Swarmed by ants

Andrew discussed the following chart in a recent blog post:

Alert! A swarm of ants has marched onto a bubble chart.

These overlapping long text labels are dominating the chart; the length of these labels encodes the length of country names, which has nothing to do with the data.

We're waiting - hoping - for the ants to march off the page.

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Andrew's blog post is about something else, the use of log scales. The chart above is a log-log plot. Both axes have log scales.

Andrew's correspondent doesn't like log scales. Andrew does.

One problem we encounter in practice with log scales is that people without science background can't read them. Andrew's correspondent said as much, while also misinterpreting the log-log chart. He says the log-log chart "visually creates a much stronger correlation than there actually is".

But that's not what happened. It's more appropriate to say that the log transformations allow us to see the correlation that exists. The correlation is not linear which is why the usual scatter plot does not reveal it. 

Nevertheless, I agree with the correspondent on avoiding log scales in data displays because most readers don't get it.

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Consider the following pair of plots.

The underlying data follow the pattern Y = 0.003 * X^2.5 but for what we're talking about, the specific pattern doesn't matter so long as X and Y has a "power" relationship. 

The left plot directly shows the relationship between X and Y using regular scales. Readers see that Y is running away from X. The slope of the line increases as X increases. The speed of growth of Y exceeds that of X. This relationship is curved, which can't be described in words succinctly.

The right plot visually shows a linear relationship between X and Y but it's not really between X and Y. It's between log(X) and log(Y). Note that log(Y) = log(0.003*X^2.5) = log(0.003) + 2.5*log(X), which is a straight line with slope 2.5 and intercept log(0.003). The gap between gridlines now represents a 10-fold jump in value (of X or of Y). The linear relationship is between X and Y in log scale; in linear scale, it's a power relationship, not linear.

The practice of printing axis labels in the original scale, rather than log scale, adds to the confusion. On the right plot, the points labeled 5,000 and 50,000 do not actually lie on the line; what fall in line are the points log(5,000) and log(50,000). The reason for this confusing practice is that humans have trouble understanding data in log scale. For example, if $50,000 is the GDP per capita for some country, then log($50,000) = $4.5 which can't be interpreted.

Whether we are talking about the gaps between gridlines or about specific points on the line, what readers see on the log-log chart is only part of the story. Readers must also recognize that for the log-log chart to work, equal gaps between gridlines do not signify equal gaps in the data, while the linear relationship is between the log of the axis labels, not the labels themselves.

The X-Y plot can be interpreted visually in a direct way while the log-log plot requires the reader to transcend the visual representation, entering an abstract realm.

 

 

Dot plots with varying dot sizes

In a prior post, I appreciated the effort by the Bloomberg Graphics team to describe the diverging fortunes of Japanese and Chinese car manufacturers in various Asian markets.

The most complex chart used in that feature is the following variant of a dot plot:

This chart plots the competitors in the Chinese domestic car market. Each bubble represents a car brand. Using the styling of the entire article, the red color is associated with Japanese brands while the medium gray color indicates Chinese brands. The light gray color shows brands from the rest of the world. (In my view, adding the pink for U.S. and blue for German brands - seen on the first chart in this series - isn't too much.)

The dot size represents the current relative market share of the brand. The main concern of the Bloomberg article is the change in market share in the period 2019-2024. This is placed on the horizontal axis, so the bubbles on the right side represent growing brands while the bubbles on the left, weakening brands.

All the Japanese brands are stagnating or declining, from the perspective of market share.

The biggest loser appears to be Volkswagen although it evidently started off at a high level since its bubble size after shrinkage is still among the largest.

***

This chart form is a composite. There are at least two ways to describe it. I prefer to see it as a dot plot with an added dimension of dot size. A dot plot typically plots a single dimension on a single axis, and here, a second dimension is encoded in the sizes of the dots.

An alternative interpretation is that it is a scatter plot with a third dimension in the dot size. Here, the vertical dimension is meaningless, as the dots are arbitrarily spread out to prevent overplotting. This arrangement is also called the bubble plot if we adopt a convention that a bubble is a dot of variable size. In a typical bubble plot, both vertical and horizontal axes carry meaning but here, the vertical axis is arbitrary.

The bubble plot draws attention to the variable in the bubble size, the scatter plot emphasizes two variables encoded in the grid while the dot plot highlights a single metric. Each shows secondary metrics.

