A visual data journalist at the Economist takes a critical eye on charts published by the Economist (link). This is a must read! (Hat tip: Fernando)
Here are some of my commentary on past Economist charts.
Yesterday, in the front page of the Business section, the New York Times published a pair of charts that perfectly captures the story of the ongoing turbulence in the stock market.
Here is the first chart:
Most market observers are very concerned about the S&P entering "correction" territory, which the industry arbitrarily defines as a drop of 10% or more from a peak. This corresponds to the shortest line on the above chart.
The chart promotes a longer-term reflection on the recent turbulence, using two reference points: the index has returned to the level even with that at the start of 2018, and about 16 percent higher since the beginning of 2017.
This is all done tastefully in a clear, understandable graphic.
Then, in a bit of a rhetorical flourish, the bottom of the page makes another point:
When viewed back to a 10-year period, this chart shows that the S&P has exploded by 300% since 2009.
A connection is made between the two charts via the color of the lines, plus the simple, effective annotation "Chart above".
The second chart adds even more context, through vertical bands indicating previous corrections (drops of at least 10%). These moments are connected to the first graphic via the beige color. The extra material conveys the message that the market has survived multiple corrections during this long bull period.
Together, the pair of charts addresses a pressing current issue, and presents a direct, insightful answer in a simple, effective visual design, so it hits the Trifecta!
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There are a couple of interesting challenges related to connecting plots within a multiple-plot framework.
While the beige color connects the concept of "market correction" in the top and bottom charts, it can also be a source of confusion. The orientation and the visual interpretation of those bands differ. The first chart uses one horizontal band while the chart below shows multiple vertical bands. In the first chart, the horizontal band refers to a definition of correction while in the second chart, the vertical bands indicate experienced corrections.
Is there a solution in which the bands have the same orientation and same meaning?
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These graphs solve a visual problem concerning the visualization of growth over time. Growth rates are anchored to some starting time. A ten-percent reduction means nothing unless you are told ten-percent of what.
Using different starting times as reference points, one gets different values of growth rates. With highly variable series of data like stock prices, picking starting times even a day apart can lead to vastly different growth rates.
The designer here picked several obvious reference times, and superimposes multiple lines on the same plotting canvass. Instead of having four lines on one chart, we have three lines on one, and four lines on the other. This limits the number of messages per chart, which speeds up cognition.
The first chart depicts this visual challenge well. Look at the start of 2018. This second line appears as if you can just reset the start point to 0, and drag the remaining portion of the line down. The part of the top line (to the right of Jan 2018) looks just like the second line that starts at Jan 2018.
However, a closer look reveals that the shape may be the same but the magnitude isn't. There is a subtle re-scaling in addition to the re-set to zero.
The same thing happens at the starting moment of the third line. You can't just drag the portion of the first or second line down - there is also a needed re-scaling.
Yesterday, I looked at the following pictograms used by Business Insider in an article about the rural-urban divide in American politics:
The layout of this diagram suggests that the comparison of 2010 to 2018 is a key purpose.
The following alternate directly plots the change between 2010 and 2018, reducing the number of plots from 4 to 2.
The 2018 results are emphasized. Then, for each party, there can be a net add or loss of seats.
The key trends are:
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.
This chart appeared on a recent issue of Princeton Alumni Weekly.
If you read the sister blog, you'll be aware that at most universities in the United States, every student is above average! At Princeton, 47% of the graduating class earned "Latin" honors. The median student just missed graduating with honors so the honors graduate is just above average! The 47% number is actually lower than at some other peer schools - at one point, Harvard was giving 90% of its graduates Latin honors.
Side note: In researching this post, I also learned that in the Senior Survey for Harvard's Class of 2018, two-thirds of the respondents (response rate was about 50%) reported GPA to be 3.71 or above, and half reported 3.80 or above, which means their grade average is higher than A-. Since Harvard does not give out A+, half of the graduates received As in almost every course they took, assuming no non-response bias.
***
Back to the chart. It's a simple chart but it's not getting a Latin honor.
Most readers of the magazine will not care about the decimal point. Just write 18.9% as 19%. Or even 20%.
The sequencing of the honor levels is backwards. Summa should be on top.
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Warning: the remainder of this post is written for graphics die-hards. I go through a bunch of different charts, exploring some fine points.
People often complain that bar charts are boring. A trendy alternative when it comes to count or percentage data is the "pictogram."
Here are two versions of the pictogram. On the left, each percent point is shown as a dot. Then imagine each dot turned into a square, then remove all padding and lines, and you get the chart on the right, which is basically an area chart.
The area chart is actually worse than the original column chart. It's now much harder to judge the areas of irregularly-shaped pieces. You'd have to add data labels to assist the reader.
The 100 dots is appealing because the reader can count out the number of each type of honors. But I don't like visual designs that turn readers into bean-counters.
So I experimented with ways to simplify the counting. If counting is easier, then making comparisons is also easier.
Start with this observation: When asked to count a large number of objects, we group by 10s and 5s.
So, on the left chart below, I made connectors to form groups of 5 or 10 dots. I wonder if I should use different line widths to differentiate groups of five and groups of ten. But the human brain is very powerful: even when I use the same connector style, it's easy to see which is a 5 and which is a 10.
