Sometimes, the line you want to highlight… is stuck all the way in the back. Nobody can see that line, because it’s all covered up. Oops!

In this video, you’ll learn how to rearrange the line chart so that the line you want your audience to see isn’t covered up.

Watch the Tutorial

What’s Inside

• 0:00 The Problem: Excel Graphs with Covered-Up Lines
• 0:23 Welcome to Dataviz On The Go
• 0:31 The Dataset
• 0:47 Another Problem: The Spaghetti Line Chart
• 1:10 3 Fixes for Spaghetti Line Charts
• 1:37 The Edited Graph – But Average Line Hiding and Covered Up
• 2:06 The Solution & Where to Click in Excel
• 2:50 Bottom of the List… Front of the Graph
• 3:04 Download the Spreadsheet
• 3:11 Don’t Forget to Like, Subscribe, and Share

Download the Spreadsheet

Want to download the Excel file and follow along?

Download the file here: https://depictdatastudio.ck.page/movelinetofront

Read the Transcript

Ann K. Emery: [00:00:00] In this video, you’re going to learn how to bring the average line to the front of your graph. So instead of covered up and hiding, it’s just going to be in the front so people can actually see it. If this sounds like something that might be relevant to you and your workplace, stick around. I’ll give you some context about this graph, and then I’ll teach you how to fix it right inside of good old Excel.

Hi, I’m Ann Emery, you’re watching Dataviz On The Go, the series where I make jet speed tutorials as I’m racing around between my workshops and meetings. And speaking of workshops, this is the real life, sort of, table that I was working on in a workshop recently. These aren’t the real state names. These aren’t the real timeframes or the real numbers, but the graph looked more or less like this.

What happened was, when we highlighted the table and we Inserted the graph. We were left with Ann Emery’s least favorite chart of all time, the spaghetti graph. This happens all the time in real projects. So if you get a spaghetti [00:01:00] graph, don’t worry. There are some common fixes and just to make it super duper clear what we’re not doing.

Let me just make it really, we’re just, we’re not doing this. Okay. Are we in agreement? We’re not doing this. Common fixes. Common fixes, uh, you could either gray everything out, highlight one, one line at a time, that’s what we’re going to do here. Another fix, you could do small multiples, or another fix is interactivity.

Okay, those are the three common fixes for really busy, really dense graphs. In this case, we wanted to gray everything out and highlight one line at a time so people could see the average. They could see the big picture. Here’s, on average, how all of our states are doing, but then a split second later, They could look a little bit further and they could see all the gray lines.

One star of the show, lots of sidekicks. But then the problem we ran [00:02:00] into is like, the average line is missing, or, not missing, but it’s hiding. It’s really hard. You can’t see it. Where’s our main character? So let me copy paste this one so we can admire them side by side. I’ll show you what to click on. You’re going to click on…

The middle of the graph. Not the side, not the top, not the bottom. Okay, the middle. You’re going to right click. You’re going to go to Select Data. I have a love- hate relationship with this menu. I think there’s a little bit of a learning curve. All you need to know is: the thing you want to be in the front of your graph needs to go on the bottom of the list.

I’ve clicked on the average here. I’m going to tap down, tap, tap, tap, tap, tap with the arrows. Is that at the bottom? Yeah. The thing that’s on the bottom of the list is going to be on the front of your graph. Bottom of the list, front of the graph. Super counterintuitive. You would have thought that Microsoft would do it the other way around, but they didn’t.

Okay. I put the average on the bottom of that list. [00:03:00] Therefore, it’s on the front of the graph so people can actually see it. If you want to download this and click around and explore some more, check below the video. I’ve got a link where you can grab it for free.

Don’t forget to like, subscribe, and share.

Here’s my definition of data storytelling, in case you missed the previous blog post. 

Great!  

Let’s remove the guesswork from our graphs. 

The next step is to figure out which message we’ll highlight. 

We can’t visualization everything—that dilutes the power of our graph. 

What Makes a Useful Data Story? 5 Questions to Ask

Here are five thought-starter questions to help you uncover useful nuggets in your data.  

1. What’s Going Well? What’s Not Going Well? 

2. Did We Reach Our Goals? Why or Why Not? 

3. What’s Surprising? What Unfolded as Expected? 

4. Which Information Needs to be Shared with Others? Who Needs to See This Information? 

5. What Increased Over Time? Decreased? Stayed the Same? 

If you analyze data for a living, then I bet you’re already asking yourself these questions. You were probably trained to approach data this way in grad school. Or, it’s become intuitive after years of on-the-job practice. 

Dabblers in data, this one’s for you.  

