Data Viz Done Right

February 25, 2018

Makeover Monday: World Economic Freedom

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For Makeover Monday week 9 we're taking a look at this map from the Fraser Institute:

What works well?

  • Nice search functionality
  • Simple colors (though maybe tough for color blind people)
  • The table clearly show the top 10 countries
  • Being able to "Play" the visualisation and watch the map change
  • Really nice tooltips
  • Clicking on a country give you specific information about each of the rankings for that country

What could be improved?

  • The filled map makes it hard to see small countries and especially to see how they change.
  • The up/down arrows next to the numbers clearly show better or worse, but compared to what?
  • There's no sense of change because you can't easily compare years.

My Goals

  • I knew I didn't want to use a map, so I wanted to focus on other chart types.
  • Verify which countries had data for which years
  • Limit the data to only years 2000-2015 and to those countries that had data for all of those years
  • Provide the user with an option to swap out for a different metric
  • Provide context for all countries against each other
  • Allow the user to select a country to highlight

February 22, 2018

Workout Wednesday: Is it a trending baby name?

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Ok, this week's workout sucked. The requirements and result you needed to achieve were super clear. What sucked was how fiddly the calculations were.

Given it's day 2 of training for DS8 in The Data School, I thought they should give this a try, as a group, with me. It was mostly them speaking the logic out loud and me translating into Tableau. It made for a really fun exercise that took us about 3.5 hours.

Looking at Rody and Luke's methods, our's was most similar to Rody's and way different than Luke's. But that's the beauty of Tableau; we all approached it very differently, yet got the same result. Tableau works with the way you think.

Thanks for the challenge Luke! You almost got us! Here's our result:

February 18, 2018

Makeover Monday: Who gets all the medicine money?

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I had no idea that drug and medicine exports were $318B in 2016 before this week's Makeover Monday. That's crazy! I went in assuming that the US would be the highest since drugs are so stupidly expensive there, but I was clearly wrong; Germany is the clear leader. Apparently there are lots of big pharma companies there.

I love learning something new! Let's take a look at the original viz:

What works well?

  • Resizing the continents by their overall exports makes it obvious that Europe is the largest exporter.
  • I hate packed bubbles, however in this view, the largest countries stand out by double encoding with color.
  • Including the percentages for each continent provides needed context

What could be improved?

  • The packed bubbles make comparisons overly difficult.
  • The color legend uses unequal intervals.
  • Plotting the data on a map doesn't add any context.
  • There's no sense of overall ranking across all countries.
  • If you ask "so what?", there's no answer.

The article that accompanied the viz provided quite a few interesting statistics. It would have been great if these were included in the original viz, but they weren't, so I decided to make something super simple that:

  1. Has an informative title and subtitle
  2. Provides context
  3. Provides insight
  4. Uses color effectively

That's it. Simple, quick. That's how I like my Makeover Mondays.

February 14, 2018

Workout Wednesday: MAX and MIN Sales by Month

For this week's Workout Wednesday, Rody challenged us to create a highlight table that:

  1. Only highlights the max and min per month
  2. Include the grand total (which is also highlighted)
  3. Resizes the rows based on the number of rows displayed

I'd done something very similar to this before when teaching in The Data School, so I got it sorted out fairly quickly. The trickiest bit was getting the nulls to display a value in the proper position. I took a different approach to Rody, I think his is simpler. I also took a different approach to the coloring; I went the discrete route and he went with continuous.

I really love how many way there are to tackle the same problem in Tableau. Click on the image for the interactive version.

February 11, 2018

Makeover Monday: The Winter Olympics

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As the Winter Olympics started a few days ago, Rody Zakovich offered up his great Winter Olympics viz for a makeover. It can often be tough to makeover an already good viz, but I gave it a go anyway. Here's Rody's viz:

What works well?

  • Simple title and subtitle
  • Noting when he combined countries
  • Simple filtering
  • Nice small multiples design
  • Including summaries under each country name
  • Sorting the countries from most medals to least
  • Including blocks for each medal won

What could be improved?

  • Right-align the filter titles so they are next to the filter itself.
  • I'd prefer the gold medals at the bottom and bronze on top.
  • Provide an option to look at the top N countries so that you can see it one screen.
  • The x-axis doesn't make sense. If it's supposed to represent a year, why does it start at zero and end at 25?

