## Redundancy is great

##### Jul 05, 2023

I have been watching some tennis recently, and noticed that some venues (or broadcasters) have adopted a more streamlined way of showing tiebreak results.

(This is an old example I found online. Can't seem to find more recent ones. Will take a screenshot next time I see this on my TV.)

For those not familiar with tennis scoring, the match is best-of-three sets (for Grand Slam men's tournaments, it's best-of-five sets); each set is first to six games, but if the scoreline reaches 5-5, a player must win two consecutive games to win the set at 7-5, or else, the scoreline reaches 6-6, and a tiebreak is played. The tiebreak is first to seven points, or if 6-6 is reached, it's first player to get two points clear. Thus, the possible tiebreak scores are 7-0, 7-1, ..., 7-5, 8-6, 9-7, etc.

A tiebreak score is usually represented in two parts, e.g., 7-6 (7-2).

At some point, some smart person discovered that the score 7-2 contains redundant information. In fact, it is sufficient to show just the score of the losing side in a tiebreak - because the winner's points can be inferred from it.

The rule can be stated as: if the displayed number is 5 or below, then the winner of the tiebreak scored exactly 7 points; and if the displayed number is 6 or above, then the winner scored two points more than that number.

For example, in the attached image, Djokovic won a tiebreak 7-6 (2) which means 7-6 (7-2) while Del Potro won a tiebreak 7-6 (6) which means 7-6 (8-6).

***

While this discovery satisfies my mathematical side - we always like to find the most concise way to do a proof or computation - it is bad for data communications!

It's just bad practice to make readers do calculations in their heads when the information can be displayed visually.

I found where I saw this single-digit display. It's on the official ATP Tour website.

***

Just for fun, if we applied the same principle to the display of the entire scoreline, we would arrive at something even more succinct :)

4-6, 7-6(6), 6-4 can simply be written as 4-, -6(6), -4

6-3, 7-6(4), 6-3 is -3, -6(4), -3

6-1, 6-4 is -1, -4

7-5, 4-6, 6-1 is -5, 4-, -1

The shortened display contains the minimal information needed to recover the long-form scoreline. But it fails at communications.

In this case, redundancy is great.

## Speedometer charts: love or hate

##### Aug 19, 2022

Pie chart hate is tired. In this post, I explain my speedometer hate. (Also called gauges,  dials)

Next to pie charts, speedometers are perhaps the second most beloved chart species found on business dashboards. Here is a typical example:

For this post, I found one on Reuters about natural gas in Europe. (Thanks to long-time contributor Antonio R. for the tip.)

The reason for my dislike is the inefficiency of this chart form. In classic Tufte-speak, the speedometer chart has a very poor data-to-ink ratio. The entire chart above contains just one datum (73%). Most of the ink are spilled over non-data things.

This single number has a large entourage:

- the curved axis
- ticks on the axis
- labels on the scale
- the dial
- the color segments
- the reference level "EU target"

These are not mere decorations. Taking these elements away makes it harder to understand what's on the chart.

Here is the chart without the curved axis:

Here is the chart without axis labels:

Here is the chart without ticks:

When the tick labels are present, the chart still functions.

Here is the chart without the dial:

The datum is redundantly encoded in the color segments of the "axis".

Here is the chart without the dial or the color segments:

If you find yourself stealing a peek at the chart title below, you're not alone.

All versions except one increases our cognitive load. This means the entourage is largely necessary if one encodes the single number in a speedometer chart.

The problem with the entourage is that readers may resort to reading the text rather than the chart.

***

The following is a minimalist version of the Reuters chart:

I removed the axis labels and the color segments. The number 73% is shown using the dial angle.

The next chart adds back the secondary message about the EU target, as an axis label, and uses color segments to show the 73% number.

Like pie charts, there are limited situations in which speedometer charts are acceptable. But most of the ones we see out there are just not right.

***

One acceptable situation is to illustrate percentages or proportions, which is what the EU gas chart does. Of course, in that situation, one can alo use a pie chart without shame.

For illustrating proportions, I prefer to use a full semicircle, instead of the circular sector of arbitrary angle as Reuters did. The semicircle lends itself to easy marks of 25%, 50%, 75%, etc, eliminating the need to print those tick labels.

***

One use case to avoid is numeric data.

Take the regional sales chart pulled randomly from a Web search above:

These charts are completely useless without the axis labels.

Besides, because the span of the axis isn't 0% to 100%, every tick mark must be labelled with the numeric value. That's a lot of extra ink used to display a single value!

## To explain or to eliminate, that is the question

##### Nov 22, 2021

Today, I take a look at another project from Ray Vella's class at NYU.

