Via twitter, no words for this one:
I need a poll function for this. What do we think happened here?
***
One last thing!
If you think you knew what the real numbers are, think again.
Bloomberg's recent article on surging UK household energy costs, projected over this winter, contains data about which I have long been intrigued: how much energy does different household items consume?
A twitter follower alerted me to this chart, and she found it informative.
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If the goal is to pick out the appliances and estimate the cost of running them, the chart serves its purpose. Because the entire set of data is printed, a data table would have done equally well.
I learned that the mobile phone costs almost nothing to charge: 1 pence for six hours of charging, which is deemed a "single use" which seems double what a full charge requires. The games console costs 14 pence for a "single use" of two hours. That might be an underestimate of how much time gamers spend gaming each day.
***
Understanding the design of the chart needs a bit more effort. Each appliance is measured by two metrics: the number of hours considered to be "single use", and a currency value.
It took me a while to figure out how to interpret these currency values. Each cost is associated with a single use, and the duration of a single use increases as we move down the list of appliances. Since the designer assumes a fixed cost of electicity (shown in the footnote as 34p per kWh), at first, it seems like the costs should just increase from top to bottom. That's not the case, though.
Something else is driving these numbers behind the scene, namely, the intensity of energy use by appliance. The wifi router listed at the bottom is turned on 24 hours a day, and the daily cost of running it is just 6p. Meanwhile, running the fridge and freezer the whole day costs 41p. Thus, the fridge&freezer consumes electricity at a rate that is almost 7 times higher than the router.
The chart uses a split axis, which artificially reduces the gap between 8 hours and 24 hours. Here is another look at the bottom of the chart:
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Let's examine the choice of "single use" as a common basis for comparing appliances. Consider this:
I prefer standardizing to a "per day" metric. If I use the microwave three times a day, the daily cost is 3 x 3p = 9 p, which is more than I'd spend on the wifi router, run 24 hours. On the other hand, I use the washing machine once a week, so the frequency is 1/7, and the effective daily cost is 1/7 x 36 p = 5p, notably lower than using the microwave.
The choice of metric has key implications on the appearance of the chart. The bubble size encodes the relative energy costs. The biggest bubbles are in the heating category, which is no surprise. The next largest bubbles are tumble dryer, dishwasher, and electric oven. These are generally not used every day so the "per day" calculation would push them lower in rank.
***
Another noteworthy feature of the Bloomberg chart is the split legend. The colors divide appliances into five groups based on usage category (e.g. cleaning, food, utility). Instead of the usual color legend printed on a corner or side of the chart, the designer spreads the category labels around the chart. Each label is shown the first time a specific usage category appears on the chart. There is a presumption that the reader scans from top to bottom, which is probably true on average.
I like this arrangement as it delivers information to the reader when it's needed.
Through my twitter feed, I found my way to this chart, made by jamie_bio.
This is produced using R code even though it looks like a slide.
The underlying dataset concerns votes at the United Nations on various topics. Someone has already classified these topics. Jamie looked at voting blocs, specifically, countries whose votes agree most often or least often with the U.K.
If you look at his Github, this is one in a series of works he produced to hone his dataviz skills. Ultimately, I think this effort can benefit from some re-thinking. However, I also appreciate the work he has put into this.
Let's start with the things I enjoyed.
Given the dataset, I imagine the first visual one might come up with is a heatmap that shows countries in rows and topics in columns. That would work ok, as any standard chart form would but it would be a data dump that doesn't tell a story. There are almost 200 countries in the entire dataset. The countries can only be ordered in one way so if it's ordered for All Votes, it's not ordered for any of the other columns.
What Jamie attempts here is story-telling. The design leads the reader through a narrative. We start by reading the how-to-read-this box on the top left. This tells us that he's using a lunar eclipse metaphor. A full circle in blue indicates 0% agreement while a full circle in white indicates 100% agreement. The five circles signal that he's binning the agreement percentages into five discrete buckets, which helps simplify our understanding of the data.
Then, our eyes go to the circle of circles, labelled "All votes". This is roughly split in half, with the left side showing mostly blue and the right showing mostly white. That's because he's extracting the top 5 and bottom 5 countries, measured by their vote alignment with the U.K. The countries names are clearly labelled.
Next, we see the votes broken up by topics. I'm assuming not all topics are covered but six key topics are highlighted on the right half of the page.
What I appreciate about this effort is the thought process behind how to deliver a message to the audience. Selecting a specific subset that addresses a specific question. Thinning the materials in a way that doesn't throw the kitchen sink at the reader. Concocting the circular layout that presents a pleasing way of consuming the data.
