There’s a misconception that accessibility takes all day, that’s it’s costly, or that it’s complicated. Those are all false.

Accessibility is woven into all my trainings, but since this is a topic I get asked about a lot, I decided to make a new talk that’s focused just on accessibility for dataviz.

In Spring 2021 I gave a talk at the Good Tech Fest conference about dataviz accessibility quick wins.

The talk was a “Choose Your Own Adventure” style where the audience chose what we discussed from a list of options. They chose:

• direct labels,
• lower the reading level, and
• lower the numeracy level.

You can watch the recording or read the highlights. Enjoy!

—–

Watch the Conversation

Here’s the main takeaway message: remove legends and directly label instead.

You probably know what a legend is, but direct labeling? What is that?

Let’s look at an example of a regular (inaccessible) graph.

Why Traditional Legends Don’t Work

When I saw this graph a few years ago, I actually liked most aspects of it.

I really liked the title in particular, and how it was phrased as a question, which gets the audience to engage. Two thumbs up to the title, “What happened to women in computer science?”

Legends Take Too Long to Read

But then I kept reading a little bit and I was like, “Wait a second… Time out.”

In full color I could mostly tell which section of the legend corresponded with which line. The turquoise lines were tricky because it’s hard figure out which is dark, which is medium, and which is lightest. Your eyes zig–zag back and forth trying to differentiate between the three. It’s really time-consuming.

Legends Don’t Work for Grayscale Printing

So it works in color, kind of, but what about grayscale printing?

Some people will view our graphs on-screen. Others will print them.

And if they’re printing the graphs, we should plan for grayscale printing. Colored ink is so expensive.

It doesn’t work at all.

Legends Don’t Work for People with Color Vision Deficiencies

What about color blindness?

If somebody has a color vision deficiency and can’t differentiate between red and green, the lines would all look yellow.

Traditional legends don’t work; they’re a thing of the past.

So what to do instead?

Directly Label the Graphs

We’re going to directly label our graphs.

What does that mean?

Direct labeling means you put the labels as close as physically possible to the data.

In this line chart, you’d just add the labels off to the side of the line.

Direct labels are:

• Faster for everyone to read (less eye zig-zagging)
• Grayscale-friendly
• Colorblind-friendly

A win-win-win!

Bonus points if you color-code the text to match the line it is labeling. (Red text for a red line, turquoise text for a turquoise line, and so on.)

How to Label Pie Charts

We’ve looked at line charts.

So, how do we label a pie chart?

Friendly reminder: Pie charts aren’t evil. They can be used as long as you follow the rule of two: you’re only allowed two slices in your pie. Maaaaybe three. The dark slice will be what you want the viewers to really look at, versus everything else in gray. Simple, right?

But we still need to directly label them, and it’s as easy as putting the labels as close as physically possible to their slices.

For example, if you have short labels, you can place the labels on top of the pie slices.

Now it’s speedier for people to read, it’s legible in grayscale, and it’s even legible for people with color vision deficiencies.

A question I get a lot is, “But if I have really long labels?” I know most of us aren’t comparing A to B.

If you have long labels, you can put your labels outside of the pie charts.

Bonus points again if you color-code the labels to the corresponding slices.

How to Label Donut Charts

Here’s another scenario for you with donuts. You’ve seen these, right? They’re just a pie chart with a hole punched in the middle.

They have the same rules as pie charts: two slices (max), with one dark slice versus everything else.

But, it’s really hard to fit any labels on top of donut segments. So how do you label these?

You have three options:

1. Outside of the donut segments
2. Inside the donut itself
3. Beside the donut

How to Label Bar Charts

Have you ever seen this, where Excel gives a legend that reads something like ‘Series1’?

This is confusing for viewers. To fix it, all you need to do is delete the legend.

How to Label Clustered Bar Charts

If your bars are long enough, you can place the labels on top of the bars, like this.

No need to label every single bar. Teach the viewers how to read the chart by labeling the top bars. Then, let them read the rest on their own.

During the Good Test Fest talk, an audience member asked how I added those labels.

You can:

• Add text boxes on top of the bars (beware: clunky and time-consuming)
• Use fancier automation techniques (e.g., concatenating the words and numbers together, a technique from this course)

How to Label Clustered Column Charts

I’m not a fan of putting the labels on the columns. The labels would need to be rotated vertically, which takes longer to read than horizontal labels.  

I typically use horizontal clustered bar charts to allow for horizontal labels, which are the fastest to read.

Download the eBook

Want to learn more about accessible data visualization?

