Makeover Monday, 2018 #1

A whole new year of chart makeovers to look forward to! And this year the data is available via too, with integration to a wider set of tools. We’re starting out with a look at per capita poultry consumption in the US since the 1960s based on data from the National Chicken Council; a nice clean line chart that tells the main story. The source data allows us to dive into a little more detail to expand upon the story. It was interesting to look at Turkey and seafood, and also to try to find equivalent data for plant-based proteins.

My version of the chart follows and is also available on Tableau Public here.


Makeover Monday, 2017 #52

A “Merry Christmas” makeover to end the year with*, looking at a Statista graph of Christmas tree sales in the US from 2004 to 2016. The original is a nice simple bar chart, which clearly shows the breakdown between sales of real and fake Christmas trees in America. It’s a little hard to see the actual proportions and that’s one thing I wanted to hit in my makeover.

A number of the community’s submissions took the angle that fake trees are re-usable, and so whilst less are sold than real trees, the actual number out there in people’s homes and business might be higher. We have a real tree that is still re-usable in our house. I bought it nearly ten years ago as a small potted fir. It lives in the garden most of the time and then comes in every other Christmas or so. Why not every Christmas? Well I absolutely love the tree, it’s convenience and the re-usability. But my partner occasionally wins the debate and we get a larger cut tree. I guess there is a place for, and joy to be shared from, all sorts of tree’s at Christmas … not so different from other aspects of life really.

Anyway below is my makeover. As ever it is available on Tableau Public too here.



* Post script: why did I asterisk ending the year with this viz? Well I set myself a goal to complete all 52 this year but am 1 short on 51. Darn it, so close! Hopefully I’ll squeeze one more makeover in and hit my goal. Well done to those that have done all 52 – a massive effort. And of course a huge thanks and shout out to the organisers – Andy and Eva. 2018 Makeover Monday looks to be even more exciting. If you’re considering joining, or maybe wavering, then my simple advice is to get amongst it! The community is really supportive and if you heed the advice and pointers that you’ll hear, then you’ll likely see some real growth and learn heaps. On top of that if you participate most weeks then you’ll build up a great portfolio of visualisations.

Happy new year everyone!

Makeover Monday, 2017 #51

Cruising towards the end of a year of weekly makeovers with a look at over 176 million daily maximum and minimum temperature readings from around the world, over three centuries. As noted by many others, this weeks original visualisation is a tough act to follow – why try to make it better? Well I didn’t! I spent all of my time digging around what was a fascinating data set. In the end my “makeover” is simply a look at how anomalies can just be down to the fact that locations for temperature readings / estimates are introduced over time. The seeming false start for Senegal being a good case perhaps! Equally when temperatures from Antarctica were introduced is it surprising that we see the minimum temperature for the year drop dramatically? What about the impact of elevation of weather stations – over time readings are being taken from more extreme locations and I didn’t even get into looking at that!

The interactive version is available here.


Makeover Monday, 2017 #50

This week we take a look at barrier free buildings in Singapore. The original visualisation is part of a site by the Building and Construction Authority in Singapore (BCA). Although the site requires Flash to be available and enabled in your browser, there is a great range of information available if you do have Flash. From the map of an area you can drill into information about individual buildings. We didn’t have quite the data to do that (not having the building ID or the depth of information about each building). Nevertheless is was interesting to attempt to makeover the map to: cover more areas and be a little easier on the eye. Most of the info is available via hover over, but I’ve also added an inset bar chart to show how the selected area compares to the “best” and “worst”.

The interactive version is available here.



Makeover Monday, 2017 #48-49

Another double header post! Brief notes on this weeks makeover and last weeks.

Week 48

Last weeks data asked what if the world was made up of just 100 people. The original is visually interesting but hard to read as all categories are combined into one overall circle. A problem with percentages is a lack of perspective of just how many people are affected by something like starvation or malnutrition. There were some great examples from the community showing the actual number of people involved in the real world. I wanted to take this a step further and provide access to a story about just one person. The power of a story over a statistic is really interesting me at the moment as a result of a human centred design project I’m involved in.


The viz is also available on Tableau Public here.

Week 49

We’re also celebrating week 100 overall for #MakeoverMonday. Wow! Well done to the organisers. For week 49 the data looked at price variation across JD Wetherspoon pubs in the UK. For my redo I wanted to look at price distributions. The data set wasn’t quite setup  for that so I’ve pulled in an additional scaffolding data set which gave me the full range of one pound price brackets. I made the axis ranges continuous rather than discrete so that the gaps are filled in. This allow us to compare the different components of the meal, but it annoys me that the grid lines are down the middle of the bars. Are there better ways to do this? Please do post back against the tweet if you think so!


This viz is also available on Tableau Public here.

Makeover Monday, 2017 #46-47

Week 46:

Original: The world’s top cities for sustainable transport.



Tableau public: link

Notes: nothing particularly special here! The breakdown per continent allow two of the key stories to emerge. Hong Kong is top but there is a high concentration of European cities at the top of the ranking.


Week 47:

Original: Snapchat is tops with American teens.



Tableau public: link

Notes: I’m pleased with the simplicity of this chart. We can clearly see that Snapchat has grown to be the main favourite. The arrow is an aligned dual axis line and shape chart to give the effect of an arrow. This allows me to highlight the combined percentage for Snapchat and Instagram before pulling in some extra information in the final paragraph about the way young people perceive those two apps.

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:



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.



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:



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.