• the graph type you select (3D exploding pie charts with 999 slices are inherently hard to read);
• font size (I suggest a minimum of 11-point font in reports and 18-point font on slides);
• text direction (we read horizontal text faster than diagonal or vertical text);
• colorblindness considerations (no red/green combos);
• grayscale printing considerations (test it beforehand); and
• the reading level of your titles, subtitles, and annotations.

Last time, I showed you this makeover:

In that before/after transformation, our goal was to make the years along the horizontal x-axis legible. Before, the labels were too small (size 9) and they were diagonal (which is slower to read than plain old horizontal text). We freed up space by abbreviating the years (1985 to ’85). We also opted to label just four points along the line to emphasize key milestones.

We also re-wrote the slide’s title. For slides, I aim for short titles (a couple key words). When the presenter is physically present, the presenter’s voice can elaborate on the cool parts of the graph.

The fewer words, the better. You don’t want to lose your audience’s attention, i.e., you don’t want them to be reading full sentences on your slides while you’re speaking. For other types of materials (like reports, handouts, and infographics), I use storytelling titles. Storytelling titles might be 6 to 12 words long. Storytelling titles give you room to elaborate on the cool parts of the graph, like the takeaway finding or “so what?”

Measure Your Text’s Reading Grade Level

While redesigning this graph, it took me a while to understand the slide’s title. There were a lot of technical terms that I wasn’t familiar with. I was also concerned about the title’s length.
I wanted to test my gut instinct. There are several free and low-cost tools for objectively measuring text readability.

I’ve used https://readable.io/ for years and love it. You’ll need to enter your email address. Then, you can access the free portion of the tool. You get 15 minutes of free usage each day. Or, you can pay $4/month for the pro version. I don’t get paid to promote Readable, but I probably should! I love sharing this tool with others.

I pasted the before title into Readable’s website: “Stage 3 (AIDS) Classifications and Deaths of Persons with Diagnosed HIV Infection Ever Classified as Stage 3 (AIDS), among Adults and Adolescents, 1985-2014 US and 6 Dependent Areas.”

The before title scored a D, yikes! The before title was a 14.2 grade level, which is equivalent to a high school diploma and two years of college.

Yes, this graph was designed for people who had college degrees. Just because your viewers can read at a 14.2 grade level doesn’t mean they want to read at a 14.2 grade level. Reading at or above your level would feel like homework. Do you want your graph to feel like homework??

(P.S. Readable’s website got a makeover since I wrote this blog post, so your view will look a little different.)

Measure Your Text’s Reading and Speaking Times

Readable also measures reading and speaking times. Our title took 6 seconds to read and 11 seconds to speak! The epidemiologist could lose her audience for an entire 6 seconds.

On another recent project, I measured a company’s entire draft report with Readable’s website. The report was around 100 pages long, and Readable told us it would take someone 16 hours to read! The report was designed for state-level policymakers. Are we really expecting a busy policymaker to set aside two full working days to read a report? We’re all inundated with information.

Cut down your document’s length by focusing on essential content. Push non-essentials to an appendix. Then, make the remaining text faster and easier to read.

Edit Your Writing and Try Again

I read the title again. And again. And again. And I finally realized that it was just talking about AIDS diagnoses and deaths.

I tried that new title—AIDS Diagnoses and Deaths. It scored an average grade level of 6.5. Hooray!

The average American adult has an 8^th grade reading level. This isn’t the time to laugh at people with 8^th grade reading levels. I could talk about the systemic problems with our educational system for hours.

Your audience may not be the general public, of course. Your audience might be your boss, or your Board of Directors, or state-level policymakers. Their reading levels may be much higher. Your audience doesn’t want to read at their peak ability for hours. Don’t make your graph feel like homework!

This shortened title will only take the audience a second to read—six times faster than the original.

Still Not Convinced? Here’s When It’s Time to Care About Reading Levels…

Look for these clues from your readers:

“Well.. I can tell that really smart people worked on this project.” The first few times I heard this feedback, I mistook it for a compliment. I thought, “YES!!!! I used all the terms from my grad school stats classes correctly!” Now, if I hear that feedback, I cringe. This “compliment” is a sign that your documents are too technical. It’s time to revamp your graphs and your writing style.

