A German obstacle course

Tagesschau_originalA twitter user sent me this chart from Germany.

It came with a translation:

"Explanation: The chart says how many car drivers plan to purchase a new state-sponsored ticket for public transport. And of those who do, how many plan to use their car less often."

Because visual language should be universal, we shouldn't be deterred by not knowing German.

The structure of the data can be readily understood: we expect three values that add up to 100% from the pie chart. The largest category accounts for 58% of the data, followed by the blue category (40%). The last and smallest category therefore has 2% of the data.

The blue category is of the most interest, and the designer breaks that up into four sub-groups, three of which are roughly similarly popular.

The puzzle is the identities of these categories.

The sub-categories are directly labeled so these are easy for German speakers. From a handy online translator, these labels mean "definitely", "probably", "rather not", "definitely not". Well, that's not too helpful when we don't know what the survey question is.

According to our correspondent, the question should be "of those who plan to buy the new ticket, how many plan to use their car less often?"

I suppose the question is found above the column chart under the car icon. The translator dutifully outputs "Thus rarer (i.e. less) car use". There is no visual cue to let readers know we are supposed to read the right hand side as a single column. In fact, for this reader, I was reading horizontally from top to bottom.

Now, the two icons on the left and the middle of the top row should map to not buying and buying the ticket. The check mark and cross convey that message. But... what do these icons map to on the chart below? We get no clue.

In fact, the will-buy ticket group is the 40% blue category while the will-not group is the 58% light gray category.

What about the dark gray thin sector? Well, one needs to read the fine print. The footnote says "I don't know/ no response".

Since this group is small and uninformative, it's fine to push it into the footnote. However, the choice of a dark color, and placing it at the 12-o'clock angle of the pie chart run counter to de-emphasizing this category!

Another twitter user visually depicts the journey we take to understand this chart:

Tagesschau_reply

The structure of the data is revealed better with something like this:

Redo_tagesschau_newticket

The chart doesn't need this many colors but why not? It's summer.

 

 

 

 


Multicultural, multicolor, manufactured outrage

Twitter users were incensed by this chart:

Twitter_worstpiechart

It's being slammed as one of the most outrageous charts ever.

Mollywhite_twitter_outrageous

***

An image search reveals this chart form has international appeal.

In Kazakh:

Eurasianbank_piechart_kazakh

In Turkish:

Medirevogrupperformans_piechart_turkey

In Arabic, but the image source is a Spanish company:

Socialpubli_piechart_spain

In English, from an Indian source:

Panipatinstitute_piechart_india

In Russian:

Russian_piechart

***

Some people are calling this a pie chart.

But it isn't a pie chart since the slices clearly add up to more than one full circle.

It may be a graph template from an infographics website. You see people are applying data labels without changing the sizes or orientation or even colors of the slices. So the chart form is used as a container for data, rather than an encoder.

***

The Twitter user who called this "outrageous" appears to want to protect the designer, as the words have been deliberately snipped from the chart.

Mollywhite_twitter_outrageous_tweet

Nevertheless, Molly White coughed up the source in a subsequent tweet.

Mollywhite_twitter_outrageous_source

A bit strange, if you stop and think a little. Why would Molly shame the designer 20 hours later after she decided not to?

 

 

According to Molly, the chart appeared on the website of an NFT company. [P.S. See note below]

Here's the top of the page that Molly White linked to:

Mollywhite_twitter_outrageous_web3isgoinggreat

Notice the author of this page. That's "Molly White",  who is the owner of this NFT company! [See note below: she's the owner of a satire website who was calling out the owner of this company.]

Who's more outrageous?

Someone creating the most outrageous chart in order to get clout from outraged Twitter users and drive traffic to her new NFT venture? Or someone creating the template for the outrageous chart form, spawning an international collection?

 

[P.S. 3/17/2022 The answer is provided by other Twitter users, and the commentors. The people spreading this chart form is more ourageous. I now realized that Molly runs a sarcastic site. When she linked to the "source", she linked to her own website, which I interpreted as the source of the image. The page did contain that image, which added to the confusion. I must also add her work looks valuable, as it assesses some of the wild claims in Web3 land.

