Makeover Monday, 2017 #9

Makeover Monday week 8 and Andy Kriebel challenged the Tableau community to improve on two graphs of his American Express card expenditure. The graphs are very clean and appear to offer some drill down functionality to view transactions. There doesn’t seem to be an intermediary level of detail, e.g. sub-categories (although that may exist and we’re just not seeing it here). Also the use of a 2016 average may not be as useful as a median given that there were a couple of one off large expenses.

For my makeover I’ve added bars to the existing line chart, showing the number of transactions per month. This gives a little extra context (the large amount in December isn’t due to an increase in transactions for example). I’ve then tried a calendar style heat map showing when each transaction occurred.



The user can chose an aspect of the heat map to highlight. In the example above the logic is picking certain categories of non-UK expenditure as most likely to be overseas travel (e.g. a Hilton in the US as opposed to Amazon in the US. Caveat: this is obviously based on some assumptions and may be inaccurate). The user can also focus on certain categories including highlighting large / largest transactions within each category. I’m not convinced this is a good way to explore the data as the labelling is quite hard to accommodate:



You can try the interactive version here on Tableau Public.

Or check out some of the other community contributions - there’s some really cool visualisations as usual.

Makeover Monday, 2017 #8

Week 8 is all about potatoes. European Union potato sector stats to be precise. Eurostat provide a very detailed analysis of the EU potato sector on their website:

Eurostat potato sector stats page.

The article is very detailed and does take some time to read, but the key points are in bold so it can be scanned to get an idea of the main stories. The use of tables provides a lot of background data but again the emphasis is on taking the time to digest the data. For my makeover I’ve tried to focus on three key points – most of the production is in a small number of countries; Germany is the biggest producer; but France achieves the best price. The viz can also be seen on Tableau Public.

EU Potato Production & Prices

Makeover Monday, 2017 #7

Love is in the air this week with a makeover of an infographic on valentines day spending in the US.

The original visualisation is pretty good although some of the key data (like average spend per person) doesn’t necessarily jump out. Also there’s nothing to show changes over time, even though the data source does contain that information.

So for my redo I wanted to focus on a very clean presentation of the main trends over time, whilst still highlighting some key stats. I also wanted to offer the viewer the ability to explore the data a little more – something I haven’t done in many of my makeovers this year.



The interactive version is available on Tableau Public here.

Getting nice clean spark lines was just a matter of turning off all formatting and adding calculations to provide label text for the first and last points only – a few people have covered that technique this week so check out some of the other blog posts. The heart shapes with the key stats are achieved by using a dual axis chart where the additional series has a single point, in a position specified by a calculation (in the example below at year 2013 and 62.5%, note that with no ELSE only the one point is plotted as the rest are NULL). The point is then given a custom shape, the size beefed up and label text added.


Makeover Monday, 2017 #6

Great fun exploring 105 million rows of Chicago taxi data for #MakeoverMonday this week using the data underpinning this article. The full data set was provided on a hosted Exasol database, purported to be the fastest in-memory analytic database in the world (and it was pretty fast considering the amount of data I was querying from the opposite side of the world).

For my makeover I’ve tried to relate the data to the topic by placing key summary information into a taxi fare style sign - like you would see on the door of a Chicago taxi. That colour scheme is carried down to graphs showing the distribution of trip distances, durations and costs by year along with an approximated heat map of drop off points (without trying to plot millions of points!). The interactive version allows you to filter to specific pick up areas, allowing you to get a feel for the cost and drop offs for those traveling from O’Hare for example.

Chicago Taxi DashboardStatic image only for now until I figure out how to make a cut down extract for Tableau public!