In this video, you’ll learn:

• how to add checkboxes to your spreadsheet,
• how much better they look than the “old” way of doing it, and
• what types of details we can edit (like the checkbox size and color).

What’s Inside

• 0:00 Welcome
• 0:21 Dataviz On The Go
• 0:32 How to Add Checkboxes to Microsoft Excel
• 0:59 The Old Way: Adding Icons (Image Files) One by One, Not Clickable, Yuck
• 1:36 Another Old Way: Webdings g’s and c’s (Filled Squares and Empty Squares)
• 2:26 Your Editing Power: Delete, Size, Color, Use in Formulas (“true” = filled in, “false” = empty)
• 3:21 Conditional Color (e.g., everything is gray, turns green when filled in)
• 5:34 Colorblind-Friendly & Grayscale-Friendly, hooray!!
• 7:28 DON’T Transfer to Word/PowerPoint, darn. NO GRAINY SCREENSHOTS!!! Keep ’em in Excel and just PDF your Excel file directly.
• 8:32 Don’t Forget to Like, Subscribe, and Share

Resources Mentioned

Colorblindness simulation tool: https://www.color-blindness.com/coblis-color-blindness-simulator/

Transcript

Ann K. Emery: [00:00:00] So today I’m here with Do you want to say your name? This is Isla, and she just got home from kindergarten, and you want to make a video together?

Alright, we’re going to show you how to make checkboxes in Excel. They’re pretty new, so you might not have seen them before. Today is the day.

Isla, should we tell them that they’re watching Dataviz on the Go?

And we make YouTube videos for them? To help them with their job and help them save time. Yeah.

So let me show you how you make these because they’re pretty easy. So what you’re going to do is you click on the empty cell where you want to add them. And then you go to insert and they’re there. They’re right there.

So you click on the checkbox box. It adds an empty one and you just drag them down and then you fill them in. Look, when you click on the square, do you know what happens? You get a little check mark like that.

What I used to do [00:01:00] is you would click on the cell, you go to insert, you go to icons. Do you see the little duck I love right there?

And then you’d have to scroll through the whole menu. Uh, if it loads, right? And do you see all these pictures in here? And you could pick one like this that’s kind of similar, right? That one has a circle. And you could add it and you could, this is a new ish feature too, you could place it in the cell, which is kind of nice.

But then you can’t click on it, you know, you get the same end result, but you don’t have the process, the clickable process in it. So that’s all right. That’s kind of the old way.

Um, what I used to do is, Isla, do you know what letter this is? Do you recognize that one? It makes the guh sound. That’s a “g.” I used to do like this, and then do you know that one?

That’s a “c.” Yeah, we were looking, that makes a kuh sound, right, like cat? We were looking at that in your Bob books last night. Okay, what I used to do is I would do g’s and c’s, uh, G for good, [00:02:00] C for, for crappy. We probably shouldn’t say that word out loud though. Um, and then you change them to webdings, if I can type with one hand, webdings, and you sort of, sort of get the same appearance, right?

Do you see how that’s not quite a checkbox? It’s like a filled in square and an empty square. So it’s. Not, not quite as cute. Yeah, it doesn’t have a check. That’s why I like these. They look like checks.

I also like them because, look, you can delete them if you change your mind. You just hit the delete key on your keyboard.

And you can change the size. You can make them like hugely huge. Whoa, too big. Probably we just want them the same size as the font though.

You can change the color. What color should we pick? Like, purple? Green. Green. We can make them green.

And we can also count them up. How many checks are filled in right here? Two. Yeah, exactly. And let’s say you had a really long list and you didn’t want to count [00:03:00] them all because it would just take forever. You could do this formula. You could do COUNTIF. And you could say, I’m going to count this list and I’m going to count the trues. True means filled in, false means not filled in, right? And then you get two to save yourself a little bit of time, right? And make sure you don’t make typos.

I also really like these because they have not just regular color, but conditional color. Conditional color means, you know, you could make them all black, you could make them all green, you could make them all red. But let me, um, let me show you, let me copy and paste these, right? I’m gonna make them all red, right? They’re all just red font. And then I’m going to make it so that when the check is checked, they’re green, right?