Mollywhite_site
]

[P.S. 3/17/2022 Molly also pointed out that her second tweet about the source came around 45 minutes after the first tweet. Twitter showed "20 hours" because it was 20 hours from the time I read the tweet.]


Graphing highly structured data

The following sankey diagram appeared in my Linkedin feed the other day, and I agree with the poster that this is an excellent example.

Spotify_revenue_sankey

It's an unusual use of a flow chart to show the P&L (profit and loss) statement of a business. It makes sense since these are flows of money. The graph explains how Spotify makes money - or how little profit it claims to have earned on over 2.5 billion of revenues.

What makes this chart work so well?

The first thing to notice is how they handled negative flows (costs). They turned the negative numbers into positive numbers, and encoded the signs of the numbers as colors. This doesn't come as naturally as one might think. The raw data are financial tables with revenues shown as positive numbers and costs shown as negative numbers, perhaps in parentheses. Like this:

Profit_Loss_QlikView

Now, some readers are sure to have an issue with using the red-green color scheme. I suppose gray-red can be a substitute.

The second smart decision is to pare down the details. There are only four cost categories shown in the entire chart. The cost of revenue represents more than two-thirds of all revenues, and we know nothing about sub-categories of this cost.

The third feature is where the Spotify logo is placed. This directs our attention to the middle of the diagram. This is important because typically on a sankey diagram you read from left to right. Here, the starting point is really the column labeled "total Spotify revenue". The first column just splits the total revenue between subscription revenue and advertising revenue.

Putting the labels of the last column inside the flows improves readability as well.

On the whole, a job well done.

***

Sankey diagrams have limitations. The charts need to be simple enough to work their magic.

It's difficult to add a time element to the above chart, for example. The next question a business analyst might want to ask is how the revenue/cost/profit structure at Spotify have changed over time.

Another question a business analyst might ask is the revenue/cost/profit structure of premium vs ad-supported users. We have a third of the answer - the revenue split. Depending on relative usage, and content preference, the mix of royalties is likely not to replicate the revenue split.

Yet another business analyst might be interested in comparing Spotify's business model to a competitor. It's also not simple to handle this on a sankey diagram.

***

I searched for alternative charts, and when you look at what's out there, you appreciate the sankey version more.

Here is a waterfall chart, which is quite popular:

Profit_loss_waterfall

Here is a stacked column chart, rooted at zero:

Profit_loss_hangingcolumns

Of course, someone has to make a pie chart - in this case, two pie charts:

Profit_loss_piechart

 

 

 

 


Pies, bars and self-sufficiency

Andy Cotgreave asked Twitter followers to pick between pie charts and bar charts:

Ac_pie_or_bar

The underlying data are proportions of people who say they won't get the coronavirus vaccine.

I noticed two somewhat unusual features: the use of pies to show single proportions, and the aspect ratio of the bars (taller than typical). Which version is easier to understand?

To answer this question, I like to apply a self-sufficiency test. This test is used to determine whether the readers are using the visual elements of the chart to udnerstand the data, or are they bypassing the visual elements and just reading the data labels? So, let's remove the printed data from the chart and take another look:

Junkcharts_selfsufficiency_pieorbar

For me, these charts are comparable. Each is moderately hard to read. That's because the percentages fall into a narrow range at one end of the range. For both charts, many readers are likely to be looking for the data labels.

Here's a sketch of a design that is self-sufficient.

Junkcharts_selfsufficientdesign

The data do not appear on this chart.

***

My first reaction to Andy's tweet turned out to be a misreading of the charts. I thought he was disaggregating the pie chart, like we can unstack a stacked bar chart.

Junkcharts_probabilities_proportions

Looking at the data more carefully, I realize that the "proportions" are not part to the whole. Or rather, the whole isn't "all races" or "all education levels". The whole is all respondents of a particular type.

 

 


Re-engineering #onelesspie

Marco tweeted the following pie chart to me (tip from Danilo), which is perfect since today is Pi Day, and I have to do my #onelesspie duty. This started a few years ago with Xan Gregg.