In this ebook, you’ll learn 10 quick wins for designing accessible data visualizations. These small edits can have a big impact for our coworkers, board members, and funders who have color vision deficiencies, hearing loss, or learning disabilities–and for all of us who are pressed for time.

For your complimentary copy, use code: goodtechfest

The post Accessibility Quick Wins: Remove Legends and Directly Label appeared first on Depict Data Studio.

If you’ve read this blog before, or heard me speak, then you know that designing data visualization makeovers is one of my favorite activities of all time.

I love redesigning pie charts, in particular.

The vast majority of data visualization trainings just advise people to stop using pie charts… without teaching people what to do instead.

Every year, I work with dozens of organizations, and every single organization still has pie charts sprinkled throughout their reports, slideshows, dashboards, and infographics.

Hearing that you’re supposed to avoid 3D exploding pie charts with a hundred tiny slices is beginner-level stuff. I need to train you on useful alternatives–that’s the advanced-level stuff.

I created this before/after pie chart makeover a million years ago but forced myself to wait until March 14 to share it. Phew! It’s been a long wait.

Before: Two Pie Charts

I recently worked with a grantmaking organization. They awarded grants to support various research projects. For anonymity, I’ve changed the names of the research projects to A, B, C, etc.

The grantmaking organization simply wanted to look for patterns in their funding over time.

Their “before” version looked like this:

Two Slices Only

A major guideline for pie charts is that they’re easiest to read with only two slices.

At the very least, we’d need to collapse the seven slices into just two slices. For example, you may choose to focus on research topic A with a dark-light contrast.

Dark-light contrast is helpful, but it’s not enough. We have to keep editing.

Don’t Make Viewers Zig-Zag Their Eyes Across Different Graphs

Another guideline—for all charts, not just pies—is that you don’t want to make viewers make comparisons across multiple charts.

If you want them to make comparisons, then put those things next to each other, not several inches apart from each other.

Here’s how my eyes have to zig-zag back and forth to read the original:

The collapsed, two-slice version still requires zig-zagging eye movements:

Let’s make the comparisons faster and easier.

The grantmaking team and I put our heads together. We came up with a few alternatives.

After: Stacked Columns

Being researchy types, the first alternative we came up with was stacked columns. Stacked columns are just rectangular versions of pie charts.

There’s nothing inherently wrong with stacked columns. But, yikes! This alternative felt way too busy. There are seven segments in each column, which is too many…

… unless…

…. you use dark-light contrast to focus your viewer’s attention one just one segment at a time.

After: Slope Graphs

Since we’re comparing patterns over time, how about a slope graph?

A slope graph is a fancy name for a line graph that has exactly two points in time.

Spaghetti Slope

Here’s what we tried:

Yikes! We had good intentions, but we accidentally created a spaghetti slope graph.

When this happens, don’t fret. It’s not your fault. It just means your particular dataset had percentages that criss-crossed and overlapped too much.

Spaghetti Slope with Highlighting

There are a couple ways to detangle spaghetti graphs.

One option is to guide viewers’ eyes to just one thing at a time with dark-light contrast:

Which line(s) will you highlight?

Use your best professional judgment.

Think about your unique audience. You might highlight something that increased, like B. Or you might highlight something that decreased, like D. Or, you might highlight something that remained steady over time—perhaps that thing was supposed to go up or down, but didn’t, and you’ve got an interesting story to discuss.

Small Multiples Slope

Another way to fix a spaghetti graph is with a small multiples layout.

Small multiples means multiple small charts.

You could produce seven mini charts, one for each of the seven research topics.

But better yet, let’s group them into categories that will give our viewers more insights into the patterns—the fact that some research topics received more funding while other topics received less funding.

I color-coded by category (one color for increases, another color for decreases).

Finally, I added icons to boost memorability.

This one’s my favorite. Swoon.

After: Dot Plot with Arrows

I intentionally sorted the categories into those that increased and those that decreased. It’s kind of like a small multiples dot plot.

I also included arrows, instead of just regular ol’ circles or dots, to reinforce the direction of the changes.

I color-coded by category (increases in one color, decreases in another color).

Finally, I added icons to boost memorability.

Join the Conversation

Team, you know the drill! Comment and let me know which alternative is your favorite and why. I’m personally drawn to the small multiples slope graph.

Bonus! Download the Materials

Purchase the spreadsheet that contains these graphs.

Purchase the Materials

The post Stop Making People Read Across Multiple Pie Charts (& Here’s What to Do Instead) appeared first on Depict Data Studio.

