Presented without comment

Weekend assignment - which of these tells the story better?

Ourworldindata_cases_log

Or:

Ourworldindata_cases_linear

The cop-out answer is to say both. If you must pick one, which one?

***

When designing a data visualization as a living product (not static), you'd want a design that adapts as the data change.


This exercise plan for your lock-down work-out is inspired by Venn

A twitter follower did not appreciate this chart from Nature showing the collection of flu-like symptoms that people reported they have to an UK tracking app. 

Nature tracking app venn diagram

It's a super-complicated Venn diagram. I have written about this type of chart before (see here); it appears to be somewhat popular in the medicine/biology field.

A Venn diagram is not a data visualization because it doesn't plot the data.

Notice that the different compartments of the Venn diagram do not have data encoded in the areas. 

The chart also fails the self-sufficiency test because if you remove the data from it, you end up with a data container - like a world map showing country boundaries and no data.

If you're new here: if a graphic requires the entire dataset to be printed on it for comprehension, then the visual elements of the graphic are not doing any work. The graphic cannot stand on its own.

When the Venn diagram gets complicated, teeming with many compartments, there will be quite a few empty compartments. If I have to make this chart, I'd be nervous about leaving out a number or two by accident. An empty cell can be truly empty or an oversight.

Another trap is that the total doesn't add up. The numbers on this graphic add to 1,764 whereas the study population in the preprint was 1,702. Interestingly, this diagram doesn't show up in the research paper. Given how they winnowed down the study population from all the app downloads, I'm sure there is an innocent explanation as to why those two numbers don't match.

***

The chart also strains the reader. Take the number 18, right in the middle. What combination of symptoms did these 18 people experience? You have to figure out the layers sitting beneath the number. You see dark blue, light blue, orange. If you blink, you might miss the gray at the bottom. Then you have to flip your eyes up to the legend to map these colors to diarrhoea, shortness of breath, anosmia, and fatigue. Oops, I missed the yellow, which is the cough. To be sure, you look at the remaining categories to see where they stand - I've named all of them except fever. The number 18 lies outside fever so this compartment represents everything except fever. 

What's even sadder is there is not much gain from having done it once. Try to interpret the number 50 now. Maybe I'm just slow but it doesn't get better the second or third time around. This graphic not only requires work but painstaking work!

Perhaps a more likely question is how many people who had a loss of smell also had fever. Now it's pretty easy to locate the part of the dark gray oval that overlaps with the orange oval. But now, I have to add all those numbers, 69+17+23+50+17+46 = 222. That's not enough. Next, I must find the total of all the numbers inside the orange oval, which is 222 plus what is inside the orange and outside the dark gray. That turns out to be 829. So among those who had lost smell, the proportion who also had fever is 222/(222+829) = 21 percent. 

How many people had three or more symptoms? I'll let you figure this one out!

 

 

 

 

 

 

 


Reviewing the charts in the Oxford Covid-19 study

On my sister (book) blog, I published a mega-post that examines the Oxford study that was cited two weeks ago as a counterpoint to the "doomsday" Imperial College model. These studies bring attention to the art of statistical modeling, and those six posts together are designed to give you a primer, and you don't need math to get a feel.

One aspect that didn't make it to the mega-post is the data visualization. Sad to say, the charts in the Oxford study (link) are uniformly terrible. Figure 3 is typical:

Oxford_covidmodel_fig3

There are numerous design decisions that frustrate readers.

a) The graphic contains two charts, one on top of the other. The left axis extends floor-to-ceiling, giving the false impression that it is relevant to both charts. In fact, the graphic uses dual axes. The bottom chart references the axis shown in the bottom right corner; the left axis is meaningless. The two charts should be drawn separately.

