The Filmography of George Lucas

Week 28 Viz

Posted by David Velleca on September 13, 2018

Data Storytelling and Dataviz Approach

When I first started looking at this dataset, my immediate thought was that I should create a timeline of George Lucas' movies. However, there was a period of time that Lucas was extremely productive, and the data was not very clear. So, I looked at jittering the points, but wasn't happy with the jitter patterns for the relatively small number of data points. So I began looking at IMDB and Rotten Tomatoes ratings as a possible Y axis.

Looking at the ratings, however, I found the disparity in the ratings (at least for some films) to be interesting. For instance "Indiana Jones and the Kingdom of the Crystal Skull" has a rating of 78% on Rotten Tomatoes and 6.2/10 on IMDB; "American Graffiti" has an almost unheard of score of 96% on Rotten Tomatoes and only 7.5/10 on IMDB. Seeing this, I decided to use a shape based scatter plot to show the relationship between the rankings.

Each point is actually 4 shapes, one each for the role that Lucas has traditionally played in the films he's directly been involved in (producer, executive producer, writer and director). This means that I actually plotted 4 points per film. To create these extra data points, I used Tableau Prep, unioning 4 copies of the dataset with some calculated fields to create x & y points for the shapes.

The extra work in Tableau Prep was much better than creating six custom shapes for each of the possible combinations of roles. Additionally, it was fun to use Tableau Prep for more than simple data cleansing and output. On my viz, I used BANS in the same colors as the shapes to indicate the role, in place of a legend. I thought this was a bit cleaner than including a popout of the diamond with descriptors. Overall, this was a fun viz to create. I hope you enjoy!

There's still time to post your own viz for this week's data source. If you'd like to participate, create your viz and post your work to Tableau Public and Twitter with the hashtag #ThrowbackDataThursday, tagging @TThrowbackThurs. We'd really love to see what you can come up with!

Data Source

This week's dataset comes from Wikipedia. Please be sure to cite the source on your viz.