Using DataWrapper to Create Data Visualisations

One of the tools that I like to promote for those looking to begin data visualisation tasks is datawrapper.de. This is an ever more powerful web based tool that takes a ‘wizard’ like approach to creating engaging interactive visualisations. These visualisations are useful for your own analytical use and more particularly for presenting your findings to others.

Datawrapper provides a very useful ‘Academy‘ (https://academy.datawrapper.de/) with a large selection of useful tutorials and discussion about which types of charts and visualisations are applicable to different types of data or arguments.

Using DataWrapper requires creating an account and this gives you substantial access to create and store your visualisations.

Once you login to DataWrapper you will have a horizontal menu giving you your options:

From this menu you can choose to create a new Chart, a New Map, a New Table, Explore visualisations created by others in the data River, and also work collaboratively with others in a team around visualisations.

If you have previously authored visualisations, they will also appear here – this is your dashboard.

A good first step to familiarise yourself with the DataWrapper interface and system is to take the First tour through Datawrapper tutorial.

Using Datawrapper involves a 4-step process – as guided by the four tabs across the top of the workspace.

All of the steps are described in detail when you choose them. The Datawrapper creators believe in a learn by doing and by backwards engineering perspective. As a result they provide a substantial series of sample datasets for you to try at the bottom of the Upload Data step. Selecting any of these datasets will give you immediate access to the raw materials necessary to explore the various options that you have available in Datawrapper.

After you upload your own data or use one of their examples, you will see that data appear in the window to the right. Choosing to Check & Describe takes you to a tabular view where Datawrapper shows you how it has interpreted the data that you have uploaded. This is how the machine ‘sees’ your data. Most important;y it flags columns of data that it has an issue with in red and you need to ensure that the data has been correctly uploaded or formatted in a way that Datawrapper can use it. When you run into problems with it, and you will – everyone does – they have useful tutorial to help rectify issues – How to change, correct & delete data.

When you progress to the next step and visualise your data, you will see an initial attempt to choose a best visualisation type for dataset. On this screen you have a wide variety of chart types to choose, as well as additional tabs to tweak and customise the appearance of the various chart types.

The final step in the process is sharing the chart, map or table once you have achieved the look that you want. You can share your data via the Publish & Embed tab. Datawrapper gives you the ability to save the visualisation as a static PNG file that you can use onscreen or in print or to embed the visualisation with interactive components on your blog or other online source. Datawrapper constructs embedded code for inserting into your HTML source as well as a unique URL that you can also use to share your constructed visualisation with others.

Datawrapper is an excellent tool for working with your data in an experimental fashion and creating compelling charts, maps or tables to enhance your data-driven narratives. It offers a wide range of types of visualations and ways in which to customise the appearance and functionality of your creations.

Try it and see what you can produce using the sample datasets (or your own), browse the variety of informative articles in their academy and learn by doing. Most importantly, think about how the pictures that you create from your data help others to understand what you have identified and see in the data you are working with.