***

Another revelation of the graph is the fragmentation of the market. There are many dots, especially medium gray dots. There are quite a few Chinese local manufacturers, most of which experienced moderate growth. Most of these brands are startups - this can be inferred because the size of the dot is about the same as the change in market share.

The only foreign manufacturer to make material gains in the Chinese market is Tesla.

The real story of the chart is BYD. I almost missed its dot on first impression, as it sits on the far right edge of the chart (in the original webpage, the right edge of the chart is aligned with the right edge of the text). BYD is the fastest growing brand in China, and its top brand. The pedestrian gray color chosen for Chinese brands probably didn't help. Besides, I had a little trouble figuring out if the BYD bubble is larger than the largest bubble in the size legend shown on the opposite end of BYD. (I measured, and indeed the BYD bubble is slightly larger.)

This dot chart (with variable dot sizes) is nice for highlighting individual brands. But it doesn't show aggregates. One of the callouts on the chart reads: "Chinese cars' share rose by 23%, with BYD at the forefront". These words are necessary because it's impossible to figure out that the total share gain by all Chinese brands is 23% from this chart form.

They present this information in the line chart that I included in the last post, repeated here:

The first chart shows that cumulatively, Chinese brands have increased their share of the Chinese market by 23 percent while Japanese brands have ceded about 9 percent of market share.

The individual-brand view offers other insights that can't be found in the aggregate line chart. We can see that in addition to BYD, there are a few local brands that have similar market shares as Tesla.

***

It's tough to find a single chart that brings out insights at several levels of analysis, which is why we like to talk about a "visual story" which typically comprises a sequence of charts.

 

Fantastic auto show from the Bloomberg crew

I really enjoyed the charts in this Bloomberg feature on the state of Japanese car manufacturers in the Southeast Asian and Chinese markets (link). This article contains five charts, each of which is both engaging and well-produced.

***

Each chart has a clear message, and the visual display is clearly adapted for purpose.

The simplest chart is the following side-by-side stacked bar chart, showing the trend in share of production of cars:

Back in 1998, Japan was the top producer, making about 22% of all passenger cars in the world. China did not have much of a car industry. By 2023, China has dominated global car production, with almost 40% of share. Japan has slipped to second place, and its share has halved.

The designer is thoughtful about each label that is placed on the chart. If something is not required to tell the story, it's not there. Consistently across all five charts, they code Japan in red, and China in a medium gray color. (The coloring for the rest of the world is a bit inconsistent; we'll get to that later.)

Readers may misinterpret the cause of this share shift if this were the only chart presented to them. By itself, the chart suggests that China simply "stole" share from Japan (and other countries). What is true is that China has invested in a car manufacturing industry. A more subtle factor is that the global demand for cars has grown, with most of the growth coming from the Chinese domestic market and other emerging markets - and many consumers favor local brands. Said differently, the total market size in 2023 is much higher than that in 1998.

***

Bloomberg also made a chart that shows market share based on demand:

This is a small-multiples chart consisting of line charts. Each line chart shows market share trends in five markets (China and four Southeast Asian nations) from 2019 to 2024. Take the Chinese market for example. The darker gray line says Chinese brands have taken 20 percent additional market share since 2019; note that the data series is cumulative over the entire window. Meanwhile, brands from all other countries lost market share, with the Japanese brands (in red) losing the most.

The numbers are relative, which means that the other brands have not necessarily suffered declines in sales. This chart by itself doesn't tell us what happened to sales; all we know is the market shares of brands from different countries relative to their baseline market share in 2019. (Strange period to pick out as it includes the entire pandemic.)

The designer demonstrates complete awareness of the intended message of the chart. The lines for Chinese and Japanese brands were bolded to highlight the diverging fortunes, not just in China, but also in Southeast Asia, to various extents.

On this chart, the designer splits out US and German brands from the rest of the world. This is an odd decision because the categorization is not replicated in the other four charts. Thus, the light gray color on this chart excludes U.S. and Germany while the same color on the other charts includes them. I think they could have given U.S. and Germany their own colors throughout.

