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:


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).


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


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:


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:


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):

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:

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:

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.


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.


Makeover Monday, 2017 #37

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

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.



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.



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


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


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


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.

Makeover Monday, 2017 #35

A quick post for Makeover Monday this week. We had data on NFL player arrests from 2000 to August 2017 with the aim to makeover the interactive visualisation here. Whilst exploring the data I was interested to see the free text outcome data. After grouping this I was surprised to see that the proportion of guilty outcomes seemed to be reducing over time whilst the proportion with an undetermined outcome was increasing . In hindsight you’d expect more recent cases to not yet be determined – perhaps they haven’t yet worked through the system? Still I suspect that there is a story here. Of course that story may not be specific to NFL player arrests and I haven’t checked for similar stats across the wider population.



You can access the interactive version on Tableau Public here.