Data visualization tools: Datawrapper
Let’s not beat around the bush in this blog post. If you are writing articles online and need to quickly insert beautiful, interactive charts, maps or tables, Datawrapper is the tool you are looking for.
The Datawrapper team, based in Berlin, built an amazing product suitable for everyone who wants to tell stories using data. The free tier is very generous – unlimited visuals, live-updating charts, easy embedding, responsivity… The professional solution is expensive but mainly aims at newsrooms and journalists, removing the Datawrapper attribution and allowing to create print-ready graphics.
No coding skills are required, you can simply copy and paste your data, upload CSV files, or link to a URL or Google Sheet which allows live-updating charts. Obviously, you are limited to a certain set of possible chart types and designs, but the talented design team (led by Lisa Charlotte Rost) has ensured that all the available options and color schemes are well-crafted and elegantly designed.
You can find plenty of examples at the Datawrapper website, but of course a blog post about this tool would not be complete without an embedded example. So let’s try to create our own chart of, let’s say, the evolution of the price of Brent Crude Oil since January 2019. Uploading the data, as a CSV file, works very smoothly. Datawrapper automatically recognizes the type of data in each column, and support for different localizations is provided out of the box:
The tool is sufficiently intelligent to suggest the best chart type, and makes some pretty good default decisions regarding axes and grid lines (taking into account Tufte’s guidelines to maximixe data-ink ratio):
The next steps allow you to quickly finetune any other parameters you would like to change: colours, axes, grid lines, linestyles, hover labels, title, description, source links,… The final result can be downloaded as a PNG image, shared as a link, or simply embedded in your blog post:
It’s magic, it’s simple, and it’s so much fun! Did I already mention I am a fan?
Read more:
This chart is trying to trick you
The original chart in this example is trying to suggest a strong correlation between sugar intake and obesity in the US between 1980 and 2000. It does so by carefully choosing the vertical axis ranges and scaling so both lines nicely fall on top of each other.
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