***

The primacy of local brands is hinted at in the following chart showing how individual brands fared in each Southeast Asian market:

 

This chart takes the final numbers from the line charts above, that is to say, the change in market share from 2019 to 2024, but now breaks them down by individual brand names. As before, the red bubbles represent Japanese brands, and the gray bubbles Chinese brands. The American and German brands are lumped in with the rest of the world and show up as light gray bubbles.

I'll discuss this chart form in a next post. For now, I want to draw your attention to the Malaysia market which is the last row of this chart.

What we see there are two dominant brands (Perodua, Proton), both from "rest of the world" but both brands are Malaysian. These two brands are the biggest in Malaysia and they account for two of the three highest growing brands there. The other high-growth brand is Chery, which is a Chinese brand; even though it is growing faster, its market share is still much smaller than the Malaysian brands, and smaller than Toyota and Honda. Honda has suffered a lot in this market while Toyota eked out a small gain.

The impression given by this bubble chart is that Chinese brands have not made much of a dent in Malaysia. But that would not be correct, if we believe the line chart above. According to the line chart, Chinese brands roughly earned the same increase in market share (about 3%) as "other" brands.

What about the bubble chart might be throwing us off?

It seems that the Chinese brands were starting from zero, thus the growth is the whole bubble. For the Malaysian brands, the growth is in the outer ring of the bubbles, and the larger the bubble, the thinner is the ring. Our attention is dominated by the bubble size which represents a snapshot in the ending year, providing no information about the growth (which is shown on the horizontal axis).

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For more discussion of Bloomberg graphics, see here.

Aligning V and Q by way of D

In the Trifecta Checkup (link), there is a green arrow between the Q (question) and V (visual) corners, indicating that they should align. This post illustrates what I mean by that.

I saw the following chart in a Washington Post article comparing dairy milk and plant-based "milks".

The article contains a whole series of charts. The one shown here focuses on vitamins.

The red color screams at the reader. At first, it appears to suggest that dairy milk is a standout on all four categories of vitamins. But that's not what the data say.

Let's take a look at the chart form: it's a grid of four plots, each containing one square for each of four types of "milk". The data are encoded in the areas of the squares. The red and green colors represent category labels and do not reflect data values.

Whenever we make bubble plots (the closest relative of these square plots), we have to solve a scale problem. What is the relationship between the scales of the four plots?

I noticed the largest square is the same size across all four plots. So, the size of each square is made relative to the maximum value in each plot, which is assigned a fixed size. In effect, the data encoding scheme is that the areas of the squares show the index values relative to the group maximum of each vitamin category. So, soy milk has 72% as much potassium as dairy milk while oat and almond milks have roughly 45% as much as dairy.

The same encoding scheme is applied also to riboflavin. Oat milk has the most riboflavin, so its square is the largest. Soy milk is 80% of oat, while dairy has 60% of oat.

***

Let's step back to the Trifecta Checkup (link). What's the question being asked in this chart? We're interested in the amount of vitamins found in plant-based milk relative to dairy milk. We're less interested in which type of "milk" has the highest amount of a particular vitamin.

Thus, I'd prefer the indexing tied to the amount found in dairy milk, rather than the maximum value in each category. The following set of column charts show this encoding:

I changed the color coding so that blue columns represent higher amounts than dairy while yellow represent lower.

From the column chart, we find that plant-based "milks" contain significantly less potassium and phosphorus than dairy milk while oat and soy "milks" contain more riboflavin than dairy. Almond "milk" has negligible amounts of riboflavin and phosphorus. There is vritually no difference between the four "milk" types in providing vitamin D.

***

In the above redo, I strengthen the alignment of the Q and V corners. This is accomplished by making a stop at the D corner: I change how the raw data are transformed into index values. 

Just for comparison, if I only change the indexing strategy but retain the square plot chart form, the revised chart looks like this:

The four squares showing dairy on this version have the same size. Readers can evaluate the relative sizes of the other "milk" types.

The curse of dimensions

Usually the curse of dimensions concerns data with many dimensions. But today I want to talk about a different kind of curse. This is the curse of dimensions in mapping.

We are only talking about a few dimensions, typically between 3 and 6, so small number of dimensions. And yet it's already a curse. Maps are typically drawn in two dimensions. Those two dimensions are usually spoken for: they show the x- and y-coordinate of space. If we want to include a third, fourth or fifth dimension of data on the map, we have to appeal to colors, shapes, and so on. Cartographers have long realized that adding dimensions involves tradeoffs.

