I made a streamgraph

The folks at FiveThirtyEight were excited about the following dataviz they published last week two weeks ago, illustrating the progression of vote-counting by state. (link) That was indeed the unique and confusing feature of the 2020 Presidential election in the States. For those outside the U.S., what happened (by and large) was that many Americans, skewing Biden supporters, voted by mail before Election Day but their votes were sometimes counted after the same-day votes were tallied.

 

538_votetalliesovertimemap

A number of us kept staring at these charts, hoping for a how-to-read-it explanation. Here is a zoom-in for the state of Michigan:

538leadchanges_michigan

To save you the trouble, here is how.

The key is to fight your urge to look at the brown area. I know, it's pretty hard to ignore the biggest areas of every chart. But try to make them disappear.

Focus on the top edge of the chart. This line gives the total number of votes counted so far. In Michigan, by hour 12, about 2.4 million votes were counted, and by hour 72, 2.8 million votes were on the book. This line gives the sum of the two major parties' vote totals [since third parties got negligible votes in this election, I'm ignoring them so as to simplify the discussion].

Next, look at the red and blue areas. These represent the gap in the number of votes between the two parties' current vote totals. If the area is red, Trump was leading; if blue, Biden was leading. Each color flip represents a lead change. Suppress the urge to interpret red as the number or share of Trump votes.

***

What have we learned about the vote counting in Michigan?

Counting significantly slowed after the 12th hour. Trump raced to a lead on Election Day, and around hour 20, the race was dead even, and after that, Biden overtook Trump and never looked back. Throughout most of this period, the vote lead was small compared to the total votes cast although at the end, the Biden lead was noticeable.

If you insist on interpreting the brown area, it is equal to twice the vote total of the second-place candidate, so it really isn't something you want to look at.

Just for contrast, here is the chart for Iowa:

538leadchanges_iowa

Trump led from beginning to end, with his lead widening slightly as more votes were counted.

***

As I was stewing over this chart, a ominous thought overcame me. Would a streamgraph work for this data? You don't hear much about streamgraphs here because I rarely favor them (see this long-ago post) but let's just try one and see.

Junkcharts_redo_538leadchange_mi_ia

(These streamgraphs were made in R using the streamgraph package. Post-processing was applied to customize the labeling.)

This chart conveys all the key points listed before. You can see how the gap evolved over time, the lead flips, which candidate was in the lead, and the total mass of votes counted at different times. The gap is shown in the middle.

I can't say I'm completely happy with the streamgraph - I hope readers don't care about the numbers because it's hard to evaluate a difference when it's split two ways on either side of the middle axis!

***

If you come up with a better idea, make sure to leave a comment.

 

 

 

 


Election visuals 4: the snake pit is the best election graphic ever

This is the final post on the series of data visualization deployed by FiveThirtyEight to explain their election forecasting model. The previous posts are here, here and here.

I'm saving the best for last.

538_snakepit

This snake-pit chart brings me great joy - I wish I came up with it!

This chart wins by focusing on a limited set of questions, and doing so excellently. As with many election observers, we understand that the U.S. presidential election will turn on so-called "swing states," and the candidates' strength in these swing states are variable, as the name suggests. Thus, we like to know which states are in play, and within these states, which ones are most unpredictable.

This chart lines up all the states from the reddest of red up top to the bluest of blue at the bottom. Each state is ranked by the voting margin predicted by 538's election forecasting model. The swing states are found in the middle.

Since each state confers a fixed number of electoral votes, and a candidate must amass 270 to win, there is a "tipping" state. In the diagram above, it's Pennsylvania. This pivotal state is neatly foregrounded as the one crossing the line in the middle.

The lengths of the segments correspond to the number of electoral votes and so do not change with the data. What change are the sequencing of the segments, and the color shading.

This data visualization is a gem of visual story-telling. The form lends itself to a story.

***

The snake-pit chart succeeds by not doing too much. There are many items that the chart does not directly communicate.

The exact number of electoral votes by state is not explicit, nor is it easy to compare the lengths of bending segments. The color scale for conveying the predicted voting margins is crude, and it's not clear what is the difference between a deep color and a light color. It's also challenging to learn the electoral vote split; the actual winning margin is not even stated.

The reality is the average reader doesn't care. I got everything I wanted from the chart, and I ain't got the time to explore every state.