***
Now, let me talk about the things that need more work.
I'm not convinced that he got his message across. What is the visual telling us? Half of the cricle are aligned with the U.K. while half aren't so the U.K. sits on the fence on every issue? But this isn't the message. It's a bit of a mirage because the designer picked out the top 5 and bottom 5 countries. The top 5 are surely going to be voting almost 100% with the U.K. while the bottom 5 are surely going to be disagreeing with the U.K. a lot.
I did a quick sketch to understand the whole distribution:
This is not intended as a show-and-tell graphic, just a useful way of exploring the dataset. You can see that Arms Race/Disarmament and Economic Development are "average" issues that have the same form as the "All issues" line. There are a small number of countries that are extremely aligned with the UK, and then about 50 countries that are aligned over 50% of the time, then the other 150 countries are within the 30 to 50% aligned. On human rights, there is less alignment. On Palestine, there is more alignment.
What the above chart shows is that the top 5 and bottom 5 countries both represent thin slithers of this distribution, which is why in the circular diagrams, there is little differentiation. The two subgroups are very far apart but within each subgroup, there is almost no variation.
Another issue is the lunar eclipse metaphor. It's hard to wrap my head around a full white circle indicating 100% agreement while a full blue circle shows 0% agreement.
In the diagrams for individual topics, the two-letter acronyms for countries are used instead of the country names. A decoder needs to be provided, or just print the full names.
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.
I reviewed another batch of projects from Ray Vella's class at NYU. The following piece by Carlos Lasso made an impression on me. There are no pyrotechnics but he made one decision that added a lot of clarity to the graphic.
The underlying dataset gauges the income disparity of regions within nine countries. The richest and the poorest regions are selected for each country. Two time points are shown. Altogether, there are 9x2x2 = 36 numbers.
***
Let's take a deeper look at these numbers. Notice they are not in dollars, or any kind of currency, despite being about incomes. The numbers are index values, relative to 100. What does the reference level of 100 represent?
The value of 100 crosses every bar of the chart so that 100 has meaning in each country and each year. In fact, there are 18 definitions of 100 in this chart with 36 numbers, one for each country-year pair. The average national income is set to 100 for each country in each year. This is a highly convoluted indexing strategy.
The following chart is a re-visualization of the bottom part of Carlos' infographic.
I shifted the scale of the horizontal axis. The value of zero does not hold special meaning in Carlos' chart. I subtracted 100 from the relative regional income indices, thus all regions with income above the average have positive values while those below the national average have negative values. (There are other challenges with the ratio scale, which I'll skip over in this post. The minimum value is -100 while the maximum value can be very large.)
The rescaling is not really the point of this post. To see what Carlos did, we have to look at the example shown in class. The graphic which the students were asked to improve has the following structure:
This one-column structure places four bars beside each country, grouped by year. Carlos pulled the year dimension out, and showed the same dataset in two columns.
This small change makes a great difference in ease of comprehension. Carlos' version unpacks the two key types of comparisons one might want to make: trend within a given country (horizontal comparison) and contrast between countries in a given year (vertical comparison).
***
I always try to avoid convoluted indexing. The cost of using such indices is the big how-to-read-this box.
An author in Significance claims that a single season of Premier League football without live spectators is enough to prove that the so-called home field advantage is really a live-spectator advantage.
The following chart depicts the data going back many seasons:
I find this bar chart challenging.
It plots the ratio of home wins to away wins using an odds scale, which is not intuitive. The odds scale (probability of success divided by probability of failure) runs from 0 to positive infinity, with 1 being a special value indicating equal odds. But all the values for which away wins exceed home wins are squeezed into the interval between 0 and 1 while the values for which home wins exceed away wins are laid out between 1 and infinity. So it's an inherently asymmetric graphic for a symmetric formula.
The section labeled "more away wins than home wins" are filled with red bars for all those seasons with positive home field advantage while the most recent season, the outlier, has a shorter bar in that section than the rest.
Here's an alternative view:
I have incorporated dual axes here - but both axes are different only by scaling. There are 380 games in a Premier League season so the percentage scale is just a re-expression of the counts.
As I'm preparing a blog about another real-world study of Covid-19 vaccines, I came across the following chart (the chart title is mine).
As background, this is the trend in Covid-19 cases in the U.K. in the last couple of months, courtesy of OurWorldinData.org.
The React-1 Study sends swab kits to randomly selected people in England in order to assess the prevalence of Covid-19. Every month, there is a new round of returned swabs that are tested for Covid-19. This measurement method captures asymptomatic cases although it probably missed severe and hospitalized cases. Despite having some shortcomings, this is a far better way to measure cases than the hotch-potch assembling of variable-quality data submitted by different jurisdictions that has become the dominant source of our data.