“The report was really… comprehensive.” Ouch! This “praise” is a sign that your documents are too dense.

“Thanks for sending the report to us. We’ll let you know if we have any questions.” Ouch! This “engagement” is a sign that readers aren’t connecting with your documents.

Lowering your document’s reading grade level won’t solve all your reporting problems, but it’s a first step.

7 Practical Tips for Improving Your Text’s Reading Level

Sold??? Ready to improve your readability???

I love Readable because it gives you practical suggestions for improving your writing. For example, in our before title, the website highlighted the word classifications in a darker color. It also told me I was using too many long words and that the sentence was too long.

Here are practical tips for lowering your text’s reading level:

1. Find synonyms for technical terms. In this project, we changed classifications to diagnoses. In another project, we changed the counterfactual to comparison group and described the nitty gritty details of the counterfactual analyses in the appendix. In another project, we changed at baseline to when people enrolled in the program.
2. Avoid acronyms. I don’t care if you define the acronym the first time you use it. I shouldn’t have to flip back to page 1 while I’m trying to understand page 10. I shouldn’t have to memorize acronyms in addition to understanding your content and visualizations. When in doubt, spell it out.
3. Use shorter words. Look for words with lots of letters and lots of synonyms, and then find replacements.
4. Write shorter sentences. One of my personal weaknesses is writing run-on sentences. As I edit my own writing, I break long sentences into several short sentences. Search for your commas and semi-colons. Replace them with periods.
5. Write shorter paragraphs. Several short paragraphs > one long paragraph.
6. Write narrower paragraphs. I was That Nerd who took a speed-reading course during high school. I learned about how our eyes scan a page from left to right to read a line of text. I was surprised to learn that we don’t actually look alllllll the way to the left or allllll the way to the right. Instead, our eyes stay towards the center and we take advantage of our peripheral vision to scan the words on the far left and far right. Accordingly, there are studies about how long we should make each line of text (i.e., how wide or narrow our paragraphs should be). Results are somewhat mixed; readers have personal preferences about exactly how many inches wide they prefer. Nobody can speed-read a super long line of text, though. In portrait layouts, I use one or two columns of text. In landscape layouts, I use two or three columns of text.
7. Remove redundancies. Say what you need to say—once! Avoid repetition across your sentences, graphs, and tables. I’ve got an upcoming before/after makeover blog post where I’ll show you how to remove redundancies.

I also have a personal preference for active voice and first-person writing. I can’t connect with reports that sound like they were written by robots.

Lowering your graph’s reading level is not the same as dumbing-down your graph. Lowering your graph’s reading level shows that you respect your audience. You recognize that these are busy, important people. Busy, important people have lots of busy, important priorities. Your graph is one of many, many pieces of information to come across their desk each day. Give your audience the information they want in a format that doesn’t take all day to decipher. Then, your audience can make informed decisions and move on.

Have you used other readability tools? Share them here!

The post How to Objectively Measure Text Readability (and Lower Your Graph’s Reading Level) appeared first on Depict Data Studio.

After my sessions, the clients who sponsored the workshop ask their participants to rate my session. Sometimes the satisfaction surveys are electronic. Other times, the satisfaction surveys are conducted with good ol’ paper and pencil. The paper surveys get scanned and a software program automatically compiles the results.

Survey software tools have come a long way. During college, I spent ten soul-crushing hours each week entering data from paper surveys into SPSS in exchange for course credit. I paid the university for the privilege of entering data! (And yes, I learned a ton along the way, and still use those statistics and research methods skills daily.) When I was a research assistant, the survey scanning tools hadn’t been invented yet. Nowadays, research assistants probably have more meaningful tasks than data entry. It’s a different world.

I’m grateful for this automated reporting technology. But, at the same time, survey scanning tools drive me crazy. Their designs still live in the dark ages—it’s 2018 software with a 1998-era knowledge of graphic design and brain science.

Before

Here’s the most recent survey report that I received. The four-page report comes from a Dashboard Design workshop that I led at a conference last fall.