There is a hover-over effect that reveals some of the additional information:

538_snakepitchart_detail

One can keep going on. I have no idea how the 40,000 scenarios presented in the other graphics in this series have been reduced to the forecast shown in the inset. But again, those omissions did not lessen my enjoyment. The point is: let your graphics breathe.

***

I'm thinking of potential variations even though I'm fully satisfied with this effort.

I wonder if the color shading should be reversed. The light shading encodes a smaller voting margin, which indicates a tighter race. But our attention is typically drawn first to the darker shades. If the shading scheme is reversed, the color should be described as how tight the race is.

I also wonder if a third color (purple) should be introduced. Doing so would require the editors to make judgment calls on which set of states are swing states.

One strange thing about election day is the specific sequence of when TV stations (!) call the state results, which not only correlates with voting margin but also with time zones. I wonder if the time zone information can be worked into the sequencing of segments.

Let me know what you think of these ideas, or leave your own ideas, in the comments below.

***

I have already praised this graphic when it first came out in 2016. (link)

A key improvement is tilting the chart, which avoids vertical state labels.

The previous post was written around election day 2016. The snake pit further cements its status as a story-telling device. As states are called, they are taken out of the picture. So it works very well as a dynamic chart on election day.

I'm nominating this snake-pit chart as the best election graphic ever. Kudos to the FiveThirtyEight team.


Ask how you can give

A reader and colleague Georgette A was frustrated with the following graphic that appeared in the otherwise commendable article in National Geographic (link). The NatGeo article provides a history lesson on past pandemics that killed millions.

Natgeo_pandemichistory

What does the design want to convey to readers?

Our attention is drawn to the larger objects, the red triangle on the left or the green triangle on the right. Regarding the red triangle, we learn that the base is the duration of the pandemic while the height of the black bar represents the total deaths.

An immediate curiosity is why a green triangle is lodged in the middle of the red triangle. Answering this question requires figuring out the horizontal layout. Where we expect axis labels we find an unexpected series of numbers (0, 16, 48, 5, 2, 4, ...). These are durations that measure the widths of the triangular bases.

To solve this puzzle, imagine the chart with the triangles removed, leaving just the black columns. Now replace the durations with index numbers, 1 to 13, corresponding to the time order of the ending years of these epidemics. In other words, there is a time axis hidden behind the chart. [As Ken reminded me on Twitter, I forgot to mention that details of each pandemic are revealed by hovering over each triangle.]

This explains why the green triangle (Antonine Plague) is sitting inside the large red triangle (Plague of Justinian). The latter's duration is 3 times that of the former, and the Antonine Plague ended before the Plague of Justinian. In fact, the Antonine occurred during 165-180 while the Justinian happened during 541-588. The overlap is an invention of the design. To receive what the design gives, we have to think of time as a sequence, not of dates.

***

Now, compare the first and second red triangles. Their black columns both encode 50 million deaths. The Justinian Plague however was spread out over 48 years while the Black Death lasted just 5 years. This suggests that the Black Death was more fearsome than the Justinian Plague. And yet, the graphic presents the opposite imagery.

This is a pretty tough dataset to visualize. Here is a side-by-side bar chart that lets readers first compare deaths, and then compare durations.

Redo_natgeo_pandemichistory

In the meantime, I highly recommend the NatGeo article.


Twitter people UpSet with that Covid symptoms diagram

Been busy with an exciting project, which I might talk about one day. But I promised some people I'll follow up on Covid symptoms data visualization, so here it is.

After I posted about the Venn diagram used to depict self-reported Covid-19 symptoms by users of the Covid Symptom Tracker app (reported by Nature), Xan and a few others alerted me to Twitter discussion about alternative visualizations that people have made after they suffered the indignity of trying to parse the Venn diagram.

To avoid triggering post-trauma, for those want to view the Venn diagram, please click here.

[In the Twitter links below, you almost always have to scroll one message down - saving tweets, linking to tweets, etc. are all stuff I haven't fully figured out.]

Start with the Questions

Xan’s final comment is especially appropriate: "There's an over-riding Type-Q issue: count charts answer the wrong question".

As dataviz designers, we frequently get locked into the mindset of “what is the best way to present this dataset?” This line of thinking leads to overloaded graphics that attempt to answer every possible question that may arise from the data in one panoptic chart, akin to juggling 10 balls at once.

