Lies, Damned Lies & Dataviz

Bad visualization, and how to avoid it

Dr. Andrew CleggDirector, Learner Analytics & Data SciencePearson

@andrew_clegg

Part I Why Visualize?

What are the benefits when its done right?

Part II Bad Dataviz

How to spot the failures and how to avoid them yourself

Warning: Contains Opinion!

Introduction

Part I Why Visualize?

Summarizing and communicating numbers

Drawing attention to trends and patterns

Exploring data interactively

Capturing attention

Telling stories

What is the goal?

Playing to your neural hardwares strengths

Your visual system excels at pattern detection & parallel processing.

Representing data graphically means you can leverage this for free.

How does visualization help?

Challenge: estimate x when y = 0

x y x y x y

27.38 24.05 32.31 31.61 75.67 14.83

62.64 7.31 51.84 28.61 34.23 31.65

50.76 16.30 59.04 18.29 51.21 7.69

42.94 26.78 74.63 1.15 47.26 22.90

8.72 42.35 56.15 11.37 66.60 3.21

30.62 30.87 47.23 19.49 17.46 40.31

62.63 9.14 59.36 8.82 65.70 12.79

63.21 18.66 44.58 19.12 52.24 12.92

40.49 23.29 47.85 20.55 62.56 14.17

22.07 41.46 68.21 11.99 40.43 19.77

Challenge: estimate x when y = 0

Challenge: estimate x when y = 0

Challenge: find most similar data point

x y x y x y

54.88 71.52 97.86 79.92 35.95 43.70

60.28 54.49 46.15 78.05 69.76 6.02

42.37 64.59 11.83 63.99 66.68 67.06

43.76 89.18 14.34 94.47 21.04 12.89

96.37 38.34 52.18 41.47 31.54 36.37

79.17 52.89 26.46 77.42 57.02 43.86

56.80 92.56 45.62 56.84 98.84 10.20

7.10 8.71 1.88 61.76 20.89 16.13

2.02 83.26 61.21 61.69 65.31 25.33

77.82 87.00 94.37 68.18 46.63 24.44

Challenge: find most similar data point

Challenge: find the outlier

x y x y x y

54.88 71.52 97.86 79.92 35.95 43.70

60.28 54.49 46.15 78.05 69.76 6.02

42.37 64.59 11.83 63.99 66.68 67.06

43.76 89.18 14.34 94.47 21.04 12.89

96.37 38.34 52.18 41.47 31.54 36.37

79.17 52.89 26.46 77.42 57.02 43.86

56.80 92.56 45.62 56.84 98.84 10.20

7.10 8.71 1.88 61.76 20.89 16.13

2.02 83.26 61.21 61.69 65.31 25.33

77.82 87.00 94.37 68.18 46.63 24.44

Challenge: find the outlier

Avoiding limitations of statistics

Showing patterns in large data sets with minimal information loss.

Revealing structure of tricky data sets where typical summary statistics do a poor job.

How does visualization help?

Showing patterns in large data sets

https://www.facebook.com/notes/facebook-engineering/visualizing-friendships/469716398919

Describing statistically tricky data

http://www.stanford.edu/~mwaskom/software/seaborn/examples/anscombes_quartet.html

All four have the same:

mean(x)variance(x)mean(y)variance(y)correlation coefficientregression coefficients

Anscombes Quartet

(Francis Anscombe, 1973)

Describing statistically tricky data

Much web data, especially involving human preferences or choices, looks like this.

There is no central tendency so typical descriptive statistics are useless.

Zipfian distribution, an example of a power law.

How does visualization help?

Illustrating a story

Visualizations are often used simply to clarify or reinforce the main points of a story, narrative or message.

This process fails when the conclusions suggested by the graphic are irrelevant to the narrative, or even contradict it.

It can also fail when the graphic has no clear message or multiple conflicting interpretations, or is largely incomprehensible.

Many of the following examples illustrate these mistakes.