Rounds 12 and 13 captured an inflection point in the pandemic in England. The period marked the beginning of the end of the belief that widespread vaccination will end the pandemic.
The chart I excerpted up top broke the data down by age groups. The column heights represent the estimated prevalence of Covid-19 during each round - also, described precisely in the paper as "swab positivity." Based on the study's design, one may generalize the prevalence to the population at large. About 1.5% of those aged 13-24 in England are estimated to have Covid-19 around the time of Round 13 (roughly early July).
The researchers came to the following conclusion:
We show that the third wave of infections in England was being driven primarily by the Delta variant in younger, unvaccinated people. This focus of infection offers considerable scope for interventions to reduce transmission among younger people, with knock-on benefits across the entire population... In our data, the highest prevalence of infection was among 12 to 24 year olds, raising the prospect that vaccinating more of this group by extending the UK programme to those aged 12 to 17 years could substantially reduce transmission potential in the autumn when levels of social mixing increase
***
Raise your hand if the graphics software you prefer dictates at least one default behavior you can't stand. I'm sure most hands are up in the air. No matter how much you love the software, there is always something the developer likes that you don't.
The first thing I did with today's chart is to get rid of all such default details.
For me, the bottom chart is cleaner and more inviting.
***
The researchers wanted readers to think in terms of Round 3 numbers as multiples of Round 2 numbers. In the text, they use statements such as:
weighted prevalence in round 13 was nine-fold higher in 13-17 year olds at 1.56% (1.25%, 1.95%) compared with 0.16% (0.08%, 0.31%) in round 12
It's not easy to perceive a nine-fold jump from the paired column chart, even though this chart form is better than several others. I added some subtle divisions inside each orange column in order to facilitate this task:
I have recommended this before. I'm co-opting pictograms in constructing the column chart.
An alternative is to plot everything on an index scale although one would have to drop the prevalence numbers.
***
The chart requires an additional piece of context to interpret properly. I added each age group's share of the population below the chart - just to illustrate this point, not to recommend it as a best practice.
The researchers concluded that their data supported vaccinating 13-17 year olds because that group experienced the highest multiplier from Round 12 to Round 13. Notice that the 13-17 year old age group represents only 6 percent of England's population, and is the least populous age group shown on the chart.
The neighboring 18-24 age group experienced a 4.5 times jump in prevalence in Round 13 so this age group is doing much better than 13-17 year olds, right? Not really.
While the same infection rate was found in both age groups during this period, the slightly older age group accounted for 50% more cases -- and that's due to the larger share of population.
A similar calculation shows that while the infection rate of people under 24 is about 3 times higher than that of those 25 and over, both age groups suffered over 175,000 infections during the Round 3 time period (the difference between groups was < 4,000). So I don't agree that focusing on 13-17 year olds gives England the biggest bang for the buck: while they are the most likely to get infected, their cases account for only 14% of all infections. Almost half of the infections are in people 25 and over.
The Financial Times published the following chart that shows the rollout of vaccines in the U.K.
(I can't find the online link to the article. The article is titled "AstraZeneca and Oxford face setbacks and success as battle enters next phase", May 29/30 2021.)
This chart form is known as a "streamgraph", and it is a stacked area chart in disguise.
The same trick can be applied to a column chart. See the "hanging" column chart below:
The two charts show exactly the same data. The left one roots the columns at the bottom. The right one aligns the middle of the columns.
I have rarely found these hanging charts useful. The realignment makes it harder to compare the sizes of the different column segments. On the normal stacked column chart, the yellow segments are the easiest to compare because they share the same base level. Even this is taken away from the reader on the right side.
Note also that the hanging version does not admit a vertical axis.
The same comments apply to the streamgraph.
***
Nevertheless, I was surprised that the FT chart shown above actually works. The main message I learned was that initially U.K. primarily rolled out AstraZeneca and, to a lesser extent, Pfizer, shots while later, they introduced other vaccines, including Johnson & Johnson, Novavax, CureVac, Moderna, and "Other".
I can also see that the supply of AstraZeneca has not changed much through the entire time window. Pfizer has grown to roughly the same scale as AstraZeneca. Moderna remains a small fraction of total shots.
I can even roughly see that the total number of vaccinations has grown about six times from start to finish.
That's quite a lot for one chart, so job well done!
There is one problem with the FT chart. It should have labelled end of May as "today". Half the chart is history, and the other half is the future.
***
For those following Covid-19 news, the FT chart is informative in a different way.
