The canonical U.S. political map

The previous posts feature the canonical political map of U.S. presidential elections, the vote margin shift map. The following realization of it, made by NBC News (link), drills down to the counties with the largest Asian-American populations:

How does this map form encode the data?

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The key visual element is the arrow. The arrow has a color, a length and also an angle.

The color scheme is fixed to the canonical red-blue palette attached to America's two major political parties.

The angle of the arrow, as seen in the legend, carries no data at all. All arrows are slanted at the same angles. Not quite; the political party is partially encoded into the angle, as the red arrows slant one way while the blue arrows always slant the other way. The degree of slant is constant everywhere, though.

So only the lengths of the arrows contain the vote margin gain/loss data. The legend shows arrows of two different lengths but vote margins have not been reduced to two values. As evident on the map, the arrow lengths are continuous.

The designer has a choice when it comes to assigning colors to these arrows. The colors found on the map above depicts the direction of the vote margin shift so red arrows indicate counties in which the Republicans gained share. (The same color encoding is used by the New York Times.)

Note that a blue county could have shifted to the right, and therefore appear as a red arrow even though the county voted for Kamala Harris in 2024. Alternatively, the designer could have encoded the 2024 vote margin in the arrow color. While this adds more data to the map, it could wreak havoc with our perception as now all four combinations are possible: red, pointing left; red, pointing right; blue, pointing left; and blue, pointing right.

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To sum this all up, the whole map is built from a single data series, the vote margin shift expressed as a positive or negative percentage, in which a positive number indicates Republicans increased the margin. The magnitude of this data is encoded in the arrow length, ignoring the sign. The sign (direction) of the data, a binary value, is encoded into the arrow color as well as the direction of the arrow.

In other words, it's a proportional symbol map in which each geographical region is represented by a symbol (typically a bubble), and a single numeric measure is encoded in the size of the symbol. In many situations, the symbol's color is used to display a classification of the geographical regions.

The symbol used for the "wind map" are these slanted arrows. The following map, pulled from CNN (link), makes it clear that the arrows play only the role of a metaphor, the left-right axis of political attitude.

This map is essentially the same as the "wind map" used by the New York Times and NBC News, the key difference being that instead of arrows, the symbol is a triangle. On proportional triangle maps, the data is usually encoded in the height of the triangles, so that the triangles can be interpreted as "hills". Thus, the arrow length in the wind map is the hill height in the triangle map. The only thing left behind is the left-right metaphor.

The CNN map added a detail. Some of the counties have a dark gray color. These are "flipped". A flip is defined as a change in "sign" of the vote margin from 2020 to 2024. A flipped county can exhibit either a blue or a red hill. The direction of the flip is actually constrained by the hill color. If it's a red hill, we know there is a shift towards Republicans, and in addition, the county flipped, it must be that Democrats won that county in 2020, and it flipped to Republicans. Similiar, if a blue hill sits on a dark gray county, then the county must have gone for Republicans in 2020 and flipped to Democrats in 2024.

 

Gaining precision by deleting data

The title is a bit of a paradox, isn't it? When we want more precise knowledge about something, we want to gather more data, at greater granularity. But it's not that simple.

Here is the famous "wind map" by the New York Times (link) showing vote margin shifts in the U.S. Presidential elections from 2020 to 2024, at the county level. A red arrow pointing rightward indicates a county in which the voters shifted toward the Republican candidate (Trump). It paints the red wave story loud and clear.

Even though every county is on the map, this map alone doesn't answer all possible questions about vote margin shift. For example, someone might be interested in the vote margin shift in counties with high Hispanic populations. It's impossible to learn this from the above map, even if one has a list of the names of these counties.

The answer is found in the following map, published by NBC News here:

The story is also very clear. This map can be thought of as the NYT map minus the counties that have negligible Hispanic populations. By deleting all unrelated data, the designer highlights the story about Hispanic voters.

The reader can use the tab up top to see partial shift maps that emphasize different demographic groups. Well done!

 

 

 

 

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.

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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.

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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.

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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.

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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.

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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.

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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.

the wtf moment

You're reading some article that contains a standard chart. You're busy looking for the author's message on the chart. And then, the wtf moment strikes.