What did I do?

  • Focused the visualisation on the cumulative medals won by the top 5 countries based on the filters; I did this by creating stepped lines based on Rody's tutorial.
  • Made the x-axis represent the number of years since a country first participated in the games; this makes comparing the cumulative medals easier. In other words, it's easier to see which country won medals the fastest.
  • Place the filter titles above each filter
  • Moved the notes to the bottom, out of the way
  • Simplified the tooltips

Thanks Rody! You're a good sport!

February 8, 2018

Workout Wednesday: Regional Sales Across the Product Hierarchy

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For week 6, Luke challenged us to build a connected dot plot. Get the requirements here. For me, it was all pretty straightforward except for the row banding. That was a pain in the ass and took some trial and error to get it just right. I also learned a nice little hack to emulate row borders.

February 5, 2018

Makeover Monday: Did the rise of Latino players signal the decline of African American players?

This week marks a month long partnership that Tableau has asked us to kick off for Black History Month. To start the month, Eva posted this visualization from about the breakdown of demographics in Major League Baseball since the year before Jackie Robinson's debut in 1947 (he was the first African American to play in MLB, also known as the person who broke the "color barrier").

What works well?

  • The x-axis is labeled every 10 years starting with the first year in the data set. This works well since there are 70 years in the data set.
  • Labeling the y-axis for every 20% keeps that axis from getting too cluttered.
  • The title is straight to the point.
  • Placing the legend in the middle of the graph allows the chart to use the entire space.
  • Stacking "White" on the bottom is a good choice since it's always the largest segment.

What could be improved?

  • As it's stacked bars, it's harder than necessary to determine the percentage that Black and Latino comprise since their position is influenced by the colors below them.
  • The bars appear to be of differing widths and that makes it look a bit blurry to me.
  • An area chart would be much easier to understand.
  • Consider more distinct color choices, particularly for White and Black.
  • The visualization doesn't flow well with the accompanying story, which was about the increase in blacks and the more recent decrease. There's no indicator to the audience that this is what the chart is about.

What did I do?

I started by exploring the data and looking for a more interesting story. Was there a reason or cause for the recent decline of blacks in MLB? Is this the same for other minorities? How does WAR come into play, if at all? All of these questions are super simple to answer with Tableau's ability to support the way your brain thinks.

In the end, the most interesting story I found was the relationship between the decline of African Americans plays and the rise of Latino players. So my viz focusses on that.

January 31, 2018

Workout Wednesday: Dynamic Quarter Highlighting

For week 5, I sent Luke and Rody a little challenge to see if they thought it was worthy of a WW. It all started with a question from a customer on Convo, which led me to creating this view, which led to it showing up on WW.

The requirements are simple:

  1. Allow the user to pick a starting month.
  2. Allow the user to filter the years.
  3. Based on the starting month, group the next 6 months into two quarters.
  4. Display the remaining months as individual months.
  5. Color the Quarters separately from the months.
  6. You can used table calcs or LODs.
  7. The quarters must appear in the correct order within the months based on each quarter's starting month.

Here's my solution. Enjoy the challenge!

January 29, 2018

Makeover Monday: What the Most Profitable Companies Make per Second

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On to week 5 we go. This week we're looking at corporate profits, more specifically, how much do the most profitable companies make per second?

What works well?

  • The companies are sorted by profit per second.
  • The chart is certainly different, which caught my attention. Capturing your audience's interest is a good first step.
  • Coloring the companies by industry makes it easy to see that tech companies and banks are dominating the top spots.
  • Including additional information like net income and Fortune 500 rank provides additional context.

What could be improved?

  • If the companies weren't sorted, it would be way too difficult to compare the companies.
  • The unusual shape of the graphic distracts from the message of the story.
  • There is lots of unnecessary decoration like the stop watch in the middle.
  • The company logos don't add anything to the visualisation.
  • What do the blue bubbles mean? I think they represent net income, but you can't even see the whole bubble and bubbles are terrible for comparisons.

My Goals

  1. Simplify the viz by removing the decoration and flattening it out
  2. See if there are any relationships between the different metrics.
  3. Consider grouping the companies by industry.
  4. Read the article and incorporate some of the text that includes some interesting facts.

