Putting vaccine trials in boxes

Bloomberg Businessweek has a special edition about vaccines, and I found this chart on the print edition:

The chart's got a lot of white space. Its structure is a series of simple "treemaps," one for each type of vaccine. Though simple, such a chart burns a few brain cells.

Here, I've extracted the largest block, which corresponds to vaccines that work with the virus's RNA/DNA. I applied a self-sufficiency test, removing the data from the boxes. 

What proportion of these projects have moved from pre-clinical to Phase 1?  To answer this question, we have to understand the relative areas of boxes, since that's how the data are encoded. How many yellow boxes can fit into the gray box?

It's not intuitive. We'd need a ruler to do this task properly.

Then, we learn that the gray box is exactly 8 times the size of the yellow box (72 projects are pre-clinical while 9 are in Phase I). We can cram eight yellows into the gray box. Imagine doing that, and it's pretty clear the visual elements fail to convey the meaning of the data.

Self-sufficiency is the idea that a data graphic should not rely on printed data to convey its meaning; the visual elements of a data graphic should bear much of the burden. Otherwise, use a data table. To test for self-sufficiency, cover up the printed data and see if the chart still works.

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A key decision for the designer is the relative importance of (a) the number of projects reaching Phase III, versus (b) the number of projects utilizing specific vaccine strategies.

This next chart emphasizes the clinical phases:

 

Contrast this with the version shown in the online edition of Bloomberg (link), which emphasizes the vaccine strategies.

If any reader can figure out the logic of the ordering of the vaccine strategies, please leave a comment below.

When the pie chart is more complex than the data

The trading house, Charles Schwab, included the following graphic in a recent article:

This graphic is more complicated than the story that it illustrates. The author describes a simple scenario in which an investor divides his investments into stocks, bonds and cash. After a stock crash, the value of the portfolio declines.

The graphic is a 3-D pie chart, in which the data are encoded twice, first in the areas of the sectors and then in the heights of the part-cylinders.

As readers, we perceive the relative volumes of the part-cylinders. Volume is the cross-sectional area (i.e. of the base) multipled by the height. Since each component holds the data, the volumes are proportional to the squares of the data.

Here is a different view of the same data:

This "bumps chart" (also called a slopegraph) shows clearly the only thing that drives the change is the drop in stock prices. Because the author assumes no change in bonds or cash, the drop in the entire portfolio is completely accounted for by the decline in stocks. Of course, this scenario seems patently unrealistic - different investment asset classes tend to be correlated.

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A cardinal rule of data visualization is that the visual should be less complex than the data.

Visualizing black unemployment in the U.S.

In a prior post, I explained how the aggregate unemployment rate paints a misleading picture of the employment situation in the United States. Even though the U3 unemployment rate in 2019 has returned to the lowest level we have seen in decades, the aggregate statistic hides some concerning trends. There is an alarming rise in the proportion of people considered "not in labor force" by the Bureau of Labor Statistics - these forgotten people are not counted as "employable": when a worker drops out of the labor force, the unemployment rate ironically improves.

In that post, I looked at the difference between men and women. This post will examine the racial divide, whites and blacks.

I did not anticipate how many obstacles I'd encounter. It's hard to locate a specific data series, and it's harder to know whether the lack of search results indicates the non-existence of the data, or the incompetence of the search engine. Race-related data tend not to be offered in as much granularity. I was only able to find quarterly data for the racial analysis while I had monthly data for the gender analysis. Also, I only have data from 2000, instead of 1990.

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As before, I looked at the official unemployment rate first, this time presented by race. Because whites form the majority of the labor force, the overall unemployment rate (not shown) is roughly the same as that for whites, just pulled up slightly toward the line for blacks.

The racial divide is clear as day. Throughout the past two decades, black Americans are much more likely to be unemployed, and worse during recessions.

The above chart determines the color encoding for all the other graphics. Notice that the best employment situations occurred on either end of this period, right before the dotcom bust in 2000, and in 2019 before the Covid-19 pandemic. As explained before, despite the headline unemployment rate being the same in those years, the employment situation was not the same.

