area graph to highlight a line

I don't use a lot of area graphs. But I found myself pausing on one that was submitted as part of the recent annotated line graph #SWDchallenge. It was created by Mike M. and the interesting thing to me was that the focus of this particular area graph wasn't on the area so much, but rather on the line that separated the areas.

This apparently stuck with me, because I found myself recommending a similar approach in a recent client makeover. 

The original graph looked something like the following (data has been modified to protect confidentiality):

Area to highlight line_1.png

This is collections data from a bank. In case you aren't familiar with how collections work, typically an automated dialer makes calls to overdue accounts. The grey bars above represent total dials made. When someone answers the phone on the other end, the dialer connects them to a collections agent, who talks to the person who hasn't paid their bill and tries to get them to make a payment. The accounts where a person is reached (a collections agent talks to someone) are considered to be "worked," which is what the teal bars above represent. The penetration ratio, depicted by the black line, is...hmm. What is a penetration ratio exactly? This one threw me. I'm familiar with penetration rate, which would be the proportion of accounts that were worked out of the total dialed. So in other words, if penetration rate is 33%, we worked a third of the accounts. The ratio seems less straightforward. I think to describe it, it would be something like "if the penetration ratio is 3, it means we dialed 3x more accounts than we talked to." This seems unnecessarily complicated. Let's see if we can make some changes to how we show this data to make it more straightforward. Oh, and let's use that cool idea that I picked up from Mike M, too.

First, I'm going to remove the secondary y-axis on the right side of the graph and the data (Penetration Ratio) that goes with it. That gets us a simple two-series bar chart:

Area to highlight line_2.png

In the above, we see accounts worked (teal) and total dials made (grey). Dials made is the sum of accounts that were worked and those that weren't reached. So I'm going to change this data slightly—from dials made in grey to those not reached—and stack the bars on top of each other.

Area to highlight line_3.png

We can get the same information out of the view above as the previous one: we can see total dials made (overall height of bars) and within that, the portion that were worked and the portion that were not reached. Notice that because worked series is on the bottom of the stack, we can easily see how it has varied over time. Total dials made have decreased over time, so has the number of accounts we've worked. But are we working a lower proportion of total dials now than we have historically? It's hard to tell here. Let's shift to 100% view to answer that question:

Area to highlight line_4.png

With the 100% stacked bar, we lose the context that overall call volume (total dials made) has decreased over time. But that's ok, because we know it now, so we can state it in words: "Call volume decreased 47% over the course of the year." With the 100% view, we can see that the proportion of accounts that we are working has decreased recently. So in spite of reduced call volume, we are reaching a lower proportion of accounts. Interesting. Perhaps we can make that a little easier to see?

Let's remove the space between the bars and turn this into an area graph:

Area to highlight line_5.png

Bingo! With this view, we can see the proportion of accounts that were worked out of the total dialed. The white line separating the teal from the grey now represents the penetration rate. We can make this clear by adding some text and calling out the most recent data point:

Area to highlight line_6.png

I might add a headline that says something like, "Despite decreasing call volume, penetration rate hit a 12-month low in December." And like that, we've used an area graph to highlight a line.

What do you think? Do you like this approach? What might you do differently? Where else could an approach like this work? Leave a comment with your thoughts!

You can download the Excel file with the above visuals.

our tools don't know the story

A question that frequently arises in our workshops is “What tools do I need to tell stories like you do?” Many are surprised to hear the answer: we’re tool-agnostic. Rather, the concepts we teach are universal. No matter if you’re using Excel, Tableau, PowerBI, R, SAS, or something else, the tools themselves don't know your data, your organization, or your audience like you do. That’s where an analyst adds value by bringing the data & its underlying story contextually to life.

Today’s post was inspired by a real-world makeover of data originally created in a tool highly regarded for data visualization. The client was visualizing advertising data across multiple countries. Their initial visualization looked similar to the one below. (Note: I’ve anonymized the data to preserve confidentiality).  


This chart shows two dimensions of advertising effectiveness: reach (how many users saw an ad) and engagement (how many users clicked on the ad) across several countries (United States, Germany, Great Britain, China, and Brazil). The higher these numbers, the better.

Upon further exploration, we see that the magnitudes of reach and engagement are very different across countries. In China, 52% of users were reached compared to 68% in Brazil. With engagement, the magnitude of the difference is even more pronounced: China’s engagement is 6%, half of Brazil’s 12%.  

Imagine yourself as a decision maker tasked with determining an action plan from these results. If the analyst presented you with the visual above, what conclusions might you draw?  An informal poll of readers might return multiple answers, which demonstrates the danger of letting our tools "tell the story" for us.

Don’t assume two different people looking at the same graph will come to the same conclusion. Add value by highlighting key takeaways for your audience.

An important distinction made in the book, storytelling with data, is the difference between exploratory and explanatory analysis. Exploratory analysis is what we do to find interesting things in our data. For example, the analyst might have asked many questions during the exploratory phase, including (but not limited to):


1. How have these metrics changed over time?
2. Are there geographical differences when drilling down by country?
3. What is the revenue impact of this data?
4. Are there noticeable patterns in users’ behavior that can be used for predicting next quarter’s results?

After exploratory analysis, then we move to explanatory analysis. Explanatory analysis is where we take the interesting thing we found via exploratory analysis and communicate it to our intended audience. In explanatory analysis, often times that requires creating a different visual or using a different tool than we used in the exploratory phase.