(The above image is a honeypot for "smart" algorithms that don't know how to handle image dimensions which don't fit their shadow "requirement". Human beings should proceed to the full image below.)

As explained in this post, the students visualized data about regional average incomes in a selection of countries. It turns out that remarkable differences persist in regional income disparity between countries, almost all of which are more advanced economies.

The graphic is by Danielle Curran.

I noticed two smart decisions.

First, she came up with a different main metric for gauging regional disparity, landing on a metric that is simple to grasp.

Based on hints given on the chart, I surmised that Danielle computed the change in per-capita income in the richest and poorest regions separately for each country between 2000 and 2015. These regional income growth values are expressed in currency, not indiced. Then, she computed the ratio of these growth rates, for each country. The end result is a simple metric for each country that describes how fast income has been growing in the richest region relative to the poorest region.

One of the challenges of this dataset is the complex indexing scheme (discussed here). Carlos' solution keeps the indices but uses design to facilitate comparisons. Danielle avoids the indices altogether.

The reader is relieved of the need to make comparisons, and so can focus on differences in magnitude. We see clearly that regional disparity is by far the highest in the U.K.

***

The second smart decision Danielle made is organizing the countries into clusters. She took advantage of the horizontal axis which does not encode any data. The branching structure places different clusters of countries along the axis, making it simple to navigate. The locations of these clusters are cleverly aligned to the map below.

***

Danielle's effort is stronger on communications while Carlos' effort provides more information. The key is to understand who your readers are. What proportion of your readers would want to know the values for each country, each region and each year?

***

A couple of suggestions

a) The reference line should be set at 1, not 0, for a ratio scale. The value of 1 happens when the richest region and the poorest region have identical per-capita incomes.

b) The vertical scale should be fixed.

## Surging gas prices

##### Oct 28, 2021

A reader finds this chart hard to parse:

The chart shows the trend in gas prices in New York in the past two years.

This is a case in which the simple line chart works very well.

I added annotations as the reasons behind the decline and rise in prices are reasonably clear.

One should be careful when formatting dates. The legend of the original chart looks like this:

In the U.S., dates typically use a M/D/Y format. The above dates are ambiguous. "Aug 19" can be August 19th or August, xx19.

## Simple charts are the hardest to do right

##### Aug 16, 2021

The CDC website has a variety of data graphics about many topics, one of which is U.S. vaccinations. I was looking for information about Covid-19 data broken down by age groups, and that's when I landed on these charts (link).

The left panel shows people with at least one dose, and the right panel shows those who are "fully vaccinated." This simple chart takes an unreasonable amount of time to comprehend.

***

The analyst introduces three metrics, all of which are described as "percentages". Upon reflection, they are proportions of the people in specific age ranges.

Readers are thus invited to compare these proportions. It's not clear, however, which comparisons are intended. The first item listed in the legend states "Percent among Persons who completed all recommended doses in last 14 days". For most readers, including me, this introduces an unexpected concept. The 14 days here do not refer to the (in)famous 14-day case-counting window but literally the most recent two weeks relative to when the chart was produced.

It would have been clearer if the concept of Proportions were introduced in the chart title or axis title, while the color legend explains the concept of the base population. From the lighter shade to the darker shade (of red and blue) to the gray color, the base population shifts from "Among Those Who Completed/Initiated Vaccinations Within Last 14 Days" to "Among Those Who Completed/Initiated Vaccinations Any Time" to "Among the U.S. Population (regardless of vaccination status)".

Also, a reverse order helps our comprehension. Each subsequent category is a subset of the one above. First, the whole population, then those who are fully vaccinated, and finally those who recently completed vaccinations.

The next hurdle concerns the Q corner of our Trifecta Checkup. The design leaves few hints as to what question(s) its creator intended to address. The age distribution of the U.S. population is useless unless it is compared to something.

One apparently informative comparison is the age distribution of those fully vaccinated versus the age distribution of all Americans. This is revealed by comparing the lengths of the dark blue bar and the gray bar. But is this comparison informative? It's telling me that people aged 50 to 64 account for ~25% of those who are fully vaccinated, and ~20% of all Americans. Because proportions necessarily add to 100%, this implies that other age groups have been less vaccinated. Duh! Isn't that the result of an age-based vaccination prioritization? During the first week of the vaccination campaign, one might expect close to 100% of all vaccinations to be in the highest age group while it was 0% for the other age groups.

This is a chart in search of a question. The 25% vs 20% comparison does not assist readers in making a judgement. Does this mean the vaccination campaign is working as expected, worse than expected or better than expected? The problem is the wrong baseline. The designer of this chart implies that the expected proportions should conform to the overall age distribution - but that clearly stands in the way of CDC's initial prioritization of higher-risk age groups.

