#DuoDare 2 | Film Body Counts

#DuoDare 2 | Film Body Counts

Welcome to #DuoDare.  The Data Duo is back at it with a little friendly competition.  Each month, one of us will pick a data source and dare the other to viz it better.  We will pass our visualizations to each other for some feedback and present them to the community (you) to be the judge.  Please fill out the Google Form to vote for your favorite duo viz.  Our bragging rights are on the line so we need you more than ever!

Don’t forget, your monthly vote gets you an entry (up to 12) in our drawing at the end of the year for some free swag!

Full rules and vote tracker


The Data

This month, I had to look for data because both Adam and I are taking turns to find an interesting dataset each month. I did search for an interesting one for quite some time and didn’t really like anything I came across. While actually searching for terms ‘interesting data sets’ I came across a dataset on movie body counts. The data is pulled together by Randy Olson and it has fields like film name, year, body counts, genre, MPAA rating, IMDB rating, length of the movie in minutes etc. I thought this was an interesting challenge for #DuoDare 2!


Adam’s Reaction to the Data

It took Pooja awhile to find some data 🤦‍♀️, but when she finally did I was pretty happy.  We have a busy few weeks coming up and we wanted to get this done by the Tableau Conference.

I was not too excited by the genre field.  I think it plays a key role in this data set, but there were 22 unique genre tags.  Each film was tagged with up to six in a single dimension creating 208 genre combinations.  There are a few ways to handle this ETL, but I chose to handle it with some LOD calculations while watching TV.


The Away Team: Adam’s viz

Pooja’s feedback:

Adam’s work is always structured, logical and well-thought of. This visualization is no different! He paid attention to detail and once again proved that with little more efforts in balancing design and function, it can elevate the overall appeal of the visualization. He broke down the movie body counts by genre and very smartly used little bar charts to show which years and which genres had higher body counts. Very easy to see even though the charts are little, because he used color to grab attention. I like the placement of ‘distinct count by year’ bar chart to show movies in specific years but I only wish the axis wasn’t reversed.

My favorite part of this viz is the scatter plot that is smartly designed to see co-relation between movie length, IMDB rating AND Adam used size on bubbles to identify body counts. Very nifty! Little tricks that he used like mentioning the fact that he truncated Y axis because a movie will be never zero minutes is what makes his work very well thought of.

Last but not the least, the use of a morbid image and appropriately deadly font, ties the whole viz together really well! And of course, the dark background always wins for The Data Duo! ðŸ˜„

Good luck Adam! Take home the crown this time, or at least I hope you do ðŸ˜‰


The Home Team: Pooja’s viz

Adam’s feedback:

This was an interesting data set and we produced very similar visualizations.  We both chose bar charts, dramatic dark backgrounds, highlight actions, a box to separate visual elements and a ghastly image (even in the same location on the viz) – damn, are we starting to think similarly or what?

Poo always does a really good job of displaying data in a simple manner.  She has the top 10 movies by total deaths across the top (I would have labeled this though in some way 😉) and she shows the total movies and deaths over time below.  I also suspected there would be more movies captured recently, so I appreciated the context by showing the charts next to each other.  I really like her use of hover labels and hover filter to show the top movie in each year.  Although, I think she missed an opportunity to update the top 10 chart at the top to filter for each year as you hover.  My only major criticism is that out of 537 movies, you can only see details for about 56 of them.  I wonder how close movie #2 was in each year, etc.

Overall, Poo produced another great viz and she will be tough to beat.  ðŸ‘ Good Luck Poo!


It’s up to you now, vote for your favorite viz below.

Voting for this month remains open until we get around to posting month 3.

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