In this video, you’ll see more than a dozen different charts that are possible inside Microsoft Excel.

What’s Inside

• 0:00 Intro
• 0:17 Dataviz On The Go
• 0:36 3 Levels of Excel Skills
• 1:05 Level 1: Overused Native Charts (Bars, Lines, Pies, etc.)
• 2:35 Level 2: Underused Native Charts (Combo, Tree, Sunburst, Maps, etc.)
• 4:01 Level 3: Non-Native Charts (Population Pyramids, Dumbbell Dots, Lollipops, etc.)
• 5:02 Disguises Needed for Non-Native Charts
• 5:54 Interactive Dashboards in Excel (Excel Tables, Pivot Tables, Pivot Charts, and Slicers)
• 6:16 Static Dashboards in Excel (Made in Excel, but Saved/Shared as PDFs)
• 6:41 Your Turn
• 6:55 A Personal Note

3 Levels of Excel Vizardry

I’ve taught data visualization in Excel a dozen different ways over the years.

Nowadays, I teach Excel dataviz based on the degree of behind-the-scenes hacking needed to produce that chart.

We start easy. Then, we work up to harder battles.

Here are the three Levels of Excel Vizardry:

• Level 1: Overused Native Charts
• Level 2: Underused Native Charts
• Level 3: Non-Native

Let’s go through some of the Excel secrets in more detail.

Level 1: Overused Native Charts

These are the familiar faces:

• Pies
• Donuts
• Bars and columns
• Clustered bars and clustered columns
• Stacked bars and stacked columns
• Line graphs

What are Native Charts?

“Native” charts mean they’re available from our menu with just a few clicks:

What’s Wrong with Overused Charts?

There’s nothing wrong with a bar chart here or there… but any chart gets boring when we show it over and over and over and over and over and over.

There’s also the issue of analytical depth — or lack of depth.. If we’re only using bar charts… then we’re only showing totals and averages. There are dozens more statistical approaches!

Beware! Formatting Needed

Stacked bar charts, for example.

They’re easy to make.

But we still have to:

• enlarge the font;
• darken the font (to pass official Accessibility rules for color contrast);
• directly label the data (so viewers aren’t relying on the colored legend alone — another Accessibility rule);
• outline the touching shapes in white (which helps with colorblindness and grayscale printing);
• show fewer increments in the scale (so it’s not so busy);
• decide whether to apply a dark-light contrast — or not (learn about data storytelling here); and
• adjust the gap width (if you want) to nudge the bars closer together.

Level 2: Underused Native Charts

This is where it starts getting fun!!

Excel can make:

• Combo charts (e.g., a column chart with a target line, as shown below)
• Overlapping Bars
• Area charts (where you shade the area underneath the line for better oomph and high color contrast)
• Slopes (a line chart with exactly 2 points in time, like pre and post)
• Small Multiples Lines (to combat the spaghetti line graph)
• Bumps (for rankings)
• Scatter plots (x and y)
• Bubble charts (x, y, and z)
• Tree maps (for nested categories)
• Heat Maps
• Sunbursts (nesting)
• Box and Whisker (to go beyond averages and show the min, quartile 1, median, quartile 3, and max)
• Waterfall (how pieces add to a net number)
• Radar (to compare several ordinal categories at once)
• Icons & Symbols (to make our graphs easier to navigate — and more memorable!)

Yes, These are Native Charts

Well… if you’re using the latest version of Excel.

If you’re on outdated software, (most of) these charts are still possible. They just get harder to make, i.e., they’re in Level 4 territory.

Yes, Underused Native Charts Add Variety (and Analytical Depth)

We’re not just adding variety for variety’s sake.

(Although common sense — and hundreds of consulting projects — has shown me that dataviz novelty is one of the best ways to increase engagement.)

Most importantly, we’re adding analytical depth. For example, a regular ol’ bar chart just compares the average or total of several categories. What if we compare them by location, too? Now we’ve got a heat map! We can spot geographical patterns, which would’ve been impossible in a bar chart.

Beware! Formatting Needed

Scatter plots are easy to make.

