A year of MakeoverMonday
Welcome to a new year, dear reader! 2020 is finally gone. Time for one last look back before we put it on the ash heap of history where it belongs. Last week, I already reflected on all the vizzes I created in 2020. A huge part of those vizzes came from MakeoverMonday. So in this post, I’ll specifically talk about my MakeoverMonday experience.
By the end of 2019, I had learned all the Tableau basics and passed the Tableau Desktop Certified Associate Exam. So what next? I already knew of MakeoverMonday and had used some of the older datasets to build my Tableau skills. But for 2020 I decided it was time to become actively involved. So when the new year arrived, I set myself a new goal: Complete all #MakeoverMonday challenges in 2020!
Now that 2020 is over I can proudly report that I did it (well, almost… I’m still missing Week 47 but I’m just going to ignore that). So here it is – at a glance – a year’s worth of MakeoverMonday vizzes:
As you would expect, not every one of those vizzes is a hit. But there are actually very few that I don’t like at all and many that I still like quite a bit.
My favorite vizzes
Here are some of my personal favorites:
The nested squares to show parts of a whole are still one of my favorite new viz types I’ve done this year. Even though they might not be 100% best practice, I think they look pretty. I’ve used them in several other vizzes since and I’m sure they’ll come up every now in then in my future vizzes as well. This viz was also the first MakeoverMonday viz I blogged about here on this blog.
Representation of women in politics
This one looks like a book cover. More specifically, like the MakeoverMonday book. I swear that wasn’t intentional, but it must have been in the back of my mind somewhere. I remember it took me quite a while to make it look neat but I think it was worth the effort. I still like the design and the use of blank space to show where representation is lacking.
Sugar consumption in Britain
This one holds a special place in my heart. This is way back from Week 3. I think this was the first time I submitted a viz to viz review. Charlie and Eva gave some great advice and I went back and revised my viz. This then became the first viz that was picked as a favorite at the end of the week.
Looking back at my year of MakeoverMonday vizzes – what are some of my key takeways?
- Consistency is key: My main motivation for starting this project was to consistently create vizzes. MakeoverMonday gives you the easiest opportunity to do exactly that. You don’t have to spend time thinking about a topic and gathering the data. You can just go ahead and create.
- Not every dataset is gonna be equally appealing to you and that’s okay: With 52 different topics in the year, it’s quite obvious that some weeks will be harder than others. Sometimes the topic might not be as interesting to you or be something you know nothing about (Looking at you, Week 35 Cricket dataset). I was very aware of that fact when I started and promised myself to not let that deter me from creating a viz anyway. Honestly, it’s been good practice for my work as well, because let’s be real – your clients data will not always be the most exciting data you’ve ever seen or you might be having a hard time coming up with an idea in the beginning. Working through all the MakeoverMonday topics helped me prepare for that situation and assured me that I can in fact make a viz out of every topic thrown at me.
- If you’re short on time – make that part of the challenge: In the beginning, I blocked a few hours on Sunday afternoon to create my viz, often with more time needed on Monday to finish it. But soon I found that I couldn’t keep up that time commitment. And the point is – you really don’t need to spend that much time on it. If you’re worried about the amount of time you’ll have to invest, I can only recommend trying time-boxing, i.e. limiting the time spent on creating a viz to maybe 1-2 hours. It’s actually a very useful exercise and something that’ll easily happen to you in real life anyway.
- When in doubt – bar charts: Okay, it’s obviously not as simple as that. But when you look at all the vizzes I created you’ll see that there are many variations of bar charts in there. You’ll find the same thing if you go through the weekly favorites on the MakeoverMonday blog. Many of the MakeoverMonday datasets are pretty simple and that’s why simple bar charts are often the easiest and most effective way to present the data. I often feel like I need to invent a new chart type or do something super outlandish. But perfecting the bar chart can be a great goal, too!
- Try something new with every viz: I tried to adhere to that rule as much as I could and incorporate something new, something I had never done before into each viz I created. It could be anything from a new chart type that I’ve never done before, or a Tableau feature I haven’t used before, to something as simple as a specific color I’ve always wanted to use.
After a year of MakeoverMondays, I still think it is one of the best datafam initiatives out there. It’s so much fun and the feedback you can get from it is so valuable. I don’t necessarily plan on doing all 52 MakeoverMondays again this year, but I’ll try my best to do as many as I can. There’s still a lot of things to learn and improve, so let’s keep practicing!
