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:

Counselors_Figure1small

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.

***

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

Trifectacheckup_junkcharts_imageThe 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.

***

Here is an attempt to illustrate those paragraphs:

Redo_junkcharts_amstat_feline

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.


Book review: Visualizing Baseball

I requested a copy of Jim Albert’s Visualizing Baseball book, which is part of the ASA-CRC series on Statistical Reasoning in Science and Society that has the explicit goal of reaching a mass audience.

Visualizingbaseball_coverThe best feature of Albert’s new volume is its brevity. For someone with a decent background in statistics (and grasp of basic baseball jargon), it’s a book that can be consumed within one week, after which one receives a good overview of baseball analytics, otherwise known as sabermetrics.

Within fewer than 200 pages, Albert outlines approaches to a variety of problems, including:

  • Comparing baseball players by key hitting (or pitching) metrics
  • Tracking a player’s career
  • Estimating the value of different plays, such as a single, a triple or a walk
  • Predicting expected runs in an inning from the current state of play
  • Analyzing pitches and swings using PitchFX data
  • Describing the effect of ballparks on home runs
  • Estimating the effect of particular plays on the outcome of a game
  • Simulating “fake” games and seasons in order to produce probabilistic forecasts such as X% chance that team Y will win the World Series
  • Examining whether a hitter is “streaky” or not

Most of the analyses are descriptive in nature, e.g. describing the number and types of pitches thrown by a particular pitcher, or the change in on-base percentage over the career of a particular hitter. A lesser number of pages are devoted to predictive analytics. This structure is acceptable in a short introductory book. In practice, decision-makers require more sophisticated work on top of these descriptive analyses. For example, what’s the value of telling a coach that the home run was the pivotal moment in a 1-0 game that has played out?

To appreciate the practical implications of the analyses included in this volume, I’d recommend reading Moneyball by Michael Lewis, or the more recent Astroball by Ben Reiter.

For the more serious student of sabermetrics, key omitted details will need to be gleaned from other sources, including other books by the same author – for years, I have recommended Curve Ball by Albert and Bennett to my students.

***

In the final chapters, Albert introduced the simulation of “fake” seasons that underlies predictions. An inquiring reader should investigate how the process is tied back to the reality of what actually happened; otherwise, the simulation will have a life of its own. Further, if one simulates 1,000 seasons of 2018 baseball, a large number of these fake seasons would crown some team other than the Red Sox as the 2018 World Series winner. Think about it: that’s how it is possible to make the prediction that the Red Sox has a say 60 percent chance of winning the World Series in 2018! A key to understanding the statistical way of thinking is to accept the logic of this fake simulated world. It is not the stated goal of Albert to convince readers of the statistical way of thinking – but you’re not going to be convinced unless you think about why we do it this way.

***

While there are plenty of charts included in the book, a more appropriate title for “Visualizing Baseball” would have been “Fast Intro to Baseball Analytics”. With several exceptions, the charts are not essential to understanding the analyses. The dominant form of exposition is first describe the analytical conclusion, then introduce a chart to illustrate that conclusion. The inverse would be: Start with the chart, and use the chart to explain the analysis.

The visualizations are generally of good quality, emphasizing clarity over prettiness. The choice of sticking to one software, ggplot2 in R, without post-production, constrains the visual designer to the preferences of the software designer. Such limitations are evident in chart elements like legends and titles. Here is one example (Chapter 5, Figure 5.8):

Albert_visualizingbaseball_chart

By default, the software prints the names of data columns in the titles. Imagine if the plot titles were Changeup, Fastball and Slider instead of CU, FF and SL. Or that the axis labels were “horizontal location” and “vertical location” (check) instead of px and pz. [Note: The chart above was taken from the book's github site; in the  Figure 5.8 in the printed book, the chart titles were edited as suggested.]