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Here is the scatter plot for white Americans:

Even though both ends of the trajectory are marked with the same shade of blue, indicating almost identical (low) rates of unemployment, we find that the trajectory has failed to return to its starting point after veering off course during the recession of the early 2010s. While the proportion of part-time workers (counted as employed) returned to 17.5% in 2019, as in 2000, about 15 percent more whites are now excluded from the unemployment rate calculation.

The experience of black Americans appears different:

During the first decade, the proportion of black Americans dropping out of the labor force accelerated while among those considered employed, the proportion holding part-time jobs kept increasing. As the U.S. recovered from the Great Recession, we've seen a boomerang pattern. By 2019, the situation was halfway back to 2000. The last available datum for the first quarter of 2020 is before Covid-19; it actually showed a halt of the boomerang.

If the pattern we saw in the prior post holds for the Covid-19 world, we would see a marked spike in the out-of-labor-force statistic, coupled with a drop in part-time employment. It appeared that employers were eliminating part-time workers first.

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One reader asked about placing both patterns on the same chart. Here is an example of this:

This graphic turns out okay because the two strings of dots fit tightly into the grid while not overlapping. There is a lot going on here; I prefer a multi-step story than throwing everything on the wall.

There is one insight that this chart provides that is not easily observed in two separate plots. Over the two decades, the racial gap has narrowed in these two statistics. Both groups have traveled to the top right corner, which is the worst corner to reside -- where more people are classified as not employable, and more of the employed are part-time workers.

The biggest challenge with making this combined scatter plot is properly controlling the color. I want the color to represent the overall unemployment rate, which is a third data series. I don't want the line for blacks to be all red, and the line for whites to be all blue, just because black Americans face a tough labor market always. The color scheme here facilitates cross-referencing time between the two dot strings.

Consumption patterns during the pandemic

The impact of Covid-19 on the economy is sharp and sudden, which makes for some dramatic data visualization. I enjoy reading the set of charts showing consumer spending in different categories in the U.S., courtesy of Visual Capitalist.

The designer did a nice job cleaning up the data and building a sequential story line. The spending are grouped by categories such as restaurants and travel, and then sub-categories such as fast food and fine dining.

Spending is presented as year-on-year change, smoothed.

Here is the chart for the General Commerce category:

The visual design is clean and efficient. Even too sparse because one has to keep returning to the top to decipher the key events labelled 1, 2, 3, 4. Also, to find out that the percentages express year-on-year change, the reader must scroll to the bottom, and locate a footnote.

As you move down the page, you will surely make a stop at the Food Delivery category, noting that the routine is broken.

I've featured this device - an element of surprise - before. Remember this Quartz chart that depicts drinking around the world (link).

The rule for small multiples is to keep the visual design identical but vary the data from chart to chart. Here, the exceptional data force the vertical axis to extend tremendously.

This chart contains a slight oversight - the red line should be labeled "Takeout" because food delivery is the label for the larger category.

Another surprise is in store for us in the Travel category.

I kept staring at the Cruise line, and how it kept dipping below -100 percent. That seems impossible mathematically - unless these cardholders are receiving more refunds than are making new bookings. Not only must the entire sum of 2019 bookings be wiped out, but the records must also show credits issued to these credit (or debit) cards. It's curious that the same situation did not befall the airlines. I think many readers would have liked to see some text discussing this pattern.

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Now, let me put on a data analyst's hat, and describe some thoughts that raced through my head as I read these charts.

Data analysis is hard, especially if you want to convey the meaning of the data.

The charts clearly illustrate the trends but what do the data reveal? The designer adds commentary on each chart. But most of these comments count as "story time." They contain speculation on what might be causing the trend but there isn't additional data or analyses to support the storyline. In the General Commerce category, the 50 to 100 percent jump in all subcategories around late March is attributed to people stockpiling "non-perishable food, hand sanitizer, and toilet paper". That might be true but this interpretation isn't supported by credit or debit card data because those companies do not have details about what consumers purchased, only the total amount charged to the cards. It's a lot more work to solidify these conclusions.

A lot of data do not mean complete or unbiased data.

The data platform provided data on 5 million consumers. We don't know if these 5 million consumers are representative of the 300+ million people in the U.S. Some basic demographic or geographic analysis can help establish the validity. Strictly speaking, I think they have data on 5 million card accounts, not unique individuals. Most Americans use more than one credit or debit cards. It's not likely the data vendor have a full picture of an individual's or a family's spending.