Part II Bad Dataviz

1. Axes of evil

Bad dataviz

http://fluffware.tumblr.com/post/4580822773/axes

http://www.google.co.uk/trends/explore#q=%22data science%22

Unlabelled axes

Firearms (skjutvapen) seizures report: http://bit.ly/1dHnFzC (PDF) via Junk Charts

Axis scale manipulation

https://twitter.com/jk_keller/status/410498080765919232/photo/1

Axis scale manipulation (totally shameless version)

Version published by Reuters Version fixed by @jk_keller

Example from Stephen Few (PDF)

Dual axes: caution

Natural interpretation:

Units sold dipped below revenue (A) and is now catching up (B).

But these impressions are meaningless.

They are just artefacts of the chosen axis scales.

A

B

Proportionality errors

From an Australian document found at The Guardian

1 row of people = roughly 43,000 nurses.

10 rows = roughly 48,000 nurses.

?!?

Cheating outright?

All found via The Guardian

Quick quiz: what happened in 2005?

Axis inversion: when down means up?!?

From Thomson Reuters via Business Insider

Version published by Reuters Version fixed by @PFedewa

Bad dataviz

2. Distance vs. area vs. volume

http://muhammadfamizwanabdullah.blogspot.co.uk/2010/11/10-introduction-of-teaching-volume-of.html

Pie charts: avoid

Bad

Colours used for separating slices, so cant easily be put to another use.

No way to show time dimension statically.

Comparing relative sizes of slices is hard.

Doing it in 3D is harder. Perspective inflates nearer slices, and the similar volume of the objects is a red herring.

Doing it with deep, discontinuous 3D objects is even harder.

Worse

Worst

Perhaps justifiable (in 2D) if numbers are sufficiently different.

Otherwise, use a much simpler design and avoid all those problems.

Pie charts: avoid

Pie chart horrors

http://junkcharts.typepad.com/junk_charts/2014/03/two-charts-that-fail-self-sufficiency.html

Pie charts are supposed to show proportions of a whole.

People expect the %s to add up to 100%.

This one shows proportions of separate quantities.

Pie chart horrors

From a World Bank report (PDF) found at The Guardian

These ones show 96% and 40% as full circles.

This one is falling apart.

This one thinks 76% is less than three quarters.

Even worse uses of 3D

https://www.tableausoftware.com/public/blog/2011/01/viz-wiz-1-11and http://www.simplexnumerica.com/Gallery/gallery_pyramid.html

Cones, pyramids, spheres etc

Are we comparing width, height, area or volume? Nobody knows!

26.76% = tiny peak

23.32% = massive slab

?!?

Stacked charts: caution

Stacked charts show how a data series breaks down by another attribute of the data.

But people often misread these as two distinct data series, reading off a separate y-axis value for each one.

Bubble charts: avoid

http://commons.wikimedia.org/wiki/File:Bubble_Chart_Chicago_Deposit_Market.jpg

Bubble charts: avoid

http://bit.ly/1okS3nE and http://bit.ly/1hdZQtO

Bad dataviz

3. Bad maps

http://xkcd.com/1138/

Non-normalized quantities are useless

http://personal.frostburg.edu/jibandy0/starbucks%20map.jpg

Dont use absolute values without a very good reason.

Normalize appropriately:

per capita, per adult, per student, per household, per square km, per journey, per voter

Remember: geopolitical boundaries are artificial

This map shows all the countries Ive visited.

The relative size of USA makes me seem much more widely travelled than I really am.

Is country the right level of aggregation?

Remember: map projections lie

http://en.wikipedia.org/wiki/File:Hobo%E2%80%93Dyer_projection_SW.jpg

http://en.wikipedia.org/wiki/File:Choropleth-density.png

Consider using fixed-size bins

http://bit.ly/O9EPta

Drawbacks of maps

Cant easily show time dimension, without animation

Hard to show multiple attributes of data at once

Physical proximity can obscure demographic/cultural differences, and vice versa

Just because you can map the data, doesnt mean you should.

Save maps for when geographical trends are the key focus.

4. Colour choice

Bad dataviz

Good colour palettes from RColorBrewer

Sequential data

http://mapsdeguap.blogspot.co.uk/2012/04/choropleth-map.html

Use a smooth transition from min to max.