***

Andrew featured some colored bubble maps in a recent post. Here is one example:

The above map shows the proportion of population in each U.S. county that is Hispanic. Each county is represented by a bubble pinned to the centroid of the county. The color of the bubble shows the data, divided into demi-deciles so they are using a equal-width binning method. The size of a bubble indicates the size of a county.

The map is sometimes called a "Dorling map" after its presumptive original designer.

I'm going to use this map to explore the curse of dimensions.

***

It's clear from the design that county-level details are regarded as extremely important. As there are about 3,000 counties in the U.S., I don't see how any visual design can satisfy this requirement without giving up clarity.

More details require more objects, which spread readers' attention. More details contain more stories, but that too dilutes their focus.

Another principle of this map is to not allow bubbles to overlap. Of course, having bubbles overlap or print on top of one another is a visual faux pas. But to prevent such behavior on this particular design means the precise locations are sacrificed. Consider the eastern seaboard where there are densely populated counties: they are not pinned to their centroids. Instead, the counties are pushed out of their normal positions, similar to making a cartogram.

I remarked at the start – erroneously but deliberately – that each bubble is centered at the centroid of each county. I wonder how many of you noticed the inaccuracy of that statement. If that rule were followed, then the bubbles in New England would have overlapped and overprinted. 

This tradeoff affects how we perceive regional patterns, as all the densely populated regions are bent out of shape.

Another aspect of the data that the designer treats as important is county population, or rather relative county population. Relative – because bubble size don't portray absolutes, plus the designer didn't bother to provide a legend to decipher bubble sizes.

The tradeoff is location. The varying bubble sizes, coupled with the previous stipulation of no overlapping, push bubbles from their proper centroids. This forced displacement disproportionately affects larger counties.

***

What if we are willing to sacrifice county-level details?

In this setting, we are not obliged to show every single county. One alternative is to perform spatial smoothing. Intuitively, think about the following steps: plot all these bubbles in their precise locations, turn the colors slightly transparent, let them overlap, blend away the edges, and then we have a nice picture of where the Hispanic people are located.

I have sacrificed the county-level details but the regional pattern becomes much clearer, and we don't need to deviate from the well-understood shape of the standard map.

This version reminds me of the language maps that Josh Katz made.

Here is an old post about these maps.

This map design only reduces but does not eliminate the geographical inaccuracy. It uses the same trick as the Dorling map: the "vertical" density of population has been turned into "horizontal" span. It's a bit better because the centroids are not displaced.

***

Which map is better depends on what tradeoffs one is making. In the above example, I'd have made different choices.

 

One final thing – it's minor but maybe not so minor. Most of the bubbles on the map especially in the middle are tiny; as most of them have Hispanic proportions that are on the left side of the scale, they should be showing light orange. However, all of them appear darker than they ought to be. That's because each bubble has a dark border. For small bubbles, the ratio of ink on the border is a high proportion of the ink for the entire object.

Two metrics in-fighting

The Wall Street Journal shows the following chart which pits two metrics against each other:

The primary metric is the change in median yearly salary between the two periods of time. We presume it's primary because of its presence in the chart title, and the blue bars being more readable than the green bubbles. The secondary metric is the median yearly salary in the later period.

That, I believe, was the intended design. When I saw this chart, my eyes went to the numbers inside the green bubbles. Perhaps it's because I didn't read the chart title first, and the horizontal axis wasn't labelled so it wasn't obvious what the blue bars coded.

As with most bubble charts, the data labels exist to cover up the inadequacy of circular areas. The self-sufficiency test - removing the data labels - shows this well:

It's simply impossible to know what values should be in each bubble, or to perceive the relative sizes of those bubbles.

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Reversing the order of the blue bars also helps:

The original order is one of the more annoying features in most visualization packages. Because internally, the categories are numbered 1, 2, 3, ..., and because the convention is to have values run higher as they run up the vertical axis, these packages would place the top-ranked item at the bottom of the chart.

Most people read top to bottom, which means that they read the least important item first, and the most important item last!

In most visualization packages, it takes only 1 click or 1 action to reverse the order of the items. Please do it!

***

For change over time, I like using a Bumps chart, otherwise called a slope graph:

What is the question is the question

I picked up a Fortune magazine while traveling, and saw this bag of bubbles chart.