But we still have to:

• enlarge the font;
• darken the font (to pass official Accessibility rules for color contrast);
• add a key (that each dot represents one student);
• label the scales (with everyday language, like More skills gains, because scatter plots are notoriously difficult to read for people who don’t stare at graphs all the time); and
• decide whether to add a dark-light contrast.

Level 3: Non-Native Charts

Are you already using a variety of charts? Have you actually analyzed your data (beyond averages, and beyond totals)? Can you adjust the gap width, annotate the data, and apply colors strategically in your sleep?

Then you’re ready for Level 3!

With behind-the-scenes elbow grease, you can make:

• Stream Graphs
• Waffles
• B’Arcs
• Small Multiples Bars
• Population Pyramids
• Diverging Stacked Bars
• Lollipops
• Dots
• Swarm Plots
• Tile Grid Maps
• Sankey Diagrams

What are Non-Native Charts?

You won’t find any buttons that automatically make these charts.

Instead, we have to insert one chart type… and disguise it as something else.

For example, we have to insert a stacked bar chart… and disguise it as a waffle chart. You’ll need a Magic Table behind the scenes, too.

A stacked bar chart gets disguised as a population pyramid. Yes, you’ll need a Magic Table with placeholder values.

A scatter plot gets disguised as a dot plot, and so on. Each value gets assigned a x-y placeholder location inside the Magic Table.

Do these maneuvers turn your brain inside out and upside down? You’re not alone.

Learn More

If you’re consistently making, editing, and applying graphs from Level 3, you’re already a vizard. Get in touch so I can send work your way!

If you’re in Level 1 or 2, you’ll love Data Storytelling in Excel. You’ll go slow and steady so you don’t feel overwhelmed. You’ll dip your toe in… and then you’ll be swimming in the deep end in no time.

The post Which Graphs Can I Make in Excel? appeared first on Depict Data Studio.

It’s *not* easy to make dot plots in Excel.

These are non-native charts — meaning we’ll have to reconfigure our table, and use a scatter plot(!) — to trick Excel into making our dot plot.

The learning curve is worth it, promise.

Download the Excel File

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

What’s Inside

• 0:00 How to Make Dumbbell Dot Plots in Excel
• 0:25 Dataviz On The Go
• 0:33 Dot Plots are Non-Native Charts
• 1:03 It’s a Scatterplot (?!?!)
• 1:50 Pep Talk for the Perfectionists
• 3:23 Live Drawing Demo
• 6:59 Color-Code Your Table
• 7:08 Stack Your Table
• 7:43 Add “Y” Values
• 8:28 Sort and Put the “Y’s” Next to Each Other
• 9:09 Insert a Scatterplot with Straight Lines and Markers
• 9:45 Ack!
• 9:52 Remove the Lightning Bolt
• 11:02 Format Format Format
• 11:46 Your Turn: Questions? Comments?
• 12:05 A Personal Note

Transcript

[00:00:00] In this tutorial, I’m going to try to teach you how to make dumbbell dot plots in Excel.

I say “try” because, usually, in workshops, this takes about 30 minutes to teach, and everybody’s got laptops, and I demo a skill, and they practice, and I demo, and they practice, and it’s a captive audience.

On YouTube, everybody wants things really, really quickly, and I don’t know if I can cram this into five minutes.

… … , we might need 10, though.

You’re watching Dataviz on the Go with me, Ann Emery. Because you’re busy, I’m busy, let’s get to it with some jet speed tutorials.

Now, the first thing you’re going to notice about dumbbell dot plots is, when you highlight your table and you go up to insert, just like you normally would to add a new chart, you can look all day long…

You are not going to find a dumbbell dot plot up there. It is not here. It is not here. It’s not there.

It is not a built in chart. It is not a native chart. It’s a non native chart.

That doesn’t mean we can’t make it, but we have to do some behind the [00:01:00] scenes magic tricks to make it happen.

So we’re going to choose a similar chart, similar ish, kind, kind of, kind of not, right?