This software program’s design drives me crazy because:

• The title—Integrated Item Analysis Report—is a mouthful. Yes, I know what an item analysis is. I took an entire graduate course about psychometrics. There were dozens of workshop instructors at this conference. We came from a variety of academic backgrounds. Let’s not assume that everyone knows what an item analysis is.
• It’s dense. There are borders and outlines around everything. There are even double outlines around the open-ended comments. There’s almost no white space. The information is straightforward—here’s how people responded to each survey question—but the dense design makes it feel more complicated than it is.
• The colors and fonts are lacking a soul. The conference had its own logo, fonts, and colors. The organization that sponsored the conference had its own logo, fonts, and colors. Let’s breathe some life and identity into this report.
• So many decimal places. I would never change my workshop approach because a tool told me that 88.89% of people answered a certain way. That’s 89%. I’m the audience for this report. I know exactly what’s useful for me and what isn’t. Decimals won’t change my life.

Let’s revamp this report. Tiny edits will make a big difference. This is the step-by-step process that I teach in all of my Dashboard Design workshops, and today I’m sharing the process with you, too.

Step 1: Start with a Table

Tables are the heart of every quantitative report. We tabulate our numbers and build visuals from those tables. I re-typed the survey questions and the responses into my spreadsheet.

Make sure that you declutter your table by removing unnecessary ink. Tables rarely need all the borders, horizontal lines, and vertical lines that we’re accustomed to seeing. Sometimes I add a few horizontal lines back at the very end of the design process.

I ignored the mean values for each of the survey questions. Agree/disagree scales are ordinal. We can only calculate means for interval or ratio variables.

I also ignored the Neither Agree Or Disagree category. I’m the audience for this report. I won’t change my workshop content based on a middle category. Here’s how I read this report: “Did anyone hate the workshop? Are there disagree responses? Darn, one person disagreed with every statement. Curmudgeon Effect? A handful of people will be dissatisfied no matter what I do. Maybe that person was having a bad day. Or, maybe they accidentally checked the strongly disagree boxes instead of the strongly agree boxes? I’ll never know for sure. Okay, let’s move on. Did anyone love the workshop? Are there any strongly agree responses? Phew, that’s almost everyone. So now I need to compare the agree and the strongly agree responses. What was holding someone back from checking the strongly agree box? How can I go from good to great?” In most projects, participants tend to be satisfied, so we’re usually comparing the top two choices. The middle categories rarely matter.

Step 2: Add Visuals

There are several ways to visualize agree/disagree scales, like stacked bar charts, diverging stacked bar charts, or even waffle charts. The existing bar charts would be easiest to automate across dozens of workshop evaluation surveys, so we’ll keep them.

This example contains miniature within-cell bar charts called data bars. You can make data bars in good ol’ Excel with just a few clicks.

Step 3: Write a Title and Subtitle

One of the most common mistakes that I see among aspiring data visualizers is a lack of text. People get really excited about data visualization. They start churning out more (and better!) visuals than ever before. Sometimes they forget that text still plays an important role. At the top of your report, add plain language that introduces your viewers to what you’re about to show them.

For the title, I changed “Integrated Item Analysis Report” to “Dashboard Design with Ann K. Emery.” At this particular conference, there were dozens of workshops and dozens of evaluation survey reports. The title’s job is to distinguish one report from another. The contents of the report are an item analysis (a question-by-question analysis). But the title of the report needs to contain the workshop’s name and the instructor’s name.

For the subtitle, I wrote, “This report shows the results from the Dashboard Design workshop evaluation survey. If you have questions about this report, please contact So-in-So.” I typically keep my dashboard subtitles to two sentences. The first sentence tells you what you’re about to learn. The second sentence tells you who to contact if you want to learn more.

Step 4: Apply a Text Hierarchy

A text hierarchy tells your viewers which text is at the top of the food chain. The title should be large, dark, and bold so that it instantly grabs your viewers’ attention. You could also apply ALL CAPS to the title or section headers. Use ALL CAPS sparingly, please. It takes longer for our brains to read ALL CAPS than Sentence case or Title Case. We like having a mix of tall and short letters.

This is especially true for people with learning disabilities.

I also made each of the survey questions bold. Later on, I tweaked the font sizes and colors again. The idea is the same. The important information needs to stand out in large, dark, and bold text.

