**One of the people involved in the original report/data collection is u/owenshen24, and they will [answer your questions here!](https://www.reddit.com/r/dataisbeautiful/comments/8cwcbu/cause_of_death_reality_vs_google_vs_media_oc/dxidjrh/
* Album with the three primary stacked bar charts [shown separately](https://imgur.com/a/Ylu86
* Single image with the three charts [side by side](https://i.imgur.com/4d9YFbR.png
* Side by side with connecting lines by u/onlyforthisair in [MS Paint](https://i.imgur.com/Tt8V8Aj.png
Source code: [GitHub (Python 3.6, numpy, pandas, matplotlib, imageio)](https://github.com/aaronpenne/data_visualization/blob/master/cause_of_death/bar_cod.py
Data: [Aggregated by Owen Shen, et al. from CDC, Google, The Guardian, & New York Times](https://github.com/owenshen24/charting-death
This animation shows the percentage share of top causes averaged from the Center for Disease Control and Prevention (1999-2016), Google search trends (2004-2016), and headlines from the Guardian and New York Times (2004-2016). The data was collected by Hasan Al-Jamaly, Maximillian Siemers, Owen Shen, and Nicole Stone for their [in-depth write up here](https://www.reddit.com/r/dataisbeautiful/comments/8cmvms/death_reality_vs_reported/
). All credit for the data goes to them.
This chart is sorted using the CDC data. The categories stay in that ordering through the charts while the sizes of each category change. Drug overdoses is the unlabelled category between suicide and homicide.
I started sharing data visualization, machine learning, and GIS stuff on [Twitter if you're into that](https://twitter.com/aaronpenne
Note: **"car accidents"** in this chart likely should be just **"accidents"** as pointed out by u/mygotaccount
>In 2015, the CDC reports that there were 43.2/733.1 deaths due to unintentional injuries or 5.89%, but motor-vehicle related injuries, which are a subset of that, are 1.55%. For comparison, poisoning which also falls under unintentional injuries is 2.01%. Your source for the data lists car accidents as 6.1% (possible rounding error). They have most likely misconstrued all accidents for car accidents.
Note on changing the term "car accidents" to the more appropriate "car crashes" by u/nattopan:
> While this has been standard nomenclature for decades, recent efforts to reduce the number of traffic-related fatalities have resulted in a shift from "car accidents" to "car crashes." [You can read more about the "crash not accident" movement here](https://www.washingtonpost.com/news/wonk/wp/2015/08/24/when-a-car-crash-isnt-an-accident-and-why-the-difference-matters/?utm_term=.d393b23adfeb
). To be even more accurate when speaking to what was formally known as "car accidents," it is best to use "traffic crashes" or "traffic fatalities," as these terms acknowledge other modes of transportation such as motorcycles, bikes, public transportation, etc. Pedestrian deaths in particular have been skyrocketing in recent years, and it is critical that we include this category in our discussion of traffic fatalities if we are to reverse this trend.
While the animation does a nice job of emphasizing that there is a large difference between the 3 cases, it makes it incredibly difficult to actually draw any information from the set. I find myself focusing on one cause of death, then attempting to memorize the rough percentages for all 3 cases. That is to say, if you want the viewer's takeaway to be "these 3 distributions are very different", great job. If you want the viewer to actually remember any of the numbers, it's very difficult in the format.