Maybe you’re already using them…

Maybe you can make ’em in your sleep…

Or maybe you have no idea what I’m even talking about! (If that’s the case, stick around! This video is for you.)

You’ll learn about the differences between clustered bar charts and dot plots. Then, you’ll see a real-life example so you can start thinking about how you’d apply these in your own workplace.

What’s Inside

• 0:00 Intro
• 0:27 Dataviz On The Go
• 0:39 Obvious Differences (Bars vs Dots)
• 0:55 Subtle Differences (Length vs Endpoints & Distance Between Dots)
• 1:35 Dot Plots Avoid the “Diagonally-Down” Comparisons
• 1:57 Real-Life Example: School Climate Survey
• 2:59 Big A Accessibility (508/ADA compliance)
• 3:22 little a accessibility (intuitive)
• 4:22 Alternative Design: Dumbbell Dot Plots
• 4:48 Portrait vs Landscape Considerations
• 5:29 Your Turn

Download the Spreadsheet

It’s here: https://depictdatastudio.ck.page/clusteredbarsvsdumbbelldots

Transcript

Ann K. Emery: [00:00:00] Maybe you’re already familiar with dot plots. Maybe you’re already using them. Maybe you can make them in your sleep.

Or maybe you have no idea what I’m even talking about! If that’s the case, stick around. This video is for you.

You’re going to learn about the differences between clustered bar charts and dot plots.

And then I’ll show you a real life example. So you can start thinking about how you might apply this chart type to your own workplace.

I’m Ann Emery. You’re watching Dataviz on the Go, the series where I make quick tutorials as I’m racing around between workshops and webinars and conferences and consulting projects.

And speaking of consulting projects, I was recently in a meeting with a client and they were asking about these chart types, clustered bar charts and dumbbell dot plots.

And right away when you look at them, they’re obviously different! One’s got bars, one’s got dots, but there is a slight, smaller nuance that I want to draw your attention to here. [00:01:00] And that’s related to attention!

It’s related to where our eyes and brains, and therefore where our precious attention, goes when we look at each of these graphs.

So when you look at a bar chart, your eyes are going to look at obviously the end point, right? That’s the really juicy part of the bar chart.

With dot plots, they don’t waste any time. They cut right to the chase. I love them for their brevity and they just plot the end point. Okay, they don’t waste our ink and waste our time with all this, all this unnecessary ink.

Another slight difference is when you want to compare the end points, you have to do a little bit of a diagonally down movement to compare them, where dot plots plot everything on the same plane, so that it’s just a little bit faster, right?

Instead of stacked endpoints, it’s side by side: same line, same plane.

Alright, let’s look at a real life example because [00:02:00] this one with A, B, C, D and group one and group two is obviously super made up!

Let’s go back into the vault in my memory, where about 15 years ago, which is a million years ago, I was working on a lot of school climate surveys.

I did a lot of consulting for the U S Department of Education. I looked at test scores, all sorts of academic performance and school climate surveys, and I don’t remember the exact details of how this was measured, but I do know that we surveyed parents. And school staff, like the teachers and the principals and all the administrative staff. And then we compared how they responded on different measures.

This is the default graph that Excel is going to give you, which of course, if you’ve watched any of my other YouTube tutorials or read any of my blog posts over the years, you know, we can’t keep that. Okay. Let me just let, you know, just make it super duper clear what we’re not doing.

We’re not keeping these default, inaccessible settings. At a bare minimum, we’re going [00:03:00] to add Big A Accessibility –508 compliance and ADA compliance. That’s the usual stuff. That’s like making sure the font is big enough and dark enough, removing the legend and adding the direct labels right here, which is a win for grayscale printing and for custom words. Colorblindness.

And then we might even, I hope, I hope you do this. I hope you keep going with “little a accessibility” edits to make sure that your graph is really intuitive.

That’s going to be things like grouping, right? Finding groups of elements where parents scored the school higher, versus staff.

That’s going to be things like adding annotations, which, guess what? That’s just a good old text box. It’s a call- out box to help people figure out what the patterns are so that they’re not just guessing and searching and hunting for any type of insight.

This one, honestly, when it’s Big A and little a accessible, I’d say [00:04:00] it’s not that bad!

I wouldn’t lose sleep over this.

If you go this far with editing with your clustered bar charts, I’m going to say: virtual high five, leave it alone. You’ve graduated. No need to keep on going with editing unless you want to, unless you’re ready to really keep boosting your skills and try out something that’s a little bit more advanced.

And that advanced, uh, approach would be the dumbbell dot plot. Which as you know, only puts the emphasis on the end point, the juicy important part. And it helps draw your attention with this connecting line, the, the dumbbell part of it to the difference between the staff and the parents- or whatever groups you’re comparing in your project.

Now, you’re going to have to put the annotations on these finished charts in a little bit different spot, depending on whether you’re doing a landscape final project or a portrait final project.

