#SWDchallenge: design for accessibility

Accessibility in graphs is more than just using colorblind-friendly palettes. It involves creating visual representations of data that are inclusive and easy to interpret for individuals with diverse needs. This includes factors such as color contrast, alternative text for images, clear labeling, and more. 

In a prior guest post, Amy Cesal shared five ways to make data visualizations accessible:

1. Add alternative text: Alt text allows those using a screen reader to understand and engage with your visual. 

2. Employ a takeaway title: People tend to rephrase the title when asked to describe a graph, so clarifying the point can make graphs easier to understand and provide direction on where to look.

3. Label data directly: Instead of asking users to scan back and forth between the legend and data, opt for labeling the data directly. This is super useful when the colors in the graph are similar.

4. Use sufficient contrast: Without enough contrast, different elements on the graph will blend into one another, so ensure you are using a colorblind-friendly palette and that the contrast between any text and background colors is distinct enough.

5. Leverage white space: Adding space around graphical elements helps them stand out and increases legibility.

For this month's challenge, we invite you to design an accessible graph that is inclusive and informative for all audiences.

The challenge

Your challenge is to create a data visualization that prioritizes accessibility by incorporating features such as high color contrast, clear labeling, and alternative text descriptions for screen readers. Find a visualization that you think would benefit from being made more accessible. This can be a visual you find publicly available or perhaps even something you created on your own that you'd like to strengthen. If you use a real-world work example, take care to anonymize appropriately. The goal is to make your graph easily understandable for everyone. 

Share your creation in the SWD community by Sunday, March 31 at 5PM ET. Take some time also to browse others’ submissions and share your input via comments and datapoints over the course of the month. We look forward to seeing your thoughtful and accessible graph designs!

Related resources

In addition to the guest post linked above, here are a few other related resources. If you are aware of other good ones, please share in your submission commentary.

• An accessible graph makeover

• An example of how text makes the data in the graph more accessible

• Urban Institute’s Do No Harm Guide

#SWDchallenge: upskill with UpSets

While our general advice in business communication is to keep your chart choices as simple and clear as possible, it’s always useful to stay aware of uncommon or specialized visualizations. You never know when you’ll have a particular need that is ideally addressed by an unusual chart type (or when you’ll have to explain how to read one to your colleagues).

One such graph innovation came up during a recent office hour (a benefit available to premium members of our SWD community)—the UpSet plot. This visual combines elements of bar charts, dot plots, and Venn diagrams to showcase relationships among items belonging to one or more of several related sets. 

Here’s an example from Jake Conway and Nils Gehlenborg, the authors of an UpSet package built for use in R, that shows the proportion of movies in a dataset that can be classified into one or more of nine distinct genres.

Focusing first on the dots themselves: that part of the UpSet plot defines, for each column, what specific intersection of possible genres is being counted in the vertical bar above it. 

• In the first column, only Drama is lit up, so the vertical bar shows the 950 movies in the database that can ONLY be classified as dramas.

• The fourth column, however, shows both Comedy and Drama lit, with those two marks connected by a solid black line. The bar above it, therefore, tells us that 171 movies in the dataset are classified as both Comedy and Drama. In set terminology, this is the intersection of Comedy and Drama, not the union.

• If we keep moving rightward, eventually we find an intersection of three genres: Comedy, Drama, and Romance. Thirty-four movies fit that bill.

Imagine trying to visualize nine different genres and all the possible intersections of them in a Venn diagram, or an Euler diagram. It would be so geometrically complex that it may well be impossible. But here, we have a crisp, orderly, compact way of communicating that information.

The challenge

It’s a new year, so try a new graph! Using real-world or training data as you deem appropriate, create and share an UpSet plot of your own. In keeping with traditional SWD guidance, however, please also try to find some meaningful insight in your analysis and make that clear, with words and design choices, in your challenge submission.

As an esoteric chart type, the UpSet plot does not come standard in many business software applications, so learning how to create one is an additional aspect of this challenge.

• The R package mentioned above can help you, as can the UpSetR Shiny app also from the same creator; other packages I’ve seen recommended include Complex UpSet and ggupset.

• There are Python libraries for it as well (I can’t vouch for their relative plusses and minuses, but upsetplot probably does what it says on the label.)