#MakeoverMonday Week 50 – Bob Ross
Another week, another MakeoverMonday and this week we’re looking at the paintings of Bob Ross. What a fun little data set! Here’s what I came up with:
For some reason, I felt like doing a mobile layout. To get some design inspiration I scouted Pexels for nature and landscape photos or paintings. This beautiful abstract photo of Bondi Beach by Max Ravier caught my eye. I knew that I had found my background. I only added some blur to make it less distracting and ended up with this beautiful sunset gradient.
The first viz is pretty simple. It’s actually very similar to the original image we gave a Makeover to this week. The only thing I changed was limiting it to the Top 20 instead of showing all possible elements.
For the second page, I wanted users to be able to find a painting by chosing the elements they want to appear in it. I grouped the elements (for example, putting all types of trees into one tree category) and created the grid layout. I then created a set and a set action that will filter the list depending on the elements chosen in the top part of the visualization. But here’s the catch – the filter based on the set will have an OR logic. For example, if you select ‚beach‘ and ‚boat‘ you’ll get all paintings that include either beach elements or a boat. But I wanted the results to be limited to only those paintings that include both elements.
Achieving the AND logic in this case requires a few LOD calculations:
1) Calculate the number of elements that are currently in the set
2) For each painting, calculate the number of elements that are both included in the picture and also included in the set
3) Create a boolean field that checks whether the number of elements in the set is the same as the elements in the painting.
Use this boolean as a filter and allow only TRUE values. Now only those paintings will be listed that include ALL of the elements chosen.
And that’s it for this week. Check out the interactive version here.
#MakeoverMonday Week 43 – Apparel exports
Week 43 of #MakeoverMonday gave us a dataset about the consequences of the COVID pandemic on US apparel suppliers.
I tried timeboxing again and limited myself to an hour. Here’s my result:
I myself are honestly suprised by this result: First time I’ve ever used a Comet chart and I rarely do dark backgrounds. I think the comet chart works well in this case where we want to compare values for two years.
But, in one respect, this viz is kind of a cop-out. I really struggeled with orientation – which field should I put on which axis? Usually, I follow two rules rather dilligently:
- Time goes left to right – so on a horizontal axis
- Categories go top to bottom – so vertical axis
In this viz, I really struggeled finding a compromise because we had two different time dimensions (years and months) and for some reason I couldn’t find a combination I liked. So due to my time-constraint I coped out in the end and simply aggregated the months to avoid the problem.
In hindsight, I should have included the months and could have ended up with something like this:
#MakeoverMonday Week 41 – Data Survey Results
Charlie gave us a pretty simple and straight-forward dataset for this week’s MakeoverMonday. It comes from DataIQ’s survey on Data Assets and Data Cultures. Basically, it’s about how many respondents of their survey came from which industry sector.
Here’s the simple bar chart I created with that dataset:
Here are some of the thoughts I had while creating this:
- I struggled to find any interesting insight in this data. I thought about grouping the sectors to find something more interesting – but I couldn’t really find a way of grouping that made sense to me. But without further grouping, the percentages become really small. You now end up with information such as ‚2% of recipients work in logistics‘ – which just doesn’t feel all that useful to me. However, the ‚Other‘ gorup was already pretty large with 11% – so putting some of the smaller percentages into that category wasn’t a viable option for me either. In the end, I just accepted the data as it is. Maybe I’m just not the right person to look at that data. Maybe it’s quite insightful for someone who works with surveys more often. I decided to not focus too much on the content and concentrate a bit more on the design aspect of it.
- A bar chart might seem a bit boring. But I think in this case it really is the best way to present the data. But I’d be happy to be proven wrong if someone comes up with something amazing. All the other vizzes I’ve seen so far have also been bar charts, though.
- I used a vibrant blue color (#11139a) that I had in my color inspirations for a while and finally got a chance to use.
- I tried something I haven’t done before with the positioning of the labels on top of the bars. I did it by creating the bars and the labels on separate worksheets and then floating them on top of each other on the dashboard. There might be a better way to do this which I might explore if I ever have time. I would also try to find a way to increase the spacing between the individual bars to give them a bit more breathing room.
- At first, I put the explainer text below the title (above the bar chart). But then I realized that there is so much whitespace in the bar chart that I’m wasting. So I placed the text there instead. I included the lines to still create a connection between the title and the text. My hope is that this guides reades to start with the title and then go to the text before actually looking at the chart.
