A twitter user alerted me to this chart put out by the Biden adminstration trumpeting a reduction in the budget deficit from 2020 to 2021:
This column chart embodies a form that is popular in many presentations, including in scientific journals. It's deficient in so many ways it's a marvel how it continues to live.
There are just two numbers: -3132 and -2772. Their difference is $360 billion, which is less than just over 10 percent of the earlier number. It's not clear what any data graphic can add.
Indeed, the chart does not do much. It obscures the actual data. What is the budget deficit in 2020? Readers must look at the axis labels, and judge that it's about a quarter of the way between 3000 and 3500. Five hundred quartered is 125. So it's roughly $3.125 trillion. Similarly, the 2021 number is slightly above the halfway point between 2,500 and 3,000.
These numbers are upside down. Taller columns are bad! Shortening the columns is good. It's all counter intuitive.
Column charts encode data in the heights of the columns. The designer apparently wants readers to believe the deficit has been cut by about a third.
As usual, this deception is achieved by cutting the column chart off at its knees. Removing equal sections of each column destroys the propotionality of the heights.
Why hold back? Here's a version of the chart showing the deficit was cut by half:
The relative percent reduction depends on where the baseline is placed. The only defensible baseline is the zero baseline. That's the only setting under which the relative percent reduction is accurately represented visually.
This same problem presents itself subtly in Covid-19 vaccine studies. I explain in this post, which I rate as one of my best Covid-19 posts. Check it out!
A twitter follower sent the following chart:
It's odd to place the focus on China when the U.S. line is much higher, and the growth in spending in the last few years in the U.S. is much higher than the growth rate in China.
In the Trifecta Checkup, this chart is Type D (link): the data are at odds with the message of the chart. The intended message likely is China is building up its military in an alarming way. This dataset does not support such a conclusion.
The visual design of the chart can't be faulted though. It's clean, and restrained. It even places line labels at the end of each line. Also, the topic of the chart - the arms race - is unambiguous.
One fix is to change the message to bring it in line with the data. If the question being addressed is which country spends the most on the military, or which country has been raising spending at the fastest rate, then the above chart is appropriate.
If the question is about spending in China, then a different measure such as average annual spending increase may work.
Neither solution requires changing the visual form. That's why data visualization excellence is more than just selecting the right chart form.
Happy holidays to all my readers! A special shutout to those who've been around for over 15 years.
The following enhanced data table appeared in Significance magazine (August 2021) under an article titled "Winning an election, not a popularity contest" (link, paywalled)
First is the antiquated style guide of academic journals, in which they turn legends into text, and insert the text into a caption. This is one of the worst journalistic practices that continue to be followed.
The table shows 50 states plus District of Columbia. The authors are interested in the extreme case in which a hypothetical U.S. presidential candidate wins the electoral college with the lowest possible popular vote margin. If you've been following U.S. presidential politics, you'd know that the electoral college effectively deflates the value of big-city votes so that the electoral vote margin can be a lot larger than the popular vote margin.
The two sub-tables show two different scenarios: Scenario A is a configuration computed by NPR in one of their reports. Scenario B is a configuration created by the authors (Leinwand, et. al.).
The table cells are given one of four colors: green = needed in the winning configuration; white = not needed; yellow = state needed in Scenario B but not in Scenario A; grey = state needed in Scenario A but not in Scenario B.
The second problem is that the above description of the color legend is not quite correct. Green, it turns out, is only correctly explained for Scenario A. Green for Scenario B encodes those states that are needed for the candidate to win the electoral college in Scenario B minus those states that are needed in Scenario B but not in Scenario A (shown in yellow). There is a similar problem with interpreting the white color in the table for Scenario B.
To fix this problem, start with the Q corner of the Trifecta Checkup.
The designer wants to convey an interlocking pair of insights: the winning configuration of states for each of the two scenarios; and the difference between those two configurations.
The problem with the current design is that it elevates the second insight over the first. However, the second insight is a derivative of the first so it's hard to get to the second spot without reaching the first.
The following revision addresses this problem:
[12/30/2021: Replaced chart and corrected the blue arrow for NJ.]
Through my twitter feed, I found my way to this chart, made by jamie_bio.
This is produced using R code even though it looks like a slide.
The underlying dataset concerns votes at the United Nations on various topics. Someone has already classified these topics. Jamie looked at voting blocs, specifically, countries whose votes agree most often or least often with the U.K.
