The nature of algorithmic feeds like Reddit inherently leads to a survivorship bias: although users may recognize certain types of posts that appear on the front page, there are many more which follow the same patterns but fail.
For IMDb's big-but-not-big data, you have to play with the data smartly, and both R and ggplot2 have neat tricks to do just that.
Although visualizing basketball shots has been done before, this time we have access to an order of magnitude more public data to do some really cool stuff.
Manipulating actually-big-data is just as easy as performing an analysis on a dataset with only a few records.
Are commenters 'late to this thread' indeed late?
Fancy machine learning approaches may not be required to help Redditors discover new things.
There is very little discussion on how to gather the data for large-scale network graph visualizations, and how to make them. It is time to fix that.
Let's plot 587,499 arrests on top of a map of San Francisco for fun and see what happens.
Spoilers: Most arrests in San Francisco happen Wednesdays at 4-5 PM. For some reason.
I had posted a visualization of NYC taxis using ggplot2. Due to popular demand, I've cleaned up the code and have released it open source, with a few improvements.