My readers are nailing it when it comes to finding charts that deserve close study. On Twitter, the conversation revolved around the inversion of the horizontal axis. Favorability is associated with positive numbers, and unfavorability with negative numbers, and so, it seems the natural ordering should be to place Favorable on the right and Unfavorable on the left.
Ordinarily, I'd have a problem with the inversion but here, the designer used the red-orange color scheme to overcome the potential misconception. It's hard to imagine that orange would be the color of disapproval, and red, of approval!
I am more concerned about a different source of confusion. Take a look at the following excerpt:
If you had to guess, what are the four levels of favorability? Using the same positive-negative scale discussed above, most of us will assume that going left to right, we are looking at Strongly Favorable, Favorable, Unfavorable, Strongly Unfavorable. The people in the middle are neutrals and the people on the edages are extremists.
But we'd be mistaken. The order going left to right is Favorable, Strongly Favorable, Strongly Unfavorable, Unfavorable. The designer again used tints and shades to counter our pre-conception. This is less successful because the order defies logic. It is a double inversion.
The other part of the chart I'd draw attention to is the column of data printed on the right. Each such column is an act of giving up - the designer admits he or she couldn't find a way to incorporate that data into the chart itself. It's like a footnote in a book. The problem arises because such a column frequently contains very important information. On this chart, the data are "net favorable" ratings, the proportion of Favorables minus the proportion of Unfavorables, or visually, the length of the orange bar minus the length of the red bar.
The net rating is a succinct way to summarize the average sentiment of the population. But it's been banished to a footnote.
Anyone who follows American politics a little in recent years recognizes the worsening polarization of opinions. A chart showing the population average is thus rather meaningless. I'd like to see the above chart broken up by party affiliation (Republican, Independent, Democrat).
This led me to the original source of the chart. It turns out that the data came from a Fox News poll but the chart was not produced by Fox News - it accompanied this Washington Postarticle. Further, the article contains three other charts, broken out by party affiliation, as I hoped. The headline of the article was "Bernie Sanders remains one of the most popular politicians..."
But reading three charts, printed vertically, is not the simplest matter. One way to make it easier is to gift the chart a purpose. It turns out there are no surprises among the Republican and Democratic voters - they are as polarized as one can imagine. So the real interesting question in this data is the orientation of the Independent voters - are they more likely to side with Democrats or Republicans?
Good house-keeping means when you acquire stuff, you must remove other stuff. After adding the party dimension, it makes more sense to collapse the favorability dimension - precisely by using the net favorable rating column:
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.
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 Numbersensebook (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.
This WSJ graphic caught my eye. The accompanying article is here.
The article (judging from the sub-header) makes two separate points, one about the total amount of money raised in IPOs in a year, and the change in market value of those newly-public companies one year from the IPO date.
The first metric is shown by the size of the bubbles while the second metric is displayed as distances from the horizontal axis. (The second metric is further embedded, in a simplified, binary manner, in the colors of the bubbles.)
The designer has decided that the second metric - performance after IPO - to be more important. Therefore, it is much easier for readers to know how each annual cohort of IPOs has performed. The use of color to map to the second metric (and not the first) also helps to emphasize the second metric.
There are details on this chart that I admire. The general tidiness of it. The restraint on the gridlines, especially along the horizontal ones. The spatial balance. The annotation.
And ah, turning those bubbles into lollipops. Yummy! Those dotted lines allow readers to find the center of each bubble, which is where the values of the second metrics lie. Frequently, these bubble charts are presented without those guiding lines, and it is often hard to find the circles' anchors.
That leaves one inexplicable decision - why did they place two vertical gridlines in the middle of two arbitrary years?
Reader Glenn T. was not impressed by the graphical talent on display in the following column chart (and others) in a Monkey Cage post in the Washington Post:
Not starting column charts at zero is like having one's legs chopped off. Here's an animated gif to show what's taking place: (you may need to click on it to see the animation)
Since all four numbers show up on the chart itself, there is no need to consult the vertical axis.
I wish they used a structured color coding to help fast comprehension of the key points.
These authors focus their attention on the effect of the "black or white cue" but the other effect of Trump supporters vs. non-supporters is many times as big.
Notice that on average 56% of Trump supporters in this study oppose mortgage assistance while 25% of non Trump supporters oppose it - a gap of about 30%.
