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.)
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.
It's a mystery to me how there are always people who ignore certain rudimentary rules of graphing data. I'm talking about such clear guidelines as:
Bar charts encode data in the heights of the bars -- therefore:
You should start each bar at height zero, and
You should not vary the width of the bars (unless you are introducing another dimension), and
You should space the bars unevenly if your measurement times are unevenly spaced.
I mean, how is it in the year 2013, the BBC shows viewers this? (tip from UK reader Clarke C.)
The chart is absurd on its face. Men did not double in height between 1871 and 1971. This chart was broadcast in the show "breakfast" which apparently is the BBC UK version of Good Morning America.
I'd just use a line chart. The figurine construct is cute but too much trouble because you have to grow the width while growing the height. If you encode data in the area, then the height is no longer proportional to the real height.
Years ago, we featured something similar: how penguins evolved into humans (link). Curiously, also a gift from British media.
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.
Dona Wong asked me to comment on a project by the New York Fed visualizing funding and expenditure at NY and NJ schools. The link to the charts is here. You have to click through to see the animation.
Here are my comments:
I like the "Takeaways" section up front, which uses words to tell readers what to look for in the charts to follow.
I like the stutter steps that are inserted into the animation. This gives me time to process the data. The point of these dynamic maps is to showcase the changes in the data over time.
I really, really want to click on the green boxes (the legend) and have the corresponding school districts highlighted. In other words, turning the legend into something functional. Tool developers, please take notes!
The other options on the map are federal, state and local shares of funding, given in proportions. These are controlled by the three buttons above. This is a design decision that privileges showing how federal funds are distributed across districts and across time. The tradeoff is that it's harder to comprehend the mix of sources of funds within each district over time.
I usually like to flip back and forth between actual values and relative values. I find that both perspectives provide information. Here, I'd like to see dollars and proportions.
I also find the line charts to be much clearer but the maps are more engaging. Here is an example of the line chart: (the blue dashed line is the New York state average)
After looking at these charts, I also want to see a bivariate analysis. How is funding per student and expenditure per student related?
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.
I generated a big data set when writing Chapter 8 of Numbersense. This chapter discusses the question of how to measure your skills in managing/coaching a fantasy sports team. The general statistical question is how to separately measure two factors that both contribute to a single outcome.
In fantasy football (NFL), there is a matchup every week. Each week, you pick nine players from a roster of 14 players (rules vary by league). These nine players will score points for your team, based on how those players actually perform in real-life NFL games that week. You notch a win that week if your team scores more points than your opponent's team.
There are many ways to pick 9 players out of 14. In fact, in any given week, there are 200-300 eligible squads, of which only one is fielded. My big data set consists of all possible squads for every week for every team in the league. This data set contains rich information; the challenge is how to surface the information.
Visualization comes to the rescue. I'll be posting a series of charts here. Today's is the first one.
There are 13 plots, each of which represents a week of the season. The 13 plots trace the decisions of a single team over the course of the season. In each plot, the vertical line indicates the points total for the 9-player squad that was actually fielded by the team owner.
The histogram shows the range of choices the team owner could have made each week. Recall there are 200-300 possible squads of nine players from which the owner selected one. For example, in week 1, the owner didn't choose very well; there are many other sets of 9 players he could have chosen that would have scored him more points (the area to the right of the vertical line).
In Week 4, though, the owner could not have done much better. There were very few changes he could have made that would have increased his points total. Similarly, in Weeks 5 and 8.
You can also see that in Week 7, the 15 players he owned all tanked (in real life). The entire histogram is on the left side, meaning the points totals are horrible. Contrast this with Week 13, when the histogram is located on the right side of the chart, implying that this team owner would score pretty high no matter which 9 players he fielded.
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