It's also unclear how much of consumer spending is captured in this dataset. Credit and debit cards are only one form of payment.

Data quality tends to get worse.

One thing that drives data analyst nuts. The spending categories are becoming blurrier. In the last decade or so, big business has come to dominate the American economy. Big business, with bipartisan support, has grown by (a) absorbing little guys, and (b) eliminating boundaries between industry sectors. Around me, there is a Walgreens, several Duane Reades, and a RiteAid. They currently have the same owner, and increasingly offer the same selection. In the meantime, Walmart (big box), CVS (pharmacy), Costco (wholesale), etc. all won regulatory relief to carry groceries, fresh foods, toiletries, etc. So, while CVS or Walgreens is classified as a pharmacy, it's not clear that what proportion of the spending there is for medicines. As big business grows, these categories become less and less meaningful.

Graphing the extreme

The Covid-19 pandemic has brought about extremes. So many events have never happened before. I doubt The Conference Board has previously seen the collapse of confidence in the economy by CEOs. Here is their graphic showing this extreme event:

To appreciate this effort, you have to see the complexity of the underlying data. There is a CEO Confidence Measure. The measure has three components. Each component is scored on a scale probably from 0 to 100, with 5o as the middle. Then, the components are aggregated into an overall score. The measure is repeatedly estimated over time, and they did two surveys during the Pandemic, pre and post the lockdown in the U.S. And then, there's the rightmost column, which provides another reference point for one of the components of the measure.

One can easily get one's limbs tied up in knots trying to tame this beast.

Of course, the tiny square stands out. CEOs have a super pessimistic outlook for the next 6 months for overall economy. The number 3 on this scale probably means almost every respondent has a negative view. 

The grid arrangement does not appear attractive but it is terrifically functional. The grid delivers horizontal and vertical comparisons. Moving vertically, we learn that even at the start of the year, the average sentiment was negative (9 points below 50), then it lost another 10 points, and finally imploded.

Moving horizontally, we can compare related metrics since everything is conveniently expressed in the same scale. While CEOs are depressed about the overall economy, they have slightly more faith about their own industry. And then moving left, we learn that many CEOs expect a V-shaped recovery, a really fast bounceback within 6 months. 

As the Conference Board surveys this group again in the near future, I wonder if the optimism still holds. 

The Conference Board has an entire set of graphics about the economic crisis of Covid-19 here. For some reason, they don't let me link to a specific chart so I can't directly link to the chart. 
 

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.

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.

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

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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.

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.

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:

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

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.

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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.

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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.

An exposed seam in the crystal ball of coronavirus recovery

One of the questions being asked by the business community is when the economy will recover and how. The Conference Board has offered their outlook in this new article. (This link takes you to the collection of Covid-19 related graphics. You have to find the right one from the carousel. I can't seem to find the direct link to that page.)

This chart summarizes their viewpoint:

They considered three scenarios, starting the recovery in May, over the summer, and in the Fall. In all scenarios, the GDP of the U.S. will contract in 2020 relative to 2019. The faster the start of the recovery, the lower the decline.

My reaction to the map icon is different from the oil-drop icon in the previously-discussed chart (link). I think here, the icon steals too much attention. The way lines were placed on the map initially made me think the chart is about cross-country travel.

On the other hand, I love the way he did the horizontal axis / time-line. It elegantly tells us which numbers are actual and which numbers are projected, without explicitly saying so.

Also notice through the use of color, font size and bolding, he organizes the layers of detail, and conveys which items are more important to read first.

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As I round out the Trifecta Checkup, I found a seam in the Data.

On the right edge, the number for December 2020 is 100.6 which is 0.6 above the reference level. But this number corresponds to a 1.6% reduction. How so?

This seam exposes a gap between how modelers and decision-makers see the world. Evidently, the projections by the analyst are generated using Q3 2019's GDP as baseline (index=100). I'm guessing the analyst chose that quarter because at the time of analysis, the Q4 data have not reached the final round of revision (which came out at the end of March).

A straight-off-the-report conclusion of the analysis is that the GDP would be just back to Q3 2019 level by December 2020 in the most optimistic scenario. (It's clear to me that the data series has been seasonally adjusted as well so that we can compare any month to any month. Years ago, I wrote this primer to understand seasonal adjustments.)

