Makeover Monday 2019 week 1 looks at NHL attendances since the 2000-01 season. A couple of things emerge from an exploration of the data set provided: firstly there are seasons where labour disputes, or lockouts, dramatically affect attendances. Secondly some teams have different stories to the general trend. I spent most of my time exploring and presenting the lockout story, but added a team selector to allow users to explore average game attendance by team. ...
Christina Gorga recently commented on Twitter that she would love the ability to copy or duplicate text boxes on Tableau dashboards. The tweet attracted favourable attention, with 44 likes. One reason the feature is seen as useful is that it could reduce the time taken to copy formatting throughout a dashboard; styling like fonts, sizes, colours, borders. How much of a pain is it to reapply these to multiple text boxes? ...
A couple of my colleagues are giving Makeover Monday a go to practice some recent Tableau Desktop training, so I’m back into it too! This week we were given a data set from Figure Eight about wearable tech products, with the challenge to makeover the charts in this article from 2014, about where we are wearing our wearable tech. ...
Where is some of the worlds priciest residential property? For week 22 of #MakeoverMonday we look at a World Economic Forum chart trying to answer that question. On first glance the chart is nice and clear, but is a tree map the right type of chart to use when we’re not looking at parts of a whole? A number of community members have suggested it is not, and for me that detail shouldn’t be left to the chart footnote just in case the chart is used in a standalone setting. The sort order of the areas isn’t super intuitive either, with the most expensive city in the top right. ...
How accurate were the Guardian Sports writers’ predictions for the 2017-18 English Premier League? According to this visualisation, which was picked for week 21 of makeover Monday, the predictions were not that great. I decided to have a play with removing inaccurate predictions; after all once you get one wrong you’ll end up with at least one other prediction wrong too right? E.g. getting first and second the wrong way around. I was intrigued to see if the Guardian had more of the sequence correct than it seemed at first glance. Arguably they did have more right – 11 was the number I got to. ...
I’m returning to #MakeoverMonday after a month or two off with family and travelling. After completing all 52 in 2017 I’m pretty relaxed about how many I participate in this year, and hope to pick up on some other community initiatives, like viz for social good. Anyway back to this weeks makeover… In week #13 the challenge was to makeover the first chart in this infographic about chocolate bar preferences in the UK. I enjoyed the original infographic and found the bump chart interesting. It took me a little while to reconcile that the bump chart plotted preferences across age brackets not years. I like the way the lack of data for some brands has been handled, although that does add to the complexity of the chart. So for my makeover I’ve simplified it down to simple lists of rankings. I’ve coloured the items by manufacturer as I think this tells the story about Cadbury more effectively for the audience. ...
Week 2: What attributes are seen as most preferable in a romantic partner: . Week 3: Distributions as a line chart similarly to one or two others, but within a tile map. Each tile shows the distribution relative to all other distributions. Shading highlights the higher proportions for a selected income bracket. I also experimented with a second chart per tile to act as a miniature x-axis and call out the selected income bracket to orientate the viewer, not so sure about this bit … I wanted to show the income bracket too but it was just too dense text-wise! There were a few tricks here – like using a dual axis with area chart to be able to show a different background colour for each tile. Feel free to download the workbook to take a look and let me know if there’s things that could be done more elegantly! ...
A whole new year of chart makeovers to look forward to! And this year the data is available via data.world 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. ...
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. ...
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! ...