Stoplight, red, green. Do you know when we’re driving and we see the stoplight on the street and it’s like, red means, [00:04:00] red means stop. Yeah, you’re right. Um, so red means like, whoa, caution, bad. Everybody kind of understands that in the data world too. Uh, let’s see. Conditional formatting here. Let’s go to. New rule, and let’s say a, which one do we want?

Format cells that contain. And we want it to be a specific text, and if it is a true, then we will set that one to Is it gonna load? Is it gonna load? Let’s try it again. Format. Uh, we want the font color to be green. Okay, so you Yeah, we made it all red, except if it’s checked, it’s green. And then click OK a couple times, and you get the stoplight coding, which is possible, but I think it looks really ugly.

I don’t know. I think that’s like too colorful almost. What do you think? It is Christmas colored, you’re [00:05:00] right. Um, you could also do, you could make it all gray, and that everything’s checked in, then is green. I like this one better, it’s not quite so busy. What do you think? With the gray? You like the first one better?

You like a lot of colors, right? Mommy more likes plain stuff, I guess. We could focus on the checks. We could also focus on the boxes. Which we would do that with, um, I can’t remember. It doesn’t matter. It would be the opposite of what we just did in conditional formatting.

Alright, um, and then another reason I like checkboxes like this is that they are colorblind friendly and they are grayscale friendly.

Even technically this one with reds and greens is colorblind friendly. Um, Isla, do you know what colorblind means? Have you heard of that before? No. I bet some kids in your class may be colorblind. It’s pretty common. It’s like, um, 1 in 12 boys, they don’t see red and [00:06:00] green. Did you know that? They see it as yellow.

I’m going to show you what it looks like if you were colorblind. I’m going to take a little picture of the screen and we’ll put it here. So people wouldn’t see my red and green. No, they wouldn’t see red and green. I’m going to show you what it would look like for maybe, maybe some of the boys in your class, since it’s pretty common.

Let’s go to color- blindness. com. We clicked on color tools, CVD simulator, and we’re going to choose that screenshot that I just put on the desktop. Here it is. Color blind test. And this is what it looks like for me and you, right? We can see like the Christmas colors right here. But if somebody had red green color blindness, they wouldn’t see red and green.

They would see a bunch of yellows. But even though this looks like a bunch of yellows, it’s okay, they can still see [00:07:00] the checks versus the empty. So, so we’re good. They can see, they can see like the, the colored in part, right? Which is nice. Or if you printed this out, does your teacher, your teacher has printouts and worksheets at school, right?

Um, your teacher, to save ink, might print in grayscale sometimes, and this is what it would look like in grayscale. So it’s super colorblind friendly and grayscale friendly. So it gets Ann Approval.

Alright, the only downside to checkboxes is they do not transfer well to Word or PowerPoint. Can you, you want to see what happens if you try to put them into this thing called Word, Isla?

It’s not, it’s not good. It’s not good. They are not gonna show up as well. They’re gonna look like, um, gonna look like much, they’re gonna look like funny little, like, machine rectangles. They are gonna look like, let’s grab, I don’t know, like this one? Let’s say you wanted to copy [00:08:00] it, and you wanted to paste it, and it just looks like trues and falses, which isn’t, that’s not good, right?

That doesn’t look like checkboxes. Or, if you, even if you paste special and you do, like, a picture, um, it just looks, it looks funny. That doesn’t look good, right? And it does the same thing in PowerPoint, it’s just not very good.

So these are really meant to live inside of Excel, okay? Or you can PDF your Excel screen, and then you can share the PDF with your audience that way.

What do you think about checkboxes? Good? Thumbs up?

Do you want to tell them, um, anything at the end of the video? If I can move the mic closer to you? What do you want to do?

All right. Don’t forget to like, subscribe, and share! Bye!

You did great! What do you think of your first video? [00:09:00] Good.

The post How to Add Checkboxes to Microsoft Excel **New Feature!** appeared first on Depict Data Studio.

Visualizing data in charts, graphs, dashboards, and infographics is one of the most powerful strategies for getting your numbers out of your spreadsheets and into real-world conversations.

But it can be overwhelming to get started with data visualization. Does data visualization leave you feeling like the numbers are about to topple over on you??

If so, this step-by-step data visualization guide is for you!

I’ll walk you through the data visualization design process so you know what to do first, second, and third as you transform your spreadsheets into data stories.

Step 1: Understand Your Audience

Wait! Don’t start making graphs on your computer! First, we have to do some planning. A little bit of up-front planning will save you hours of blood, sweat, and tears in the long run.