• With some effort, you can create one in Tableau as this video from Sean Miller demonstrates.

• Excel will probably take even more handcrafting; I’d suggest creating three separate graphs (a connected dot plot for the switchboard-looking part, plus two bar charts), and carefully placing them together on one sheet, slide, or canvas.

• Of course, you can always go the analog route and simply hand draw or illustrate part or all of your UpSet plot…it doesn’t matter how you make it, as long as the audience sees what you want them to see. (It’s true for professional magicians, and it’s true for data visualizers just the same.)

Submit your UpSet plot and any commentary here in the SWD community by 5pm PT on Wednesday, January 31, 2024. 

Related resources

• There’s nothing wrong with checking out the UpSet Plot entry on wikipedia to get a fuller understanding of its history and potential applications

• If you prefer not to rely on anonymous sources, here’s a nice explainer from Kieran Healy on UpSet plots

• How to create an UpSet plot in Tableau

• The R Shiny app for UpSetR

#SWDchallenge: create a clever combo chart

When I hear the term combination chart, I think of dual-axis charts with a mix of bars and lines that overlap. Personally, I find these creations cluttered, confusing, and unnecessarily complex. 

However, that’s not the only kind of combination chart out there. A more accurate definition of the term might be, “a mashup of two or more graphs with different encodings (length, area, position, intensity, text, etc.) in a single chart.” The charts—or underlying datasets, I should say—often share a common dimension like time or categories, which allows them to be shown in a single view. 

With this more exhaustive definition in mind, a combo chart could pair up bars and tables, areas and lines, lines and Gantt charts…the possibilities are endless. The best part is that nowhere in this definition are two vertical axes required!

If we use this more inclusive definition of combo charts, then we’d have to say that these kinds of visuals are commonplace at storytelling with data (shown above). When executed well, combo charts can be clever and insightful—providing a more robust understanding than a graph with a single dimension. On the other hand, when not done well, they are subject to all of the negative descriptions mentioned above.

This month, your task is to redefine combo charts by thoughtfully merging two or more charts into one.

The challenge

Find some data of interest that will lend itself well to this task, and cleverly construct your combination chart—any two graphical encodings will work. If you need help getting started, check out this list of publicly available data sources. You’re also welcome to use a real work example if you can anonymize appropriately.

Share your creation in the SWD community by November 30th at 5PM PT. If there is any specific feedback or input that you would find helpful, include that detail in your commentary. Take some time also to browse others’ submissions, and share your input via comments and datapoints over the course of the month.

Related resources

Here are a few related resources. If you are aware of other good ones, please share in your submission commentary.

• A line + area graph combination

• A diverging bar + data table combination

• A line + Gantt chart combination

• A matrix + map combination

• A histogram + data table combination
  

#SWDchallenge: annotate it!

Words play an important role when communicating with data because they help our graphs make sense to your audience. One place we can incorporate meaningful text is in the form of chart annotations that highlight key data points, explain trends, or add clarity to complex information. When we don’t use annotations, it often requires our audience to do unnecessary work to understand our graphs. 

Here’s an example from storytelling with data: a guide for business professionals. Note how the annotation makes the data more accessible in the graph on the right compared to the one on the left.

You can annotate almost any type of chart by adding words next to the data of interest. For example, words next to the 2021 bar in the graph below tell us that major and violent crime has increased by 7.5% since 2020.

When you have multiple data points you want to highlight, you can apply color to the text callouts to visually link the words and the data, like in the scatterplot below.

This month we challenge you to practice thoughtful application of annotations to a chart of your choosing.

The challenge

Find some data of interest, visualize the information in whatever way you think would be the most effective, and include annotations to highlight noteworthy details.

If you need help finding a data source, check out this list of publicly available data for inspiration. Real-world work examples are also acceptable as long as you anonymize appropriately.

Share your creation in the SWD Community by Tuesday, October 31, 2023 at 5PM PT. If there is any specific feedback or input that you would find helpful, include that detail in your commentary. Take some time to browse others’ submissions, too, and share your input via comments and datapoints over the course of the month.

Related resources

Below are some resources to help you annotate your graph and a few examples for inspiration. If you are aware of other good ones, please share them in your submission commentary.