The chart analyzes the location relative to the strike zone of pitches that were missed versus pitches that were hit (not missed). By default, the software takes the name of the binary variable (“Miss”) as the legend title, and lists the values of the variable (“True” and “False”) as the labels of the two colors. Imagine if True appeared as “Miss” and False as “Hit” .

Finally, the chart exhibits over-plotting, making it tough to know how many blue or gray dots are present. Smaller dot size might help, or else some form of aggregation.

***

Visualizing Baseball is not the book for readers who learn by running code as no code is included in the book. A github page by the author hosts the code, but only the R/ggplot2 code for generating the data visualization. Each script begins after the analysis or modeling has been completed. If you already know R and ggplot2, the github is worth a visit. In any case, I don’t recommend learning coding from copying and pasting clean code.

All in all, I can recommend this short book to any baseball enthusiast who’s beginning to look at baseball data. It may expand your appreciation of what can be done. For details, and practical implications, look elsewhere.


Clarifying comparisons in censored cohort data: UK housing affordability

If you're pondering over the following chart for five minutes or more, don't be ashamed. I took longer than that.

Ft_ukgenerationalhousing

The chart accompanied a Financial Times article about inter-generational fairness in the U.K. To cut to the chase, a recently released study found that younger generations are spending substantially higher proportions of their incomes to pay for housing costs. The FT article is here (behind paywall). FT actually slightly modified the original chart, which I pulled from the Home Affront report by the Intergenerational Commission.

Uk_generational_propincomehousing

One stumbling block is to figure out what is plotted on the horizontal axis. The label "Age" has gone missing. Even though I am familiar with cohort analysis (here, generational analysis), it took effort to understand why the lines are not uniformly growing in lengths. Typically, the older generation is observed for a longer period of time, and thus should have a longer line.

In particular, the orange line, representing people born before 1895 only shows up for a five-year range, from ages 70 to 75. This was confusing because surely these people have lived through ages 20 to 70. I'm assuming the "left censoring" (missing data on the left side) is because of non-existence of old records.

The dataset is also right-censored (missing data on the right side). This occurs with the younger generations (the top three lines) because those cohorts have not yet reached certain ages. The interpretation is further complicated by the range of birth years in each cohort but let me not go there.

TL;DR ... each line represents a generation of Britons, defined by their birth years. The generations are compared by how much of their incomes did they spend on housing costs. The twist is that we control for age, meaning that we compare these generations at the same age (i.e. at each life stage).

***

Here is my version of the same chart:

Junkcharts_redo_ukgenerationalhousing_1

Here are some of the key edits:

  • Vertical blocks are introduced to break up the analysis by life stage. These guide readers to compare the lines vertically i.e. across generations
  • The generations are explicitly described as cohorts by birth years
  • The labels for the generations are placed next to the lines
  • Gridlines are pushed to the back
  • The age axis is explicitly labeled
  • Age labels are thinned
  • A hierarchy on colors
  • The line segments with incomplete records are dimmed

The harmful effect of colors can be seen below. This chart is the same as the one above, except for retaining the colors of the original chart:

Junkcharts_redo_ukgenerationalhousing_2

 

 


Clearing a forest of labels

This chart by the Financial Times has a strong message, and I like a lot about it:

Ft-europe-growth

The countries are by and large aligned along a diagonal, with the poorer countries growing strongly between 2007-2019 while the richer countries suffered negative growth.

A small issue with the chart is the thick forest of text - redundant text. The sub-title, the axis titles, the quadrant labels, and the left-right-half labels all repeat the same things. In the following chart, I simplify the text:

Redo_fteuropegrowth_text

Typically, I don't put axis titles as a sub-header (or, header of the graphic) but as this may be part of the FT style, I respected it.


The unreasonable effect of chart labels

In discussing the bar-density and pie-density charts with a buddy (thanks LB!), it became obvious that the labeling is a challenge. And he's right.

Here is the pie-density chart for the Youtube views with the labels as originally conceived.

