Beyond Graphs 1: I made a Great Graph after just a Few Course Modules

Soon after the course started, I brought Ann data about who connected with me on LinkedIn after I was listed as one of Nick Martin’s 9 Amazing Humans to Follow. Nick has a HUGE network and I got over 69 connection requests in the first day. And requests continued for more than a week!

So I made a graph to go with a post on LinkedIn, applying all the learnings from the first few course modules.

Beyond Graphs 2: 2 Things I Learned in 10 Minutes of Help in 1 Office Hour Session

I examined the few data variables on the LinkedIn connection requests. My impression was validated. Only 2 of 136 requests had a personalized message (despite LinkedIn experts emphasizing the need to personalize connection messages).

I tried different ways to display this finding (waffle chart, pie chart, and this one). Luckily, Office Hours were the next day. (Office Hours are a CAN’T MISS opportunity for immediate feedback!)

Ann took one look and exclaimed, “Ooh, let’s try the WeePeople font!” (Well, maybe not exactly like that!)

She then quickly used WeePeople to show the data.

Learning 1: More relevant and representative visuals with icons showing diverse silhouettes

(Hooray – No more using just the standard male icon.)

And then Ann taught us all how to make a gif which was even more effective at telling the “only 2 of 136 people” story.

Learning 2: Using a gif can give readers a quick result from your data

And, for those who follow LinkedIn stats to see how their posts engage, the post with the bar graph got 4,765 impressions and the 2^nd post (the next day) with the gif got 8,778 impressions!

Beyond Graphs 3: Now I’m Applying a Mental Checklist to Graphs and Charts

No – not only to the few graphs I’m making.

The course taught me and heightened my awareness to look at all the visual elements in the many graphics we see each day. There was so much learning from the course modules. And then many great opportunities in Office Hours to learn from what others were working on.

Here are things I find I am automatically looking for in these graphics:

• Color choice(s)
• Is it a Traditional or Storytelling title?
• The clarity of the data message
• Is the graph or chart the best for that message and the data being used?
• What elements could be removed for better clarity?

And a Beyond Graphs Bonus: Consistency and Efficiency

I consider myself a digital pioneer. But I didn’t know what I didn’t know, even being a longtime Word, PowerPoint, and Excel user.

I jumped into the course, and my efficiency increased in the first week! The course started – not with graphs – with ensuring basics including branding by setting my color and font defaults.

And then, a couple weeks later, I set up branding for a 3-part seminar series I did for Waey, the Association for Community Health in Saudi Arabia.

And I now have the consistency across Excel, Word, and PowerPoint and across my different PCs. What a difference!

This is just the tip of the iceberg of everything I am doing differently after Great Graphs – Excel!

Connect with Sue Griffey

LinkedIn: https://www.linkedin.com/in/suegriffey/

Twitter: @SueMentors

Youtube: https://www.youtube.com/channel/UC-rjWX4ZmTdo0S3ssKbut_A

SueMentors Resources: https://suegriffey.fyi.to/suementors-resources-for-your-professional-presence

A no-cost short course: Build and Update Your Professional Presence in 4 Steps at this page: https://www.linkedin.com/company/4-steps-to-build-update-your-professional-presence

The post When a Course is More Than a Course: 3 Ways “Great Graphs in Excel” Was Beyond Graphs 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.

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.

The Project: Need to Display Demographic Data about Survey Respondents

A little context: I recently spoke to graduate students about visualizing their survey data. A group of students had surveyed an art museum’s visitors to gather feedback about their experiences at the museum.

I’m pretending that I’m going to meet with the museum’s higher-ups and share data in a slideshow.

I’d begin with a single slide about the survey respondents. Same thing for a report: Limit your background information to just a page.

In the vast majority of cases, I suggest that you only present one graph per slide. Otherwise, the slide gets cramped and the audience is reading Graphs B, C, and D while you’re still droning on about Graph A. But, nobody really cares about demographic data. I mean, they want to know how many people responded. And they want to know whether the small sample was generally a good representation of everyone else as a whole. Reassure your audience that you gathered a nice big number of surveys and that you had a balanced mix of people, and then move on. Don’t drone on for four separate slides.