On the left chart, the organizing principles are to keep each connector to its own row, and within each category, to start with 10-group, then 5-group, then singletons. The anti-principle is to allow same-color dots to be separated. The reader should be able to figure out Summa = 10+3, Magna = 10+5+1, Cum Laude = 10+5+4.
The right chart is even more experimental. The anti-principle is to allow bending of the connectors. I also give up on using both 5- and 10-groups. By only using 5-groups, readers can rely on their instinct that anything connected (whether straight or bent) is a 5-group. This is powerful. It relieves the effort of counting while permitting the dots to be packed more tightly by respective color.
Further, I exploited symmetry to further reduce the counting effort. Symmetry is powerful as it removes duplicate effort. In the above chart, once the reader figured out how to read Magna, reading Cum Laude is simplified because the two categories share two straight connectors, and two bent connectors that are mirror images, so it's clear that Cum Laude is more than Magna by exactly three dots (percentage points).
***
Of course, if the message you want to convey is that roughly half the graduates earn honors, and those honors are split almost even by thirds, then the column chart is sufficient. If you do want to use a pictogram, spend some time thinking about how you can reduce the effort of the counting!
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 most time-consuming part of this exercise was finding the appropriate structure which can be easily explained in a visual manner.
A reader submitted the following chart from Pew Research for discussion.
The reader complained that this chart was difficult to comprehend. What are some of the reasons?
The use of color is superfluous. Each line is a "cohort" of people being tracked over time. Each cohort is given its own color or hue. But the color or hue does not signify much.
The dotted lines. This design element requires a footnote to explain. The reader learns that some of the numbers on the chart are projections because those numbers pertain to time well into the future. The chart was published in 2014, using historical data so any numbers dated 2014 or after (and even some data before 2014) will be projections. The data are in fact encoded in the dots, not the slopes. Look at the cohort that has one solid line segment and one dotted line segment - it's unclear which of those three data points are projections, and which are experienced.
The focus on within-cohort trends. The line segments indicate the desire of the designer to emphasize trends within each cohort. However, it's not clear what the underlying message is. It may be that more and more people are not getting married (i.e. fewer people are getting married). That trend affects each of the three age groups - and it's easier to paint that message by focusing on between-cohort trends.
***
Here is a chart that emphasizes the between-cohort trends.
A key decision is to not mix oil and water. The within-cohort analysis is presented in its own chart, next to the between-cohort analysis. It turns out that some of the gap between cohorts can be explained by people deferring marriage to later in life. The steep line on the right indicates that a bigger proportion of people now gets married between 35 and 44 than in previous cohorts.
I experimented a bit with the axes here. Several pie charts are used in lieu of axis labels. I also plotted a dual axis with the proportion of unmarried on the one side, and the corresponding proportion of married on the other side.
This Buzzfeed article proves that foodies love their food served with dataviz (tip: Chris P.). Menus are an undertapped resource when it comes to data visualization.
There are several examples worth discussing.
Venn diagrams are not easy to read, people.
Plus they are hard to construct well... note the asymmetric areas.
Here is one without circles:
Then, I pared it down to its essence:
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This beer map is pretty great:
Some of its virtues:
Potential problems:
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This next menu contains an error:
When the drink comes in one size, only one price is listed. If it comes in two sizes, two prices should be listed.
Is the cafe owner shading Americans as not good at math?
It's not often I come across a piece of data journalism that pleases me so much. Here it is, the "Happy 700" article by Washington Post is amazing.
When data journalism and dataviz are done right, the designers have made good decisions. Here are some of the key elements that make this article work:
(1) Unique
The topic is timely but timeliness heightens both the demand and supply of articles, which means only the unique and relevant pieces get the readers' attention.
(2) Fun
The tone is light-hearted. It's a fun read. A little bit informative - when they describe the towns that few have heard of. The notion is slightly silly but the reader won't care.
(3) Data
It's always a challenge to make data come alive, and these authors succeeded. Most of the data work involves finding, collecting and processing the data. There isn't any sophisticated analysis. But a powerful demonstration that complex analysis is not always necessary.
(4) Organization
The structure of the data is three criteria (elevation, population, and terrain) by cities. A typical way of showing such data might be an annotated table, or a Bumps-type chart, grouped columns, and so on. All these formats try to stuff the entire dataset onto one chart. The designers chose to highlight one variable at a time, cumulatively, on three separate maps. This presentation fits perfectly with the flow of the writing.
(5) Details
The execution involves some smart choices. I am a big fan of legend/axis labels that are informative, for example, note that the legend doesn't say "Elevation in Meters":
The color scheme across all three maps shows a keen awareness of background/foreground concerns.
I came across this chart from the OurWorldinData website, and this one would make the late Hans Rosling very happy.
If you went to Professor Rosling's talk, he was bitter that the amazing gains in public health, worldwide (but particularly in less developed nations) during the last few decades have been little noticed. This chart makes it clear: note especially the dramatic plunge in extreme poverty, rise in vaccinations, drop in child mortality, and improvement in education and literacy, mostly achived in the last few decades.
This set of charts has a simple but powerful message. It's the simplicity of execution that really helps readers get that powerful message.
The text labels on the left and right side of the charts are just perfect.
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Little things that irk me:
I am not convinced by the liberal use of colors - I would make the "other" category of each chart consistently gray so 6 colors total. Having different colors does make the chart more interesting to look at.
Even though the gridlines are muted, I still find them excessive.
There is a coding bug in the Vaccination chart right around 1960.