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How can you communicate your data with others? How can you even communicate it with yourself? The answer is data visualisation.
Consider the following dataset:
| X | 1 | 1.1 | 1.2 | 1.5 | 1.8 | 2 | 2.2 | 2.5 | 2.8 | 3 | 3.1 | 3.2 | 3.3 | 3.4 | 3.5 | 3.6 | 3.7 | 3.8 | 3.9 | 4 | 4.1 | 4.2 | 4.3 | 4.4 | 4.8 | 5.3 | 5.5 | 5.6 | 6 | 6.1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| A | 20 | 0 | 7 | 10 | 7 | 0 | 1 | 2.5 | 4 | 5 | 5 | 3 | 2 | 0 | 1 | 5 | 0 | 1 | ||||||||||||
| B | 5 | 10 | 9 | 5 | 5 | 7 | 8 | 10 | 5 | 20 | ||||||||||||||||||||
| C | 5 | 1 | 0 | 0 | 0 | 2 | 20 | 4 | 1 | 0 | 2 | 20 | 4 | 1 | 0 | 6 | 8 | 9 | 10 | 8 | 5 |
It's hard to make much sense of it just by looking at numbers in a table. Perhaps adding some conditional colouring might help pick out some patterns — a deeper green for a higher number in this case:
| X | 1 | 1.1 | 1.2 | 1.5 | 1.8 | 2 | 2.2 | 2.5 | 2.8 | 3 | 3.1 | 3.2 | 3.3 | 3.4 | 3.5 | 3.6 | 3.7 | 3.8 | 3.9 | 4 | 4.1 | 4.2 | 4.3 | 4.4 | 4.8 | 5.3 | 5.5 | 5.6 | 6 | 6.1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| A | 20 | 0 | 7 | 10 | 7 | 0 | 1 | 2.5 | 4 | 5 | 5 | 3 | 2 | 0 | 1 | 5 | 0 | 1 | ||||||||||||
| B | 5 | 10 | 9 | 5 | 5 | 7 | 8 | 10 | 5 | 20 | ||||||||||||||||||||
| C | 5 | 1 | 0 | 0 | 0 | 2 | 20 | 4 | 1 | 0 | 2 | 20 | 4 | 1 | 0 | 6 | 8 | 9 | 10 | 8 | 5 |
Perhaps that's helped a bit. There's clearly a progression in the X category, and rises, falls, and occasional spikes in the other categories. But there's probably more to see. Let's try making a graph...
By using the X category as an x-axis, and plotting the other categories against it, the data suddenly speaks for itself!
Admittedly, this is perhaps a bit of an extreme example, but it nonetheless demonstrates the power visualisation has in bringing a dataset to life.
Visualisation gives us a new lens with which to explore our data, but it also gives us an opportunity to communicate our research.
Take one of the most famous data visualisations — John Snow's cholera map:
The map shows the location of all the waterpumps between Oxford Street and Piccadilly in London. On it, black bars are used to denote a cholera case in a given location, and these are stacked to highlight multiple outbreaks. The bigger the stacks, the more cases of cholera. And right next to the biggest concentration of cholera cases: the Broad Street pump.
This is what we'd call a correlation: a coincidence of information. There may be a causal link between the two details or there may not. That would be something for us to investigate.
But Snow's map was not part of his investigation. It wasn't giving him a lead. He'd already worked out the relationship between contaminated water and cholera. The purpose of Snow's map was to communicate his findings — it was a tool of persuasion.
So visualising can help us make sense of our data but it can also allow others to make sense of our research. What might be complicated or difficult to explain in words might be a lot easier to follow in pictures...
There are a range of visualisation options built into Google Sheets and Excel. Here we take a look at a few of them:
There's a load of free tools out there too, which can produce some impressive results:
For pretty much every visualisation tool you'll need to get your data into a specific shape: a shape it can work with an understand. You may need to restructure your data to fit the structure needed by the tool you're using...
One specific form of visualisation is the infographic, as exemplified very effectively by the work of Information is Beautiful.
Infographics typically use simple graphics and isotypes to convey statistics.
There are various free (or freemium) tools to help you make infographics. For instance, Piktochart and Infogram let you enter tabulated data tables for some great charts graphics, while Canva is more about the graphics than the data.
Alternatively, you could make very effective infographics in something like PowerPoint. There's a lot of relevant help on our Posters with a Powerful Point practical guide:
Set the page size (A4’s probably fine)
Draft some ideas out on paper: think about reading layout (columns like a newspaper? rows like a comic strip? boxes? does it flow logically?)
Consider the important details you need to convey. If it’s not essential, you probably should leave it off the page
Set up guidelines on your page to help create a balanced structure
Draw your graphics. PPT has tools or you could just import things from elsewhere. You can always draw over the top of something too
Add your content; add your text (use shapes, not text boxes; space text out – empty space helps readability)
Think about your choice of colour – remember, minimalist simplicity is often best for infographics (you don’t want to distract from the message)
Remember that infographics typically rely upon simple graphics to convey their information. PowerPoint has a collection of useful icons in its image library. Iconfinder, the Noun Project, and the Doodle Library are also good sources of creative commons icon-style graphics.
Visualisation needn't be visual. Or, at least, the communication of data needn't be visual. You could sculpt or 3D-print your data for a tangible 'visualisation'. Or you could communicate your data aurally...
Here's a deck of slides that picks through a few weird and wonderful examples of sonification, and offers up some tools for you to try, too:
So we can communicate data visually, aurally, tangibly... That leaves two senses still to exploit. Maybe you can make your own research stand out by finding a way to communicate it through taste or smell...?
For an example/lesson on using sonification with historical data, see the Programming Historian site's lesson on The Sound of Data. The lesson explores some of the ways sonification can be useful to exploring data about the past.
We take a mad dash through over 60 inspiring visualisations in just under eleven minutes:
An introduction to some principles of data visualisation, exploring the possibilities and hacks available, and taking a look at some tools
Forthcoming sessions on :
There's more training events at:
A really good source of advice on data visualisation is the data.europa.eu Data Visualisation Guide:
There are several short courses available with tips and tricks for data visualisation:
