So apparently there was a pretty exciting solar eclipse this week. I guess that’s why Eva picked a NASA data set on solar eclipses for Makeover Monday! Go check out the original article as it’s got a great map of eclipse paths. The data we got for our makeover didn’t include paths, instead it included one coordinate for each eclipse over 5 millennia along with data on the type and duration. Still, the community produced some amazing and informative visualisations.

Like others, I was seeing interesting patterns across longitudes and latitudes during initial exploration. I started off with bar charts based on longitude and latitude bins but decided there was an opportunity to try a radial bar chart in Tableau. My trigonometry is a bit rusty so I turned to Rajeev Pandey’s “how to” guide to get a start on the maths. Thanks Rajeev!

Here’s what I ended up with:

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For longitudes it felt appropriate to use the full 360 degrees, but for latitude which stretches from pole to pole I felt a semi circle was a more accurate representation. Labelling the max values on each chart gives some context and I added subtle Greenwich / South pole labels to the first chart on each row to orientate the viewer.

I loved what people were doing to explain the different types of eclipse so added that as hover over text on a central circle. Colour choice was intended to help the charts pop although in hindsight I should have tried a black background. Have a look at Michael Mixon’s excellent submission to see what I mean. I also loved Colin Wojtowycz’s viz which highlighted the interesting data around latitude of partial eclipses.

Finally if you want to try the interactive version of my viz, head over to Tableau Public.

Earlier this year the pudding published an excellent analysis into the myth that various events were a trigger for mini baby booms. The analysis was based on CDC data on natality in America. Fast forward to August and the data set was selected by Andy for week 33 of Makeover Monday.

Instead of the usual makeover I tried to look for a different angle in the data set; after all visually exploring data and finding the stories is one of the strengths of Tableau right? Like a few other participants the variations in the mothers average age and average baby weight at birth intrigued me. My first thought was that I’d made a mess of averaging as we were provided with pre-aggregated data at county level not state level. Luckily we have the number of births so can calculate a weighted average to use at state level:

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If you’re not sure what’s going on here then check out Charlie Hutcheson’s blog and his link to Andy’s article on averages of averages.

I’ve focussed on weight at birth in my viz, highlighting the states with the highest and lowest average over the whole period (check out the LOD calculation and accompanying ranking table calculation if interested … oh and if you can tell me how to calculate a sortable column based on the rank so that I can programmatically ensure that the highest and lowest lines always sit on top then I’d love to hear how as I ended up fudging that bit!).

To encourage engagement I’ve also added the ability to choose a state to highlight, so that US users can see where their state sits.

Here is the finished viz:

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The viz is also available on Tableau Public here.

One final comment – why did I make the viz so small?

Well, I’ve been trying smaller charts and dashboards a lot with Makeover Monday. There are a couple of reasons for this. The initial reason was to make them more mobile friendly as we all consume so much of our content on smart phones these days. I find it frustrating when I see a cool viz in my twitter feed, go to interact with it on my phone and can’t really engage.

A secondary reason, which I’m mulling over as a good piece of advice, is that small =  less space for clutter = you have to really work to simplify and distil your story to the key points. We often see advice from Andy and Eva in their weekly write ups to keep things simple – e.g. don’t just chuck several graphs at a dashboard and call it a day. If you suffer from this then perhaps aiming for smaller and smaller dashboards each week would be a good learning exercise?

Of course there’s a point where things just don’t suit a small screen, or get oversimplified, or the author just makes the font smaller to make things fit! Have I done that this week? It’d be great to get your thoughts back on Twitter.

This week a makeover of an article and Tableau chart about sanitation in rural Indian schools, based on data from the ASER Centre. Here is the original chart posted by Eva and Andy:

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This is a great topic as it reminds us about what we take for granted and the challenges that others face. For me a geographical map is good in some ways (I can see where the states are and potentially spot regional trends) and not so good in others (the data for Goa is hidden). The headings don’t grab me and although the colour highlighting is a nice idea, I’m not sure I’ve understood it as there are some states that seem to lag further behind those highlighted. Showing the specific situation for girls compared to the overall is great as this looks like an important part of the story, but it is quite hard to compare the two figures for any one state as you have to shift your attention from one map to the other.

Made over

A tile map seemed like a good way to retain some of the benefits of the map whilst mitigating the downsides. It was an interesting exercise to make a tile map for India and the end result still retains some similarity to an outline of India. The addition of imagery featuring the Indian flag reinforces the fact that the viz is about India, and I like the analogy of a child putting their hand up to ask to go to the toilet!.

I’ve attempted to reframe the figures so that I could highlight the additional proportion of schools with no working girls toilet. And the final design decision was a headline and sub-title to bring the implications and key points into focus for the reader.

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The visualisation is also available on Tableau Public here.

Back into it with a revisualisation of a 2015 Southeast Asian Games infographic. A simple and engaging infographic telling the history of the games. The data provided was a little different; only covering 2007 to 2015. I’ve focussed on a medal table for 2015 and area chart showing medal count trends over the period. Firstly this keeps things simple, secondly it was something I could compare against medal tables on Wikipedia. The reason I wanted to be able to sense check the data against something was due to the structure. For team events both the team medal and individual participants were included so some extra filtering was needed and I wanted to check I had that right.

Here is the end result – you can also check it out on Tableau Public.

I was on holiday for week 27 to 30 so caught up with these four later in the year…

Week 27: Tourism in Berlin and Brandenburg

For week 27 a makeover of a filled map showing visitor stats for Berlin and Brandenburg. I’ve retained the filled maps but focussed on an angle that interested me; whilst Berlin ranks top for both number of visitors and total number of nights in all years, when you look at average nights per visitor it is one of the regional districts that usually tops the list. Check out the interactive version here on Tableau Public where you can look at different years, and explore how the figures differ for domestic or international tourists.

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Week 28: Tour de France

I was cycling in France around the time of the tour. Not quite at the same pace, even if we did pass a few signs! I can highly recommend a barge and bike holiday in Burgandy if you want to immerse yourself in the French countryside. Anyway here is a quick catch-up based on the week 28 data set; is the Tour de France getting easier, or the riders better? Available on Tableau Public here.

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Week 29: White House Salaries

Is the Trump administration payroll top heavy? Well there is a bump, but actually I’m not so sure! Makeover below. Also available on Tableau Public here.

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Week 30: How thirsty is our food?

That’s a wrap! My final makeover for the year completed on the 29th December.

Statista put together a nice summary of how much water is used when producing various foods, using data from the Unesco Institute for Water Education. The statista version is a pretty clear bar chart which has been jazzed up into an infographic style. I recall that there were lots of great submissions from this week but didn’t remind myself of these before attempting my own makeover. Goal one was to show the breakdown by water type and goal two was to visualise the rankings - e.g. which food type uses most green water, which uses most grey.  A bump chart eluded me with the blended data set I used, but I quite like the alternative I came up with anyway! Next to the ranking there is a simple bar chart for each water type so we can see the absolute amount of water used (otherwise the ranking could be a little misleading). Both charts are ordered by total water consumption and text boxes added for title, overview and column headings.

I’m intrigued to see that pulses require the most grey water to dilute generated waste water sufficiently. A shame as I do like a good bean surprise for dinner ;o)

The makeover is also available on Tableau Public.