#SWDchallenge: stack it up!

September 02, 2024 by Amy Esselman in Data Viz Challenge, SWDchallenge, Bar chart

This month, we invite you to explore the power of a stacked graph. Your mission is to find a scenario where a stacked graph shines as a visualization that makes the point of your communication unmistakable.

Both stacked bar charts and area graphs (which are essentially stacked line graphs) can represent data in either absolute values or percentages. Each of these graph types has its strengths and challenges, demanding thoughtful design to convey your insights effectively.

Stacked charts can be powerful visuals in a variety of scenarios, like showing opportunity or highlighting a change in relative proportions. They are particularly effective when you need to:

show how individual components contribute to a total,
compare the parts of multiple totals, or
illustrate changes in composition over time.

Regardless of the situation and your choice of stacked graph, you’ll want to apply thoughtful design considerations when using them:

Limit the number of categories. Too many categories can make the chart cluttered and difficult to read. Aim for simplicity.
Be intentional with your sort order. Place the most important category at the baseline to make it easier to compare across groups.
Apply color strategically. Rather than using color simply to make categorical distinctions, employ it to highlight key insights and ensure that your chart is accessible to all viewers.
Use your words. Add annotations and labels to guide your audience through the data and highlight the most important points.

The challenge

Identify a dataset or scenario where a stacked chart would be appropriate, and design one that clearly communicates an intended message. As you build your graph, keep in mind the design considerations mentioned above.

Share your creation in the SWD community by September 30 at 5PM PT. Provide a brief explanation of why you chose a stacked chart and how your design choices help to make the data story clear. If there is any specific feedback or input that you would find helpful, also include that detail in your commentary. Take some time to browse others’ submissions, and share your input via comments and datapoints.

Related resources

In addition to the examples linked above, explore these related articles:

#SWDchallenge: visualize the Olympics

August 01, 2024 by Mike Cisneros

"We're going to France! Parlez-vous Olympics?"

D.B. Sweeney's classic line, from the less-than-classic 1992 movie The Cutting Edge, revealed that the film's two mismatched protagonists—a petulant figure skater (Moira Kelly) and a former college hockey player (Sweeney)—were indeed getting the chance to compete in pairs skating at the Winter Olympics.

“It’s a love/skate relationship,” as the movie’s tagline would have had us believe.

The season is different, but Sweeney's quotation aptly describes the venue and the event we'll focus on for our monthly challenge. Once again, the world's sporting attentions turn to the Olympic Games in Paris, and we'll be watching along for the pageantry, competition, and athletic excellence on display.

We'll also pay close attention to the broadcasts. As dataviz enthusiasts, we can't help but think about how networks show data in real-time, what information they share with the home viewer that provides critical context, and how to cater visual displays to audiences with widely varying levels of knowledge, interest, and understanding.

Inspired by all of this data and information, let’s make sense of it by visualizing the Olympics!

The challenge

This month, we challenge you to visualize any subset of data about the Olympics that you find interesting. The definition of "about the Olympics" here is extremely broad:

you could focus on one competitor, one event, one specific instance of a race, or one country's history at the Games;
you could look into attendance, geographic distribution of host cities, nations who have participated and in what volumes;
if you can find data on Opening or Closing Ceremonies, the events that have come and gone (did you know breakdancing is in the Olympics this year?), or the people who have lit the Olympic torch...go for it!

The important thing is that you choose an Olympics-related topic that interests you, and that your final submitted challenge entry is presented in a way that you believe would interest others.

Share your creation in the SWD community by August 31st at 5PM PT. If you would find any specific feedback or input helpful, include that detail in your commentary. Also, take some time to browse others' submissions and share your input via comments and datapoints throughout the month.

Related resources

There are lots of resources out there with data you could use, including:

Olympic.org: Olympic history, athletes, results, and records; includes past and current games, athlete biographies, and country performance

International Olympic Committee (IOC): Detailed reports, statistics, and official documents; includes data on hosting cities, participating countries, and sports; the factsheets page seems interesting

Statista : Statistics and facts on various aspects of the Olympic Games, including economic impact, TV viewership, and social media engagement (some data requires a subscription)

World Athletics (formerly the IAAF): Athlete profiles, event results, and statistics on track and field events, specifically

Olympedia: A wiki-style collection of historical data from all modern Olympic Games

US Olympic and Paralympic Committee: This is the United States's national Olympic Committee website; many countries maintain their own similar site with information on their specific nation's historical participation and performance at the Games

If you are aware of other good resources, please share in your submission commentary.

#SWDchallenge: create a clever combo chart

November 01, 2023 by Alex Velez in Data Viz Challenge, SWDchallenge

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.

#SWDchallenge: annotate it!

October 02, 2023 by Amy Esselman in Data Viz Challenge, SWDchallenge

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.

#SWDchallenge: makeover magic

September 01, 2023 by Cole Nussbaumer Knaflic in SWDchallenge, Data Viz Challenge

“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: making or missing the mark?

May 01, 2023 by Amy Esselman in SWDchallenge

Businesses often set targets to measure progress toward their desired results, such as setting a minimum customer satisfaction level or a monthly sales quota. Goals are also commonplace in personal life, like aiming to save money or improving health measurements. Keeping track of how you’re doing compared to a target can provide positive reinforcement (when the results are good) or highlight where changes may be needed (when the results leave something to be desired).

There are many ways to visualize data with an eye toward emphasizing performance against a projected target. Common chart types like pairwise bars or time series data in a line chart could work. Other options may involve more novel approaches, like a bullet graph, a slopegraph, or even simple text.

For this month’s #SWDChallenge, we want you to show us what is making or missing the mark by graphing actual data against a projected goal or forecasted metric.

CHALLENGE

Find some data of interest that includes historical projections and actual performance, then visualize the information in whatever way you think would be the most effective. We encourage you to employ thoughtful use of color and words–including a takeaway title–to make it clear whether the desired goal was obtained.

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

Share your creation in the SWD Community by May 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

A few suggestions for comparing actual data against a target are mentioned above, and below are some additional related resources. If you are aware of other good ones, please share them in your submission commentary.