I don’t usually get to attend Tableau User Groups. We don’t (yet?) have one down in the depths of New Zealand’s south island, and it’s a long drive to the nearest one in Christchurch. But with New Zealand and much of the world in some form of lock down, Tableau has encouraged and supported virtual user group meetings. So I was excited to dial into this weeks virtual New Zealand Tableau User Group meeting jointly arranged by Alex, Thabata, Jeff and Paul from the Auckland, Christchurch and Wellington groups. The icing on the cake was being invited to speak about my experience with Makeover Monday!

The topic of my 30 minute slot was Hone your skills with Makeover Monday.  For those after the tips and checklist I mentioned, please read on. Or you can watch the recording of the whole session on YouTube, including the other great speakers:

• How to do Tableau in lock-down! – Alex Waleczek
• Hiring: Score yourself a unicorn – Sarah Burnett
• Set It Up: When to use Set vs Parameter Actions – Heidi Kalbe

Here is the summary of the 13 tips and the example checklist I covered in my presentation.

Tips

1. First up, my last tip: please, please, please don’t be discouraged from participating by some of the brilliant submissions you’ll see from others. Everyone has to start somewhere. And there’s nothing wrong with a quick and simple makeover. Often a simple bar chart is just what the data and story deserves.
2. Do read the article, it’s tempting to save time by not reading it. But there is often useful context and background that you can dig into. Spending some time understanding context will usually pay you back as an analyst
3. Remember the purpose of makeover Monday. Take the time to ask yourself what works well with the original chart and what could be improved. Doing so will help you focus on what you want to make over. Sometimes the improvements might only need to be minor – e.g. better use of colour. Sometime you may be doing a completely different chart.  Invest the time that suits you
4. Do dig into any nuances in the data. E.g. does the data start and end partway through a year, which would impact seasonal comparisons? Or are there fields that need to be transformed or pivoted to make analysis easier? Have you understood an unusual outlier, or some peaks and troughs?
5. Once into visual exploration I like to build up working sheets as I explore various angles. That way I can come back to points I want to focus on and refine later. As I refine these, ideas for a dashboard and sequencing start to emerge. My best advice here is to watch one of Andy Kriebel’s live Makeover Monday’s. You’ll get to see where he spends his time, how he goes about exploring data and creating a better data viz.
6. If you plan to blog about each makeover you’ll find that that takes some time. It can help to keep notes as you review the original chart and explore the data if you plan to blog. That way you can structure them into a blog post at the end
7. Have a checklist to go through before publishing. Some people keep a written checklist to remind themselves of key things, Otherwise it’s easy to forget about tool tips or spell checking! I’ve included an example checklist below.
8. Do share your work – take the plunge! It’s a good way to engage and get feedback, which is a crucial part of improving your data visualisation and story telling skills.
9. Try not get too disheartened if you get no feedback on Twitter, or even unexpected feedback. It’s very difficult to deliver feedback in a way that suits everyone on Twitter.
10. If you want feedback register for the weekly viz review webinar. Remember to to only use the #MMVizReview hashtag if you will register for and attend the webinar otherwise it makes it harder for the organisers to prepare.
11. Do work through and incorporate any feedback that you’re given in the viz review webinar. It helps you to reinforce the learnings, and it shows Eva and Charlie that their input is worthwhile!
12. Don’t be discouraged if you don’t get selected in the weekly favourites. You’re one of a thousand people participating (as of May 2020) and Charlie and Eva can’t practicably see and recall every Makeover Monday tweet! Remember why you decided to take part and ensure that you’re getting what you want out of it – e.g. after two months look back at how much you’ve improved.
13. If you benefit try to pay it forward in the future. I’ll leave that up to you, but it could be helping new Tableau users on the Tableau Forums (hint: many people find that trying to pass on their knowledge is a wonderful way to gain deeper knowledge themselves), or it could be getting involved in your local user group. Maybe you’ll take the time to encourage new Makeover Monday participants in your area!

Checklist

Here is the example checklist I provided – over time you’ll find the things that help you to check that you’ve got a great makeover before you submit:

• Right chart type
• Improved what you set out to improve
• Remember your audience (e.g. mobile)
• Clear title & annotations
• If your title is a question is it answered?
• Consistent fonts, tool tips, etc.
• Consistent use of colour (helps the story)
• Simple is good (remove till you can’t)
• Spell check and read back through it
• Source and image credits

Those tips are things that have worked for, or stuck out to me. You can find much more information on the Makeover Monday website, including how to buy the book which covers a whole heap of data visualisation advice.

Finally as I said in the presentation, I look forward to seeing more NZ user group members in the #MakeoverMonday feed soon. Please do reach out if you need some encouragement!

An interesting and deceptively simple data set on alcohol consumption by country for 2019 week 26.

I like the simplicity of the table of data and the factors affecting the top 25 that are discussed in the article. The chart itself would be better as bars not columns in my opinion, allowing the country names to be laid out for easier reading. As Eva noted in her submission showing liters of pure alcohol consumed per capita per year isn’t that easy to relate to. Digging into the definitions for standard drinks / units I was surprised to find that there is quite a range, and that some countries still don’t define a standard drink. I decided to focus on that aspect for my makeover.

Interactive version on Tableau Public: here.

Andy Kriebel selected a data set about US workers paid at/below the minimum wage for those choosing to participate in week 3, 2019.

The original viz highlights some of the regional differences for 2017 by showing the data geographically. I like that I can see regional differences, but I found myself wanting to see the trend over time (as it’s available in the data set) to see if the geographical trends are part of an ongoing story.

So for my makeover I’ve kept things pretty simple and separated the different regions and sub-regions. Adding the overall line for the US and differentiating values above / below this in different colours helps to tell the story. A state highlighter allows users to focus in on one state if they want to – this is quick built in functionality for Tableau (right click a dimension and set as highlighter). I spent a lot of time in the depths of SQL Server geography queries for last week’s makeover, so it was refreshing to step back to simple built in Tableau functionality for week 3!

Interactive viz: here on Tableau Public.

Static image:

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.

Interactive version on Tableau Public is available here.

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.

The charts are simple, clear bar charts. For me it could be made clearer that the charts don’t indicate what products sell well, and hence what tech is actually worn most, and where. Also we don’t get to see the inter-relationships; are lifestyle products worn on a different part of the body to health products or entertainment products? For my makeover I wanted to take a look at these angles whilst retaining the simplicity of the bar charts. I’ve minimised styling because one of the team is keen to see how to move away from Tableau defaults for fonts, grid lines, etc.

The makeover follows below. Or you can click through to the interactive version where the highlight picker at the bottom lets you explore the inter-relationships (e.g. try picking entertainment to see where those devices are worn and who produces them).

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.

I felt that areas worked well for the topic – square meters of real estate – but have overlaid them to allow the different cities to be more easily compared. This approach also removes the issue of not showing parts of a whole. I’ve tried for a blue-print like look and feel. Picking courier new to complement that. In hindsight that perhaps doesn’t work with a theme of wealth and costliness.

Tableau public version.

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.

Tableau public version here.

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.

Available on Tableau Public here.

Week 2: What attributes are seen as most preferable in a romantic partner:

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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!

Available on Tableau Public here.

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.

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