Newcomers to data, this one’s for you.  

What’s Going Well? And What’s Not?

Everyone loves a success story.  

Look through your dataset.  

Find the good news and highlight that finding with dark colors and takeaway text.  

I often start with positive findings so that my audience can celebrate a small victory right away. 

But, facilitating an honest conversation with data visualization requires balance.  

After presenting positive news, I present the less-than-positive news.  

For example, the next graph in my report might use a darker color to draw attention to something that isn’t going well. 

Did We Reach Our Goals? Why or Why Not? 

I consult to dozens of grantmakers and grantees each year—Federal, state, and local government agencies, foundations, and nonprofit organizations.  

In the grantmaking world, it’s common for funders to ask their grantees to explain whether they are meeting their targets.  

For example, one goal of a parenting program for teenage mothers and fathers might be to avoid repeat pregnancies. The health centers and high schools that are running the program might have to report whether there was, in fact, a decrease compared to a control group.  

Graphing these goals is an obvious choice. 

What’s Surprising? What Unfolded as Expected? 

Take off your data nerd hat. 

Put on your human hat.  

Step outside the math for a bit.  

Trust your gut instinct.  

I look for numbers that are surprising and unexpected.  

What’s surprising to you, personally?  

Surprising new facts make for interesting reports.  

Nobody wants to read the same old stories over and over and over.

Which Information Needs to be Shared with Others? Who? 

This thought-starter question keeps your data actionable.  

Examine your numbers.  

Who, in particular, needs to see these numbers?  

Think about all the different people who are involved in your project.  

Are there certain takeaway findings that your boss should probably know about? Or the boss’ boss? Or someone outside the organization?  

Who might act differently or make a different decision based on this new information?

What Increased Over Time? Decreased? Stayed the Same? 

Most projects have numbers available at multiple points in time.  

Examine how your numbers are changing over time, if at all.  

Sometimes a number will increase over time. Other times, a number will decrease over time.  

And other times, you might not notice any difference whatsoever. Flat lines can be useful, too! 

Data Storytelling Example: Highlighting a Flat Line in a Workforce Development Project 

I changed around the details, but this example is based loosely on a past project.  

Let’s pretend that you’re leading a career coaching program for adults who recently immigrated to the country. I consulted on a project like this a couple years ago.  

The purpose of the career coaching program was to get those adults into higher-paying jobs.  

A few times a year, the career counselors collected data on the participants. For example, they asked the participants how much they were being paid. The career counselors might even verify their wages by looking at pay slops or tax forms.  

The person responsible for compiling all this data should see whether wages are improving, declining, or staying steady.  

Imagine that you uncovered that wages for most program participants were staying steady—despite hundreds of thousands of tax dollars being poured into this program. 

That flat line has to be shared and talked about! Something needs to be adjusted ASAP. 

Your things-stayed-steady-over-time graph might look like this. 

We applied several data storytelling techniques. I bet you recognized them right away: 

1. We’ve got color contrast (all 30 participants’ individual lines are grayed out, and the average is highlighted in a darker brand color). 

2. We’ve got a takeaway title (“Wages Did Not Increase”). 

3. We’ve got numeric labels on a handful of key data points (the $18.27 average wage at the beginning, and the $18.30 average wage at the end). 

4. We’ve got (light) narrative annotations (“Average hourly wages: $18.30”) 

Looking for Useful Stories throughout the Analytical Process 

When do you look for possible data stories? 

Not the day before your project’s due!!!!!!!!!!!!!!!!!! 

Revisit these questions at each stage of your project’s analytical process. 

Look for Useful Stories in the Raw Data 

I start with my spreadsheets of raw data.  

I ask myself, “What’s going well? Did we reach our goals? What increased over time? What’s surprising? Which information needs to be shared with others?”  

I keep a running list of interesting nuggets in a notebook. 

Look for Useful Stories as You’re Compiling Tables for Your Appendices 

Later, I compile my analyses in tables. The tables often go in the appendix of a technical report.  

This means that I write the last pages of my report first.  

As I’m designing the tables, I ask myself those five questions again, and I add to my running list. 

Look for Useful Stories as You’re Designing Your Full Reports or Slideshows 

Next, I write my report (or create my slideshow, or whatever the finished product will be).  

I look through the tabulated data as I’m designing the report: Which numbers deserve to go into the body of the report?  

Look for Useful Stories as You’re Designing Summaries (One-Pagers, Infographics, Briefs, etc.) 

Finally, when my full report/slidedeck is complete, I pull out graphs that are so interesting that they deserve to go in a summary.  

