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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: