I played around a bit with the data, and ended up finding this weird data point on this channel that had a small number of video, and subscribers… but had a ton of views. A click and a watching on the video later I think I figured out the reason.
This week we took a look at Andy’s American Express usage for 2016. Pretty normal except for the new car purchase. I really like the action I did with the list of monthly transactions and the monthly spend time line.
One of the things I love most about #MakeoverMonday is the data sets… and the variety of those data sets. This week did not disappoint… Potatoes! Who knew Ireland was lacking in potato production?
Ah, my least favorite holiday… Valentines spending is crazy… people buy for their pets. This week’s data was all about VD spending… Who and What we spend our money on…
This weeks data was all about Chicago taxi usage… and more importantly the decline. No comparative data to Uber or Lyft, but one can only assume that the rise of the Ride Share ecosystem has negatively effected the taxis in large metro areas. This data set was large, so I really focused in on the […]
Another simple dataset this week focused on the “G7” and their share employment by county. Mapping was my first thought but I thought that would be to simple. At work I have been utilizing Dan Montgomery’s published trick using MIN(1) to allow for separate formatting of elements. I still included a mapping element, but it […]
Week 4 of Makeover Monday gave us New Zealand tourism data, by region and month. Very simple data set, so as always you look for the story. It took me a few minutes but they I found the international travel spike for Matamata-Piako, where Lord of the Rings was filmed. I wanted to show all the […]
I struggled with this one… I was ready to give up on my New Year’s Resolution because I despised the data set and subject. I found a reason to Viz; learning. I’ve been meaning to do something with polygons and custom maps but had never found the right opportunity. I wanted to say something political, […]
Week 2 of #MakeoverMonday gave us iPhone sales. The story line was that 2016 was the first decline in total sales. I added the quarter in to the data and you will see that Q1 sales were up slightly, so early adopters continued to grow. The mid to late adopters were weaker, and I would […]
My 2017 resolution… complete all 52 #MakeOverMondays Week 1 – The Australian Wage Gap So may angles to look at this… but at the level of detail the data sat at, the sampling per occupation skewed the wage gaps. So I limited the data to occupations that had more than 8000 people in this positions, […]