I’m using the word summary loosely here.

A summary could be a one-page handout, an infographic, a shorter brief, etc. 

Yes, this is the place for those stories to shine.

Yes, you should’ve found stories along to way to include in your summaries. Hopefully!!!

This stage gives you one more chance to think carefully about useful gems in your dataset.

Don’t wait until the end of a project to think about the “so what?”  

This should be an ongoing, intentional process.  

When we think deeply about the data, our audiences will benefit from the added clarity. 

Your Turn 

What’s your process for uncovering interesting stories in your data? 

Do you have more thought-starter questions to add to the list? 

If you’ve read this blog before, or heard me speak, then you know that designing data visualization makeovers is one of my favorite activities of all time.

I love redesigning pie charts, in particular.

The vast majority of data visualization trainings just advise people to stop using pie charts… without teaching people what to do instead.

Every year, I work with dozens of organizations, and every single organization still has pie charts sprinkled throughout their reports, slideshows, dashboards, and infographics.

Hearing that you’re supposed to avoid 3D exploding pie charts with a hundred tiny slices is beginner-level stuff. I need to train you on useful alternatives–that’s the advanced-level stuff.

I created this before/after pie chart makeover a million years ago but forced myself to wait until March 14 to share it. Phew! It’s been a long wait.

Before: Two Pie Charts

I recently worked with a grantmaking organization. They awarded grants to support various research projects. For anonymity, I’ve changed the names of the research projects to A, B, C, etc.

The grantmaking organization simply wanted to look for patterns in their funding over time.

Their “before” version looked like this:

Two Slices Only

A major guideline for pie charts is that they’re easiest to read with only two slices.

At the very least, we’d need to collapse the seven slices into just two slices. For example, you may choose to focus on research topic A with a dark-light contrast.

Dark-light contrast is helpful, but it’s not enough. We have to keep editing.

Don’t Make Viewers Zig-Zag Their Eyes Across Different Graphs

Another guideline—for all charts, not just pies—is that you don’t want to make viewers make comparisons across multiple charts.

If you want them to make comparisons, then put those things next to each other, not several inches apart from each other.

Here’s how my eyes have to zig-zag back and forth to read the original:

The collapsed, two-slice version still requires zig-zagging eye movements:

Let’s make the comparisons faster and easier.

The grantmaking team and I put our heads together. We came up with a few alternatives.

After: Stacked Columns

Being researchy types, the first alternative we came up with was stacked columns. Stacked columns are just rectangular versions of pie charts.

There’s nothing inherently wrong with stacked columns. But, yikes! This alternative felt way too busy. There are seven segments in each column, which is too many…

… unless…

…. you use dark-light contrast to focus your viewer’s attention one just one segment at a time.

After: Slope Graphs

Since we’re comparing patterns over time, how about a slope graph?

A slope graph is a fancy name for a line graph that has exactly two points in time.

Spaghetti Slope

Here’s what we tried:

Yikes! We had good intentions, but we accidentally created a spaghetti slope graph.

When this happens, don’t fret. It’s not your fault. It just means your particular dataset had percentages that criss-crossed and overlapped too much.

Spaghetti Slope with Highlighting

There are a couple ways to detangle spaghetti graphs.

One option is to guide viewers’ eyes to just one thing at a time with dark-light contrast:

Which line(s) will you highlight?

Use your best professional judgment.

Think about your unique audience. You might highlight something that increased, like B. Or you might highlight something that decreased, like D. Or, you might highlight something that remained steady over time—perhaps that thing was supposed to go up or down, but didn’t, and you’ve got an interesting story to discuss.

Small Multiples Slope

Another way to fix a spaghetti graph is with a small multiples layout.

Small multiples means multiple small charts.

You could produce seven mini charts, one for each of the seven research topics.

But better yet, let’s group them into categories that will give our viewers more insights into the patterns—the fact that some research topics received more funding while other topics received less funding.

I color-coded by category (one color for increases, another color for decreases).

Finally, I added icons to boost memorability.

This one’s my favorite. Swoon.

After: Dot Plot with Arrows

I intentionally sorted the categories into those that increased and those that decreased. It’s kind of like a small multiples dot plot.

I also included arrows, instead of just regular ol’ circles or dots, to reinforce the direction of the changes.

I color-coded by category (increases in one color, decreases in another color).

Finally, I added icons to boost memorability.

Join the Conversation

Team, you know the drill! Comment and let me know which alternative is your favorite and why. I’m personally drawn to the small multiples slope graph.

Bonus! Download the Materials

Purchase the spreadsheet that contains these graphs.

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