Adobe Illustrator is my preferred tool for data visualisation. The majority of my work focuses on data storytelling and this software works well in an area requiring very custom graphics. Experimenting with different tools is not something I do often - like many, I stick with what I know. But data visualisation tools have increased in both number and quality on what was available even a year ago. More tools are available for more effective data story visualisation.
Datawrapper has been on my radar to try for a while (mainly because Lisa Charlotte Rostwrites amazing data viz blogs about their product) but it's not something I've given myself time to try out. Therefore after reading the first #SWDChallenge of 2019, I had the perfect opportunity.
"I challenge you to try a new tool for visualizing data" - Storytelling with Data
My Datawrapper project
Instead of starting from scratch (sourcing the right data and designing the visualisation) which can take a while, I've used one of my existing graphs created in Adobe Illustrator, and attempted to recreate the same design using Datawrapper.
Original graph created using Adobe Illustrator
Graph recreated using Datawrapper
Differences in graphing software
Disclaimer: I haven't spent a lot of time learning Datawrapper. With the help of their tutorials and Google I've created the above graph. If I point out differences below that can be corrected please let me know - thank you!
I used the basic (free) version of Datawrapper, allowing only one font choice. Font size and weight choice was also limited. The only font size I was able to change under the basic plan was the annotation. If I paid 499€/month to upgrade to their full solution I could have custom branded this graph.
The original graph was part of a bigger infographic and its title placement was to the right of the graph. This was a purposeful design choice to decrease the proximity of the takeaway title to the data points it describes (the high proportion of female hatchlings).
Gestalt principle of proximity: the closer an object is to another, the closer their relationship.
In Datawrapper there was only one option of title placement I could find - at the top left. A design choice that makes sense for most graphs.
The colour gradient in the original graph symbolises the nest temperature; warming from blue to red.
Although Datawrapper was able to seperate and shade areas of the background, I couldn't find a way to run an overall colour gradient through it.
Data points and legend
Solid points in the original graph represent a higher proportion of female hatchlings and the more subdued outlined points representing a higher proportion of males (the graph takeaway title was related to the female hatchlings so I've made these stand out more).
While it was possible to outline the points in Datawrapper, the outline didn't appear in the legend making the white point disappear (as below).
I've shaded these points with a light grey in the final graph so it is visible in the legend.
I like to have as few unnecessary lines in my graphs as possible as they can add to the clutter. By shading the background of this graph, the border is already outlined so I don't need the axis lines.
In Datawrapper I couldn't find a way to remove my axis lines without losing my axis labels (as below) - even though it's okay for a scatter plot axis to not start at zero 😉
It's easy to customise the graph size in Datawrapper but not the canvas size. Axis labels were always inside the graphing area (as below), so I couldn't accommodate labels and annotations outside of this.
I like to include units in my axis whenever possible as this helps to decrease the cognitive load on a reader if they have to search out the measurement or scale.
After various attempts at Googling number formats, I couldn't find a way to include the temperature unit (°C) in each axis value so had to settle for incorporating it into the axis label.
I'll use Datawrapper again
Datawrapper doesn't have the flexibility of a graphic design software such as Adobe Illustrator but I was pleasantly surprised by how much it could be customised - something really important for the data storytelling I do. I turned off tool tips for this graph but it's also a function I can see using for more interactive/explorative data stories.
You can find out more about Datawrapper on their website: www.datawrapper.de
If you're interested in data visualisation I'd recommend taking part in this month's #SWDChallenge - whatever your professional role. It's easy to get wrapped up in learning an organisation's tool of choice, without looking up to see what else is out there.