Kaiserfung_piedensity_youtube_orig_labels

These labels are trying too hard to provide precise data to the reader.

Here are some simplified labels that get at the message rather than the data:

Kaiserfung_piedensity_youtube_labels_2b


Here is a slightly different version:

Kaiserfung_piedensity_youtube_labels_3b


 

 

 


Nice example of visual story-telling in the FT

I came across this older chart in the Financial Times, which is a place to find some nice graphics:

Ft_uklifeexpectancy

The key to success here is having a good story to tell. Blackpool is an outlier when it comes to improvement in life expectancy since 1993. Its average life expectancy has improved, but the magnitude of improvement lags other areas by quite a margin.

The design then illustrates this story in two ways.

On the right side, one sees Blackpool occupying a lone spot on the left side of the histogram. On the left chart, the gap between Blackpool and the national average is plotted over time. The gap is clearly widening; the size of the gap is labeled so the reader immediately knows it went from 1.8 to 4.9.

Although they're not labeled, the reader understand that the other two lines are the best and worst areas. The comparison between Glasgow City and Blackpool is also informative. Glasgow City, which has the worst life expectancy in the U.K. is fast catching up with Blackpool, the second worst.

I also like color-coded titles. It draws attention to Blackpool and it links the conclusion to both charts in an efficient manner.


Not following direction or order, the dieticians complain

At first glance, this graphic's message seems clear: what proportion of Americans are exceeding or lagging guidelines for consumption of different food groups. Blue for exceeding; orange for lagging. The stacked bars are lined up at the central divider - the point of meeting recommended volumes - to make it easy to compare relative proportions.

Figure-2-1-eatingpatterns

The original chart is here, on the Health.gov website.

The little icons illustrating the food groups are cute and unintrusive.

It's when you read further that things start to get complicated. The last three rows display a flipping of the color scheme, with orange on the right, blue on the left. Up to this point, you may understand blue to mean over the recommended value, and orange is under. Suddenly, the orange is shown on the right side.

The designer was wrestling with a structural issue in the data. The last three food groups - sugars, fats and sodium - are things to eat less. So, having long bars on the right side is not good. The orange/blue colors should be interpreted as bad/good and not as under/over.

***
The problem with this design is that it draws attention to this color flip - that is to say, it draws attention to which food groups are favored and which ones are to be avoided. This insight is actually in the metadata, not what this dataset is about.

In the following chart, I enforce the bad/good color scheme while ignoring the direction of good. The text is adjusted to use words that do not suggest direction.

Redo_foodgroups1

Dieticians are probably distressed by this chart, given that most Americans are lagging on almost all of the recommendations.

In a final edit, I re-ordered the categories.

Redo_foodgroups2

 


Five steps to let the young ones shine

Knife stabbings are in the news in the U.K. and the Economist has a quartet of charts to illustrate what's going on.

Economist_20190309_WOC479

I'm going to focus on the chart on the bottom right. This shows the trend in hospital admissions due to stabbings in England from 2000 to 2018. The three lines show all ages, and two specific age groups: under 16 and 16-18.

The first edit I made was to spell out all years in four digits. For this chart, numbers like 15 and 18 can be confused with ages.

Redo_econ_ukknives_1

The next edit corrects an error in the subtitle. The reference year is not 2010 as those three lines don't cross 100. It appears that the reference year is 2000. Another reason to use four-digit years on the horizontal axis is to be consistent with the subtitle.

Redo_econ_ukknives_2

The next edit removes the black dot which draws attention to itself. The chart though is not about the year 2000, which has the least information since all data have been forced to 100.

Redo_econ_ukknives_3

The next edit makes the vertical axis easier to interpret. The indices 150, 200, are much better stated as + 50%, + 100%. The red line can be labeled "at 2000 level". One can even remove the subtitle 2000=100 if desired.

