VizWiz

Data Viz Done Right

February 13, 2019

Tableau Tip Tuesday: How to Convert a Reference Line into a Table Calculation

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In this tip, I show you how a reference line is merely a table calculation that Tableau makes easy for you. I'll show you how to write any reference line as a table calc for use later.

February 12, 2019

Makeover Monday: When did President Trump spend the most Executive Time?

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I've already written about what works and what doesn't about the original visualization (read it here) and since the two current The Data School cohorts had to create a Makeover Monday viz in an hour, I thought I should do the same.

I wanted to create a calendar view that only show the Executive Time. It's easy enough to filter to just that data, however, there are days when there was no executive time, which led to holes in the calendar. To overcome this, I created an Excel spreadsheet with every day from 1 December 2018 through 31 January 2019, then I joined the two data sets, ensuring that my Excel spreadsheet was the primary data source so that all dates would be in the data set (in other words, NOT an inner join).

From there, creating the calendar was simple, adding the color was simple. I spent most of my time fiddling with the formatting.

Click on the image below for the interactive version.

February 11, 2019

Makeover Monday: How President Trump Spends His Executive Time

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Axios published a fascinating article and data set last week with details of President Trump's hourly schedule. To say "Executive Time" is a major part of his day would be a gross understatement. The article doesn't give any specifics about how that time is actually spent, however it does provide some interesting insight:

  • Trump usually spends the first 5 hours of the day in Executive Time.
  • He spends his mornings in the residence, watching TV, reading the papers, and responding to what he sees and reads by phoning aides, members of Congress, friends, administration officials and informal advisers.
  • Trump doesn't take an intelligence briefing until 11am or 11:30am, and they only last 30 minutes.

The list, sadly, goes on. The viz they posted that we're making over this week is this simple stacked bar chart.


What works well?


  • Using a color that stands out over the others to highlight executive time
  • The title tells me what the viz is about.
  • The subtitle provides context as to the amount of data that the chart summarizes.
  • Simple labeling
  • Including the total time at the bottom and stretching the lines to the ends of the stacked bar chart

What could be improved?

  • It's hard to compare the executive time to all other time. A percentage would be helpful.
  • Would the stacked chart be better as a horizontal bar chart with two rows?

What I did

  • I wanted to look at the frequency of executive time by hour of day and day of week. Does Trump spend the same amount of executive time each day?
    RESULT: The first couple heatmaps looked terrible, but visualizing by weekday looks ok.
  • Do big numbers help tell the story in the data?
    RESULT: Yes, they help summarize the data well, but didn't help my end product.
  • Are there any trends in the data? That is, is executive time increasing or decreasing? Or has it been consistent?
    RESULT: The trends are not very useful.

In the end, I thought visualizing the data as stacked bar charts by weekday looked the best. I built quite a few charts that turned out completely useless. However, there comes a point when something is good enough. That's where I ended up. Click on the image below for the interactive version.

February 6, 2019

Hospital Closures in Rural America

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Today is my first time participating in Lindsay Betzendahl's great collaboration project #ProjectHealthViz. She first told me about back at TC and I told her I would participate when time permitted. So here I am, participating for the first time.

The data set Lindsay posted was about hospital closures in rural parts of America. My mind immediately went to poverty in the South (I wasn't too far off), access to medical care, and the cost of healthcare.

After exploring the data and getting feedback from Lindsay, I settled on a simple story that answers  few simple questions:

  1. How many hospitals have closed?
  2. How many beds are no longer available?
  3. How many people are impacted (I added data from the US Census)?
  4. How many hospital bed days have been lost?

In the end, this is a pretty simple viz that I hope communicates the message well. In my opinion, access to healthcare should be a right, not a privilege. Click on the image for the interactive version.

February 3, 2019

Makeover Monday: How Chinese New Year Compares With Thanksgiving

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This week, Eva chosen another viz from Statista, this time the chart attempts to compare Chinese New Year with Thanksgiving in the U.S.


What works well?

