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!
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.
Another double header post! Brief notes on this weeks makeover and last weeks.
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.
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.
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.
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.
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:
- Josh Jackson’s mobile friendly tool tells you how much time do you have left. Gulp! Use it wisely on Makeover Mondays
- S P Vishnu Sekhar did a nice job of one of the popular options this week, highlighting both some horrific events and income disparity.
- Emily Chen’s beautifully clear highlighting of war and famine is a clear favourite! I love the shaded area under the lines, the legend, call outs and overall flow
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.
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!
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.
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:
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).
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.
I’ve gone with three key elements in my makeover:
- Retain the line chart and highlighting of the UK, but show a wider period of time and highlight the referendum date
- 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
- Provide a ranking table to highlight the low ranking for the UK in the last two quarters
The viz is also available on Tableau Public.
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!