For week 20 Makeover Monday is collaborating with #VizForSocialGood and Inter-American Development Bank to look at youth employment trends in Latin America and the Caribbean. Great data set, great cause and a great opportunity for our data visualisations to make a difference. For some reason I also felt an increased sense of responsibility to understand and accurately represent the data! ...
We’re redoing a list based on Dutch car registration data this week for #MakeoverMonday. The list doesn’t really do the data set justice, but I don’t speak Dutch so haven’t dug into the rest of the story! The actual figures were hard to reproduce and seemed like a niche part of the data, so I looked for a different story and decided to focus on the most popular makes of car. Headline figures give the reader some context as to how many registrations there were in 2015 and 2016, as well as the general growth rate. The slope chart then shows registrations for the top 5 makes and how these have changed: ...
A look at Sydney ferry patronage for week 18 of Makeover Monday based on Transport for New South Wales Open Data. Thoughts on the original chart The chart being made over is actually a series of Tableau dashboards within a story (set of tabs). I like the way I can work through the story from an overview of the data to some summary charts and then down to some detail. The card type dashboard interested me. They key story that jumped out to me was that around 70% of trips were made using an Adult Opal Card. I don’t think we need to see this proportion visually per month and then again per line. Perhaps other angles from the data could have been visually represented too? Nice dashboard though and I enjoyed clicking through to the map for some context. ...
This week Tableau themselves chose the data; a celebration of the importance of data skills based on LinkedIn’s annual Top Skills reports. The Tableau example includes a bump chart and box and whisker plot, whereas the LinkedIn version is a simple list per country in a slide share style: Thoughts on the original charts The LinkedIn version is simple and clear. You can see which skills are ranked highest and how that ranking has changed since last year. However it is hard to compare the countries to each other or to the overall global ranking (you have to flick back and forth in the slide deck). Also you can’t see how rankings have changed from years prior to 2015. ...
Week 16 and it’s back to some big data on EXASOL thanks to Eva and Johannes. This time over 750 million rows of GP practice prescribing data for the UK from 2010 – 2017. The nominal challenge to makeover some of the charts in a House of Commons research briefing based on the data. ...
I don’t usually participate in Tableau #WorkoutWednesday, but this week the chart we had to reproduce featured my team so I had to give it a go! Here is the finished chart, which is also available on Tableau Public: <th> Team </th> <th> Total points </th> <td> Arsenal </td> <td> </td> <td> Arsenal </td> <td> 3 </td> So the challenge is to: ...
Uh oh! Making over a 2009 graph by Andy Kriebel this week. No pressure. The graph looks at the correlation between gold and oil prices with the premise that they’re closely related. You can see Andy’s own makeover here. Another of my favourites is one by Mike Cisneros, which you can read about here. I love the story telling element Mike achieves; something I was aiming for myself. ...
The Guardian recently reported on the a PWC report into the risk of UK job losses from breakthroughs in robotics and artificial intelligence. Whilst technological unemployment isn’t a new concept and the report and article certainly do comment on job creation too, the percentages at risk in some sectors are quite striking. ...
I found week 13 of Makeover Monday quite hard. The visualisation in question was an infographic purporting to show the perceived reasons for success by different social strata. The subtitle of the graphic seemed quite inflammatory and so I was keen to trace the data back to source to see what grounds the author had for their claims. Unfortunately, as others found too, it was pretty hard to find the actual Russian survey results used. This University of Illinois blog post has a great critique of the visualisation and is well worth a read. ...
Learning about March Madness / the NCAA college basketball tournament this week. Is it getting harder to pick the winners at each stage? Tableau public version available here.