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.

For my makeover I wanted to highlight the difference between the Social Blade ranking / rating and raw views / subscribers. I also wanted to practice a few tricks raised by Rody Zakovich on making your dashboard pop and from Andy and Eva on the Makeover Monday blog for week 8 and 9. I think this is an improvement on the list – and on my previous makeovers – but there’s still more I’d like to have nailed if time had allowed.

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The visualisation is also available on Tableau Public.

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.

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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:

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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.

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:

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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.

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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.

A quick redo of the pie charts in this Business Insider article for #MakeoverMonday week 5.

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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?

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.

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:

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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.

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:

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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.

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.