For those who have not read the mega-post about the Oxford models, let me give a brief description of what these charts are saying. The four colors refer to four different models - these models have the same structure but different settings. The top chart shows the proportion of the population that is still susceptible to infection by a certain date. In these models, no one can get re-infected, and so you see downward curves. The bottom chart displays the growth in deaths due to Covid-19. The first death in the UK was reported on March 5.  The black dots are the official fatalities.

b) The designer allocates two-thirds of the space to the top chart, which has a much simpler message. This causes the bottom chart to be compressed beyond cognition.

c) The top chart contains just five lines, smooth curves of the same shape but different slopes. The designer chose to use thick colored lines with black outlines. As a result, nothing precise can be read from the chart. When does the yellow line start dipping? When do the two orange lines start to separate?

d) The top chart should have included margins of error. These models are very imprecise due to the sparsity of data.

e) The bottom chart should be rejected by peer reviewers. We are supposed to judge how well each of the five models fits the cumulative death counts. But three design decisions conspire to prevent us from getting the answer: (i) the vertical axis is severely compressed by tucking this chart underneath the top chart (ii) the vertical axis uses a log scale which compresses large values and (iii) the larger-than-life dots.

As I demonstrated in this post also from the sister blog, many models especially those assuming an exponential growth rate has poor fits after the first few days. Charting in log scale hides the degree of error.

f) There is a third chart squeezed into the same canvass. Notice the four little overlapping hills located around Feb 1. These hills are probability distributions, which are presented without an appropriate vertical axis. Each hill represents a particular model's estimate of the date on which the novel coronavirus entered the UK. But that date is unknowable. So the model expresses this uncertainty using a probability distribution. The "peak" of the distribution is the most likely date. The spread of the hill gives the range of plausible dates, and the height at a given date indicates the chance that that is the date of introduction. The missing axis is a probability scale, which is neither the left nor the right axis.

***

The bottom chart shows up in a slightly different form as Figure 1(A).

Oxford_covidmodels_Fig1A

Here, the green, gray (blocked) and red thick lines correspond to the yellow/orange/red diamonds in Figure 3. The thin green and red lines show the margins of error I referred to above (these lines are not explicitly explained in the chart annotation.) The actual counts are shown as white rather than black diamonds.

Again, the thick lines and big diamonds conspire to swamp the gaps between model fit and actual data. Again, notice the use of a log scale. This means that the same amount of gap signifies much bigger errors as time moves to the right.

When using the log scale, we should label it using the original units. With a base 10 logarithm, the axis should have labels 1, 10, 100, 1000 instead of 0, 1, 2, 3. (This explains my previous point - why small gaps between a model line and a diamond can mean a big error as the counts go up.)

Also notice how the line of white diamonds makes it impossible to see what the models are doing prior to March 5, the date of the first reported death. The models apparently start showing fatalities prior to March 5. This is a key part of their conclusion - the Oxford team concluded that the coronavirus has been circulating in the U.K. even before the first infection was reported. The data visualization should therefore bring out the difference in timing.

I hope by the time the preprint is revised, the authors will have improved the data visualization.

 

 

 


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

[Note: As of Monday afternoon, Typepad is having problems rendering images. Please try again later if the charts are not loading properly.]

DC sent me the following chart over Twitter. It supposedly showcases one sector that has bucked the economic collapse, and has conversely been boosted by the stay-at-home orders around the world.

Covid19-pornhubtraffic


At first glance, I was drawn to the yellow line and the axis title on the right side. I understood the line to depict the growth rate in traffic "vs a normal day". The trend is clear as day. Since March 10 or so, the website has become more popular by the week.

For a moment, I thought the thin black line was a trendline that fits the rather ragged traffic growth data. But looking at the last few data points, I was afraid it was a glove that didn't fit. That's when I realized this is a dual-axis chart. The black line shows the worldwide total Covid-19 cases, with the axis shown on the left side.

As with any dual-axis charts, you can modify the relationship between the two scales to paint a different picture.

This next chart says that the site traffic growth lagged Covid-19 growth until around March 14.

Junkcharts_ph_dualaxis1

This one gives an ambiguous picture. One can't really say there is a strong correlation between the two time series.

Junkcharts_ph_dualaxis2

***

Now, let's look at the chart from the DATA corner of the Trifecta Checkup (link). The analyst selected definitions that are as far apart as possible. So this chart gives a good case study of the intricacy of data definitions.

First, notice the smoothness of the line of Covid-19 cases. This data series is naturally "smoothed" because it is an aggregate of country-level counts, which themselves are aggregates of regional counts.

By contrast, the line of traffic growth rates has not been smoothed. That's why we see sharp ups and downs. This series should be smoothed as well.