First, we need to consider our audience and context. Who, exactly, is going to be using the data to make decisions?

Here are some discussion-starter questions to talk about with your colleagues.

Who is Your Audience?

A chart designed for a group of foundation program officers will not be appropriate for a group of high school principals, and vice versa.

List all your audience types on a piece of paper, or a whiteboard, or in a spreadsheet, or even on the back of a napkin. Share the list with your colleagues and make sure you’re on the same page.

Have you reached consensus about who you’re targeting with your data?

What’s Your Audience’s Numeracy Level?

Do they enjoy or fear data? Unless you’re designing charts for a group of economists or statisticians, you can usually leave out details like the effect size, power analysis, and margin of error. Laypeople are often more interested in practical significance (the “so what?” and implications of findings) than in statistical significance.

What’s Your Audience’s Data Visualization Familiarity Level?

If they’re brand new to dataviz, stick with the traditional charts like pie charts, bar charts, and line charts—otherwise they’ll spend more timing ooh-ing and aah-ing over the chart’s novelty than paying attention to the information contained in the chart.

How Much Time Does Your Audience Have?

Little time or interest: Simple static chart.

Lots of time and interest: Interactive charts.

What Types of Decisions Does Your Audience Make?

What information do they need? What information do they already have? What information are they expecting? How will your chart(s) add value for them?

If you can’t think of how your chart will add value for the readers, don’t make one. Every chart needs a purpose and so what?

How Much Precision is Necessary?

As the data visualization designer, you have the freedom (and responsibility) to select how much precision is necessary. Your selection should be well thought-out and intentional. Your decision plays out in two ways: the chart type you select, and how you label the data points.

When selecting chart types, remember that some charts are better than others in displaying precision. For example, charts that rely on angles and area to show differences, like pie charts, are for communicating general patterns. Charts that rely on length to show differences, like bar charts, are for communicating specific details.

How Many Decimal Places Are Necessary?

A related decision is how exact your data labels will be. Will you include decimal places? How many?

In most scenarios, you can safely round your decimal places to the nearest whole number. Your audience is rarely using the tenths, hundredths, or thousandths place to make decisions.

Are My Viewers Expecting a Story?

Think about whether your audience is expecting a traditional or storytelling graph.

You’ll learn about the distinctions in this video:

Step 2: Choose the Right Chart

It takes a while to understand all the different chart types and to pick the best one for your desired takeaway message. There are tons of great graphs to choose from!

Consult a Chart Chooser

My interactive Chart Chooser includes dozens of chart types, resources, tutorials, and templates.

New to Dataviz? Start with Classic Chart Types

If you’re not sure which chart to use, stick with classics like the bar chart to compare categories and the line chart to visualize how things change over time.

These charts will be “right” most of the time, so they’re a safe bet.

Use Pie Charts Sparingly

Contrary to popular belief, pie charts are not evil and don’t have to be avoided altogether. I have seven guidelines for using pie charts and donuts. In this pie chart makeover, I show you how to transform a 3D pie chart with way too many slices into a storytelling bar chart with icons:

Getting Comfortable with Dataviz? Branch Out and Try Other Chart Types

Once you’ve mastered the classic chart types, you can play around with less-familiar chart types like bubble charts, bullet charts, dot plots, heat maps, scatter plots, slope graphs, social network maps, tree maps, waterfall charts, and more.

Surround Yourself with Positive Inspiration

Surround yourself with great graphs so you can expand your worldview of what’s possible with data visualization. I suggest following top-notch data journalism teams like @PostGraphics, @NYTgraphics, and @WSJgraphics.

You can even create a physical or digital library of great graphs. For example, you might print full-page, full-color charts and tape them near your desk. Surrounding myself with a variety of chart types, all of which have been used in different reports and for different groups of people, helps me create brand new charts easily. All I do is glance up at my gallery, and then I quickly figure out which chart is best for my new situation.

Dive Into Your Dataset with Exploratory Data Visualization Techniques

I also use exploratory computer strategies, like Microsoft Excel’s spark lines, data bars, and conditional formatting, to help me narrow down the focus of my charts.