• 3 tips to annotate your graph

• 88 annotated line graphs

• an annotated area graph

• peak break-up times on Facebook

#SWDchallenge: makeover magic

“That’s not the best graph for this data.” 

“This is confusing.”

“I’d change the colors.”

“Toss it and start over!”

Have you ever found yourself reacting with any of the preceding or similar sentiments when faced with a graph or slide? Perhaps you’ve even thought to yourself, “I could do this better!” This month, I present you with exactly this opportunity.

Before sharing the example, there is one important point I would be remiss not to mention. When we critique and makeover graphs in the wild, we typically lack insight into the context and constraints faced by the original designer. For this exercise, I release you from these considerations—in fact, I welcome you to make assumptions liberally for the purpose of your makeover (please outline those you do make in your commentary). Outside of this exercise, however, please bear this facet of data visualization critique in mind and frame your approach accordingly (for more on this and related musings, have a listen to our very first podcast episode, the art of feedback).

The challenge

The remit this month is a simple one: consider the following data visualization and how you can make it better. Specifically, you might identify clutter to eliminate, reflect on an appropriate chart type, determine a specific takeaway to highlight, and perhaps even use this data as the basis of a more robust story. Show us all the magic of a great makeover!

Download the graph and data.

Share your creation in the SWD community by September 30th at 5PM ET. If there is specific feedback or input that you would find helpful, include that detail in your commentary. Please take some time also to browse others’ submissions, and share your input via comments and datapoints over the course of the month.

Related resources

Here are a few of our related resources. If you are aware of other great ones, please share in your submission commentary.

• Watch SWD makeovers (YouTube playlist)

• Read SWD makeovers (many more on our blog!)

• Recognize this example? It’s a modified version of Let’s Practice! Exercise 8.6; you’ll find a plethora of makeover examples in the practice with cole sections of the book

#SWDchallenge: what does your audience know?

It's no surprise that we have to know our audience in order to communicate effectively with our data. Unless we consider who they are, what they care about, and how they prefer to consume information, we'll be unable to tailor our messaging so that it connects and resonates as best as is possible.

Sometimes we think about basing our messaging around whether our audience is “knowledgeable” or not. After all, the audience’s familiarity and expertise levels would drive, for instance, how much background information we would feel compelled to provide, or how technical we would get with the detail we include in our charts. We might worry that we're wasting people's time if they already know the basics of the topic under discussion. By the same token, we wouldn’t want to barge forward with technical details and dense jargon in a presentation to an audience that could use a refresher or a broader overview.

We don’t always think to consider whether our audience is "knowledgeable" in a data literacy sense. We are all multifaceted humans, and we are all familiar with different things. And if we are considering the knowledgeability of our audience, we need to include more than just “familiarity with the data” or “works in the subject area” as our criteria for assessing knowledgeability. We should consider whether a chart type might be seen as brand new or old hat.

Sometimes you will have an audience that isn’t familiar with the subject, but is very comfortable with data and complex visualizations. Sometimes you’ll have audiences that are industry leaders in their particular fields, but are unfamiliar with charts beyond the most basic forms.

I’ve struggled with this distinction in my own work. I've spent a lot of time collaborating with people in the data visualization space, so my idea of “what chart is easy to understand?” can be skewed compared to the broader business world. So at times, even when I correctly assessed and tailored my work to the specific subject matter expertise of my audience, the graphs I delivered didn't resonate, because they were too unfamiliar.

For this challenge, consider two different extremes of “knowledgeable.” Using any data set you like, find a message that you’d use a data visualization to convey. (For example, if you wanted to build a visualization about, say, Sanrio characters or Minecraft, kids may be subject matter experts but would be unfamiliar with a lot of graph types, whereas adults might know very little about the topic area but could be educated via more complex visuals.) 

Create two visualizations that get that message across, one for each of two different audiences: 

• For version 1, imagine that your audience really knows the subject matter, but is used to seeing data in tables and is not particularly familiar with data visualization.  

• For version 2, imagine that your audience knows next to nothing about the subject you’re visualizing, but would be comfortable with whatever esoteric chart you could throw at them.

Share your creations in the SWD Community by August 31, 2023 at 5PM PT. If there is any specific feedback or input that you would find helpful, include that detail in your commentary. Take some time to browse others’ submissions, too, and share your input via comments and datapoints over the course of the month.