Junkcharts_ph_smoothedtrafficgrowth

The seven-day moving average line indicates a steady growth in traffic. The day-to-day fluctuations represent noise that distracts us from seeing the trendline.

Second, the Covid-19 series is a cumulative count, which means it's constantly heading upward over time (on rare days, it may go flat but never decrease). The traffic series represents change, is not cumulative, and so it can go up or down over time. To bring the data closer together, the Covid-19 series can be converted into new cases so they are change values.

Junkcharts_ph_smoothedcovidnewcases

Third, the traffic series are growth rates as percentages while the Covid-19 series are counts. It is possible to turn Covid-19 counts into growth rates as well. Like this:

Junkcharts_ph_smoothedcovidcasegrowth

By standardizing the units of measurement, both time series can be plotted on the same axis. Here is the new plot:

Redo_junkcharts_ph_trafficgrowthcasegrowth

Third, the two growth rates have different reference levels. The Covid-19 growth rate I computed is day-on-day growth. This is appropriate since we don't presume there is a seasonal effect - something like new cases on Mondays are typically larger than new cases on Tuesday doesn't seem plausible.

Thanks to this helpful explainer (link), I learned what the data analyst meant by a "normal day". The growth rate of traffic is not day-on-day change. It is the change in traffic relative to the average traffic in the last four weeks on the same day of week. If it's a Monday, the change in traffic is relative to the average traffic of the last four Mondays.

This type of seasonal adjustment is used if there is a strong day-of-week effect. For example, if the website reliably gets higher traffic during weekends than weekdays, then the Saturday traffic may always exceed the Friday traffic; instead of comparing Saturday to the day before, we index Saturday to the previous Saturday, Friday to the previous Friday, and then compare those two values.

***

Let's consider the last chart above, the one where I got rid of the dual axes.

A major problem with trying to establish correlation of two time series is time lag. Most charts like this makes a critical and unspoken assumption - that the effect of X on Y is immediate. This chart assumes that the higher the number Covid-19 cases, the more people stays home that day, the more people swarms the site that day. Said that way, you might see it's ridiculous.

What is true of any correlations in the wild - there is always some amount of time lag. It usually is hard to know how much lag.

***

Finally, the chart omitted a huge factor driving the growth in traffic. At various times dependent on the country, the website rolled out a free premium service offer. This is the primary reason for the spike around mid March. How much of the traffic growth is due to the popular marketing campaign, and how much is due to stay-at-home orders - that's the real question.


Proportions and rates: we are no dupes

Reader Lucia G. sent me this chart, from Ars Technica's FAQ about the coronavirus:

Arstechnica_covid-19-2.001-1280x960

She notices something wrong with the axis.

The designer took the advice not to make a dual axis, but didn't realize that the two metrics are not measured on the same scale even though both are expressed as percentages.

The blue bars, labeled "cases", is a distribution of cases by age group. The sum of the blue bars should be 100 percent.

The orange bars show fatality rates by age group. Each orange bar's rate is based on the number of cases in that age group. The sum of the orange bars will not add to 100 percent.

In general, the rates will have much lower values than the proportions. At least that should be the case for viruses that are not extremely fatal.

This is what the 80 and over section looks like.

Screen Shot 2020-03-12 at 1.19.46 AM

It is true that fatality rate (orange) is particularly high for the elderly while this age group accounts for less than 5 percent of total cases (blue). However, the cases that are fatal, which inhabit the orange bar, must be a subset of the total cases for 80 and over, which are shown in the blue bar. Conceptually, the orange bar should be contained inside the blue bar. So, it's counter-intuitive that the blue bar is so much shorter than the orange bar.

The following chart fixes this issue. It reveals the structure of the data, Total cases are separated by age group, then within each age group, a proportion of the cases are fatal.

Junkcharts_redo_arstechnicacovid19

This chart also shows that most patients recover in every age group. (This is only approximately true as some of the cases may not have been discharged yet.)

***

This confusion of rates and proportions reminds me of something about exit polls I just wrote about the other day on the sister blog.

When the media make statements about trends in voter turnout rate in the primary elections, e.g. when they assert that youth turnout has not increased, their evidence is from exit polls, which can measure only the distribution of voters by age group. Exit polls do not and cannot measure the turnout rate, which is the proportion of registered (or eligible) voters in the specific age group who voted.

