Junk Charts

Book Preview: How Charts Lie, by Alberto Cairo

If you’re like me, your first exposure to data visualization was as a consumer. You may have run across a pie chart, or a bar chart, perhaps in a newspaper or a textbook. Thanks to the power of the visual language, you got the message quickly, and moved on. Few of us learned how to create charts from first principles. No one taught us about axes, tick marks, gridlines, or color coding in science or math class. There is a famous book in our field called The Grammar of Graphics, by Leland Wilkinson, but it’s not a For Dummies book. This void is now filled by Alberto Cairo’s soon-to-appear new book, titled How Charts Lie: Getting Smarter about Visual Information.

As a long-time fan of Cairo’s work, I was given a preview of the book, and I thoroughly enjoyed it and recommend it as an entry point to our vibrant discipline.

In the first few chapters of the book, Cairo describes how to read a chart. Some may feel that there is not much to it but if you’re here at Junk Charts, you probably agree with Cairo’s goal. Indeed, it is easy to mis-read a chart. It’s also easy to miss the subtle and brilliant design decisions when one doesn’t pay close attention. These early chapters cover all the fundamentals to become a wiser consumer of data graphics.

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How Charts Lie will open your eyes to how everyone uses visuals to push agendas. The book is an offshoot of a lecture tour Cairo took during the last year or so, which has drawn large crowds. He collected plenty of examples of politicians and others playing fast and loose with their visual designs. After reading this book, you can’t look at charts with a straight face!

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In the second half of his book, Cairo moves beyond purely visual matters into analytical substance. In particular, I like the example on movie box office from Chapter 4, titled “How Charts Lie by Displaying Insufficient Data”. Visual analytics of box office receipts seems to be a perennial favorite of job-seekers in data-related fields.

The movie data is a great demonstration of why one needs to statistically adjust data. Cairo explains why Marvel’s Blank Panther is not the third highest-grossing film of all time in the U.S., as reported in the media. That is because gross receipts should be inflation-adjusted. A ticket worth $15 today cost $5 some time ago.

This discussion features a nice-looking graphic, which is a staircase chart showing how much time a #1 movie has stayed in the top position until it is replaced by the next higher grossing film.

Cairo’s discussion went further, exploring the number of theaters as a “lurking” variable. For example, Jaws opened in about 400 theaters while Star Wars: The Force Awakens debuted in 10 times as many. A chart showing per-screen inflation-adjusted gross receipts looks much differently from the original chart shown above.

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Another highlight is Cairo’s analysis of the “cone of uncertainty” chart frequently referenced in anticipation of impending hurricanes in Florida.

Cairo and his colleagues have found that “nearly everybody who sees this map reads it wrongly.” The casual reader interprets the “cone” as a sphere of influence, showing which parts of the country will suffer damage from the impending hurricane. In other words, every part of the shaded cone will be impacted to a larger or smaller extent.

That isn’t the designer’s intention! The cone embodies uncertainty, showing which parts of the country has what chance of being hit by the impending hurricane. In the aftermath, the hurricane would have traced one specific path, and that path would have run through the cone if the predictive models were accurate. Most of the shaded cone would have escaped damage.

Even experienced data analysts are likely to mis-read this chart: as Cairo explained, the cone has a “confidence level” of 68% not 95% which is more conventional. Areas outside the cone still has a chance of being hit.

This map clinches the case for why you need to learn how to read charts. And Alberto Cairo, who is a master visual designer himself, is a sure-handed guide for the start of this rewarding journey.

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Here is Alberto introducing his book.

This chart advises webpages to add more words

A reader sent me the following chart. In addition to the graphical glitch, I was asked about the study's methodology.

I was able to trace the study back to this page. The study uses a line chart instead of the bar chart with axis not starting at zero. The line shows that web pages ranked higher by Google on the first page tend to have more words, i.e. longer content may help with Google ranking.

On the bar chart, Position 1 is more than 6 times as big as Position 10, if one compares the bar areas. But it's really only 20% larger in the data.

In this case, even the line chart is misleading. If we extend the Google Position to 20, the line would quickly dip below the horizontal axis if the same trend applies.

The line chart includes too much grid, one of Tufte's favorite complaints. The Google position is an integer and yet the chart's gridlines imply that 0.5 rank is possible.

Any chart of this data should supply information about the variance around these average word counts. Would like to see a side-by-side box plot, for example.

Another piece of context is the word counts for results on the second or third pages of Google results. Where are the short pages?

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Turning to methodology, we learn that the research team analyzed 1 million pages of Google search results, and they also "removed outliers from our data (pages that contained fewer than 51 words and more than 9999 words)."

When you read a line like this, you have to ask some questions:

How do they define "outlier"? Why do they choose 51 and 9,999 as the cut-offs?

