The Brewster Rockit: Space Guy! comic strip from last Sunday makes an important point about designing charts and graphs: don’t get carried away.
Brewster Rockit May 3, 2020
The goal of using a chart or graph is to distill the information behind it into an easy-to-read format for making a quick point. A reader’s eye is drawn to a chart or graph and it should be easy to figure out the point the graphic is making.
If the graph or chart is too complicated, it loses its potency. If it looks great or clever but cannot help the reader interpret the data correctly, it is not very useful. If the researcher spends a lot of time tweaking the graphic to really make it eye-popping, it may not be worth it compared to simply getting the point across.
In sum: graphs and charts can be fun. They can break up long text and data tables. They can focus attention on an important data point or relationship. At the same time, they can get too complicated and become a time suck both for the producer of the graphic and those trying to figure them out.
In a look at COVID-19 cases across countries, the New York Times changed the Y-axis on the different graphs:
Typically, readers of graphs should beware when someone changes the scale on the Y-axis; this leads to issues when interpreting the data and can make it look like trends are present when they are not. See two earlier posts – misleading charts of 2015, State of the Union data presented in 2013 – for examples.
But, in this case, adjusting the scale makes some sense. The goal is to show exponential curves, the type of change when a disease spreads throughout a population, and then hopefully a peak and decline on the right side. Some countries have very few cases – such as toward the bottom like in Morocco or Hungary or Mexico – and some have many more – like Italy or South Korea – but the general shape can be similar. Once the rise starts, it is expected to continue until something stops it. And the pattern can look similar across countries.
Also, it is helpful that the creators of this point out at the top that “Scales are adjusted in each country to make the curve more readable.” It is not always reported when Y-axes are altered – and this lack of communication could be intentional – and then readers might not pick up on the issue.
Pie graphs can be great at showing relative differences between a small number of categories. A recent example of this comes from CBS:
CBS This Morning co-host Tony Dokoupil set up a table at a mall in West Nyack, New York, with a pie that represented $98 trillion of household wealth in the United States. The pie was sliced into 10 pieces and Dokoupil asked people to divide up those pieces onto five plates representing the poorest, the lower middle class, middle class, upper middle class, and wealthiest Americans. No one got it right. And, in fact, no one was even kind of close to estimating the real ratio, which involves giving nine pieces to the top 20 percent of Americans while the upper middle class and the middle class share one piece between the two of them. The lower middle class would effectively get crumbs considering they only have 0.3 percent of the pie. What about the poorest Americans? They wouldn’t get any pie at all, and in fact would get a bill, considering they are, on average, around $6,000 in debt…
To illustrate just how concentrated wealth is in the country, Dokoupil went on to note that if just the top 1 percent are taken into account, they would get four of the nine pieces of pie that go to the wealthiest Americans.
A pie chart sounds like a great device for this situation because of several features of the data and the presentation:
1. There are five categories of social class. Not too many for a pie chart.
2. One of those categories, the top 20 of Americans, clearly has a bigger portion of the pie than the other groups. A pie chart is well-suited to show one dominant category compared to the others.
3. Visitors to a shopping mall can easily understand a pie chart. They understand how it works and what it says (particularly with #1 and #2 above).
Together, a pie chart works in ways that other graphs and charts would not.
(Side note: it is hard to know whether the use of food in the pie chart helped or hurt the presentation. Do people work better with data when feeling hungry?)
An article discussing changes in American household arrangements includes this graph:
A summary of the data:
It all represents an increasing distance from the nuclear-family structure considered traditional for decades. The changes solidify shifts that have been mounting since then, erasing the notion of one dominant family type. In the early 1960s, two-thirds of children were raised in male-breadwinner, married-couple families. By contrast, today there is no one family-and-work arrangement that encompasses the majority of children, demographers say.
“That dominant model declined, but it’s not like it was replaced by one thing,” says Philip Cohen, professor of sociology at the University of Maryland. “It was replaced by a peacock’s tail, a plethora of different arrangements.”
The graph is most effective at showing the biggest change: the decline of the “mother-father married, father only earner” group over nearly six decades. Two other categories have significant increases – married and dual earners, single mother – while the five categories at the bottom involve relatively fewer households.
The graph is unusually skinny from left to right and this helps emphasize the straight lines up or down over time. Would a wider x-axis show some more variation over time or are the trends always pretty consistent?
The colors are a little hard to distinguish. I am not usually in favor of dotted lines and so on but this might be an opportunity to differentiate between trend lines.
Just thinking about other graph options, a pie chart for each time period might also communicate the big change well (though the smaller categories might not show up as well) or a clustered bar graph with the two years side to side could show the relative changes for each group.
In sum, graphing significant social change is not necessarily easy and this format clearly communicates a big change.