For complex datasets, it is often helpful to narrow down the list of questions, and provide a series of charts, each addressing one or two questions. I’ll come back to this point. I want to first show some of the nicer visuals that others have produced, which brings out the structure and complexity of this dataset.

 

The UpSet chart

The primary contender is the “UpSet” chart form, as best exemplified by Bart’s effort

Upset_bartjutte

The centerpiece of this chart is the matrix of dots. The horizontal rows of dots represent the presence of specific symptoms such as cough and anosmia (loss of smell and taste). The vertical columns are intuitive, once you get it. They represent combinations of symptoms, and the fill/no-fill of the dots indicates which symptoms are being combined. For example, the first column counts people reporting fatigue plus anosmia (but nothing else).

The UpSet chart clearly communicates the structure of the data. In many survey questions (including this one conducted by the Symptom Tracker app), respondents are allowed to check/tick more than one answer choices. This creates a situation where the number of answers (here, symptoms) per respondent can be zero up to the total number of answer choices.

So far, we have built a structure like we have drawn country outlines on a map. There is no data yet. The data are primarily found in the sidebar histograms (column/bar charts). Reading horizontally to the right side, one learns that the most frequently reported symptom was fatigue, covering 88 percent of the users.* Reading vertically, one learns that the top combination of symptoms was fatigue plus anosmia, covering 16 percent of users.

***

Now come the divisive acts.

Act 1: Bart orders the columns in a particular way that meets his subjective view of how he wants readers to see the data. The columns are sorted from the most frequent combinations to the least. The histogram has a “long tail”, with most of the combinations receiving a small proportion of the total. The top five combinations is where the bulk of the data is – I’d have liked to see all five columns labeled, without decimal places.

This is a choice on the part of the designer. Nils, for example, made two versions of his UpSet charts. The second version arranges the combinations from singles to quintuples.

Nils Gehlenborg_upsetplot_sortedbynumberofsymptoms

 

Digression: The Visual in Data Visualization

The two rendering of “UpSet” charts, by Nils and Bart, is a perfect illustration of the Trifecta Checkup framework. Each corner of the Trifecta is an independent dimension, and yet all must sync. With the same data and the same question types, what differentiates the two versions is the visual design.

See how many differences you can find, and make your own design choices!

 

I place the digression here because Act 1 above has to do with the Q corner, and both visual designs can accommodate the sorting decisions. But Act 2 below pertains to the V corner.

Act 2: Bart applies a blue gradient to the matrix of dots that reinforces his subjective view about identifying frequent combinations of symptoms. Nils, by contrast, uses the matrix to show present/absent only.

I’m not sure about Act 2. I think the addition of the color gradient overloads the matrix in the chart. It has the nice effect of focusing the reader’s attention on the top 5 combinations but it also requires the reader to have understood the meaning of columns first. Perhaps applying the gradient to the histogram up top rather than the dots in the matrix can achieve the same goal with less confusion.

 

Getting Obtuse

For example, some readers (e.g. Robin) expressed confusion.

Robin is alleging something the chart doesn’t do. He pointed out (correctly) that while 16 percent experienced fatigue and anosmia only (without other symptoms), more than 50 percent reported fatigue and anosmia, plus other symptoms. That nugget of information is deeply buried inside Bart’s chart – it’s the sum of each column for which the first two dots are filled in. For example, the second column represents fatigue+anosmia+cough. So Robin wants to aggregate those up.

Robin’s critique arises from the Q(uestion) corner. If the designer wants to highlight specific combinations that occur most frequently in the data, then Bart’s encoding makes perfect sense. On the other hand, if the purpose is to highlight pairs of symptoms that occur most frequently together (disregarding symptoms outside each pair), then the data must be further aggregated. The switch in the Question requires more Data manipulation, which then affects the Visualization. That's the essence of the Trifecta Checkup framework.

Rest assured, the version that addresses Robin’s point will not give an easy answer to Bart’s question. In fact, Xan whipped up a bar chart in response:

Xan_symptomscombo_barchart

This is actually hard to comprehend because Robin’s question is even hard to state. The first bar shows 87 percent of users reported fatigue as a symptom, the same number that appeared on Bart’s version on the right side. Then, the darkened section of the bar indicates the proportion of users who reported only fatigue and nothing else, which appears to be about 10 percent. So 1 out of 9 reported just fatigue while 8 out of 9 who reported fatigue also experienced other symptoms.

