Carl Bialik used to be the Numbers Guy at Wall Street Journal - he's now with FiveThirtyEight. Apparently, he left a huge void. John Eppley sent me to this set of charts via Twitter.
This chart about Citibike is very disappointing.
Using the Trifecta checkup, I first notice that it addresses a stale question and produces a stale answer. The caption below the chart says "the peak times ... seem to be around 9 am and 6 pm." What a shock!
I sense a degree of meekness in usnig "seem to be". There is not much to inspire confidence in the data: rather than the full statistics which you'd think someone at Citibike has, the chart is based on "a two-day sample last autumn". The number of days is less concerning than the question of whether those two autumn days are representative of the year. Curious readers might want to know what data was collected, how it was collected, and the sample size.
Finally, the graph makes a mess of the data. While the black line appears to be data-rich, it is not. In fact, the blue dots might as well be randomly scattered and connected. As you can see from the annotations below, the scale of the chart makes no sense.
Plus, the execution is sloppy, with a missing data label.
The next chart is not much better.
The biggest howler is the choice of pie charts to illustrate three numbers that are not that different.
But I have to say the chart raises more questions than it answers. I am not an expert in pregnancy but doesn't a pregnant woman's weight include the weight of the baby she's carrying? So the more weight the woman gains, on average, the heavier is her baby. What a shock!
The last and maybe the least is this chart about basketball players in the playoff.
It's the dreaded bubble chart. The players are arranged in a perplexing order. I wonder if there is a natural numbering system for basketball positions (center = #1, etc.), like there is in soccer. Even if there is such a natural numbering system, I still question the decision to confound that system with a complicated ranking of current-year playoff players against all-time players.
Above all, the question being asked is uninteresting, and so the chart is uninformative. A more interesting question to me is whether the best players are playing in this year's playoff. To answer this question, the designer should be comparing only currently active players, and showing the all-time ranks of those players who are playing in the playoffs versus those who aren't.
The question on the table is motivated by the extraordinary performance of a young baseball player Mike Trout. The early success can be interpreted either as evidence of future potential or as evidence of a future drought. As an analogy, someone wins a lottery. You can argue that the odds are so low that winning again is impossible. Or you can argue that winning once indicates that this person is "lucky" and lucky people might win again.
The chart shows the proportion of players who performed even better after the initial success, given the age at which they first broke out. One way to read this chart is to mentally replace the bubbles with dots (or columns), and then interpret the size of the bubbles as the statistical significance of the corresponding probability estimate. The legend says number of players, which is the sample size, which governs the error bar associated with that particular number.
This bubble chart is no different from others: it is impossible to judge the relative sizes of bubbles. Even though the legend provides us two reference points (a nice enough idea on its own), it is still impossible to know, for example, what proportion of players did better later in life when they first peaked at age 24. The bubble for age 23 looks like it's exactly five players but I still cannot figure out how many players the adjacent bubble represents.
The designer should have just replaced each bubble with an error bar, and the chart is instantly more readable. (I have another version of this at the end of the post.)
The rest of the design elements are clean and well-done, particularly use of notes to point out interesting aspects of the data.
From a Trifecta checkup perspective, I am uncertain about how the nature of the data used to investigate the interesting question posed above.
Readers should note the concept of "early success" and "later success" are not universally defined. The author here selects two proxies. Reaching an early peak is equated to "batters first posting 15+ WAR over two seasons". Next, reversion to the mean is defined as not having a better two-year span subsequent to the aforementioned early peak.
Why two seasons? Why WAR and not a different metric? Why 15 as the cutoff? These are all design decisions made while working with the data.
One can make reasonable arguments to justify the above two questions. A bigger head-scratcher relates to the horizontal axis, which identifies the first time a player reaches his "early peak," as defined above. The way the above chart is set up, it is almost preordained to exhibit a negative slope. The older the player is when he reaches the first peak, the fewer years left in his playing career to try to emulate or surpass that feat.
This last point is nicely illustrated in the next chart of the article:
This chart is excellent on many levels. It's not clear, though, whether it says anything other than aging.
Near the end of the post, the author rightfully pointed out that "there’s not really enough data to demonstrate this effect". Going back to the first chart, it appears that no single bubble contains a double-digit count of players. So every sample size is between one and, say, seven. We should be wary of conclusions based on so little data.
