Why Vizioneer?

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Atlanta, Georgia, United States
The "Vizioneer" comes from mashing two words that have shaped my world for most of my adult life - Engineer and [data] Visualizations (or Vizes to those who know what's up). Graduating from first from Oglethorpe University in Atlanta, followed by Georgia Tech with my Bachelors and Masters in Civil Engineering, all of which taught me to think through anything and everything - problem solving, "engineering" solutions, teaching to the "ah ha" moments - is what I love to do. In 2010 that investigative, engineering mindset intersected a job change and a plunge into the world of Data Analysis. In the search for the next great thing I stumbled on to a data visualization and dashboarding product called Tableau software and things just took off. So now I guess you could call me that engineer with the sweet data visualizations - or just "The Vizioneer" :)

In 2013, I joined the incredible team at Slalom, focusing on Tableau and it's been an amazing experience. Recently in 2014, I was honored and humbled to receive Tableau's highest recognition of being named a Tableau Zen Master. Follow along to see what happens next :)

Wednesday, April 23, 2014

Day 23: 45 Degree Line

One of the best things about working at Slalom is being surrounded by awesome people.  We push each other to do great work and great things for the community.  Today I welcome the first of a few guest blogs on the #Tableau30for30.  I'm proud to bring this Cy Young out of the bullpen - the recent Sports Iron Viz champion John Mathis. This awesome post was first seen on his blog, The Datographer.  I give you Day 23 - the 45 Degree Line Reference line.

Creating a 45 Degree Reference Line in a Tableau Scatter Plot (without SQL!) 

A scatter plot chart is useful to compare two measures and quickly identify clusters, outliers and trends. One can quickly identify relationships between the two measures, for example Sales vs. Profit or Age vs. Income. While we would expect to see correlations and patterns in these examples, it does not make sense to see how close they are to parity (e.g. 42 years old vs. $51,000 in income). However when there are certain, similar measures, it may provide additional insight to see how closely the measures equal each other or if there is a divergence. Several examples of this would be goals scored at home vs. away or average male education vs. female education.

In situations where it is logical to make this comparison, a simple 45 degree line on a scatter plot can show how close or far the points are to parity. While working on my Hans Rosling - Fighting Ignorance viz, I wanted to include a reference line to help users understand educational inequality between genders. Below I've outlined the steps used to create this reference line in Tableau without using any SQL.

We begin with a scatter plot showing male education on the X axis and female education on the Y axis. At a glance, it is hard to tell how close the average male education compares to the average female education:
Our starting point - while this chart provides some insight, the gender education inequality is not obvious.
The first step is to create a calculated field that will be plotted as the reference line. I will title it 'Reference Line'. We are going to make it equal to Male Education so that at any point X the value for this field will be X: 
By creating  a field equal to male education we can plot it on the male education axis resulting in a simple 45 degree line.
Next we add the newly created calculated measure to Rows and right click to set it as 'Dual Axis' so both the educational data and reference line are on the same chart. Note that due to Tableau defaults, it colors and formats the reference line data just like the educational data. We'll be correcting that shortly!
Tableau defaults the reference line formatting to match that of education data so it is segmented by size (year) and color (geography).

Tableau defaulted the Reference Line field to Sum so we need to adjust it back to Average:
This ensures the line is at the same scale as the educational data.

Because we want to compare apples-to-apples between our data points and reference line it is critical we synchronize the axes between the data and reference line or otherwise we'll mislead the user:
It is essential the axes are synchronized, otherwise users will be misled.

Now we need to format the reference line by taking several actions. First remove the color and size variation for geography and year. Second, to increase the range of the line, we need to increase the granularity of the data so I have added the country dimension ensuring that no matter how the chart is filtered by the user, the line reaches from the lowest point to the highest. Lastly, we change the color from the default light grey to black so that it is more visible on the chart:

We must remember to clean up the default tooltip generated by Tableau. I have removed all of the variables and given it the description "Reference Line - Avg. Male Education = Avg. Female Education". I also removed the command buttons as they do not serve a purpose on the reference line:
Remove the default text and provide a description to help your user understand the significance of the line.

Finally we hide the second axis and our new and improved scatter plot chart with a 45 degree reference line is complete! It is now easy to see which geographies and countries have near parity between male and female education and which ones have a large gender gap.
With the reference line in place, it is now apparent that most geographies are nearing gender educational equality while Africa has diverged further from educational equality.


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