In the end, I found that a particular section of article about Apple would be a nice way to tell a story and provide some perspective.

January 24, 2018

Workout Wednesday: Who are the customers that have grown every year?

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Rody's week 4 2018 challenge called for a simple table to created. All you had to do was:

  1. Only include customers that had sales increases every year
  2. Sort by 2017 sales
  3. You could not use table calcs or LODs in the solution

10 minutes! BOOM! (I got lucky!)

Makeover Monday: How far have the turkey vultures flown?

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Inspired by Mike Cisneros' viz this week, I thought I would look at the cumulative distance each turkey vulture traveled. And, as a bonus, I wanted to use stepped lines, which are not yet available in Tableau. Fortunately, Rody Zakovich has written a great, easy to follow tutorial.

In addition to the stepped lines, I wanted to be able to compare all the birds on the same baseline. I created a calculation for quarters since each bird was tagged and then calculated the running total of the distance along the quarters. Standardizing the data this way helps me to see which bird flew the farthest (Leo) and which bird has flown to longest (Irma, which really means Irma has been tagged the longest).

Lastly, I sent the viz to Eva for some feedback and she suggested swapping the axes so that distance was on the X and time on the Y. This definitely makes more sense since distance is the independent variable.

Fun learning exercise and Rody's blog really makes creating stepped lines so, so much easier.

January 23, 2018

Makeover Monday: Distance Traveled by Turkey Vultures (Power BI Edition)

My goal was to recreate my Tableau viz from week 4 in Power BI. Here are ten thoughts about the process:

  1. I had to install a custom chart type in order to create the heat map.
  2. The heat map did not allow me to have year across the top and months down the side.
  3. I had no control over the colors in the heat map.
  4. I had to create a separate column for each of the years in the data set so that they were each individual measures that I could then use in the rows. That took way too long!
  5. I could not create a distance per bird calculation like I did in Tableau that could then be used in the heat map; I went with total distance instead.
  6. I could not connect the dots on the map to show the paths for each bird.
  7. I could not make the dots really small on the map nor could I color the dots by the bird name.
  8. I love how PBI automatically resizes to the screen. The viz I created is about 1300x900 in PBI, yet I can embed it as 800x600 and it rescales perfectly. This is fantastic!
  9. I love the pixel perfect layout; it's super easy to get everything just right.
  10. Mapping options are limited and you can't use a custom map (that I could see) so I had to match the dashboard background to the map background as close as I could.

With that, here's my Makeover Monday week 4 2018 created in Power BI.

January 22, 2018

Makeover Monday: When Do Turkey Vultures Travel the Farthest?

I had never heard of turkey vultures before Eva posted the data for week 4. Since I had no context, I went straight to the article referenced and read through parts of the abstract to understand what the data was about.

Eva posted these maps to makeover:

What works well?

  • Using a map helps show the migration patterns.
  • Using color to indicate the speed by which the birds travel (FYI, they're FAST!!!)
  • Splitting the view between outbound and return migration.

What could be improved?

  • Remove the latitude and longitude axes
  • Remove the region labels
  • Change the red/green colors for the middle two speeds
  • Add a title so we know what the viz is about
  • Make it interactive so that we can see the patterns of individual birds

What I did

  • I focused on the distance the birds traveled. To do this, I exported the data to excel and added a column for distance which was calculated row by row for each bird. This then makes aggregating the distance easier.
  • I created a calculation for distance per bird. This is a better measure than the average distance because the denominator is the number of birds, not the number of measurements.
  • I create a marginal histogram to show the monthly/yearly patterns.
  • I included filters to make it easier for the user to look at a specific bird or birds.
  • I include the map for context and it draw a line for the pattern for each bird, whereas the original was a series of dots.
  • I created a custom mapbox map that only includes the country borders (for decluttering) and has a background color that matches the dashboard color.

With that, here's my Makeover Monday week 4 2018 visualisation.

January 18, 2018

Gender Bias: Interruptions in the Supreme Court

This morning I was listening to the Justice, Interrupted, an episode of Radiolab's great podcast More Perfect. The episode refers to a paper on gender bias in the Supreme Court with respect to Justices interrupting each other. In that paper, there's data about which Justices interrupt other Justices the most, so I thought I'd turn it into a Tableau story.