Spark Lines

Here’s a tutorial that shows you how to get started with spark lines:

Data Bars

And here’s a tutorial that shows you how to get started with data bars:

Conditional Formatting

You can set up rules in your spreadsheet that automatically change the color of certain cells based on their values. I regularly use heat tables to scan my dataset for patterns. You can follow my step-by-step tutorial to make heat tables for your data.

Sketch Rough Drafts on Paper

Step back from your software program. This is especially crucial if you’re using Excel or R (versus Tableau) where you usually need a solid idea of your chart’s design before implementing that design on the computer.

I sketch, draw, and doodle plenty of drafts before I create anything on the computer.

Here’s how it works: First, sketch plenty of rough drafts on paper. Give yourself permission to doodle as many drafts as you need. Share drafts with colleagues early and often. Gather as much feedback as you can. Next, create one or two of those promising drafts on the computer. Finally, edit, edit, edit! Put your easiest-to-follow chart in your final presentation or report. You might sketch five or more drafts. Only the single best chart will survive the editing process.

Step 3: Select a Software Program

Once you’ve got a rough mental idea of what your visualization might look like, sit down and build the first draft of your visualization on the computer.

There are dozens of software programs available for building data visualizations. Some are free. Others are low-cost. And others are quite costly, at least for smaller organizations.

I’m software-agnostic at my core, meaning that I don’t care which program you use. You can create great — or terrible — graphs in any software program.

That being said, 99% of my data visualization consulting is done in Microsoft products: Excel, Word, and PowerPoint. Those are the common denominator for the companies that hire me. I’d never create a dashboard in a specialty software program… if you don’t also have access to it and know how to use it. It would be useless!

Here’s an example of an interactive dashboard made in good ol’ Excel. You can learn how to make these, and many other types, inside my Dashboard Design online course.

Step 4: Declutter

After you’ve got the first draft of your data visualization created on the computer, it’s time to refine your visualization and make your message shine. No computer program is perfect. You’ll have to roll up your sleeves and make intentional edits no matter which software program you’re using. The very first edit I make is to declutter my visualization. Software programs come with way too many borders, lines, and unnecessary ink. Examine each and every speck of ink on the chart. Does it have a specific purpose? If you can’t articulate a reason for that ink, you don’t need it.

Apply the Squint Test

In these before scatter plot on the left, the cluttered appearance distracts us from the data. All these extra lines make the charts look overly scientific—and outdated. In the after version on the right, I removed the background shading and borders. I kept the x and y axes and some of the grid lines, but I intentionally changed the black ink to gray ink.

How do you know when you’re done decluttering? Apply the Squint Test. Here’s how it works: Squint your eyes so that you’re peering at the chart through your eyelashes. Everything should look a little blurry. Can you see the overall shape of the data? For example, you should be able to tell if a line chart is jutting upwards or downwards over time. If not, try removing more clutter.

Outline Shapes in White

You’ve got the gist of decluttering. Now, let’s fine-tune!

Sometimes reducing clutter means outlining shapes in white, rather than black, so that they match the chart’s background color.

Step 5: Clarify Your Message with Color

There are three goals for color:

1. Branding (Using your company’s colors, which saves time and helps you look professional)
2. Accessibility (Making sure your colors pass official guidelines so they’re legible for people with disabilities, like ADA/508 compliance in the United States)
3. accessibility (Using colors to make the graph feel intuitive)

Brand Your Visuals with Custom Colors

I’m begging you! Do not use the default colors from Excel, Tableau, or Google Charts. Nothing screams novice! or 2002! more than default color schemes. If you’re designing charts for a report, handout, or presentation for a client, use their color scheme. Consultants, this means the report will look like it came from the client. It will not have your firm’s look and feel.

In this example, Johanna Morariu and I were designing a slidedoc for the Working Families Success Network. We began by investigating the Working Families Success Network’s logo, website, and publications. Their logo has a distinctive blue, orange, and pink and their publications use dark gray text rather than black. Throughout their website they use color blocks with white text and white outlines. Next, we adapted that layout and color scheme for our slidedoc. The images on the right are separate slides (pages) of the report.

You can locate custom color codes in style guides, with a free eyedropper tool, or even with Microsoft Paint. Then, enter your custom color codes in Microsoft Excel or in Tableau.

