Junk Charts

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.

***

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.

***

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.

 

 

 

 

 

Graphical advice for conference presenters - demo

Yesterday, I pulled this graphic from a journal paper, and said one should not copy and paste this into an oral presentation.

So I went ahead and did some cosmetic surgery on this chart.

I don't know anything about the underlying science. I'm just interpreting what I see on the chart. It seems like the key message is that the Flowering condition is different from the other three. There are no statistical differences between the three boxplots in the first three panels but there is a big difference between the red-green and the purple in the last panel. Further, this difference can be traced to the red-green boxplots exhibiting negative correlation under the Flowering condition - while the purple boxplot is the same under all four conditions.

I would also have chosen different colors, e.g. make red-green two shades of gray to indicate that these two things can be treated as the same under this chart. Doing this would obviate the need to introduce the orange color.

Further, I think it might be interesting to see the plots split differently: try having the red-green boxplots side by side in one panel, and the purple boxplots in another panel.

If the presentation software has animation, the presenter can show the different text blocks and related materials one at a time. That also aids comprehension.

***

Note that the plot is designed for an oral presentation in which you have a minute or two to get the message across. It's debatable as to whether journal editors should accept this style for publications. I actually think such a style would improve reading comprehension but I surmise some of you will disagree.

Graphical advice for conference presenters

I've attended a number of talks in the last couple of days at the Joint Statistical Meetings. I'd like to offer some advice to presenters using graphics in their presentations.

Here is an example of the style of graphics that are being presented. (Note: I deliberately picked an example from a Google image search - this graphic was not used in a presentation but is representative of those I've seen.)

Here are some tips to make your graphic much more impactful:

• Use much larger font sizes. Typically, the same graphic published in a journal is used in the presentation. Other than the people sitting in the front row, no one can see any of the text, which means no one can understand anything. Most of us realize that for the bullet points on the slides, you have to pick a large font, say 20 points. The same goes for any labels or annotation on your graphics!
• Use much thicker lines, larger dots, etc. Similar to the above, if you'd like people in the second to the last rows to be able to see your chart, you must enlarge everything. (For R users, cex comes in handy.)
• Put a lot of text on the graphic itself. The graphic shown above has words but it lacks any context. In many of these presentations, the audience are statisticians, many of whom work in different industries or disciplines so we don't know what OpN, LIN, LIC mean. You may have explained this five slides prior but it's hard to expect the audience to remember. Why not just spell that out. Kendall's tau may be known to some in the audience but we still don't know - just based on what's on this chart - what correlation is being assessed. Any other text that helps explain what's on the chart should be added.
• Add an informative title. These presentations are only 20 minutes long, and you'll spend maybe one minute explaining the graphic to someone who hasn't read the paper. You should spell out what is the message of your graphic - then we can look at the evidence to see how you drew that conclusion. In this example, it seems like there is a story around Flowering.
• Avoid complex graphics. In a few occasions, the presenters show a grid of charts. These work well in a journal paper when we have time to figure out the layout. It's hard to grasp the message plus figure out how to read the chart all in a matter of a minute or so! Just like we recommend usually one message per slide, you should stick to one message per graphic used in an oral presentation.

The larger lesson is that the chart that is perfect for publication in a journal is less than perfect for an oral presentation.

 

PS. Please see here for an example of how one can remake the above chart for use in a conference presentation.

Two good charts can use better titles

NPR has this chart, which I like:

It's a small multiples of bumps charts. Nice, clear labels. No unnecessary things like axis labels. Intuitive organization by Major Factor, Minor Factor, and Not a Factor.

Above all, the data convey a strong, surprising, message - despite many high-profile gun violence incidents this year, some Democratic voters are actually much less likely to see guns as a "major factor" in deciding their vote!

Of course, the overall importance of gun policy is down but the story of the chart is really about the collapse on the Democratic side, in a matter of two months.

The one missing thing about this chart is a nice, informative title: In two months, gun policy went from a major to a minor issue for some Democratic voters.