Your speaking points would be brief, too: “We surveyed 60 people. 50 percent were first-time visitors and 30% were Harvard affiliates. Most of the visitors were from Massachusetts, but we did have a bunch of other states and countries represented, like Canada and even China! And the people were surveyed tended to be on the younger side; 39 percent were in that 18 to 29 year old bracket.”

The content is fine as-is. We just need to tweak the visuals.

Step 1. Instead of a List, Experiment with a Two-Column or Three-Column Layout

First, break out of the regular ol’ one-column layout and experiment with two-column layouts. As you’re rearranging your key phrases on the slide, don’t forget to delete the bullet points altogether. Those little circles are chartjunk.

Step 2. Add One Visual Per Key Point

Then, add one visual per key point. I used two-slice donut charts to show that 50 percent of survey respondents were first-time visitors to a museum and 30 percent were connected with the nearby university. I used a map to emphasize how most of the survey respondents were from Massachusetts. I used a histogram to show the distribution of age ranges of survey respondents. All of the visuals have been decluttered. In other words, there aren’t any unnecessary lines, borders, tick marks, or legends on the graphs.

Step 3. Apply a Text Hierarchy

Size your text intentionally to guide viewers through the content. The most important information should be large, bold, and dark. The least important information should be small, but not too small. I suggest using at least size 18 for the less-important info on slides, like the “18-29” age bracket labels.

You’ll use your audience’s font to enhance their branding (not my font).

Step 4. Re-Write Your Slide’s Title

Do something, anything, I’m begging you, to translate that jargony slide title for non-technical audiences.

Step 5. Apply Intentional Color-Coding

Finally, color-code by category. Intentional color-coding breaks up your dense visuals and text into a few simple categories.

You’ll use your audience’s color palette to enhance their branding (not my colors).

Bonus! Watch a Video Tutorial

And better yet, watch a sample lesson from my online course, Great Graphs: Transform Spreadsheets into Stories with Better Data Visualization so that you can see the entire step-by-step transformation. Most lessons, like this one, are just a few minutes long. I know you’ve got other things going on. Learn a new technique while you eat lunch, and build your new skills steadily. Enjoy!

The post How to Visualize Demographic Data: From Boring Bullet Points into Great Graphs 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.

Rules for Using Pie Charts and Donut Charts

Here are my guidelines for using pie and donut charts. Pie charts are okay when they:

1. are well-formatted. No 3D, exploding slices, leader lines, or legends.
2. display nominal variables. Ordinal variables don’t belong in a pie chart.
3. add to 100%. I’ve seen pies that only add to 90% because the designer deleted the “other” category and forgot to recalculate the new percentages.
4. contain positive numbers. I’ve seen designers place a mix of positive and negative numbers inside the same pie chart, which doesn’t make any sense.
5. display a single point in time. Patterns over time belong in a time series graph, like a slope chart, line chart, or dot plot.
6. only have two or three slices. Four slices is pushing it.
7. are displayed individually. Only show one pie chart at a time. No small multiples pies. Comparisons across multiple pies are time-consuming.

Finally, while I don’t consider this to be a strict guideline, pie charts tend to be easiest to read with common fractions, like a one-fourth vs. three-fourths pie or a one-third vs. two-thirds pie.

Circumstances When Pie Charts Are Okay

Given these guidelines, I use pie charts to show:

• male/female/etc. gender categories;
• yes/no survey responses;
• students who graduated high school on time vs. didn’t graduate high school on time;
• adults who live in single-family homes vs. adults who live in other housing types; and/or
• other binary data.

Alternatives to Pie Charts

Let’s tackle these pie charts! It’s not sufficient to tell you to avoid pie charts. You need to know what to do instead. Here are some pie chart makeovers that are inspired by my real projects.

If Your Pie Chart is Poorly Formatted… Then Format It!