Like the coronavirus data, the scales of these two metrics are different even though they are both percentages: the turnout rate is typically a number between 30 and 70 percent, and summing the rates across all age groups will exceed 100 percent many times over. Summing the proportions of voters across all age groups should be 100 percent, and no more.

Changes in the proportion of voters aged 18-29 and changes in the turnout rate of people aged 18-29 are not the same thing. The former is affected by the turnout of all age groups while the latter is a clean metric affected only by 18 to 29-years-old.

Basically, ignore pundits who use exit polls to comment on turnout trends. No matter how many times they repeat their nonsense, proportions and rates are not to be confused. Which means, ignore comments on turnout trends because the only data they've got come from exit polls which don't measure rates.

 

P.S. Here is some further explanation of my chart, as a response to a question from Enrico B. on Twitter.

The chart can be thought of as two distributions, one for cases (gray) and one for deaths (red). Like this:

Junkcharts_redo_arstechnicacoronavirus_2

The side-by-side version removes the direct visualization of the fatality rate within each age group. To understand fatality rate requires someone to do math in their head. Readers can qualitatively assess that for the 80 and over, they accounted for 3 percent of cases but also about 21 percent of deaths. People aged 70 to 79 however accounted for 9 percent of cases but 30 percent of deaths, etc.

What I did was to scale the distribution of deaths so that they can be compared to the cases. It's like fitting the red distribution inside the gray distribution. Within each age group, the proportion of red against the length of the bar is the fatality rate.

For every 100 cases regardless of age, 3 cases are for people aged 80 and over within which 0.5 are fatal (red).

So, the axis labels are correct. The values are proportions of total cases, although as the designer of the chart, I hope people are paying attention more to the proportion of red, as opposed to the units.

What might strike people as odd is that the biggest red bar does not appear against 80 and above. We might believe it's deadlier the older you are. That's because on an absolute scale, more people aged 70-79 died than those 80 and above. The absolute deaths is the product of the proportion of cases and the fatality rate. That's really a different story from the usual plot of fatality rates by age group. In those charts, we "control" for the prevalence of cases. If every age group were infected in the same frequency, then COVID-19 does kill more 80 and over.

 

 

 


How to read this chart about coronavirus risk

In my just-published Long Read article at DataJournalism.com, I touched upon the subject of "How to Read this Chart".

Most data graphics do not come with directions of use because dataviz designers follow certain conventions. We do not need to tell you, for example, that time runs left to right on the horizontal axis (substitute right to left for those living in right-to-left countries). It's when we deviate from the norms that calls for a "How to Read this Chart" box.

***
A discussion over Twitter during the weekend on the following New York Times chart perfectly illustrates this issue. (The article is well worth reading to educate oneself on this red-hot public-health issue. I made some comments on the sister blog about the data a few days ago.)

Nyt_coronavirus_scatter

Reading this chart, I quickly grasp that the horizontal axis is the speed of infection and the vertical axis represents the deadliness. Without being told, I used the axis labels (and some of you might notice the annotations with the arrows on the top right.) But most people will likely miss - at a glance - that the vertical axis utilizes a log scale while the horizontal axis is linear (regular).

The effect of a log scale is to pull the large numbers toward the average while spreading the smaller numbers apart - when compared to a linear scale. So when we look at the top of the coronavirus box, it appears that this virus could be as deadly as SARS.

The height of the pink box is 3.9, while the gap between the top edge of the box and the SARS dot is 6. Yet our eyes tell us the top edge is closer to the SARS dot than it is to the bottom edge!

There is nothing inaccurate about this chart - the log scale introduces such distortion. The designer has to make a choice.

Indeed, there were two camps on Twitter, arguing for and against the log scale.

***

I use log scales a lot in analyzing data, but tend not to use log scales in a graph. It's almost a given that using the log scale requires a "How to Read this Chart" message. And the NY Times crew delivers!

Right below the chart is a paragraph:

Nyt_coronavirus_howtoreadthis

To make this even more interesting, the horizontal axis is a hidden "log" scale. That's because infections spread exponentially. Even though the scale is not labeled "log", think as if the large values have been pulled toward the middle.

