Data Viz Done Right

March 17, 2019

#MakeoverMonday: To what extent are women and men viewed equally in leadership positions?

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The chart Eva chose this week shows that, not surprisingly, people are less likely to view women and men equally in leadership positions. Scores of 100 mean that people view women and men are equally suited to leadership roles. Sadly, women still have a long way to go in their seemingly never ending fight for equality.

Let's have a look at the chart:

Source: World Economic Forum


  • Ordering the countries from highest to lowest in terms of people that view women and men equally in leadership positions
  • Including the G7 average for context
  • Assigning a different color to the G7 average
  • Labeling the end of the lines


  • Circular bar charts are horrible for comparisons.
  • The title is meaningless.
  • The lines start thin, get thicker, then get thin again. Why?
  • The title and the center of the chart are the same. That's certainly unnecessary redundancy.


I started by creating a simple bar chart and that was fine. I also added a grey bar to have it as a stacked bar for each country that goes up to 100%. I then thought about doing a waffle chart (with circles) and then I remembered this viz from Andy Cotgreave back in Makeover Monday week 4 2016. I decided to replicate Andy's work since it looks great and gives lots of context. I created a mobile version like Andy did too.

With that in mind, here's my makeover for week 12.

March 12, 2019

#TableauTipTuesday: How to add a one pixel line to a dashboard

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In this week's tip, I show you how to use text boxes combined with containers to add divider lines in your dashboards. You can download the workbook here.

March 11, 2019

#MakeoverMonday: Has Philadelphia recovered from the Great Recession?

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For this week's Makeover Monday, we're looking at this dashboard from OpenDataPhilly.

What works well?

  • Consistency of colors
  • Simple design
  • Using an area chart with a bold line at the top
  • Bar chart is sorted
  • Interactive actions
  • Automatic proportional brushing

What could be improved?

  • Reduce the outline of the zip codes on the map
  • Remove the background from the map
  • Add a dashboard title
  • Change the chart titles to be more meaningful

And here's my makeover. Click to interact.

March 5, 2019

Makeover Monday Power BI Edition: Births Attended by Skilled Health Staff vs. Female Life Expectancy as a Motion Chart

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One of the things I couldn't do with my Makeover Monday this week was animate the visualization. The animations is what makes the story unfold and neither Tableau Public nor Tableau Server support animation.

What's one to do? Try a tool that does support animation. In this case, Power BI. Scatter plots in Power BI support animation natively and it took less than five minutes to create this.

  1. Upload the data.
  2. Choose the measure for the x-axis and place it on the X Axis shelf.
  3. Choose the measure for the y-axis and place it on the Y Axis shelf.
  4. Add a dimension to the Details shelf to draw more dots.
  5. Place the dimension to animation across, i.e., years, on the Play Axis shelf.
  6. Add a title.
  7. Add a text box as a footer.

BOOM! Done! Easy peasy. Check it out below.

March 4, 2019

Makeover Monday: Are skilled health staff an indicator of female life expectancy in fistula countries?

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For Makeover Monday week 10, Eva presented us with this map of adolescent fertility rates on Data Wrapper:

What work well?

  • Using a continuous color palette
  • There are no exceptionally large countries compared to the others, so a filled map is a good choice.
  • Normalizing the data to make comparisons across countries more relevant.
  • Using grey for countries with no data.
  • Good title and subtitle

What could be improved?

  • If there is data across years, it would provide additional context to the data. In other words, is the situation improving?
  • Make the title bigger; it's too small compared to the large map.

My Goals

  • Compared the metrics between fistula and non-fistula countries
  • Look at change over time
  • Figure out how to deal with all of the nulls
  • Be done

February 26, 2019

Tableau Tip Tuesday: How to Convert a Reference Line into a Level of Detail Expression

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This week's tip builds upon my tip from two weeks ago: How to Convert a Reference Line into a Table Calculation. In this tip, instead of using table calcs, I'll show you how to convert a reference line into a LOD.


February 25, 2019

Makeover Monday: The Economic Value of the Bicycle Industry in the UK

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Spring is trying to peek out here in London. I saw so many people cycling in Richmond Park yesterday that I thought it was a good time to look at the state of cycling in the UK, focusing on the economic value.

Here's the original chart:

What works well?

  • Using a line chart for representing data over time
  • Minimal use of color
  • Y-axis is properly labeled
  • Including the sources

What could be improved?

