how to make a picture worth a thousand words: effectively ...guiding principles • make the data...

How to make a picture worth a thousand words:

Effectively communicating your research results using statistical graphics

Yates Coley, PhD Kaiser Permanente Washington Health Research Institute

Seattle , WA

Joint work with Mike Jackson, PhD, KPWHRI

April 4, 2018

Seminar Outline

• Introduction

• Fundamentals of Statistical Graphics

• Data Visualization Best Practices

• Resources

Data Visualization is…

• a scientific discipline.

• both a principled and subjective art.

• work!

• important!

• an organizing framework.

Objectives

• Present organizing framework for data visualization

• Describe conceptual best practices for creating statistical graphics and give concrete examples

• Provide sources and references for future consultation

Seminar Outline

• Introduction



• Resources

Components of a data visualization

• Visual Cues

• Coordinate System

• Scale

• Context

Yau (2013) Data PointsFIGURE 33 Visual cuesYau, Nathan. Data Points, edited by Nathan Yau, Wiley, 2013. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/jhu/detail.action?docID=1158630.Created from jhu on 2017-05-28 16:03:12.

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Visual Cues


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Visual Cues

Quantitative Variables


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Visual Cues

Categorical Variables

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PSA

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● ●Low volume High volume

Diagnostic characteristics of patients in active surveillance

Diagnostic biopsy

Components of a data visualization

• Visual Cues

• Coordinate System

• Scale

• Context

Data Visualization Process

• What data do you have?

• What do you want to know about your data?

• What visualization method should you use?

• What do you see and does it make sense?

Yau (2013) Data Points



• Continuous, ordinal, or categorical?

• Time series?





Data Visualization Process• What data do you have?


• Distributions of single variables?

• Relationships between variables?

• Summaries or unit-level detail?




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Age (years)

PSA

(ng/

mL)

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05

1015

20PSA observations for patients throughout active surveillance

0

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PSA observations for patients throughout active surveillance

Age (years)

PSA

(ng/

mL)

Diagnostic biopsy

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Seminar Outline

• Introduction



• Resources

How do we define an “effective” statistical graphic?

• An effective statistical graphic enables the reader to

• extract information accurately

• with reasonable effort and

• high confidence.

Enrico Bertini Lecture #3

https://drive.google.com/drive/folders/0B-9uY9BLNUVFajg1bGg5YWp3V0k

Expressiveness Principle

Statistical graphic “should express all and only the information in the data” (and statistical results).




0 100 200 300 400 500 600 1200700 800 900 1000 1100

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line chart with categorical data (wrong!)


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Effectiveness Principle• “The importance of the information should match

the salience of the mode of visual encoding”.

• “Salience” is characterized by:

• Accuracy

• Discriminability

• Separability

• “Pop-out”

• GroupingEnrico Bertini Lecture #4



Quantitative Variables Categorical Variables


Diagnostic biopsy

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PSA

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! !Low volume High volume

Diagnostic characteristics of patients in active surveillance AccuracyDiscriminability

SeparabilityPop-out

Grouping

Source: New York Times

https://www.nytimes.com/interactive/2018/03/06/business/china-tariffs.htmlhttps://drive.google.com/drive/folders/0B-9uY9BLNUVFajg1bGg5YWp3V0k

Source: The Economist

https://www.economist.com/blogs/graphicdetail/2018/03/daily-chart-13

Diagnostic biopsy

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Diagnostic biopsy



Perceptionvs.

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Guiding Principles• Make the data stand out. Maximize the data-to-

ink ratio.

• Avoid superfluidity. Remove “chartjunk”. Reduce non-data ink and redundant data-ink.

• Strive for clarity.

• Clear vision.

• Clear understanding.

Cleveland (1983) Elements of Graphing Data Edward Tufte (1985) Visual Display of Quantitative Information

Visual Cues“Make graphical elements encoding data visually prominent.”

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CARDIOVASCULAROTHER

FIRSTCARDIOVASCULAR

CARE UNIT

1950 1960 1970 1980

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Cleveland (1983) VDQI, Ch. 2

Visual ProminencePlotting symbols are large, dark enough to be easily seen

! CARDIOVASCULAROTHER

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CARE UNIT

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Plotting symbols are large, dark enough to be easily seen

Visual ProminencePlotting symbols aren’t obscured by connecting lines


FIRSTCARDIOVASCULAR

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Plotting symbols aren’t obscured by connecting lines

Visual ProminenceOverlapping plotting symbols are easily distinguishable


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Visual ProminenceSuperposed data readily visually discriminated


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Superposed data readily visually discriminated

Visual ProminenceGraphical elements do not interfere with data


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Graphical elements do not interfere with data

Visual hierarchy


Visual Hierarchy | 203

point of interest. This creates a visual hierarchy that helps readers immediately focus on the vital parts of a data graphic and use the surroundings as context, as opposed to a flat graphic that a reader must visually rummage through.