It's the moment when you discover that the chart designer has done something unexpected, something that changes how you should read the chart. It's when you learn that time is running right to left, for example. It's when you realize that negative numbers are displayed up top. It's when you notice that the columns are ordered by descending y-value despite time being on the x-axis.

Tell me about your best wtf moments!

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The latest case of the wtf moment occurred to me when I was reading Rajiv Sethi's blog post on his theory that Kennedy voters crowded out Cheney voters in the 2024 Presidential election (link). Was the strategy to cosy up to Cheney and push out Kennedy wise?

In the post, Rajiv has included this chart from Pew:

The chart is actually about the public's confidence in scientists. Rajiv summarizes the message as: 'Public confidence in scientists has fallen sharply since the early days of the pandemic, especially among Republicans. There has also been a shift among Democrats, but of a slightly different kind—the proportion with “a great deal” of trust in scientists to act in our best interests rose during the first few months of the pandemic but has since fallen back.'

Pew produced a stacked column chart, with three levels for each demographic segment and month of the survey. The question about confidence in scientists admits three answers: a great deal, a fair amount, and not too much/None at all. [It's also possible that they offered 4 responses, with the bottom two collapsed as one level in the visual display.]

As I scan around the chart understanding the data, suddenly I realized that the three responses were not listed in the expected order. The top (light blue) section is the middling response of "a fair amount", while the middle (dark blue) section is the "a great deal" answer.

wtf?

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Looking more closely, this stacked column chart has bells and whistles, indicating that the person who made it expended quite a bit of effort. Whether it's worthwhile effort, it's for us readers to decide.

By placing "a great deal" right above the horizon, the designer made it easier to see the trend in the proportion responding with "a great deal". It's also easy to read the trend of those picking the "negative" response because of how the columns are anchored. In effect, the designer is expressing the opinion that the middle group (which is also the most popular answer) is just background, and readers should not pay much attention to it.

The designer expects readers to care about one other trend, that of the "top 2 box" proportion. This is why sitting atop the columns are the data labels called "NET" which is the sum of those responding "a great deal" or "a fair amount".

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For me, it's interesting to know whether the prior believers in science who lost faith in science went down one notch or two. Looking at the Republicans, the proportion of "a great deal" roughly went down by 10 percentage points while the proportion saying "Not too much/None at all" went up about 13%. Thus, the shift in the middle segment wasn't enough to explain all of the jump in negative sentiment; a good portion went from believer to skeptic during the pandemic.

As for Democrats, the proportion of believers also dropped by about 10 percentage points while the proportion saying "a fair amount" went up by almost 10 percent, accounting for most of the shift. The proportion of skeptics increased by about 2 percent.

So, for Democrats, I'm imagining a gentle slide in confidence that applies to the whole distribution while for Republicans, if someone loses confidence, it's likely straight to the bottom.

If I'm interested in the trends of all three responses, it's more effective to show the data in a panel like this:

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Remember to leave a comment when you hit your wtf moment next time!

 

Dizziness

Statista uses side-by-side stacked column charts to show the size of different religious groups in the world:

It's hard to know where to look. It's so colorful and even the middle section is filled in whereas the typical such chart would only show guiding lines.

What's more, the chart includes gridlines, as well as axis labels.

The axis labels compete with the column section labels, the former being cumulative while the latter isn't.

The religious groups are arranged horizontally in two rows at the top while they are stacked up from bottom to top inside the columns.

The overall effect is dizzying.

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The key question this chart purportedly address is the change in the importance of religions over the time frame depicted.

Look at the green sections in the middle of the chart, signifying "Unaffiliated" people. The change between the two time points is 16 vs 13 which is -3 percent.

Where is this -3 percent encoded?

It's in the difference in height between the two green blocks. On this design, that's a calculation readers have to do themselves.

One might take the slope of the guiding line that links the tops of the green blocks as indicative of the change, but it's not. In fact the top guiding line slopes upwards, implying an increase over time. That increase is associated with the cumulative total of the top three religious groups, not the share of the Unaffiliated group.