  • The x-axis label is completely wrong.
  • The title needs to be more specific. As a standalone chart, we have no idea if this is about a country, a store, whatever.
  • Don't include all of the axis ticks between each of the quarters.
  • Remove the diamonds as markers for each point.
  • Change the units of measure on the y-axis to thousands.

My goals

  • Create something that's easy to understand.
  • Stick with the minimal use of color.
  • See if the change between periods is important. If so, what is changing and why?
  • Is there seasonality? If so, how is that seasonality changing?
  • Consider other metrics, like value added per bike. How does that change through time? What do the changes mean?

With those goals in mind, here is my Makeover Monday week 9 2019. I made value added per bike the primary focus of my viz to help explain that the downturn in the cycling industry is due to volume, not value added per bike.

February 22, 2019

Where are New York's Parking Meters?

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There are times when you come across a data set that you immediate know will look cool as a visualization. New York City open data had, what I thought, was a pretty obscure dataset: Parking Meters GPS Coordinates and Status.

Their map is impossible to read with some many big dots overlapping each other. This also makes it hard to see the concentration of parking meters. My assumption going in was that you'd see way more in Manhattan.

All I really did was create a map, plot each point, and change the mark type to Density. From there it was formatting:

  1. Using a custom mapbox map, which I customized based on the mapbox template Metropolis.
  2. Play around with lots of colors, then intensity and opacity of each of those colors, before settling on a choice. 

I probably could have done this process for days and days without ever finding a "perfect" solution for the formatting, so I decided it's good enough and wanted to get it published. Enjoy!

February 20, 2019

Improving Upon Strava Activity Summaries: RIDE IT London Osterley Sportive

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A few weeks ago, I voluntold Luke Stoughton (Head of Data School Recruitment and good friend of mine) to join me for a 52 mile cycling sportive hosted by Evans, a UK-based chain of cycling shops. Now, there's one thing to keep in mind...Luke had only ridden up to about 20 miles on a single ride. He WAS NOT happy with me, but he went anyway.

Coach Carl joined us as well, so I met him at a train station on the way and we rode the rest of the way to the start at Osterley House, an incredibly beautiful National Trust park and house. We met Luke there and headed off on our trek, stopping at two feed stops (I ate about eight pieces of cake at the second), peddling up three steep hills (they weren't that bad, but Luke hated it and threatened me), before looping back to the start and hitting the pub for a couple well deserved refreshments.

Along the way, I was thinking about how I could visualize the data. Strava does a decent job (see my ride here) and I thought I'd use that as a basis for my viz. My goals:

  1. Use a Mapbox map background as similar as I could to the Strava map (which is proprietary).
  2. Use the Strava colors (I found the hex codes in their brand guidelines and added them to my preferences file).
  3. Prep the data in Alteryx. There is a web data connector, but I like being able to create my own row level calculations and extract only the data I'm interested in. Plus it gives me an excuse to practice Alteryx more.
  4. I like how the elevation timeline, but for me it's lacking context by not including speed. I decided to keep its wide and short style while creating a dual-axis chart to overlay speed on top of the elevation.
  5. I included key moments as viz in tooltips. Hover over the gold ribbon (fastest speed) to see a picture of Luke and Carl speeding down the hill. That was fun! You can also see a selfie of us at the end.
  6. Include interactivity like Strava. Basically link the map to the chart and vice versa.
  7. Include speed and elevation on each point.
  8. Everyone loves a good BAN! FYI, Strava calculations elevation climb differently.

This isn't anything complicated and I think it's a viz that provides ever so slightly more context than Strava. Click on the image below for the interactive version.

February 18, 2019

Makeover Monday: Which States have invested the most per home in wind energy?

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For week 8, we're making over this chart from

What works well?

  • The States are sorted from most installed capacity to least.
  • The title clearly indicates what the chart is about.

What could be improved?

  • The log scale is misleading.
  • The colors are double encoding the data.
  • The windmill symbols are chart junk.
  • The labels for the Stated get smaller and smaller, to the point where you can hardly read them.
  • The investment labels are not consistently formatted, they are aligned on an angle, then vertically, and they get smaller and smaller.

What I did

  • Since this is a simple data set, I wanted to use a tool other than Tableau. I find this useful so that I can speak about other tools from an educated perspective.
  • Create a simple viz
  • Look at the relationship between installed capacity and development
  • Consider metrics that normalize the data

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.