Step 5: Brand with Customs Colors and Fonts

Another common mistake that I see among aspiring data visualizers is when people think that adding their logo will sufficiently brand their document. Sure, you can add your logo to your report.

Just make sure to place your logo in the lower corner—not in the top—so that it doesn’t distract from the report’s contents. In addition to using logos, your fonts and can reinforce your brand.

For fonts, I’m using a combination of Lato Heavy and Lato Light. You would use your own fonts.

You would also use your own colors, not mine. Learn how to read your organization’s style guide, locate your color codes with an eyedropper, or locate your color codes with Microsoft Paint. Then, enter your color codes in Excel or in Tableau.

Step 6: Re-Arrange Until Everything Fits on the Page

This step is more of an art than a science…

I opted for a landscape layout instead of portrait layout. I reserve portrait layout for materials that are going to be printed. Are workshop facilitators printing out documents like this? I doubt it. I won’t. Landscape layout is best for documents that are going to be read on-screen because our computer monitors are already landscape-shaped. You open the document and it just fits so nicely.

No wasted space around the margins.

I adjusted row heights and column widths to get the page breaks just right.

I merged a few cells. When you’re working in spreadsheets, don’t merge cells together too early!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! You’ll kick yourself. Unmerging cells is drudgery.

I added page numbers. My report was designed entirely within Excel, but it would get shared with others as a PDF. Page numbers make my “spreadsheet” feel more like a regular document.

I added horizontal lines so that readers could across the rows more easily. The lines are light gray, not black. They don’t distract from the more important information.

And speaking of lines… I removed the double outlines (!!!) around the open-ended responses. This is one of my favorite parts of the makeover. Now, we can actually see the comments because they aren’t competing with the outlines for attention.

I color-coded the report by category. The introductory section is purple, the closed-ended questions are blue, and the open-ended questions are turquoise. I wanted readers to know when a new topic was starting.

Finally, for bonus points, I added icons. Michelle Borkin and her team found that icons make graphs more memorable, so I add icons whenever I can. I’ll teach you how to add icons in a future post.

Here’s the full makeover. I hope you like it.

Within minutes, we’ve provided workshop facilitators with useful information without burning their eyeballs.

Learn More

Want to learn the technical how-to’s in Microsoft Excel?

Want to see additional before/after data visualization makeovers?

All these skills are covered inside Dashboard Design.

Purchase the Template

Want to explore my survey report makeover in more detail? See how I arranged the text, graphs, and icons. Or, get ideas from the template to use in your own project. You can purchase the template below.

Purchase the Dashboard Template

The post How to Visualize Workshop Evaluation Results appeared first on Depict Data Studio.

As part of the most-practical-keynote-you’ve-ever-heard emphasis, we included makeovers from the grantees’ real projects. In case it’s useful for my blog readers, I’m sharing one of those makeovers with you today.

Before: A Table

This table came from a grantee’s slidedeck. I’ve anonymized the county names where the grantee was operating, hid the program’s name, and changed the numbers of families, but hopefully you can get the gist of it anyway. This group displayed four months’ worth of data at a time because they held quarterly Board of Directors meetings. They opted to display the current month’s data (December) plus the prior three months (September, October, and November). We both agreed this was the “right” amount of historical context for their Board.

After: A Better Table

At the bare minimum, I would rearrange the table. I transposed the table–rotated the table so that the columns and rows are swapped–so that the time periods go horizontally from left to right. I always display ordinal data like time periods from left to right for consistency.

Then, I would declutter the table. Removing the background fills makes the data easier to read. There’s more contrast between the black numerals and the white background. Removing the background fills also allows me to overlay a heat map later on if I choose.

Finally, at the bare minimum, I would apply this grantee’s branding. They used Calibri and this exact shade of green.

After: A Line Chart

A second option is a line chart. Here’s Microsoft’s default:

Now, declutter that default graph! Remove the border:

Declutter the vertical axis and grid lines. In a moment, I’m going to add numeric labels to individual data points, so the axis doesn’t need to demarcate 0 and 20 and 40 and 60 and 80 and 100. And with exact values labeled, the grid lines no longer have a purpose, so they’ve got to go.

Add numeric labels through the center of each line–Edward Tufte’s graph table, my favorite chart type–as demonstrated in my tutorial.

