Posts Tagged ‘graphs’

Wherefore Pop Art Thou?It’s time for another look at the kind of CSD you’ve been drinking lately when you toss down a sweet LRB. And the results may be scarier than than you expect. (But hang on — the charts are better than ever!)

Beverage Digest has brought out their yearly summary, leading off with the ominous words, “2013 Was A Challenging Year for U.S. Beverage Business.” Among other things, note that this capitalizes “Was” and even “A” but not “for” in that spooky opening line. That may give you some idea that there’s trouble ahead.

First some terminology you need to know when discussing this topic as though you know what you’re talking about:

  • BD = Beverage Digest
  • LRB = Liquid Refreshment Beverage
  • CSD = Carbonated Soft Drink

I think insiders pronounce those bud, lurb, and cussed, as in, “Bud says the cussed lurbs are losing market share again.”


US CSD sales were down a bit to a mere $76 billion in 2013. That’s a big number even if you’re a billionaire, so to give it some perspective: If a country only made, bought/sold and drank that much CSDs and did nothing else, it would have a GDP of about the 85th largest country in the world. Imagine everyone in the country of Jordan doing nothing but buying and selling that much Coke and Pepsi all year long. They’d be as much of an economic powerhouse as they are now, except the people would probably burp a lot more.

On the other hand, that much money is only enough to give everyone on the planet a $10 bill. Once. So maybe it’s not so much after all. But on the third hand, it’s still comfortably above the comparatively meager $56 billion that Americans spent on their pets last year.

Per Capita Consumption

Things start to get scary with this factoid: BD says that US consumption per person was down in 2013 but still equivalent to just shy of two drinks each day for every man, woman, and newborn baby in the country. This could help to explain many, many things. But we won’t try.

 Soft Drink Pie

If you’re the kind of person who can’t help but go to the Google or Bing image search page and type in oddball search terms like “soft drink pie chart”, then for the last few years you will have seen the 2010 pie chart near the top of the results list. So obviously it’s time to provide another pie chart, for 2013, to try to get more of these images in the top of those searches. Because … well, because of some important reason that has temporarily slipped our mind.

These pie charts may show up for other image searches like the slightly more reasonable “soft drink market share” although performance is sporadic for such reasonable search terms.


2103 approximate relative market share for the top 10 soft drinks (CSDs)


CSD Market Share Can

The pie chart can be somewhat disorienting to the excessively logical types, who are disturbed by a food-based diagram used to depict drinks. So in yet another hopeless effort to appease such folks, here’s another look at 2013 CSD market. If you were a person with super-typical tastes who…

  • bought soft drinks in exact proportion to the rest of the country
  • selected the top ten most common flavors
  • mixed those all together well
  • and then poured the mixture back into all the empty cans

.. then each of the combo-cans that you drank would be composed as shown in the following picture. Bottoms up!We are what we drinkAnd if you’re not feeling good after drinking all that, go see the doctor and mention you suspect it’s cussed lurbs.


Data presentation tip: strive for charts and graphs that (a) communicate with a zing, or at least (b) make people hungry or thirsty. If a 6-year old can quickly grasp the message, you’re on the right track. In this case, the little tyke will soon be asking for some sugary drinks. Anyway send in requests for new ways to depict these things so we can help people to just get it. Finally.



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Time to look at some gripping data, statistics, and data presentation methods, along with a rare chance to use the word ‘oxygenated.’

I drive a 5-speed Honda Civic EX which I bought new in 2006. Not too long ago I looked down at the dashboard and saw the big 100k miles approaching. Somehow the following picture managed to show up in my cell phone, obviously taken by a professional driver on a closed off road. And not by a simple-minded commuter who happened to be in the middle of heavy freeway city traffic and going the indicated 60 mph at the time.

Don't try this at home. Because it's hard to get a car going 60 mph inside.

The odometer shows 99,999 miles, so I missed the 100,000 mile milestone by one, but there was only so much driving-by-knee in heavy traffic that I could do that day.


But this post is not about odometer / speedometer pictures. It’s about something even more fascinating: gas mileage. (“Wow!”) I’ve been methodically collecting mileages and gas numbers at each fill-up over the last 7 years. (“Why??”) I managed to gather up all the numbers over that time, calculating and compiling mileage numbers tankful by tankful. Here’s a graph of the car mileage over that time. Click/tap on the picture for a larger (more fun!) version.

