VizWiz

Data Viz Done Right

July 18, 2018

Financial Times Visual Vocabulary: Tableau Edition

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We're all in the never ending search for resources that will help us pick the "best" chart for the situation. The Financial Times Graphics team created the Visual Vocabulary to help all of us make better chart choices.


Over the past month, I've been building all of these charts in Tableau so that everyone in the Tableau Community would have examples they could use and learn from. This has been quite the labor of love and I would like to thank the (best) team at The Information Lab for their support, reviews and feedback along the way.

There are 72 charts in total, most of which I built myself or with help of tutorials from the community. To build the violin plot, equalized cartogram, and heat map examples, I prepared the data in Alteryx and the output was shape files. The scaled cartogram was built using Tilegrams by Pitch Interactive based on this tutorial from Ken Flerlage.

While the people listed below may not have been the original creators of the charts, they are the resources I used to create the charts in my workbook.

Chart
Person
Link
Diverging Stacked Bar Steve Wexler Data Revelations
Surplus/Deficit Filled Line Jeffrey Shaffer Data +Science
Violin Plot Ben Moss YouTube / Alteryx App
Sunburst Chart Leonid Golub Super Data Science
Arc Chart Ken Flerlage KenFlerlage.com
Venn Diagram Leonid Golub Super Data Science
Radar Chart Adam McCann Dueling Data
Scaled Cartogram Ken Flerlage KenFlerlage.com
Sankey Diagram Leonid Golub Super Data Science
Chord Diagram Noah Salvaterra DataBlick

How to use this workbook

  1. Start on the Visual Vocabulary tab.
  2. Click on the text in any section to get to the chart types associated with that topic.
  3. To go back to the beginning, click on the Visual Vocabulary tab (NOTE: I'll add dashboard navigation buttons once Tableau releases that feature.)
  4. Download the workbook to see how the charts are built. You should be able to swap your data out for any chart type fairly easily.
  5. Give credit to the creator of the chart as appropriate.

This has taken up a tremendous amount of my time, so I would appreciate it not being downloaded and then re-posted as if it's your own work. If you see someone has done that, please tweet me with a link to the person/page that has done so and I'll take it from there. Feel free to show these charts to customers and prospects to show the capabilities of Tableau. 

Notes

  • This is NOT meant to be an exhaustive list of charts that can be built with Tableau. This is based on the charts created by the Financial Times for the Visual Vocabulary.
  • Actions are quite slow to respond on Tableau Public. If you download the workbook, it's much more responsive. 
  • There's a mobile version as well.
  • Images of each set of charts can be found on Google Photos.

If you find what I've created useful, please share a link to this blog post to them. Any feedback you have is very much appreciated. Click on the gif below for the interactive version. Enjoy!


July 16, 2018

Makeover Monday: NBA Team Salaries Against the Cap

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Shortly after I finished this week's Makeover Monday, I reached out to Eva to give her some ideas for how to approach the data, given her disdain for sports data. I told her to basically think of the data as actuals (team salaries) vs. budget (salary cap). Then it struck me, this is pretty much how I approached the Visual Profit & Loss Statement I created last summer.

With that in mind, here's a second Makeover Monday from this week, a scorecard of NBA steam salaries vs. the salary cap. Click on the image below for the interactive version. From there, click or lasso any set of years and the bullet chart and BANs will update accordingly.

July 15, 2018

Makeover Monday: Historical NBA Team Salaries Against the Cap in the Salary Cap Era

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I've been complaining to Eva the past few weeks about her choices of data sets. She basically tells me to suck it up and moves on. Tough love! So what better way for me to respond than to give her some sports data to play with. (I've given her some hints on how to approach it.)

Let's start by reviewing the original viz from What's the Cap?:


What works well?

  • Title and subtitle clearly explain what the chart is about
  • Good labelling of the y-axis
  • Using colors that are easy to distinguish from each other
  • Quick interactivity on the tooltips
  • Using lines for three of the metrics works well for time series data

What could be improved?

  • Over other season is labeled, which isn't hard to figure out, but it looks messy
  • Season labels are on a diagonal; make them horizontal
  • Make the salary cap a line as well for consistency
  • The legend could use some work. Why are they boxes?
  • There's no option to pick a team. What if I want to know my favorite team's salary vs. the salary cap?