Delete the legend and place the category labels directly beside the line:

Add a title. For live presentations, I opt for short titles that describe the graph’s contents but don’t give away the story. In many sense-making presentations, that’s the whole point of the meeting–to interpret the graphs together. My voice and talking points would describe the “so what?”–that County 2’s line went down while County 1’s line went up.

For handouts, I add subtitles. When I’m not physically present to discuss the graph, I type my talking points into the text box right below the title. There are always folks with schedule conflicts who can’t attend the presentation. Subtitles ensure that they know what you highlighted from each graph.

Match the font to the organization’s branding (Calibri, in this case).

Match the colors to the organization’s branding. Rather than using my navy and orange, I matched the organization’s style guide, which used these particular shades of green and orange.

Make sure your graph’s still legible in grayscale. Don’t wait until 4:59 pm for a 5 pm deadline! Print a draft a few days ahead of time. Compare the shades of gray. Can you still tell the lines apart?

Make sure the graph’s still legible for people with color vision deficiencies. Colorblindness is pretty common. It affects roughly one in ten people so you absolutely work with people who are colorblind and you should absolutely strive to make your graph accessible for those people. Head over to www.color-blindness.com and use their Color Blindness Simulator to preview what your graphs will look like for people who are red blind, blue blind, or green blind, among other scenarios. My orange-green graph turns into an orange-brown graph for people with red-green color blindness.

After: A Clustered Column Chart

A third option is a clustered column chart. This is the go-to chart for a lot of the people I work with, and I’d like to show you why it shouldn’t be your go-to anymore.

Obviously we’re not going to keep those default settings. I decluttered the graph, wrote a title and subtitle, and applied the grantee’s branding.

Make sure your graph’s legible when printed in grayscale. This is where the clustered column chart falls short. Clustered charts often need separate legends, and it’s much too difficult to distinguish the shades of gray apart from each other.

The separate legend doesn’t help anyone with color vision deficiencies, either. The viewers with red-green colorblindness would have to spend absurd amounts of their precious attention on distinguishing those muted oranges and browns apart from each other.

In theory, we could delete that separate legend and place the category labels directly on top of the columns. But then the text is sideways, which takes longer to read. And yes, in theory, we could transpose the graph (i.e., flip the graph from columns into rows). But then our ordinal data wouldn’t flow from left to right like the rest of our slidedeck.

Yes, direct labels hold up better in grayscale.

And yes, direct labels hold up better for people with color vision deficiencies.

Both legends and sideways text take longer to read, a drawback we can’t ignore… Yet another reason I despise clustered charts.

After: A Stacked Column Chart

A fourth option is a stacked column chart in which the County 1 and County 2 numbers are stacked on top of each other. Microsoft gives us this default:

In this edited version, I decluttered the graph, added a title and subtitle, and applied the organization’s fonts and colors for a hint of branding. I also added the totals on top of each column. Software packages call this a stacked column chart.

If our viewers care about proportions, we could convert those raw numbers into percentages. Software packages call this a 100% stacked column chart.

Deleting the legend and placing labels directly on top of the columns means that our graph would do fine when photocopied in grayscale. Notice the intentional white line between the green and orange counties, which helps to distinguish the shades of gray from one another.

The directly-labeled columns also hold up for people with color vision deficiencies.

Your Choice of Charts Depends on Your Message

“But Ann, which of these choices is correct?!” They’re all correct. Yes, all of them. Your choice of charts depends on your message.

The table puts viewers in the driver’s seat. Viewers have an opportunity to interpret data for themselves and come up with their own messages. I use tables for internal audiences, e.g., when you’re bringing data to your staff meeting in which the whole purpose of the meeting is to think about what the numbers mean.

The line chart shows whether lines are going up, going down, or holding steady. If you want to focus on County 2’s decline and County 1’s upswing, then this is the chart for you.

The clustered column chart directly compares the two columns. If you want to focus on the difference between the orange column and the green column, then place them beside each other in close physical proximity.

The stacked column chart focuses on part-to-whole patterns–how the orange segment and the green segment add up to a total. If you want to focus on combined numbers (289 families) vs. on break-outs (107 in County 1 and 182 in County 2), then the stacked bar chart is for you.

