#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 is closed for this month, but check out the #DuoDare page for the next one.

3 thoughts on “#DuoDare 2 | Film Body Counts

  1. Mohit Choudary

    Adam did a good job of showing death data by genre by year, but it’s not clear that how many movies of that genre were released in that year. It seems that there are more deaths in action movies but that might be because there are more action movies (Of course that’s not the case). I am trying to make a point that it could have been better if one can show ratio of Deaths/Genre by year.

    Either poo was very busy or she was lazy. She walked out with simple vizz or I can say that my expectations were high after voting her for last viz.

    Great visuals by Adam & looks like he put a significant effort into this, so my vote goes to Adam!

  2. Adam Crahen

    Hi Mohit-

    I agree the distinct counts aren’t clear by genre. The data was pretty complex around genre. Movies had a 1 to many relationship with the genre field. I don’t think it is fair to classify a movie a single genre so I tried to address it by including the movie in each genre it was tagged with. Why don’t you give it a shot and show me how you would approach it? I am sure there are a few other ways. Here is the raw data: https://figshare.com/articles/On_screen_movie_kill_counts_for_hundreds_of_films/889719

    I really don’t appreciate you calling Poo lazy. The data was difficult and she did a great job with her design. She always takes complex data and presents it in a simple fashion for people to understand. That is not easy to do.

    We both take time away from our families to do this. We appreciate you reading and taking the time to vote, but we could have done without the insult.

  3. Mohit Choudary

    “Either poo was very busy or she was lazy. She walked out with simple vizz or I can say that my expectations were high after voting her for last viz.”
    It’s a compliment for poo. I was trying to say that my expectations from her is very high and I expect better vizz from her. It’s just a way to motivate her. It’s funny that you thought I was trying to insult. No! I wanted to ignite fire in her so that she can come up with awesome vizz next time! Once you earn a fan you have to live upto expectations 😉 I hope poo will understand that.

    Thanks for sharing the data, I appreciate that.

    Good luck to both of you for the #3

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