I recently partnered with a school system to visualize data about the percentage of their students who quality for free or reduced-price meals. In the United States, Free and Reduced Meals (a.k.a. FARMs) is basically how we measure poverty in schools. The more students who are eligible for free lunch (based on their family’s income), the more students who are low-income.

Before

Here’s the before version.

They wanted to compare one of the schools to the district as a whole.

The nested design almost made sense because it almost easily allows us to compare the school and district.

But the concentric circles throw off the proportions. Do you see the magenta segments? The outer ring is larger than the inner ring, so the outer five percent is incorrectly larger than the inner five percent. Oops!

After

Let’s look at three possible ways to better tell our story.

Stacked Columns

Here’s my first attempt. At the very least, we need to transform our circles into rectangles.

For all of these makeovers, I selected two colors: one color for the district and another color for the school. I also made the District and School text stand out in a large font size. You would use your own branded colors and fonts, not mine.

My Former Researcher Brain loves stacked charts. Researchy types like me tend to default to this layout. I’m not sure why… just because we’re used to stacked charts?

There’s nothing inherently wrong with stacked charts. They just tend to be overused. Even though I design data visualizations for a living, I still can’t shake my Former Researcher tendencies, and I tend to draft reports with way too many bar charts. As I’m editing my own work, I look for places where I can swap out my overused bar chart for another chart type. I don’t include variety just for variety’s sake. I look for places where the chart’s messaging might be clearer through another chart type.

Waffle Charts

Another option is a waffle chart, like these two waffles. Waffles contain 100 little squares. They’re like square pie charts.

This approach almost works. I like it, but I don’t love it. I’m not in love with the labels. I tried creating text boxes for each of the segments—e.g., 27% free meals or 5% reduced meals—but it was impossible to arrange the text boxes on top of the waffle without looking cluttered or busy. Then, I moved the text boxes to the top of the graph, where they are now… but the viewers have to zig-zag their eyes back and forth between the words and the squares. Good. But not great.

Icon Arrays

Or, go big-picture with icon arrays. I love this approach, and the school district did, too.

In this icon array, we intentionally collapsed the categories—we combined free and reduced into a single category—to focus on big patterns so that our audience wouldn’t get lost in the weeds.

We also intentionally transformed the percentages into whole numbers, which is a better approach for non-data people (i.e., regular people who don’t stare at spreadsheets all day). Whole numbers are a lower numeracy level than percentages, so more people will understand the information.

And we intentionally focused on small numbers—3 in 10 students instead of 30 in 100 students—so that the numbers feel more tangible.

Want to try icon arrays yourself? A heads up: You must be comfortable with rounding. Thirty-two percent of students in the district qualified for free or reduced meals, but I rounded that 32 down to 30. I don’t suggest coloring-in part of an icon. Partially-colored icons can get confusing, especially if you’re using those little people icons.

Icon arrays shine when you need an at-a-glance overview. For example, you might use icon arrays for slideshows or executive summaries. You can save the nuanced stacked columns or waffles for the body of the report.

Your Turn

This isn’t an exhaustive list of options.

What additional designs can you come up with?

Bonus: Download the Materials

Want to explore how I made these visuals? Download the slides and use them however you’d like.

Download the Slides

The post Students Receiving Free and Reduced Meals: From Nested Donuts to an Icon Array appeared first on Depict Data Studio.

It’s intimidating to sit in front of a blank screen and try to start developing your report, infographic, or dashboard from scratch. What data sources will you include? How will you analyze the data? What type of graph will best present your results?

The NHVRC provides hundreds of pages of data about home visiting across the United States. Our team works together behind the scenes to create and finesse each visualization. We want to make sure the numbers are accurate, easy to understand, and visually appealing.

As we tweak the design elements of the 2018 Home Visiting Yearbook—the NHVRC’s third major publication since launching in 2017—we’ve revisited some of our earliest conversations around data visualization. This brief presents our process in five simple steps, along with images of our products at various phases. We hope this inside look can help guide readers looking to share their own home visiting data.

Step 1. Select Your Data Sources

Will you use publicly available datasets? Will you need to collect new data? Which specific variables are available? Sit down with your teammates and select data sources together, just like the NHVRC did during one of our earliest brainstorming sessions. Your process doesn’t have to be fancy. Handwritten notes and whiteboards work just fine.

