Makeover Monday, 2017 #45

Not so much a makeover for me this week. The original chart was a WHO map of life expectancy data. I’ve just looked at a simple comparison of a country of the viewers choice with the two countries with the highest and lowest life expectancy at birth for a given year:

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Want to try different countries or years? Click through to the Tableau public version.

Some of my favourites from the community this week:

Makeover Monday, 2017 #44

For week 44 of #MakeoverMonday Eva selected a Daily Telegraph article mapping the countries with the most public holidays. Nice map – although as ever with filled maps there are data points that get a little lost (the smaller countries). The lists work well to make up for this, although they’re pretty basic and unexciting.

The dataset was quite challenging this week - broader than the original article, lots of variation and some data quality issues. I’ve decided to focus on a specific public holiday, labour day, because it was easier to hone that subset of the data for analysis and visualising. Labour day is also reasonably prevalent and it’s roots are a great reminder to us all about striking a suitable work-life balance!

First up I wanted to map those countries celebrating Labour Day to get a feel for how much of the world does celebrate the day. An image and large KPI-style number lend a bit of focus here. The rest of my makeover focusses on drilling into the variation in when the world celebrates the day – not everyone celebrates it at the same time or for the same duration. For example I was really surprised to see that China counts a weekend as part of it’s celebrations.

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The viz is also available on Tableau Public where you get a little more hover over – e.g. to see what dates each country celebrates and for how long.

Makeover Monday, 2017 #43

A tough week for me with a hectic work week and a struggle for #MakeoverMonday inspiration.

The aim was to makeover this Myers Briggs chart. I like the 4×4 grid representation of the original as we are basically looking at a mix of four attributes. I don’t really get a sense of the percentages / proportions though as every segment of the grid is the same size. I also had to flick back and forth between another page on the site to get a more detailed explanation of what the letters mean.

Perhaps foolishly I decided I wanted an overlapping area chart showing all four dimensions. I couldn’t do this in Tableau so should have fallen back on a tree map or marimekko, or maybe this beautifully simple reviz from Henrik Lindberg (nice). Instead I banged my head against producing the following:

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It’s also available on Tableau Public.

Most of the work was actually done in an SQL script as I couldn’t wrap my head around the Tableau table calculations. Was it worth it? Yes and no. The representation isn’t totally accurate due to fudging the bottom row and left hand column start points.  On the other hand it’s quite visually engaging and it was good to nail those calculations even if it was back in SQL!

Makeover Monday, 2017 #42

Making over a table of Formula E racing results from the FIA Formula E website this week.

I was interested in how drivers progressed from practice to qualification so went with a bump chart. I’ve highlighted the winner but dashboard actions also allow the user to highlight the driver that they are hovering over. This way they can see how each driver’s performance changes during an event because the bump chart is quite hard to follow otherwise.

The viz asks a question and gives the user room to explore the data and come to their own conclusions, however a micro chart in the left hand margin also provides a general answer for those with less time or inclination to interact. The micro chart is based on average rank in qualification and race with a trend line to show the correlation.

You’ll spot that for the “super pole” session only six drivers are involved. To avoid a gap in the bump chart for the other drivers I’ve filled this rank from the previous session but not shown a mark / rank.

If I had more time and data I’d dig into the impact that fanboost has on the eventual winner.

The viz is also available on Tableau Public.

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Makeover Monday, 2017 #41

Just a brief write up for now! Andy and Eva chose charts relating to adult obesity in America for the 41st makeover with the source data coming from the CDC. The original charts are part of an interactive dashboard and it is well worth checking out.

Similarly to Klaus Schulte I decided to dig into gender differences. Klaus’s makeover is very nice so definitely go take a look at that.

Here is my attempt:

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It is also available on Tableau Public here.

Challenges were laying out the maps to include the more far flung states and territories, and also getting some decent labelling on the area and line charts. I’ve resorted to reference lines for the latest value and standard header labelling for the states (I would rather have had a label in the area shading).

Makeover Monday, 2017 #40

This week was a makeover of a Financial Times article on the UK economy since the Brexit referendum using data from the OECD.

The original

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The original visualisation allows us to easily see the UK, but doesn’t allow us to compare to another G7 country as there is no labelling of the other lines or ability to highlight. The chart also lacks context around when the Brexit vote occurred and what the historical patterns have been; does the UK usually have slower growth than the rest of the G7? Missing 2017 Q2 data is a bit of a shame.

