The response to 52Vis has exceeded expectations and there have been great entries for both weeks. It’s time to award some prizes!
### Week 1 – Send in the Drones
I’ll take [this week](https://github.com/52vis/2016-13) in comment submission order (remember, the rules changed to submission via PR in Week 2).
NOTE: WordPress seems to have “eaten” the animations on upload, so _please_ check out the direct links to them (they are worth the clicks).
First up is a straightforward but really colorful take take on the data by J. Alexander Branham’s (in R):
You can see his code and commentary (he went into detail on both the code and thought process) on [his blog](http://jabranham.com/blog/2016/03/ggplot-maps.html). Extra points for the Albers projection!
Next up is Jérôme Laurent who did an equally great job explaining his thought process behind his approach and some great code exposition that produced this really neat time-lapse animation:
You can see Jérôme’s [blog](https://jerome-laurent-pro.github.io/2016-04-01-dataviz-week13/) for all the code and ‘splainin.
Timothy Kiely took a super neat approach and mapped out the drone geographic density, but then expanded on his vision to look at the density over time (with a paired bar chart!).
Balázs Dukai also took the density approach in his [RPubs submission](http://rpubs.com/BalazsDukai/vis_2016-13) (with code) but attacked it from a small multiples perspective. It was interesting to see California move in and out of prominence and I think this will be an interesting project to replicate as the FAA collects more data.
Fellow rOpenSci 2016 participant Julia Silge wanted to see the distribution of sightings throughout the week and took a straightforward faceted bar chart approach:
BUT she also doubled-down on stats to make her case. Read her [superb exposition](http://rpubs.com/juliasilge/168308) to see the conclusion!
I was _extremely_ excited to see a [non-R submission](https://github.com/xangregg/dronetimes) from Xan Gregg. Using JMP & JSL (SAS components), Xan walks us through both the dreaded data-munging component (that R folks got for free from me) and found some really interesting oddities in the data that suggest there are issues with data quality. I really loved the in-depth explanation and the scatterplot that was produced to help diagnose data issues is really well-crafted.
Philipp Ottolinger was super-kind enough to go back and PR his submission so all his code is in the 52vis repo, but you should also [check it out in his repo](https://github.com/ottlngr/52Vis/blob/master/01/01_52Vis.md) since he has alot of other really cool things to persuse. He tried to see if drones were a weekend or daily occurrence in his approach:
Jacob Barnett was the first Leaflet submission (with [full code](https://gist.github.com/barnettjacob/58601c78f22616a02c3d3e1fa1aea724#file-flight_data-png)) and explored the geographic prevalence of the drone sightings:
@patternproject [explored](https://github.com/patternproject/r.rudis.challenge1) the density by week of year:
and this is going to be a really neat graph to watch over time as more of these flying annoyances take to the skies. I’m re-running that code later this year to see if the weeks with greater density stay the same.
Mukul Chaware did some [text analysis](https://github.com/mukul13/2016-13/tree/master/mukul) as well as temporal queries on the data one of the more interesting charts is the day vs night one:
but he also used the text analysis to see where there were LEO notifications or not and did multiple views using that as a pivot (really neat idea). Check out his [full submission](https://github.com/mukul13/2016-13/tree/master/mukul) for some great exposition and analysis.
Lastly is Andrew Heiss‘ [inquiry to discover](https://www.andrewheiss.com/blog/2016/04/03/drone-sightings-in-the-us-visualized/) whether we have a hobbyist problem or an official problem with these flying digital buzzards:
The pairing of external data, combined with the gorgeous map truly ruled the day, but he went further to answer another question: which state had the most (per capita) drone sightings:
### The Results Are In
It was really excellent work by everyone and the different approaches by the the submitters shows exactly what I was hoping this project would show: that there are many ways to approach the data that you have and finding the question that really takes hold of you can ultimately deliver amazing results.
For this week:
– Andrew Heiss takes the #1 spot and gets not only a digital copy of [Data-Driven Security](http://dds.ec/amzn) but also a $25.00 Amazon gift cart (I actually had a sponsor who wanted to remain anonymous for this one)
– Xan Gregg takes a #2 spot and gets a copy of Data-Driven Security for an excellent analysis and being willing to be the first non-R submitter!
Andrew & Xan: please e-mail me at bob at rudis dot net so I can get your prizes to you!
Next post will be the Week 2 winners! Then, Week 3’s challenge!