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.

No Latin honors for graphic design

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.

***

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!

 

 

 

 

 

Some Tufte basics brought to you by your favorite birds

Someone sent me this via Twitter, found on the Data is Beautiful reddit:

The chart does not deliver on its promise: It's tough to know which birds like which seeds.

The original chart was also provided in the reddit:

I can see why someone would want to remake this visualization.

Let's just apply some Tufte fixes to it, and see what happens.

Our starting point is this:

First, consider the colors. Think for a second: order the colors of the cells by which ones stand out most. For me, the order is white > yellow > red > green.

That is a problem because for this data, you'd like green > yellow > red > white. (By the way, it's not explained what white means. I'm assuming it means the least preferred, so not preferred that one wouldn't consider that seed type relevant.)

Compare the above with this version that uses a one-dimensional sequential color scale:

The white color still stands out more than necessary. Fix this using a gray color.

What else is grabbing your attention when it shouldn't? It's those gridlines. Push them into the background using white-out.

The gridlines are also too thick. Here's a slimmed-down look:

The visual is much improved.

But one more thing. Let's re-order the columns (seeds). The most popular seeds are shown on the left, and the least on the right in this final revision.

Look for your favorite bird. Then find out which are its most preferred seeds.

Here is an animated gif to see the transformation. (Depending on your browser, you may have to click on it to view it.)

 

PS. [7/23/18] Fixed the 5th and 6th images and also in the animated gif. The row labels were scrambled in the original version.

 

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.

 

 

 

 

Common charting issues related to connecting lines, labels, sequencing

The following chart about "ranges and trends for digital marketing salaries" has some problems that appear in a great number of charts.

The head tilt required to read the job titles.

The order of the job titles is baffling. It's neither alphabetical nor by salary.

The visual form suggests that we could see trends in salaries reading left-right, but the only information about trends is the year on year salary change, printed on top of the chart.

Some readers will violently object to the connecting lines between job titles, which are discrete categories. In this case, I also agree. I am a fan of so-called profile charts in which we do connect discrete categories with connecting lines - but those charts work because we are comparing the "profiles" of one group versus another group. Here, there is only one group.

The N=3,567 is weird. It doesn't say anything about the reliability of the estimate for say Chief Marketing Officer.

***

A dot plot can be used for this dataset. Like this:

The range of salaries is not a great metric as the endpoints could be outliers.

Also, the variability of salaries is affected by two factors: the variability between companies, and sampling variability (which depends on the sample size for each job title). A wide range here could mean that different companies pay different salaries for the same job title, or that very few survey responders held that job title.

 

 

Discoloring the chart to re-discover its plot

Today's chart comes from Pew Research Center, and the big question is why the colors?

The data show the age distributions of people who believe different religions. It's a stacked bar chart, in which the ages have been grouped into the young (under 15), the old (60 plus) and everyone else. Five religions are afforded their own bars while "folk" religions are grouped as one, and so have "other" religions. There is even a bar for the unaffiliated. "World" presumably is the aggregate of all the other bars, weighted by the popularity of each religion group.

So far so good. But what is it that demands 9 colors, and 27 total shades? In other words, one shade for every data point on this chart.

Here is a more restrained view:

***

Let's follow the designer's various decisions. The choice of those age groups indicates that the story is really happening at the "margins": Muslims and Hindus have higher proportions of younger followers while Jews and Buddhists have higher concentrations of older followers.

Therein lies the problem. Because of the lengths, their central locations, and the tints, the middle section of each bar is the most eye-catching: the reader is glancing at the wrong part of the chart.

So, let me fix this by re-ordering the three panels:

Is there really a need to draw those gray bars? The middle age group (grab-all) only exists to assure readers that everyone who's supposed to be included has been included. Why plot it?

The above chart says "trust me, what isn't drawn here constitutes the remaining population, and the whole adds to 100%."

***

Another issue of these charts, exacerbated by inflexible software defaults, is the forced choice of imbuing one variable with a super status above the others. In the Pew chart, the rows are ordered by decreasing proportion of the young age group, except for the "everyone" group pinned as the bottom row. Therefore, the green bars (old age group) are not in a particular order, its pattern much harder to comprehend.

In the final version, I break the need to keep bars of the same religion on the same row:

Five colors are used. Three of them are used to cluster similar religions: Muslims and Hindus (in blue) have higher proportions of the young compared to the world average (gray) while the religions painted in green have higher proportions of the old. Christians (in orange) are unusual in that the proportions are higher than average in both young and old age groups. Everyone and unaffiliated are given separate colors.

The colors here serve two purposes: connecting the two panels, and revealing the cluster structure.

 

 

 

 

Using a bardot chart for survey data

Aleks J. wasn't amused by the graphs included in Verge's report about user attitudes toward the major Web brands such as Google, Facebook, and Twitter.

Let's use this one as an example:

Survey respondents are asked to rate how much they like or dislike the products and services from each of six companies, on a five-point scale. There is a sixth category for "No opinion/Don't use."

In making this set of charts, the designer uses six different colors for the six categories. This means he/she thinks of these categories as discrete so that the difference between categories carries no meaning. In a bipolar, five-point scale, it is more common to pick two extreme colors and then use shades to indicate the degree of liking or disliking. The middle category can be shown in a neutral color to express the neutrality of opinion.

The color choice baffles me. The two most prominent colors, gray and dark blue, correspond to two minor categories (no opinion and neutral) while the most important category - "greatly like" - is painted the modest yellow, paling away.

Verge sees the popularity of Facebook as the key message, which explains its top position among the six brands. However, readers familar with the stacked bar chart form are likely looking to make sense of the order, and frustrated.

