I love a chart where you don't need to write any commentary to understand the chart or to understand the analysis. This is one of those charts.

Click on your birthday to see how popular it is.

In this tip, you will learn how to show the rank of each team in MLB by homeruns for 24 seasons. 

This week I decided to turn Makeover Monday into a Workout Wednesday of sorts. I wanted to try a technique I haven't used in a while...showing elements on either side of a rank and filtering out the rest.

This week was a fairly simple data set. Rankings by year by country for a single measure. One thing I found tough to get my head around was the 1-7 scale of the quality ratings. I converted the values to a 1-10 scale instead.

Makeover Monday is back! Every Monday I'll be running a #WatchMeViz and every Wednesday, Eva will be running #VizReview. Subscribe to my YouTube channel for reminders of the latest events.

https://youtube.com/andykriebel

This week was a makeover of a visualization by Visual Capitalist about the top 10 military spenders. During Watch Me Viz, I iterated through 15 different charts before settling on a bump chart. If you want to learn about sets, parameters, table calculations, containers and more, watch the video below.

Below the video you can see my viz, or click here. Enjoy! If you need clarifications on anything, please comment on the video here.

In this tip, I show you how to create a hexbin map in Tableau. But first, let me explain the purpose, benefits and drawback of a hexbin map.

There are countless times when I have been asked to display way too much data on a map. Sometimes I’ve been asked to display thousands of points on a map..but why? I know no one will understand it. 

A hexbin map uses hexagons to divide an area into multiple parts and assign a color gradient to each hexagon. This chart type is used to visualize density, where the hexagons dividing the whole space into discrete units of equal size. 

This video is going to help you communicate the concentration of data on maps more effectively and give you another option for visualizing geospatial data. I’ll show you how to create a hexbin map based on both the value and the rank, giving you two options depending on your use case.

If you want to follow along, these are the data sources I used:

1. Austin Bike Accidents - https://bit.ly/AustinBikeAccidents

2. Austin Zip Codes Shapefile - https://bit.ly/AustinZipCodes

3. Price Summary - https://bit.ly/PriceSummary

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.

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.

Close enough for me! 

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 this tip, I show you how to use a parameter to filter the rank across multiple worksheets or an entire dashboard.

In this week's Watch Me Viz, I covered the following charts: 

1. Line chart
2. Trellis chart
3. Slope graph
4. Connected scatter plot
5. Bar charts with comparisons
6. Diverging bar charts
7. Heatmap
8. Hex map
9. Tile map
10. Barbell chart
11. Peas in a pod chart
12. Bump Chart
13. Comet chart

In the end, I settled on the bump chart using highlighting and BANs. Other topics covered include:

1. Sorting calculations
2. Level of detail expressions
3. Rank table calculations
4. Parameters
5. Filter actions
6. Padding in dashboards
7. Cleaning tooltips
8. Divider lines in dashboards

The original viz for this week was so good that I struggled to come up with something different. In the end, I wanted to learn by recreating the original. Check out #WatchMeViz and interact with the viz below.

In this video, I show you how to use the RANK table calculation to identify which sub-category has the most sales in each State. While this example answers a question that works best on a map, you can easily apply this technique to any dimension or combination of dimensions for which you need to find the largest.

One of the drawback of filtering a dimension is that you will lose the rank of a dimension relative to others. This happens because of Tableau's order of operations which dictate that a dimension filter takes effect before a table calculation (rank in this case).

This video shows you how to use Set Actions to filter a dimension while also maintaining the rank.

Have you ever needed to compare the rank of items, but only show two of them? For example, you want to show any player compared to Player A. Player A should always be in the view and another play should only be shown when selected.

And, when you display any players, you need to show their rank amongst everyone. This is where set actions come into play.

In this example, I show you how to compare any car to a car that you selected from a parameter. The car you select from the scatterplot appears next to the car selected from the parameter and the overall rank for each car is displayed.

Today DS13 was supposed to have most of the day to work on their client project. However, after a training session where I showed them how I approach a new data set and then design a dashboard, we brought Sophie Sparkes in to throw a surprise dashboard week challenge at the team. After all, they only had three days of dashboard week anyway.

Sophie's Challenge

While Tableau is an amazing tool, when you use it all the time you can fall into data-viz-auto-pilot mode. You build the same kinds of charts; you construct similar kinds of dashboards; you fall back on the same formatting styles. While familiarity with tool, and a workflow, is a good thing, it also narrows your view of what’s possible.

For today’s Dashboard Week challenge, I want you to step outside your data viz comfort zones and try building a viz using Flourish. Flourish is a free tool that lets your build interactive, responsive, and embeddable vizzes and data stories, all within the browser using your own data. Flourish is focused at the communication side of data viz (more than the data exploration side), and I’d like DS13 to really think about communication in today’s challenge.

Why Flourish? I really like their wide (and ever expanding) range of templates and interactivity (transitions, stories and ‘Talkies’ to name a few); also they are based in London – so why not viz-local?

Using any part (years, geographic locations, genders) of the England and Wales baby names data sets, I want DS13 to find and communicate one specific story from this data set.

Here are the rules for today, and what I’d like to see as output:

1. They must work independently.
2. Everything must be finished by 5pm.
3. They must use Tableau and Alteryx for the data prep and exploration.
4. The final viz must be made in Flourish.

My Approach

First, I had to get some data. I decided to download the data from the ONS for 1996-2016 because it was in a relatively decent format.

Next, I opened the "Plotting Competitors" example because I loved the animation. The great thing about Flourish is you can immediate use the template. All you need to do is upload your own data, assign the columns, and you're done!

For Makeover Monday week 48, Eva chose this visualization from Thrillist (created by Statista):

What works well?

What could be improved?

What I did

This tip is inspired by Craig Bloodworth, CTO of The Information Lab and Tableau Zen Master Hall of Fame member. Craig demonstrated this when an attendee at our Zen Master session asked how you can filter a dimension and maintain the rank.

I gave one solution which would show and hide dimensions, but Craig's is way way better. It's a neat way of using table calcs to filter a dimension since a table calc filter happens AFTER other table calcs are computed.

Also, in the video I reference our new meetup, Let's Talk Data. If you're in London, there will be tons of events at the new home of The Data School. If not, don't fret! We have some virtual events as well.

With that, here's the how you can filter a dimension and maintain the rank.

The 2018-19 Premier League season kicks off tomorrow night with an enticing match between Manchester United and Leicester City. This reminded me about a viz I had created at the end of the last season as a way of practicing stepped lines in Tableau.

When I originally created this, I had to use table calcs to get the stepped lines to work, which can get complicated and is very time consuming. Now with stepped lines, it merely a matter of changing the lines type.

I decided to add in a couple of user options:

1. Which team do you want to highlight?
2. How do you want to compare the teams? By total points for the season of the final position in the table?
3. Not all teams have been in the EPL for all 17 years, so I provided an option to filter down to just the teams that have been in the EPL for the user specified number of years.