lollipop chart

Extreme Data Viz Makeover: COVID Edition

by Lindsay Lamb

Andrea and I have mentioned it before: we are in a golden age, albeit a morbid golden age, of data and data visualizations. One way we can stay ahead of Covid-19 is by reading the reports and analyses that are readily available to the public. Sometimes this information can be super helpful, and sometimes the story gets lost in the data.

Generally, there are two places I typically go to check out recent statistics. These are The New York Times and Johns Hopkins University.

Recently however, I stumbled upon a site which examines data on COVID-19 and consumer behavior. The referenced survey was administered in mid March.

Take a look at the below charts. (These are similar to the ones from the site but have been remade in Excel.) As you look at them, see if you can spot some areas for improvement.

Chart about fears concerning the coronavirus outbreak.
Data source & graph inspiration from Astound commerce; figures remade from data
Note. Survey administered in March 11-13, 2020
Chart showing predictions on when shoppers feel COVID-19 will be under control or eradicated.
Data source & graph inspiration from Astound commerce; figures remade from data
Note. Survey administered in March 11-13, 2020
Chart showing how many people have altered their day-to-day activities in order to be as "contactless" due to COVID-19.
Data source & graph inspiration from Astound commerce; figures remade from data
Note. Survey administered in March 11-13, 2020

There is a lot to take in. Like, a LOT. These figures are pretty overwhelming.

Before I get into it, let me be clear: it is not my intention to trash talk the folks who created this. I am sure they worked really hard on it and that it took them a ton of time. Not to mention that they are working on top of the stress of COVID-19, childcare issues and job security. We are all in this together. I’m simply looking at this as data and thinking of ways to make it easier for readers to understand the story.

What these charts do right

The percentages are right there on the chart and I like the use of icons. Focusing on consumers is an interesting reference point. I haven’t seen this side of the COVID-19 story before, which is why I thought it would be interesting to highlight in our blog. Overall this chart succeeds at drawing in the reader, but at that point things get murky.

What’s missing?

The use of different types of charts is confusing and might lead the reader to think we’re looking at different types of data, or data in a different way. Really, I think the graphic designer was trying to create visual interest by breaking things up a bit, which is admirable, but not at the cost of easy interpretation. Additionally, the colors used throughout are really similar and readers must carefully search the legend over and over.

We ended up with some questions. What are the key takeaways? What are we really comparing? What is that wheel shape? And why are the hands floating?? These are not questions that you want your readers to be asking themselves, because in all likelihood, they will just walk away.

Let’s Do a Makeover!

Let’s start with the donut chart and the bar chart.

Chart about fears concerning the coronavirus outbreak.
Data source & graph inspiration from Astound commerce; figures remade from data
Note. Survey administered in March 11-13, 2020

Here are some ways I came up with to make the story pop a little bit more.

Change to a Lollipop Chart & Add Icons

First, I redid the donut charts – which are not a great substitute for a bar graph – and used my favorite chart type: a lollipop chart. I added images of each country/region for some flair and to make it easier to see which country was which. Now readers can more easily find the story behind the data. It is easier to see that respondents from the Middle East expressed greater fears concerning COVID-19 than did survey respondents in other countries.

Add a Graphic

Andrea edited this to include a graphic (a scared face) because we planned to use the chart again, and she wanted to make sure the two charts were distinctly different at-a-glance. I think you could leave out the graphic out though.

I also made the chart title more informative. I gave a short statement of what I felt was the main message of the chart. Then I added some subtext. Readers should get enough information from the title to be able to easily interpret the chart.

Middle East Citizens Report Highest Fear of COVID-19. Collectively, three quarters of all people surveyed had fears concerning COVID-19. Fears were similar for the US, Canada, and Europe, but much higher among those surveyed in the Middle East.

Reworked chart showing that Middle East citizens reported the highest level of fear of COVID-19.
Data source Astound commerce survey – March 11-13, 2020

Let’s take a look at the second chart with the floating hands. We could do another lollipop chart. The use of the same chart type informs the readers we are looking at similar types of data and it invites them in to make comparisons. I bet that the story will pop here for you, even without a helpful title.

Before:

Chart showing how many people have altered their day-to-day activities in order to be as "contactless" due to COVID-19.
[Before makeover] Data source: Astound commerce survey – March 11-13, 2020

and here’s the after:

Middle East Citizens Report Largest Behavior Changes Due to COVID-19. Around half of people surveyed in the US, Canada and Europe had altered behaviors, but over 80% of those in the Middle East had made changes.

Reworked chart showing that Middle East citizens reported the largest level of behavior changes due to COVID-19.
[After makeover] Data source: Astound commerce survey – March 11-13, 2020

Let’s get back to that busy bar chart.

Before:

Chart showing predictions on when shoppers feel COVID-19 will be under control or eradicated.
Data source Astound commerce survey – March 11-13, 2020

What makes this difficult to interpret?

The response options are included as a bar within the chart, making it difficult to read. It took me a while to even notice the survey questions, probably because the text is insanely small, making it difficult to read (particularly when you have old eyes and got up at 5:30 AM to get some work done before the kids wake up and chaos ensues… but maybe that is just me!).

It is also difficult to know what we are supposed to compare. Are we interested in comparisons across country? Are we interested when participants think COVID-19 will be under control? Do responses within country and across survey options add up to 100? All of these questions are difficult to answer.

Slider chart!