Make Sure Your Colors Are Legible for People with Color Vision Deficiencies

Here’s how:

1. First, by proactive and avoid using red-green color combos.
2. Second, make sure you directly label your data.

Although we’re used to seeing legends, we rarely need them. Legends can lead to unnecessary zig-zagging around the screen or page, and legends can also be difficult to interpret if your graph is printed in grayscale.

Instead of using legends, directly label the data. Direct labels mean that you add labels as close as possible to the data. For example, in a line graph, you would delete the separate legend and place the category labels off to the right of each line. For bonus points, color-code the text in the labels to match the line.

This is what direct labels look like:

Then, you can upload your draft to www.color-blindness.com’s Color Vision Deficiency Simulator to get a preview of what it’ll look like for people with protanopia and deuteranopia.

Emphasize the Takeaway Message with the Action Color

When you want to tell a story with data, you can guide your viewer’s attention to your desired takeaway finding by creating a dark/light contrast. This example comes from one of my graduate school projects a decade ago, so I used the exact shade of green from my university’s logo. Then, I used dark green to draw my audience’s attention to a couple key parts of the slide. This slide comes from the fourth section or chapter of the presentation, the Limitations section, so that tab was highlighted in dark green so that it contrasted with the other tabs, which are in gray. The topic of this particular slide was Brevity of open-ended survey responses, so that text is in green so that it stands out against the rest of the text. And the box-and-whisker plot itself also uses dark green.

Step 6: Clarify Your Message with Text

It’s hard to get wording just right, so I usually save my titles, subtitles, and annotations for the end.

Brand Visuals with Custom Fonts

Rather than using Microsoft’s plain ol’ Calibri, make sure your visualization’s fonts match the project’s branding.

Write the Takeaway Finding in the Graph’s Title

Need to tell a story with data? Rather than using a generic title (“Figure 1” or “Number of youth served”), state the takeaway message in the title.

I first learned about this technique through Cole Nussbaumer’s Storytelling with Data workshop back in 2012—but geez, was it tough to apply! This is one of the hardest practices for social scientists to learn because we’re so comfortable with APA formatting and its generic figure titles.

Think Twitter-like and aim for six- to eight-word titles. Look to newspaper articles for inspiration; journalists know how to include the “so what?” in their title. You may or may not read the full newspaper story for additional details. Same thing with charts: your audience may or may not read your full chart, so your title must give them the gist of your findings.

Add Context with Annotations

Annotations are call-out boxes that provide important contextual details. In PowerPoint, Word, or Excel, you can easily create annotations by inserting a text box. No fancy software required!

Here’s a great example from Mother Jones. A generic title would’ve been “Number of children living in poverty” or “Relationship between poverty and geographic location.” This 6-word title, “In Climbing Income Ladder, Location Matters,” ensures that readers grasp the chart’s message instantly. A 2-line caption adds more details underneath the title, and a few cities are annotated. The tweet’s text also reinforces this message.

This is how likely poor kids are to grow up and move out of poverty based on where they live http://t.co/5A5VIZkLBN pic.twitter.com/7BBZQJ9bdg — Mother Jones (@MotherJones) January 31, 2014

Establish a Text Hierarchy

Size your fonts according to their importance. A text hierarchy tells your viewers which information is most important (headings) and which information is least important (the regular ol’ paragraphs). In this example, I transformed a university’s annual report simply by adding an intentional text hierarchy. I call this makeover a two-hour turnaround because these are changes that anyone can make in two hours or less. Before, all the font was the same size, so the headings didn’t stand out. The report looked like a sea of words. After, we made the headings stand out by with larger fonts and by overlaying the text on top of a photograph. We also used a different color for each section to break up the sea of words into manageable chunks.

Lower the Reading Level

The vast majority of reports, handouts, infographics, dashboards, and slideshows that I review with clients are written at a reading grade level that’s so high that reading the documents feels like homework. In this example, we assessed our draft’s reading grade level with a free tool called readable.io. Then, we re-worded the title so that it was a closer match for our intended audience.

Finally, go share your chart!

You’ll need to edit it slightly depending on the medium — a chart for a presentation should look different than a chart for a dashboard. You can learn about presentation-specific, dashboard-specific, and report-specific techniques.

Learn More

Sign up for my free online course called Soar Beyond the Dusty Shelf Report. There are several quick lessons that help you get started with data storytelling.

Or, contact me about online courses, private workshops, and conference keynotes.