Decision-makers might push back on that conclusion because the reference level of Q3 2019 seems arbitrary. Instead, what they like to know is the year-on-year change to GDP. A small calculation is completed to bridge between the two numbers.

The decision-makers are satisfied after finding the numbers they care about. They are not curious about how the sausage is made, i.e., how the monthly numbers result in the year-on-year change. So the seam is left on the chart.

 

Graphing the economic crisis of coronavirus 2

Last week, I discussed Ray's chart that compares the S&P 500 performance in this crisis against previous crises.

A reminder:

Another useful feature is the halo around the right edge of the COVID-19 line. This device directs our eyes to where he wants us to look.

In the same series, he made the following for The Conference Board (link):

Two things I learned from this chart:

The oil market takes a much longer time to recover after crises, compared to the S&P. None of these lines reached above 100 in the first 150 days (5 months).

Just like the S&P, the current crisis is most similar in severity to the 2008 Great Recession, only worse, and currently, the price collapse in oil is quite a bit worse than in 2008.

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The drop of oil is going to be contentious. This is a drop too many for a Tufte purist. It might as well symbolize a tear shed.

The presence of the icon tells me these lines depict the oil market without having to read text. And I approve.

Graphing the economic crisis of Covid-19

My friend Ray Vella at The Conference Board has a few charts up on their coronavirus website. TCB is a trusted advisor and consultant to large businesses and thus is a good place to learn how the business community is thinking about this crisis.

I particularly like the following chart:

This puts the turmoil in the stock market in perspective. We are roughly tracking the decline of the Great Recession of the late 2000s. It's interesting that 9/11 caused very mild gyrations in the S&P index compared to any of the other events. 

The chart uses an index with value 100 at Day 0. Day 0 is defined by the trigger event for each crisis. About three weeks into the current crisis, the S&P has lost over 30% of its value.

The device of a gray background for the bottom half of the chart is surprisingly effective.

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Here is a chart showing the impact of the Covid-19 crisis on different sectors.

So the full-service restaurant industry is a huge employer. Restaurants employ 7-8 times more people than airlines. Airlines employ about the same numbers of people as "beverage bars" (which I suppose is the same as "bars" which apparently is different from "drinking places"). Bars employ 7 times more people than "Cafeterias, etc.".

The chart describes where the jobs are, and which sectors they believe will be most impacted. It's not clear yet how deeply these will be impacted. Being in NYC, the complete shutdown is going to impact 100% of these jobs in certain sectors like bars, restaurants and coffee shops.

Food coma and self-sufficiency in dataviz

The Hustle wrote a strong analysis of the business of buffets. If you've read my analysis of Groupon's business model in Numbersense (link), you'll find some similarities. A key is to not think of every customer as an average customer; there are segments of customers who behave differently, and creating a proper mix of different types of customers is the management's challenge. I will make further comments on the statistics in a future post on the sister blog.

At Junk Charts, we'll focus on visualizing and communciating data. The article in The Hustle comes with the following dataviz:

This dataviz fails my self-sufficiency test. Recall: self-sufficiency is a basic requirement of visualizing data - that the graphical elements should be sufficient to convey the gist of the data. Otherwise, there is no point in augmenting the data with graphical elements.

The self-sufficiency test is to remove the dataset from the dataviz, and ask whether the graphic can stand on its own. So here:

The entire set of ingredient costs appears on the original graphic. When these numbers are removed, the reader gets the wrong message - that the cost is equally split between these five ingredients.

This chart reminds me of the pizza chart that everyone thought was a pie chart except its designer! I wrote about it here. Food coma is a thing.

The original chart may be regarded as an illustration rather than data visualization. If so, it's just a few steps from becoming a dataviz. Like this:

P.S. A preview of what I'll be talking about at the sister blog. The above diagram illustrates the average case - for the average buffet diner. Underneath these costs is an assumption about the relative amounts of each food that is eaten. But eaten by whom?

Also, if you have Numbersense (link), the chapter on measuring the inflation rate is relevant here. Any inflation metric must assume a basket of goods, but then the goods within the basket have to be weighted by the amount of expenditure. It's much harder to get the ratio of expenditures correct compared to getting price data.