For this chart, I am going to use a slider chart (which is basically two lollipop charts combined). This makeover was a bit more complicated, but I think it tells a better story. First, I combined categories such that we now only report “within 1-2 months” and “6 months or more” (combining 6 months, one year and over a year). I felt like these two categories were more meaningful than looking at each one separately. I then created the two lollipop charts, added text boxes for labels and added my icons.

Color!

I selected the two tones from the green color family since the data were depicting participants’ likelihood to spend money. The main point is that we want two different tones in the same color family so people can distinguish them. I chose a deeper color for the “a year or more” category because that response was more intense.

Now the story is much more clear.

Now we can clearly see that most survey participants believe COVID-19 will be under control within 6 months. As always, a descriptive title helps.

Across regions, half of participants felt that COVID-19 would be under control in 6 months or more. Only one-third of participants across regions felt that COVID-19 would be under control in just a couple of months.

Reworked chart showing when citizens of varying countries think COVID-19 will be eradicated.
Data source Astound commerce survey – March 11-13, 2020

As a business owner, I would find this information helpful. I could mentally prepare for customers to not feel comfortable coming back to my business until 6 months at the earliest (but maybe more). This information might help me plan what to do to pivot my work for the next 6 months to keep things afloat until our economy starts to normalize. Obviously, opinions may have changed since the time this survey was administered, but it probably still gives business owners a good ballpark.

Regardless, this story got lost in the jumble of infographics. I’m sure there are other stories buried in the data, but that is what is fun about creating data visualizations. Sometimes you do not know the full story until you see it.

What are some key takeaways?

  1. Multiple pie charts (and their cousin, the donut chart) are difficult to interpret, and it is usually best to represent the data differently.
  2. Make data visualizations clean and not too busy. Use white space, and make sure the reader can walk away from your visualization understanding the information without having to read a full report.
  3. If presenting several visualizations together, make sure the images/graphics/colors all work well together and tell a consistent story.
  4. Use icons and images to draw interest and help the reader interpret the graph.
  5. Add a descriptive title.

Chart Makeover: Line to Lollipop

By Lindsay Lamb

A few weeks ago, we talked about one of the big controversies in data visualization – the pie chart. When used properly, pie charts can be a great way to display certain types of information. Sometimes it’s hard to know what those rules are, and which graph should be used where.

Are there guidelines on the best ways to present data? The answer is yes –  there are many great tools, like Stephanie Evergreen’s chart chooser, that we often use to help me make these kinds of decisions.

One rule of thumb we use in our data visualizations is to only use line graphs when displaying time-related data.

Here’s an example based on confirmed Covid-19 cases (as of 4/1/20):

Confirmed cases for selected countries

Horizontal axis shows the number of days since the case count exceeded 500 in each country

Source: Johns Hopkins University CSSE

These data are perfect for a line graph. Readers naturally start reading the graph from left to right; without reading anything else we can quickly understand:

  • the number of cases are increasing over time.
  • some lines have a steeper slope compared to others,
  • some countries have had confirmed cases of Covid-19 for a longer period of time than other countries.

We can visualize in our mind projections of each line (unfortunately for us here in the United States, that line seems to be trending straight upward, but who knows! Maybe you are reading this blog in the future and are saying, “What are they so worried about?!? We found a cure, and everyone was fine!” Let’s hope so).

What would the data look like if it were presented differently? Let’s look at the same data in column chart format (updated 4/1/20).

Source: Johns Hopkins University CSSE data map
Note: Data in this graph were pulled on 4/1/20. Some numbers are estimates, but numbers from days 15 and 20 were accurate as of the date and time in which they were pulled.

This graph just doesn’t tell the same story. We don’t get the same sense of time, or quite frankly, urgency. We cannot see how some countries flattened the curve compared to other countries. We cannot see where the U.S. is in terms of our trajectory compared to China and Italy. Sure, this graph is informative, and we do see that South Korea has far fewer cases over time, compared to other countries, but the power in that story is gone. Having the lines, slopes, and trajectories for each country in one graph together tells a much more compelling story. This is exactly why line graphs, or time series graphs, are so powerful. The time series graph created by Johns Hopkins CSSE contains a lot of valuable information all in one place (if you like data, I highly recommend taking a look at their dashboard). You can look at the graph and understand the story at first glance, without having to do much reading.

Now let’s walk through an example from a report we were working on with one of our clients. In this example, a line graph did not make sense with the data we had.

When I looked at this figure, as with the Johns Hopkins figure above, I started reading the data from left to right, and assumed the data progressed over time. Instead, the data were based on categories of information (e.g., scores on different components of the ACT) and displayed scores for treatment and control group students in each category. It was difficult to compare the two groups within each category. I was spending too much time thinking about the data within the figure, and if I was thinking too much, our clients certainly would be as well.

I talked to Andrea about the graph (I’m calling her out – it was her graph, originally) – and she said that she had considered a column chart, but it just wasn’t having the same impact as a line chart.

We needed a different visualization.

I reworked the data using one of my favorite graphs I learned from Stephanie Evergreen – a lollipop chart!

Now, the reader can look at the data and quickly compare the two groups (i.e., treatment and control) within each category. It is easy to see that the treatment students outperformed matched control group peers in each subject.

Want to know how to make a lollipop chart? Check out Stephanie Evergreen’s website where she walks you through an example. This is the type of skill we include in our telling stories with data training we are excited to offer. This training is available online so if you are looking to hone your skills while quarantined at home, let us know!