It's always fun to find examples of the Law of Small Numbers, courtesy of Kahneman & Tversky.
Here is a sketch of how I might re-make the first chart (I made up data; see the note below).
While making this chart, I realize another issue with the original bubble chart. When the proportion of players improving on their early peak is zero percent, how many players did not make it is quite hidden. In the revised chart, this data is clearly seen (look at age 22).
Note: I wonder if I totally missed the point of the original chart.... I actually had trouble eyeballing the data so I ended up making up numbers. The bubble at age 22 looks like it should stand for 5 players and yet it sits at precisely 50%, which would map to 2.5 players. If I assume the 22 bubble to be 4 players, then I don't know what the 26 bubble is. If it is 4 players also, then the minimum non-zero proportion should have been 1/4, but the bubble clearly lies below 25%. If it is 3 players, the minimum non-zero proportion is 1/3, which should be at 33%.
My twitter followers have been sending in several howlers.
Twitter (link) made a bunch of bold claims about its own influence by using the number of tweets about the Oscars as fodder. They also adopt the euphenism common to the digital marketing universe, the so-called "view", which credit to them, they define as "how many times tweets are displayed to users". Yes, you read that right, displaying is the same as viewing in this world - and Twitter is just a follower not a trend setter here.
In the meantime, @wilte found this unfortunate donut chart, created by PWC in the Netherlands.
Both designers basically used appropriated a graphical form and deprived it of data. In one, the designer threw the concept of scale to the wind. In the other, the designer dumped the law of total probability. In either case, the fundamental rationale for the particular graphical form is sacrificed.
Both are examples that fail our self-sufficiency test. This test says if a visual display cannot be understood unless the entire data set is printed on the chart, then why create a visual display? In both charts, if you block out the numbers, you are left with nothing!
The PWC chart was submitted by @graphomate, who also submitted the following KPMG chart:
The complaint was the total adding up to 101%. I'm not really bothered by this as it is a rounding issue. That said, I like to "hide" such rounding issues. I have never understood why it is necessary to display the imperfection. Flip a coin and remove the decimals from one of the categories!
Jens M., a long-time reader, submits a good graphic! This small-multiples chart (via Quartz) compares the consumption of liquor from selected countries around the world, showing both the level of consumption and the change over time.
Ordered the countries by the most recent data point rather than alphabetically
Scale labels are found only on outer edge of the chart area, rather than one set per panel
Only used three labels for the 11 years on the plot
Did not overdo the vertical scale either
The nicest feature was the XL scale applied only to South Korea. This destroys the small-multiples principle but draws attention to the top left corner, where the designer wants our eyes to go. I would have used smaller fonts throughout.
Having done so much work to simplify the data and expose the patterns, it's time to look at whether we can add some complexity without going overboard. I'd suggest using a different color to draw attention to curves that are strangely shaped -- the Ukraine comes to mind, so does Brazil.
I'd also consider adding the top liquor in each country... the writeup made a big deal out of the fact that most of the drinking in South Korea is of Soju.
One way to appreciate the greatness of the chart is to look at alternatives.
Here, the Economist tries the lazy approach of using a map: (link)
For one thing, they have to give up the time dimension.
A variation is a cartogram in which the physical size and shape of countries are mapped to the underlying data. Here's one on Worldmapper (link):
One problem with this transformation is what to do with missing data.
Wikipedia has a better map with variations of one color (link):
The Atlantic realizes that populations are not evenly distributed on the map so instead of coloring countries, thay put bubbles on top of the map (link):
Unfortunately, they scaled the bubbles to the total consumption rather than the per-capita consumption. You guess it, China gets the biggest bubble and much larger than anywhere else but from a per-capita standpoint, China is behind many other countries depicted on the map.
PS. A note on submissions. I welcome submissions, especially if you have a good chart to offer. Please ping me if I don't reply within a few weeks. I may have just missed your email. Also, realize that submissions take even more time to research since it is likely in the area I have little knowledge about, and mostly because you sent it to me since you hope I'll research it. Sometimes I give up since it's taking too much time. If you ping me again, I'll let you know if I'm working on it.
The above does not apply to emails from people who are building traffic for their infographics.
PPS. Andrew Gelman chimes in with his take on small multiples.
At the NY Tech Meetup, Andrei Scheinkman showed off some work his team at Huffington Post did relating to gun violence in America.