There is a misleading statement going around blaming the U.K.'s recent surge in cases on the Astrazeneca vaccine, claiming that the U.K. mostly uses AZ. This chart shows that from the start, about a third of the shots administered in the U.K. are Pfizer, and Pfizer's share has been growing over time.
U.K. compared to some countries mostly using mRNA vaccines
U.K. is almost back to the winter peak. That's because the U.K. is serious about counting cases. Look at the state of testing in these countries:
What's clear about the U.S. case count is that it is kept low by cutting the number of tests by two-thirds, thus, our data now is once again severely biased towards severe cases.
We can do a back-of-the-envelope calculation. The drop in testing may directly lead to a proportional drop in reported cases, thus removing 500 (asymptomatic, or mild) cases per million from the case count. The case count goes below 250 per million so the additional 200 or so reduction is due to other reasons such as vaccinations.
A few weeks ago, I did a guest lecture for Ray Vella's dataviz class at NYU, and discussed a particularly hairy dataset that he assigns to students.
I'm happy to see the work of the students, and there are two pieces in particular that show promise.
The following dot plot by Christina Barretto shows the disparities between the richest and poorest nations increasing between 2000 and 2015.
The underlying dataset has the average GDP per capita for the richest and the poor regions in each of nine countries, for two years (2000 and 2015). With each year, the data are indiced to the national average income (100). In the U.K., the gap increased from around 800 to 1,100 in the 15 years. It's evidence that the richer regions are getting richer, and the poorer regions are getting poorer.
(For those into interpreting data, you should notice that I didn't say the rich getting richer. During the lecture, I explain how to interpret regional averages.)
Christina's chart reflects the tidy, minimalist style advocated by Tufte. The countries are sorted by the 2000-to-2015 difference, with Britain showing up as an extreme outlier.
***
The next chart by Adrienne Umali is more infographic than Tufte.
It's great story-telling. The top graphic explains the underlying data. It shows the four numbers and how the gap between the richest and poorest regions is computed. Then, it summarizes these four numbers into a single metric, "gap increase". She chooses to measure the change as a ratio while Christina's chart uses the difference, encoded as a vertical line.
Adrienne's chart is successful because she filters our attention to a single country - the U.S. It's much too hard to drink data from nine countries in one gulp.
This then sets her up for the second graphic. Now, she presents the other eight countries. Because of the work she did in the first graphic, the reader understands what those red and green arrows mean, without having to know the underlying index values.
Two small suggestions: a) order the countries from greatest to smallest change; b) leave off the decimals. These are minor flaws in a brilliant piece of work.
Reader Mirko was concerned about a video published in Germany that shows why the new coronavirus variant is dangerous. He helpfully provided a summary of the transcript:
The South African and the British mutations of the SARS-COV-2 virus are spreading faster than the original virus. On average, one infected person infects more people than before. Researchers believe the new variant is 50 to 70 % more transmissible.
Here are two key moments in the video:
This seems to be saying the original virus (left side) replicates 3 times inside the infected person while the new variant (right side) replicates 19 times. So we have a roughly 6-fold jump in viral replication.
Later in the video, it appears that every replicate of the old virus finds a new victim while the 19 replicates of the new variant land on 13 new people, meaning 6 replicates didn't find a host.
As Mirko pointed out, the visual appears to have run away from the data. (In our Trifecta Checkup, we have a problem with the arrow between the D and the V corners. What the visual is saying is not aligned with what the data are saying.)
***
It turns out that the scientists have been very confusing when talking about the infectiousness of this new variant. The most quoted line is that the British variant is "50 to 70 percent more transmissible". At first, I thought this is a comment on the famous "R number". Since the R number around December was roughly 1 in the U.K, the new variant might bring the R number up to 1.7.
However, that is not the case. From this article, it appears that being 5o to 70 percent more transmissible means R goes up from 1 to 1.4. R is interpreted as the average number of people infected by one infected person.
Mirko wonders if there is a better way to illustrate this. I'm sure there are many better ways. Here's one I whipped up:
The left side is for the 40% higher R number. Both sides start at the center with 10 infected people. At each time step, if R=1 (right side), each of the 10 people infects 10 others, so the total infections increase by 10 per time step. It's immediately obvious that a 40% higher R is very serious indeed. Starting with 10 infected people, in 10 steps, the total number of infections is almost 1,000, almost 10 times higher than when R is 1.
The lines of the graphs simulate the transmission chains. These are "average" transmission chains since R is an average number.
P.S. [1/29/2021: Added the missing link to the article in which it is reported that 50-70 percent more transmissible implies R increasing by 40%.]
