Today’s challenge was to assume a situation where we are limited by the specific technology we have on hand to work with. For these reasons, we were only allowed to use the Tableau suite to construct our datasets. Additionally, the further objective was to source and incorporate at least one external form of data within our final dashboards.

To build today’s project we used Tableau Prep, rather than Alteryx; to clean, organise and join our data.

External Sources

Of three datasets provided, I selected the Solar Power dataset specific to Small Households (<100kW generated). Additionally, I brought in socio-economic data from the Australian 2016 Census Data, Solar Data, and ABS Spatial Data using a combination of joins (through Tableau Prep) and relationships (in Tableau).

Building in Tableau

When I saw the topic was on Solar Panels, I thought a great visual element to represent this was a simple but clear little chart – the Waffle Chart, with it’s ‘Solar Panel’ like boxes.

Similar to the Power Gauge charts I built earlier in the week, this was a chart type I hadn’t attempted before so I was interested to try them out. Also similar to the Power Gauge charts, I learned there are a few limitations.

For example, due to the way the chart is structured, it’s best for displaying simple findings such as percentage results. The use of filtering in these charts can also be fairly restrictive. I created a more streamlined approach by building multiple views that could by switched used a parameter control.

For each Australian State I chose three key areas for comparison; ‘% of Dwellings with Solar Installations’, ‘% Kilowatt Power Capacity’ and ‘% of the Upper 25% Socio-Advantaged Scoring Population’. The latter was to indicate when there might be a trend between States with more economically advantaged individuals, and the proportion of Solar Installations or Capacity.

The Resulting ‘Solar Panels’

By the end, I had some neat little waffle charts to display. I also decided to changing the shapes to provide some additional visual contrast between each element. While I feel other types of charts can be a bit more versatile in the information they can yield, these provide a nice, simple visualisation and were a interesting chart to try!

In Conclusion

Overall, I felt this dashboard challenge provided some great practice for thinking resourcefully and problem-solving how to acquire information and develop insights relating to the assigned topic.

Tamara Allcock
Author: Tamara Allcock

Tamara has an interesting background in veterinary science, data analytics and retail. She discovered her passion for analytics while working on a range of research projects involving Australian and exotic wildlife. She was excited to learn about the Data School and the opportunities it provided to develop this interest into a career path. It may be a common preference, but she thinks you can’t go wrong the variety of options a delicious pizza offers. In her spare time, Tamara is an avid reader and watcher of fantasy, science-fiction or assorted pop culture and also enjoys painting, craft projects and writing.