***

In revising this chart, I introduce a second level of grouping: the six categories fit into three color groups: red for dislike, gray for no opinion/neutral, and orange for like. The like and dislike groups are plotted at the left and right ends of the chart while the two less informative categories are lumped toward the middle.

I take great pleasure in dumping the legend box.

***

Now, when a five-point scale is used, many analysts like to analyze the Top 2, or Bottom 2 boxes. The choice of colors in the above chart facilitates this analysis. Adding some subtle dots makes it even better!

Because this chart is a superposition of a stacked bar chart and a dot plot, I am calling this a bardot chart.

Also notice that the brands are re-ordered by Top 2 box popularity.

 

 

The visual should be easier to read than your data

A reader sent this tip in some time ago and I lost track of who he/she is. This graphic looks deceptively complex.

What's complex is not the underlying analysis. The design is complex and so the decoding is complex.

The question of the graphic is a central concern of anyone who's retired: how long will one's savings last? There are two related metrics to describe the durability of the stash, and they are both present on this chart. The designer first presumes that one has saved $1 million for retirement. Then he/she computes how many years the savings will last. That, of course, depends on the cost of living, which naively can be expressed as a projected annual expenditure. The designer allows the cost of living to vary by state, which is the main source of variability in the computations. The time-based and dollar-based metrics are directly linked to one another via a formula.

The design encodes the time metric in a grid of dots, and the dollar-metric in the color of the dots. The expenditures are divided into eight segments, given eight colors from deep blue to deep pink.

Thirteen of those dots are invariable, appearing in every state. Readers are drawn into a ranking of the states, which is nothing but a ranking of costs of living. (We don't know, but presume, that the cost of living computation is appropriate for retirees, and not averaged.) This order obscures any spatial correlation. There are a few production errors in the first row in which the year and month numbers are misstated slightly; the numbers should be monotonically decreasing. In terms of years and months, the difference between many states is immaterial. The pictogram format is more popular than it deserves: only highly motivated readers will count individual dots. If readers are merely reading the printed text, which contains all the data encoded in the dots, then the graphic has failed the self-sufficiency principle - the visual elements are not doing any work.

***

In my version, I surface the spatial correlation using maps. The states are classified into sensible groups that allow a story to be told around the analysis. Three groups of states are identified and separately portrayed. The finer variations between states within each state group appear as shades.

Data visualization should make the underlying data easier to comprehend. It's a problem when the graphic is harder to decipher than the underlying dataset.

 

 

 

Fifty-nine intersections supporting forty dots of data

My friend Ray V. asked how this chart can be improved:

Let's try to read this chart. The Economist is always the best at writing headlines, and this one is simple and to the point: the rich get richer. This is about inequality but not just inequality - the growth in inequality over time.

Each country has four dots, divided into two pairs. From the legend, we learn that the line represents the gap between the rich and the poor. But what is rich and what is poor? Looking at the sub-header, we learn that the population is divided by domicile, and the per-capita GDP of the poorest and richest regions are drawn. This is a indirect metric, and may or may not be good, depending on how many regions a country is divided into, the dispersion of incomes within each region, the distribution of population between regions, and so on.

Now, looking at the axis labels, it's pretty clear that the data depicted are not in dollars (or currency), despite the reference to GDP in the sub-header. The numbers represent indices, relative to the national average GDP per head. For many of the countries, the poorest region produces about half of the per-capita GDP as the richest region.

Back to the orginal question. A growing inequality would be represented by a longer line below a shorter line within each country. That is true in some of these countries. The exceptions are Sweden, Japan, South Korea.

***
It doesn't jump out that the key task requires comparing the lengths of the two lines. Another issue is the outdated convention of breaking up a line (Britian) when the line is of extreme length - particularly unwise given that the length of the line encodes the key metric in the chart.

Further, it has low data-ink ratio a la Tufte. The gridlines, reference lines, and data lines weave together in a complex pattern creating 59 intersections in a chart that contains only 40  36 numbers.

***

 I decided to compute a simpler metric - the ratio of rich to poor.  For example, in the UK, the richest area produces about 20 times as much GDP per capita as the poorest one in 2015.  That is easier to understand than an index to the average region.

I had fun making the following chart, although many standard forms like the Bumps chart (i.e. slopegraph) or paired columns and so on also work.

This chart is influenced by Ed Tufte, who spent a good number of pages in his first book advocating stripping even the standard column chart to its bare essence. The chart also acknowledges the power of design to draw attention.

 

 

PS. Sorry I counted incorrectly. The chart has 36 dots not 40. 

Three pies and a bar: serving visual goodness

If you are not sick of the Washington Post article about friends (not) letting friends join the other party, allow me to write yet another post on, gasp, that pie chart. And sorry to have kept reader Daniel L. waiting, as he pointed out, when submitting this chart to me, that he had tremendous difficulty understanding it:

 

This is not one pie but six pies on a platter. There are two sources of confusion: first, the repeated labels of Republicans and Democrats to refer to different groups of people; and second, the indecision between using two or four categories of "how many".

Let me begin by re-ordering and re-labeling the chart:

From this version, one can pull out the key messages of the analysis. (A) Most voters, regardless of party, have mostly friends from the same party. and (B) Republicans are more likely to have more friends from the other party than Democrats. A third, but really not that interesting, point is that regardless of party, people have about the same likelihood to befriend Independents.

In visualization, less is more is frequently appropriate. So, here is a view of the same chart, using two categories instead of four.

The added advantage is only two required colors, and thus even grayscale can work.

The new arrangement of the pie platter makes it clear that there really isn't that much difference between Republican and Democratic voters along this dimension. Thus, visualizing the aggregate gets us to the same place.

After three servings of pies, the reader might be craving some energy bars. 

One can say that for very simple data like this, pie charts are acceptable. However, the stacked bar is better.

Thanks again Daniel, and it's a pleasure to serve you!