The post The Data Visualization Design Process: A Step-by-Step Guide for Beginners appeared first on Depict Data Studio.

The graph title tells us which line is which.

In dataviz lingo, we call it “embedding the legend” in the graph title.

What a clever style!

But it’s not colorblind-friendly or grayscale-friendly.

Let’s compare embedded legends (on the left, YUCK) with direct labels (on the right, YAY).

Not Legible for Colorblind People

Although embedding the legend in the graph title is popular, it’s not colorblind-friendly.

Here’s a preview of what the two styles would look like for someone with red-green colorblindness. (I uploaded a screenshot to https://www.color-blindness.com/coblis-color-blindness-simulator/.)

If you’re required to follow 508 compliance in your workplace (if your project is funded by the U.S. Federal government), then embedding the legend in the graph title isn’t 508 compliant, either.

One of the 508 guidelines goes something like this: Viewers shouldn’t have to rely on color alone to understand the graphic.

In the embedded legend version, we are asking our audience to rely on color alone. Oops! That’s where the direct labels save the day.

“But Ann, just choose colors that are colorblind-friendly!”

No no no no. I don’t think we should be choosing random colors for our graphs.

I want you to use your organization’s brand colors in your graphs. Brand colors remove guesswork. No more sitting down to think about which colors look like. Brand colors also help us avoid Frankensteined graphs. No more pages 1-5 of your document in colors that Bob likes, followed by pages 6-10 in colors that Joe likes.

Not Legible in Grayscale

Embedding the legend isn’t grayscale-printing friendly, either. We wouldn’t want our audience to guess which shade of gray is which.

(Again, I uploaded a screenshot to https://www.color-blindness.com/coblis-color-blindness-simulator/.)

How to Directly Label Graphs in Excel, PowerPoint, or Word

“Ann, how do I directly label the graph in Excel??”

I don’t recommend using text boxes. They’re such a pain! It takes forever to add the text boxes, align them, and group them. When we re-size the graph—making it taller or smaller, for example—the text boxes have to be re-aligned. Ugh.

There’s a better way.

Add the Numeric Labels

In today’s example, I’m using PowerPoint. You can do this through Excel and Word, too.

And in today’s example, I’m labeling the endpoints. Sometimes labeling every. single. point. pulls our audience into the weeds when we need them to be thinking at a higher, strategic level.

Here’s how to add percentages to the endpoints.

Click on the line once. All the dots will be selected.

Click on the right-most point again. Only the right-most point will be selected.

Right-click on that right-most point. On the pop-up window, choose Add Data Label. It should be singular (Add Data Label) and not plural (Add Data Labels). If you see the plural version, it means you’ve still got all the points selected.

Then, do the same thing to the left-most point.

Don’t forget to move the label to the left of the point.

Here’s how: Once you’ve got the percentage label, click on it again, twice. Right-click and choose Format Data Label. In the sidebar, go to Label Position and choose Left.

Repeat the steps to add percentages to the other lines.

Color-Code the Labels to Match the Lines

This is an extra visual cue for our audience. We want them to know which label belongs with which line.

Click on the labels to activate them.

Go to the Home tab and change the font color just as you normally would.

Make sure that colored fonts are bold, not regular. A good rule of thumb for color contrast accessibility is that colored fonts should be bold.

(It’s harder to read colored font than black font, so the way we make the colored font easier to read is to make the letters thicker. You can learn more about color contrast rules at https://webaim.org/resources/contrastchecker/.)

Add the Category Label

Click on that right-most percentage, twice.

Right-click and choose Format Data Label.

Check the box for either Series Name or Category Name. (Series Name vs. Category Name depends on how your data table is organized. Just check and uncheck the boxes until you get the right label.)

Make sure to un-check the Leader Lines box, too. Otherwise you’ll get cluttered connecting lines later on.

Make Sure the Labels Fit

The wrapped text is awkward and hard to read.

We need to make the graph wider.

Sometimes I need to expand the interior of the chart, too. In Excel lingo, this inner border is called the Plot Area.

It looks like this:

Adjust the Separator

This step is optional.

The “separator” is the comma, period, space, or new line that separates the percentage from the words.

In this example, I’m going to use New Line. The percentage will be on one line, and the words will be on a second line. (I’m controlling the text wrapping.)

To adjust the separator: Click on the right-most label, twice. Right-click and choose Format Label. Go to the Separator drop-down menu.