***

In my version of the chart, I illustrate the proportion of people in each age group who have been fully vaccinated.

Among those fully vaccinated, some did it within the most recent two weeks:

***

Elsewhere on the CDC site, one learns that on these charts, "fully vaccinated" means one shot of J&J or 2 shots of Pfizer or Moderna, without dealing with the 14-day window or other complications. Why do we think different definitions are used in different analyses? Story-first thinking, as I have explained here. When it comes to telling the story about vaccinations, the story is about the number of shots in arms. They want as big a number as possible, and abandon any criterion that decreases the count. When it comes to reporting on vaccine effectiveness, they want as small a number of cases as possible.

## Did prices go up or down? Depends on how one looks at the data

##### Jul 06, 2021

The U.S. media have been flooded with reports of runaway inflation recently, and it's refreshing to see a nice article in the Wall Street Journal that takes a second look at the data. Because as my readers know, raw data can be incredibly deceptive.

Inflation typically describes the change in price level relative to the prior year. The month-on-month change in price levels is a simple seasonal adjustment used to remove the effect of seasonality that masks the true change in price levels. (See this explainer of seasonal adjustment.)

As the pandemic enters the second year, this methodology is comparing 2021 price levels to pandemic-impacted price levels of 2020. This produces a very confusing picture. As the WSJ article explains, prices can be lower than they were in 2019 (pre-pandemic) and yet substantially higher than they were in 2020 (during the pandemic). This happens in industry sectors that were heavily affected by the economic shutdown, e.g. hotels, travel, entertainment.

Here is how they visualized this phenomenon. Amusingly, some algorithm estimated that it should take 5 minutes to read the entire article. It may take that much time to understand properly what this chart is showing.

Let me save you some time.

The chart shows monthly inflation rates of hotel price levels.

The pink horizontal stripes represent the official inflation numbers, which compare each month's hotel prices to those of a year prior. The most recent value for May of 2021 says hotel prices rose by 9% compared to May of 2020.

The blue horizontal stripes show an alternative calculation which compares each month's hotel prices to those of two years prior. Think of 2018-9 as "normal" years, pre-pandemic. Using this measure, we find that hotel prices for May of 2021 are about 4% lower than for May of 2019.

(This situation affects all of our economic statistics. We may see an expansion in employment levels from a year ago which still leaves us behind where we were before the pandemic.)

What confused me on the WSJ chart are the blocks of color. In a previous chart, the readers learn that solid colors mean inflation rose while diagonal lines mean inflation decreased. It turns out that these are month-over-month changes in inflation rates (notice that one end of the column for the previous month touches one end of the column of the next month).

The color patterns become the most dominant feature of this chart, and yet the month-over-month change in inflation rates isn't the crux of the story. The real star of the story should be the difference in inflation rates - for any given month - between two reference years.

***

In the following chart, I focus attention on the within-month, between-reference-years comparisons.

Because hotel prices dropped drastically during the pandemic, and have recovered quite well in recent months as the U.S. reopens the economy, the inflation rate of hotel prices is almost 10%. Nevertheless, the current price level is still 7% below the pre-pandemic level.

## Reading this chart won't take as long as withdrawing troops from Afghanistan

##### May 06, 2021

Art sent me the following Economist chart, noting how hard it is to understand. I took a look, and agreed. It's an example of a visual representation that takes more time to comprehend than the underlying data.

The chart presents responses to 3 questions on a survey. For each question, the choices are Approve, Disapprove, and "Neither" (just picking a word since I haven't seen the actual survey question). The overall approval/disapproval rates are presented, and then broken into two subgroups (Democrats and Republicans).

The first hurdle is reading the scale. Because the section from 75% to 100% has been removed, we are left with labels 0, 25, 50, 75, which do not say percentages unless we've consumed the title and subtitle. The Economist style guide places the units of data in the subtitle instead of on
the axis itself.

Our attention is drawn to the thick lines, which represent the differences between approval and disapproval rates. These differences are signed: it matters whether the proportion approving is higher or lower than the proportion disapproving. This means the data are encoded in the order of the dots plus the length of the line segment between them.

The two bottom rows of the Afghanistan question demonstrates this mental challenge. Our brains have to process the following visual cues:

1) the two lines are about the same lengths

2) the Republican dots are shifted to the right by a little

3) the colors of the dots are flipped

What do they all mean?

A chart runs in trouble when you need a paragraph to explain how to read it.

It's sometimes alright to make complicated data visualization that illustrates complicated concepts. What justifies it is the payoff. I wrote about the concept of return on effort in data visualization here.

