#makeovermonday: Data breaches from informationisbeautiful.net

I’m going to try to do something quick for Andy Kriebel and Andy Cotgreave’s #makeovermonday every week so I can continue showcasing some of the different types of graphs you can make in my graphing web app Playfair and so I can identify and quash bugs! This week they chose an interactive from David McCandless’s informationisbeautiful.net showing the number of records leaked in various data breaches between 2004 and 2016. Andy gives a nice run-down of the pros and cons of the original graphic.

I made the mistake of looking at a few early entries and a major theme that seems to have struck several people is the division between data breeches that were the result of hacks and those that weren’t (including user error and lost equipment). A quick look at the data shows that the former are increasing rapidly. My first thought was that an area chart would show this trend nicely, but then I remembered that I recently implemented a variation on area charts that might be neat here. I’m actually not sure what you call this kind of chart, but it’s simply an area chart with two categories where both areas originate from y=0. The area for one category is above the x-axis and the area for the other is below it. Here’s my entry for this data set:

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Quick #makeovermonday in Playfair

Andy Kriebel runs a twitter hashtag #makeovermonday where he revises an existing data visualization every Monday and invites his Twitter followers to do the same. The competition is Tableau-centric (or maybe even exclusive) but I’m going to inject a teensy bit of Playfair into it as an excuse to make a new Playfair graph every now and then and show off some of the app’s capabilities.

This week’s visualization is pretty basic – it started out as a mildly crummy bubble chart and the obvious thing to do is to make it a bar chart. I added a tiny bit of complexity by dividing my bars into owned and chartered capacity (all available at the original datasource, alphaliner), ordering the dataset by owned capacity, and fading out the chartered portion of the bars a little bit (you don’t have to do this by bar in Playfair, you can just fade out the chartered key element by right-clicking on it). This lends some additional visual importance to owned capacity. I have no domain knowledge here so I’m not sure if this division between owned and chartered capacity is interesting, but it seemed like a significant division in the data.

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