  • Good title and subtitle
  • Using colors that are easy to distinguish from each other
  • Including the numbers to give the circles context

What could be improved?

  • Comparing circles is very difficult; what are we to compare? The size? The diameter? Either way, it's very difficult.
  • Remove the background image
  • Make the numbers comparable. China's population with way bigger than the US. Converting them to per capita would make for better comparisons.

What did I do

  • Transposed the data so that I had a column for each measure
  • Create per capita calculations for each measure
  • The trips and spending data looked like the most interesting, do I discarded the viewership data since that really has nothing to do with the other data.
  • Changed the circles to simple bar charts
  • Made the titles of the charts state the message of the chart

January 31, 2019

Set Actions: Dynamic Reference Lines and Coloring

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CONFESSION: I have been avoiding using set actions because I've struggled to wrap my head around getting them to work the way I think they should work.

Fortunately, we're surrounded by brilliant people at The Data School, so today Coach Carl asked Harry Cooney from DS11 (graduating tomorrow) to run a lesson for the team. Harry clearly understand them and walked us through several practical use cases to help us understand the basics. He then assigned each of us the task of recreating one of the vizzes from Lindsey Poulter's amazing resource of Set Action use cases. I was tasked with recreating her dynamic reference lines and colors viz.

I took on this challenge as I would a Workout Wednesday:

  1. Understand the requirements
  2. Play with the viz to see what it's doing
  3. Rebuild the viz
  4. Don't look at the method for the original until I'm done

I find this my best method for learning. Simply downloading the workbook and seeing how it was built doesn't help me learn as effectively as I would like.

Before showing the viz, I want to recap some of the things I have done differently that I think make the visualization simpler.

  • I built it all with one worksheet. Lindsey floated one sheet on top of another, meaning two sheets have to be maintained if changes need to be made.
  • I labeled all of the sub-categories in the upper right of each box. Lindsey had it as a label for the last dot.
  • I included tooltips and the x-axis.
  • I added the circles directly on the line rather than as a separate chart. This allowed me to use the dual axis for labeling the sub-categories.

Other than that, we used pretty much the same techniques. 

The team noticed that Lindsey used a lot of floating sheets on top of floating sheets to get the look she wanted. If that works for her, great! Our preference was to create them in a single sheet so that they are easier to maintain and debug by others later on if necessary.

I found this a really fun challenge and I learned a ton in a short amount of time. And thank you, Harry, for your fantastic teaching!

January 29, 2019

Using an LOD to Count Marks in a View

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Last week, Lorna showed you how to use the SIZE table calculation to count the number of marks in a view (watch it here). Since there are lots and lots of people that are intimidated by table calcs, I decided to build upon her work and show how you can use a Level of Detail expression to count the marks instead.

I also show how to combine multiple, disparate BANs into a single sheet.

January 27, 2019

Makeover Monday: The Digital Economy and Society Index

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For this week's makeover, I chose this chart from the European Commission:


What works well?

  • The countries are sorted from best to worst.
  • The scale and gridlines help guide the eye across the view.
  • Using the country abbreviations so they are easier to read.

What could be improved?

  • The title could include a subtitle to explain the DESI.
  • Stacked bars are hard to compare across countries as they are influenced by the bars below them.
  • The colors are too bright; everything is competing for attention.
  • The legend does not need the numbers before each indicator.

What I did

  • Added a subtitle to explain the DESI
  • Split the indicators apart so they are easier to compare across countries
  • Include a parameter to allow the user to select a country and have it highlighted
  • Made the line representing the EU black so that it's in context for comparison
  • Simplified the colors
  • Added BANs to show the change vs. 2014 for the chosen country and for the EU (for context)
  • Shaded every other column to guide the eye down the viz

Yes, I know this is the same highlighting technique I used in week 3. I used it again because it works. 

January 21, 2019

Makeover Monday: Electricity Use at 10 Downing Street

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For the week 3 makeover, Eva picked his viz about energy usage at 10 Downing Street. For those of you that might not be familiar with the building, it's the headquarters of the U.K. government and home of the Prime Minister. Basically, it's the equivalent of the White House. I go by it quite often on my commute to work. You might not even notice it if not for the throngs of tourists and the guards with really big guns.