Step 2. Choose Your Unit of Analysis

Next, think about how you’ll analyze your numbers. For the NHVRC, we decided to provide both national- and state-level analysis of the numbers. The 2017 Home Visiting Yearbook and follow-up Data Supplement) present a map of the United States, so that readers can see where home visiting programs operate across the country. Later, we provide individual state profiles, so that readers can learn about the children and families who benefit from home visiting in particular states and territories. We also decided to provide model-level details in a series of model profiles.

Step 3. Brainstorm Your Page Layout

It’s one thing to design a single graph. It’s another to design an entire page or an entire report! The graphs need a logical, intentional flow from one to the next. Start broad and then go deeper. For the NHVRC State Profiles, we started with the broadest details, like the name of each state and the number of children reached by home visiting programs. Then, we dove into demographic details about those children and their caregivers.

Step 4. Choose the Right Chart for Each Variable

Will you use a pie chart, a bar chart, or something else altogether? Here are a few familiar chart types and some that you may not have encountered before.

Pies and Donuts

Let’s start with the most familiar chart of all, the pie chart. Some data visualization trainers advise against using pie charts at all. That’s probably because so many of us accidentally include too many slices, like this impossible-to-read pie. Here’s what not to do.

Two-slice pies are fine. You can also try an alternative, like a two-slice donut, two-slice waffle, or two-slice icon array. Icon arrays contain miniature shapes, like the miniature coffee cups below.

Bar Charts

Bar charts are another familiar chart type. My advice—if you’re not sure which chart to use, start with a bar chart instead of a pie chart. Bars are often easier to read than pies, especially when you’ve got more than two categories.

The only problem with bar charts is that they tend to be overused. Try an alternative, like a lollipop chart, which places a dot at the end of the bar. Lollipops tend to look cleaner than bars, and they focus viewers’ attention where you want it—on the endpoints.

If you decide to continue using bar charts, consider adding valuable context. Does your organization track your progress toward meeting year-end goals or specific outcomes outlined in grants or contracts? If so, add a target line or use overlapping bars to showcase your achievements.

Line Charts

Need to show how something has unfolded over time? Try a line chart to let your viewers see all the peaks and valleys. Want to focus on the big picture? Try a slope chart, in which viewers focus on the steepness of the slope over time.

When you’re using line charts, beware of the spaghetti line! Spaghetti lines crisscross and intersect—just like a pile of tangled spaghetti—making them near-impossible to decipher. If you see a spaghetti line chart in your project, don’t worry. You can try making it easier to read by highlighting a single line of interest in a darker color or testing a small multiples layout in which you build multiple small charts, one per line.

Step 5. Draft Your Visualizations with Sticky Notes

You may be tempted to sit down at your computer and begin creating charts right away. I’ve found it helpful to create drafts with good old paper and pencil before creating anything on the computer. I also like using sticky notes as I’m drafting because they allow me to move graphs around the page and get the placement just right. Your sticky notes might contain some of the pies charts, bar charts, or line charts shown previously.

While drafting the NHVRC State Profiles, we drew one graph covering one variable per sticky note. Then, we arranged and rearranged the sticky notes. As a team, we talked about which variables should come first, second, and third on each page. When we reached consensus, we started building the graphs on the computer. Drafting on paper saved us a lot of time and helped us develop a final product that reflected everyone’s input.

We hope this behind-the-scenes peek into our data visualization process and early images can help you during your own drafting process. Click through the NHVRC website to see our results so far and feel free to send us your data masterpieces!

The post Five Simple Steps to Creating Home Visiting DataViz appeared first on Depict Data Studio.

Before

Here’s a screenshot from one group’s (public-facing) research report. The authors of the report talked about wheelchairs that were given to people in other countries. This was the demographic section of the report so there were a few graphs showing who those wheelchairs went to — men, women, kids, adults, and so on. There are 3D pie charts but I don’t fault anyone for their before graphs. We’ve all been there.

After

The first pie chart was about gender–the proportion of wheelchairs distributed to men and women. According to my seven pie chart guidelines, gender can stay in a pie chart. Gender is a nominal/categorical variable and there are only a couple slices. We just need to reformat things a bit to make the graph easier to read. The before version is 3D, so the chart’s height makes the slices look larger or smaller than they really are. 3D distorts data.

There’s a legend below the chart so viewers would have to zig-zag their eyes back and forth to figure out which color corresponds to which slice. And the viewers would literally be zig-zagging because men are on the left side of the pie and on the right side of the legend, a software oddity. The after version is 2D; has labels directly on top of the slices to avoid wasting time hunting for information in the legend; and it uses colors from the organization’s logo rather than the software program’s default color palette.

