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July 19, 2017

Workout Wednesday: Who sits where at the Data School?

This might seem a bit stalkerish, yet I promise this was done as a learning exercise. Since DS2 started, I've been tracking where each person sits around the table every day (well, almost every day). We sit around a table, so the seats are numbered 1-8. I started doing this because I noticed some people in DS1 (ahem Pablo and Damiana) sitting in the same spots EVERY SINGLE DAY, so when DS2 started, I wanted to see if it would continue.

BTW, we also literally tracked how many times Bethany said "literally" just to get to her to stop saying it ALL THE TIME! This is a great way to change behaviours.

Your task for Workout Wednesday Week 29 is to visualise the favorite seats of each person in each cohort. Here are the rules:

  1. The cohorts must be listed in ascending ordered top-to-bottom from DS2-DS6.
  2. Within each cohort, the DSers should be ordered by the percentage of time they sit in their favorite seat.
  3. Create a donut chart for each DSer.
    1. The blue slice (Hex #2C5573) represents the time they spent in their favorite seat. 
    2. The grey slice (Hex #D3D3D3) represents the time spent in any other seat.
  4. In the middle of the donut chart, you need to include 3 bits of information:
    1. The time they spent in their favorite seat as a BAN.
    2. Their name.
    3. The seat number of their favorite seat.
  5. Match the title and subtitle.
  6. Match the formatting.
  7. Match the tooltips.
  8. The viz should be 1000x700.

Download the data here.

That's it! Good luck! When you finish, remember to post an image to twitter with the hashtag #WorkoutWednesday and tag @EmmaWhyte and @VizWizBI. 

Click on the image for the interactive version.


  1. Here's a question - how many of those who spent the most time in a specific seat got in earlier than others? Or how many who had the least amount of time spent were those who got in late/last? Somehow, I suspect, that there's a great probability that those who spent a greater percentage in the same seat were those who got in early/first.

    1. We didn't track when people arrived but I doubt that would have impacted it anyway. They knew they were being tracked so that helped make it more random.

  2. Hi Andy, while exploring the dataset, I noticed that for DS2 Simona had no entries for seat 5. Yet seat 5 is her favorite seat. Also there are cases where more than 1 seat are coming up as favorites. How do we handle these cases (Eg: Peter; DS4; Seat 4 & 5)

    1. Thanks. Plan to use that. How about the DS2 Simona case where there are no entries for seat 5

    2. I don't remember off the top of my head. You can download my workbook to see.