If we are to interpret the roughly +/- 5% swing attributed to black/white cues as "racist" behavior on the part of Trump supporters, then the +/- 3% swing on the part of non-Trump supporters in the other direction should be regarded as a kind of "reverse racist" behavior. No?
So from this experiment, one should not conclude that Trump voters are racist, which is what the authors are implying. Trump voters have many reasons to oppose mortgage assistance, and racist reaction to pictures of black and white people has only a small part of play in it.
The reporting of the experimental results irks me in other ways.
The headline claimed that "we showed Trump voters photos of black and white Americans." That is a less than accurate description of the experiment and subsequent analysis. The authors removed all non-white Trump voters from the analysis, so they are only talking about white Trump voters.
Also, I really, really dislike the following line:
When we control for age, income, sex, education, party identification, ideology, whether the respondent was unemployed, and perceptions of the national economy — other factors that might shape attitudes about mortgage relief — our results were the same.
Those are eight variables they looked into for which they provided zero details. If they investigated "interaction" effects, only of pairs of variables, that would add another 28 dimensions for which they provided zero information.
The claim that "our results were the same" tells me nothing! It is hard for me to imagine that the set of 8+28 variables described above yielded exactly zero insights.
Even if there were no additional insights, I would still like to see the more sophisticated analysis that controls for all those variables that, as they admitted, shape attitudes about mortgage relief. After all, the results are "the same" so the researcher should be indifferent between the simple and the sophisticated analyses.
In the old days of printed paper, I can understand why journal editors are reluctant to print all those analyses. In the Internet age, we should put those analyses online, providing a link to supplementary materials for those who want to dig deeper.
On average, 56 percent of white Trump voters oppose mortgage relief. Add another 3-5 percent (rounding error) if they were cued with an image of a black person. The trouble here is that 90% of the white Trump voting respondents could have been unaffected by the racial cue and the result still holds.
While the effect may be "statistically significant" (implied but not stated by the authors), it represents a small shift in the average attitude. The fact that the "average person" responded to the racial cue does not imply that most people responded to it.
The last two issues I raised here are not specific to this particular study. They are prevalent in the reporting of psychological experiments.
This chart is in the Sept/Oct edition of Harvard Magazine:
Pretty standard fare. It even is Tufte-sque in the sparing use of axes, labels, and other non-data-ink.
Does it bug you how much work you need to do to understand this chart?
Here is the junkchart version:
In the accompanying article, the journalist declared that student progress on NAEP tests came to a virtual standstill, and this version highlights the drop in performance between the two periods, as measured by these "gain scores."
The clarity is achieved through proximity as well as slopes.
The column chart form has a number of deficiencies when used to illustrate this data. It requires too many colors. It induces involuntary head-shaking.
Most unforgivingly, it leaves us with a puzzle: does the absence of a column means no progress or unknown?
PS. The inclusion of 2009 on both time periods is probably an editorial oversight.
There is an artistic (or data journalistic) license behind the way the data are processed. Most likely, a 50% cutoff is applied to determine which map a county sits atop. The analysis is at the county level so there is neccessarily some simplification... in fact, this aggregation is needed to make the "islands" and other features contiguous.
I am a bit sad that at this moment, we are so focused on what sets us apart, and not what binds us together as a nation.
PS. Via twitter, Maciej reacted negatively to these maps: "Horribly tendentious map visualization from the NYT makes the candidate who won more votes look like a tiny minority."
This is a good illustration of selecting the chart form to bring out one's message. If the goal of the chart is to show that Clinton has more votes, I agree that these maps fail to convey that message.
What I believe the NYT designer wants to point out is that the supporters of Clinton are clustered into these densely populated urban areas, leaving the Republicans with most of the land mass. (Like I said above, because of the 50% cutoff criterion, we are over-simplifying the picture. There are definitely Democrats living somewhere in Trump's nation, and likewise Republicans residing in Clinton strongholds.)
Someone at the Wall Street Journal noticed that Denver's transit agency has outspent other top transit agencies, after accounting for number of rides -- and by a huge margin.
But the accompanying graphic conspires against the journalist.
For one thing, Denver is at the bottom of the page. Denver's two bars do not stand out in any way. New York's transit system dwarfs everyone else in both number of rides and total capital expenses and funding. And the division into local, state, and federal sources of funds is on the page, absorbing readers' mindspace for unknown reasons.