Miles per gallon (MPG) for each of 298 tankfuls over 7 years.
Vertical color bands are by season of the year.

A few comments on the graph:

  • The blue spiky line (with diamonds) is the mileage for each tank, varying between 28 and 40 mpg.
  • The orange line is the average mileage over time (total miles divided by total gallons). It slowly increased and settled to about 33.7 mpg.
  • The wavy green line is the average mileage over the last 10 tankfuls at each fill-up. That is, it represents the mileage of the most recent tank at a given fill-up date, and the 9 fill-ups before that.
  • The vertical color bars (green, yellow, orange, purple) show the season of the year (spring, summer, fall, winter)

One interesting part of the graph is the green line with the triangles, which gives a 10-tank running average of mileage. The tank was filled on a roughly weekly basis, so each point reveals the mileage over the previous 2 – 3 months. This line always peaks in late summer to early fall, and its recurring minimums occur each winter. I get around 8% to 10% difference between the high and low peaks.

Some other questions / insights one might have regarding the mileage graph:

  • Why does the overall mileage (orange line) slowly but consistently improve from about 32 mpg to 33.7? Does the car ‘break-in’ or get otherwise happier over time?
  • The worst of single tank mileages (blue points) almost all occur toward the winter, with an occasional bad one in the spring/summer.
  • The top half-dozen tankfuls all occur in summer, but there was one good one (>37 mpg) in January of 2013.
  • What causes the seasonal cycle — higher mileage in summer, lower in winter?

Weather Impact?

For that last point, on the seasonal variation: One cause of course might be temperature change having a direct impact. Obviously that follows the same pattern of high-in-summer, low-in-winter. There’s lots of data available for the curious on various historical weather effects, for example from this Weatherspark site.* The graph below is temperature data for my area over the same time period, 2006-2013.

Global warming / cooling is staying carefully concealed over this period.

Temperature data for Seattle (Boeing Field) for 2006 – 2013.

Incidentally and as a slight aside, this site also allows exploration of cloud cover statistics. Here’s how things looked (in the sky) for the same time period, 2006-2013 (that’s 0 to 100% on the vertical direction, for the shaded gray graph):

But it only rains at night

Cloud cover stats for Seattle, 2006 – 2013

So apparently there have been a few clear days here and there in the last 7 years. And not to depress the locals or anything, but here’s how the cloud cover numbers (or lack thereof) look for Los Angeles over the same time period:

Yes but it's only sunny during the day

Cloud cover stats for Los Angeles

Anyway it doesn’t look like cloud cover statistics is going to help account for the seasonal variation in gas mileage. Some other reasons are proposed at THIS SITE, and include the following:

  • Colder fuel doesn’t explode as easily (because it doesn’t atomize so well)
  • Oxygenated fuels, often applied in the winter months to improve air quality, reduce mileage by a few percent
  • Changes in driving style, which can be an indirect effect of the winter climate and planet orbit. More darkness and wet roads leads to slower speeds and more congestion, which reduce mileage. My commutes are frequently longer in winter for those reasons.

Data Presentation

Let’s use this data as an opportunity to explore how to bias or distort data to communicate a point you want to make. One good trick is the careful use of Y-axis manipulation, i.e. scaling the vertical axis to suit your purposes. Among the 298 fill-ups, I broke out the top 8 station brands** that I used (for 279 of the purchases), and then shifted the data so the mileage from each station could be compiled correctly (using the excel pivot table function).

This first chart shows the average mileage from each of the 8 stations. Note that the vertical axis starts at 0. The normal passerby would probably say that all of the stations give about the same mileage — it doesn’t really appear to matter much where you buy your gas.

All gas is created equal, but some is more equal than others

Mileage data by gas station brand. All the same, right?

But if you want to bias the audience toward or away from some station(s), how about changing the vertical axis (without changing any of the data). This time (next graph) just show the mileage numbers starting at 32.80 mpg. This is the exact same data as the previous chart, but now a quick glance from that average passerby will get a different reaction. It looks clear that Safeway gas is for losers, while Union 76 is the champion — why, maybe it’s 20% better than the others! But in fact, the Union 76 mileage in my data is less than a half-percent better than Shell, and only 4% better than Safeway.

But zoom in far enough on small differences, and they can look big.

Zoom in on average mileage per station, with vertical axis starting at 32.8 mpg.
All wildly different, right?