What proportion of the data was removed at either end of the distribution?

If these proportions are small, then the outliers are not going to affect that average word count by much, and thus there is no point to their removal. If they are large, we'd like to see what impact removing them might have.

In any case, the median is a better number to use here, or just show us the distribution, not just the average number.

It could well be true that Google's algorithm favors longer content, but we need to see more of the data to judge.

 

 

Labels, scales, controls, aggregation all in play

JB @barclaysdevries sent me the following BBC production over Twitter.

He was not amused.

This chart pushes a number of my hot buttons.

First, I like to assume that readers don't need to be taught that 2007 and 2018 are examples of "Year".

Second, starting an area chart away from zero is equally as bad as starting a bar chart not at zero! The area is distorted and does not reflect the relative values of the data.

Third, I suspect the 2007 high point is a local peak, which they chose in order to forward a sky-is-falling narrative related to China's growth.

So I went to a search engine and looked up China's growth rate, and it helpfully automatically generated the following chart:

Just wow! This chart does a number of things right.

First, it confirms my hunch above. 2007 is a clear local peak and it is concerning that the designer chose that as a starting point.

Second, this chart understands that the zero-growth line has special meaning.

Third, there are more year labels.

Fourth, and very importantly, the chart offers two "controls". We can look at China's growth relative to India's and relative to the U.S.'s. Those two other lines bring context.

JB's biggest complaint is that the downward-sloping line confuses the issue, which is that slowing growth is still growth. The following chart conveys a completely different message but the underlying raw data are the same:

 

Men and women faced different experiences in the labor market

Last week, I showed how the aggregate statistics, unemployment rate, masked some unusual trends in the labor market in the U.S. Despite the unemployment rate in 2018 being equal, and even a little below, that in 2000, the peak of the last tech boom, there are now significantly more people "not in the labor force," and these people are not counted in the unemployment rate statistic.

The analysis focuses on two factors that are not visible in the unemployment rate aggregate: the proportion of people considered not in labor force, and the proportion of employees who have part-time positions. The analysis itself masks a difference across genders.

It turns out that men and women had very different experiences in the labor market.

For men, things have looked progressively worse with each recession and recovery since 1990. After each recovery, more men exit the labor force, and more men become part-timers. The Great Recession, however, hit men even worse than previous recessions, as seen below:

For women, it's a story of impressive gains in the 1990s, and a sad reversal since 2008.

P.S. See here for Part 1 of this series. In particular, the color scheme is explained there. Also, the entire collection can be viewed here. 

What to make of the historically low unemployment rate

One of the amazing economic stories of the moment is the unemployment rate, which at around 4% has returned to the level last reached during the peak of the tech boom in 2000. The story is much more complex than it seems.

I devoted a chapter of Numbersense (link) to explain how the government computes unemployment rates. The most important thing to realize is that an unemployment rate of 4 percent does NOT mean that four out of 100 people in the U.S. are unemployed, and 96 out of 100 are employed.

It doesn't even mean that four out of 100 people of working age are unemployed, and 96 out of 100 of working age are employed.

What it means is of the people that the government decides are "employable", 96 out of 100 are employed. Officially, this employability is known as "in labor force." There are many ways to be disqualified from the labor force; one example is if the government decides that the person is not looking for a job.

On the flip side, who the government counts as "employed" also matters! Part-timers are considered employed. They are counted just like a full-time employee in the unemployment metric. Part-time, according to the government, is one to 34 hours worked during the week the survey is administered.

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So two factors can affect the unemployment rate a lot - the proportion of the population considered "not in labor force" (thus not counted at all); and the proportion of those considered employed who are part-timers. (Those are two disjoint groups.)

The following chart then shows that despite the unemployment rate looking great, the U.S. labor market in 2018 looks nothing like what it looked like from 1990 to 2008.

Technical notes: all the data are seasonally adjusted by the Bureau of Labor Statistics. I used a spline to smooth the data first - the top chart shows the smoothed version of the unemployment rates. Smoothing removes month-to-month sharp edges from the second chart. The color scale is based on standardized values of the smoothed data.

 

P.S. See Part 2 of this series explores the different experiences of male and female workers. Also, the entire collection can be viewed here.

The merry-go-round of investment bankers

Here is the start of my blog post about the chart I teased the other day:

 

Today's post deals with the following chart, which appeared recently at Business Insider (hat tip: my sister).

It's immediately obvious that this chart requires a heroic effort to decipher. The question shown in the chart title "How many senior investment bankers left their firms?" is the easiest to answer, as the designer places the number of exits in the central circle of each plot relating to a top-tier investment bank (aka "featured bank"). Note that the visual design plays no role in delivering the message, as readers just scan the data from those circles.

