Confuse, confuses, confused, confusing

Via Twitter, @Stoltzmaniac sent me this chart, from the Economist (link to article):

Econ_vehicles

There is simply too much going on on the right side of the chart. The designer seems not to be able to decide which metric is more important, the cumulative growth rate of vehicles in use from 2005 to 2014, or the vehicles per 1,000 people in 2014. So both set of numbers are placed on the chart, regrettably in close proximity.

In the meantime, the other components of the chart, such as the gridlines and the red line indicating 2005 = 100 are only relevant to the cumulative vehicle growth metric. Perhaps noticing the imbalance, the designer then paints the other data series in rainbow-colored boxes, and prints the label for this data series in a big white box. This decision tilts the chart towards the vehicle per capita metric, as our eyes now cannot help but stare at the white box.

***

There are really three trends: the growth in population, the growth in vehicles, and the resultant growth in vehicle per capita. They are all be accommodated in a small-multiples setting, as follows:

Jc_econ_vehicles2

There are some curious angular trends revealed here. The German population somehow dipped into negative territory around 2007-8 but since then has turned around. Nigeria's vehicle growth declined sharply after 2006 so that the density of vehicles has stabilized.

 


If Clinton and Trump go to dinner, do they sit face to face, or side by side?

One of my students tipped me to an August article in the Economist, published when last the media proclaimed Donald Trump's campaign in deep water. The headline said "Donald Trump's Media Advantage Falters."

Who would have known, judging from the chart that accompanies the article?

Economist_20160820_woc352_1

There is something very confusing about the red line, showing "Trump August 2015 = 1." The data are disaggregated by media channel, and yet the index is hitched to the total of all channels. It is also impossible to figure out how Clinton is doing relative to Trump in each channel.

Here is a small-multiples rendering that highlights the key comparisons:

Redo_economist_earnedmedia1b

Alternatively, one can plot the Clinton advantage versus Trump in each channel, like this:

Redo_economist_earnedmedia2b

One sees that Clinton has caught up in the last month (July 2016), primarily through more coverage by "online news."

Imagine Mr. Trump and Mrs. Clinton dining at a restaurant. Are they seated side by side (Economist) or face to face (junkcharts)?


Graphical inequity ruins the chart

This Economist chart has a great concept but I find it difficult to find the story: (link)

Economist_brexit

I am a fan of color-coding the text as they have done here so that part is good.

The journalist has this neat idea of comparing those who are apathetic ("don't care about whether Britain is in or out") and those who are passionate ("strongly prefer" that Britain is either in or out).

The chosen format suffers because of graphical inequity: the countries are sorted by decreasing apathy, which means it is challenging to figure out the degree of passion.

This chosen order is unrelated to the question at hand. One possible way of interpreting the chart is to compare individual countries against the European average. The journalist also recognizes this, and highlighted the Euro average.

The problem is that there are two different averages and no good way to decide whether a particular country is above or below average.

Here is my version of the chart:

Redo_econ_brexit2

The biggest change is to create the new metric: how many people say they really care about Brexit/Bremain for every person who say they don't care. In Britain, over four people really care for each one who doesn't while in Slovenia, you can only find fewer than half a person who really cares for each one who doesn't.

 

 


Confusion is not limited to complex dataviz

This chart looks simple and harmless but I find it disarming.

Econ_puertorico

I usually love the cheeky titles in the Economist but this title is very destructive to the data visualization. The chart has nothing to do with credit scores. In fact, credit scoring is associated with consumers while countries have credit ratings.

Also, I am not a fan of the Economist way of labeling negative axes. The negative sign situated between 0 and 1 looks like a stray hyphen that the editor missed.

A line chart would have brought out the pattern more sharply:

Redo_puerto1

The pairing of columns in the original chart signals that readers should compare GDP growth to population growth. A good point, since GDP scales with population.

Controlling for population size can be accomplished by the per-capita GDP growth rate.

Redo_puerto2

The last three years are clearly different. By this metric, different in a good way.