Let’s assume that what’s relevant in this data is the varying levels of reach & engagement and therefore, each country needs its own strategy for next year. If that’s the interesting conclusion, how might the analyst communicate this? One option is to use the initial design and visual cues like color and annotations to focus attention appropriately:


In this version, I’ve preserved the horizontal bars, sorted by reach in descending order, and decluttered by removing the border and grid lines. While this a step in the right direction, it still takes a lot of work to read all this text and mentally process the different takeaways:


1. Low engagement/high reach
2. High engagement/high reach
3. High engagement/low reach
4. Low engagement/low reach

Perhaps a different visual would make this more visually apparent. Since these takeaways fall into four quadrants, a scatterplot is another alternative:


We now have a visual with a well-labeled construct on how to interpret the data. The categories on the axes (Many/Few, Low/High) help the audience understand the range of values and where each country falls on that range. For further reading on the importance of categorization, check out this post.

Finally, I’d add back the color & annotations, while being thoughtful about how the audience will intake the information. For example, in a setting where only the Brazil team is present, I might focus attention only on their data:


Or the European countries, where the results are mixed:


Both views would be important considerations in knowing the audience for our explanatory analysis.  

Scatterplots are often used with scientific data, but in this case work well for visualizing categories of differing takeaways. This works because of the additions of text and categorization, which helps the audience process the information. Remember, never make your audience do more work than necessary to understand a graph!

If all the takeaways need to be on one view, I can still leverage the scatterplot while being strategic about the use of color to focus attention appropriately.  


In conclusion, there’s a huge difference between simply showing data from the exploratory phase vs. using data tell the a story in explanatory analysis. Check out the difference between the remade view above vs where we started:  


We have a wide disposal of great tools for visualizing data, but our tools will never know our data’s story like we do. We can add value to our roles and our organizations by bringing the story to life.

If interested, you can download the Excel file with the above graphs.

Want more on story? Check out Episode 2 of the SWD podcast, where Cole discusses her thoughts on, "What is story?" She makes a distinction between story with a lower case 's' (the takeaway, or the so what—the way "story" was used in this post) and Story with a capital 'S,' which has a shape (plot, twists, ending—a narrative arc). Also stay tuned for the next post here, where Cole will recap and share the 75+ annotated line graphs received in response to the latest #SWDchallenge.

Update from Cole: We have a couple of additional views to share based on reader comments. First, the following view is similar to the final visual above, only with text moved out of the graph itself to the side. 

Scatterplot - words on side.png

This next graph was created by Daniel Zvinca, which follows what he calls his "obsessive concern for a flexible design."

Scatterplot alternative_DanZvinca.png

He notes that he preserved color for potential additional enhancement and outlines the following benefits of this view:

  1. More metrics can be added or just one can be used (works fine for 1, 2, ...5 metrics).
  2. More countries can be added. When number is higher, gridlines ever 5 countries or so would help localize the associated values.
  3. Any metric is clearly encoded/decoded and can be used for sorting.
  4. Comments do not require special care, they never overlap (unless they are too long).
  5. They can be defined for several performance levels (e.g. Likert scale intervals). For purpose of this design, bad=dark background, good=light background.

Nice idea, Dan, and thanks for sharing! Thanks also to everyone who has commented and contributed to the discussion, both here and on other posts.

Elizabeth Hardman Ricks is a Data Visualization Designer on the storytelling with data team. She has a passion for helping her audience understand the ’so-what?’ Connect with Elizabeth on LinkedIn or Twitter.  

how I'm building this

“How did you start storytelling with data?” is a question I receive frequently. In this latest episode of the podcast, I’m interviewed by my husband, Randy, and field questions on how it all came to be, including finding balance between work and family, some book-writing tips, and my outlook on where SWD will go from here.

Listening time: 45:30. Links mentioned during the show:

Subscribe in your favorite podcast platform to be updated when new episodes are available. If you like what you hear, please rate or review the SWD podcast. You can find past episodes on the podcast page, including sessions focusing on feedback in data visualization and discussion of what is story?


SEARCH STORYTELLING WITH DATA: © 2010-2018 Cole Nussbaumer Knaflic. All rights reserved. STORYTELLING WITH DATA and the STORYTELLING WITH DATA logo are trademarks of Cole Nussbaumer Knaflic.

introducing the 2018 #SWDchallenge

The inaugural challenge was an annotated line graph. Lines are most often used with continuous data: days, months, quarters, years. Click the link below to see the full details, including an example and submission instructions.

Read More

what is story?

The year is winding down! While you were attending that holiday party or baking one more batch of gingerbread cookies, here at the Knaflic household, we've been recording another podcast. In this final 2017 episode, I discuss my perspective on what is story? and why those working with data should care. I also tackle listener Q&A on data visualization vs. storytelling, NFL football stats, and how to tell when a visualization is complete.

Listening time: 36:51. Links mentioned during the show:

Subscribe in your favorite podcast platform to be updated when new episodes are available. If you like what you hear, please rate or review the SWD podcast. Also, if you haven't already, listen to Episode 1, which focuses on feedback in data visualization. 

If you have any resources to share related to the topic of story, please do so in the comment box at the bottom of this post.

Next up for me: an early morning flight to the chilly midwest for a couple weeks off with family (hoping for a white Christmas!). Happy holidays!


SEARCH STORYTELLING WITH DATA: © 2010-2018 Cole Nussbaumer Knaflic. All rights reserved. STORYTELLING WITH DATA and the STORYTELLING WITH DATA logo are trademarks of Cole Nussbaumer Knaflic.