***

 I am impressed by this Financial Times effort:

The key here is the analysis. Most lazy analyses compare millennials to other generations but at current ages but this analyst looked at each generation at the same age range of 18 to 33 (i.e. controlling for age).

Again, the data convey a strong message - millennials have significantly higher un(der)employment than previous generations at their age range. Similar to the NPR chart above, the overall story is not nearly as interesting as the specific story - it is the pink area ("not in labour force") that is driving this trend.

Specifically, millennial unemployment rate is high because the proportion of people classified as "not in labour force" has doubled in 2014, compared to all previous generations depicted here. I really like this chart because it lays waste to a prevailing theory spread around by reputable economists - that somehow after the Great Recession, demographics trends are causing the explosion in people classified as "not in labor force". These people are nobodies when it comes to computing the unemployment rate. They literally do not count! There is simply no reason why someone just graduated from college should not be in the labour force by choice. (Dean Baker has a discussion of the theory that people not wanting to work is a long term trend.)

The legend would be better placed to the right of the columns, rather than the top.

Again, this chart benefits from a stronger headline: BLS Finds Millennials are twice as likely as previous generations to have dropped out of the labour force.

 

 

 

 

Digital revolution in China: two visual takes

The following map accompanied an article in the Economist about China's drive to create a "digital silkroad," roughly defined as making a Silicon Valley. 

The two variables plotted are the wealth of each province (measured by GDP per capita) and the level of Internet penetration. The designer made the following choices:

• GDP per capita is presented with less precision than Internet penetration. The former is grouped into five large categories while the latter is given as a percentage to one decimal place.
• The visual design favors GDP per capita which is encoded as the shade of color of each province. The Internet penetration data appeared added on as an afterthought.

If we apply the self-sufficiency test (i.e. by removing the printed data from the chart), it's immediately clear that the visual elements convey zero information about Internet penetration. This is a serious problem for a chart about the "digital silkroad"!

***

If those two variables are chosen, it would seem appropriate to convey to readers the correlation between the two variables. The following sketch is focused on surfacing the correlation.

(Click on the image to see it in full.) Here is the top of the graphic:

The individual maps are not strictly necessary. Just placing provincial names onto the grid is enough, because regional pattern isn't salient here.

The Internet penetration data were grouped into five categories as well, putting it on equal footing as GDP per capita.

 

Several problems with stacked bar charts, as demonstrated by a Delta chart designer

In the Trifecta Checkup (link), I like to see the Question and the Visual work well together. Sometimes, you have a nice message but you just pick the wrong Visual.

An example is the following stacked column chart, used in an investor presentation by Delta.

From what I can tell, the five types of aircraft are divided into RJ (regional jet) and others (perhaps, larger jets). With each of those types, there are two or three subtypes. The primary message here is the reduction in the RJ fleet and the expansion of Small/Medium/Large.

One problem with a stacked column chart with five types is that it takes too much effort to understand the trends of the middle types.

The two types on the edges are not immune to confusion either. As shown below, both the dark blue (Large) type and the dark red (50-seat RJ) type are associated with downward sloping lines except that the former type is growing rapidly while the latter is vanishing from the mix!

 In this case, the slopegraph (Bumps-type chart) can overcome some of the limitations.

***

This example was used in my new dataviz workshop, launched in St. Louis yesterday. Thank you to the participants for making it a lively session!

The tech world in which everyone is below average

Laura pointed me to an infographic about tech worker salaries in major tech hubs (link).

What's wrong with this map?

The box "Global average" is doubly false. It is not global, and it is not the average!

The only non-American cities included in this survey are Toronto, Paris and London.

The only city with average salary above the "Global average" is San Francisco Bay Area. Since the Bay Area does not outweigh all other cities combined in the number of tech workers, it is impossible to get an average of $135,000.

***

Here is the second chart.

What's wrong with these lines?

This chart frustrates the reader's expectations. The reader interprets it as a simple line chart, based on three strong hints:

• time along the horizontal axis
• data labels show dollar units
• lines linking time

Each line seems to show the trend of average tech worker salary, in dollar units.

