How to create your first Datawrapper chart

This is a very detailed tutorial on how to create and play around with your very first chart in Datawrapper

In this exercise, you will create a stacked bar chart – which is a normal bar chart that contains different parts in each bar. This is how it looks like: 

On the way, you will also try out other chart types, like a donut chart, split bars, stacked column chart, grouped column chart and dot plots. 

But first, you'll need to open Datawrapper. To do so, go to datawrapper.de in our browser.

You should see a website that looks like this:

Now click on the big green “Start creating” button. You can use Datawapper without being signed in, but you will need to sign up to publish the chart you build. 

Clicking on the “Start Creating” button will bring you directly into the chart editor: Here you can see four steps on the top: Upload Data, Check & Describe, Visualize and Publish & Embed. We will walk through these four steps to build our chart.

How to select and check data

Every data visualization needs data, so that’s what Datawrapper asks us to upload first. We can simply copy & paste our data in the white text field at the right, or we can upload an Excel file or link to a Google Spreadsheet or another external data set. That’s great if you want live-updating charts, for example during an election. 

But in this exercise, we’re lazy and we don’t upload data but take one of the sample datasets that Datawrapper prepared for us. 

To do so, click on “Select a sample dataset” at the bottom of your page:

You’ll get a list of sample datasets. You can play around with the other datasets later. For now, choose the dataset “Trust in Media Reporting”. 

Once you selected “Trust in Media Reporting”, you see the dataset in the text field. It looks a bit chaotic right now, but don’t worry, it’ll be tidied up and readable once we go to the next step, Check & Describe. 

To do so, click on “Proceed” in the bottom right. 

Now we see our data in a proper table! Let’s look at the data we just selected. 

In the first column, we see media topics, like “the financial crisis in Greece” or the “Ukraine Conflict”. The following columns contain values for “Very high trust”, “high trust” etc. It seems to be a poll, where people could reply how much they trusted the media reporting on the topics we see. 

You can see that the columns are differently colored. The first column is black – that tells us that Datawrapper recognizes this column as a text column. Great! That’s what it is. The following columns are blue, which means Datawrapper thinks these are columns filled with numbers. That’s also right, so it seems like we’re good to go. 

When you’re creating a data visualization, it’s always helpful to know if Datawrapper understands the values you uploaded. Make sure the columns are in the right color before continuing. 

Ok, let’s do that; let’s continue. Click on “Proceed” in the lower-left to go to step 3: “Visualize”.

Finally, a data visualization! Datawrapper already selected a stacked bar chart for us.

We can see that there are many other chart types available: normal bar charts, line charts, pie charts, and more uncommon ones like dot plots and scatter plots. We will try a few of them later. 

How to refine your stacked bar chart

For now, let’s make our stacked bar chart more readable. To do so, click “Refine” at the top of the chart types.

This brings us to a page on which we have different settings. Play around with them! You can turn “Thicker bars” off, for example, or see what happens when you turn on “Show values on hover”. 

When you’re ready to change the colors, click on “customize colors” right below “Appearance”. 

This will open a little list of categories that we can color differently. 

Click on each category, then click on the little color rectangle that appears to its right. Then, select a color and then check the little checkmark in the lower right. 

Choose some colors and see if and how they improve the chart.

We chose this color combination: Two shades of blue for the positive responses, two shades of orange for the negative responses:

Orange-blue is a color combination that color-blind people can perceive well. And it’s clear for the reader that the two blue and the two orange categories belong together. 

We made the most extreme answers (“Very high trust” and “Very low trust”) the darkest, to highlight them a bit. 

And, like in many data visualizations, we chose to color the “No answer” category in a grey color, so that it gets the least attention. 

To increase the visual difference between the positive and the negative values, we can also use a “diverging” value alignment:

Try it out and see if you prefer that over the left alignment. 

Alright, almost done! Our chart looks good, but it needs a proper title. 

To add one, go to the “Annotate” tab:

Here you can add a title, description, notes, a data source and a byline.

Almost everything is already filled out. To give the chart a title, click in the first text field and start typing. 

The description already tells us what kind of data we see in the chart; so we can use the title to communicate what we can read out of this chart. We decided on this title:

Datawrapper saves and remembers all settings we do. 