The payoff for this chart escaped me. Take the Democratic response to troop withdrawal. About 3/4 of Democrats approve while 15% disapprove. The thick line says 60% more Democrats approve than disapprove.

***

Here, I show the full axis, and add a 50% reference line

Small edits but they help visualize "half of", "three quarters of".

***

Next, I switch to the more conventional stacked bars.

This format reveals some of the hidden data on the chart - the proportion answering neither approve/disapprove, and neither yes/no.

On the stacked bars visual, the proportions are counted from both ends while in the dot plot above, the proportions are measured from the left end only.

***

## Dreamy Hawaii

##### Jan 12, 2021

I really enjoyed this visual story by ProPublica and Honolulu Star-Advertiser about the plight of beaches in Hawaii (link).

The story begins with a beautiful invitation:

This design reminds me of Vimeo's old home page. (It no longer looks like this today but this screenshot came from when I was the data guy there.) In both cases, the images are not static but moving.

The tour de force of this visual story is an annotated walk along the Lanikai Beach. Here is a snapshot at one of the stops:

This shows a particular homeowner who, according to documents, was permitted to rebuild a destroyed seawall even though officials were supposed to disallow reconstruction in order to protect beaches from eroding. The property is marked on the map above. The image inside the box is a gif showing waves smashing the seawall.

As the reader scrolls down, the image window runs through a carousel of gifs of houses along the beach. The images are synchronized to the reader's progress along the shore. The narrative makes stops at specific houses at which point a text box pops up to provide color commentary.

***

The erosion crisis is shown in this pair of maps.

There's some fancy work behind the scenes to patch together images, and estimate the boundaries of th beaches.

***

The following map is notable for its simplicity. There are no unnecessary details and labels. We don't need to know the name of every street or a specific restaurant. Removing excess details makes readers focus on the informative parts.

Clicking on the dots brings up more details.

***

Enjoy the entire story here.

## These are the top posts of 2020

##### Jan 08, 2021

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:

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.

## Convincing charts showing containment measures work

##### Dec 03, 2020

The disorganized nature of U.S.'s response to the coronavirus pandemic has created a sort of natural experiment that allows data journalists to explore important scientific questions, such as the impact of containment measures on cases and hospitalizations. This New York Times article represents the best of such work.

The key finding of the analysis is beautifully captured by this set of scatter plots:

Each dot is a state. The cases (left plot) and hospitalizations (right plot) are plotted against the severity of containment measures for November. The negative correlation is unmistakable: the more containment measures taken, the lower the counts.

There are a few features worth noting.

The severity index came from a group at Oxford, and is a number between 0 and 100. The journalists decided to leave out the numerical labels, instead simply showing More and Fewer. This significantly reduces processing time. Readers won't be able to understand the index values anyway without reading the manual.

The index values are doubly encoded. They are first encoded by the location on the horizontal axis and redundantly encoded on the blue-red scale. Ordinarily, I do not like redundant encoding because the reader might assume a third dimension exists. In this case, I had no trouble with it.

The easiest way to see the effect is to ignore the muddy middle and focus on the two ends of the severity index. Those states with the fewest measures - South Dakota, North Dakota, Iowa - are the worst in cases and hospitalizations while those states with the most measures - New York, Hawaii - are among the best. This comparison is similar to what is frequently done in scientific studies, e.g. when they say coffee is good for you, they typically compare heavy drinkers (4 or more cups a day) with non-drinkers, ignoring the moderate and light drinkers.

Notably, there is quite a bit of variability for any level of containment measures - roughly 50 cases per 100,000, and 25 hospitalizations per 100,000. This indicates that containment measures are not sufficient to explain the counts. For example, the hospitalization statistic is affected by the stock of hospital beds, which I assume differ by state.

Whenever we use a scatter plot, we run the risk of xyopia. This chart form invites readers to explain an outcome (y-axis values) using one explanatory variable (on x-axis). There is an assumption that all other variables are unimportant, which is usually false.

***

Because of the variability, the horizontal scale has meaningless precision. The next chart cures this by grouping the states into three categories: low, medium and high level of measures.

This set of charts extends the time window back to March 1. For the designer, this creates a tricky problem - because states adapt their policies over time. As indicated in the subtitle, the grouping is based on the average severity index since March, rather than just November, as in the scatter plots above.

***

The interplay between policy and health indicators is captured by connected scatter plots, of which the Times article included a few examples. Here is what happened in New York:

Up until April, the policies were catching up with the cases. The policies tightened even after the case-per-capita started falling. Then, policies eased a little, and cases started to spike again.

The Note tells us that the containment severity index is time shifted to reflect a two-week lag in effect. So, the case count on May 1 is not paired with the containment severity index of May 1 but of April 15.

***