#MakeoverMonday Week 40 -US GDP by State
It’s TC20 week! What an exciting time for the datafam. This is my first Tableau Conference since I’ve become more active in the community. I was supposed to go to TC Europe in London earlier this year but… the pandemic. So now TC20 is gonna have to be virtual again. But the cool thing about that is how many people from all over the world will be able to participate. I hope I’ll see you there!
But let’s get to the topic this post is actually about: This week’s MakeoverMonday!
Since it is TC20 week, there was also a special Live MakeoverMonday. I managed to join the Monday evening session on Youtube.
So what was different this week? Well, the idea is the same. But the topic was introduced on the livestream and we then had an hour to work with the dataset and submit our viz. After that, the MakeoverMonday team took some time to review some of the results on the livestream. So the big difference for me this week was timeboxing.
I usually don’t limit the time I work on my MakeoverMonday viz. And it often takes me quite a while. Typically it’s not because I lack the technical skills (though that happens too, of course). I typically have a hard time settling on an idea and tend to overthink things. But getting things done quickly is an important skill. So this was an interesting and exciting exercise for me!
So here’s what I ended up with after an hour:
I’m actually quite surprised how much I like the result. It’s simple, you can easily compare different states and you can see some interesting patterns (check out Michigan’s 2008 dip, for example).
How did I end up with that viz?
I’ll let you in on a secret – it was a panicked last-ditch effort in the last 20 minutes or so. And isn’t that how it works quite often? How often do you start working with a dataset, build a viz, only to scratch it and try something else? I guess some people are well-organized, sketch out their vizzes in advance and actually stick to their plan. Well, I’m not one of those people. But in the end, you only see my finished viz and you don’t get to see all of my failed attempts. This week I’m going to change that and show you what happened before I settled on the final viz.
My first attempt was a simple map. I’ve never done a county-level map, so this seemed like a good dataset to try. However, the few very big values make the map pretty useless. And then you also have the usual problem of choropleth maps: Regions with small areas completely vanish. Do you see New York in that map? Me neither….
My second attempt was a slope chart. I was hung up on that idea for quite some time. But as you can see it doesn’t work very well. There are way too many similar lines that overlap into a big mess and I don’t feel like you can gain any real insights from that chart.
#MakeoverMonday Week 39 – Child Marriage
This week’s dataset consisted of only 4 fields: the country name, percentage of girls married by 15, percentage of girls married by 18 and percentage of boys married by 18. I decided to concentrate on the girls and not use the boys percentage. The next thing I noticed was that the girls married by 15 are a subset of the girls married by 18 and I wanted my viz to reflect that somehow. That’s how the idea of the squares within squares formed in my head.
But how to get there?
I knew I had to use polygons – something I’ve never really worked with before. Luckily, the Flerlage Twins are there to help you out with a handy blog post. Some data prep was needed (pivot the measure columns and quadruple the data to be able to draw the 4 corners of the square), do some calculations to get the positions of the corners – and voilà!
Next, I had to get the country labels look nice. For that, I used another trick that was in Kevin’s blog post mentioned above: create the labels on a separate sheet and float them behind the polygon viz. I positioned the labels using transparent shapes – another trick I learned from the Flerlage’s blog. This has quickly become one of my favorite and most-used little tricks ever since I read the 14 use cases for transparent shapes blog post.
All that was left after that was implementing the sort functionality. I actually handled this in data prep by creating index fields based on the three sort options. Depending on the sorting parameter I would then use those indexes to calculate the X and Y position of each country in the small multiples grid. I then created the sort buttons and configured the parameter action.
All in all, I really liked how this turned out. It was really fun to try something new and explore polygons. I also rarely do small multiples, so that was a good opportunity for me to get more used to them. What’s also cool is that this was one of those cases where I had an idea in my head and I actually managed to recreate that exact idea – something that sadly doesn’t happen all the time. I do feel like this viz got more attention than I would usually get – so I guess other people out there liked it, too.
However, let’s be very clear about something: This is not necessarily what I would call a best practice approach. Is it compelling to look at? I would say so. But is it the best way to communicate the data? Maybe not. Because check out the example on the right: If the white square is 100% – how much do you think the grey square represents? The correct answer is 76%! The first time I saw this I thought my calculations must be wrong. But I double-checked. They’re not. It’s just that we are super bad at estimating and comparing areas!