 

Xan’s bar chart can be flipped 90 degrees and replace Bart’s histogram on top of the matrix. But you see, we end up with the same problem as I mentioned up top. By jamming more insights from more questions onto the same chart, we risk dropping the other balls that were already in the air.

So, my advice is always to first winnow down the list of questions you want to address. And don’t be afraid of making a series of charts instead of one panoptic chart.

***

Act 3: Bart decides to leave out labels for the columns.

This is a curious choice given the key storyline we’ve been working with so far (the Top 5 combinations of symptoms). But notice how annoying this problem is. Combinations require long text, which must be written vertically or slanted on this design. Transposing could help but not really. It’s just a limitation of this chart form. For me, reading the filled dots underneath the columns as column labels isn’t a show-stopper.

 

Histograms vs Bar Charts

It’s worth pointing out that the sidebar “histograms” are not both histograms. I tend to think of histograms as a specific type of bar (column) chart, in which the sum of the bars (columns) can be interpreted as a whole. So all histograms are bar charts but only some bar charts are histograms.

The column chart up top is a histogram. The combinations of symptoms are disjoint, and the total of the combinations should be the total number of answer choices selected by all respondents. The bar chart on the right side however is not a histogram. Each percentage is a proportion to the whole, and adding those percentages yields way above 100%.

I like the annotation on Bart’s chart a lot. They are succinct and they give just the right information to explain how to read the chart.

 

Limitations

I already mentioned the vertical labeling issue for UpSet charts. Here are two other considerations for you.

The majority of the plotting area is dedicated to the matrix of dots. The matrix contains merely labels for data. They are like country boundaries on a map. While it lays out the structure of data very clearly, the designer should ask whether it is essential for the readers to see the entire landscape.

In real-world data, the “long tail” phenomenon we saw earlier is very common. With six featured symptoms, there are 2^6 = 64 possible combinations of symptoms (minus 1 if they filtered out those not reporting symptoms*), almost all of which will be empty. Should the low-frequency columns be removed? This is not as controversial as you think, because implicitly both Bart and Nils already dropped all empty combinations!

 

Data and Code

Kieran Healy left a comment on the last post, and you can find both the data (thank you!) and some R code for UpSet charts at his blog.

Also, Nils has a Shiny app on Github.

 

(*) One must be very careful about what “users” are being represented. They form a tiny subset of users of the Symptom Tracker app, just those who have previously taken a diagnostic test and have self-reported at least one symptom. I have separately commented on the analyses of this dataset by the team behind the app. The first post discusses their analytical methods, the second post examines how they pre-processed the data, and a future post will describe the data collection practices. For the purpose of this blog post, I’ll ignore any data issues.

(#) Bart’s chart is conceptual because some of the columns of dots are repeated, and there is one column without fills, which should have been removed by a pre-processing step applied by the research team.


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

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

Nature tracking app venn diagram

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

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

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

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

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

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

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

***

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

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

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

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

 

 

 

 

 

 

 


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:

Tcb_COVID-19-CEO-confidence-1170

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. 
 


Gazing at petals

Reader Murphy pointed me to the following infographic developed by Altmetric to explain their analytics of citations of journal papers. These metrics are alternative in that they arise from non-academic media sources, such as news outlets, blogs, twitter, and reddit.

The key graphic is the petal diagram with a number in the middle.

Altmetric_tetanus

I have a hard time thinking of this object as “data visualization”. Data visualization should visualize the data. Here, the connection between the data and the visual design is tenuous.

There are eight petals arranged around the circle. The legend below the diagram maps the color of each petal to a source of data. Red, for example, represents mentions in news outlets, and green represents mentions in videos.

Each petal is the same size, even though the counts given below differ. So, the petals are like a duplicative legend.

The order of the colors around the circle does not align with its order in the table below, for a mysterious reason.

Then comes another puzzle. The bluish-gray petal appears three times in the diagram. This color is mapped to tweets. Does the number of petals represent the much higher counts of tweets compared to other mentions?

To confirm, I pulled up the graphic for a different paper.

Altmetric_worldwidedeclineofentomofauna

Here, each petal has a different color. Eight petals, eight colors. The count of tweets is still much larger than the frequencies of the other sources. So, the rule of construction appears to be one petal for each relevant data source, and if the total number of data sources fall below eight, then let Twitter claim all the unclaimed petals.

