Junk Charts

Welcome

Junk Charts is made by Kaiser Fung, the Web’s first data visualization critic. I discuss what makes graphics work, and how to make them better. Think chartjunk + junk art.

Here is my first post from more than ten years ago. These are the posts with the most (clicks): 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. There are some things I say over and over again: the Trifecta Checkup, the self-sufficiency test, bumps charts, small multiples, the power of aggregation. I carefully tag all posts with topics so clicking on map shows all posts about maps. A list of topics is on the right column. Here is a call for more dataviz criticism.

Here is my other blog, about statistics and Big Data. Here is my twitter feed, aggregating both blogs. If you prefer RSS, there's one feed for Junk Charts, and one for Big Data Plainly Spoken.

I also write popular books about statistics applied to daily life. Here is Numbers Rule Your World, and Numbersense. Tom Peters tweeted: On my 13-hour Boston-Dubai flight, I re-read cover-2-cover Kaiser Fung's superb-useful-fun book Number Sense. Trust him, or trust me.

I am a business statistician and speaker/trainer for hire. This is about a few hours in my life. I built the data teams at Vimeo (still part of the family) and Sirius XM Radio. Click here to write me. Some citations in the media. Talks. A free course.

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Oct 2014: I'm teaching my data visualization workshop. This is a great way for you to learn how to make great graphics in a fun, immersive setting. See a fuller description here.

 

 

SCMP's fantastic infographic on Hong Kong protests

In the past month, there have been several large-scale protests in Hong Kong. The largest one featured up to two million residents taking to the streets on June 16 to oppose an extradition act that was working its way through the legislature. If the count was accurate, about 25 percent of the city’s population joined in the protest. Another large demonstration occurred on July 1, the anniversary of Hong Kong’s return to Chinese rule.

South China Morning Post, which can be considered the New York Times of Hong Kong, is well known for its award-winning infographics, and they rose to the occasion with this effort.

This is one of the rare infographics that you’d not regret spending time reading. After reading it, you have learned a few new things about protesting in Hong Kong.

In particular, you’ll learn that the recent demonstrations are part of a larger pattern in which Hong Kong residents express their dissatisfaction with the city’s governing class, frequently accused of acting as puppets of the Chinese state. Under the “one country, two systems” arrangement, the city’s officials occupy an unenviable position of mediating the various contradictions of the two systems.

This bar chart shows the growth in the protest movement. The recent massive protests didn't come out of nowhere. 

This line chart offers a possible explanation for burgeoning protests. Residents’ perceived their freedoms eroding in the last decade.

If you have seen videos of the protests, you’ll have noticed the peculiar protest costumes. Umbrellas are used to block pepper sprays, for example. The following lovely graphic shows how the costumes have evolved:

The scale of these protests captures the imagination. The last part in the infographic places the number of protestors in context, by expressing it in terms of football pitches (as soccer fields are known outside the U.S.) This is a sort of universal measure due to the popularity of football almost everywhere. (Nevertheless, according to Wikipedia, the fields do not have one fixed dimension even though fields used for international matches are standardized to 105 m by 68 m.)

This chart could be presented as a bar chart. It’s just that the data have been re-scaled – from counting individuals to counting football pitches-ful of individuals. 

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Here is the entire infographics.

Three estimates, two differences trip up an otherwise good design

Reader Fernando P. was baffled by this chart from the Perception Gap report by More in Common. (link to report)

Overall, this chart is quite good. Its flaws are subtle. There is so much going on, perhaps even the designer found it hard to keep level.

The title is "Democrat's Perception Gap" which actually means the gap between Democrats' perception of Republicans and Republican's self-reported views. We are talking about two estimates of Republican views. Conversely, in Figure 2 (not shown), the "Republican's Perception Gap" describes two estimates of Democrat views.

The gap is visually shown as the gray bar between the red dot and the blue dot. This is labeled perception gap, and its values are printed on the right column, also labeled perception gap.

Perhaps as an after-thought, the designer added the yellow stripes, which is a third estimate of Republican views, this time by Independents. This little addition wreaks havoc. There are now three estimates - and two gaps. There is a new gap, between Independents' perception of Republican views, and Republican's self-reported views. This I-gap is hidden in plain sight. The words "perception gap" obstinately sticks to the D-gap.

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Here is a slightly modified version of the same chart.

 

The design focuses attention on the two gaps (bars). It also identifies the Republican self-perception as the anchor point from which the gaps are computed.

