# DataViz: Chart of Cousins (Flowing Data)

From Flowing Data.

I have a big family, and it seems at every get-together there are new additions and more kids running around. At some point, I lost track of how I was related to everyone else. I mean, I know that some people are my cousins and I can trace everyone back to a common ancestor, but I’m not sure what to call everyone.

So after the latest get-together, I figured I’d chart it out. It turns out there are a lot of charts and explainers available (not surprising), but they were kind of hard to read. Hopefully the revised chart above makes it a bit easier.

Here’s how it works. Figure out the common ancestor between two relatives. Then select the relationship of the first relative to the common ancestor in the top row. Move down to the row that corresponds to the relationship of the second person to the common ancestor. The result is the relationship of the second person to the first.

For example, say the first person is the grandchild of the common ancestor, and the second person is a great-grandchild. Therefore, the second person is the first cousin once removed from the first.

There is of course a pattern to all of this. Wikipedia explains:

There is a mathematical way to identify the degree of cousinship shared by two individuals. In the description of each individual’s relationship to the most recent common ancestor, each “great” or “grand” has a numerical value of 1. The following examples demonstrate how this is applied.

Example: If person one’s great-great-great-grandfather is person two’s grandfather, then person one’s “number” is 4 (great + great + great + grand = 4) and person two’s “number” is 1 (grand = 1). The smaller of the two numbers is the degree of cousinship. The two people in this example are first cousins. The difference between the two people’s “numbers” is the degree of removal. In this case, the two people are thrice (4 — 1 = 3) removed, making them first cousins three times removed.

Now I don’t have to think about it all now, and can just say “Hi, first cousin once removed.” What a load off of my shoulders.

# Dataviz: Crayola Crayons – How Color Has Changed

When I was a young boy, I loved to color with my big box of Crayola Crayons. I would pull out blank sheets of paper and create multi-colored masterpieces (at least my mother said so).

Crayola’s crayon chronology tracks their standard box, from its humble eight color beginnings in 1903 to the present day’s 120-count lineup. According to Crayola, of the seventy-two colors from the official 1975 set – sixty-one survive. [1]

A creative dataviz type who goes by the name Velociraptor (referred from here as “Velo”) created the chart below to show the historical crayonology (I just made that word up!) of Crayola Crayons colors.

Velo gently scraped Wikipedia’s list of Crayola colors, corrected a few hues, and added the standard 16-count School Crayon box available in 1935.

Except for the dayglow-ski-jacket-inspired burst of neon magentas at the end of the ’80s, the official color set has remained remarkably faithful to its roots!

Ever industrious, Velo also calculated the average growth rate: 2.56% annually. For maximum understandability, he reformulated it as “Crayola’s Law,” which states:

The number of colors doubles every 28 years!

If the Law holds true, Crayola’s gonna need a bigger box, because by the year 2050, there’ll be 330 different crayons! [1]

### A Second Version

Velo was not satisfied with his first version, so he produced the second version below. [2]

### A Third Version (and interactive too!)

Click through to the interactive version for a larger view with mouseover color names!

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References:

[1] Stephen Von Worley, Color Me A Dinosaur, The History of Crayola Crayons, Charted, Data Pointed, January 15, 2010, http://www.datapointed.net/2010/01/crayola-crayon-color-chart/.

[2] Stephen Von Worley, Somewhere Over The Crayon-Bow, A Cheerier Crayola Color Chronology, Data Pointed, October 14, 2010, http://www.datapointed.net/2010/10/crayola-color-chart-rainbow-style/.

# Stephen Few: Now You See It

I was in Portland, Oregon last week attending three data visualization workshops by industry expert, Stephen Few. I was very excited to be sitting at the foot of the master for three days and soak in all of this great dataviz information.

Last Thursday, was the third workshop, Now You See It which is based on Steve’s best-selling book (see photo below).

To not give away too much of what Steve is teaching in the workshops, I have decided to discuss one of our workshop topics, human perceptual and cognitive strengths.

You can find future workshops by Steve on his website, Perceptual Edge.

