Resources

1. Back to Viz Basics
2. Week 4 Challenge
3. My Workbook

KPI Samples from Tableau Public:

If you're not participating in the Back to Viz Basics community project yet, you should be. It's all about creating effective, simple charts, which is exactly what you need to be able to do before you build anything fancy. This will definitely help you master data visualization best practices.

For week 3, the objective was to build a text table of Olympic medal counts. Sounds simple, but unfortunately, building good looking tables isn't one of Tableau's strengths. 

I also started the #WatchMeViz livestreams again. You can find all of them on this playlist. Thank you to those that attended live and asked good questions and left thoughtful comments. Below is the livestream as well as my final viz.

Until next week...

A Merimekko Chart, also known as a variable width bar chart, is useful for comparing both high-level data and low-level data in the same chart. However, since we have size variables for both the width of the bars AND the height of the bars, comparing different segments of the chart is more challenging than it needs to be. Read more about the problems with Merimekko charts here.

In this example, I show you how to make an alternative to a traditional Merimekko Chart. I show you how to compare both category sales and regions sales within each category at the same time.

This can be easily replicated by simply swapping out the dimensions and updating one LOD calculation. Alternatively, you could use two parameters to allow the user to pick both the high-level and low-level dimensions and a third parameter for the measure to compare.

In this tip I show you how to use the INDEX and RANK functions to label the top N values in a chart. I also explain the difference in how they work, and how the RANK function is simpler to configure.

This tip comes from a comment from a viewer. They wanted to know how they could build a dual-axis chart based on a single dimension (e.g., Order Date) and a single measure (e.g., Sales). In this tip I show you how to compare the monthly sales for the current year (as a line) to the prior year (as a bar).

In this tip I show you how to create a jitter plot. A jitter plot is very similar to a dot strip plot other than it reduces the overlapping of the data points. The data is plotted like a dot plot and then we use either the RANDOM() or INDEX() functions to spread out the dots.

Data - https://data.world/vizwiz/mock-car-sales

NOTE: The insights you see in this post are based on an article by The Upshot from September 2018. Some of the insights and use cases demonstrated are the same and are shown in Tableau for demonstration purposes.

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When I first saw the map that The Upshot created in How Connected Is Your Community to Everywhere Else in America? I was blow away by the simplicity of the map and how easy it is to understand the relationships of people in the United States via their Facebook friendships. The first thing you need to understand is the metric "Social Connectedness Index". You can access the data I used via the same link. 

Here's the formula Facebook uses to calculate the index:

From Facebook:

Social Connectednessi,j measures the relative probability of a Facebook friendship link between a given Facebook user in location i and a user in location j. Put differently, if this measure is twice as large, a Facebook user in i is about twice as likely to be connected with a given Facebook user in j.

In each dataset, we scale the measure to have a fixed maximum value (by dividing the original measure by the maximum and multiplying by 1,000,000,000) and the lowest possible value of 1. We also round the measure to the nearest integer.

I was not able to match the color scale in The Upshot exactly, so instead I used a table calculation that ranks each County in the U.S. compared to the County selected by the user.

The data has columns for the State/County of the user and for State/County of the friend. To ensure that I was only looking at friends for the County selected, I used Parameter Actions to filter the user to the County and State selected. The rank calculation then only uses the SCI for the friends.

Now let's look through some of the use cases as described in The Upshot.

DISTANCE IS MOST IMPORTANT

STATE LINES ARE BOUNDARIES

In some counties (like the four below, friendships drop significantly outside State borders.

MIGRATION PATTERNS

People from certain areas of the country have migrated to other areas in the country over the course of many decades. We can see these patterns by looking at Chicago and Milwaukee. The southern counties were typically related to the slave trade, and the people in the south gradually migrated north after they were freed.

Migration patterns aren't limited to history. Consider counties in the Northeast. Nearly all of them have a strong relationship with coastal areas in South Carolina and Georgia and all of Florida. These are called snowbirds, people that migrate south for the winter.

PHYSICAL BOUNDARIES

Friendships in some counties are limited by geographical boundaries. For example, friendships for people living in Belmont County, Ohio don't cross the Appalachian Mountains in West Virginia.

While people in Scott County, Arkansas don't have friends on the other side of the Mississippi River.

Have some fun with the interactive version below.