Another thing not revealed in these plots is how many purchases were made from each place. A small data set for something like mileage tends to vary more than a large collection, which would be more settled toward its average value (if there is one). The next chart changes the vertical axis again, to start at 32.0 this time, and also adds some color and thickness effects to try to show frequency of visits. But on the very likely chance that this doesn’t work, the labels on the bars makes it kind of obvious.

Similar graph with vertical axis now starts at 32 mpg. Also adds number of time each station brand was used.

Yet another vertical axis starting point. With ‘depth’ and color added to the columns in a lame attempt to indicate frequency of purchases.

This still can be misleading because, for example, perhaps all the Safeway fill-ups occurred during the winter when mileage is typically lower as discussed above. But a careful look through the numbers shows that 7 of the 9 Safeway visits occurred during the spring and summer. Which means that Safeway is actually biased somewhat more favorably than deserved in these graphs, and it still loses. (By a little. Or was it a lot?)

Gas Prices

Finally let’s see how gas prices have varied during the last 7 years. This next graph might lead one to the following creative conclusion: gas prices above $4.40 are a strong indicator of imminent economic collapse. Maybe there’s something to that. If you’re so inclined, you can look up gas prices at your location (US and Canada) during this century HERE.

Gas price paid over the years

Gas price paid over the years

Wrap-up on Fill-ups

Some summary numbers for my 298 fill-ups over this period:

  • Total gallons: 3119
  • Total gas cost: $9863
  • Average cost per gallon: $3.16
  • Total miles to this point: 105k

* That site allows for interactive modes, click on its ‘View in dashboard’ option
** FMeyer is Fred Meyer, a local-ish department store sometimes including gas stations.

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Your favorite soda newsletter, Beverage Digest, has just released data for 2010. It contains the shocking statistics showing that Diet Coke is now the #2 drink, pushing Pepsi down to #3. This is troubling and demands some further serious investigation. Especially since Beverage Digest is one of the self-described authoritative publications covering non-alcoholic drinks. Let’s assume they deserve to be listened to.

But I’ll leave the investigating for someone else. Instead, I’ll look at the exciting subject of how to communicate through the effective presentation of data — with a focus on how to transform people who are innocently viewing data into people who think they are thirsty and need to buy a soft drink. Now.

To increase thirst levels, you should be clicking on links and pictures (for larger views) throughout this post.

Here is the data from the Beverage Digest (BD) study graphed in a variety of ways. The first two graphs show a rather dull way to present the data of which carbonated soft drink (that’s CSD to the BD people) sold the most in 2010. The graph on the right is slightly better because it has a mild 3D look and the bars may rather weakly invoke images of soft drink cans in the mind of the reader – albeit very tall and skinny cans in some cases. But the winners and losers don’t just immediately jump right out at you. We can do better.

The following graph is a big improvement. You don’t have to be a mathematician to instantly grasp where people are spending their soft drink money and which drinks contribute the most to tooth decay.

Pop Chart

Another possibility is just to stretch or shrink the size of the can to represent its sales numbers. The following chart does this, in a mildly deceptive way. While sales are relative to the can height, its width also grows (or shrinks) with the height, so that the apparent sales numbers are exaggerated for better or for worse. The little guys really drop off rapidly in this approach. This is a good example of data that’s not only accurate but at the same time intentionally distorted. That’s a trick some good marketing people strive for. The rest don’t bother with the accurate part.

No data presentation is really complete without a pie chart, even though it’s about liquids in this case. Here’s the relative market share of the top 10 soft drinks, where the slice size and owners are quickly identified.

Relative market share, top 10 soft drinks in 2010

Next up is a personal twist, my relative soft drink preference. I add in some Root Beer players even though they didn’t make the top 10 for Beverage Digest. Maybe their non-alcoholic market analysis got confused by the ‘Beer’ part. Vote for your favorite, on the off-hand chance it’s not Dr Pepper.

Finally, those interested in more on visual data presentation should check out the book, The Visual Display of Quantitative Information. It contains the famous (for the few who know of it) chart of Napoleon’s Russian campaign of 1812, created by one Charles Joseph Minard in 1869. It is probably safe to claim that this is the only chart you’ll ever see that combines army size, location, time, direction of army movement, and temperature while readily communicating the devastating losses Napoleon suffered on the campaign. The book also contains amusing examples of data distortion in graphs and charts, and even quantifies the amount of deception.

And speaking of devastating losses, let’s all go buy a Fanta to give it a boost in its battle against the mighty Coke products.

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