The chart on the lower left is poorly formatted. This one is 3D, so the slices look larger or smaller than they really are… and, it’s exploding, which is distracting for viewers… and instead of the percentages being right on top of the pie slices, now there’s a tiny legend down below the pie, which means our viewers would have to zig-zag their eyes around the slide to tell which slice is which.

The final sin in this poorly-formatted pie chart is that there are leader lines, those gray lines connecting the 25% and 75% to their corresponding slices. Plus, So much ink is on the page, yet so little is actually focused on the data.

The well-formatted pie chart on the lower right is fair game. Gender is nominal or categorical, so that works. We’re only showing a single point in time, so that works too. And we’ve only got two different slices.

If You’ve Got Ordinal Data… Then Use a Stacked Bar Chart or a Column Chart

Ordinal or sequential variables have a natural order, like responses to a survey that go from strongly agree to agree to disagree to strongly disagree.

In this case, you’d swap out your pie chart and use a stacked bar chart instead, so that viewers can tell which category is at which end of the spectrum – the agrees on one side and the disagrees on the other side.

Another type of ordinal or sequential variable is age ranges. Histograms are a great alternative to pie charts when you’ve got ordinal or sequential groupings. Let your audience read across the screen from left to right (i.e., from lowest to highest).

If You’ve Got Negative Numbers…Then Use a Deviation Chart

I mentioned that pie charts are only for positive numbers, not negative numbers. Sometimes we have negative numbers when we’re dealing with changes over time.

In this example, we’re looking at four products and whether they increased or decreased in sales compared to the previous quarter. For example, Product A’s sales decreased 20% compared to the prior quarter while Product B’s sales improved 40% compared to the prior quarter.

Instead of a pie chart, we’d use a column chart or bar chart. In your software program, the negative numbers will automatically flip in the opposite direction of your positive numbers. The axis line runs across the middle at 0% and we can see which products went down (like Product A) and which products went up (like Products B, C, and D).

If You’ve Got Patterns Over Time… Then Use a Time Series Chart

What if you have time series data, that is, patterns over time? Maybe you’re trying to show data for each Quarter – Quarter 1, Quarter 2, Quarter 3, and Quarter 4 – or for each month, or for each year in the grant cycle.

Swap out your pie chart and use a line chart instead. You want viewers to see the beginning point – Quarter 1 – over to the end point – which is Quarter 4.

If You’ve Got More than Two or Three Categories… Then Use a Bar Chart

Pie charts are easiest to read with only two or three slices.

What if you have lots of different slices, like favorite ice cream flavors? This pie chart has too many slices – vanilla, chocolate, strawberry, mint, and cookie dough. It’s too hard for our brains to compare the slices to each other.

Swap out the pie chart for a bar chart and order the bars from greatest to least (or least to greatest). Chocolate would be listed first because it’s the most popular, and cookie dough would be listed last because it’s the least popular.

If You’re Tempted to Display More than One Pie at a Time… Then Use a Grouping of Stacked Bar Charts

What if you want to compare several companies, organizations, outcomes, etc. all at once? Pie charts are hard enough to read. Our brains don’t do well deciphering the angles, area, or circumference of circles. Two or three or four different pie charts can be understood, but with way too much mental energy.

In this example, we’re asking our viewers to look first at the 20% angle, and then at the 40% angle, and then their eyes have to zig-zag to the 60% angle, and then their eyes have to zig-zag over to the 80% angle. So. Much. Work.

In this case, you’d swap your small multiples pie chart for a small multiples stacked bar chart. The part-to-whole pattern is still there, but now our viewers’ eyes only have to make a single, diagonal swooping motion down the page to compare all four companies at once. Less energy required for reading, more energy reserved for making decisions based on that data.

Join the Conversation

Have you seen real-life pie charts or donut charts that are in desperate need of a makeover? Comment below and include a link to the example, and I may even include it in a future pie chart makeover blog post.

The post When Pie Charts Are Okay (Seriously): Guidelines for Using Pie and Donut Charts appeared first on Depict Data Studio.