Here is an over-simplified way to see this. A disease that spreads at a rate of fifteen people at a time is not 3 times worse than one that spreads five at a time. In the former case, the first sick person transmits it to 15, and then each of the 15 transmits the flu to 15 others, thus after two steps, 241 people have been infected (225 + 15 + 1). In latter case, it's 5x5 + 5 + 1 = 31 infections after two steps. So at this point, the number of infected is already 8 times worse, not 3 times. And the gap keeps widening with each step.

P.S. See also my post on the sister blog that digs deeper into the metrics.

 


How to read this cost-benefit chart, and why it is so confusing

Long-time reader Antonio R. found today's chart hard to follow, and he isn't alone. It took two of us multiple emails and some Web searching before we think we "got it".

Ar_submit_Fig-3-2-The-policy-cost-curve-525

 

Antonio first encountered the chart in a book review (link) of Hal Harvey et. al, Designing Climate Solutions. It addresses the general topic of costs and benefits of various programs to abate CO2 emissions. The reviewer praised the "wealth of graphics [in the book] which present complex information in visually effective formats." He presented the above chart as evidence, and described its function as:

policy-makers can focus on the areas which make the most difference in emissions, while also being mindful of the cost issues that can be so important in getting political buy-in.

(This description is much more informative than the original chart title, which states "The policy cost curve shows the cost-effectiveness and emission reduction potential of different policies.")

Spend a little time with the chart now before you read the discussion below.

Warning: this is a long read but well worth it.

 

***

 

If your experience is anything like ours, scraps of information flew at you from different parts of the chart, and you had a hard time piecing together a story.

What are the reasons why this data graphic is so confusing?

Everyone recognizes that this is a column chart. For a column chart, we interpret the heights of the columns so we look first at the vertical axis. The axis title informs us that the height represents "cost effectiveness" measured in dollars per million metric tons of CO2. In a cost-benefit sense, that appears to mean the cost to society of obtaining the benefit of reducing CO2 by a given amount.

That's how far I went before hitting the first roadblock.

For environmental policies, opponents frequently object to the high price of implementation. For example, we can't have higher fuel efficiency in cars because it would raise the price of gasoline too much. Asking about cost-effectiveness makes sense: a cost-benefit trade-off analysis encapsulates the something-for-something principle. What doesn't follow is that the vertical scale sinks far into the negative. The chart depicts the majority of the emissions abatement programs as having negative cost effectiveness.

What does it mean to be negatively cost-effective? Does it mean society saves money (makes a profit) while also reducing CO2 emissions? Wouldn't those policies - more than half of the programs shown - be slam dunks? Who can object to programs that improve the environment at no cost?

I tabled that thought, and proceeded to the horizontal axis.

I noticed that this isn't a standard column chart, in which the width of the columns is fixed and uneventful. Here, the widths of the columns are varying.

***

In the meantime, my eyes are distracted by the constellation of text labels. The viewing area of this column chart is occupied - at least 50% - by text. These labels tell me that each column represents a program to reduce CO2 emissions.

The dominance of text labels is a feature of this design. For a conventional column chart, the labels are situated below each column. Since the width does not usually carry any data, we tend to keep the columns narrow - Tufte, ever the minimalist, has even advocated reducing columns to vertical lines. That leaves insufficient room for long labels. Have you noticed that government programs hold long titles? It's tough to capture even the outline of a program with fewer than three big words, e.g. "Renewable Portfolio Standard" (what?).

The design solution here is to let the column labels run horizontally. So the graphical element for each program is a vertical column coupled with a horizontal label that invades the territories of the next few programs. Like this:

Redo_fueleconomystandardscars

The horror of this design constraint is fully realized in the following chart, a similar design produced for the state of Oregon (lifted from the Plan Washington webpage listed as a resource below):

Figure 2 oregon greenhouse

In a re-design, horizontal labeling should be a priority.

 

***

Realizing that I've been distracted by the text labels, back to the horizontal axis I went.

This is where I encountered the next roadblock.

The axis title says "Average Annual Emissions Abatement" measured in millions metric tons. The unit matches the second part of the vertical scale, which is comforting. But how does one reconcile the widths of columns with a continuous scale? I was expecting each program to have a projected annual abatement benefit, and those would fall as dots on a line, like this:

Redo_abatement_benefit_dotplot

Instead, we have line segments sitting on a line, like this:

Redo_abatement_benefit_bars_end2end_annuallabel

Think of these bars as the bottom edges of the columns. These line segments can be better compared to each other if structured as a bar chart:

Redo_abatement_benefit_bars

Instead, the design arranges these lines end-to-end.