A third sample paper confirms this rule:

Altmetric_dnananodevices

None of the places we were hoping to find data – size of petals, color of petals, number of petals – actually contain any data. Anything the reader wants to learn can be directly read. The “score” that reflects the aggregate “importance” of the corresponding paper is found at the center of the circle. The legend provides the raw data.

***

Some years ago, one of my NYU students worked on a project relating to paper citations. He eventually presented the work at a conference. I featured it previously.

Michaelbales_citationimpact

Notice how the visual design provides context for interpretation – by placing each paper/researcher among its peers, and by using a relative scale (percentiles).

***

I’m ignoring the D corner of the Trifecta Checkup in this post. For any visualization to be meaningful, the data must be meaningful. The type of counting used by Altmetric treats every tweet, every mention, etc. as a tally, making everything worth the same. A mention on CNN counts as much as a mention by a pseudonymous redditor. A pan is the same as a rave. Let’s not forget the fake data menace (link), which  affects all performance metrics.


Bubble charts, ratios and proportionality

A recent article in the Wall Street Journal about a challenger to the dominant weedkiller, Roundup, contains a nice selection of graphics. (Dicamba is the up-and-comer.)

Wsj_roundup_img1


The change in usage of three brands of weedkillers is rendered as a small-multiples of choropleth maps. This graphic displays geographical and time changes simultaneously.

The staircase chart shows weeds have become resistant to Roundup over time. This is considered a weakness in the Roundup business.

***

In this post, my focus is on the chart at the bottom, which shows complaints about Dicamba by state in 2019. This is a bubble chart, with the bubbles sorted along the horizontal axis by the acreage of farmland by state.

Wsj_roundup_img2

Below left is a more standard version of such a chart, in which the bubbles are allowed to overlap. (I only included the bubbles that were labeled in the original chart).

Redo_roundupwsj0

The WSJ’s twist is to use the vertical spacing to avoid overlapping bubbles. The vertical axis serves a design perogative and does not encode data.  

I’m going to stick with the more traditional overlapping bubbles here – I’m getting to a different matter.

***

The question being addressed by this chart is: which states have the most serious Dicamba problem, as revealed by the frequency of complaints? The designer recognizes that the amount of farmland matters. One should expect the more acres, the more complaints.

Let's consider computing directly the number of complaints per million acres.

The resulting chart (shown below right) – while retaining the design – gives a wholly different feeling. Arkansas now owns the largest bubble even though it has the least acreage among the included states. The huge Illinois bubble is still large but is no longer a loner.

Redo_dicambacomplaints1

Now return to the original design for a moment (the chart on the left). In theory, this should work in the following manner: if complaints grow purely as a function of acreage, then the bubbles should grow proportionally from left to right. The trouble is that proportional areas are not as easily detected as proportional lengths.

The pair of charts below depict made-up data in which all states have 30 complaints for each million acres of farmland. It’s not intuitive that the bubbles on the left chart are growing proportionally.

Redo_dicambacomplaints2

Now if you look at the right chart, which shows the relative metric of complaints per million acres, it’s impossible not to notice that all bubbles are the same size.


Tennis greats at the top of their game

The following chart of world No. 1 tennis players looks pretty but the payoff of spending time to understand it isn't high enough. The light colors against the tennis net backdrop don't work as intended. The annotation is well done, and it's always neat to tug a legend inside the text.

Tableautennisnumberones

The original is found at Tableau Public (link).

The topic of the analysis appears to be the ages at which tennis players attained world #1 ranking. Here are the male players visualized differently:

Redo_junkcharts_no1tennisplayers

Some players like Jimmy Connors and Federer have second springs after dominating the game in their late twenties. It's relatively rare for players to get to #1 after 30.


The Periodic Table, a challenge in information organization

Reader Chris P. points me to this article about the design of the Periodic Table. I then learned that 2019 is the “International Year of the Periodic Table,” according to the United Nations.

Here is the canonical design of the Periodic Table that science students are familiar with.

Wiki-Simple_Periodic_Table_Chart-en.svg

(Source: Wikipedia.)

The Periodic Table is an exercise of information organization and display. It's about adding structure to over 100 elements, so as to enhance comprehension and lookup. The canonical tabular design has columns and rows. The columns (Groups) impose a primary classification; the rows (Periods) provide a secondary classification. The elements also follow an aggregate order, which is traced by reading from top left to bottom right. The row structure makes clear the "periodicity" of the elements: the "period" of recurrence is not constant, tending to increase with the heavier elements at the bottom.