Interactive version is here. The animation shows day by day, where the victims of gun violence were located. The table below contains the details of each victim, and links to the news story covering the event.
What is not seen on the chart is even more impressive. Andrei described how they looked around for databases that would provide them the raw materials for creating this chart but no timely source exists. This means that a team of 15 (if I heard correctly) spent a month or so manually collecting all the data on a spreadsheet.
It's also the reason why they cannot continue the map indefinitely, as people have other things to do.
Andrei also contrasted this visualization with a text article that describes the state of gun violence in words. You guessed it, the visual presentation is hands-down more compelling.
The bubble chart is one of the most hopeless data graphics ever invented. It is sometimes useful for conceptual charts but trying to express data with it is a lost cause.
The Wall Street Journal used a bubble chart to show the trend in whistle-blower lawsuits in the U.S. The original chart looks like this:
Focus on the top part of the chart. Now apply the self-sufficiency test (link), as follows:
First, cover up the data labels. You'll notice that no information is conveyed by the bubbles in and of themselves.
Second, give yourself a hint. The size of the first bubble corresponds to 363 suits. What does that tell you about the second bubble? Unfortunately, the answer is still nothing.
Third, give yourself two hints. The second bubble from the left has size 311. Now try to estimate the size of the rightmost bubble given those two pieces of data. This exercise is still extremely taxing.
Thus, the conclusion about bubble charts is:
That is to say, it fails the self-sufficiency test (link). The chart cannot exist without the data labels. The graphical elements do not provide any additional value.
Augustine F. (@acfou) was not amused by a set of charts made by the Bureau of Labor Statistics, via Business Insider (link). Here's one of them:
The article's message is that the book, periodical and music stores industry has shrunk drastically (over 50%) in the last 10 years but unless you spend time studying the chart, you're not likely to get this picture.
The bubbles are going right and up, which usually is indicative of an increasing trend. What is tripping us up is the employment level occupying the horizontal axis rather than the expected time dimension. The only real way to see the plunge in employment is to focus on the horizontal axis, and to notice the deepening color of the bubbles.
The chart is actually a scatter plot of number of firms versus number of employees. The slope of the line gives us the number of firms per employee, which is also unexpected since the usual metric is its reciprocal, the number of employees per company. However, since the slope is essentially constant, highlighting this number is pointless. While the industry is collapsing, the average workforce of the surviving firms has remained more or less the same.
I added a cone to the chart to visualize the narrow range in which the employees per firm varied during the past decade.
As if it's not confusing enough, the reciprocal of the slope is coded to the size of the bubbles on the chart. This requires a legend to explain. All of this means that readers' attention is directed to the average work force metric, instead of the drop in employment.
The following indexed chart shows that the number of employees and the number of firms dropped in step during the ten years. Both dropped about 55% during the decade. This just confirms that the average employee per firm metric is not meaningful.
If you follow the link to the BLS analysis, you'll find some other interesting data, namely the "internet publishing" industry. Does it make sense to talk about the drastic decline in traditional publishing without talking about the rise of the "substitute" industry? The chart below shows that the new jobs created in Internet publishing filled almost all of the hole left in the traditional publishing industry. The decline from 2009 on may not be specific to the industry; it could just be the Great Recession. (As defined, I don't think the two industry sectors are exactly what I'm looking for, but it's close enough.)
I enjoy looking at the New York Times' summation of National Convention speeches via visualization. (link)
It's a disguised word cloud combined with a bubble chart with a little bar chart thrown in for good measure.
The size of the bubble is the total number of mentions of particular words or phrases. So the bubbles tell us the importance of specific concepts in aggregate of two parties.
It's the split within each bubble that represents the relative emphasis by party. Helpfully, the bubbles are sorted from left to right with the most Democratic words on the left. This splitting uses a bar chart paradigm. The diameter of the bubble is being partitioned, not the areas of the segments.
I wanted to see this as a straight-out word cloud. In the following, I use the red-blue-purple color gradient to indicate the Republican-Democratic bias, and the size of the words to indicate the number of mentions.
This word cloud is created using the Wordle tool, advanced options. My colleague John helped me pick the colors. (By the way, I don't like the insertion of small words within large letters, like what happened here inside the O in Obama.)
Also, I'd line the colors up so that the red words are on one side, blue on the other and purple in the middle. I'd need a different tool to be able to exercise this type of control.