Makeover

I’ve gone with three key elements in my makeover:

  1. Retain the line chart and highlighting of the UK, but show a wider period of time and highlight the referendum date
  2. Show the range of growth across the G7 without showing too many lines. Instead allow the user to choose which G7 country to compare to
  3. Provide a ranking table to highlight the low ranking for the UK in the last two quarters

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The viz is also available on Tableau Public.

Final thoughts

My usual approach would be to plot all of the G7 countries as individual lines in a deemphasised colour. This week I wanted to experiment with showing the range of data in a different way. I had hoped that a light area chart would allow me to highlight the UK in red, show another selected country in a mid-grey and still see the G7 range in the background. Whilst the approach does allow the user to get a better idea of stability / variability for each country as they explore the data, the area chart doesn’t really sit in the background when it comes to interacting – e.g. hover over.

The area chart has two parts with the bottom part being shown in white to give the impression that you’re just seeing one area. This required calculations based on min/max values to get the two parts.  An additional challenge was that Tableau doesn’t handle area charts crossing the 0 line particularly well (or didn’t for me at any rate – I ended up with empty shards in the chart!). To work around this I calculated an offset and pushed all data points up by that amount so that everything was above 0. The real, none offset, values are used for labels and tooltips and a reference line was added for a fake 0 line. Upshot: it was a lot of work and fudging and perhaps not worth it. Feel free to download the workbook from the Tableau Public page linked above to see what you think.

I like the way the table at the end tells the ranking part of the story in quite an effective way. I prefer it over a bump chart in this case and in hindsight perhaps the table was actually all that the makeover needed!

Makeover Monday, 2017 #39

In August 2016 Nielson released a report what’s in our food and on our mind. Page 8 of the report included a set of spiral bar charts showing restricted dietary requirements around the world:

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The charts on this page were chosen for week 39 of Makeover Monday. Whilst the infographic is visually engaging I’m not a big fan of the spiral bars. That and the way the different diets are laid out makes it quite hard to visually compare various categories (e.g. low sodium to vegetarian).

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The finished makeover follows and is also available on Tableau Public.

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Initially I wanted to present the data in a Gantt chart format to show the range of responses for each dietary category across the continents, with each individual continent plotted as a mark within the Gantt bars. There were two problems with this as you can see in the screenshot below. Notice how the coloured circles at the extremes of each bar are centred on the ends of the bar and hence spill out of it. Also for some bars you can only see four circles due to two continents having the same percentage:

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I could have switched the circles for thin bars that were the same depth as the Gantt bars. Instead I decided to try something else. I was sure I remembered a submission in week 24 with rounded ends to a Gantt chart, so figured I’d have a go at that. I ended up achieving it using lines. Is that the best way? Check out my steps below.

Using thick lines instead of a Gantt

First up I defined the minimum and maximum percentage (followers) for each diet regardless of continent. This was done using two LOD calculations. Here is an example:

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Next I figured I’d need two points with different values to use for the ”path” of each line. Point 1 being the start of the line and point 2 being the end. Luckily we have five points in the data so I just made two of them return a value and the rest return nothing (NULL – implicitly due to the lack of an ELSE):
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Now I had a path for points 1 and 2, but I also needed a position to plot those points at on the horizontal axis. I used the same approach as above but this time returned the percentage when min or max:
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To put this together I dragged the point onto columns, and the path onto the path card having selected a line mark type. A little bit of formatting to boost up the size and a label at the end of the line resulted in this:
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The final step was to add the dual axis chart back in showing each continents actual percentage. I ended up boosting the size of these up and colouring them white except for a continent selected by the user. That way the focus is on the continent that the user wants to look at.

Interesting feedback was that the visual look was quite similar to iOS on/off switches. Oops! That wasn’t something I was aiming for, but I can see why people thought so. The danger of being too similar visually to a concept as familiar as on/off switches is that some users may think that they can interact with the bars as if they are switches.