Here's the viz Eva chose:


What works well?


  • Really nice BANs that also have context included. I give people feedback quite often that BANs can be great, but they're meaningless without context.
  • Nice filter options with the buttons at the bottom
  • The chart shows the peaks and troughs well.
  • Using different colors for peak usage
  • Data updates as you click on the BANs

What could be improved?

  • Include a legend so you know what the colors signify
  • A better x-axis is needed
  • Remove the buttons that don't have any data, District Heat and Gas in this case

My Plan

  • Hold off on working on my viz until we have our weekly Makeover Monday time at the Data School. I've written this section and the two above Sunday night.
  • Explore the data with line charts to get a sense for the patterns in the data.
  • Keep something similar to the BANs; consider different or additional context.
  • Should the timeline show all of the data? Play about with different filter options.
  • Consider a heatmap that shows usage by hour of the day compared to day of the week or perhaps month.
  • Will reporting energy use, money, and carbon impact in the same dashboard be too crowded?
  • Explore relationships between the metrics with scatterplots. Is a connected scatterplot an option?
  • Would a mobile version be better so that people can look at it on the go?
  • Is there any additional data?

What I Uncovered

  • The data set only included 2017, so I downloaded back to 2008 as well. But data only existed back to 2013, so I had to deleted 2008-2012. Tableau Prep doesn't allow you to skip the first three rows, which is required for 2013-2016, so I used Alteryx instead and then unioned those years with 2017.
  • Only data for electricity usage is consistent across the years; I was expecting to see money and carbon impact as well. I wonder why don't they include those as well. Anyway, this eliminates a scatter plot.
  • Data was missing for December 2015, so I excluded that month from the data set.
  • There were lots of zeros, so I removed those as well.

And here's my viz after working on it for 60 minutes at the Data School.

January 15, 2019

Tableau Tip Tuesday: How to Compare Current YTD to Prior YTD

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This week's tip came from a question from Andrzej Szczurko on week one's tip from Lorna:

How would you do year to date percent difference when you only have data for first 3 months in the current year? For example when you have data for the current year from January to March for 2018 and you want to compare it to January to March from 2017 and calculate percent difference then?

This is a very common business question. In this video I show you how to use level of detail expressions to calculate these two fields plus the difference.

NOTE: In the video, I have the Year over Year calculation backwards. The formula should be: SUM([CY Sales])-SUM([PY Sales])

January 14, 2019

Makeover Monday: Workers Making Minimum Wage or Less in America

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For Makeover Monday week 3, the Community is making over the viz below from Business Insider. I had bookmarked this data set back in 2016 and stumbled across it again over the weekend. I was then able to get more data from the Bureau of Labor Statistics for 2002-2017. Win!


What works well?

  • Maps are easy to understand
  • Positioning of Alaska and Hawaii in the available space
  • Including notes about the data in the footer
  • Simple, effective title
  • Using a single color gradient; a diverging palette would not be appropriate

What could be improved?

  • Ranges are not the same size
  • Smaller States are nearly impossible to compare; this is a good use case for a hex or tile map
  • No context for good vs. bad

What I did

  • Looked at the data over time
  • Included a comparison to the US average for context
  • Included all States for context
  • Allowed the user to highlight the State they are interested in
  • Included labels on the ends to the lines to show change over the entire period

January 6, 2019

Makeover Monday: How Has Press Freedom Changed Around the World?

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For week 2, Eva chose this visualization from Freedom House about the changing freedom of press in countries around the World.


What works well?

  • The colors contrast well and represent the values well (i.e., green = good, etc.).
  • The map zooms in nicely as you select a region across the top.
  • Good tooltips
  • The white borders around each country make them easy to distinguish.
  • Using dots to represent the small islands.

What could be improved?