This chart creates a problem for the journalist. The article is about the deal to "save" Puerto Rico which some has criticized as colonial. Presumably, the territory has been in dire straits. There are plenty of metrics to illustrate this point but GDP growth is not one of them.


Batmen not as interesting as it seems

When this post appears, I will be on my way to Seattle. Maybe I will meet some of you there. You can still register here.

I held onto this tip from a reader for a while. I think it came from Twitter:

20160326_woc432_1 batman

The Economist found a fun topic but what's up with the axis not starting at zero?

The height x weight gimmick seems cool but on second thought, weight is not the same as girth so it doesn't make much sense!

In the re-design, I use bubbles to indicate weight and vertical location to indicate height. The data aren't as interesting as one might think. All the actors pretty much stayed true to the comic-book ideal, with Adam West being the closest. I also changed the order of the actors.

Redo_batman

I left out the Lego, as it creates a design challenge that does not justify the effort.

 

 


Raw data and the incurious

The following chart caught my eye when it appeared in the Wall Street Journal this month:

Wsj_fedratehike

This is a laborious design; much sweat has been poured into it. It's a chart that requires the reader to spend time learning to read.

A major difficulty for any visualization of this dataset is keeping track of the two time scales. One scale, depicted horizontally, traces the dates of Fed meetings. These meetings seem to occur four times a year except in 2012. The other time scale is encoded in the colors, explained above the chart. This is the outlook by each Fed committee member of when he/she expects a rate hike to occur.

I find it challenging to understand the time scale in discrete colors. Given that time has an order, my expectation is that the colors should be ordered. Adding to this mess is the correlation between the two time scales. As time treads on, certain predictions become infeasible.

Part of the problem is the unexplained vertical scale. Eventually, I realize each cell is a committee member, and there are 19 members, although two or three routinely fail to submit their outlook in any given meeting.

Contrary to expectation, I don't think one can read across a row to see how a particular member changes his/her view over time. This is because the patches of color would be less together otherwise.

***

After this struggle, all I wanted is some learning from this dataset. Here is what I came up with:

Redo_wsjfedratehike

There is actually little of interest in the data. The most salient point is that a shift in view occurred back in September 2012 when enough members pushed back the year of rate hike that the median view moved from 2014 to 2015. Thereafter, there is a decidedly muted climb in support for the 2015 view.

***

This is an example in which plotting elemental data backfires. Raw data is the sanctuary of the incurious.

 

 


Circular but insufficient

One of my students analyzed the following Economist chart for her homework.

Economist_book_sales_printversion

I was looking for it online, and found an interactive version that is a bit different (link). Here are three screen shots from the online version for years 2009, 2013 and 2018. The first and last snapshots correspond to the years depicted in the print version.

  Economist_booksales_all

The online version is the self-sufficiency test for the print version. In testing self-sufficiency, we want to see if the visual elements (i.e. the circular sectors on the print version) pull their own weights. The quick answer is no. The reader can't tell how much sales are represented in each sector, nor can they reliably estimate the relative scales of print versus ebook (pink/red vs yellow/orange) or year-to-year growth rates.

As usual, when we see the entire data set printed on the chart itself, it is giveaway that the visual elements are mere ornaments.

The online version does not have labels unless you hover over the hemispheres. But again it is a challenge to learn anything from the picture.

In the Trifecta checkup, this is a Type V chart.

***

This particular dataset is made for the bumps-style chart:

Redo_economistbooksales

 

 

 


The missing Brazil effect, and BYOC charts

Announcement: I'm giving a free public lecture on telling and finding stories via data visualization at NYU on 7/15/2014. More information and registration here.

***

The Economist states the obvious, that the current World Cup is atypically high-scoring (or poorly defended, for anyone who've never been bothered by the goal count). They dubiously dub it the Brazil effect (link).

Perhaps in a sly vote of dissent, the graphic designer came up with this effort:

Economist_worldcup

(Thanks to Arati for the tip.)