For example, Figure 5-1 is the scatterplot from the previous chapter that shows NBA players’ usage percentage versus points per game. The dots, fitted line, grid, border, and labels are of the same color and thickness, so there is no clear visual focus. It’s a flat image, where all the elements are on the same level.

FIGURE 51 All visual elements on the same level

This is easily remedied with a few small changes. In Figure 5-2, the line width of the grid lines is reduced so that they are no longer as thick as the fitted line. In this example, you want the data to stand out. The grid lines also alternate in width so that it is easier to see where each data point lies in the coordinate system, and there’s no imaginary blur that you get in the original chart.

FIGURE 52 Width of grid lines reduced to fit in background

Yau, Nathan. Data Points, edited by Nathan Yau, Wiley, 2013. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/jhu/detail.action?docID=1158630.Created from jhu on 2017-05-28 16:12:31.

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Place visual elements on different “levels” to shift focus, draw attention to most important aspect of data or results.

Visual hierarchyPlace visual elements on different “levels” to shift focus, draw attention to most important aspect of data or results.


204 | CHAPTER 5: Visualizing with Clarity

Still though, the fitted line is obscured by all the dots, because (1) it’s thin com-pared to the radius of each dot and (2) it still blends in with the grid behind it. Figure 5-3 changes the color to blue to make the data stand out more, and the width of the fitted line is increased so that it clearly rests on top of the dots.

FIGURE 53 Focus of chart shifted to fitted line with color and width

The chart is a lot more readable now, but if you imagine people viewing the graphic like they would a body of text—from top to bottom and left to right—more descriptive axis labels and less prominent value labels can help, as shown in Figure 5-4. The text within the chart works similar to how it does in an essay or a book. Headers are often printed bigger and in a bold font to provide both structure and a sense of flow. In this case, the bolder labels provide immediate context for what the chart is about. Also, notice fewer and less prominent gridlines, which directs focus further to the upward trend.

FIGURE 54 Grid and value labels adjusted and fewer, less prominent gridlines


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ink ratio.



• Clear vision.



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Cardiovasculardeaths

Other deaths

First cardiovascularcare unit

Year

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Scales• “Choose the scales

so that data fill up as much of the data region as possible.”

• “Choose the range of the tick marks to include or nearly include the range of the data.”

Cleveland (1983) Elements of Graphing Data

Visualization Components | 109

FIGURE 315 Scales

Numeric

The visual spacing on a linear scale is the same regardless of where you are on the axis. So if you were to measure the distance between two points on the lower end of the scale, it’d be the same if they were at the high end of the scale.

On the other hand, a logarithmic scale condenses as you increase values. This scale is used less than the linear scale and is not as well understood or straightforward for those who don’t regularly work with data, but it’s useful if you’re interested in percent differences more than you are raw counts or your data has a wide range.

For example, when you compare state populations in the United States, you deal with numbers from the hundreds of thousands up to the tens of millions. As of this writing, California has a population of approximately 38 million peo-ple, whereas Wyoming has a population of approximately 600,000. As shown


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Dual y-axes: NOT clear

Dual y-axes: NOT clear

250,000

300,000

350,000

2 million

1 million

1.5 million

0.5 million

# Abortions # Cancer screening,Prevention

Source Evergreen Data

http://stephanieevergreen.com/two-alternatives-to-using-a-second-y-axis/

Clear Understanding• Provide clear explanations for error bars,

confidence bands, etc.

• Make legends comprehensive and informative.

1. Describe everything that is graphed.

2. Draw attention to the important features of the data.

3. Describe the conclusions that are drawn from the data on the graph.


Keep it simple. Or not.

• “A large amount of quantitative information can be packed into a small region.” (p. 90)

• “Many useful graphs require careful, detailed study.” (p. 94)


Proofread. Edit. Revise. Repeat.

• Creating statistical graphics is an iterative process.

• Consider alternative graphical approaches.

• Share graphics with collaborators, colleagues to gauge understanding.

• For presentation: evaluate figures (size, color) when projected on big screen

Seminar Outline

• Introduction



• Resources

Books on Data Visualization• William Cleveland The Elements of Graphing Data (1985)• Edward Tufte:

• The Visual Display of Quantitative Information (1983, 2001)

• Envisioning Information (1990, 2001) • Visual Explanations (1997) • Beautiful Evidence (2006)

• Leland Wilkinson Grammar of Graphics (1999) • Nathan Yau

• Visualize This (2011) • Data Points (2013)

Online Resources

• Flowing Data (Nathan Yau)

• Information Visualization course from Enrico Bertini

• Data Remixed (Ben Jones)

• Dear Data (Giorgia Lupi and Stefanie Posavec)

• WTF Visualizations

http://flowingdata.com/http://enrico.bertini.io/teaching/http://dataremixed.comhttp://www.dear-data.comhttp://viz.wtf/

Short course by Mike Jackson, October 22

how to make a picture worth a thousand words: effectively ...guiding principles • make the data...

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