Anyone persistent enough to explore the rest of the chart will eventually discover these features...

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The entire post including an alternative view of the dataset is a guest blog at the JMP Blog here. This is a situation in which plotting everything will make an unreadable chart, and the designer has to think hard about what s/he is really trying to accomplish.

Webinar Wednesday

I'm delivering a quick-fire Webinar this Wednesday on how to make impactful data graphics for communication and persuasion. Registration is free, at this link.

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In the meantime, I'm preparing a guest lecture for the Data Visualization class at Yeshiva University Sims School of Management. The goal of the lecture is to emphasize the importance of incorporating analytics into the data visualization process.

Here is the lesson plan:

1. Introduce the Trifecta checkup (link) which is the general framework for effective data visualizations
2. Provide examples of Type D data visualizations, i.e. graphics that have good production values but fail due to issues with the data or the analysis
3. Hands-on demo of an end-to-end data visualization process
4. Lessons from the demo including the iterative nature of analytics and visualization; and sketching
5. Overview of basic statistics concepts useful to visual designers

 

The French takes back cinema but can you see it?

I like independent cinema, and here are three French films that come to mind as I write this post: Delicatessen, The Class (Entre les murs), and 8 Women (8 femmes). 

The French people are taking back cinema. Even though they purchased more tickets to U.S. movies than French movies, the gap has been narrowing in the last two decades. How do I know? It's the subject of this infographic: 

How do I know? That's not easy to say, given how complicated this infographic is. Here is a zoomed-in view of the top of the chart:

 

You've got the slice of orange, which doubles as the imagery of a film roll. The chart uses five legend items to explain the two layers of data. The solid donut chart presents the mix of ticket sales by country of origin, comparing U.S. movies, French movies, and "others". Then, there are two thin arcs showing the mix of movies by country of origin. 

The donut chart has an usual feature. Typically, the data are coded in the angles at the donut's center. Here, the data are coded twice: once at the center, and again in the width of the ring. This is a self-defeating feature because it draws even more attention to the area of the donut slices except that the areas are highly distorted. If the ratios of the areas are accurate when all three pieces have the same width, then varying those widths causes the ratios to shift from the correct ones!

The best thing about this chart is found in the little blue star, which adds context to the statistics. The 61% number is unusually high, which demands an explanation. The designer tells us it's due to the popularity of The Lion King.

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The one donut is for the year 1994. The infographic actually shows an entire time series from 1994 to 2014.

The design is most unusual. The years 1994, 1999, 2004, 2009, 2014 receive special attention. The in-between years are split into two pairs, shrunk, and placed alternately to the right and left of the highlighted years. So your eyes are asked to zig-zag down the page in order to understand the trend. 

To see the change of U.S. movie ticket sales over time, you have to estimate the sizes of the red-orange donut slices from one pie chart to another. 

Here is an alternative visual design that brings out the two messages in this data: that French movie-goers are increasingly preferring French movies, and that U.S. movies no longer account for the majority of ticket sales.

A long-term linear trend exists for both U.S. and French ticket sales. The "outlier" values are highlighted and explained by the blockbuster that drove them.

 

P.S.

1. You can register for the free seminar in Lyon here. To register for live streaming, go here.
2. Thanks Carla Paquet at JMP for help translating from French.

Crazy rich Asians inspire some rich graphics

On the occasion of the hit movie Crazy Rich Asians, the New York Times did a very nice report on Asian immigration in the U.S.

The first two graphics will be of great interest to those who have attended my free dataviz seminar (coming to Lyon, France in October, by the way. Register here.), as it deals with a related issue.

The first chart shows an income gap widening between 1970 and 2016.

This uses a two-lines design in a small-multiples setting. The distance between the two lines is labeled the "income gap". The clear story here is that the income gap is widening over time across the board, but especially rapidly among Asians, and then followed by whites.

The second graphic is a bumps chart (slopegraph) that compares the endpoints of 1970 and 2016, but using an "income ratio" metric, that is to say, the ratio of the 90th-percentile income to the 10th-percentile income.

Asians are still a key story on this chart, as income inequality has ballooned from 6.1 to 10.7. That is where the similarity ends.

Notice how whites now appears at the bottom of the list while blacks shows up as the second "worse" in terms of income inequality. Even though the underlying data are the same, what can be seen in the Bumps chart is hidden in the two-lines design!

In short, the reason is that the scale of the two-lines design is such that the small numbers are squashed. The bottom 10 percent did see an increase in income over time but because those increases pale in comparison to the large incomes, they do not show up.

What else do not show up in the two-lines design? Notice that in 1970, the income ratio for blacks was 9.1, way above other racial groups.

Kudos to the NYT team to realize that the two-lines design provides an incomplete, potentially misleading picture.

