The carousel never stops turning


You’ve heard of Grey’s Anatomy — it’s the medical drama where everyone dies and all the characters were once in love with each other. I was introduced at the start of my Prime subscription more than a year ago, right before the universal scourge that left me with several hours a day to watch surgeons agonise about family troubles over a bursting appendix.

After my recent criticism of how Grey’s handled the pandemic, I was left pondering how the show has changed over the sixteen (!) years it’s aired and what may have led to its decrease in ratings. Of course, I couldn’t think of a better way to analyse this history than through dataviz.

Part 1: Character turnover

Grey’s Anatomy has been both celebrated and lambasted for its characters, mainly composed of surgical interns, residents, and attending physicians at an imaginary teaching hospital in Seattle, Washington. Its pilot introduced the stories of hapless surgeons-in-training Meredith (Ellen Pompeo), Alex (Justin Chambers), George (T.R. Knight), Izzie (Katherine Heigl), and Cristina (Sandra Oh), fondly known throughout the series as “MAGIC” — this cast experienced substantial growth and transformation as staff members were recruited, long-lost family members were found, and new intern classes rose up the ranks.

For my first serious attempt at constructing a Sankey diagram, I plotted how main characters transformed from series regulars to exiting the cast in a variety of fashions. I also captured the handful of instances where former cast members, whether deceased or relocated, came back to visit.

Image by author. Source: Grey’s Anatomy Wiki. Height of character nodes indicates number of episodes since their first appearance. Made with Flourish’s Sankey template and Sketchbook for Mac.

This chart shows that of the five original interns, only Meredith remains. Of the main cast members present in the pilot, the only surgeons remaining are Dr Bailey and Dr Webber. No characters appeared on screen for every single episode of Grey’s, though characters who were more recently introduced were more likely to appear exclusively in the credits.

Though the diagram above reveals rich insights, it doesn’t tell the full story. For example, Dr Altman appeared in a handful of early episodes but then left and returned seasons later, resulting in a possible impact on series ratings. And speaking of ratings, it’s near-impossible to illustrate audience perceptions of each episode with this format. To do so, I decided to switch to a more interactive visualisation.

Part 2: IMDb ratings

Due to the large size of this dataset, I chose an exclusively web-based scatterplot style containing a filter control for each episode. By clicking the “play” button, you can watch character appearances evolve over time — alternatively, you can choose to investigate the specific episode you’re interested in.

Made with Flourish’s Scatter template.

It’s difficult to draw conclusions about the entire series from this fluid viz, but I noticed that the addition of a new character seems to correlate with an immediate slight drop in ratings, and that character deaths seem linked with the same. More careful perusal of this data might lead me to theorise that the presence of certain characters is associated with higher ratings, but that would be impossible to gather from visualisations alone. Maybe a regression is next on my journey …

Despite what I have said about Grey’s Anatomy’s declining quality in the past, I still think it’s worth a watch. I would recommend focusing on earlier seasons (for example, many fans reminisce about Cristina’s comical awareness of her own superior medical knowledge, which you can relive from seasons 1–11). The brazen idealism of these doctors in the face of trauma and a borderline unliveable healthcare system, though frequently unrealistic, should be applauded. Besides, it’s said to have inspired young people to enter healthcare and helped viewers diagnose themselves or their loved ones with rare conditions (see this interview).

There are also notable moments that I hope non-regular viewers can appreciate, and I’ve embedded one of them below.

[TW: sexual assault]

In a gem from season 15, resident Dr Jo Karev treats Abby, a survivor of sexual violence who enters the emergency room and is eventually admitted for an operation. Jo can relate to her patient’s experience and, with the help of her fellow surgeons, brings the hospital’s female staff members together to support Abby. If you feel comfortable, watch this scene — I find that a different element of its direction and writing moves me to tears every time. Today, it was the quiet in the hallway and the stoic expressions on each woman’s face, nuances that add to the power of this extraordinary act.

Thank you for reading, and my appreciation for the medical profession can’t be overstated.

*This is a work in progress.

  • In viz 1, I want to make font styles and sizes consistent (perhaps using more sophisticated software), add more context, and increase image and text resolution.
  • In viz 2, I want to fix redundancies in the x-axis positioning of character names, as well as the visual overwhelm that can occur after more than ten characters appear on screen. I’m concerned, too, about what appearance classifications I should use, and whether “credits only” is relevant for the viewer.

Here is the dataset I used (the “ratings + char” tab is the one where I entered data, while “long data” is the result of wrangling in R and is what was used in both visualisations).

Please let me know your thoughts!




she/her. Population Health student @ UCL. Perpetual dataviz nerd. Published on Towards Data Science and UX Collective.

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Yaning Wu

Yaning Wu

she/her. Population Health student @ UCL. Perpetual dataviz nerd. Published on Towards Data Science and UX Collective.

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