So, if we use those guiding lines, we have to take the difference of two lines, not just the top line. The line linking the bottoms of the green blocks is also relevant. However, the top and bottom lines will in general not be parallel, so readers have to somehow infer from the parallelogram bounded by the guiding lines and vertical block edges that the change in the Unaffiliated group is 3 percent.

Ouch.

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I generally like to use Bumps charts (also called slopegraphs) to show change across two points in time:

What's sacrificed is the cumulation of percentages. I also am pleased that Christian and Muslim, where the movements are greatest, are found at the top of the chart. (There isn't a need to use so many colors; I just inherited them from the original chart.)

Election coverage prompts good graphics

The election broadcasts in the U.S. are full-day affairs, and they make a great showcase for interactive graphics.

The election setting is optimal as it demands clear graphics that are instantly digestible. Anything else would have left viewers confused or frustrated.

The analytical concepts conveyed by the talking heads during these broadcasts are quite sophisticated, and they did a wonderful job at it.

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One such concept is the value of comparing statistics against a benchmark (or, even multiple benchmarks). This analytics tactic comes in handy in the 2024 election especially, because both leading candidates are in some sense incumbents. Kamala was part of the Biden ticket in 2020, while Trump competed in both 2016 and 2020 elections.

In the above screenshot, taken around 11 pm on election night, the MSNBC host (that looks like Steve K.) was searching for Kamala votes because it appeared that she was losing the state of Georgia. The question of the moment: were there enough votes left for her to close the gap?

In the graphic (first numeric column), we were seeing Kamala winning 65% of the votes, against Trump's 34%, in Douglas county in Georgia. At first sight, one would conclude that Kamala did spectacularly well here.

But, is 65% good enough? One can't answer this question without knowing past results. How did Biden-Harris do in the 2020 election when they won the presidency?

The host touched the interactive screen to reveal the second column of numbers, which allows viewers to directly compare the results. At the time of the screenshot, with 94% of the votes counted, Kamala was performing better in this county than they did in 2020 (65% vs 62%). This should help her narrow the gap.

If in 2020, they had also won 65% of the Douglas county votes, then, we should not expect the vote margin to shrink after counting the remaining 6% of votes. This is why the benchmark from 2020 is crucial. (Of course, there is still the possibility that the remaining votes were severely biased in Kamala's favor but that would not be enough, as I'll explain further below.)

All stations used this benchmark; some did not show the two columns side by side, making it harder to do the comparison.

Interesting side note: Douglas county has been rapidly shifting blue in the last two decades. The proportion of whites in the county dropped from 76% to 35% since 2000 (link).

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Though Douglas county was encouraging for Kamala supporters, the vote gap in the state of Georgia at the time was over 130,000 in favor of Trump. The 6% in Douglas represented only about 4,500 votes (= 70,000*0.06/0.94). Even if she won all of them (extremely unlikely), it would be far from enough.

So, the host flipped to Fulton county, the most populous county in Georgia, and also a Democratic stronghold. This is where the battle should be decided.

Using the same format - an interactive version of a small-multiples arrangement, the host looked at the situation in Fulton. The encouraging sign was that 22% of the votes here had not yet been counted. Moreover, she captured 73% of those votes that had been tallied. This was 10 percentage points better than her performance in Douglas, Ga. So, we know that many more votes were coming in from Fulton, with the vast majority being Democratic.

But that wasn't the full story. We have to compare these statistics to our 2020 benchmark. This comparison revealed that she faced a tough road ahead. That's because Biden-Harris also won 73% of the Fulton votes in 2020. She might not earn additional votes here that could be used to close the state-wide gap.

If the 73% margin held to the end of the count, she would win 90,000 additional votes in Fulton but Trump would win 33,000, so that the state-wide gap should narrow by 57,000 votes. Let's round that up, and say Fulton halved Trump's lead in Georgia. But where else could she claw back the other half?

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From this point, the analytics can follow one of two paths, which should lead to the same conclusion. The first path runs down the list of Georgia counties. The second path goes up a level to a state-wide analysis, similar to what was done in my post on the book blog (link).

Around this time, Georgia had counted 4.8 million votes, with another 12% outstanding. So, about 650,000 votes had not been assigned to any candidate. The margin was about 135,000 in Trump's favor, which amounted to 20% of the outstanding votes. But that was 20% on top of her base value of 48% share, meaning she had to claim 68% of all remaining votes. (If in the outstanding votes, she got the same share of 48% as in the already-counted, then she would lose the state with the same vote margin as currently seen, and would lose by even more absolute votes.)