The scatterplot, the scatterplot especially with the straight connecting lines. We’re going to use a scatterplot as the foundation. And we’re going to disguise it and make it look like a dot plot.

This is a very advanced, very sophisticated, very brain bending way of thinking about Excel. It’s a scatterplot that looks like a dot plot.

So what that’s going to mean is we have to assign each of these dots X, Y coordinates, which means our table, like this, It’s going to have to be totally reconfigured and that’s going to be a little bit tricky to figure out, but that’s why I’m here to walk you through it.

So if you’re a perfectionist, please go easy on yourself. Just give yourself a break. There is a learning curve here. This is not an easy chart type at all, but it’s worth it. I [00:02:00] just, please keep going. Please, please, please.

And if you’re tired, like I clearly am just scroll down below the video, because I’m going to give you this spreadsheet with the template that I’m using so you can just download it and just punch in your numbers and not have to start from scratch. So you don’t have to fight with Excel so much.

All right, let’s keep going. Let’s transform our original table into a magic reconfigured table.

The first thing we’ve got to do though, especially the first few times you’re doing this is You’ve got to sketch it, please, please don’t skip this step. You need to draw out your dot plot and figure out where each dot’s going to go so that later as you’re making it, you can compare and say, “wait, is this in the right spot?””

No, I need to sort my table different.” “Wait, I think this is going wrong.” And you can kind of diagnose your own errors that are going on as you’re learning.

So I’m going to use my draw feature on my computer, but you might just use a good old paper and pencil when you draw your dot plots.

Okay, so let’s draw this out and I’m going to show you how it’s [00:03:00] a scatter plot with XY coordinates, that is ultimately gonna look like a dot plot.

Okay? That’s the most important thing for you to remember. It’s a scatter plot that looks like a dot plot. And then the other thing is just draw it. Please. Please,

Alright, let’s draw it. So first we’re gonna draw our xy. Okay, this looks like eighth grade math class, doesn’t it?

Here’s our x, y, x, y coordinates. That’s what a scatterplot has going on behind the scenes. Then you’re going to draw your scale.

This fictional scale goes from, uh, zero to ten. Yours in real life might go from zero to ten million. It might have percentages. It might have currency. This works with all the units. It doesn’t have to be zero to 10. It can be whatever your real life units are.

And then the height, uh, these are gonna be our categories that we’re comparing. We’re going to have category [00:04:00] A, B, C, D. I’m going to draw some grid lines in here. That’s why I’m using gray. And then let’s figure out where our dots go.

And remember, um, we’re just, I’m just drawing this. Okay. I’ve already made the finished version to show you where we’re heading, but in real life, you’d be like, what, where, what is this going to look like with my numbers and my percentages?

Okay. And let’s be consistent. Um, I think, what do I have group one in green? Yeah. Let’s always do group one in green. Let’s always do group two in purple. We’re going to use Mardi Gras colors. Why not? Why not? Consistent color coding absolutely is going to help your brain to figure this out. So please do this as you’re sketching on your own paper.

Uh, group one, let’s do those in green. Group two, we’ll do in purple.

Let’s draw out our dots and then we’re going to assign them X, Y coordinates. So that first 8. 1, where is that going to [00:05:00] go? It’s going to go over on the X, 8. And then it’s, it’s the A, so it’s going to go right here. Okay. That’s that dot and let’s assign it an XY coordinate. So that is eight.

What is it? 8. 1. That’s my X comma, the Y. is, it goes up, one, two, three, four. Let me write that out for you just to make it really easy to follow. One, two, three, four. It’s very hard to write with a mouse. 8. 1 comma four. Okay, the next one 5. 6 that goes over on the X about this far, but that’s about where the 5.

6 would be. And then it goes, that’s 5. 6. It goes up on our, [00:06:00] like, Y, Y in air quotes, right? Our fictional Y, our placeholder Y, it goes up three. Let’s do maybe one or two more. Uh, the 4. 5. Okay. So it goes over 4. 5. That’s approximately here. XY coordinates, 4. 5 comma 2. Yep. You’re right. And the next one is 8. 6.

