The Giants QB Eli Manning is in the news for the wrong reason this season. His hometown paper, the New York Times, looked the other way, focusing on one metric that he still excels at, which is longevity. This is like the Cal Ripken of baseball. The graphic (link) though is fun to look at while managing to put Eli's streak in context. It is a great illustration of recognition of foreground/background issues. (I had to snip the bottom of the chart.)
After playing around with this graphic, please go read Kevin QuigleyQuealy's behind-the-scenes description of the various looks that were discarded (link). He showed 19 sketches of the data. Sketching cannot be stressed enough. If you don't have discarded sketches, you don't have a great chart.
Pay attention to tradeoffs that are being made along the way. For example, one of the sketches showed the proportion of possible games started:
I like this chart quite a bit. The final selection arranges the data by team rather than by player so necessarily, the information about proportion of possible games started fell by the wayside.
(Disclosure: I'm on Team Philip. Good to see that he is right there with Eli even on this metric.)
Business Insider links to this blog with a chart depicting the top beer brands by state.
I like the quilt-like appearance brought on by using the packaging of different brands. The nine glowing yellow islands sitting in the Atlantic Ocean I find annoying. This happens a lot because those New England states are smaller in area than most.
The design problem evaporates if you choose a small multiples approach. As shown below, there is the added benefit that the regional pattern of brand preference is clearly visible whereas in the original chart, it is rather hard to figure out.
I won't comment on the data source here. It's highly suspect.
New York/Tri-State residents: Meet me at NYU Bookstore tonight, 6-7:30 pm. (link)
When I wrote about the graphic showing the vote distribution around Syria in the Congress a few posts ago (link), readers offered opinions about what's a better graphic might look like. Having considered these submissions, I came up with a new visualization.
This graphic is one that facilitates an assessment of the prospect of the Syria resolution passing, given the known and leaning votes. It addresses various scenarios of how the undecided votes would break out. It also considers the extreme -- and unlikely -- case in which all leaning yes votes are sustained, all leaning no votes reverse, and all undecided vote yes. In that scenario, the President would have 131% of the votes needed for passing the resolution.
In this graphic, the real story of the data is revealed: based on the then known and leaning votes, the President would face certain defeat. Even if all the undecided broke in his favor, he would still only get to 86% of the votes needed to pass.
The top bar, showing composition, is a concession to those who wanted to understand how each party is voting under each scenario. It's a minor concern here.
Comparison to the original chart, reproduced below, is almost unfair. What is the prospect of the resolution passing? It's impossible to tell.
My graphic exposes less data, hides all No and Leaning No votes, displays no vote totals, and focuses on a computed metric, the proportional progress towards the 271 vote goal.
Notice the inspired touch of the black circles to trace the outline of Blackberry's market share. They are a guide to experiencing the chart.
I wish they had put the Palm section above Blackberry. In an area chart, the only clean section is the bottom section in which the market share is not cumulated. Given the focus on Blackberry, it's a pity readers have to perform subtractions to tease out the shares.
I also wonder if the black circles should contain Blackberry's market share rather than the year labels.
But I enjoyed this chart. Thanks for producing it.
Kevin Drum shows the following graphic (link) to illustrate where the House stood on authorizing force in Syria.
What interests me is whether the semi-circle concept adds to the chart. It evokes the physical appearance of a chamber, presumably where such a debate has taken place -- although most televised hearings tend to exhibit lots of empty seats.
The half-filled circles in particular do not make peace with me.
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.
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.
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.
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.
Rick (via Twitter) tells me he is baffled by this chart that showed up in Financial Review:
I'm baffled as well. What might the designer have in mind?
Based on the cues such as length of the curves, one would expect the US, Singapore, Japan, etc. to be leaders and India and China to be laggards. But what is being plotted on the vertical axis? It's not explained.
The title of the chart seems to indicate there is a time dimension but it's not on the horizontal axis where you'd expect it. The vertical axis does not appear to be time either, as it runs negative. The length of the lines could encode time but it is counterintuitive since China's line should then be much longer than that of the U.S., given its history.
Finally, how does one explain the placement of the callout box, noting China's GDP per capita. It literally points to nowhere.
Note: The winner of the Book Quiz Round 2 was announced on my book blog. Congratulations to the winners. You can get your own copy of Numbersensehere.
