Data Viz Done Right

October 14, 2019

#MakeoverMonday: Ironman World Championship Medalists

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Sunday was the 43rd Iron World Championship. It was the first time I spent a lot of time watching it and I found it pretty cool. I figured I should pay more attention to how it all works given I'm doing Challenge Roth in July. I'm a bit daunted by the prospect of competing in an Ironman distance, but I know I can do it with proper training.

The viz to makeover is a simple table from Wikipedia:

What works well?
  • The years are listed in order from most recent to oldest.
  • Table can be sorted
  • Including separate columns for each medal
  • Including links to each athlete

What could be improved?
  • I'm not convinced that both the flag and country abbreviations are necessary; one is probably enough.
  • Some of the athletes have red text and some have blue. I couldn't find anything on the page that explained this.
  • Comparing athletes across years is difficult because of the precision of the times/

What I did
After exploring the data for a few minutes, I remembered that Rody Zakovich created an incredible viz about the Winter Olympics (check it out here) and I've been wanting to emulate it. This data set proved perfect for it. This is the beauty of Tableau Public; you can download workbooks, see how someone created their work, and use it to help create your own.

Here's my viz for Makeover Monday week 42 (click on the image for the interactive version).

October 8, 2019

#TableauTipTuesday: How to Automatically Apply Worksheet Actions as Dashboard Actions

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This week's tip was brought to my attention by Zen Master Rosario Gauna. One of the really annoying things about Tableau for the longest time has been needing to create a separate dashboard action that mimics a worksheet action. In other words, you create a worksheet action, you like it, then when you add the sheet to a dashboard, the action is no longer there and you have to create it again.

Well in this tip, I show you how to make a worksheet action automatically apply to a dashboard action without having to create a separate dashboard action. Genius!

October 7, 2019

#MakeoverMonday: Bearwood Corporate Services - The Money Behind David Cameron's Conservative Party

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What a fascinating data set! More on that in a minute. First, let's have a look at this week's viz to makeover. It comes from The Electoral Commission and focuses on political donations in the UK, which are required to be reported.


  • Placing the filters on the upper right let me know immediately that I can interact with the data to find my own story.
  • The bar chart is sorted in descending order.
  • The summary numbers provide some context, but not much.

  • The bar chart would be easier to read if it was horizontal.
  • Why are all of the bars colored? There are way too many colors and they have no meaning.
  • The packed bubbles would be much better as a bar chart or BANs.

I started by exploring each field in the data set. Many of them didn't have information I found useful, so I hid all of those fields so that they would not distract from my analysis. 

As I explored the donors and who gave what to whom, I saw that Bearwood Corporate Services was donating A LOT to the Conservative Party over a period of a few years. I hadn't heard of it before so I did some research on Google and it turns out that they were pretty controversial and closely linked to the rise to power of David Cameron.

That's where my story begins...

September 30, 2019

#MakeoverMonday: London's Aging Population

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This week, Eva chose a viz/data from the London Datastore about population projections. Here's the original viz:

SOURCE: London Datastore

What works well?
  • It's a simple bar chart, which is very easy to read.
  • Uses a single color; we often see the bars double encoded with the same value as the length of the bar.
  • The axis starts at zero.
  • Simple, clear axis labels

What could be improved?
  • I think a line chart would be even easier to read.
  • The title says "Population of London" but it's really the projected population plus the past population; some clarification would be good.
  • The legend isn't needed.
  • The y-axis title could either be removed or changed. "Number" doesn't mean a whole lot.

For my makeover, I was interested in comparing the distribution of the population by age for one year compared to the distribution of the population in 2050. For example, what was % of the population for 45 year olds for 2018 and 2050, then compare those two values. This then shows how the population distribution will change.

September 24, 2019

#TableauTipTuesday: How to Use Parameter Action to Drill Down

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In this week's tip, I show you a trick the Hesham Eissa showed to use a parameter action instead of a set action for drill down. Parameter actions are much simpler to implement, so give this a try.

September 23, 2019

#MakeoverMonday Week 39 - Are tenants in cool neighborhoods less likely to be evicted?

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This week Sophie Sparks thought it would be a good idea for me to live stream building my viz. You can watch the video here. I started with critiquing the original viz.

SOURCE: Humanistic Data Science
What works well?
  1. The title tells us what each line represents.
  2. The daily view helps show the major outliers and the dominant neighborhood.

What could be improved?
  1. The colors are hard to distinguish.
  2. The data is too granular.
  3. I'm not sure what the purpose is.

What I did
Watch the video to see everything I did and how I explored the data, then refined my analysis. Along the way I kept notes of things that stuck out to me like the Ellis Act, any outliers, and locations that are dominants.

I started by asking the following questions: When, Where, Why, Who, How. I explored each of these questions by building several charts for each and deciding which one worked the best together. I then built the dashboard, applied all of the formatting, clean up the tooltips, added some interactivity, and published. Done!

And here's where I ended up. Click on the viz for the interactive version.

September 16, 2019

#MakeoverMonday: Committed Forever to Positive Impact Events

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This week is a partnership between the Makeover Monday community and the UN SDG Action Campaign. Here's the viz they've asked us to makeover:

What works well?
  • Everything looks very crisp, as it always does on Google Data Studio.
  • Good filtering capability
  • Allows you to explore the data in any way you desire
  • Colors of the bar chart are easy to distinguish

What could be improved?
  • It's way too long; I know I would never read all of it.
  • The stacked bar / treemap charts things don't make sense at all.
  • There's no structure to guide the user; like what is most important?