That’s around here. 8. 6 up 1. You do the same thing for your purples. Uh, that’s a 5 around here. That is five comma four. This one is What is it? Oh, 2. 3. That’s around here. 1. 3, 4. 4. You get the gist of it? Okay. Draw it out so you can envision, like, where’s everything going? Did I get it right?

It takes about this long.

Next up, let’s take our original table and [00:07:00] we’re going to color code it. You’re just going to add some fill behind it, okay, to keep yourself all organized.

Then, we have to re orient, you’re just going to do a copy paste, you’re going to stack it. Instead of group 1, group 2, beside each other, you’re going to stack them.

You’re just going to say group 1, group 1, group 1, group 2, group 2, group 2, and, oops, this isn’t group, this is, this is your value, okay, which is also known as your X. Your X in quotes, cause it’s not a real X. It’s like the X value that we have to type into Excel to make this all, you know, work and be figured out behind the scenes.

All right. The next thing we’re going to do is we’re going to add some Y values. And again, this is supposed to be X. This is Y. Okay, so we’ve got the groups repeated, we’ve got the X’s, and then we’ve got the Y’s. Remember, we already figured out what the Y’s would be. Uh, here is another tip to make sure you’re doing it right.

[00:08:00] Figure out how many dots you’re going to have. It’s one, two, three, four, five, six, seven, eight. 1, 2, 3, 4, 5, 6, 7, 8. Okay. Eight dots means eight entries on your table. 1, 2, 3, 4, 5, 6, 7, 8. Eight dots, eight entries, eight sets of X and Y coordinates. All right, let’s keep going. The next thing you’re going to do is you’re going to sort it and you’re going to put your Y’s next to each other.

So your fours are going to go together. Your threes are going to go together, your twos and your ones. Don’t overthink sorting. Okay. It just means you take your table with your X’s and your Y’s and you go to data and you go to sort and you say, I’m going to sort by my Y. And it doesn’t matter if you do smallest to largest or largest to smallest.

Okay. It doesn’t matter. It just means they have to be next to each other. Like here, the ones are next to each other. Okay. [00:09:00] Here, the fours are next to each other. Do you see how the color coding changed? It’s green, purple, green, purple now, right? The next thing we’re going to do is we’re going to highlight just the inside of the table, just the interior that I’ve made darker for you.

And I outlined it in black to make sure you can see it. You’re going to go to insert and you’re going to insert our. Scatterplot. If you want it to look like a dumbbell dot plot with a connecting line in there, you’re going to pick this one, the scatter with straight lines and markers. If you just want the dots by themselves, you can pick this one.

Okay. I wouldn’t pick wavy. That would be super weird. I wouldn’t pick this one. That scribble, scrabble. Okay, I’m going to do this one for us. And then you get the Harry Potter lightning bolt and you’re like, and, uh, that’s not what I wanted. That’s okay. We can remove the lightning bolt. It’s going to look more like this.

Can you start to see it? Can you see your dot plot to remove that connecting lightning bolt? Okay. So like, here’s the [00:10:00] connecting line I want to remove. It depends how your table is sorted of which dot you click on. It’s going to be either this dot or this dot. Okay. So you’re just going to try one. And if the wrong line is removed, you’re just going to click undo and you’re going to try again.

So let’s try this one. Okay. I’m going to click on this dot cause I’m going to guess that it controls this line. So if you click on this dot the first time, All of the points are selected. You click on it a second time so that just that dot’s there. You’re going to hold your mouse over that spot. You’re going to do a right click, go to outline and say, no outline.

No, thank you. Okay. Part of the lightning bolt’s gone. Let’s do it again. This dot controls this connecting line. Click on it once, twice, right click. Outline no outline. Okay, you’re gonna get in the rhythm of it. It’s just gonna take this long. Don’t don’t worry It’s really really quick. Okay, there’s your dot plot kind of right you have to [00:11:00] Format format format that takes a little bit more time Things to keep on your radar would be you can control the color of the dot You can add the, uh, labels right here.

