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October 3, 2022

#MakeoverMonday 2022 Week 40 - Income Inequality Around the World

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Of course, as soon as I posted the data set this week, the UK PM decided to backtrack on the tax cuts she has promised before. In my opinion, this is a good. But it kinda made that part of the analysis irrelevant.

Still, though, I was curious to see how the UK compared to the rest of the World. During Watch Me Viz (below), I started by rebuilding the original chart, which I quite liked. I then looked at the data over time, but it was quite sparse and difficult to do any meaningful analysis of.

So I decided to stick with a single chart that looked like the original, but it includes all countries and some filtering options.

To learn how I approached the analysis and built the charts, watch the video below. My final dashboard is below the video.

Enjoy!


March 22, 2021

#WorkoutWednesday 2021 Week 11 - Gapminder: Income vs. Life Expectancy

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As Lorna mentions in the week 11 challenge, the key is in the data prep. Once you have that, the visualization is really simple.

I did not use the new relationships model; I stuck with the traditional method of unions and a join as that's the most straightforward way to ensure you get the data in the correct shape.

First, you want to union together the three CSV files: life expectancy, population, and income. When you do that, you'll get this strange looking view that is super wide and doesn't have headers that mean anything. 


What you should see, though, is that the headers are in the first row. To fix that, click on the drop down triangle next to the unioned data sources and choose Field names are in first row.


The years are nicely in the headers now. The next step is to select all of the columns with the years and pivot the data. Be sure to ONLY select the years.

I then renamed Pivot Field Names to "Year" and changed the data type to Number (whole) and also renamed Pivot Field Values to "Values".

Next, add the data source with the list of countries and drag it into the data prep area to create a join. You want to join "country" to "name". And now everything should look good. That's it for the data prep.


Now that the data is pivoted, in order to build the view, you need to create a calculated field for each measure: life expectancy, population, and income



All three calculations are the same. All you need to do is swap out the name of the csv. Lastly, build the view.


Note that the x-axis is a logarithmic scale and both axes have the option to start at 0 turned off.  That's it! I hope you found this helpful.

January 15, 2018

Makeover Monday: U.S. Household Income Distribution by State

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The viz this week comes from the Visual Capitalist. I used this data set for the final interviews for DS8 last week; it was quite impressive what people brand new to Tableau can create in just a week.


What works well?

  • The ordering of the states by the highest income level from smallest to largest is easy to see.
  • The colors are all pretty distinct from each other (as I'm not color blind).
  • The colors seem like an intentional choice to go from red (low) to green (high) with steps of color in between.
  • The labels help add context to the stacked bars and they aren't distracting.
  • Including the data source

What could be improved?

  • There's no indication as to which year this represents.
  • As it's not interactive, you can't sort by a different income level, therefore it's not easy to compare different states without reading the labels, which is slow.
  • Are there region differences? I can't tell from this view.

What I did:

  • To be able to see regional trends, I included a tile map.
  • I used bars that represent the change since 2009 so that we could see which income groups grew and shrank the most.
  • I included context in the tooltips that show each income level for 2016 and the change since 2009 for the selected income level.
  • I added some annotations to aid understanding (thanks Eva for the idea).

November 28, 2016

Makeover Monday: Patterns of Change in Wealth Inequality

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No one loves iterating in Tableau more than me. So when I saw this tweet by Matt Hoover, I knew I had to give a connected scatterplot a try as well.


A connected scatterplot is great for visualising paired time series data. In this case, the pair is the bottom 90% vs. the top N% as picked by the user. The line is colored by the difference between the bottom 90% and the top N%. I added dots on the ends of the lines to make the start and end easier to find.


Makeover Monday: Wealth Inequality in the United States

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For Makeover Monday week 48, we look at this visualisation from Business Insider about wealth inequality in the US.


What works well?
  • Line chart is an appropriate chart choice as we're comparing two values over time
  • Title is clear and simple
  • Good sourcing and footnotes
  • Tells a simple story effectively

What could be improved?
  • Only one axis is needed
  • Title is a bit misleading as the values aren't actually equivalent
  • Color choices imply democrat vs. republican
  • Feels like there's a bit of extra visual clutter
  • Difference could be accentuated more

This week, I again recorded all of my work along the way. In 45 minutes, I created 175 images. But this doesn't include parameters, filters and all the work done inside the dashboard, otherwise it would probably be twice as many.



You'll see in my final version that I put a lot of focus on the difference between the lines. I also used a parameter so the user can pick their own comparison. I also have a dynamic subtitle that updates based on the values picked in the parameter.

December 6, 2011

When income grows, who gains? Find out for yourself.

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Anyone who follow this blog know how much I despise pie charts, but there are times when I tip my cap to someone that does them well.  As much as it pains me to say it, pie charts are not ALWAYS evil. 

The viz below is a great example of how to use a pie chart well (from the State of Working America blog):

  1. There are a maximum of four slices to this pie chart
  2. You can very quickly see how dominant one slice is versus the others
  3. The colors contrast well enough to not have to constantly refer to the legend

Click on the image to interact (you will be taken to the source site).

I guess what caught me most off-guard about this chart is the summary text when you choose 2002-2008.  All income growth went to the top 10%.  I had no idea!  A great chart can indeed tell a great story, or better yet, let the reader discover the story for themselves.

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November 7, 2011

Makeover of a Makeover – Waterfall vs. Side-by-side Bar Chart

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One of the great things about the data viz world is that people are always willing to listen, learn and share.  Cole Nussbaumer over at storytelling with data (a great blog you should follow) recently conducted a visual makeover on some horrible charts submitted during a class she was teaching. and she was willing to share her data with me so that I could make my own viz.  Where else do you experience such camaraderie? 

The improvements she recommends are fantastic, but I recommended one improvement to this chart she created:

Typically when I see side-by-side bar charts I’m looking to compare the bars that are next to each other.  However, in this case, the bars are not necessarily related; they are simply a list of expenses and income next to each other.  I recommended she create a waterfall chart like this one done with Tableau:

Storytelling with Data Waterfall

To me, a waterfall chart communicates the expenses vs. income story of this data more effectively

  1. The bar sizes make comparisons easy.  It’s clear that Programs are the largest expense and Grants are the largest income.
  2. You can easily see the total variance without having to do the math in your head.
  3. Other Expenses are much larger than Other Income.  I wonder what’s included in those expenses.  Looks like an area for investigation.
  4. This group should probably focus a bit more on Sponsorships so that they’re not so dependent on Grants.

I could have included labels for all of the bars, but I wanted to show the patterns and relative sizes without the numbers being a distraction.

You can download the original Excel data here and/or the Tableau workbook here.

P.S. I chose red/green bars for two reasons: (1) most people understand red as negative and green as positive when reading financial figures and (2) to annoy my friend Steve Wexler of the Data Revelations blog (another you should follow), who hates this color scheme more than anyone I know.

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