Check out this good quick overview of visualization errors – here are a few good moments:
Everything is relative. You can’t say a town is more dangerous than another because the first one had two robberies and the other only had one. What if the first town has 1,000 times the population that of the first? It is often more useful to think in terms of percentages and rates rather than absolutes and totals…
It’s easy to cherrypick dates and timeframes to fit a specific narrative. So consider history, what usually happens, and proper baselines to compare against…
When you see a three-dimensional chart that is three dimensions for no good reason, question the data, the chart, the maker, and everything based on the chart.
In summary: data visualizations can be very useful for highlighting a particular pattern but they can also be altered to advance an incorrect point. I always wonder with these examples of misleading visualizations whether the maker intentionally made the change to advance their point or whether there was a lack of knowledge about how to do good data analysis. Of course, this issue could arise with any data analysis as there are right and wrong ways to interpret and present data.
Here are some improved charts first put forward by politicians, advocacy groups, and the media in 2015.
I’m not sure exactly how they picked “the most misleading charts” (is there bias in this selection?) but it is interesting that several involve a misleading y-axis. I’m not sure that I would count the last example as a misleading chart since it involves a definition issue before getting to the chart.
And what is the purpose of the original, poorly done graphics? Changing the presentation of the data provides evidence for a particular viewpoint. Change the graphic depiction of the data and another story could be told. Unfortunately, it is actions like these that tend to cast doubt on the use of data for making public arguments – the data is simply too easy to manipulate so why rely on data at all? Of course, that assumes people look closely at the chart and the data source and know what questions to ask…
Several new apps transform spreadsheet data into a chart or graph without having to spend much or any time with the raw data:
It’s called Project Elastic, and he unveiled the thing this fall at a conference run by his company, Tableau. The Seattle-based company has been massively successful selling software that helps big businesses “visualize” the massive amount of online data they generate—transform all those words and numbers into charts and graphics their data scientists can more readily digest—but Project Elastic is something different. It’s not meant for big businesses. It’s meant for everyone.
The idea is that, when someone emails a spreadsheet to your iPad, the app will open it up—but not as a series of rows and columns. It will open the thing as chart or graph, and with a swipe of the finger, you can reformat the data into a new chart or graph. The hope is that this will make is easier for anyone to read a digital spreadsheet—an age-old computer creation that’s still looks like Greek to so many people. “We think that seeing and understanding your data is a human right,” says Story, the Tableau vice president in charge of the project.
And Story isn’t the only one. A startup called ChartCube has developed a similar tool that can turn raw data into easy-to-understand charts and graphs, and just this week, the new-age publishing outfit Medium released a tool called Charted that can visualize data in similar ways. So many companies aim to democratize access to online data, but for all the different data analysis tool out on the market, this is still the domain of experts—people schooled in the art of data analysis. These projects aim to put the democracy in democratize.
Two quick thoughts:
1. I understand the impulse to create charts and graphs that communicate patterns. Yet, such devices are not infallible in themselves. I would suggest we need more education in interpreting and using the information from infographics. Additionally, this might be a temporary solution but wouldn’t it be better in the long run if more people know how to read and use a spreadsheet?
2. Interesting quote: “We think that seeing and understanding your data is a human right.” I have a right to data or to the graphing and charting of my data? This adds to a collection of voices arguing for a human right to information and data.
AllMusic had a recent poll asking readers about their favorite Beatles album. Interesting topic but the pie chart used to display the results didn’t work out so well:
Two main complaints:
1. There are a lot of categories to represent here:14 different albums. While it is relatively easy to see some of the larger categories, it gets more difficult to judge the proportions of the smaller categories.
2. There are some categories clearly bigger than others but the color scene seems to have little to do with the actual album title. The palette runs from black to light gray but it does not appear to be in any order. For example, they might have used the same palette but light gray would have been Please Please Me while the darkest color could have been Past Masters. As it currently stands, the reader has to pick out the category and then try to figure out where it is in the key.
Given this comes from an app intended to help create infographics, this one isn’t so great as it suffers from two issues – lots of categories and a limited color design – that I often warn my statistics students about when using pie charts.
The Census regularly puts together new data visualizations to highlight newly collected data. The most recent visualization looks at population change in metropolitan areas between 2010-2011 and breaks down the change by natural increase, international migration, and domestic migration.
Several trends are quickly apparent:
1. Sunbelt growth continues at a higher pace and non-Sunbelt cities tend to lose residents by domestic migration.
2. Population increases by international migration still tends to be larger in New York, Los Angeles, and Miami.
3. There are some differences in natural increases to population. I assume this is basically a measure of birth rates.
However, I have two issues with this visualization. My biggest complaint is that the boxes are not weighted by population. New York has the largest natural increase to the population but it is also the largest metropolitan areas by quite a bit. A second issue is that the box sizes are not all the 50,000 or 10,000 population change as suggested by the key at the top. So while I can see relative population change, it is hard to know the exact figures.