For my blog post this week, I decided to take a look at the Titanic dataset provided. It provided information on the different classes that were aboard (distinguished as crew, first class, second class, and third class), the sex of the passengers, their age (distinguished as “adult” or “child”), and whether or not they survived the tragic sinking of the ship.
I specifically focused on exploring the relationship between the passengers’ class and whether or not they survived. For anyone that has seen the movie, it was obvious that the first class was privileged in more ways than one, most importantly when it came to escaping first as soon as tragedy struck. I wanted to see how this story compared to the “real” data (i.e. if there really was a correlation between class and survival). Looking at the dataset, this was not immediately evident as there were several data points.
Using Tableau, I was able to create a pie chart to better visualize the information I was given. Each of the colors correspond to a particular class and the size of each “slice” denotes the sum of how many in that particular class survived. As can be seen from this data visualization, first class and crew did indeed have a certain privilege when it came to survival, while the second class was not so lucky. The most peculiar thing, however, is that the visualization shows that the third class somehow had a large number of survivors. I did some research online and found that although third class passengers were mainly immigrants forced to stay below deck, no one knows exactly how such a large number survived. Overall, this data visualization was able to show the relationship between class and survival, and even a mysterious component which was not obvious from simply looking at the overwhelming dataset.
I really liked your explanation of the implications you saw from your data. It is very clear and easy to understand. Pie charts are misleadingly simple, but I think your depiction points out a glaring detail on social class and privilege.