A common advice for anyone living in the U.S. is "read the fine print." If you receive a notice or see an ad, and there is an asterisk or some copy in almost invisible font located at the bottom of the page, you better pull out your magnifying glass.
If you are a data analyst, you better have a magnifying glass in your pocket at all times. One of the recurring themes in Numbersense is that details matter... a lot. This is particularly relevant to Chapters 6 and 7 on economic data.
Last week, on the first Friday of the month, the jobs report came out. For the best reporting on the data itself, with succinct commentary but no hand-waving, I go to Calculated Risk blog.
One of the charts highlighted (in this post) is the unemployment rate by educational attainment. This is the chart that leads to horribly misleading statements saying that the solution to the unemployment crisis is more education. I ranted about this before--see here and here.
Taking this chart at face value, you'd say that the unemployment rate is lower, the more education one has. One can also say that the unemployment rate is less volatile, the more education one has.
Bill makes two succinct comments, basically letting his readers know this chart is next to worthless.
1. Although education matters for the unemployment rate, it doesn't appear
to matter as far as finding new employment - and the unemployment rate
is moving sideways for those with a college degree!
The issue behind this is the "cohort effect". The chart above aggregates everyone from 25 years old and over. This means it treats equally people who just graduated from college last year and people who got their degrees thirty years ago. Why does this matter? A jobs recession hits certain types of people harder than others, and one important determinant is work experience (another would be the industry one works in.) The low unemployment rate for all college graduates masks the challenging job market for recent college graduates. The misinterpretation of this chart leads to wrongheaded policies such as make more college gradutes.
2. This says nothing about the quality of jobs - as an example, a college
graduate working at minimum wage would be considered "employed".
This is where the magnifying glass is critical. You should not assume that your idea of "employed" is the same as the official definition of "employed". Bill raised the issue of minimum wage. Elsewhere, other commentators noted the issue of "part-timers". Part-time employment is not distinguished from full-time employment in the official aggregate statistics.
Taking this further, isn't it plausible that unemployment "trickles down"? As the college graduates grab whatever job they can find, including the minimum-wage ones, they push the high-school graduates out of jobs.
In data, there is often no fine print to be found. In Big Data, this problem is aggravated by a thousand times. Unfortunately, magnifying blank is still blank. So, having the magnifying glass is not enough.
The solution then is to create your own fine print. Spend inordinate amounts of time understanding how data is collected. Dig deeply into how data is defined.
No, this work is not sexy. (PS. If you can't stand it, you really shouldn't be in data science.)
In Chapter 6 of Numbersense, I did this work for you as it relates to jobs data. What I show there is that there is no "right" way to measure employment--it's not as clearcut as you'd like to think. If you were to put forth your definition of "employed" for comment, your definition will absolutely get criticized, just the same way you're criticizing the government's definition.
PS. Larry at Good Stats, Bad Stats pulled out his magnifying glass and wrote a series of posts about education, employment and income. He mildly disagrees with me.
Reader Steph G. didn't like the effort by WRAL (North Carolina) to visualize the demographics of protestors in Raleigh. It sounds like the citizens of NC are making their voices heard. Maybe my friends in Raleigh can give us some background.
There are definitely problems with the choice of charts. But I rate this effort a solid B. In the Trifecta Checkup, they did a good job describing the central question, as well as compiled an appropriate dataset. I love it when people go out to collect the right data rather than use whatever they could grab. The issue was the execution of the charts.
The first was a map showing where the arrested protestors came from.
Maps are typically used to show geographical distribution. The chosen color scheme (two levels of green and gray) compresses the data so much that we learn almost nothing about distribution. I clicked on Wake County to learn that there were 178 arrests there. The neighboring Randolph County had only 1 arrest but you can't tell from the colors.
The next chart shows the trend of arrests over time. I like the general appearance (except for the shadows). The problem is the even spacing of the columns when the gaps between the arrests are uneven.
Here's a quick redo, with proper spacing:
The final set of charts is inspired. They compare the demographics of those arrested protestors against the average North Carolina resident. For example:
For categories like Age with quite a few levels, the pie chart isn't a good choice. It's also hard to compare across pie charts. A column or dot chart works better.