I just added the group names in text boxes, but the labels are built in. Uh, you can do this fancy ways to add whatever your category labels are. Honestly, I usually just do text boxes because I find it’s actually, uh, faster in the long run. I should say I don’t do text boxes, plural. I do see one text box that I very carefully format so that it’s lined up with its grid line.

You’re going to adjust the min and the max as you need to. You might make the connecting line thicker. All the normal things. All the normal formatting things that I cover in all my other videos and blog posts. All right, it is your turn. Comment below the video. Let me know. Are you totally lost? Are you kind of following?

How are you feeling about this scatterplot into dotplot thing? Do you want the spreadsheet? It’s there. All right. Good luck. Good luck with [00:12:00] your dotplots. Please sketch. And have fun. Bye.

Finished the video. Made a quick dinner. Now we’re outside doing turkey trot training. I’m gonna try to run with the twins and the big guy.

Hi, big guy. Well, the girls, where are they? Running with daddy.

The post How to Make Dumbbell Dot Plots in Excel appeared first on Depict Data Studio.

Maybe you’re already using them…

Maybe you can make ’em in your sleep…

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

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

What’s Inside

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

Download the Spreadsheet

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

Transcript

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I wouldn’t lose sleep over this.

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

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

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

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

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

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

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

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

She’s sharing examples of slider plots and step-by-step instructions for making them in Excel. Thanks for sharing, Lauren! –Ann

—

Hey everyone! Lauren Fox here from the Research & Evaluation Division (RED) of The University of Arkansas for Medical Sciences. Our group focuses on translating research into practice in fields like early childhood education, child nutrition, abuse prevention, and HIV education.

Much of my job involves working with faculty members and project leads to develop evaluation questions that lead to actionable data.

One of the biggest puzzles we face is how to translate those results (visually and verbally) so that everyone from expert audiences to laypeople can understand our findings and benefit from them.

The data viz world is full of options for visualizing basic data such as change over time, pre/post differences, and percentages/frequencies for a single point in time.

Sometimes however, your data (or your audience) demands a little more.

Case-in-point: When displaying margin of error is important.

The Challenge: Displaying Margin of Error Data

Back before the pandemic, one of our faculty asked for some help in visualizing her data for a conference on childhood nutrition.

I dressed it up the best I could, but it still fell far short of the best practices for data visualization. I figured there had to be a better way to display data with margins of error (a.k.a., the “95% confidence interval”), and set out to find it.

Spoiler alert: I didn’t find anything. So, a little bit at a time, over the course of several months, I built it myself. I call them “slider plots.”

Full disclosure: I didn’t know until after I developed these, but Stephanie Evergreen posted a rough sketch version of this idea using auto-calculated standard error bars back in 2017. Her title used “confidence intervals,” instead of “margin of error” so I missed it in my initial search.

While that means the basic idea behind my “slider plots” isn’t completely new, I’m still excited to build on her work and share this as a small step forward in chart design!

The Old Way

The “old way” involves using column charts with error bars.

The New Way: Slider Plots

Slider plots can be vertical or horizontal. Here’s an example of a vertical slider plot that shows policy ratings from four different neighborhoods.

Here’s a second example of a vertical slider plot that shows teacher ratings in four different schools.

Here’s what a horizontal slider plot of those policy ratings would look like.

And finally, here’s the horizontal version of the teacher ratings.

Download the Excel File with Step-by-Step Instructions

The process to create slider plots follows many of the same steps as creating dot plots and adds a few more to create and customize your margin of error bars.

Start to finish (from creating a data table, to building your dot plot, through creating and customizing your error bars), there are 15 steps, plus a few optional sub-steps.

I’d like to list them all here, but this post would definitely get a TLDR citation from the blog police (Too Long, Didn’t Read).

While many of the steps are similar, vertical slider plots are easier to build so I recommend you start with those first.

The horizontal version may be harder to build, but it has the same readability advantages of classic dot plot we all know and love.

As a bonus, you can download a free Excel file with step-by-step instructions and screenshots, as well as an end-product template you can use to make the process much faster.

Winning Hearts & Minds with Slider Plots

While slider plots do take some time to set up, the payoff for your effort is helping to expand the reach of data viz.