The second pie chart was about age ranges–the proportion of wheelchairs given out to younger people and older people. According to my seven pie chart guidelines, age ranges can’t stay in a pie chart because age range is an ordinal variable. There’s a natural order or progression from younger people over to older people, so we need the chart to reflect that built-in characteristic. You could display these age ranges in a stacked bar chart or in a histogram. I went with a histogram because the 0-5 years segment was tiny and nearly invisible in a stacked bar chart. It’s faster to read left to right (the histogram) than to start at 12 o’clock and read clockwise (the pie).

I never cease to be amazed how small edits lead to a big impact. This page of the report looks completely different!

No, I didn’t keep the tables. When we see tables and graphs beside each other, our brains wonder whether the table matches the graph. Is this the same thing? Or different? Wait, it’s the same, right? So why did they include both? Oh, for the raw numbers? The graph has the percentages but not the numbers? That’s the only added value of the table? Redundant tables and graphs are unnecessarily burdensome for viewers. My rule of thumb is to display raw numbers for anything below 100 (3 of 7 people, not 43%) and percentages for anything above 100. We’re talking about well over 100 units here (either 886 or 866 wheelchairs, what an unfortunate typo) so we could’ve just displayed percentages in the pie chart and left off the raw numbers altogether.

However, this report was written for a technical audience, and technical audiences love extra details like numbers and percentages, so I simply included both within the after version.

Sometimes workshop attendees are afraid that including graphs will lengthen their reports. On the contrary, data visualization often decreases your report’s length. I freed up space by deleting the redundant tables. I decided to use that space for titles and subtitles to explain each graph. Before, the graphs were just slapped into the report without any explanatory text. I’m a visual person and prefer to read graphs over paragraphs. Other people prefer to read the paragraphs over graphs. Both viewers’ preferences are met when we add explanatory text alongside graphs.

The post Pie Chart Makeover: Transforming a Research Report appeared first on Depict Data Studio.

Let’s walk through a real-life example!

A few weeks ago I was in Indianapolis with the juvenile detention alternatives initiative. This group wanted to graph:

• how many youth went to juvenile detention centers,
• by gender, and
• by quarter.

Brainstorming

There are a dozen ways to graph each dataset. For this data, we could utilize:

1. table
2. heat table
3. quarter-by-quarter pies (well, not the best choice)
4. one year-end pie
5. line graph with numbers
6. small multiples line graphs with numbers
7. clustered bar
8. small multiples bar
9. stacked bars with numbers
10. stacked bars with percentages
11. stacked area with numbers
12. stacked area with percents
13. and probably a few more that I haven’t thought of yet

Before: Quarter-by-Quarter Pies

Naturally, the quarter-by-quarter-no-way-these-don’t-work-pies were currently being used by a few people in the room. I adjusted the wording from youth going to detention centers to youth being served so that this example is relevant to even more of you.

The quarter-by-quarter pies are off the table. According to my pie chart guidelines, gender breakdowns are one of the only times it’s okay to use a pie chart (nominal variable, just a few slices, etc.).

But it’s really hard to make comparisons across multiple pies. Your eyes would have to jump from the 1st quarter pie to the 2nd quarter pie to the 3rd quarter pie to the 4th quarter pie and back again. I’m tired just thinking about it.

After

After we sketched ideas and discussed the pros and cons of each approach, we decided that stacked bars with numbers were the most promising. Stacked bars can display the number of males, females, and the totals. They’re also easy to label (compared to, say, a stacked area chart). We used vertical bars because time is ordinal.

My rule of thumb is to display numbers for anything less than 100 and percentages for anything over 100. Fewer than 100 youth went to these detention centers each quarter, so we displayed numbers.

For a Slideshow: Stacked Columns with a Short Title

Here’s the after version for a slideshow:

For a Report: Stacked Columns with a Subtitle

Here’s the after version for a handout or report, which intentionally contains a bit more explanatory text.

Bonus: Make This Yourself

No, I didn’t add the totals to each quarter’s bar chart with text boxes. Yes, that’s possible. It’s also a ton of work to crunch the numbers, create text boxes, and center them perfectly above each bar. This is a stacked bar chart with three segments: males, females, and totals. The total segment has a transparent fill so it looks invisible.

If you want to explore the strategy in more detail, watch the video or download the template.

Bonus: Download the Templates

Purchase the templates ($5)

The post Stop Putting Quarterly Trends in Pie Charts appeared first on Depict Data Studio.