You’ll see me widening the label, too.

Left-Align the Label

Left-aligned text is faster to read than centered text.

And, left alignment ensures that the label is right beside the line.

Simply select the label, go to the Home tab, and use the alignment button.

Change Which Comes First: The Percentage or the Words

This step is optional.

We can change which comes first (well, which one’s on top): the percentage or the words.

Kudos to Nick Visscher for teaching me this tip!

Here’s how: You double-click within the label until you see the gray fill around the percentages/words. Then, on the drop-down menu, select which element should come first or second.

That was a lot of steps, sheesh!

I covered them as thoroughly and slowly as possible. In real life, once you get the hang of it, this would take 60 seconds from start to finish.

The Bottom Line

Embedding the legend is fine.

But it’s not enough on its own.

We also need to directly label the data.

Download the Spreadsheet

Want to explore the graph a bit more? You can download my Excel spreadsheet here: https://depictdatastudio.gumroad.com/l/EmbeddedLegendsArentEnough

The post Embedded Legends Aren’t Enough appeared first on Depict Data Studio.

Accessibility means different things to different people.

I see two main types: Big A and little a.

1. Big A Accessibility means that our graphs meet official accessibility guidelines so that they’re understandable for people with disabilities. In the United States, that means 508 Compliance.
2. Little a accessibility means that our graphs are understandable for non-technical audiences; skimmable; and generally not a garbage heap of jargon.

Both Big A and little a are central in my work.

Here’s where you can learn more.

Big A Accessibility

Here’s a (partial) list of everything you’d need to do to pass 508 compliance guidelines.

Directly Label

Remove legends and directly label the data.

Add White Outlines

Outline the touching, filled shapes with white borders (i.e., cells in a heat map, slices in a pie chart, locations in maps, rectangles in stacked bar charts).

Make Sure Graphs are Legible for People with Color Vision Deficiencies

Red-green colorblindness isn’t the only type, but it’s most common.

And, it’s the most problematic for data visualization. We love to use “stoplight coding,” especially in dashboards. We’ve all seen dashboards where green means “we met the target” and red means “we didn’t.”

For people with color vision deficiencies, those reds and greens just look like yellows all blended together.

Tips:

• Avoid red-green color combos (try red-blue, orange-green, or orange-blue for stoplight coding instead).
• Use direct labels.
• Test your drafts.

Make Sure Graphs are Grayscale-Friendly

Printing is less common nowadays, especially with so many people working from home indefinitely. Who wants to pay for their own color ink?!

Your infographics, reports, slideshows, and reports might still be printed.

And they might be printed in grayscale.

Let’s plan for grayscale printing ahead of time to make sure the visuals will still be legible, just in case they’re printed.

Tips:

• Use direct labels.
• Add white outlines.
• Test your draft.

Use Plenty of Color Contrast

The other night, as I was reading my 4-year-old a bedtime story, I was struggling to read the words on the page. The book used colored font against a colored background, and the words were kinda small. Those were all fixable problems!

Tips:

• Don’t use colored text against a colored background. Here’s how to place text on top of photos so that it’s legible.
• Make sure all your colored text (e.g., for headings) is bold.
• Make sure your grays are dark enough. Software defaults are terrible at this. Enlarge and darken those defaults!!

Use Larger Fonts

Increase the font size (I recommend 11+ for documents and 18+ for slideshows, not Excel and Tableau’s puny size 9 defaults).

Again, the software defaults don’t help us here. Their defaults are usually way too small.

Add Alt Text

Add alt text to all images for standalone documents (reports, dashboards, infographics, etc.), including graphs.

Molly Burke’s Instagram is my favorite alt text inspo.

little a accessibility

These are techniques that won’t necessarily help you pass official accessibility guidelines… but are still a good idea if you want to make charts that people actually understand and use.

Actually Use Graphs

Use less text and more graphs!!!

Add graphs alongside those boring bullet points.

Choose the Right Chart

Go beyond the bar chart.

Use Data Storytelling

Use data storytelling, which I define as:

• writing takeaway titles instead of topical titles and
• highlighting one key finding at a time in a darker color.

Color-Code by Category

One of my favorite techniques of all time.

Color-coding by category helps us chunks the information into manageable pieces.

We can color-code in presentations, reports, dashboards, and one-pagers.