This chart is visually appealing, that must be said. Each circle represents the reported revenues of a corporation that belongs to the “Global 500 Companies” list. It is labeled by the location of the company’s headquarters. The largest bubble shows Beijing, the capital of China, indicating that companies based in Beijing count $6 trillion dollars of revenues amongst them. The color of the bubbles show large geographical units; the red bubbles are cities in Greater China.

I appreciate a couple of the design decisions. The chart title and legend are placed on the top, making it easy to find one’s bearing – effective while non-intrusive. The labeling signals a layering: the first and biggest group have icons; the second biggest group has both name and value inside the bubbles; the third group has values inside the bubbles but names outside; the smallest group contains no labels.

Note the judgement call the designer made. For cities that readers might not be familiar with, a country name (typically abbreviated) is added. This is a tough call since mileage varies.

***

As I discussed before (link), the bag of bubbles does not elevate comprehension. Just try answering any of the following questions, which any of us may have, using just the bag of bubbles:

• What proportion of the total revenues are found in Beijing?
• What proportion of the total revenues are found in Greater China?
• What are the top 5 cities in Greater China?
• What are the ranks of the six regions?

If we apply the self-sufficiency test and remove all the value labels, it’s even harder to figure out what’s what.

***

Moving to the D corner of the Trifecta Checkup, we aren’t sure how to interpret this dataset. It’s unclear if these companies derive most of their revenues locally, or internationally. A company headquartered in Washington D.C. may earn most of its revenues in other places. Even if Beijing-based companies serve mostly Chinese customers, only a minority of revenues would be directly drawn from Beijing. Some U.S. corporations may choose its headquarters based on tax considerations. It’s a bit misleading to assign all revenues to one city.

As we explore this further, it becomes clear that the designer must establish a target – a strong idea of what question s/he wants to address. The Fortune piece comes with a paragraph. It appears that an important story is the spatial dispersion of corporate revenues in different countries. They point out that U.S. corporate HQs are more distributed geographically than Chinese corporate HQs, which tend to be found in the key cities.

There is a disconnect between the Question and the Data used to create the visualization. There is also a disconnect between the Question and the Visual display.

One bubble is a tragedy, and a bag of bubbles is...

From Kathleen Tyson's twitter account, I came across a graphic showing the destinations of Ukraine's grain exports since 2022 under the auspices of a UN deal. This graphic, made by AFP, uses one of the chart forms that baffle me - the bag of bubbles.

The first trouble with a bag of bubbles is the single bubble. The human brain is just not fit for comparing bubble sizes. The self-sufficiency test is my favorite device for demonstrating this weakness. The following is the European section of the above chart, with the data labels removed.

How much bigger is Spain than the Netherlands? What's the difference between Italy and the Netherlands? The answers don't come easily to mind. (The Netherlands is about 40% the size of Spain, and Italy is about 20% larger than the Netherlands.)

While comparing relative circular areas is a struggle, figuring out the relative ranks is not. Sure, it gets tougher with small differences (Germany vs S. Korea, Belgium vs Portugal) but saying those pairs are tied isn't a tragedy.

***

Another issue with bubble charts is how difficult it is to assess absolute values. A circle on its own has no reference point. The designer needs to add data labels or a legend. Adding data labels is an act of giving up. The data labels become the primary instrument for communicating the data, not the visual construct. Adding one data label is not enough, as the following shows:

Being told that Spain's value is 4.1 does little to help estimate the values for the non-labelled bubbles.

The chart does come with the following legend:

For this legend to work, the sample bubble sizes should span the range of the data. Notice that it's difficult to extrapolate from the size of the 1-million-ton bubble to 2-million, 4-million, etc. The analogy is a column chart in which the vertical axis does not extend through the full range of the dataset.

The designer totally gets this. The chart therefore contains both selected data labels and the partial legend. Every bubble larger than 1 million tons has an explicit data label. That's one solution for the above problem.

Nevertheless, why not use another chart form that avoids these problems altogether?

***

In Tyson's tweet, she showed another chart that pretty much contains the same information, this one from TASS.

This chart uses the flow diagram concept - in an abstract way, as I explained in previous post.

This chart form imposes structure on the data. The relative ranks of the countries within each region are listed from top to bottom. The relative amounts of grains are shown in black columns (and also in the thickness of the flows).