Best Regards,

Michael

### Designed for Humans

Good visualizations and good visualization tools are carefully designed to take advantage of human perceptual and cognitive strengths and to augment human abilities that are weak. If the goal is to count the number of circles, this visualization isn’t well designed. It is difficult to remember what you have and have not counted.

Quickly, tell me how many blue circles you see below.

The visualization below, shows the same number of circles, however, is well designed for the counting task. Because the circles are grouped into small sets of five each, it is easy to remember which groups have and have not been counted, easy to quickly count the number of circles in each group, and easy to discover with little effort that each of the five groups contains the same number of circles (i.e., five), resulting in a total count of 25 circles.

The arrangement below is even better yet.

Information visualization makes possible an ideal balance between unconscious perceptual and conscious cognitive processes. With the proper tools, we can shift much of the analytical process from conscious processes in the brain to pre-attentive processes of visual perception, letting our eyes do what they do extremely well.

# Stephen Few: Information Dashboard Design

I am in Portland, Oregon this week attending three data visualization workshops by industry expert, Stephen Few. I am very excited to be sitting at the foot of the master for three days and soak in all of this great dataviz information.

Today, was the second workshop, Information Dashboard Design which is based on Steve’s best-selling book (see photo below).

To not give away too much of what Steve is teaching in the workshops, I have decided to discuss one of the dashboard exercises we did in class. The goal here was to find what we feel is wrong with the dashboard.

I will show you the dashboard first. Then, you can see our critique below.

You can find future workshops by Steve on his website, Perceptual Edge.

Best Regards,

Michael

### Critique Key Points

• Top left chart – Only left hand corner chart has anything to do with flight loading
• Top left chart – are flight numbers useful?
• Two Expand/Print buttons – Need more clarity (right-click on chart would be a better choice)
• Top right chart – Poor use of pie charts – size of pies are telling largest sales channel – use small multiple bar charts, total sales as a fourth bar chart
• Redundant use of “February” – In the title and in charts
• Bottom left chart – why does it have a pie chart in it?
• Bottom right chart  – map may be better as a bar chart (geographical display could be useful if we had more information). Current way bubbles are being expressed is not useful (use % cancellations instead). Symbols may have a different meaning every day
• Bottom right chart – CORDAir Logo – is this necessary?
• Location of drop-down. Not clear if it applies to top left chart or all charts
• Backgrounds – heavy colors, gradients
• Instructions should be in a separate help document. Only need to learn this once.
• Top left chart: Faint Image in background. Suppose to look like a flight seating map. Do you really want to see this every day? It is a visual distraction.
• IMPORTANT: Is there visual context offered with any of the graphs? No. This is critical.

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Dashboard Example Source: Website of Corda Technologies Incorporated, which has since been acquired by Domo.

# Stephen Few: Show Me The Numbers

I am in Portland, Oregon this week attending three data visualization workshops by industry expert, Stephen Few. I am very excited to be sitting at the foot of the master for three days and soak in all of this great dataviz information.

Yesterday, was the first workshop, Show Me the Numbers which is based on Steve’s best-selling book (see photo below).

To not give away too much of what Steve is teaching in the workshops, I have decided for today to show a “before and after” example with Steve’s explanation of why he made the changes he did.

You can find future workshops by Steve on his website, Perceptual Edge.

Best Regards,

Michael

### “Before” Example

In the example below, the message contained in the titles is not clearly displayed in the graphs. The message deals with the ratio of indirect to total sales – how it is declining domestically, while holding steady internationally. You’d have to work hard to get this message the display as it is currently designed.

### “After” Example

The revised example below, however, is designed very specifically to display the intended message. Because this graph, is skillfully designed to communicate, its message is crystal clear. A key feature that makes this so is the choice of percentage for the quantitative scale, rather than dollars.

### Additional Thoughts From Steve

The type of graph that is selected and the way it’s designed also have great impact on the message that is communicated. By simply switching from a line graph to a bar graph, the decrease in job satisfaction among those without college degrees in their later years is no longer as obvious.