Lower the Reading Level

I’ve written about reading levels several times:

• In this blog post, you’ll see a re-written chart title for a conference slide.
• Here are some common data phrases, re-written.
• Here are some common research methods phrases, re-written.

Lower the Numeracy Level

Here’s how.

Use a Consistency Text Hierarchy

Make sure all the Heading 1s match, all the Headings 2s match, and so on.

Use Horizontal Text

It’s faster to read than diagonal and vertical text.

Avoid Underlines

Only use underlines for hyperlinks (not for headings).

Avoid ALL CAPS

It feels like shouting and takes longer to read than mixed case letters.

Place Text Next to the Graph

Not on the next page so that it’s faster to read.

Add Symbols and Icons

They make graphs easier to navigate and they boost the memorability of our findings.

• Here’s an example with state icons.
• Here’s how to use the StateFace font.
• Here’s an example with realistic human icons (instead of gingerbread people).
• And the realistic human icons, in a GIF!

Presentation-Specific Techniques

Talk about one thing at a time so that what your audience is hearing and seeing match.

Use a microphone!!! In-person and online. I don’t care if you think your voice can fill the room. It can’t, especially for people who are hearing-impaired. For virtual presentations, purchase a microphone. Or, at the bare minimum, wear the earbuds that came with your phone (because they likely have a built-in microphone). Tinny, echo-y sounds are hard for all of us.

Report-Specific Techniques

Follow the 30-3-1 approach. Limit the body to 30 pages (or less!), and then create a separate 3-pager and 1-pager.

Use visual appendices instead of black and white appendices to make the patterns more obvious.

Dashboard-Specific Techniques

Don’t expect busy, non-technical audiences to interact with your dashboards.

Involve the End Users

Involve the end users in the sensemaking process, e.g., by using data placemats and by following the Choose Your Own Adventure method in presentations.

Listen to what users say they need… but give them what they actually need.

Remember the Humans Behind the Data

I love this example.

Your Turn

Which of these techniques are you already using?

Why techniques might you try in the future?

The post “Big A Accessibility” and “little a accessibility” Tips for Data Visualization appeared first on Depict Data Studio.

Sometimes, it’s easier to understand several small graphs than a single graph.

I was recently working with an organization to visualize which states were using their software programs.

States might use:

• Software A
• Software B
• Or, both software A and B

Before: A Single Multicolor Map

Here’s what their visualization looked like.

They had a single U.S. map with one color for each scenario:

• one color for states using Software A
• another color for states using Software B, and
• another color for states using A and B.

Fairly straightforward, right?

It took us a while to spot patterns, though. Three colors is a lot to understand at once. It’s not impossible, but we had to think about it for a moment.

Multicolor (well, multi-hue) maps take a while to interpret.

Multi-hue maps aren’t colorblind-friendly. Here’s a simulation of what the map would look thanks to https://www.color-blindness.com/coblis-color-blindness-simulator/.  

Multicolor maps aren’t grayscale-friendly, either.

After: Small Multiples Maps with One Color Each

In lieu of a multicolor map, try small multiples!

In the redesign, we created three maps instead of one.

Now, we’re showing a single variable on each map, so the audience can understand it at a glance.

Small multiples binary maps (your dark brand color + light gray) are often faster to read than multi-hue maps. It’s counterintuitive, I know. We’re asking people to read three maps instead of one. But, three fast maps will beat one slow map any day of the week.

Small multiples binary maps are colorblind-friendly. Everyone can spot the dark brand color vs. the light gray.

Finally, small multiples binary maps are grayscale-friendly. Everyone can distinguish the dark gray vs. light gray.

Side-by-Side Comparison

Both styles fit on a single page (a goal in their project).

Both styles have room for explanatory sentences (something I recommend in all one-pagers).

Only the small multiples version is colorblind-friendly and grayscale-friendly.

I’d argue that the small multiples version is faster to read, too.

Download the Files

Want to explore my Excel file and Word doc?

You’ll see:

• How I formatted the Excel table that feeds into the maps
• How I arranged everything inside good ol’ Word

Download them here: https://depictdatastudio.gumroad.com/l/SmallMultiplesMapsInExcel

Your Turn

Have you split your multicolor map into small multiples?! Get in touch when you apply this technique to your own projects.

The post Do You Need a Single Map, or Several Maps? 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.