Redo_econ_ukknives_4

Finally, I surmise the message the designer wants to get across is the above-average jump in hospital admissions among children under 16 and 16 to 18. Therefore, the "All" line exists to provide context. Thus, I made it a dashed line pushing it to the background.

Redo_econ_ukknives_5

 

 

 


The ebb and flow of an effective dataviz showing the rise and fall of GE

Wsj_ebbflowGE_800A WSJ chart caught my eye the other day – I spotted someone looking at it in a coffee shop, and immediately got a hold of a copy. The chart plots the ebb and flow of GE’s revenues from the 1980s to the present.

What grabbed my attention? The less-used chart form, and the appealing but not too gaudy color scheme.

The chart presents a highly digestible view of the structure of GE’s revenues. We learn about GE’s major divisions, as well as how certain segments split from or merged with others over time. Major acquisitions and divestitures are also depicted; if these events are the main focus, the designer should find ways to make these moments stand out more.

An interesting design decision concerns the sequence of the divisions. One possible order is by increasing or decreasing importance, typically indicated by proportional revenues. This is complicated by the changing nature of the business over the decades. So financial services went from nothing to the largest division by far to almost disappearing.

The sequencing need not be data-driven; it can be design-constrained. The merging and splitting of business units are conveyed via linking arrows. Longer arrows are unsightly, and meshes of arrows are confusing.

On this chart, the long arrow pointing from the orange to the gray around 2004 feels out of place. What if the financial services block is moved to the right of the consumer block? That will significantly shorten the long arrow. It won’t create other entanglements as the media block is completely disjoint and there are no other arrows tying financial services to another division.

 

***


To improve readability, the bars are spaced out horizontally. The addition of whitespace distorts the proportionality. So, in 2001, the annotation states that financial services (orange) accounted for “about half of the revenues,” which is directly contradicted by the visual perception – readers find the orange bar to be clearly shorter than the total length of the other bars. This is a serious deficiency of the chart form but this chart conveys the "ebb and flow" very well.


Environmental science can use better graphics

Mike A. pointed me to two animated maps made by Caltech researchers published in LiveScience (here).

The first map animation shows the rise and fall of water levels in a part of California over time. It's an impressive feat of stitching together satellite images. Click here to play the video.

Caltech_groundwater_map1

The animation grabs your attention. I'm not convinced by the right side of the color scale in which the white comes after the red. I'd want the white in the middle then the yellow and finally the red.

In order to understand this map and the other map in the article, the reader has to bring a lot of domain knowledge. This visualization isn't easy to decipher for a layperson.

Here I put the two animations side by side:

Caltech_groundwater_side

The area being depicted is the same. One map shows "ground deformation" while the other shows "subsidence". Are they the same? What's the connection between the two concepts (if any)?  On a further look, one notices that the time window for the two charts differ: the right map is clearly labeled 1995 to 2003 but there is no corresponding label on the left map. To find the time window of the left map, the reader must inspect the little graph on the top right (1996 to 2000).

This means the time window of the left map is a subset of the time window of the right map. The left map shows a sinusoidal curve that moves up and down rhythmically as the ground shifts. How should I interpret the right map? The periodicity is no longer there despite this map illustrating a longer time window. The scale on the right map is twice the magnitude of the left map. Maybe on average the ground level is collapsing? If that were true, shouldn't the sinusoidal curve drift downward over time?

Caltech_groundwater_sineThe chart on the top right of the left map is a bit ugly. The year labels are given in decimals e.g. 1997.5. In R, this can be fixed by customizing the axis labels.

I also wonder how this curve is related to the map it accompanies. The curve looks like a model - perfect oscillations of a fixed period and amplitude. But one suppose the amount of fluctuation should vary by location, based on geographical features and human activities.

The author of the article points to both natural and human impacts on the ground level. Humans affect this by water usage and also by management policies dictated by law. It would be very helpful to have a map that sheds light on the causes of the movements.