Makeover Monday (#43)

This weeks Tableau Makeover Monday was a challenge to visualise a small amount of data; two data points – total size of US National Debt versus the rest of the world. The original visualisation can be seen on the visualcapitalist website and also include comparisons of the US$ 19.5 trillion debt to thinks like company sizes, oil exports, cash held, etc. The pie chart works well here and the comparisons give some idea of scale.

I’ve stuck to the challenge and just used the two data points. Presented as a tree map but formatted similarly to a credit card – perhaps a good way to make the concept more real?



[Edit: Was it a good idea to use credit card imagery in this visualisation? Maybe not! As touched on by Andy Cotgreave in his Makeover Monday blog post, national debt is different to house hold debt. A better analogy might be a business loan – borrowing money to build a business (aka grow the economy). If I were revisiting the visualisation I’d also try changing the title and caption to refer to “Sovereign Debt” (with explanatory note) as the term ”global” could be misconstrued as all debt not just national or sovereign debt.]

Makeover Monday (#42)

This weeks #MakeoverMonday was a look at US presidential election forecasting data by Drew Linzer on Daily Kos Elections.

The original charts plot the average percentage being polled by Clinton and Trump over time, along with percentage undecided and other (independents). Personally I wasn’t sure I could improve on the existing charts or some of the community versions (loving the tile maps!) so instead I’ve focussed on a different angle - it wasn’t always easy to see at a glance who was predicted to win the election and why. Particularly with the complexity of the electoral college voting system.

Showing the states each candidate was predicted to win, ordered by the scale of the candidate’s lead with a running sum of electoral votes seems to work well:



Colour coding those states where the lead is smaller than the percentage undecided adds another potential indicator of confidence allowing us to make our own judgements of the likelihood of a particular result – how many close states would have to swing Trump’s way for him to win at this stage?

I would have liked to have added the overall country wide line chart – like the original – and to have dug deeper into the two states where electoral votes are allocated differently. Animating the chart over time, using the pages shelf, was an interesting exercise that I would have liked to explore further.

The full viz can be found on Tableau Public.

Or check out other community efforts via the #MakeoverMonday webstite.

Makeover Monday (#41)

Having a go at Tableau #MakeoverMonday this week, with a reworking of a FT visualisation of European public transportation satisfaction survey results in 2015. A good opportunity to look into ways to visualise Likert scale survey results, and to practice some table calculations in Tableau! Adding the ranking by country along with an indicator of the number of places gained/lost gives a quick idea of how satisfaction has changed.

Public transport satisfaction in Europe in 2015

View in Tableau Public. Areas for improvement are: the table calculations for last years ranking (could I use LOD here?); tool tips for the diverging stacked bars (some of the other community makeovers do a very nice job here); drill down to city level and the legend.