Whenever we have data on a topic that’s a bit different from the ‘heavier’ datasets about government debt, employment rates or crime statistics, it can admittedly feel a bit self-indulgent. I like cycling, I like bikes and I like when people conquer really difficult physical challenges. So this week we looked at Tour de France data and I was a bit worried that people wouldn’t be interested.
Yet our Makeover Monday community taught me a good lesson once again and blew me away with their analysis, creativity and the diversity of stories that came out of a single dataset. It seems that people enjoyed this week’s challenge a lot and in return there will be plenty of pats on the back and a few lessons for everyone as well.
There were lots of great submissions this week, so here is a list of what I particularly noticed:
TAKING ON FEEDBACK AND ITERATING ON YOUR WORK
Since the beginning of the year many of our community members have made huge progress in their work and are now providing guidance for newbies. This has resulted in a very collaborative conversation on Twitter where feedback is given, suggestions are made, questions are asked and answered and people improve their vizzes over the course of a few days. Sometimes it’s only minor tweaks, other times, a viz changes substantially and becomes so much better than the first version.
Firstly, Andy and I really appreciate having others help out, give feedback and ‘teach’ those who need help. We simply can’t get to every question ourselves, even just because of time zone differences.
Secondly, and more importantly, we are excited to see so many people take that feedback on board and treat the weekly challenge as the learning and practice exercise they were designed to be (rather than a race or competition).
Our main goal is to help YOU get better at data analysis, data visualisation and at using Tableau (or whatever tool you choose to use). And you will only improve through regular practice, through asking questions, as well as by not being too proud to change what you did and make it better with the help of others.
Jamie Briggs went through a number of iterations this week, making minor changes to his dashboard following our discussion on Twitter:
PUTTING YOUR OWN SPIN ON IT
We love the creativity with which you make each week’s challenge your own. Whether you want to try out a new chart type, experiment with a design idea you have seen, try your hand at long form story telling or build a viz that is very personal… The more fun you have when you do it and the more you are interested in the creation process, the content and the skills you pick up, the more it will stick, the more you’ll enjoy learning, improving and contributing to the community.
We saw some great examples of that happening this week, so check out some of these examples:
The upcoming IronViz contender Tristan Guillevin used this week’s dataset to see what he can create in 20min in preparation for his appearance on the big stage in Las Vegas at TC17:
Sue Grist decided to provide a comprehensive overview of Le Tour while also creating her first ever long-form story, after finding inspiration in the community:
A mainly English viz accompanied by French tweets came from Staticum
And Joe Macari shows us some serious dedication to #MakeoverMonday, drawing his viz in the sand, retro style, while on holiday in Crete
FINDING THOSE INSIGHTS
Admittedly, I often take the easy way out and create a simple makeover of the existing chart or focus on a simple story. Again this week people found interesting ways to analyse the data, draw interesting conclusions or simply focus on an aspect of this famous cycling race, that doesn’t normally get a lot of attention.
Here are a couple of vizzes which I liked for exactly that reason:
Sean Hughes analysed the diversity of winners by looking at riders’ nationalities and noting that France’s and Belgium’s dominance has stalled
Corey Jones also looked at winners but focused on the 21 riders who won more than once and how many years lay between their wins. A great take on the data and a stunning way of visualizing his findings!
So from me/us to you all: well done on your work this week!
As you know, however, aside from the praise and compliments, we’re all here to learn something, too. So for this week I have five lessons based on the submissions received. If you find yourselve ‘guilty’ of some of these, don’t worry. It’s about progress, not perfection. And the whole point of writing this stuff up every week is so that we can all create something better next week. None of the points of critique were done by just one person, they instead popped up a number of times, so made it worthwhile to mention them.
LESSON 1: CHECK YOUR DATA – ARE THERE ANY GAPS?
While you should always, always look at your data and not just ‘blindly’ put together a viz and call it a day, this week it was important to notice that at a yearly level there was no data for the years of World War 1 and 2, and there were no winners for the years 1999-2005, because results were voided due to doping (if you’re interested, I highly recommend a couple of books: ‘It’s not about the bike’ by Lance Armstrong and – for a bit of contrast and reality check ‘Cycle of Lies: The Fall of Lance Armstrong’ by Juliet Macur).
For your analysis, you need to consider those ‘gaps’ in the data. What do they mean for your story? Does your viz reflect them effectively or are they hidden? I saw a number of line charts which connected years ‘across the gap’, thereby seemingly showing data points where there weren’t any.
Either leave a gap for the years without data (WW1 and WW2), so that your viz conveys the interruption of the yearly rhythm of the race (If you want to know how to do this, check the vizzes of Adam Crahen, Andy or me) or pick a subsection of the data to work with. And I’d always recommend to call out the gaps, either with an annotation, a text box or in a footnote, depending on the overall viz and story you’re creating.
The same goes for the years where results were voided. Including them isn’t wrong as such, but as the winner, winning team and nationality for those years were all set to ‘Results voided’, it looks a bit odd to keep those labels in a viz among proper team, rider or country names. Again, some annotations will be useful, or you could try changing your sort order to keep all the normal dimension members together and maybe sort ascending or descending by a measure, then move ‘Results voided’ to the very bottom, to separate it from the rest.
