MakeoverMonday

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. ...

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. ...

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. ...

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. ...