The reason why the situation was more hopeless than it even sounded here is that the 48% base value came from the 2024 votes that had been counted; thus, for example, it included her better-than-benchmark performance in Douglas county. She would have to do even better to close the gap! In Fulton, which has the biggest potential, she was unable to push the vote share above the 2020 level.

That's why in my book blog (link), I suggested that the networks could have called Georgia (and several other swing states) earlier, if they used "numbersense" rather than mathematical impossibility as the criterion.

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Before ending, let's praise the unsung heroes - the data analysts who worked behind the scenes to make these interactive graphics possible.

The graphics require data feeds, which cover a broad scope, from real-time vote tallies to total votes casted, both at the county level and the state level. While the focus is on the two leading candidates, any votes going to other candidates have to be tabulated, even if not displayed. The talking heads don't just want raw vote counts; in order to tell the story of the election, they need some understanding of how many votes are still to be counted, where they are coming from, what's the partisan lean on those votes, how likely is the result going to deviate from past elections, and so on.

All those computations must be automated, but manually checked. The graphics software has to be reliable; the hosts can touch any part of the map to reveal details, and it's not possible to predict all of the user interactions in advance.

Most importantly, things will go wrong unexpectedly during election night so many data analysts were on standby, scrambling to fix issues like breakage of some data feed from some county in some state.

Criminal graphics graphical crime

One of my Twitter followers disliked the following chart showing FBI crime statistics for 2023 (link):

If read quickly, the clear message of the chart is that something spiked on the right side of the curve.

But that isn't the message of the chart. The originator applied this caption: "The age-crime curve last year looked pretty typical. How about this year? Same as always. Victims and offenders still have highly similar, relatively young ages."

So the intended message is that the blue and the red lines are more or less the same.

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What about the spike on the far right? 

If read too quickly, one might think that the oldest segment of Americans went on a killing spree last year. One must read the axis label to learn that elders weren't committing more homicides, but what spiked were murderers with "unknown" age.

A quick fix of this is to plot the unknowns as a column chart on the right, disconnecting it from the age distribution. Like this:

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This spike in unknowns appears consequential: the count is over 2,000, larger than the numbers for most age groups.

Curiously, unknowns in age spiked only for offenders but not victims. So perhaps those are unsolved cases, for which the offender's age is unknown but the victim's age is known.

If that hypothesis is correct, then the same pattern will be seen year upon year. I checked this in the FBI database, and found that every year about 2,000 offenders have unknown age.

In other words, the unknowns cannot be the main story here. Instead of dominating our attention, it should be pushed to the background, e.g. in a footnote.

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Next, because the amount of unknowns is so different between the offenders and victims, comparing two curves of counts is problematic. Such a comparison is based on the assumption that there are similar total numbers of offenders and victims. (There were in fact 5% more offenders than there were victims in 2023.)

The red and blue lines are not as similar as one might think.

Take the 40-49 age group. The blue value is 1,746 while the red value is 2,431, a difference of 685, which is 40 percent of 1,746! If we convert each to proportions, ignoring unknowns, the blue value is 12% compared to the red value of 15%, a difference of 3% which is a quarter of 12%.

By contrast, in the 10-19 age group, the blue value is 3,101 while the red value is 2,147, a difference of about 1,000, which is a third of 3,101. Converted to proportions, ignoring unknowns, the blue value is 21% compared to the red value of 13%, a difference of 8% which is almost 40% of 21%.

It's really hard to argue that these age distributions are "similar".

As seen from the above, offenders are much more likely to be younger (10-29 years old) than victims, and they are also much more likely to be 90+! Meanwhile, the victims are more likely to be 60-89.

