More visuals of the economic crisis











I have three additional proposals:

Disclosure of Biases and Limitations




Disclosure of Definitions and Proxies





Data as a Public Good



Graph literacy, in a sense

Ben Jones tweeted out this chart, which has an unusual feature:


What's unusual is that time runs in both directions. Usually, the rule is that time runs left to right (except, of course, in right-to-left cultures). Here, the purple area chart follows that convention while the yellow area chart inverts it.

On the one hand, this is quite cute. Lines meeting in the middle. Converging. I get it.

On the other hand, every time a designer defies conventions, the reader has to recognize it, and to rationalize it.

In this particular graphic, I'm not convinced. There are four numbers only. The trend on either side looks linear so the story is simple. Why complicate it using unusual visual design?

Here is an entirely conventional bumps-like chart that tells the story:


I've done a couple of things here that might be considered controversial.

First, I completely straightened out the lines. I don't see what additional precision is bringing to the chart.

Second, despite having just four numbers, I added the year 1996 and vertical gridlines indicating decades. A Tufte purist will surely object.


Related blog post: "The Return on Effort in Data Graphics" (link)

Elegant way to present a pair of charts

The Bloomberg team has come up with a few goodies lately. I was captivated by the following graphic about the ebb and flow of U.S. presidential candidates across recent campaigns. Link to the full presentation here.

The highlight is at the bottom of the page. This is an excerpt of the chart:


From top to bottom are the sequential presidential races. The far right vertical axis is the finish line. Going right to left is the time before the finish line. In 2008, for example, there are candidates who entered the race much earlier than typical.

This chart presents an aggregate view of the data. We get a sense of when most of the candidates enter the race, when most of them are knocked out, and also a glimpse of outliers. The general pattern across multiple elections is also clear. The design is a stacked area chart with the baseline in the middle, rather than the bottom, of the chart.

Sure, the chart can disappoint those readers who want details and precise numbers. It's not immediately apparent how many candidates were in the race at the height of 2008, nor who the candidates were.

The designer added a nice touch. By clicking on any of the stacks, it transforms into a bar chart, showing the extent of each candidate's participation in the race.


I wish this was a way to collapse the bar chart back to the stack. You can reload the page to start afresh.


This elegant design touch makes the user experience playful. It's also an elegant way to present what is essentially a panel of plots. Imagine the more traditional presentation of placing the stack and the bar chart side by side.

This design does not escape the trade-off between entertainment value and data integrity. Looking at the 2004 campaign, one should expect to see the blue stack halve in size around day 100 when Kerry became the last man standing. That moment is not marked in the stack. The stack can be interpreted as a smoothed version of the count of active candidates.


I suppose some may complain the stack misrepresents the data somewhat. I find it an attractive way of presenting the big-picture message to an audience that mostly spend less than a minute looking at the graphic.

Pretty circular things




What to Like






What's baffling





A couple of other comments on the circular design.








Check out the Lifespan of News project




















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:



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.



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.

No Latin honors for graphic design

Paw_honors_2018This chart appeared on a recent issue of Princeton Alumni Weekly.




























Made in France stereotypes

France is on my mind lately, as I prepare to bring my dataviz seminar to Lyon in a couple of weeks.  (You can still register for the free seminar here.)

The following Made in France poster brings out all the stereotypes of the French.


(You can download the original PDF here.)

It's a sankey diagram with so many flows that it screams "it's complicated!" This is an example of a graphic for want of a story. In a Trifecta Checkup, it's failing in the Q(uestion) corner.

It's also failing in the D(ata) corner. Take a look at the top of the chart.


France exported $572 billion worth of goods. The diagram then plots eight categories of exports, ranging from wines to cheeses:


Wine exports totaled $9 billion which is about 1.6% of total exports. That's the largest category of the eight shown on the page. Clearly the vast majority of exports are excluded from the sankey diagram.

Are the 8 the largest categories of exports for France? According to this site, those are (1) machinery (2) aircraft (3) vehicles (4) electrical machinery (5) pharmaceuticals (6) plastics (7) beverages, spirits, vinegar (8) perfumes, cosmetics.

Compare: (1) wines (2) jewellery (3) perfume (4) clothing (5) cheese (6) baked goods (7) chocolate (8) paintings.

It's stereotype central. Name 8 things associated with the French brand and cherry-pick those.

Within each category, the diagram does not show all of the exports either. It discloses that the bars for wines show only $7 of the $9 billion worth of wines exported. This is because the data only capture the "Top 10 Importers." (See below for why the designer did this... France exports wine to more than 180 countries.)

Finally, look at the parade of key importers of French products, as shown at the bottom of the sankey:


The problem with interpreting this list of countries is best felt by attempting to describe which countries ended up on this list! It's the list of countries that belong to the top 10 importers of one or more of the eight chosen products, ordered by the total value of imports in those 8 categories only but only including the value in any category if it rises to the top 10 of the respective category.

In short, with all those qualifications, the size or rank of the black bars does not convey any useful information.


One feature of the chart that surprised me was no flows in the Wine category from France to Italy or Spain. (Based on the above discussion, you should realize that no flows does not mean no exports.) So I went to the Comtrade database that is referenced in the poster, and pulled out all the wine export data.

How does one visualize where French wines are going? After fiddling around the numbers, I came up with the following diagram:


I like this type of block diagram which brings out the structure of the dataset. The key features are:

  • The total wine exports to the rest of the world was $1.4 billion in 2016
  • Half of it went to five European neighbors, the other half to the rest of the world
  • On the left half, Germany took a third of those exports; the UK and Switzerland together is another third; and the final third went to Belgium and the Netherlands
  • On the right half, the countries in the blue zone accounted for three-fifths with the unspecified countries taking two-fifths.
  • As indicated, the two-fifths (in gray) represent 20% of total wine exports, and were spread out among over 180 countries.
  • The three-fifths of the blue zone were split in half, with the first half going to North America (about 2/3 to USA and 1/3 to Canada) and the second half going to Asia (2/3 to China and 1/3 to Japan)
  • As the title indicates, the top 9 importers of French wine covered 80% of the total volume (in litres) while the other 180+ countries took 20% of the volume

 The most time-consuming part of this exercise was finding the appropriate structure which can be easily explained in a visual manner.



Why line charts are better than area charts

I saw this chart on Business Insider recently:


This links to Market Insider, where there is a tool to make different types of charts. Despite the huge drop depicted above, by last week, the Dow Jones index has recovered to the level at the start of 2018:


The same chart can be made as an area chart (called a "mountain chart" by Market Insider).


The painting of the area serves no purpose here because the area doesn't mean anything.

Imagine adding an inch of space to the bottom of each chart. The area chart is sensitive to the choice of the minimum value of the vertical axis while the line chart isn't. Since the data did not change, it's not a good idea for the display to shift perception. That's why I prefer the line chart.