How to create a range plot

Range plots are a relatively new chart type. It's an extension of the dot plot, which William Cleveland created in the 1990s. Dot plots show one or multiple dots on one line. Range plots show exactly two dots on a line and connect them with an extra bold line. They are great if you want to draw the attention to the distance between the values more than to the values themselves. You could also call this chart type the “gap chart” because it’s great for situations where the gap is important (e.g. gender pay gap).

That's what every range plot shows:

  • You have two points that mark absolute quantities of two comparable categorical dimensions and
  • A connecting line between these points that indicates the quantitative difference

This guide will show you how to prepare data to create this chart type. 

1

Preparing and importing the data

If you want to try to create this chart type, your data needs to be in a certain format. You'll need:

  • One header row containing labels
  • The first column contains main categories
  • Two other columns are categorical dimensions containing values of comparable measures

That's how the data looks like for the chart you can see above. (You can simply copy the data to recreate the chart. Just make sure you copy all the rows and columns.)

Education Women Men
Less than 9th grade 21284 34479
9th to 12 grade (no diploma) 22472 36837
High school graduate 29119 46833
Some college, no degree 35499 52852
Associate degree 38149 55795
Bachelor's degree 54411 84486
Master's degree 69022 99363
Doctorate 90906 151019   

Source:  US Census, 2 016

In this case, we have nine different categories, i.e. different education levels. Be aware that the cells of your table must contain values of the same measures. Each quantitative value will be represented by a dot and the difference of the two values in one row will be recalculated into the length of the connecting line automatically. Once your dataset looks like this, you can upload or copy & paste the table to Datawrapper.


2

Check & Describe

This is what the dataset looks like once it is uploaded into Datawrapper. Make sure that the box  "First row as  label " is ticked so that Datawrapper correctly assigns the values to the labels.

One hint: This example is all about salaries, the pay gap between women and men. But note that we did not add a currency sign. It or course possible to add a $ sign, but why? This information would be redundant and clutter the chart. If all values are of the same type it is often much better to mention this above the chart in a few words. 

Click on "Proceed" and Datawrapper will take you to the next step.

3

Visualize

Once you're in the "Visualize" tab, choose "Range Plot" and Datawrapper will create the first iteration of your data. Continue with the steps refine, annotate, and design to finish your chart. We'll cover this in a separate short tutorial.

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