To unravel this mystery, we go back to the objective of the chart, as announced by the book reviewer. Here it is again:

policy-makers can focus on the areas which make the most difference in emissions, while also being mindful of the cost issues that can be so important in getting political buy-in.

The primary goal of the chart is a decision-making tool for policy-makers who are evaluating programs. Each program has a cost and also a benefit. The cost is shown on the vertical axis and the benefit is shown on the horizontal. The decision-maker will select some subset of these programs based on the cost-benefit analysis. That subset of programs will have a projected total expected benefit (CO2 abatement) and a projected total cost.

By stacking the line segments end to end on top of the horizontal axis, the chart designer elevates the task of computing the total benefits of a subset of programs, relative to the task of learning the benefits of any individual program. Thus, the horizontal axis is better labeled "Cumulative annual emissions abatement".

 

Look at that axis again. Imagine you are required to learn the specific benefit of program titled "Fuel Economy Standards: Cars & SUVs".  

Redo_abatement_benefit_bars_end2end_cumlabel

This is impossible to do without pulling out a ruler and a calculator. What the axis labels do tell us is that if all the programs to the left of Fuel Economy Standards: Cars & SUVs were adopted, the cumulative benefits would be 285 million metric tons of CO2 per year. And if Fuel Economy Standards: Cars & SUVs were also implemented, the cumulative benefits would rise to 375 million metric tons.

***

At long last, we have arrived at a reasonable interpretation of the cost-benefit chart.

Policy-makers are considering throwing their support behind specific programs aimed at abating CO2 emissions. Different organizations have come up with different ways to achieve this goal. This goal may even have specific benchmarks; the government may have committed to an international agreement, for example, to reduce emissions by some set amount by 2030. Each candidate abatement program is evaluated on both cost and benefit dimensions. Benefit is given by the amount of CO2 abated. Cost is measured as a "marginal cost," the amount of dollars required to achieve each million metric ton of abatement.

This "marginal abatement cost curve" aids the decision-making. It lines up the programs from the most cost-effective to the least cost-effective. The decision-maker is presumed to prefer a more cost-effective program than a less cost-effective program. The chart answers the following question: for any given subset of programs (so long as we select them left to right contiguously), we can read off the cumulative amount of CO2 abated.

***

There are still more limitations of the chart design.

  • We can't directly read off the cumulative cost of the selected subset of programs because the vertical axis is not cumulative. The cumulative cost turns out to be the total area of all the columns that correspond to the selected programs. (Area is height x width, which is cost per benefit multiplied by benefit, which leaves us with the cost.) Unfortunately, it takes rulers and calculators to compute this total area.

  • We have presumed that policy-makers will make the Go-No-go decision based on cost effectiveness alone. This point of view has already been contradicted. Remember the mystery around negatively cost-effective programs - their existence shows that some programs are stalled even when they reduce emissions in addition to making money!

  • Since many, if not most, programs have negative cost-effectiveness (by the way they measured it), I'd flip the metric over and call it profitability (or return on investment). Doing so removes another barrier to our understanding. With the current cost-effectiveness metric, policy-makers are selecting the "negative" programs before the "positive" programs. It makes more sense to select the "positive" programs before the "negative" ones!

***

In a Trifecta Checkup (guide), I rate this chart Type V. The chart has a great purpose, and the design reveals a keen sense of the decision-making process. It's not a data dump for sure. In addition, an impressive amount of data gathering and analysis - and synthesis - went into preparing the two data series required to construct the chart. (Sure, for something so subjective and speculative, the analysis methodology will inevitably be challenged by wonks.) Those two data series are reasonable measures for the stated purpose of the chart.

The chart form, though, has various shortcomings, as shown here.  

***

In our email exchange, Antonio and I found the Plan Washington website useful. This is where we learned that this chart is called the marginal abatement cost curve.

Also, the consulting firm McKinsey is responsible for popularizing this chart form. They have published this long report that explains even more of the analysis behind constructing this chart, for those who want further details.