If you look at his Github, this is one in a series of works he produced to hone his dataviz skills. Ultimately, I think this effort can benefit from some re-thinking. However, I also appreciate the work he has put into this.
Let's start with the things I enjoyed.
Given the dataset, I imagine the first visual one might come up with is a heatmap that shows countries in rows and topics in columns. That would work ok, as any standard chart form would but it would be a data dump that doesn't tell a story. There are almost 200 countries in the entire dataset. The countries can only be ordered in one way so if it's ordered for All Votes, it's not ordered for any of the other columns.
What Jamie attempts here is story-telling. The design leads the reader through a narrative. We start by reading the how-to-read-this box on the top left. This tells us that he's using a lunar eclipse metaphor. A full circle in blue indicates 0% agreement while a full circle in white indicates 100% agreement. The five circles signal that he's binning the agreement percentages into five discrete buckets, which helps simplify our understanding of the data.
Then, our eyes go to the circle of circles, labelled "All votes". This is roughly split in half, with the left side showing mostly blue and the right showing mostly white. That's because he's extracting the top 5 and bottom 5 countries, measured by their vote alignment with the U.K. The countries names are clearly labelled.
Next, we see the votes broken up by topics. I'm assuming not all topics are covered but six key topics are highlighted on the right half of the page.
What I appreciate about this effort is the thought process behind how to deliver a message to the audience. Selecting a specific subset that addresses a specific question. Thinning the materials in a way that doesn't throw the kitchen sink at the reader. Concocting the circular layout that presents a pleasing way of consuming the data.
Now, let me talk about the things that need more work.
I'm not convinced that he got his message across. What is the visual telling us? Half of the cricle are aligned with the U.K. while half aren't so the U.K. sits on the fence on every issue? But this isn't the message. It's a bit of a mirage because the designer picked out the top 5 and bottom 5 countries. The top 5 are surely going to be voting almost 100% with the U.K. while the bottom 5 are surely going to be disagreeing with the U.K. a lot.
I did a quick sketch to understand the whole distribution:
This is not intended as a show-and-tell graphic, just a useful way of exploring the dataset. You can see that Arms Race/Disarmament and Economic Development are "average" issues that have the same form as the "All issues" line. There are a small number of countries that are extremely aligned with the UK, and then about 50 countries that are aligned over 50% of the time, then the other 150 countries are within the 30 to 50% aligned. On human rights, there is less alignment. On Palestine, there is more alignment.
What the above chart shows is that the top 5 and bottom 5 countries both represent thin slithers of this distribution, which is why in the circular diagrams, there is little differentiation. The two subgroups are very far apart but within each subgroup, there is almost no variation.
Another issue is the lunar eclipse metaphor. It's hard to wrap my head around a full white circle indicating 100% agreement while a full blue circle shows 0% agreement.
In the diagrams for individual topics, the two-letter acronyms for countries are used instead of the country names. A decoder needs to be provided, or just print the full names.
In the prior post about Canadian elections, I suggested that designers expand beyond plots of one variable at a time. Today, I look at a project by DataWrapper on the German elections which happened this week. Thanks to long-time blog supporter Antonio for submitting the chart.
The following is the centerpiece of Lisa's work:
CDU/CSU is Angela Merkel's party, represented by the black color. The chart answers one question only: did polls correctly predict election results?
The time period from 1994 to 2021 covers eight consecutive elections (counting the one this week). There are eight vertical blocks on the chart representing each administration. The right vertical edge of each block coincides with an election. The chart is best understood as the superposition of two time series.
You can trace the first time series by following a step function - let your eyes follow the flat lines between elections. This dataset shows the popular vote won by the party at each election, with the value updated after each election. The last vertical block represents an election that has not yet happened when this chart was created. As explained in the footnote, Lisa took the average poll result for the last month leading up to the 2021 election - in the context of this chart, she made the assumption that this cycle of polls will be 100% accurate.
The second time series corresponds to the ragged edges of the gray and black areas. If you ignore the colors, and the flat lines, you'll discover that the ragged edges form a contiguous data series. This line encodes the average popularity of the CDU/CSU party according to election polls.
Thus, the area between the step function and the ragged line measures the gap between polls and election day results. When the polls underestimate the actual outcome, the area is colored gray; when the polls are over-optimistic, the area is colored black. In the last completed election of 2017, Merkel's party underperformed relative to the polls. In fact, the polls in the entire period between the 2013 and 2017 uniformly painted a rosier picture for CDU/CSU than actually happened.
