Blog

This week a look at the top 500 gamer channels on YouTube based on this list on socialblade.com. What intrigued me about this week was the immediately interesting disparity between the video views, channel subscribers and the Social Blade ranking (score) and rating (grade). The more influential channels according to Social Blade are not necessarily those with more views or more subscribers. The list itself is pretty handy but not visual engaging and there’s no real story telling or summary. ...

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. ...

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: . 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. ...

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. ...

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). ...

A quick redo of the pie charts in this Business Insider article for #MakeoverMonday week 5. . If you’re thinking that something seems dodgy with these charts then you may well be right and should have a read of @ChrisLuv’s comments which are an excellent read. ...

I spent more time looking into the data than on the visualisation for this weeks #MakeoverMonday because the data related to New Zealand. The task this week was to make over the international and domestic tourism spend charts on figure.nz. The international chart is shown below: The charts are very clean, but showing each year side-by-side makes it hard to read for me. The key seasonality of tourism spend emerges nicely but also makes it harder to spot trends. ...

This week’s Makeover Monday challenge was to redo this graphic of the accounts Donald Trump retweeted during his US Presidential election campaign. The original bubble chart gives an idea of the top accounts being retweeted, but doesn’t cover the depth that the article goes into or allow for easy comparison. I’ll acknowledge up front that I haven’t improved on the comparability as I wanted to learn how to produce multiple donut charts in Tableau! Depth was added by showing which platform the retweets were made from (which may indicate how much retweeting Trump did himself?) and column charts showing volume of retweets over time (and onward retweeting by others) to see what happened at the point that Trump’s campaign was launched. ...

A reviz of global iPhone sales over the last decade for week two of Makeover Monday in 2017. On first glance the only thing I wanted to change from the original chart was the slight 3D affect on the columns, and maybe the background colour. Other than that the chart has a clear and simple title and highlights the data point addressing the question posed. ...

The first Tableau Makeover Monday for 2017 looked at an article about gender inequality in Australian pay. The article is based on 2013-14 tax year data from data.gov.au. The original article presented the data in two tabular lists which made the comparisons being drawn hard to visualise. Unsurprisingly many of the makeovers represented the gap between male and female taxable income in a selection of occupations. One of the problems with the article, and a number of makeovers, is the assumption that taxable income is the same as pay; that is not necessarily the case as can be seen by digging into the original source data (which seems to cover taxable income from sources other than main occupation). I’ve steered away from mentioning pay in my version and simply tried to represent that in the bulk of cases men will generally have a higher taxable income than their female counterparts. Click on the image to see the interactive version, where hovering over a bubble shows you the detailed figures. ...