Conclusion

Great to learn a new technique. Perhaps not so great to have ended up mirroring a UI component in a way that doesn’t follow the conventions for that component! Arguably I haven’t allowed people to compare continents that easily. If I were redoing the visualisation I’d take a look at both of those aspects. There was some great analysis by others in the community looking at different categories of dietary restrictions. I could have used two colours to differentiate diets most likely to be related to health as compared to those more associated with moral/ethical/religious choices. Finally a similar visualisation had a radio button selector for continent instead of a drop down – I definitely should have thought of that given the small number of values.

Makeover Monday, 2017 #38

A day at the races! This week we got a wealth of Strava data from Andy and Eva across two recent events that they have competed in. Find out more here. You’ll see that they each had a set of questions for the community to consider. I was busy trying to reproduce some of the graphs I’ve seen produced from sports watch / trackers and got very bogged down in the data. A quick look at the submissions that were coming through on twitter showed me that those submitting were focussing on a subset of the questions and only tackling one person’s data. Phew – it was good to take a step back! Eva’s question about the second kilometer of the run intrigued me. Friends that participate in triathlons have mentioned the initial pain over the first 500 meters but I hadn’t picked up on a lull after the first 1000 meters.

I’m sure I’ve learnt more from the exercise than Eva will, but here is what I came up with.

The viz is on Tableau Public too.

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Makeover Monday, 2017 #37

For week 37 of Makeover Monday we’re looking at UK bicycle thefts based on data from data.police.uk.

The reference site linked above has pretty extensive visualisations and I love the way they unfold as you work your way through the site. I’m not going to try to improve upon the site, instead I’m looking for a different angle to show and, given the bleakness of the data (less than 1% of bike thefts resolved!), it’s an opportunity to try a paired back black and white viz.

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Trying to remove unnecessary colour is some advice from Andy and Eva in their weekly roundups. Does it work in my viz? Feel free to let me know on the twitter thread - any feedback is welcome. Also check out the Tableau Public interactive version where you can dig into a particular constabulary to find your local story.

A note about the resolution calculation:

The source data came with a range of outcome descriptions which I’ve mapped to three resolution categories: unknown; resolved and not resolved. If you’re interested in digging into this mapping I’ve included it below. The reason for the unknown category is that there seems to be a lead time on getting a resolution (positive or negative) as a case works its way through the system. Having a yet to be determined / unknown category means that we can factor those out of a resolution rate. Otherwise we’d expect to see a tail off in the most recent months as the thefts are yet to be investigated, etc. Arguably a couple of the outcomes that I’ve categorised as resolved are a little unclear and a proportion of those could actually be unresolved or still in progress.

outcome-groupings

 

Makeover Monday, 2017 #36

The UN, #MakeoverMonday and #VizForSocialGood came together to challenge the data visualisation community to visualise results from the MYWorld survey on the UN Sustainable Development Goals. You can read more about the challenge on the Makeover Monday blog.

The design is geared towards a tablet as there were indications that the UN would like to use the finished viz on stands with tablets at various UN events like the forthcoming UN General Assembly in New York.

I’ve split the story into three pages to meet different aspects of the brief:

  1. The overall demographics of survey participants (I’ve also used this page as an opportunity to introduce the goals and the MYWorld survey questions);
  2. Responses across the 17 goals for question 2 and 3 of the survey (a sort order selector in the column headings allows the user to consider different rankings); and
  3. The ability for users to compare their country to another country (indications were that stakeholders were interested in, and motivated by, comparisons to their neighbours).

The proposed visualisation is available on Tableau Public with data extracted as at 23-Aug-2017 – screenshots below.

Page 1

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It would be possible to include a country filter on this page (in the right hand column header) and to include a call to action prompting the user to consider what participation is like across various demographics within their country – and what they could do to increase or maintain participation.

The graph of participation over time is intended to show how participation is trending in terms of the goal of 1,000,000 respondents per year and a good place to show how many participants are aware of the goals over time.

Page 2

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The continent and country filters at the bottom allow the user to focus in on their area of interest. They can move to page 3 for more detailed comparisons of specific goals. An improvement would be to allow the user to click a goal on page 2 to jump to page 3 with that goal selected.

Page 3

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As well as the country comparisons this page also allows the user to compare responses by some of the other categories in the survey data – e.g. education level and HDI (human development index). Finally the page ends with a call to action to take the survey and a thank you for exploring the results. An alternative use of this space would be a call to action geared towards meeting the sustainable development goals.