Many in the evaluation community have begun to adopt better data visualization practices to help communicate their work over the last few years, but there are still many spaces (workplaces, conferences, etc.) where we find resistance.

Some of that is fear of judgement; that we won’t be taken seriously as scientists by our colleagues if we present data in non-traditional ways.

If there’s one thing I’ve learned from being an evaluator in the early education space, it’s that if you want to change people’s minds (and then their behavior), you have to meet them where they are.

I’m under no illusions this chart type will suddenly convert all the data viz detractors or revolutionize the field.

However, the changes are small enough and familiar enough that they might be a bridge to expert audiences; a way they can slowly grow more comfortable with the idea that presenting data differently doesn’t make you less scientific.

Know Your Audience

As cool as it is to do something new, it’s important that I leave you with this reminder:

Most of the time, margins of error will not be important enough to visualize unless you’re dealing with an expert audience.

It will most likely confuse or distract less-advanced audiences from the point you’re trying to make.

However, you can try adding a little more explanation in the graph subtitle to bridge the gap (see my slider plots above for examples) if it’s critical for your lay audience to see the margins of error as well.

Connect with Lauren

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

The post How to Visualize Margin of Error Data in Excel with “Slider Plots” appeared first on Depict Data Studio.

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For the past few months, I’ve been developing dashboards to support students’ understanding of local ecology and equip them to use that local understanding as a baseline to explore the rest of the world.

Imagine, for example, being a 10-year-old in St. Louis.

Your neighborhood has plenty of trees since you’re at the western edge of the forests typical of the eastern US. This can only happen because the temperature is warm enough and there is enough precipitation to support tree growth.

Heading west from here the ecology shifts pretty quickly to grasslands, with the grass getting shorter as you approach the Rocky Mountains. A quick look at the data shows decreasing levels of precipitation as you head west.

For a more extreme contrast, Yuma, Arizona is much hotter, but the area gets about 10% of the precipitation St. Louis does. How is this heat and lack of precipitation reflected in the plants and animals in southern Arizona?

All of this learning is wrapped under the heading “What’s It Like Where You Live?” – a program I used as a 4th grade teacher 25 years ago, developed by the Missouri Botanical Garden and now undergoing a major reworking.

As we flesh out the curriculum, we’ll be supporting kids’ local field work with dashboards synthesizing climate data and images of plants and animals typically found in different ecoregions.

First Forays

At a basic level, students can compare temperature and precipitation data for their local community with data for other cities around the world. Is it warmer or cooler, wetter or drier?

A simple table or a scatter plot serves the purpose quite well. The limit in this approach is the image students often develop when data from one city represents “the desert” or “the rainforest.”

Resolving this conundrum has opened the door to some exploratory work crafting dashboards which encompass both similarities and variation within an ecoregion. (Mostly I’ve just been geeking out with the data, but “exploratory research and development” sounds so much better!)

Refinements

Taking the data visualizations further has pushed me to walk a fine line between interesting visualizations and the developmental capacities pre-teen students bring to the task.

Most kids have limited experience with data tables and graphs, and what work they have done is pretty specific (such as graphing pizza preferences among class members).

Graphs showing means (or even means of means) risks becoming too abstract without the right supports.

After exploring a few options, I settled on a representation which captured both the spread of data typical of cities in a given ecoregion and the mean value of these cities.

Major thanks are due here to Ann Emery for streamlining the look and feel of this version. Her focused, uncluttered design aesthetic is a perfect match for this work.

I’ve tested this out with a few kids with good results, but COVID restrictions have kept me from seeing how a broader pool of students make sense of this display. I’m hopeful that restrictions will be lifted in the new school year so we can move forward with some pilot testing.

Going Further

To be sure students remain connected to their local base, I needed an anchor which is ideally movable so that students in other areas can use the materials.  For this, I’m indebted to Jon Schwabish of the Urban Institute and PolicyViz.

While participating in a workshop he led, a couple of techniques we were using came together. By combining a single point scatter plot and error bars, a reference line can be inserted to mark local conditions.