So if this was going to be landscape, you’re going to have [00:05:00] space for the annotations off to the side. If it’s portrait, It’s going to be a lot narrower.

You’re just not going to have the space. So you’re probably going to have to put the call out boxes above each chart, something like this.

Here’s what I mean: landscape versus portrait. You’re just going to have to think very carefully about where everything fits. So it’s not so condensed that people can’t actually notice those important differences between your groups.

It’s your turn. Comment below this video. Let me know, are you using dot plots? For what? You probably aren’t doing school climate surveys. You’re probably using them for something completely different from this. And also let me know what types of how to questions you have. These are possible in good old Excel and PowerPoint and Word, but they require some advanced behind the scenes magic tricks to make them happen, which I am happy to share with you in future [00:06:00] videos.

The post What’s the Difference between Clustered Bars and Dumbbell Dots?? appeared first on Depict Data Studio.

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.

The Survey Questions

Last month I spoke with Harvard University graduate students about visualizing survey results. This is one small portion of their survey.

My First Attempt

I started by visualizing the first question.

• I’m pretending that 30 people completed the survey. I focus on raw numbers of people when I’m dealing with smaller numbers (less than 100) and I convert numbers into percentages when I’m dealing with larger numbers (greater than 100). You’ll notice that the scale along the bottom intentionally goes from 0 to 30 people rather than from 0 to 100 percent.
• Once in a while, when I sense that the audience is really hungry for details, I’ll also include percentages alongside the raw numbers. In Excel, I simply concatenated the values from a few cells together.
• I built stacked bar charts instead of regular bar charts because I wanted to remind viewers that while some people checked the survey boxes (dark blue) others did not (light blue). I only labeled the dark blue bars because that’s the segment that really matters.
• Rather than following the order of questions from the survey (super boring), I listed the items from greatest to least (more useful).
• I outlined the dark and light segments in white to provide just the right amount of distinction between categories. White outlines also make printing look a hair crisper.
• I deleted all the garbage (borders, grid lines, and so on).

I flew through the visualization of the first question and bolted just as quickly into the second question. Within minutes, I had built this one-pager that the researchers could pass around at a meeting.

Oops! My Second Attempt

Then, at the very end, I took a moment to actually read the survey questions. You know, that thing you should probably do first. The categories looked familiar. Too familiar. Eerily familiar. I wondered, Didn’t I already read about ‘industrial and Naval history?’ I think ‘studio art and graphic design’ sounds familiar, too… 

In my zest for building the graphs, I had forgotten to think strategically about the page layout. Would viewers want to read about the survey respondents’ prior academic experience first, and then read about respondents’ prior work experience second? Probably not. Viewers could care less about the order that you asked questions on the survey. There’s always a better way to order your results than by the order in which you asked the questions on the survey. Viewers were probably trying to design museum exhibits by topic–were visitors already familiar with American history?

How? Through their work experience or academic experience? In this second draft, I rearranged the page by topic instead of by survey question.

Oops! My Third Attempt

Then, I took another moment to reflect, and was disgusted by the stacked bar charts I had designed. Clustered bar charts are my least favorite chart of all time because 1) there’s (almost) always a more effective alternative but 2) despite these alternatives they’re used over and over and over and over and over.

In this scenario, dot plots are the better choice because they align the academic experience and work experience numbers on the same plane. If I want viewers to compare academic experience and work experience, then it’s my responsibility to place those numbers as close as physically possible to each other. They also take up less ink and space on the page. It’s cheaper to print little dots than big rectangles. You save viewers’ energy and trees; everybody wins.

Bonus: Download the Materials

If you want to explore how I designed the stacked bar charts, the clustered bar charts, the dot plots, or the handouts, you can purchase the templates here:

Purchase the templates

The post How to Visualize Survey Data with Check-All-That-Apply Questions appeared first on Depict Data Studio.

Most of these charts from my prior work were ineffective. Of course they were. Data visualization is a new skill for most researchers and evaluators.

One of my old charts was so horrific that I just had to share it with you:

What’s so bad? you ask. I see these all the time, you say. Can’t they just tilt their head to read the words, you argue.

What’s Wrong with my Previous Chart

Each of these mistakes–from the before I knew better period of my career, never to be revisited again–kept viewers from understanding the information:

• Generic and centered title (Should have a “so what?” and be left-justified.)
• No subtitle or annotations (So the viewer can’t skim the information.)
• Border (Should be removed.)
• Full grid lines (Should be lightened or removed altogether.)
• Legend (Should have direct labeling instead of the legend to avoid those back-and-forth eye movements.)
• Speaking of direct labeling… why did I label the axes and the bars? Overkill.
• Diagonal text (Should swap this vertical bar chart for a horizontal bar chart, leaving more room for the labels.)
• Default color scheme (Should use the client’s brand colors).
• An action color is there, kind of. Not really. It’s used incorrectly. Our brains are drawn to darker colors. Why was I drawing attention to the “possible items correct” section with the dark blue?
• Most importantly, why did I display the raw scores vs. the percentages? Can you imagine how much mental energy it must’ve taken my viewers to figure out what 2.9 vs. 2.6 vs. 6 means?