However, that isn't the designer's intention. Let's zoom in on Chicago and Denver:

The number $112,000 (Denver) sits below the number $107,000 (Chicago). It appears that each chart has its own scale. But that's not the case either.

For a small-multiples setup, we expect all charts should use the same scale. Even though the data labels are absolute dollar amounts, the vertical axis is on a relative scale (percent change). To make things even more complicated, the percent change is computed relative to the minimum of the three annual values, no matter which year it occurs.

That's why $106,000 (Chicago) is at the same level as $112,000 (Denver). Those are the minimum values in the respective time series. As shown above, these line charts are easier to understand if the axis is displayed in its true units of percent change.

The choice of using the minimum value as the reference level interferes with comparing one city to the next. For Chicago, the line chart tells us 2015 is about 2 percent above 2016 while 2017 is 6 percent above. For Denver, the line chart tells us that 2016 is about 2 percent above the 2015 and 2017 values. Now what's the message again?

Here I index all lines to the earliest year.

 

In a Trifecta Checkup analysis (link), I'd be suspicious of the data. Did tech salaries in London really drop by 15-20 percent in the last three years?

 

 

Saying no thanks to a box of donuts

As I reported last week, the Department of Education for Delaware is running a survey on dashboard design. The survey link is here.

One of the charts being evaluated is a box of donuts, as shown below:

I have written before about the problem with donut charts (see here). A box of donuts is worse than one donut. Here, each donut references a school year. The composition by race/ethnicity of the student body is depicted. In aggregate, the composition has not changed drastically although there are small changes from year to year.

In the following alternative, I use a side-by-side line charts, sometimes called slopegraphs, to illustrate the change by race/ethnicity.

The key decisions are:

• using slopes to encode the year-to-year changes, as opposed to having readers compute those changes by measuring and dividing
• using color to show insights (whether the race/ethnicity has expanded, contracted or remained stable across the three years) as opposed to definitions of the data
• not showing that the percentages within each year summing to 100% as opposed to explicitly presenting this fact in a circular arrangement
• placing annual data side by side on the same plot region as opposed to separating them in three charts

***

There is still a further question of how big a change from year to year is considered material.

This is a good example of why there is never "complete data." In theory, the numbers on this chart are "complete," and come from administrative records. Even when ignoring the possibility that some of the records are missing or incorrect, you still have the issue that the students in the system from year to year varies, so a 1 percent increase in the proportion of Hispanic students can indicate a real demographic trend, or it does not.

 

 

Excellent visualization of gun violence in American cities

I like the Guardian's feature (undated) on gun violence in American cities a lot.

The following graphic illustrates the situation in Baltimore.

The designer starts by placing where the gun homicides occured in 2015. Then, it leads readers through an exploration of the key factors that might be associated with the spatial distribution of those homicides.

The blue color measures poverty levels. There is a moderate correlation between high numbers of dots (homicides) and deeper blue (poorer). The magenta color measures education attainment and the orange color measures proportion of blacks. In Baltimore, it appears that race is substantially better at explaining the prevalence of homicides.

This work is exemplary because it transcends description (first map) and explores explanations for the spatial pattern. Because three factors are explored together in a small-multiples layout, readers learn that no single factor can explain everything. In addition, we learn that different factors have different degrees of explanatory power.

Attentive readers will also find that the three factors of poverty, education attainment and proportion black are mutually correlated.  Areas with large black populations also tend to be poorer and less educated.

***

I also like the introductory section in which a little dose of interactivity is used to sequentially present the four maps, now superimposed. It then becomes possible to comprehend the rest quickly.

 ***

The top section is less successful as proportions are not easily conveyed via dot density maps.

Dropping the map form helps. Here is a draft of what I have in mind. I just pulled some data from online sources at the metropolitan area (MSA) level, and it doesn't have as striking a comparison as the city-level data, it seems.

 

 PS. On Twitter, Aliza tells me the article was dated January 9, 2017.