It all depends on your message. Sometimes you know the message beforehand. You might be trying to sway an audience to adopt a particular course of action and need to find data that support that course of action. Other times, you brainstorm several possible charts share those drafts with a colleague, and then choose your chart (and therefore your message).

Your Turn

I brainstormed four options for visualizing this dataset. Can you come up with additional ideas?

As usual, you can purchase the templates for the table, line chart, clustered column chart, and stacked bar chart.

Purchase the templates ($5)

The post How to Transform a Table of Data into a Chart: Four Charts with Four Different Stories appeared first on Depict Data Studio.

Before

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

After

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

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

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

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

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

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

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

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

Let’s walk through a real-life example!

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

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

Brainstorming

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

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

Before: Quarter-by-Quarter Pies

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

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

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

After

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

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

For a Slideshow: Stacked Columns with a Short Title

Here’s the after version for a slideshow:

For a Report: Stacked Columns with a Subtitle

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

Bonus: Make This Yourself

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

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

Bonus: Download the Templates

Purchase the templates ($5)

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

And here’s another thought-starter question to consider: Do your viewers want to see the data presented as-is, or do they want you to cut to the chase and interpret the data?

Sometimes we need to serve as unbiased collectors and disseminators of information. This is especially true for my workshop participants in research-y roles who publish their data in places like peer-reviewed journal articles or formal research reports with lengthy appendices.

Other times, we need to get a message across in our graphs — and fast! This is especially true for my workshop participants who are consultants or those who work in communications-y roles.

Their viewers are busy, busy, busy. Their viewers are hoping that someone else — you! — will dig through mountains of data and uncover the handful of nuggets worth paying attention to.

It’s not that one visualization style is better or worse than the other. They’re apples and oranges. I want you to figure out when your viewers are expecting to see each style and then learn how to switch back and forth.

Storytelling Strategies I Use in My Graphs

The as-is approach is the easy one. You create a graph. You clean up the default settings a little, especially those cruddy parts like borders or too-thick grid lines. You select colors from the viewers’ color palette. I’ve been doing a lot of design projects with USAID contractors lately; this blog post has USAID’s exact shades of blue and red.

The storytelling approach can seem like the harder one. But! It’s not impossible! This is just a newer style for most of us.

I want to make it easier for you. Here are four design strategies you can use to tell a story in your graph:

• Descriptive titles
• Descriptive subtitles
• Annotations
• Saturation

Use one technique or all four, it’s up to you. Let’s check out a few examples. The graphs on the left present the data as-is while the graphs on the right interpret the data. 

Example 1. Tell a Story in Bar Charts

This first bar chart uses a descriptive title and saturation to show how chocolate is the preferred ice cream flavor.

Example 2. Tell a Story in Slope Graphs

The descriptive title and saturation emphasize how Project A is performing particularly well.

Example 3. Tell a Story in Line Graphs

A descriptive title, a descriptive subtitle, and an annotation explain how the agency is funding more studies to measure the effectiveness of their programming, which is due to their new policy. Annotations are call-out boxes that give viewers more background information about a specific data point or two, like why we’re seeing a sudden increase or decrease.

Example 4. Tell a Story in Donut Charts

The as-is version on the left gives equal emphasis to both subgroups of students because the red and blue are both relatively dark colors. This is USAID’s exact shade of red (with data that is obviously not from USAID). The red is tricky because we’re accustomed to stoplight color-coding in which green means “good” and red means “caution!” or “bad!” In addition to the red and blue being equally saturated, we also have to be careful with the cultural connotations of using red here.

The interpreted-version on the right uses saturation to highlight the percentage of students who qualify for free and reduced meals.

Example 5. Tell a Story in Dot Plots

Finally, this dot plot uses a descriptive subtitle and saturation to draw viewers’ eyes towards the teachers’ survey responses.

Join the Conversation

Which approach do you follow most often? And who are your viewers? Their preferences drive every decision about how you’ll format your graph, after all. Are your viewers expecting you to present the data as-is, or do they prefer that you offer interpretations through titles, subtitles, annotations, or saturation?

The post How to Tell a Story with Data: Titles, Subtitles, Annotations, Dark/Light Contrast, and Selective Labeling appeared first on Depict Data Studio.