What I did
  • I focused on the high-level goals.
  • I noticed that there were a lot of people that said they would commit to action forever, so I made that the focus of my viz.
  • I had originally split the view up by gender, but that didn't add anything to the analysis, so I took it out.
  • I used the colors and fonts from the Positive Impact Events website.

And here's my makeover...

September 10, 2019

#MakeoverMonday: Alex Cross vs. Women's Murder Club

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I saw a data set (I don't recall where from) about library checkouts at Seattle public libraries and thought it would be a fun data set to play with. The original viz that I wanted everyone to makeover was about Jane Austen, but the checkouts for Jane Austen books was pretty sparse.

I thought about James Patterson, an author I used to read quite often and is extremely popular. This has provided something worth vizzing.

Before I get to that, let's look at the original:

What works well?
  • The timeline is in sequential order.
  • The dark background lets the dots pop out.
  • The colors are easy to distinguish.
  • Simple title and subtitle.

What could be improved?
  • The y-axis is missing.
  • The dots are double encoded by size and position on the y-axis.
  • Google trends are included for some reason.
  • The legend could be simplified.

What I did
I wanted to look at seasonality and trends for each of the books. I got nowhere really quickly. I struggled and struggled with ideas and ended up not having enough time to finish on Monday.

In the end, I decided to take some inspiration from Workout Wednesday week 10 2018. I also chose to focus on only two book series: Alex Cross and Women's Murder Club. Why? Because those are the two series of his books that I read the most. The color theme comes from James Patterson's official website.

#TableauTipTuesday: How to Create a Waffle Chart Using Data Densification

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In this tip, I show you how to use a calculated join to create a waffle chart. This method of data densification can remove the need for any complex data blending.

September 1, 2019

#MakeoverMonday: Which season do Americans prefer?

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On to week 36 we go. This week Eva has chosen a simple data set and simple viz. Let's have a look:


What works well?

  • The seasons are sorted according to when Americans experience them.
  • Nice clean design
  • Labeling the tops of the bars
  • Hiding the axis
  • Color are easy to distinguish

What could be improved?
  • I keep having to refer back to the legend. Maybe label each bar once?
  • This show a comparison of the ages within a season. If the viewer wants to compare the seasons by age, then that become difficult.

And here's my version, which allows you compare both across seasons and across age groups:

August 27, 2019

#TableauTipTuesday: Using Measure Names to Update a Parameter

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Before parameter actions, we've never been able to use Measure Names in a calculation. This week, I show you how to make a dynamic scatterplot and dynamic line chart by leveraging the power of parameter actions to pass the Measure Names field to a parameter, and thus a calculation.

Thank you to Hesham Eissa and Sara Hamdoun of The Data School for coming up with the solution.

August 25, 2019

#MakeoverMonday: Mobile is taking over the global gaming market

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After a week on holiday and skipping writing a Makeover Monday blog posts for the first time in 4+ years, I'm back with a topic that I can attest to give my boys' affinity for gaming.

This week's chart is from Statista:

What works well?

  • The title clearly explains the story in the chart.
  • The colors are easy to distinguish.
  • The bar chart helps us see the increasing revenue.
  • Placing mobile at the bottom of the stacked bars makes it easy to see the increase in the mobile share.

What could be improved?

  • All of the labels make the chart look quite busy.
  • Is the image of the platforms necessary?

What I did

  • I wanted to look at the relationship of share and revenue over time, which led me to a connected scatterplot.
  • I thought it was important to look at each platform independently, so I placed them inside "cards".
  • I included BANs based on the estimated revenue for 2021.

Let me know what you think.

August 13, 2019

#TableauTipTuesday: How to Compare Ranks within a Dimension with Set Actions

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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.

August 11, 2019

#MakeoverMonday: The Social Investment of Clinical Trials

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This week's viz is from Aero Data Lab analyzes clinical trial registration records from 10 large pharmaceutical companies. Here's how to read it:

  1. The trial start date on the x-axis.
  2. The disease/condition of interest on the y-axis.
  3. Each bubble in the figure corresponds to a trial. 
  4. The bubble color corresponds to the company
  5. The bubble size corresponds to the number of human subjects enrolled. 
  6. The bubble shape indicates the trial’s current status.

So basically you need to be able to understand quadruple encoding of the bubbles, which is quite difficult. It's very hard to visualize data that is this dense; kudos to the author for giving it a go.


  • The article reference provides a good overall analysis of the data.
  • Including the trial start dates on the x-axis helps understand when the trial took place.

  • Too many colors
  • The x-axis labels are slanted.
  • It's way too long (but I understand why it was designed that way).
  • There aren't any legends.
  • There are too many shapes.
  • The bubbles are trying to do too many things at once.
  • Comparisons are very difficult.

I had thought about examining the costs, but there isn't an information about the costs, even though the article references it. Instead, I decided to focus on the scale of the enrollees; that is, the number of people participating in each trial.

  • I used BANs for the enrollees per pharmaceutical company. I then used this as a set action to highlight that company in the line chart.
  • I looked at the enrollees per status, but I don't think that makes sense since it's the latest status. 
  • The same applies to the phase; it also only shows the latest phase.
  • I looked at how many enrollees there have been since the start the first trial for each company.
  • I included the enrollees per condition and used a set action for proportional brushing for the company selected.

I'm about to head on holiday for eight days, so I had to get this one out the door.