The last vertical block is interpreted a little differently. Since the reference level is the last month of polls (rather than the actual popular vote), the abundance of black indicates that Merkel's party has been suffering from declining poll numbers on the approach of this week's election.
The picture shown above seems to indicate that these polls are not particularly good. It appears they have limited ability to self-correct within each election cycle. Aside from the 1998-2002 period, the area colors seldom changed within each cycle. That means if the first polling average overestimated the party's popularity, then all subsequent polling averages were also optimistic. (The original post focused on a single pollster, which exacerbates this issue. Compare the following chart with the above, and you'll find even fewer color changes within cycle here:
Each pollster may be systematically biased but the poll aggregate is less so.)
Here's the chart for SDP, which is CDU/CSU's biggest opponent, and likely winner of this week's election:
Overall, this chart has similar features as the CDU/CSU chart. The most recent polls seem to favor the SPD - the pink area indicates that the older polls of this cycle underestimates the last month's poll result.
Both these parties are in long-term decline, with popularity dropping from the 40% range in the 1990s to the 20% range in the 2020s.
One smaller party that seems to have gained followers is the Green party:
The excess of dark green, however, does not augur well for this election.
Stephen Taylor reached out to me about his work to visualize Canadian elections data. I took a look. I appreciate the labor of love behind this project.
He led with a streamgraph, which presents a quick overview of relative party strengths over time.
I am no Canadian election expert, and I did a bare minimum of research in writing this blog. From this chart, I learn that:
- the Canadians have an irregular election schedule
- Canada has a two party plus breadcrumbs system
- The two dominant parties are Liberals and Conservatives. The Liberals currently hold just less than half of the seats. The Conservatives have more than half of the seats not held by Liberals
- The Conservative party (maybe) rebranded as "progressive conservative" for several decades. The Reform/Alliance party was (maybe) a splinter movement within the Conservatives as well.
- Since the "width" of the entire stream increased over time, I'm guessing the number of seats has expanded
That's quite a bit of information obtained at a glance. This shows the power of data visualization. Notice Stephen didn't even have to include a "how to read this" box.
The streamgraph form has its limitations.
The feature that makes it more attractive than an area chart is its middle anchoring, resulting in a form of symmetry. The same feature produces erroneous intuition - the red patch draws out a declining trend; the reader must fight the urge to interpret the lines and focus on the areas.
The breadcrumbs are well hidden. The legend below discloses that the Green Party holds 3 seats currently. The party has never held enough seats to appear on the streamgraph though.
The bars showing proportions in the legend is a very nice touch. (The numbers appear messed up - I have to ask Stephen whether the seats shown are current values, or some kind of historical average.) I am a big fan of informative legends.
The next featured chart is a dot plot of polling results since 2020.
One can see a three-tier system: the two main parties, then the NDP (yellow) is the clear majority of the minority, and finally you have a host of parties that don't poll over 10%.
It looks like the polls are favoring the Conservatives over the Liberals in this election but it may be an election-day toss-up.
The purple dots represent "PPC" which is a party not found elsewhere on the page.
This chart is clear as crystal because of the structure of the underlying data. It just amazes me that the polls are so highly correlated. For example, across all these polls, the NDP has never once polled better than either the Liberals or the Conservatives, and in addition, it has never polled worse than any of the small parties.
What I'd like to see is a chart that merges the two datasets, addressing the question of how well these polls predicted the actual election outcomes.
The project goes very deep as Stephen provides charts for individual "ridings" (perhaps similar to U.S. precincts).
Here we see population pyramids for Vancouver Center, versus British Columbia (Province), versus Canada.
This riding has a large surplus of younger people in their twenties and thirties. Be careful about the changing scales though. The relative difference in proportions are more drastic than visually displayed because the maximum values (5%) on the Province and Canada charts are half that on the Riding chart (10%). Imagine squashing the Province and Canada charts to half their widths.
Analyses of income and rent/own status are also provided.
This part of the dashboard exhibits a problem common in most dashboards - they present each dimension of the data separately and miss out on the more interesting stuff: the correlation between dimensions. Do people in their twenties and thirties favor specific parties? Do richer people vote for certain parties?
The riding-level maps are the least polished part of the site. This is where I'm looking for a "how to read it" box.