So we can try out other chart types with the colors we already chose while refining the Stacked Bar Chart. 

How to try out other chart types

Let’s see how this looks like! Go back to the tab “Chart type”. 

There, choose the chart type “Multiple donuts":

If we do so, you will see that it doesn’t show the colors we just chose. But the whole chart also doesn’t make sense: It shows us which share of media topics got “very high trust”, which share of media topics got “high trust” etc. That’s not helpful.

 We want it the other way round: For each media topic, we want to see how many people gave the reporting “very high trust”, “high trust” etc. 

Datawrapper has a feature inbuilt that can help us. It’s called “Transpose the data”, and you will find it at the bottom of the chart type grid:

When we transpose data, columns become rows and rows become columns.

That’s what happens in the background, but we don’t need to care about it so much. When we look at our chart, it looks great – and that’s what counts. 

Every time a chart looks odd – like, “the wrong way around” – try transposing the data with a click on this link down here. 

But you can refine the chart even further. 

To do so, go to the “Refine” tab at the top again. And again you'll get a list of options you can change:

It makes sense to increase the minimum grid column width so that your donuts fit nicely next to each other. 

Also, consider selecting “Keep order” below “Sort by”. 

Why? Datawrapper assumes that we want to sort our donut slices automatically….and for lots of datasets, that makes sense.

Not for ours, though. So let’s change it to “Keep order”. 

You can try out as many chart types as you’d like! Just go back to the “Chart type” tab and play around. 

Remember that a click on “Transpose data” can help when the chart doesn’t look right. 

Here we selected a Grouped column chart. That seems to work fairly well for your data!  

But we need to keep in mind how our data visualization will look like on different screen sizes. 

That’s why Datawrapper offers these three buttons below each chart. 

With these buttons, you can check how the chart looks like for mobile readers, tablet readers, and readers on desktop computers. 

When we click the “mobile screen” button, this is the result: 

Datawrapper automatically turns the x-axis labels. That’s good, because otherwise they would overlap. 

But this rotated text is still hard to read for mobile readers. 

So while grouped column charts can work great for some datasets – even on mobile screens – they’re not the right fit for our data. 

The same is true for the Stacked Column Chart: The rotated labels are hard to read! 

But note how this chart is basically the first chart as the Stacked Bar Chart we created at the beginning – just 90 degrees rotated. 

See? Here’s our Stacked Bar Chart again. The labels have more space and are easier to read on this one.

Here’s a fancy chart type: The dot plot. 

It’s a great chart type if you want to show high values that are close together. For a dataset like ours, it can be a bit confusing. 

Maybe Split Bars are a better fit? 

They make it easier to compare the values of “High trust”, “Low trust” etc which each other; easier than our Stacked Bar Chart does it. 

But the whole chart looks really crammed; especially on mobile phones. Our bars can be longer in the Stacked Bar Chart.

The Stacked Bar Chart really does seem like a good solution for your data. 

Now that you figured that out, you're ready to publish!  

How to publish your chart

To do so, click on the arrow for “step 4: Publish & Embed”.

You will see your chart again, and a big button that says “Publish chart”. If you don't see this button yet, you're not signed up yet. You will be asked to enter your email address.

As soon as you click on that "Publish chart" button, your chart becomes visible for the public, and you can embed it, or share it on social media. 

So let’s hit publish! 

Once you've done that, you'll get some Share & Embed option. 

The “responsive iframe” is great for embedding your chart in an online article. 

If you want to share your chart on social media or just in an email to a coworker, choose the “Share via URL” option and then click on “normal size”. 

If you click on the URL (datawrapper.de/_/ezkEv/), you will see your chart on a separate page: 

Try to hover over the data, and you will notice that the chart is interactive, too.  

And that’s it! Congratulations on your first Datawrapper chart, and thank you so much for following this tutorial.

Do you have questions about options in a specific chart type? Click on the categories to the left of this Academy article to find out more about pie charts, bar charts, dot plots, etc. 

To find out more about Datawrapper in general, go to our website. And if you have any questions, we're always happy to help at support@datawrapper.de.

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