Light entertainment: people of color

What colors do the "average" person like the most and the least? The following chart found here (Scott Design) tells you favorite and least favorite colors by age groups:

Color-preferences-by-age

(This is one of a series of charts. A total of 10 colors is covered by the survey. The same color can appear in both favorites and least favorites since these are aggregate proportions. Almost 40% of the respondents are under 18 and only one percent are over 70.)

Here's one item that has stumped me thus far: how are the colors ordered within each figurine?


Women workers taken for a loop or four

I was drawn to the following chart in Business Insider because of the calendar metaphor. (The accompanying article is here.)

Businessinsider_payday

Sometimes, the calendar helps readers grasp concepts faster but I'm afraid the usage here slows us down.

The underlying data consist of just four numbers: the wage gaps between race and gender in the U.S., considered simply from an aggregate median personal income perspective. The analyst adopts the median annual salary of a white male worker as a baseline. Then, s/he imputes the number of extra days that others must work to attain the same level of income. For example, the median Asian female worker must work 64 extra days (at her daily salary level) to match the white guy's annual pay. Meanwhile, Hispanic female workers must work 324 days extra.

There are a host of reasons why the calendar metaphor backfired.

Firstly, it draws attention to an uncomfortable detail of the analysis - which papers over the fact that weekends or public holidays are counted as workdays. The coloring of the boxes compounds this issue. (And the designer also got confused and slipped up when applying the purple color for Hispanic women.)

Secondly, the calendar focuses on Year 2 while Year 1 lurks in the background - white men have to work to get that income (roughly $46,000 in 2017 according to the Census Bureau).

Thirdly, the calendar view exposes another sore point around the underlying analysis. In reality, the white male workers are continuing to earn wages during Year 2.

The realism of the calendar clashes with the hypothetical nature of the analysis.

***

One can just use a bar chart, comparing the number of extra days needed. The calendar design can be considered a set of overlapping bars, wrapped around the shape of a calendar.

The staid bars do not bring to life the extra toil - the message is that these women have to work harder to get the same amount of pay. This led me to a different metaphor - the white men got to the destination in a straight line but the women must go around loops (extra days) before reaching the same endpoint.

Redo_businessinsider_racegenderpaygap

While the above is a rough sketch, I made sure that the total length of the lines including the loops roughly matches the total number of days the women needed to work to earn $46,000.

***

The above discussion focuses solely on the V(isual) corner of the Trifecta Checkup, but this data visualization is also interesting from the D(ata) perspective. Statisticians won't like such a simple analysis that ignores, among other things, the different mix of jobs and industries underlying these aggregate pay figures.

Now go to my other post on the sister (book) blog for a discussion of the underlying analysis.

 

 


Where are the Democratic donors?

I like Alberto's discussion of the attractive maps about donors to Democratic presidential candidates, produced by the New York Times (direct link).

Here is the headline map:

Nyt_demdonormaps

The message is clear: Bernie Sanders is the only candidate with nation-wide appeal. The breadth of his coverage is breath-taking. (I agree with Alberto's critique about the lack of a color scale. It's impossible to know if the counts are trivial or not.)

Bernie's coverage is so broad that his numbers overwhelm those of all other candidates except in their home bases (e.g. O'Rourke in Texas).

A remedy to this is to look at the data after removing Bernie's numbers.

Nyt_demdonormap_2

 

This pair of maps reminds me of the Sri Lanka religions map that I revisualized in this post.

Redo_srilankareligiondistricts_v2

The first two maps divide the districts into those in which one religion dominates and those in which multiple religions share the limelight. The third map then shows the second-rank religion in the mixed-religions districts.

The second map in the NYT's donor map series plots the second-rank candidate in all the precincts that Bernie Sanders lead. It's like the designer pulled off the top layer (blue: Bernie) to reveal what's underneath.

Because all of Bernie's data are removed, O'Rourke is still dominating Texas, Buttigieg in Indiana, etc. An alternative is to pull off the top layer in those pockets as well. Then, it's likely to see Bernie showing up in those areas.

The other startling observation is how small Joe Biden's presence is on these maps. This is likely because Biden relies primarily on big donors.

See here for the entire series of donor maps. See here for past discussion of New York Times's graphics.