in the raw data; in other words I could not simply aggregate them to get the national picture. If you’re unsure why this is then let’s look at this example with house prices: if the average price in one state with just one house is $100k, and the average price in another state with 100 houses is $50k then the overall average price of a house in both states is not ($100k + $50K) / 2, instead it is (1 x 100k + 100 x 50k) / (1 + 100). In mathematical terms this is what we call a weighted average, because the average is weighted by the number of houses.

In the same way my national average yield per colony (in lbs) is calculated as a weighted average at the national level:

sum([Number of Honey Producing Colonies]*[Yield per colony (pounds)])/sum([Number of Honey Producing Colonies])

Design

As with any visualisation the initial engagement comes not from the story and analysis but from the design, a visualisation needs to pull in a user if it’s to really grab their attention beyond a cursory “uh-hu”. Again, fundamental techniques don’t present a problem here with a little imagination and design flair and I was able to produce what I believe is an engaging format.

I opted for floating elements to bring the visualisation together and allow me to position the elements as I wanted, it also gave me the control I needed to be able to float bee photos over the charts and give the feeling of bees invading the users screen. Sourcing the images wasn’t too much of a problem thanks to Flickr (remember to only take images from the internet if people have allowed it and always attribute photographers accordingly) and with some rudimentary Fill tool knowledge in Snag It Editor I was able to make their background transparent.

The overall clean design of black text and charts with yellow bees was something that evolved over time, and as with anything in design then you know when you’ve hit something you like and this was certainly the case here. My advice for new users with anything design-wise is to look for plenty of inspiration, inside and outside the data-viz world, and keep iterating designs until something works.

Conclusion

It was a tough job to revisit Carl’s viz, I expected it to be easier, however winding back my knowledge to the point where I only allowed simple techniques actually released me from worrying too much about complexity and instead I think focussed me on the analysis of the story and the final design. Overall I’m happy with the result, but I’d love to hear your feedback. Are you a new user? Do you think you could have created this? Where is the difficulty for you?

 

Chris Love

Nottingham, UK

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