It took me a while to realize that the colors represent the parties. If I haven't come in from the front page, I'd have been totally lost.
Next, I got confused by the use of the word "poll". Clicking on any of the subdivisions bring up details of an actual race, with party colors, candidates and a donut chart showing proportions. The title gives a "poll id" and the name of the riding in parentheses. Since the poll id changes as I mouse over different subdivisions, I'm wondering whether a "poll" is the term for a subdivision of a riding. A quick wiki search indicates otherwise.
My best guess is the subdivisions are indicated by the numbers.
Back to the donut charts, I prefer a different sorting of the candidates. For this chart, the two most logical orderings are (a) order by overall popularity of the parties, fixed for all ridings and (b) order by popularity of the candidate, variable for each riding.
The map shown above gives the winner in each subdivision. This type of visualization dumps a lot of information. Stephen tackles this issue by offering a small multiples view of each party. Here is the Liberals in Vancouver.
Again, we encounter ambiguity about the color scheme. Liberals have been associated with a red color but we are faced with abundant yellow. After clicking on the other parties, you get the idea that he has switched to a divergent continuous color scale (red - yellow - green). Is red or green the higher value? (The answer is red.)
I'd suggest using a gray scale for these charts. The hardest decision is going to be the encoding between values and shading. Should each gray scale be different for each riding and each party?
If I were to take a guess, Stephen must have spent weeks if not months creating these maps (depending on whether he's full-time or part-time). What he has published here is a great start. Fine-tuning the issues I've mentioned may take more weeks or months more.
Stephen is brave and smart to send this project for review. For one thing, he's got some free consulting. More importantly, we should always send work around for feedback; other readers can tell us where our blind spots are.
To read more, start with this post by Stephen in which he introduces his project.
Art sent me the following Economist chart, noting how hard it is to understand. I took a look, and agreed. It's an example of a visual representation that takes more time to comprehend than the underlying data.
The chart presents responses to 3 questions on a survey. For each question, the choices are Approve, Disapprove, and "Neither" (just picking a word since I haven't seen the actual survey question). The overall approval/disapproval rates are presented, and then broken into two subgroups (Democrats and Republicans).
The first hurdle is reading the scale. Because the section from 75% to 100% has been removed, we are left with labels 0, 25, 50, 75, which do not say percentages unless we've consumed the title and subtitle. The Economist style guide places the units of data in the subtitle instead of on
the axis itself.
Our attention is drawn to the thick lines, which represent the differences between approval and disapproval rates. These differences are signed: it matters whether the proportion approving is higher or lower than the proportion disapproving. This means the data are encoded in the order of the dots plus the length of the line segment between them.
The two bottom rows of the Afghanistan question demonstrates this mental challenge. Our brains have to process the following visual cues:
1) the two lines are about the same lengths
2) the Republican dots are shifted to the right by a little
3) the colors of the dots are flipped
What do they all mean?
A chart runs in trouble when you need a paragraph to explain how to read it.
It's sometimes alright to make complicated data visualization that illustrates complicated concepts. What justifies it is the payoff. I wrote about the concept of return on effort in data visualization here.
The payoff for this chart escaped me. Take the Democratic response to troop withdrawal. About 3/4 of Democrats approve while 15% disapprove. The thick line says 60% more Democrats approve than disapprove.
Here, I show the full axis, and add a 50% reference line
Small edits but they help visualize "half of", "three quarters of".
Next, I switch to the more conventional stacked bars.
This format reveals some of the hidden data on the chart - the proportion answering neither approve/disapprove, and neither yes/no.
Read all my posts about Economist charts here
This chart is giving me feelings:
I first saw it on TV and then a reader submitted it.
Let's apply a Trifecta Checkup to the chart.
Starting at the Q corner, I can say the question it's addressing is clear and relevant. It's the relationship between Trump and McConnell's re-election. The designer's intended message comes through strongly - the chart offers evidence that McConnell owes his re-election to Trump.
Visually, the graphic has elements of great story-telling. It presents a simple (others might say, simplistic) view of the data - just the poll results of McConnell vs McGrath at various times, and the election result. It then flags key events, drawing the reader's attention to those. These events are selected based on key points on the timeline.
The chart includes wise design choices, such as no gridlines, infusing the legend into the chart title, no decimals (except for last pair of numbers, the intention of which I'm not getting), and leading with the key message.