The aggregate value of movements within each region is called out in that middle section. It is impossible to learn this from the bag of bubbles version.

The designer did print the entire dataset onto this chart (except for the smallest countries grouped together as "other"). This decision takes away from the power of the underlying flow chart. Instead of thinking about the proportional representation of each country within its respective region, or the distribution of grains among regions, our eyes hone in on the data labels.

This brings me back to the principle of self-sufficiency: if we expect readers to consume the data labels - which comprise the entire dataset, why not just print a data table? If we decide to visualize, make the visual elements count!

Showing both absolute and relative values on the same chart 1

Visual Capitalist has a helpful overview on the "uninsured" deposits problem that has become the talking point of the recent banking crisis. Here is a snippet of the chart that you can see in full at this link:

This is in infographics style. It's a bar chart that shows the top X banks. Even though the headline says "by uninsured deposits", the sort order is really based on the proportion of deposits that are uninsured, i.e. residing in accounts that exceed $250K.  They used a red color to highlight the two failed banks, both of which have at least 90% of deposits uninsured.

The right column provides further context: the total amounts of deposits, presented both as a list of numbers as well as a column of bubbles. As readers know, bubbles are not self-sufficient, and if the list of numbers were removed, the bubbles lost most of their power of communication. Big, small, but how much smaller?

There are little nuggets of text in various corners that provide other information.

Overall, this is a pretty good one as far as infographics go.

***

I'd prefer to elevate information about the Too Big to Fail banks (which are hiding in plain sight). Addressing this surfaces the usual battle between relative and absolute values. While the smaller banks have some of the highest concentrations of uninsured deposits, each TBTF bank has multiples of the absolute dollars of uninsured deposits as the smaller banks.

Here is a revised version:

The banks are still ordered in the same way by the proportions of uninsured value. The data being plotted are not the proportions but the actual deposit amounts. Thus, the three TBTF banks (Citibank, Chase and Bank of America) stand out of the crowd. Aside from Citibank, the other two have relatively moderate proportions of uninsured assets but the sizes of the red bars for any of these three dominate those of the smaller banks.

Notice that I added the gray segments, which portray the amount of deposits that are FDIC protected. I did this not just to show the relative sizes of the banks. Having the other part of the deposits allow readers to answer additional questions, such as which banks have the most insured deposits? They also visually present the relative proportions.

***

The most amazing part of this dataset is the amount of uninsured money. I'm trying to think who these account holders are. It would seem like a very small collection of people and/or businesses would be holding these accounts. If they are mostly businesses, is FDIC insurance designed to protect business deposits? If they are mostly personal accounts, then surely only very wealthy individuals hold most of these accounts.

In the above chart, I'm assuming that deposits and assets are referring to the same thing. This may not be the correct interpretation. Deposits may be only a portion of the assets. It would be strange though that the analysts only have the proportions but not the actual deposit amounts at these banks. Nevertheless, until proven otherwise, you should see my revision as a sketch - what you can do if you have both the total deposits and the proportions uninsured.

Lay off bubbles

Wall Street Journal says that the scale of layoffs in the tech industry recently is worse than those caused by the pandemic lockdown. Here is the chart:

It's the dreaded bubble chart, complete with overlapping circles. Each bubble represents the total number of employees laid off in the U.S. in a given month.

The above isn't really the chart you find in the Journal. I have removed the two data labels from the chart. Look at the highlighted months of April 2020 and November 2022. Can you guess how much larger is the number of laid-off employees in November 2022 relative to April 2020?

***

If you guessed it's 100% - that the larger bubble is twice the size of the smaller one, then you're much better than I at reading bubble charts. Here is the published chart with the data labels:

I like to run this exercise - removing data labels - in order to reveal whether the graphical elements on the page are sufficient to convey the underlying data. Bubbles are typically not great at this. (This is what I call the self-sufficiency test.)

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Another problem with bubble charts is that the sizes of the bubbles are arbitrary. This allows the designer to convey different messages with the same data.

Take a look at these two bubble charts:

The first one has huge bubbles, and lots of overlapping while the second one is roughly the same as the WSJ chart (I pulled a different dataset so the numbers may not be exactly the same).

Both charts are made from exactly the same data! In the second chart, the smallest bubbles are made very small while in the first chart, the smallest bubbles are still quite large.

Think twice before you make a bubble chart.