What I did

  • I wanted to show all teams so that they could be compared. I settled on a dot plot for each season.
  • I created a calculation to get the starting year for each season so that the x-axis labels would look nicer and could be displayed horizontally.
  • I made the focus on the variance to the salary cap. I had no idea so many teams were over the salary cap.
  • I included a line that displays the NBA average of the variance to the cap for the team selected (via a parameter).
  • Since teams have moved to other cities and changed names, I created a calculation to make them franchises.


To understand how many outliers there were, I used box plots and hid the marks behind the boxes.


The problem I saw with this, though, was that I didn't feel like I had much context for the distribution of the teams, even though that it the point of a box plot. I decided to scrap the box plot and created this version in the end.

July 9, 2018

Makeover Monday: Have volcanoes nearest to a tectonic plate erupted more recently?

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Week 28 focused on volcanoes around the world and when they last erupted. Here's the original visualization:

What works well?

  • Including a description for how to interpret the chart
  • Ordering the volcanoes from front to back according to elevation above sea level
  • Coloring by the number of eruptions since 1893
  • Excellent tooltips

What could be improved?

  • Where is sea level? Some of the volcanoes are below sea level. You can't really size by negative feet below sea level.
  • There's no explanation for why some of the volcanoes have labels.
  • Make it more clear where the based of the volcano starts. I assume it's at the bottom of the viz.
  • Include reasoning for why 1893 is when the counting of the eruptions starts.

What I did

I started by creating an Alteryx workflow that took the volcanic eruptions data and plotted the volcanoes onto a 250 miles grid of the world.



I then created a custom Mapbox map on which I included the tectonic plates, which I got the boundaries for as a shapefile from sciencebase.gov, imported it into Alteryx, created points, got the lat/lon and exported as a CSV so that I could import it as a layer in Mapbox. Here's what I ended up with, which was fun to create, but not insightful at all.



I had to start all over, so this time I decided to look at how far each volcano was from the boundary of the nearest tectonic plate. Again, Alteryx to the rescue!


Once I had the data I needed, I created a few calculation to help me create a simple quadrant chart that clearly show that the nearer a volcano is to a boundary of a tectonic plate, the more recently it erupted. All of that totally makes sense given what we learned about geology in school.

July 1, 2018

Makeover Monday: Where are New York's rats?

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We begin this second half of Makeover Monday 2018 with data about rat sightings in New York City. I've been aware of this data set for quite a while and thought it would be funny to look at rats this week after analyzing London's bikes last week. It's kind of the same data, just replace the city and the subject. I'm curious to see how different the vizzes are this week from everyone.

The original article by Jowanza Joseph contains several fantastic visualization, most which look like they were created in R. For this week's makeover, we need to try to make this visualization better:


What works well?

  • Simple title that tells us what the data is about and the time period
  • Axes are clearly labeled
  • Including light gridlines for context that aren't distracting
  • Including every sighting as a dot for context; it's interesting how these show cyclical patterns
  • Including an average line which confidence bands to show the overall pattern
  • Excellent color choices; the purple really works well again the grey background

What could be improved?

  • Include an explanation of what the line represents
  • Include the data source and author's name
  • Remove the word "Date" from the x-axis. That's implied by the title and the year labels.

What I did

  • We were doing Alteryx spatial training this week at The Data School this week, so I wanted to do something using the locations of the rats, but not a simple dot of where each sighting occurred.
  • I wanted to use Alteryx as I'm working to improve my skills in that area.
  • I create a tile grid map using Alteryx for London crimes last year and wanted to do that again, as I need to practice techniques several times to reinforce them.
  • Create the tile grid map so every 1/2 mile and have them cut off at a Borough's edge
  • Create a simple, minimalist map and line chart in Tableau
  • Use the Magma color palette as I really like how it works as a heat map

Alteryx Workflow


The workflow is pretty simple. It takes the individual sightings, converts them to spatial points, assigns them to a 1/2 mile grid based on shape files available for each borough, then I export it as a shape file.

Tableau Visualization

In Tableau, it's simply a matter of double clicking on the spatial object, adding the borough and grid ID to give it the right level of detail, adding color by number of sightings, creating a line chart, adding a borough filter, and cleaning up the tooltips. 

Because all of the heavy work was done in Alteryx, it takes about 10 minutes to create the visualization in Tableau, most of that time being formatting. With that, here's my Makeover Monday week 27 about rat sightings in New York City.