I can nitpick a few things. Get rid of the vertical axis. Also, expand the scale so that the difference between 51%-40% and 58%-38% becomes more apparent. Space the time points in proportion to the dates. The box at the bottom is a confusing afterthought that reduces rather than assists the messaging.
But the designer got the key things right. The above suggestions do not alter the reader's expereince that much. It's a nice piece of visual story-telling, and from what I can see, has made a strong impact with the audience it is intended to influence.
This chart is proof why the Trifecta Checkup has three corners, plus linkages between them. If we just evaluate what the visual is conveying, this chart is clearly above average.
In the D corner, we ask: what the Data are saying?
This is where the chart runs into several problems. Let's focus on the last two sets of numbers: 51%-40% and 58%-38%. Just add those numbers and do you notice something?
The last poll sums to 91%. This means that up to 10% of the likely voters responded "not sure" or some other candidate. If these "shy" voters show up at the polls as predicted by the pollsters, and if they voted just like the not shy voters, then the election result would have been 56%-44%, not 51%-40%. So, the 58%-38% result is within the margin of error of these polls. (If the "shy" voters break for McConnell in a 75%-25% split, then he gets 58% of the total votes.)
So, the data behind the line chart aren't suggesting that the election outcome is anomalous. This presents a problem with the Q-D and D-V green arrows as these pairs are not in sync.
In the D corner, we should consider the totality of the data available to the designer, not just what the designer chooses to utilize. The pivot of the chart is the flag annotating the "Trump robocall."
Here are some questions I'd ask the designer:
What else happened on October 31 in Kentucky?
What else happened on October 31, elsewhere in the country?
Was Trump featured in any other robocalls during the period portrayed?
How many robocalls were made by the campaign, and what other celebrities were featured?
Did any other campaign event or effort happen between the Trump robocall and election day?
Is there evidence that nothing else that happened after the robocall produced any value?
The chart commits the XYopia (i.e. X-Y myopia) fallacy of causal analysis. When the data analyst presents one cause and one effect, we are cued to think the cause explains the effect but in every scenario that is not a designed experiment, there are multiple causes at play. Sometimes, the more influential cause isn't the one shown in the chart.
Finally, let's draw out the connection between the last set of poll numbers and the election results. This shows why causal inference in observational data is such a beast.
Poll numbers are about a small number of people (500-1,000 in the case of Kentucky polls) who respond to polling. Election results are based on voters (> 2 million). An assumption made by the designer is that these polls are properly conducted, and their results are credible.
The chart above makes the claim that Trump's robocall gave McConnell 7% more votes than expected. This implies the robocall influenced at least 140,000 voters. Each such voter must fit the following criteria:
- Was targeted by the Trump robocall
- Was reached by the Trump robocall (phone was on, etc.)
- Responded to the Trump robocall, by either picking up the phone or listening to the voice recording or dialing a call-back number
- Did not previously intend to vote for McConnell
- If reached by a pollster, would refuse to respond, or say not sure, or voting for McGrath or a third candidate
- Had no other reason to change his/her behavior
Just take the first bullet for example. If we found a voter who switched to McConnell after October 31, and if this person was not on the robocall list, then this voter contributes to the unexpected gain in McConnell votes but weakens the case that the robocall influenced the election.
As analysts, our job is to find data to investigate all of the above. Some of these are easier to investigate. The campaign knows, for example, how many people were on the target list, and how many listened to the voice recording.
The Washington Post reported a surge in donations to the Democrats after the death of Justice Ruth Ginsberg (link). A secondary effect, perhaps unexpected, was that donors decided to spread the money around; the proportion of donors who gave to six or more candidates jumped to 65%, where normally it is at 5%.
There is something frustrating about this chart, though. It's that the spike is shown upside down. The level that the arrow points at is 45%, which is the total of the blue columns. The visual suggests the proportion of multiple beneficiaries (2 or more) should be 55%. There is a divergence between what the visual is saying and what the data are saying. Whichever number is correct, the required proportion is the inverse of the level shown on the percentage axis!
This is the same chart flipped over.
Now, the number we need can be read off the vertical axis.
I also moved the color legend to the right side so that the entries can be printed vertically, in the same direction as the data. This is one of the unspoken rules of data visualization I featured in my feature for DataJournalism.com.
In the Trifecta Checkup (link), the issue is with the green arrow between the D corner and the V corner. The data and the visual are not in sync.