Dont cycle more than once.

This map goes purple-green twice.

A better choice would be:

Diverging data

http://www-03.ibm.com/press/us/en/pressrelease/35359.wss

Here the yellow section indicates the median. Red/green = above/below median.

However, the red and green ranges are not scaled well. 75 (close to median) is almost the same colour as 108 (max).

Sequential data, but with a well-defined midpoint.

Two directions from this midpoint -- two poles:

above/below average, positive/negative, female/male, Democrat/Republican etc.

Categorical data

Also known as nominal or qualitative.

Colours should not form a pattern, as this can imply a false relationship.

The ethnicity colours here are reasonable, although quite close in colour space.

The location colours are badly chosen. They suggest a linear progression, which is meaningless.

http://www.visualizing.org/full-screen/10886

Consistency

Dont do this.

http://www.raterush.com/pages/digg-reddit

Other considerations

Colour blindness -- nearly 10% of men -- rare in women Print and photocopy friendliness Characteristics of different screens, esp. projectors

ColorBrewer is a great help:

See also brewer2mpl (Python) RColorBrewer (R) ColorBrewer (Matlab)

http://colorbrewer2.org/

Bad dataviz

5. Correlation vs. causation

http://xkcd.com/552/

Beware of bogus correlations

http://gizmodo.com/5977989/internet-explorer-vs-murder-rate-will-be-your-favorite-chart-today/and http://pubs.acs.org/doi/abs/10.1021/ci700332k

Correlation does not prove causation, even with a good R2 score.

Beware of bogus correlations

Even respectable journals sometimes get carried away.

Ask yourself:

Are these both effects of a common cause?

Or just sheer chance? (Multiple comparisons)

http://www.nejm.org/doi/full/10.1056/NEJMon1211064

Bad dataviz

6. Trying to say too much

Each visualization needs a clear purpose. But some designers and analysts try to include every possible piece of information.

This is not a good idea.

Unnecessary detail and ostentatiously clever presentation can obscure the real message.

http://bit.ly/1gVzeUe

Dont do this.

7. Tips for developing a critical eye

Here are some techniques you can use for critical analysis.

They are often subjective, debatable, context-dependent and partly based on aesthetics So dont expect absolute rules.

Bad dataviz

Usability

Does the chart need detailed instructions in order for it to be comprehensible and usable?

Acceptable if this is a standard visualization method used in a particular domain

Less acceptable if this is a one-off for general consumption

First impressions test

What is the first thing you infer from looking at the visualization?

(Dont stop to read every detail -- see what you get from a glance.)

Does this impression prove to be accurate,on closer inspection?

If not, then there may be a problem.

Many people will only glance and neverperform the close inspection.

Return on effort (Kaiser Fung)

http://bit.ly/1dKewGo

Self-sufficiency test (Kaiser Fung)

Would the chart make sense without the numbers printed on each data point?

If not, the chart has failed the self-sufficiency test.

http://junkcharts.typepad.com/junk_charts/2013/03/blowing-the-whistle-at-bubble-charts.html

Trifecta checkup (Kaiser Fung)

Ask the following:

What practical question does the graphic attempt to address?

What answer does the data imply? What answer does the graphic imply?

Can you answer these clearly?Do the three answers align?

If not, there is something wrong.

http://junkcharts.typepad.com/junk_charts/2014/02/pets-may-need-shelter-from-this-terrible-chart.html

Data-ink score (Edward Tufte)

Main principle: Remove redundant or uninformative elements from the design, to reduce distraction. High data-ink ratio = clarity.

http://www.infovis-wiki.net/index.php/Data-Ink_Ratio

And finally

Ask yourself how much you trust the data.

Professional presentation does not imply reliable numbers.

Is there enough data to be sure of statistical significance?What are the margins of error?

Is there a plausible mechanism of action?

What about sources of bias (accidental or intentional), confounding factors, missing data, or measurement error (noise)?

Thank you!

http://www.makefive.com/categories/entertainment/other/pie-charts-that-explain-simple-material/percentage-of-chart-which-resembles-pac-man