Before/After Makeovers

The first phase in learning about data visualization is usually critiquing charts. What, exactly, is wrong with your work or someone else’s? How can you learn from those mistakes?

The second phase is articulating specific ideas for how you’d make charts better.

The third phase is actually remaking your charts.

Some people stay in the first phase forever. My goal is to move more people into the third phase: actually improving your work.

So I’ll lead by example. Here’s how I’d re-do this chart today. I designed five different remakes.

Makeover #1: A Horizontal Clustered Bar Chart

First and foremost, I changed the chart type from a vertical column chart to a horizontal bar chart. Horizontal bar charts are great when the data labels are pretty long, like in this example. Your goal is to avoid diagonal text at all costs. It’s harder to read, so viewers get distracted, bored, or generally turned off and stop trying to decode your messy chart. I’m not a fan of vertical text either. (Not sure how to transform your vertical bar chart into a horizontal bar chart? I’ve got a tutorial.)

Next, I transformed the raw scores (“2.6”) into percentages (“43%”).

Finally, I addressed all the Low Hanging Fruit formatting issues. For example, I added a 10-word title and a 1-sentence caption; I deleted the border, grid lines, and tick marks; I placed the percentages on the bars so I could delete the axis; and I placed the years directly on the bars so I could delete the legend.

Why the black and white? Just for fun, I want to show you that you can still emphasize patterns without using bright, showy colors. In real life, would I match the action color to my client’s RGB codes? Absolutely.

What do you think of remake #1?

Makeover #2: A Clustered Stacked Bar Chart

I’ve experimented plenty with real-life clients to see whether they prefer regular bar charts or stacked bar charts. Here’s the typical response:

• Me: Here’s the first chart. [Regular bar chart.] What’s the message here?
• Client: Oh wow! Our participants are doing so well! 46%! That’s high! … Right? Or is that number low? Out of what? Out of 100%, right? Hmm…
• Me: You’re on the right track. Here’s the second chart. [Stacked bar chart.] What’s the message in this one?
• Client: Oh darn, we’ve got a long way to go. 46 out of 100%?! I need to speak with our program director about this. In fact, our whole team better see this. We need to figure out what we’re doing wrong before it’s too late!

A dozen conversations later, and the result is still the same: Nearly all my clients gain deeper insights about the findings through stacked bar charts instead of regular bar charts. What’s the response in your projects?

Makeover #3: A Small Multiples Bar Chart

The first two remakes are better than the original. That being said… clustered bar charts are my least favorite chart in the history of the world. They’re so cluttered. And worse, the comparisons are lost. With so many bars smushed together, it’s nearly impossible to see at-a-glance patterns between the two series of data.

In this example, I created a side-by-side bar chart so viewers could more easily see 1) the 2009 pattern on its own, 2) the 2010 pattern on its own, and 3) the difference between the two. Still in the bar chart family, but different patterns pop out, don’t you think?

I also used the action color (dark gray) to emphasize the Social and Ethical scores, and I added an annotation (the call-out box on the chart) to make my viewer’s comprehension even easier.

Want to make your own side-by-side bar chart? I’ve got a tutorial.

Makeover #4: A Dot Plot

Dot plots are often the superior chart. I use them to compare two points in time (like this example); two distinct groups (Program A and Program B); or, when I triangulate data, two distinct viewpoints (students’ perspectives vs. teachers’ perspectives).

But they’re not always superior. This dot plot doesn’t work. It’s too cluttered, isn’t it? Its more confusing than helpful. It’s because students improved on some areas, declined on other areas, and didn’t change at all on other areas.

The annotation isn’t helping, either; instead of adding clarity, it adds clutter.

For the rare viewer who’s willing to spend 60+ seconds interpreting the chart, it’s great because it shows more nuanced patterns than the other charts. But my guess is that the majority of viewers will lose interest after a few seconds because they can’t immediately grasp what it means.

Makeover #5: A Slope Graph

A slope chart is basically a line chart for two or three points in time.

This chart type is also effective at showing rankings (i.e., it’s easy to see how the skills are ordered on the left-hand side). 

For me, this chart is the winner. It’s easiest to understand at-a-glance. It ranks each of the skills areas. It shows differences over time. There’s a lot of cool stuff going on for the viewer to explore. It’s supplemented with a non-intimidating title, subtitle, and annotation. Yet, the information takes up very little ink and space.

Your Turn

Which remake would suit your audience best?

The post Avoiding Diagonal Text in Your Charts appeared first on Depict Data Studio.