Onelesspie2021

This chart supposedly was published in an engineering journal. I don't have a clue what the question might be that this chart is purportedly answering. Maybe the reason for picking a cellphone?

The particular bits that make this chart hard to comprehend are these:

Junkcharts_onelesspie2021_problems

The chart also fails the ordering rule, as it spreads the largest pieces around.

It doesn't have to be so complicated.

Here is a primitive chart that doesn't even require a graphics software.

Junkcharts_redo_onelesspie2021_1color

Younger readers have not experienced the days (pre 2000) when color printing was at a premium, and most graphics were grayscale. Nevertheless, restrained use of color is recommended.

Junkcharts_redo_onelesspie2021_2colors

Happy Pi Day!


These are the top posts of 2020

It's always very interesting as a writer to look back at a year's of posts and find out which ones were most popular with my readers.

Here are the top posts on Junk Charts from 2020:

How to read this chart about coronavirus risk

This post about a New York Times scatter plot dates from February, a time when many Americans were debating whether Covid-19 was just the flu.

Proportions and rates: we are no dupes

This post about a ArsTechnica chart on the effects of Covid-19 by age is an example of designing the visual to reflect the structure of the data.

When the pie chart is more complex than the data

This post shows a 3D pie chart which is worse than a 2D pie chart.

Twitter people upset with that Covid symptoms diagram

This post discusses some complicated graphics designed to illustrate complicated datasets on Covid-19 symptoms.

Cornell must remove the logs before it reopens in the fall

This post is another warning to think twice before you use log scales.

What is the price of objectivity?

This post turns an "objective" data visualization into a piece of visual story-telling.

The snake pit chart is the best election graphic ever

This post introduces my favorite U.S. presidential election graphic, designed by the FiveThirtyEight team.

***

Here is a list of posts that deserve more attention:

Locating the political center

An example of bringing readers as close to the insights as possible

Visualizing change over time

An example of designing data visualization to reflect the structure of multivariate data

Bloomberg made me digest these graphics slowly

An example of simple and thoughtful graphics

The hidden bad assumption behind most dual-axis time-series charts

Read this before you make a dual-axis chart

Pie chart conventions

Read this before you make a pie chart

***
Looking forward to bring you more content in 2021!

Happy new year.


Using comparison to enrich a visual story

Just found this beauty deep in my submission pile (from Howie H.):

Iwillvote_texas

What's great about this pie chart is the story it's trying to tell. Almost half of the electorate did not vote in Texas in the 2016 Presidential election. The designer successfully draws my attention to the white sector that makes the point.

There are a few problems.

Showing two decimals is too much precision.

The purple sector is not labeled.

The white area seems exaggerated. The four sectors do not appear to meet at the center of the circle. The distortion is not too much but it's schizophrenic: the pie slices are drawn with low precision while the data labels have high precision.

***

The following fixes those problems, and also adds a second chart to contrast the two ways of thinking:

Redo_junkcharts_iwillvotecomtexas


Avoid concentric circles

A twitter follower sent me this chart by way of Munich:

Msc_staggereddonut

The logo of the Munich Security Conference (MSC) is quite cute. It looks like an ear. Perhaps that inspired this, em, staggered donut chart.

I like to straighten curves out so the donut chart becomes a bar chart:

Redo_junkcharts_msc_germanallies_distortion

The blue and gray bars mimic the lengths of the arcs in the donut chart. The yellow bars show the relative size of the underlying data. You can see that three of the four arcs under-represent the size of the data.

Why is that so? It's due to the staggering. Inner circles have smaller circumferences than outer circles. The designer keeps the angles the same so the arc lengths have been artificially reduced.

Junkcharts_redo_munichgermanallies_donuts

***

The donut chart is just a pie chart with a hole punched in the middle. For both pie charts and donut charts, the data are encoded in the angles at the center of the circle. Under normal circumstances, pie charts can also be read by comparing sector areas, and donut charts using arc lengths, as those are proportional to the angles.

The area and arc interpretation fails when the designer alters the radii of the sections. Look at the following pair of pie charts, produced by filling the hole in the above donuts:

Junkcharts_redo_munichgermanallies_pies

The staggered pie chart distorts the data if the reader compares areas but not so if the reader compares angles at the center. The pie chart can be read both ways so long as the designer does not alter the radii.

