Over the past year or so I’ve been building an online portfolio of speaking and writing engagements.

Before that, I collected paper copies of reports I worked on:

Before

The great sobering thing about portfolios is that you can bask in the glory of your wonderful work bow your head in shame.

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

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

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

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

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

Introducing the Data Visualization Checklist

Now I know better. I’ve studied, practiced, gotten trained, and trained others. I want you to learn from my mistakes and go forth and make charts that people actually understand. That’s why Stephanie Evergreen and I published the Data Visualization Checklist earlier this month.

Guess how my old chart scored on the Data Visualization Checklist? 39%. Which means I’m certain that nobody would take the time to understand my poorly-titled, redundantly-labeled, cluttered, confusing chart.

Preview the checklist below. Download it. Avoid the agony. Your viewers will thank you. Maybe they’ll even hire you again.

Remakes

The first phase in learning about data visualization is usually critiquing charts. What, exactly, is wrong with your work or someone else’s? How can you learn from those mistakes? The second phase is articulating specific ideas for how you’d make charts better (Data Visualization Checklist to the rescue!). The third phase is actually remaking your charts. Some people stay in the first phase forever. My goal is to move more people into the third phase: actually improving your work.

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

Remake #1: A horizontal clustered bar chart

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

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

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

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

What do you think of remake #1?

Remake #2: A stacked bar chart

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

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

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

Remake #3: A side-by-side bar chart

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

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

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

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

Remake #4: A dot plot

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

But they’re not always superior. This dot plot doesn’t work. It’s too cluttered, isn’t it? Its more confusing than helpful. It’s because students improved on some areas, declined on other areas, and didn’t change at all on other areas. The annotation isn’t helping, either; instead of adding clarity, it adds clutter.

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

Remake #5: A slope chart

A slope chart is basically a line chart for two or three points in time. This chart type is also effective at showing rankings (i.e., it’s easy to see how the skills are ordered on the left-hand side). Want to make your own slope chart? Download Cole Nussbaumer’s template.

This chart is the winner. It’s easiest to understand at-a-glance. It ranks each of the skills areas. It shows differences over time. There’s a lot of cool stuff going on for the viewer to explore. It’s supplemented with a non-intimidating title, subtitle, and annotation. Yet, the information takes up very little ink and space. The score on our Data Visualization Checklist–98% of the possible points–reflects the strengths of this remake over the others.

Which remake would suit your audience best? Have you re-done any of your charts using our Data Visualization Checklist? If so, please contact me. I’d love to see how you’re improving your work.

P.S. Want to learn more about titles, subtitles, and annotations? Go check out Stephanie Evergreen’s post (http://stephanieevergreen.com/how-to-rock-the-text-in-your-data-visualization/) plus other Data Visualization Checklist resources on her blog (stephanieevergreen.com/tag/data-visualization-checklist).

P.P.S. Are you in DC next week? I’m leading an evaluation workshop on June 2 and a data visualization workshop on June 6. No diagonal text, I promise.

I love stacked bar charts because they’re pretty versatile, and because they’re a great chart for lots of evaluation and survey data. In my example, I looked at the percentage of survey respondents who selected strongly agree, agree, disagree, and strongly disagree on a satisfaction survey. But stacked bar charts can be used in dozens of different ways.

So when can you use a stacked bar chart?

• Stacked bar charts are for part-to-whole relationships. Use them when you want readers to see both a) one portion of the bar and b) compare that piece to the entire bar.
• Stacked bar charts can be used for tallies or percentages. A tally is the number of actual people, dollars, etc. For example, a nonprofit could display their funding sources in a stacked bar chart – $100K from a foundation, $200K from a government grant, and so on. The reader can see the size of each grant as well as how the grants stack up as a whole.
• Stacked bar charts can be used for nominal, ordinal, or diverging data. An example of nominal data is the racial/ethnic categories of your survey respondents. Ordinal data has a natural order – from best to worst, most to least, something to nothing – like my example. Diverging data is a subtype of ordinal data – when the categories are polar opposites and there’s a clear middle ground or neutral zone in between two ends.

And when can you use a diverging stacked bar chart? Diverging stacked bar charts are just for comparing several sets of ordinal data at once. They work best when you’ve got an even number of categories (like the 4 survey choices). Then, you can easily line up the midpoints along an invisible y-axis.

The dataviz challenge: Re-create the “after” version in Excel, R, or some other free software program. When you’re finished, email me or tweet a screenshot to @annkemery.

Bonus! 1) Adapt this chart for own data. Think outside the box! 2) There are at least two different ways to create diverging stacked bar charts in Excel. Can you find more than one solution? (And these charts are so awesome that you’ll even see one solution on Stephanie Evergreen’s blog next week!) 3) Don’t forget to use custom colors!

The prize for playing: Beer or coffee, my treat, the next time you’re in DC; a professional development opportunity; and bragging rights.

I’ll post the how-to guide in 3 weeks, on September 6, 2013. Happy charting!

Getting excel to do complex work is easy; making the output look nice is a different matter. I took the challenge to recreate Ann’s graph because I’m always looking for better ways to display information.

The solution required a stacked bar chart, so the only problem was lining up the starting point for the second dataset. A buffer needed to sit between the end of the first set of bars and the start of the second so that it would force the second set to line up correctly. Hidden data had to fill the gap. Here’s how I did it.

First, start by inputting the original data. Sorting the rows from large to small will help make the chart easier to build.

First, input the original data

The next step is creating the dummy data. Insert a column between the two existing datasets and populate it by subtracting the first column from “1.” This gives you a series of percentages that start at the end of the first bar and end at “1.” The problem of lining up the second dataset is solved at this point.

Create a column of dummy data

With all of the data in place, creating the chart is easy. Use a stacked bar and include all 3 columns.

Insert a stacked bar chart

Once the chart has been built, it’s time to hide the dummy column. Highlight the red data series and choose the format menu. Fill the series with the same color as your background. This makes the bars blend into the background and leaves them invisible. Do the same for the data labels. The dummy information is now hidden and your second dataset lines up properly.

Make the dummy data invisible

The y-axis still needs to be fixed since the values are organized from low to high. Invert it by highlighting it and opening the format axis menu. Tick the “categories in reverse order” radio button to have the bars arrange themselves from high to low.

Flip the y-axis

Here is the primary display difference between this solution and Ann’s. Due to the hidden data, the x-axis begins at 0 and goes up to 200%. Since it’s a big challenge to make repeating axes in one chart, and one that I haven’t solved, it’s probably best to remove the x-axis altogether. Since the data values are shown in the bars, removing the axis should not make data interpretation more difficult. In fact, it will be easier for people looking at the chart since they don’t have to rescale the second data series in their heads.

Finally, decrease the gap between the bars and invert the fill and text colors for the overall bar. This makes it stand out from the other series. It’s personal preference whether you want to make the data labels bold or not.

Expand, invert the colors and remove the x-axis

Your chart is finished and you have improved on excel’s built in data presentation immensely. Other than the problem with the x-axis, this solution gives the same result as Ann’s. It’s a visually appealing way to compare two or more groups across the same categories.

When he isn’t playing with Excel and learning to use R better, Paul Denninger is trying to start a company. He currently manages investments for people and is on a constant search for data to analyze. He spends enough time working with nonprofits that he might make a career change and do it full time. You can see more of his work on his blog, Risk Assets.