The list of problems with this chart is long but let's start with the absence of the host country and the absence of the current tournament, both conspiring against our ability to find an answer to the posed question: did Brazil make them do it?

***

Turns out that without 2014 on the chart, the only other year in which Brazil hosted a tournament was 1950. But 1950 is not even comparable to the modern era. In 1950, there was no knock-out stage. They had four groups in the group stage but divided into two groups of four, one group of three and one group of two. Then, four teams were selected to play a round-robin final stage. This format is so different from today's format that I find it silly to try to place them on the same chart.

This data simply provide no clue as to whether there is a Brazil effect.

***

The chosen design is a homework assignment for the fastidious reader. The histogram plots the absolute number of drawn matches. The number of matches played has tripled from 16 to 48 over those years so the absolute counts are highly misleading. It's worse than nothing because the accompanying article wants to make the point that we are seeing fewer draws this World Cup compared to the past. The visual presents exactly the opposite message! (Hint: Trifecta Checkup)

Unless you realize this is a homework assignment. You can take the row of numbers listed below the Cup years and compute the proportion of draws yourself. BYOC (Bring Your Own Calculator). Now, pay attention because you want to use the numbers in parentheses (the number of matches), not the first number (that of teams).

Further, don't get too distracted by the typos: in both 1982 and 1994, there were 24 teams playing, not 16 or 32. The number of matches (52 in each case) is correctly stated.

***

Wait, the designer provides the proportions at the bottom of the chart, via this device:

Econ_worldcup_sm

As usual, the bubble chart does a poor job conveying the data. I deliberately cropped out the data labels to demonstrate that the bubble element cannot stand on its own. This element fails my self-sufficiency test.

***

I find the legend challenging as well. The presentation should be flipped: look at the proportion of ties within each round, instead of looking at the overall proprotion of ties and then breaking those ties by round.

The so-called "knockout round" has many formats over the years. In early years, there were often two round-robin stages, followed by a smaller knockout round. Presumably the second round-robin stage has been classified as "knockout stage".

Also notice the footnote, stating that third-place games are excluded from the histogram. This is exactly how I would do it too because the third-place match is a dead rubber, in which no rational team would want to play extra-time and penalty shootout.

The trouble is inconsistency. The number of matches shown underneath the chart includes that third-place match so the homework assignment above actually has a further wrinkle: subtract one from the numbers in parentheses. The designer gets caught in this booby trap. The computed proportion of draws displayed at the bottom of the chart includes the third-place match, at odds with the histogram.

***

Here is a revised version of the chart:

Redo_econ_worldcup1

Redo_econ_worldcup2

A few observations are in order:

  • The proportion of ties has been slowly declining over the last few Cups.
  • The drop in proportion of ties in 2014 is not drastic.
  • While the proportion of ties has dropped in the 2014 World Cup, the proportion of 0-0 ties has increased. (The gap between the two lines shows the ties with goals.)
  • In later rounds, since the 1980s, the proportion of ties has been fairly stable, between 20 and 35 percent.

Another reason for separate treatment is that the knockout stage has not started yet in 2014 when this chart was published. Instead of removing all of 2014, as the Economist did, I can include the group stage for 2014 but exclude 2014 from the knockout round analysis.

In the Trifecta Checkup, this is Type DV. The data do not address the question being posed, and the visual conveys the wrong impression.

 ***

Finally, there is one glaring gap in all of this. Some time ago (the football fans can fill in the exact timing), FIFA decided to award three points for a win instead of two. This was a deliberate effort to increase the point differential between winning and drawing, supposedly to reduce the chance of ties. Any time-series exploration of the frequency of ties would clearly have to look into this issue.

 


Update on Dataviz Workshop 1

Happy to report on the dataviz workshop, a first-time offering at NYU. I previously posted the syllabus here.

I made minor changes to the syllabus, adding Alberto Cairo's book, The Functional Art (link), as optional reading, some articles from the recent debate in the book review circle about the utility of "negative reviews" (start here), and some blog posts by Stephen Few.

