Data Viz Done Right

November 18, 2019

#MakeoverMonday: Tween and Teen Smartphone Ownership

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This week, we're looking at the change in smartphone ownership for tweens and teens between 2015 and 2019.

SOURCE: Morning Brew

What works well?

  • Clear title
  • X-axis is clearly labeled
  • Including the data source
  • Colors are easy to distinguish
  • Vertical lines help draw the eye to compare the years within each age
  • Including labels since the y-axis is hidden

What could be improved?

  • The title could be less bold.
  • The title uses the color for 2015, but it's not related to one year only.
  • The dots are distracting since they are so large.
  • The labels are helpful, but do they need to be so big?
  • With the vertical lines connecting the dots within the year, and the line connecting the ages across the years, I'm not sure which is more important. Given the title, the focus seems like it should be on comparing years within an age.
  • The vertical lines don't need to be so broad.

What I did

  • Removed the lines to make the focus comparing the ownership within an age group
  • Surrounded the dots with a band to ensure the user reads the data within each age group
  • Colored the bands by the change to accentuate the ages that have changed the most
  • Included the labels, but made them very small as to not distract from the analysis
  • Created a mobile version for practice

November 12, 2019

#MakeoverMonday: Literacy Rates Around the World

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It's TC19 week and Eva has provided a data set about literacy rates around the World for the 1200+ people in Vegas to viz live plus the hundreds more of you that aren't live with us.

Here's the original viz:

What works well?

  • The data by region is ordered alphabetically, making it easy to find each region.
  • The bar chart is sorted by largest to smallest.
  • Nice filtering options

What could be improved?

  • A diverging color palette should only be used when there is a logical midpoint or goal. I don't see those in this viz.
  • The squares are hard to understand.
  • I don't find the map very useful. It would be more useful if it zoomed in when a region is selected.
  • There's no title.
  • There's too much text.
  • The bar chart seems to go out past the edge, or at least visually it appears that way.

What I did

  • I created a KPI scorecard so that I could understand the patterns for the overall or an individual country. Are literacy rates improving or regressing?
  • Show the distribution of the rates of the countries within each region
  • Within each region, which countries are above or below the median for that region?
  • How has the literacy rate changed over time?
  • Allow simple filtering options.

I drew inspiration from Workout Wednesday week 51 2018: Container Fun from Rody Zakovich. I love finding reasons to practice techniques I've tried before and want to master. Consider challenging yourself to learn something new each week.


November 5, 2019

#TableauTipTuesday: Using Level of Detail Expressions to Count Items Exceeding a Threshold

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In this tip, I show three examples of using Level of Detail expressions to count members of a dimension in a view. I also show how parameters can be used for counting members based on thresholds.

I ended up babbling quite a bit as I created more examples; sorry for that, but I was on a roll.

November 4, 2019

How Many Rats Are Near Hungry Cat?

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Well, I don't really mean near YOU, I mean near people that live in New York. One of the fun datasets we play with at The Data School when I'm teaching them spatial analytics is Rat Sightings in New York.

And right after I taught this class to DS16, Lorna Eden posted the Workout Wednesday week 43 challenge. In this challenge, you had to find all casinos within X miles of a casino you click on. This required using the new DISTANCE function that came into Tableau 2019.3.1.

So, why not practice this technique more, but with rats? Instead of clicking on a casino, you can click on a rat to make it the Hungry Cat and find all rats within X miles of the cat. Silly, yes, and fun to practice too. The rats all have names too.

Lastly, I wanted to resize the dots based on the number of rats in the view. I used this blog post from The Data School, except I used an LOD instead of a table calc.

Enjoy! Find the rats near you.

November 3, 2019

#MakeoverMonday: Is Las Vegas Convention Attendance a Recession Indicator?

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Tableau Conference is headed to Vegas shortly, so I thought for Makeover Monday week 45 we should look at data about visitors to Las Vegas. The original viz comes from Calculated Risk:

SOURCE: Calculated Risk

What works well?
  • The time goes from oldest to latest.
  • The colors are easy to distinguish.
  • The axes are well labeled.
  • Including the caveat for 2019 since it's not a complete year in the data.

What could be improved?
  • The axes aren't synchronized; I'd like to see how they would look synchronized.
  • Without referring back to the color legend, I don't know which axis goes with which metric.
  • Using a dual axis chart implies there's a correlation between the two measures. There might be, but it could be displayed other way to make that more evident.
  • There no indicator of the data source.

What I did
I started by reading the original blog post. What caught my attention in particular was the last sentence:
Historically, declines in Las Vegas visitor traffic have been associated with economic weakness, so the slight declines over the last two years was concerning.

Super interesting! So this is where my worked started. I first annualized the data to make 2019 comparable to the rest of the years. From there, I created a connected scatterplot, which takes the two metrics in the original chart, plots one on the x-axis and the other on the y-axis, and connect the points by the year. This lead to a swirly look at the end, which made the relationship difficult to understand.

Instead, I chose to focus on the "red" line of the original, i.e., convention visitors. I wanted to see if convention visitors was indeed a recession indicator. The chart was simple to make, then some googling turned up the recession dates. Low and behold, convention visitors to Vegas sure do look like a leading indicator for a recession. If this is true, then we're on the verge of a recession very soon.

Click on the image for the interactive version.

October 22, 2019

#TableauTipTuesday: Using Distribution Lines to Provide Space for Labels

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Credit for this tip goes to Rody Zakovich (@RodyZakovich). In the past, I've always created complicated table calcs to give labels room above the max and below the min of a line chart.

With distribution lines, you no longer need to do that. Simply set a percentage offset and you're good to go! So simple!

October 20, 2019

#MakeoverMonday: The Age at Which Most People Are Dying by Suicide Has Increased Over Time

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We're heading into the home stretch for #MakeoverMonday 2019. This week, I chose a visualization from the ONS about the age at which people commit suicide in the UK. I picked this data set for two primary reason:

  1. It's a simple data set.
  2. The original visualization is really good.

Let's have a look.

What Works Well?
  • The patterns are super intuitive to see and understand.
  • Amazing interactivity that allows you to look at an entire year without the overlap of the other years.
  • Nice annotations
  • Years and ages are in the appropriate order
  • Using the intensity of a single color to emphasize the number of suicides

What could be improved?
  • The number of axis ticks could be reduced to every five or ten years.

What I did
I thought I would try to build a horizon chart, but it became too complex and the data wasn't quite suited for it. I then built a series of area charts like the original, but without the overlap. It didn't look good; the original has nice rounded lines, whereas a Tableau area chart has sharp edges.

I tried line charts, small multiples, running totals, none of them worked. I ended up keeping it very simple and went with a heatmap that I think gets pretty close to the same analysis as the original.

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.