 


Bloomberg made me digest these graphics slowly

Ask the experts to name the success metric of good data visualization, and you will receive a dozen answers. The field doesn't have an all-encompassing metric. A useful reference is Andrew Gelman and Antony Urwin (2012) in which they discussed the tradeoff between beautiful and informative, which derives from the familiar tension between art and science.

For a while now, I've been intrigued by metrics that measure "effort". Some years ago, I described the concept of a "return on effort" in this post. Such a metric can be constructed like the dominating financial metric of return on investment. The investment here is an investment of time, of attention. I strongly believe that if the consumer judges a data visualization to be compelling, engaging or  ell constructed, s/he will expend energy to devour it.

Imagine grub you discard after the first bite, compared to the delicious food experienced slowly, savoring every last bit.

Bloomberg_ambridge_smI'm writing this post while enjoying the September issue of Bloomberg Businessweek, which focuses on the upcoming U.S. Presidential election. There are various graphics infused into the pages of the magazine. Many of these graphics operate at a level of complexity above what typically show up in magazines, and yet I spent energy learning to understand them. This response, I believe, is what visual designers should aim for.

***

Today, I discuss one example of these graphics, shown on the right. You might be shocked by the throwback style of these graphics. They look like they arrived from decades ago!

Grayscale, simple forms, typewriter font, all caps. Have I gone crazy?

The article argues that a town like Ambridge in Beaver County, Pennslyvania may be pivotal in the November election. The set of graphics provides relevant data to understand this argument.

It's evidence that data visualization does not need whiz-bang modern wizardry to excel.

Let me focus on the boxy charts from the top of the column. These:

Bloomberg_ambridge_topboxes

These charts solve a headache with voting margin data in the U.S.  We have two dominant political parties so in any given election, the vote share data split into three buckets: Democratic, Republican, and a catch-all category that includes third parties, write-ins, and none of the above. The third category rarely exceeds 5 percent.  A generic pie chart representation looks like this:

Redo_junkcharts_bloombergambridgebox_pies

Stacked bars have this look:

Redo_junkcharts_bloombergambridgebox_bars

In using my Trifecta framework (link), the top point is articulating the question. The primary issue here is the voting margin between the winner and the second-runner-up, which is the loser in what is typically a two-horse race. There exist two sub-questions: the vote-share difference between the top two finishers, and the share of vote effectively removed from the pot by the remaining candidates.

Now, take another look at the unusual chart form used by Bloomberg:

Bloomberg_ambridge_topboxes1

The catch-all vote share sits at the bottom while the two major parties split up the top section. This design demonstrates a keen understanding of the context. Consider the typical outcome, in which the top two finishers are from the two major parties. When answering the first sub-question, we can choose the raw vote shares, or the normalized vote shares. Normalizing shifts the base from all candidates to the top two candidates.

The Bloomberg chart addresses both scales. The normalized vote shares can be read directly by focusing only on the top section. In an even two-horse race, the top section is split by half - this holds true regardless of the size of the bottom section.

This is a simple chart that packs a punch.

 


Making better pie charts if you must

I saw this chart on an NYU marketing twitter account:

LATAMstartupCEO_covidimpact

The graphical design is not easy on our eyes. It's just hard to read for various reasons.

The headline sounds like a subject line from an email.

The subheaders are long, and differ only by a single word.

Even if one prefers pie charts, they can be improved by following a few guidelines.

First, start the first sector at the 12-oclock direction. Like this:

Redo_junkcharts_latamceo_orientation

The survey uses a 5-point scale from "Very Good" to "Very Bad". Instead of using five different colors, it's better to use two extreme colors and shading. Like this:

Redo_junkcharts_latamceo_color

I also try hard to keep all text horizontal.

Redo_junkcharts_latamceo_labels

For those who prefers not to use pie charts, a side-by-side bar chart works well.

Redo_junkcharts_latamceo_bars

In my article for DataJournalism.com, I outlined "unspoken rules" for making various charts, including pie charts.