Light entertainment: Making art by making data

Chris P. sent in this link to a Wired feature on "infographics."

The first entry is by Giorgia Lupi and Stefanie Posavec.

These are fun images and I enjoy looking at it as hand-drawn art. But it's a stretch to call them "data visualization," "data," or "data analysis," which are all tags used by the Wired editing staff.

(PS. Wired chose a particular example of their work. There are many examples of Lupi's work that strike a balance between handicraft and data communications.)

 

An enjoyable romp through the movies

Chris P. tipped me about this wonderful webpage containing an analysis of high-grossing movies. The direct link is here.

First, a Trifecta checkup: This thoughtful web project integrates beautifully rendered, clearly articulated graphics with the commendable objective of bringing data to the conversation about gender and race issues in Hollywood, an ambitious goal that it falls short of achieving because the data only marginally address the question at hand.

There is some intriguing just-beneath-the-surface interplay between the Q (question) and D (data) corners of the Trifecta, which I will get to in the lower half of this post. But first, let me talk about the Visual aspect of the project, which for the most part, I thought, was well executed.

The leading chart is simple and clear, setting the tone for the piece:

I like the use of color here. The colored chart titles are inspired. I also like the double color coding - notice that the proportion data are coded not just in the lengths of the bar segments but also in the opacity. There is some messiness in the right-hand-side labeling of the first chart but probably just a bug.

This next chart also contains a minor delight: upon scrolling to the following dot plot, the reader finds that one of the dots has been labeled; this is a signal to readers that they can click on the dots to reveal the "tooltips". It's a little thing but it makes a world of difference.

I also enjoy the following re-imagination of those proportional bar charts from above:

This form fits well with the underlying data structure (a good example of setting the V and the D in harmony). The chart shows the proportion of words spoken by male versus female actors over the course of a single movie (Tin Men from 1987 is the example shown here). The chart is centered in the unusual way, making it easy to read exactly when the females are allowed to have their say.

There is again a possible labeling hiccup. The middle label says 40th minute which would imply the entire movie is only 80 minutes long. (A quick check shows Tin Men is 110 minutes long.) It seems that they are only concerned with dialog, ignoring all moments of soundtrack, or silence. The visualization would be even more interesting if those non-dialog moments are presented.

***

The reason why the music and silence are missing has more to do with practicality than will. The raw materials (Data) used are movie scripts. The authors, much to their merit, acknowledge many of the problems that come with this data, starting with the fact that directors make edits to the scripts. It is also not clear how to locate each line along the duration of the movie. An assumption of speed of dialog seems to be required.

I have now moved to the Q corner of the Trifecta checkup. The article is motivated by the #OscarSoWhite controversy from a year or two ago, although by the second paragraph, the race angle has already been dropped in favor of gender, and by the end of the project, readers will have learned also about ageism but  the issue of race never returned. Race didn't come back because race is not easily discerned from a movie script, nor is it clearly labeled in a resource such as IMDB. So, the designers provided a better solution to a lesser problem, instead of a lesser solution to a better problem.

In the last part of the project, the authors tackle ageism. Here we find another pretty picture:

At the high level, the histograms tell us that movie producers prefer younger actresses (in their 20s) and middle-aged actors (forties and fifties). It is certainly not my experience that movies have a surplus of older male characters. But one must be very careful interpreting this analysis.

The importance of actors and actresses is being measured by the number of words in the scripts while the ages being analyzed are the real ages of the actors and actresses, not the ages of the characters they are playing.

Tom Cruise is still making action movies, and he's playing characters much younger than he is. A more direct question to ask here is: does Hollywood prefer to put younger rather than older characters on screen?

Since the raw data are movie scripts, the authors took the character names, and translated those to real actors and actresses via IMDB, and then obtained their ages as listed on IMDB. This is the standard "scrape-and-merge" method executed by newsrooms everywhere in the name of data journalism. It often creates data that are only marginally relevant to the problem.

 

 

 

Butcher: which part of the leg do you want? Me: All of it, in five pieces please

This ABC News chart seemed to have taken over the top of my Twitter feed so I better comment on it.

Someone at ABC News tried really hard to dress up the numbers. The viz is obviously rigged - Obama at 79% should be double the length of Trump's 40% but not even close!

In the Numbersense book (Chapter 1), I played the role of the Devious Admissions Officer who wants to game the college rankings. Let me play the role of the young-gun dataviz analyst, who has submitted the following chart to the highers-up:

I just found out the boss blew the fuse after seeing my chart. The co-workers wore dirty looks, saying without saying "you broke it, you fix it!"

How do I clean up this mess?

Let me try the eye-shift trick.

The solid colors draw attention to themselves, and longer bars usually indicate higher or better so the quick reader may think that Obama is the worst and Trump is the best at ... well, "Favorability on taking office," as the added title suggests.

Next, let's apply the foot-chop technique. This fits nicely on a stacked bar chart. 

I wantonly drop 20% of dissenters from every President's data. Such grade inflation actually makes everyone look better, a win-win-win-win-win-win-win proposition. While the unfavorables for Trump no longer look so menacing, I am still far from happy as, with so much red concentrated at the bottom of the chart, eyes are  focused on the unsightly "yuge" red bar, and it is showing Trump with 50% disapproval.

I desperately need the white section of the last bar to trump its red section. It requires the foot-ankle-knee-thigh treatment - the whole leg.

Now, a design issue rears its head. With such an aggressive cut, there would be no red left in any of the other bars.

I could apply two cuts, a less aggressive cut at the top and a more aggressive cut at the bottom.

The Presidents neatly break up into two groups, the top three Democrats, and the bottom four Republicans. It's always convenient to have an excuse for treating some data differently from others.

Then, I notice that the difference between Clinton and GW Bush is immaterial (68% versus 65%), making it awkward to apply different cuts to the two neighbors. No problem, I make three cuts.

The chart is getting better and better! Two, three, why not make it five cuts? I am intent on making the last red section as tiny as possible but I can't chop more off the right side of GHW Bush or Reagan without giving away my secret sauce.

The final step is to stretch each bar to the right length. Mission accomplished.

This chart will surely win me some admiration. Just one lingering issue: Trump's red section is still the longest of the group. It's time for the logo trick. You see, the right ends of the last two bars can be naturally shortened.

The logo did it.

***

Faking charts can take as much effort as making accurate ones.

The ABC News chart encompasses five different scales. For every President, some percentage of dissenters were removed from the chart. The amount of distortion ranges from 15% to 47% of respondents.

 

 

 

 

Layered donuts have excess fats and oils

Via Twitter, Nicholas S. sent this chart:

It's a layered donut. There isn't much context here except that the chart comes from USDA. Judging from the design, I surmise that the key message is the change in proportion by food groups between 1970 and 2014. I am assuming that these food groups are exhaustive so that it makes sense to put them in a donut chart, with all pieces adding up to 100%.

The following small-multiples line chart conveys most of the information:

The story is the big jump in "Added fats and oils".  In the layered donut, the designer highlighted this by a moire effect, something to be avoided.

Note the parenthetical 2010 next to the Added fats and oils label. The data for all other food groups come from 2014 but the number for the most important category is four years older. The chart would be more compelling if they used 2010 data for everything.

One piece of information is ostensibly absent in the line chart version - the growth in the size of the pie. The total of the data increased about 20% from 1970 to 2014. In theory, the layered donut can convey this growth by the perimeters of the circles. But it doesn't appear that the designer saw this as an important insight since the total area of the outer donut is clearly more than 20% of the area of the inner donut.