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The third chart in the series is a marvellous scatter plot (with one small snafu, which I'd get t0).

What are all the things one can learn from this chart?

• There is, as expected, a strong correlation between having college degrees and earning higher salaries.
• The Asian immigrant population is diverse, from the perspectives of both education attainment and median household income.
• The largest source countries are China, India and the Philippines, followed by Korea and Vietnam.
• The Indian immigrants are on average professionals with college degrees and high salaries, and form an outlier group among the subgroups.

Through careful design decisions, those points are clearly conveyed.

Here's the snafu. The designer forgot to say which year is being depicted. I suspect it is 2016.

Dating the data is very important here because of the following excerpt from the article:

Asian immigrants make up a less monolithic group than they once did. In 1970, Asian immigrants came mostly from East Asia, but South Asian immigrants are fueling the growth that makes Asian-Americans the fastest-expanding group in the country.

This means that a key driver of the rapid increase in income inequality among Asian-Americans is the shift in composition of the ethnicities. More and more South Asian (most of whom are Indians) arrivals push up the education attainment and household income of the average Asian-American. Not only are Indians becoming more numerous, but they are also richer.

An alternative design is to show two bubbles per ethnicity (one for 1970, one for 2016). To reduce clutter, the smaller ethnicites can be aggregated into Other or South Asian Other. This chart may help explain the driver behind the jump in income inequality.

 

 

 

 

 

Education deserts: places without schools still serve pies and story time

I very much enjoyed reading The Chronicle's article on "education deserts" in the U.S., defined as places where there are no public colleges within reach of potential students.

In particular, the data visualization deployed to illustrate the story is superb. For example, this map shows 1,500 colleges and their "catchment areas" defined as places within 60 minutes' drive.

It does a great job walking through the logic of the analysis (even if the logic may not totally convince - more below). The areas not within reach of these 1,500 colleges are labeled "deserts". They then take Census data and look at the adult population in those deserts:

This leads to an analysis of the racial composition of the people living in these "deserts". We now arrive at the only chart in the sequence that disappoints. It is a pair of pie charts:

 The color scheme makes it hard to pair up the pie slices. The focus of the chart should be on the over or under representation of races in education deserts relative to the U.S. average. The challenge of this dataset is the coexistence of one large number, and many small numbers.

Here is one solution:

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The Chronicle made a commendable effort to describe this social issue. But the analysis has a lot of built-in assumptions. Readers should look at the following list and see if you agree with the assumptions:

• Only public colleges are considered. This restriction requires the assumption that the private colleges pretty much serve the same areas as public colleges.
• Only non-competitive colleges are included. Precisely, the acceptance rate must be higher than 30 percent. The underlying assumption is that the "local students" won't be interested in selective colleges. It's not clear how the 30 percent threshold was decided.
• Colleges that are more than 60 minutes' driving distance away are considered unreachable. So the assumption is that "local students" are unwilling to drive more than 60 minutes to attend college. This raises a couple other questions: are we only looking at commuter colleges with no dormitories? Is the 60 minutes driving distance based on actual roads and traffic speeds, or some kind of simple model with stylized geometries and fixed speeds?
• The demographic analysis is based on all adults living in the Census "blocks" that are not within 60 minutes' drive of one of those colleges. But if we are calling them "education deserts" focusing on the availability of colleges, why consider all adults, and not just adults in the college age group? One further hidden assumption here is that the lack of colleges in those regions has not caused young generations to move to areas closer to colleges. I think a map of the age distribution in the "education deserts" will be quite telling.
• Not surprisingly, the areas classified as "education deserts" lag the rest of the nation on several key socio-economic metrics, like median income, and proportion living under the poverty line. This means those same areas could be labeled income deserts, or job deserts.

At the end of the piece, the author creates a "story time" moment. Story time is when you are served a bunch of data or analyses, and then when you are about to doze off, the analyst calls story time, and starts making conclusions that stray from the data just served!

Story time starts with the following sentence: "What would it take to make sure that distance doesn’t prevent students from obtaining a college degree? "

The analysis provided has nowhere shown that distance has prevented students from obtaining a college degree. We haven't seen anything that says that people living in the "education deserts" have fewer college degrees. We don't know that distance is the reason why people in those areas don't go to college (if true) - what about poverty? We don't know if 60 minutes is the hurdle that causes people not to go to college (if true).We know the number of adults living in those neighborhoods but not the number of potential students.

The data only showed two things: 1) which areas of the country are not within 60 minutes' driving of the subset of public colleges under consideration, 2) the number of adults living in those Census blocks.

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So we have a case where the analysis is incomplete but the visualization of the analysis is superb. So in our Trifecta analysis, this chart poses a nice question and has nice graphics but the use of data can be improved. (Type QV)