If this strategy proves useful in our pilot testing, I expect that we will be able to support localization so that students anywhere could enter their own data and have VLOOKUP or a similar procedure to change my St. Louis reference line to one appropriate for any student’s home city.

The work so far has been an enjoyable way to explore data and apply the many things I’ve learned in Ann’s workshops and elsewhere. I’m looking forward to seeing how students use the data when we begin pilot testing. 

The post Dashboards for 10-Year-Olds: Connecting Data to Students’ Lived Experience appeared first on Depict Data Studio.

I am so excited to share how grateful I am to Ann and her colleagues for putting together an online training to share their talents with me. 

In return, I am paying it forward by teaching my Advanced Excel students at Central Washington University a tiny portion of how they can use Excel as a data viz tool and application. 

Here’s how my own work and my students’ work evolved after taking the data visualization course.

My Own Challenges to Communicating Results

One of the biggest challenges I faced as an evaluator was to show “differences” between “pre & post” or “before & after” an intervention other than a typical table of numbers.

Most evaluation reports were made up of narrative and making references to tables of results. When I heard about Ann K. Emery and how she used Excel to produce dynamic and creative stories about programs, I was all about learning more and so I enrolled in Simple Spreadsheets and Great Graphs last year.

Before: Tables to Show Pre-Post Differences

Here’s what my own work looked like before taking two of Ann’s courses—a table with key statistics.

After: Dot Plots to Show Pre-Post Differences

I used several of Ann’s lessons in Great Graphs to create more visually appealing and easier to read charts to show differences in the intervention of leadership education for teens.

For example, I used Ann’s step-by-step process to create a dot plot chart to show the pre- to post-test difference.

She provided a template that walked me through each step of creating the dot plot and now I have the template to keep creating dot plots:

Here’s what my work looks like now:

Editing My Own Visuals

In addition to the dot plots, Ann provided clear instructions for how to work create, edit, declutter, and place charts in a variety of reporting formats.

Before: A Black and White Table with Little Font Size Variation

The next example is related to an individual survey item which was statistically significant between the participant and comparison group.

After: Using Dark Colors and Larger Font Sizes to Highlight Key Details

The same information is included in each chart, but hopefully the “After” is easier on the eyes!

Training University Students on Data Visualization

I also wanted to expose these tools and applications to students that are enrolled in the Advanced Excel Spreadsheets course at Central Washington University.

First, I require students to sign up for Ann’s Soar Beyond the Dusty Shelf Report mini course so they can get acquainted with what data visualization is and how they can use Excel beyond just pivot tables, what if statements, and business intelligence applications.

Next, there is one assignment for the students to use an existing set of data (i.e. work-related, volunteer project for non-profit, or choose a data set I provided for them) and create a “before” and “after” data viz using the tools and techniques from the mini course and an extensive review of Ann’s blog.   

A University Student’s Before-After Transformation

One of my students, Kelly, volunteers for a small newly organized non-profit organization. They needed help developing an overview of their organization and information showing how they are good stewards of financial donations.

Before: A Table

Here’s what the information used to look like: a table.

After: A One-Pager with Graphs and Narrative

Kelly provided a one-page document that briefly explained the organization’s mission and program, along with their non-profit status. 

She included two column charts as the data visualization portion of the document representing dashboard of finances (income) and stewardship of funds (expense/costs) to clearly define the majority of the contributions are in direct support of immigrant families. 

She also included small narrative blocks to briefly highlight grant and corporate donors, in the first block, and that the majority of funds raised go to directly support immigrant families.  Both corporate contributions and direct support of immigrant families were the two items that this organization wanted clearly stated.

I love to learn and one of my professional goals continues becoming a better communicator of information using data visualization tools and techniques.  I am going to continue seeking out opportunities with Ann and her team and learn more about data viz every day! 

Connect with Lori

Connect with Lori Thompson on LinkedIn: https://www.linkedin.com/in/lori-a-thompson-ph-d-8023b85a/.

The post Teaching + Knowledge = Passion for Data Viz appeared first on Depict Data Studio.