The Cairo and Few readings, together with Tufte, are closest to what I want to accomplish in the first two classes, before we start discussing individual projects: encouraging students to adopt the mentality of the course, that is to say, to think of dataviz as an artform. An artform implies many things, one of which is a seriousness about the output, and another is the recognition that the work has an audience. 

The field of data visualization is sorely lacking high-level theory, immersed as so many of us are in tools, data, and rules of thumb. It is my hope that these workshop discussions will lead to a crytallization of the core principles of the field.

We went on a tour of many dataviz blogs, and documented various styles of criticism. In the next class, we will discuss what style we'd adopt in the course.

***

The composition of the class brings me great excitement. There are 12 enrolled students, which is probably the maximum for a class of this type.  One student subsequently dropped out, after learning that the workshop is really not for true beginners.

The workshop participants come from all three schools of dataviz: computer science, statistics, and design. Amongst us are an academic economist trained in statistical methods, several IT professionals, and an art director. This should make for rewarding conversation, as inevitably there will be differences in perspective.

***

REQUEST FOR HELP: A variety of projects have been proposed; several are using this opportunity to explore data sets from their work. That said, some participants are hoping to find certain datasets. If you know of good sources for the following, please write a comment below and link to them:

  • Opening-day ratings from sites like Rotten Tomatoes
  • New York City water quality measures by county (or other geographical unit), probably from an environmental agency
  • Data about donors/donations to public media companies

***

Since this is a dataviz blog, I want to include a chart with this post. I did a poll of the enrolled students, and one of the questions was about what dataviz tools they use to generate charts. I present here two views of the same data.

The first is a standard column chart, plotting the number of students who include a particular tool in his or her toolset (each student is allowed to name more than one tools). This presents a simple piece of information simply: Excel is the most popular although the long tail indicates the variety of tools people use in practice.

Class_tools_2

What the first option doesn't bring out is the correlation between tools, indicated by several tools used by the same participant. The second option makes this clear, with each column representing a student. This chart is richer as it also provides information on how many tools the average student uses, and the relationship between different tools.

Class_tools_1

The tradeoff is that the reader has to work a little more to understand the relative importance of the different tools, a message that is very clear in the first option. 

This second option is also not scalable. If there are thousands of students, the chart will lose its punch (although it will undoubtedly be called beautiful).

Which version do you like? Are there even better ways to present this information?

 


Hate the defaults

One piece of  advice I give for those wanting to get into data visualization is to trash the defaults (see the last part of this interview with me). Jon Schwabish, an economist with the government, gives a detailed example of how this is done in a guest blog on the Why Axis.

Here are the highlights of his piece.

***

He starts with a basic chart, published by the Bureau of Labor Statistics. You can see the hallmarks of the Excel chart using the Excel defaults. The blue, red, green color scheme is most telling.

Schwabish_bls1

 

Just by making small changes, like using tints as opposed to different colors, using columns instead of bars, reordering the industry categories, and placing the legend text next to the columns, Schwabish made the chart more visually appealing and more effective.

Redo_schwabishbls1

 The final version uses lines instead of columns, which will outrage some readers. It is usually true that a grouped bar chart should be replaced by overlaid line charts, and this should not be limited to so-called discrete data.

Redo_schwabishbls2

Schwabish included several bells and whistles. The three data points are not evenly spaced in time. The year-on-year difference is separately plotted as a bar chart on the same canvass. I'd consider using a line chart here as well... and lose the vertical axis since all the data are printed on the chart (or else, lose the data labels). 

This version is considerably cleaner than the original.

***

I noticed that the first person to comment on the Why Axis post said that internal BLS readers resist more innovative charts, claiming "they don't understand it". This is always a consideration when departing from standard chart types.

Another reader likes the "alphabetical order" (so to speak) of the industries. He raises another key consideration: who is your audience? If the chart is only intended for specialist readers who expect to find certain things in certain places, then the designer's freedom is curtailed. If the chart is used as a data store, then the designer might as well recuse him/herself.