I have chosen to describe the Republican dot as "self-perception" rather than "actual view," which connotes a form of "truth." Rather than considering the gap as an error of estimation, I like to think of the gap as the difference between two groups of people asked to estimate a common quantity.

Also, one should note that on the last two issues, there is virtual agreement.

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Aside from the visual, I have doubts about the value of such a study. Only the most divisive issues are being addressed here. Adding a few bipartisan issues would provide controls that can be useful to tease out what is the baseline perception gap.

I wonder whether there is a self-selection in survey response, such that people with extreme views (from each party) will be under-represented. Further, do we believe that all survey respondents will provide truthful answers to sensitive questions that deal with racism, sexism, etc.? For example, if I am a moderate holding racist views, would I really admit to racism in a survey?

 

 

Putting the house in order, two Brexit polls

Reader Steve M. noticed an oversight in the Guardian in the following bar chart (link):

The reporter was discussing an important story that speaks to the need for careful polling design. He was comparing two polls, one by Ipsos Mori, and one by YouGov, that estimates the vote support for each party in the future U.K. general election. The bottom line is that the YouGov poll predicts about double the support for the Brexit Party than the Ipsos-Mori poll.

The stacked bar chart should only be used for data that can be added up. Here, we should be comparing the numbers side by side:

I've always found this standard display inadequate. The story here is the gap in the two bar lengths for the Brexit Party. A secondary story is that the support for the Brexit Party might come from voters breaking from Labour. In other words, we really want the reader to see:

Switching to a dot plot helps bring attention to the gaps:

Now, putting the house in order:

Why do these two polls show such different results? As the reporter explained, the answer is in how the question was asked. The Ipsos-Mori is unprompted, meaning the Brexit Party was not announced to the respondent as one of the choices while the YouGov is prompted.

This last version imposes a direction on the gaps to bring out the secondary message - that the support for Brexit might be coming from voters breaking from Labour.

 

 

Tightening the bond between the message and the visual: hello stats-cats

The editors of ASA's Amstat News certainly got my attention, in a recent article on school counselling. A research team asked two questions. The first was HOW ARE YOU FELINE?

Stats and cats. The pun got my attention and presumably also made others stop and wonder. The second question was HOW DO YOU REMEMBER FEELING while you were taking a college statistics course? Well, it's hard to imagine the average response to that question would be positive.

What also drew me to the article was this pair of charts:

Surely, ASA can do better. (I'm happy to volunteer my time!)

Rotate the chart, clean up the colors, remove the decimals, put the chart titles up top, etc.

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The above remedies fall into the V corner of my Trifecta checkup.

The key to fixing this chart is to tighten the bond between the message and the visual. This means working that green link between the Q and V corners.

This much became clear after reading the article. The following paragraphs are central to the research (bolding is mine):

Responses indicated the majority of school counselors recalled experiences of studying statistics in college that they described with words associated with more unpleasant affect (i.e., alarm, anger, distress, fear, misery, gloom, depression, sadness, and tiredness; n = 93; 66%). By contrast, a majority of counselors reported same-day (i.e., current) emotions that appeared to be associated with more pleasant affect (i.e., pleasure, happiness, excitement, astonishment, sleepiness, satisfaction, and calm; n = 123; 88%).

Both recalled emotive experiences and current emotional states appeared approximately balanced on dimensions of arousal: recalled experiences associated with lower arousal (i.e., pleasure, misery, gloom, depression, sadness, tiredness, sleepiness, satisfaction, and calm, n = 65, 46%); recalled experiences associated with higher arousal (i.e., happiness, excitement, astonishment, alarm, anger, distress, fear, n = 70, 50%); current emotions associated with lower arousal (n = 60, 43%); current experiences associated with higher arousal (i.e., n = 79, 56%).

These paragraphs convey two crucial pieces of information: the structure of the analysis, and its insights.

The two survey questions measure two states of experiences, described as current versus recalled. Then the individual affects (of which there were 16 plus an option of "other") are scored on two dimensions, pleasure and arousal. Each affect maps to high or low pleasure, and separately to high or low arousal.

The research insight is that current experience was noticably higher than recalled experience on the pleasure dimension but both experiences were similar on the arousal dimension.

Any visualization of this research must bring out this insight.

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Here is an attempt to illustrate those paragraphs:

The primary conclusion can be read from the four simple pie charts in the middle of the page. The color scheme shines light on which affects are coded as high or low for each dimension. For example, "distressed" is scored as showing low pleasure and high arousal.

A successful data visualization for this situation has to bring out the conclusion drawn at the aggregated level, while explaining the connection between individual affects and their aggregates.