As with most complex datasets, these elements defy simple organization, due to a curse of dimensionality. The general goal is to put the similar elements closer together. Similarity can be defined in an infinite number of ways, such as chemical, physical or statistical properties. The canonical design, usually attributed to Russian chemist Mendeleev, attained its status because the community accepted his organizing principles, that is, his definitions of similarity (subsequently modified).

***

Of interest, there is a list of unsettled issues. According to Wikipedia, the most common arguments concern:

  • Hydrogen: typically shown as a member of Group 1 (first column), some argue that it doesn’t belong there since it is a gas not a metal. It is sometimes placed in Group 17 (halogens), where it forms a nice “triad” with fluorine and chlorine. Other designers just float hydrogen up top.
  • Helium: typically shown as a member of Group 18 (rightmost column), the  halogens noble gases, it may also be placed in Group 2.
  • Mercury: usually found in Group 12, some argue that it is not a metal like cadmium and zinc.
  • Group 3: other than the first two elements , there are various voices about how to place the other elements in Group 3. In particular, the pairs of lanthanum / actinium and lutetium / lawrencium are sometimes shown in the main table, sometimes shown in the ‘f-orbital’ sub-table usually placed below the main table.

***

Over the years, there have been numerous attempts to re-design the Periodic table. Some of these are featured in the article that Chris sent me (link).

I checked how these alternative designs deal with those unsettled issues. The short answer is they don't settle the issues.

Wide Table (Janet)

The key change is to remove the separation between the main table and the f-orbital (pink) section shown below, as a "footnote". This change clarifies the periodicity of the elements, especially the elongating periods as one moves down the table. This form is also called "long step".

Mg32190402_long_conventional

As a tradeoff, this table requires more space and has an awkward aspect ratio.

In this version of the wide table, the designer chooses to stack lutetium / lawrencium in Group 3 as part of the main table. Other versions place lanthanum / actinium in Group 3 as part of the main table. There are even versions that leave Group 3 with two elements.

Hydrogen, helium and mercury retain their conventional positions.

 

Spiral Design (Hyde)

There are many attempts at spiral designs. Here is one I found on this tumblr:

Hyde_periodictable

The spiral leverages the correspondence between periodic and circular. It is visually more pleasing than a tabular arrangement. But there is a tradeoff. Because of the increasing "diameter" from inner to outer rings, the inner elements are visually constrained compared to the outer ones.

In these spiral diagrams, the designer solves the aspect-ratio problem by creating local loops, sometimes called peninsulas. This is analogous to the footnote table solution, and visually distorts the longer periodicity of the heavier elements.

For Hyde's diagram, hydrogen is floated, helium is assigned to Group 2, and mercury stays in Group 12.

 

Racetrack

I also found this design on the same tumblr, but unattributed. It may have come from Life magazine.

Tumblr_n3tbz5rIKk1s3r80lo3_1280

It's a variant of the spiral. Instead of peninsulas, the designer squeezes the f-orbital section under Group 3, so this is analogous to the wide table solution.

The circular diagrams convey the sense of periodic return but the wide table displays the magnitudes more clearly.

This designer places hydrogen in group 18 forming a triad with fluorine and chlorine. Helium is in Group 17 and mercury in the usual Group 12 .

 

Cartogram (Sheehan)

This version is different.

Elements_relative_abundance

The designer chooses a statistical property (abundance) as the primary organizing principle. The key insight is that the lighter elements in the top few rows are generally more abundant - thus more important in a sense. The cartogram reveals a key weakness of the spiral diagrams that draw the reader's attention to the outer (heavier) elements.

Because of the distorted shapes, the cartogram form obscures much of the other data. In terms of the unsettled issues, hydrogen and helium are placed in Groups 1 and 2. Mercury is in Group 12. Group 3 is squeezed inside the main table rather than shown below.

 

Network

The centerpiece of the article Chris sent me is a network graph.

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This is a complete redesign, de-emphasizing the periodicity. It's a result of radically changing the definition of similarity between elements. One barrier when introducing entirely new displays is the tendency of readers to expect the familiar.

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I found the following articles useful when researching this post:

The Conversation

Royal Chemistry Society