Makeover Monday, 2017 #5

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.

In addition to these observations the pie chart colouring doesn’t highlight the message – for me Germany sticks out more than the US based on the colour choice. Other #MakeoverMonday participants have commented on the use of pie charts; personally I don’t mind the pie charts here as the message is to compare the US segment to the rest of the segments combined. Although if doing this I would have coloured the other segments similarly and highlighted US as mentioned.

For my redo I went with a bar chart but combined the other G-7 countries into a single bar. Doing so allows the message in the article (whether right or wrong) to stand out more for me, whilst still providing some of the detail for the other G-7 countries.

I’ve experimented with labelling the other G-7 countries by creating duplicate sheets where mark colours are set to fully transparent so that we seem to get a set of labels aligned under each chart – a bit of a hack and I’m not sure it really works given the distance from the labels to the bar segments?


G-7 Employment Growth

Makeover Monday, 2017 #4

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 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.

The charts use RTI (regional tourism indicator), but that is an old superseded measure. MBIE now use MRTE (monthly regional tourism estimates) which is a $ figure rather than an index.

You can read more about MRTE and RTI here on the MBIE website. One of the key differences I was interested in was that the RTI uses “a relatively coarse definition of domestic tourism … [which] conflicts with the official definition” – in essence some domestic tourist spend is discounted under the RTI.

Running short of time for my makeover I’ve focussed on showing the differences between the two measures:


A number of community submissions focussed on events such as the premier of the Hobbit movie, instead I’ve shown the marked temporary increase in tourist spend during the 2011 Rugby World Cup which jumped out from the seasonally smoothed 12-month moving averages.

The visualisation is also available on Tableau Public.

Makeover Monday, 2017 #3

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.


Retweets by @RealDonaldTrump

Check out the full workbook on Tableau Public or jump onto Twitter to see some of the other great #MakeoverMonday submissions.

Caveats: Data not vetted. Also there is a bug with my ranking and layout calculations for the 5×5 grid of donut charts once you get outside of the top 20 or so – still some learning to do for this but a good opportunity to use nested table calculations.

Thanks to Trump Twitter Archive – @realtrumptweet for the source data.

Makeover Monday, 2017 #2

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.

Digging into the quarter-by-quarter data there seemed to be a bit more of a story and, for me, the addition of a moving average helped to smooth out seasonality and see this story:

Global iPhone sales 2007-2016


Adding the bounding boxes around 2015 and 2016 was a mistake as they impinge upon the hover over functionality in the underlying workbook on Tableau Public. They also add some visual clutter, but I was keen to help differentiate and highlight the two years.

On a wider note, there was some good discussion amongst the community about the dangers of data quality and drawing false conclusions (for an example see my previous blog entry on the pitfalls of using taxable income data to draw conclusions about salaries and wages, or dive into this blog post by Steve Wexler and Jeffrey Shaffer and resulting twitter discussions).

To an extent these sorts of issue are inevitable within the constraints of Makeover Monday. Not everyone can commit the time to really dig into the data, and the recommended time of one hour arguably doesn’t encourage it.  For most people the focus seems to be on honing Tableau and data presentation skills. But this is where I think Makeover Monday can turn a weakness into a strength (isn’t that what it’s all about after all?). Whilst most people might focus on presentation in any given week some will focus on, and discuss, the data. That’s great – as a group we’ve then covered multiple aspects of the original viz and data set.

If we were to somehow curate those discussions back to the datasets page on the Makeover Monday website, and every viz had a standard footer re data source (they should have this anyway) with a caveat that linked back to that datasets page, would that address some concerns? Might it also ensure that any one viz viewed in isolation would guide people into the wider discussion and in depth analysis pulled together by the community? Might that be a positive thing in terms of improving understanding of the original story being told?

I’ve had a go at adding such a standard caveat to my viz this week. It could certainly be refined, as could the viz itself, but seems like a good start.

Makeover Monday, 2017 #1

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 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.