# DataViz Chart: The Buildings of Detroit – Erin Jang

As many of you know, I am a born and raised Detroiter. Despite living in Arizona most of my adult life, I still root for the hometown sports team, seem to be able to find another Detroiter in a crowded room, and long for Sanders Hot Fudge and Lafayette Coney Island hot dogs and hamburgers.

I came across this beautiful chart of buildings in Detroit created by Eric Jang on her blog, The Indigo Bunting. Erin Jang is an art director and designer living in New York City. She has worked as a designer/illustrator/art director at several publications including Esquire magazine and was the senior art director at Martha Stewart Living magazine.

She now runs a design studio called The Indigo Bunting (theindigobunting.com) and works on custom freelance design projects and wedding invitations. Her design and illustration work has been recognized by PRINT magazine, Communication Arts, and the Society of Publication Design. She can be contacted at erin [at] theindigobunting [dot] com.

Check out Erin’s other work on her blog. It is truly beautiful.

Best regards,

Michael

### Illustrations of 16 historical buildings in Detroit

Art Director: Jessica Decker
DBusiness Magazine, March 2013.

References:
[1] Erin Jang, NEW WORK: Detroit Building Chart, The Indigo Bunting, 4/2/13, http://theindigobunting.blogspot.com/2013/04/new-work-detroit-building-chart.html.
[2] Bella Figura, Erin Jang Photo and Bio, http://www.bellafigura.com/designers/jang.html.

# Small Multiples, Tableau and Ben Jones

My BI world is changing a bit as I move more towards using Cognos and Tableau at work. In particular, I have a lot of status reports and dashboards to create for my leadership and I have been doing these mostly in Tableau.

I had a situation recently where I wanted to create a small multiples chart versus using a 3D Bar Chart that already existed. I have created small multiples charts fairly easily in MicroStrategy in my previous work, but have never created one before in Tableau. I reached out to Ben Jones (photo, right) at Tableau. I have been a big fan of Ben’s DataRemixed blog for quite some time and have blogged about Ben many times in the past. Ben was gracious enough to create a simple example small multiples chart for me to use to accomplish what I wanted to visualize. I was really impressed that Ben and Tableau did not put me through any red tape for him to help me. He saw I had a need and he helped me.

Much thanks to Ben for his help and I hope this example is useful to you.

Best Regards,

Michael

### Small Multiples

A small multiple (sometimes called trellis chart, lattice chart, grid chart, or panel chart) is a series or grid of small similar graphics or charts, allowing them to be easily compared. The term was popularized by data visualization pioneer, Edward Tufte.

According to Tufte (Envisioning Information, p. 67):

At the heart of quantitative reasoning is a single question: Compared to what? Small multiple designs, multivariate and data bountiful, answer directly by visually enforcing comparisons of changes, of the differences among objects, of the scope of alternatives. For a wide range of problems in data presentation, small multiples are the best design solution.

### A Small Multiples Example by Andrew Gelman

One of the most well-known examples of the use of small multiples is Andrew Gelman’s analysis of public support for vouchers, broken down by religion/ethnicity, income, and state (see image below).

Mr. Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), and A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina).

Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.

[Click on Image to Enlarge]

### My Small Multiples Chart

Since I cannot show you what I used the small multiples chart for related to my job, I made an illustrative, simple example related to home sales in different regions for the past six months. Below is an example of my chart, which I created using Tableau.

[Click on Image to Enlarge]

### Adding Trend Lines

One of the key features I wanted to use in my chart was to be able to show trend lines for each small multiple.

However, when I clicked on Trend Lines -> Show Trend Lines, I kept getting the following error message:

Ben pointed out that in my original chart, the Columns shelf, Month needed to be a Continuous data type (green pill) rather than a Discrete data type (blue pill).  If you click in the Month pill, you should be able to select “Change to Continuous” and then you should be able to add a trend line. This occurs because you can only calculate a trend line when two axes are involved. The way I had it set up, the Columns were just different categories or attributes, rather than continuous measures.

I thought this would be a nice tip to pass along.

I hope to be able to share more Tableau tips as I become more proficient with the tool.