 

 

 

 

 

 

 

Book review: Getting (more out of ) Graphics by Antony Unwin

Antony Unwin, a statistics professor at Augsburg, has published a new dataviz textbook called "Getting (more out of) Graphics", and he kindly sent me a review copy. (Amazon link)

I am - not surprisingly - in the prime audience for such a book. It covers some gaps in the market:
a) it emphasizes exploratory graphics rather than presentation graphics
b) it deals not just with designing graphics but also interpreting (i.e. reading) them
c) it covers data pre-processing and data visualization in a more balanced way
d) it develops full case studies involving multiple graphics from the same data sources

The book is divided into two parts: the first, which covers 75% of the materials, details case studies, while the final quarter of the book offers "advice". The book has a github page containing R code which, as I shall explain below, is indispensable to the serious reader.

Given the aforementioned design, the case studies in Unwin's book have a certain flavor: most of the data sets are relatively complex, with many variables, including a time component. The primary goal of Unwin's exploratory graphics can be stated as stimulating "entertaining discussions" about and "involvment" with the data. They are open-ended, and frequently inconclusive. This is a major departure from other data visualization textbooks on the market, and also many of my own blog posts, where we focus on selecting a good graphic for presenting insights visually to an intended audience, without assuming domain expertise.

I particularly enjoyed the following sections: a discussion of building graphs via "layering" (starting on p. 326), enumeration of iterative improvement to graphics (starting on p. 402), and several examples of data wrangling (e.g. p.52).

Unwin does not give "advice" in the typical style of do this, don't do that. His advice is fashioned in the style of an analyst. He frames and describes the issues, shows rather than tells. This paragraph from the section about grouping data is representative:

Sorting into groups gets complicated when there are several grouping variables. Variables may be nested in a hierarchy... or they may have no such structure... Groupings need to be found that reflect the aims of the study. (p. 371)

He writes down what he has done, may provide a reason for his choices, but is always understated. He sees no point in selling his reasoning.

The structure of the last part of the book, the "advice" chapters, is quite unusual. The chapter headers are: (data) provenance and quality; wrangling; colour; setting the scene (scaling, layout, etc.); ordering, sorting and arranging; what affects interpretation; and varieties of plots.

What you won't find are extended descriptions of chart forms, rules of visualization, or flowcharts tying data types to chart forms. Those are easily found online if you want them (you probably won't care if you're reading Unwin's book.)

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For the serious reader, the book should be consumed together with the code on github. Find specific graphs from the case studies that interest you, open the code in your R editor, and follow how Unwin did it. The "advice" chapters highlight points of interest from the case studies presented earlier so you may start there, cross-reference the case studies, then jump to the code.

Unfortunately, the code is sparsely commented. So also open up your favorite chatbot, which helps to explain the code, and annotate it yourself. Unwin uses R, and in particular, lives in the "tidyverse".

To understand the data manipulation bits, reviewing the code is essential. It's hard to grasp what is being done to the data without actually seeing the datasets. There are no visuals of the datasets in the book, as the text is primarily focused on the workflow leading to a graphic. The data processing can get quite involved, such as Chapter 16.

I'm glad Unwin has taken the time to write this book and publish the code. It rewards the serious reader with skills that are not commonly covered in other textbooks. For example, I was rather amazed to find this sentence (p. 366):

To ensure that a return to a particular ordering is always possible, it is essential to have a variable with a unique value for every case, possibly an ID variable constructed for just this reason. Being able to return to the initial order of a dataset is useful if something goes wrong (and something will).

Anyone who has analyzed real-world datasets would immediately recognize this as good advice but who'd have thought to put it down in a book?

Visualizing extremes

The New York Times published this chart to illustrate the extreme ocean heat driving the formation of hurricane Milton (link):

The chart expertly shows layers of details.

The red line tracks the current year's data on ocean heat content up to yesterday.

Meaning is added through the gray line that shows the average trajectory of the past decade. With the addition of this average line, we can readily see how different the current year's data is from the past. In particular, we see that the current season can be roughly divided into three parts: from May to mid June, and also from August to October, the ocean heat this year was quite a bit higher than the 10-year average, whereas from mid June to August, it was just above average.

Even more meaning is added when all the individual trajectories from the last decade are shown (in light gray lines). With this addition, we can readily learn how extreme is this year's data. For the post-August period, it's clear that the Gulf of Mexico is the hottest it's been in the past decade. Also, this extreme is not too far from the previous extreme. On the other hand, the extreme in late May-early June is rather more scary.