  • Without using the zoom feature, small countries are very hard to see.
  • The title doesn't really tell me what the data map is about.
  • You can't see trends over time. In other words, you don't know if the press are getting more or less free.
  • There are no definitions for what the scores mean, unless you hover over a country.

What I did

  • I started by building a trellis chart that showed every country and its score over time. It was so messy and crowded so I scrapped it.
  • I wanted to see the overall trends, so I focused on the percentage of countries within each year that fall into each status.
  • I used the same color palette.
  • I wanted to show the change vs. 1993, so I included that as text on the end of each line.

I found this to be a particularly interesting, and a bit alarming data set. According to this data, the Press are becoming less free. That's not good for democracy.

December 30, 2018

Makeover Monday: Team by Team NHL Attendance

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Year 4 of Makeover Monday is here! I can't believe how quickly it's gone. We've made a number of changes for this year which you can read about here. We want to have more visibility into who is participating and how often, so we're also asking people to help us track their work by filling out the simple weekly submission form here. After we get a few weeks in, we'll build a dashboard to share with everyone.

For week 1, we're making over this chart from NHL to Seattle. Granted the chart is from 2013, but it's still worth a makeover. If you want to see what John Barr has done since then, you can find a more recent visualization of NHL attendance from him here.


What works well?

  • The teams are ordered alphabetically, which makes it easy to find a specific team.
  • Axes are clearly labeled

What could be improved?

  • You should NEVER EVER truncate the axis of a bar chart.
  • You should not use a line chart for non-ordinal data (e.g., team names).
  • There's no title.
  • 3D bar charts are meh
  • Labeling each square makes the viz feel cluttered.
  • Ordering the bars from highest to lowest would make it easier to see where a team ranks.

What I did

  • I grouped the team into their respective conferences and divisions.
  • I create a couple of KPIs and repeated them for each team.
  • I started by lining up the teams horizontally, which kept me under an hour. Then I sent a screenshot to Eva and she said it would make more sense to have them vertically and geographically west to east. THAT TOOK FOREVER! Three sheets for each team, each within a "team container", which is inside a "Division" container, which is inside a horizontal container to give each division container the same space. What a pain!

It's done. So with that, here's my first Makeover Monday for 2019. Click the image for the interactive version.

December 23, 2018

Makeover Monday: Spending on Christmas Gifts in America

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We've done it! Another year in the books for Makeover Monday, the most fun project I've been a part of and the largest in the Tableau community (perhaps in data viz as well??). From year one with Andy Cotgreave, to years two and three with Eva Murray, to Makeover Monday the Book, this has been an incredible journey.

I believe Charlie Hutcheson is the only community member to complete all 156 weeks, though Simona Loffredo has only a couple of weeks to catch up on before the end of the year to join Charlie (and me) in the 100% club. As of this writing, Charlie has 307 vizzes on his Tableau Public profile, while Simona has 197. That's an incredible achievement and a testament to their dedication to improve week by week.

It's nearly Christmas Day, so Eva picked a Christmas-themed data set. Let's have a look at the viz:

Original viz by Statista

What works well?

  • It's a line chart based on time, so it's easy to understand what it's telling us.
  • Using one color
  • I kind of like the banding for every other year.
  • Good axis title for the measure

What could be improved?

  • Remove all of the numbers except the first and last years.
  • Add a title
  • Add the data source; surely Statista didn't come up with the data themselves. 
  • Remove the paywall so we can see information about the source and the publisher.
  • Remove the paywall for downloading the data. All you really need to do is type the numbers into Excel anyway.
  • Is there any insight?

What I did

  • Create something simple
  • Supplement with additional data to see if it can add any context. 
  • Looked at year-over-year change
  • Compare the statistics to look for relationships

I found absolutely no relationships between the average spending data and the other metrics. You might see that as a waste of time, but for me, that's part of the analytical process. Just because you don't find something, that doesn't mean the analysis is wasted. It means you have confirmed there is no relationship.

With that, here's my final Makeover Monday for 2018, focusing on the year-over-year change to highlight the Great Recession.