LESSON 2: WILL YOUR AUDIENCE UNDERSTAND YOUR NUMBERS THE WAY YOU INTENDED?
This point is one I noticed early on and I actually called Andy out on it too, or at least questioned his approach. What do I mean? In the data we have the number of total riders who entered and those who finished. Some people added all of those up across the 103 races and provided some nice big numbers on their dashboards.
Stating that there have been 14,588 entrants or riders over the year is technically correct. There were. A LOT of those riders, however, participated over the course of many years, so those 14,588 riders are literally ‘bums on bikes’, not unique people. And while it isn’t wrong to have those big summary numbers on a dashboard, I want to challenge you to think about your audience.
When your audience (let’s assume they know nothing or only very little about the Tour de France) sees these numbers, what will they conclude? What does ‘14,588 entrants’ sound like to you? To me at least it sounds like 14,588 people entered the race over the years, which is a wrong assumption to make. But if I am your audience and assuming average intelligence here, you can see how easy it would be for your story to be misinterpreted.
The dataset didn’t contain unique rider numbers across all years, so I would simply recommend to make a note to that effect. Let your audience know what’s going on to avoid any confusion.
LESSON 3: USE COLOURS AND IMAGES SPARINGLY AND DELIBERATELY
I get it, the Tour de France logo is yellow/golden and black and it’s a fun color combination to use, so of course I braced myself for a lot of yellow this week. I think there can be too much of a good thing. Too much color means nothing stands out anymore and your audience doesn’t know what to focus on. Simplicity is key. The same goes for images and icons. Using them sparingly can effectively enhance your viz, add an element of fun and easy recognition (e.g. using a flag for a country or a logo for a team). If you have many different icons and images on your viz, your reader gets confused, it will be hard to differentiate between the elements and quite honestly, it can quickly look tacky and unprofessional.
Keeping your viz or dashboard neat and simple and adding colour to highlight, to draw attention or to identify a specific data point will make it much easier for your audience to go through the story you’ve created.
Tableau have published a lot of content on this topic, so I’d recommend you check out their webinar on ‘The Science of Color and Visual Design Principles’ and go from there.
Here are also a couple of examples where minimal use of color draws the attention to the key point of the story and makes the important data points stand out:
Matt Chambers uses yellow as a highlight color for the outier in his viz and explains the color meaning by reflecting it in his heading and annotation, therefore not needing an additional color legend.
Paweł Wróblewski also uses a single color for highlighting the focus for his story, the ‘voided’ results. By using red font in his title for the number of years and the key message ‘dishonestly’, the audience is immediately drawn to the 5 data points in his viz that are colored red.
LESSON 4: GO EASY ON THE FONTS
The recommendations I made for colors also apply for fonts. Do you need 5 different fonts in a viz? No. Does your audience want to strain their eyes deciphering decorative fonts? Highly unlikely.
As much as I want to tell you to unleash your creativity, I also want to remind everyone that the purpose of data visualization is to communicate often complex information in a clear and simple way. We visualize data so our audience can quickly see trends, outliers, can draw conclusions and find insights. If we then use a font (or multiple fonts) that are hard to read, we essentially are working against ourselves.
Yes, I understand that using Tableau Book, Calibri, Arial or Tahoma might get a bit boring, even if you bravely use Georgia for some of your text boxes, you know ‘newspaper style’. But in the end those fonts are supported and will always work when publishing, because they are good fonts to use. Tableau know what they’re doing and have put thought into this, believe me.
What happens if you use a funky custom font and then publish to Tableau Public? Well, it will turn your viz to a default font anyway, so if you really want to guarantee your custom font, you need to work with images.
Firstly, stick to simple fonts, at least for the main parts of the viz, i.e. your annotations, tooltips, subtitles, labels and any other parts that convey information and data.
For decorative purposes or if you really really want a different font, then keep this minimal and potentially use such a font only for your title or a single element.
Here is a great example by Brian Harris who used a custom font in his title but kept the rest of the fonts in his viz very simple and easy to read. Also note the minimal use of an icon to draw attention (and put a smile on my face), plus the ‘changed’ logo that further underlines his key point, which is to look at the number of riders not finishing the race.
LESSON 5: DON’T JUST COPY AND PASTE FROM WIKIPEDIA
We’ve talked a lot in the past about providing context and it’s been great to see so many of you include descriptions of the Tour de France to give your audience some background about the race before they look at your viz and your findings.
What I found this week is that some people took their description almost word for word from Wikipedia. If you do that, clearly make it a quote, better yet: paraphrase the information and put your own spin on it.
While it can be tricky finding your own words for descriptions, it’s the right thing to do and a good habit to get into.
Use a thesaurus to find synonyms or approach it all from a completely different angle: using questions can be helpful or taking the viewpoint of a specific person. Why not describe the race from a rider’s perspective? Add a bit of humor into the mix or stay serious and formal, you choose. It will become easier to find your own words if you practice over time.
Go to Wikipedia to aid your own understanding, then create text that isn’t a straight copy and paste job. Your viz will be better for it.
Those were my 5 lessons for this week and now it’s time for…
What I like about it:
- I’m getting used to being surprised by Shawn. He usually delivers something that is different from everyone else and I like that
- A simple viz with a very different focus and story from the rest of the submissions. I like the attention being on the drug scandal because Le Tour is only just recovering from all the drama of those years
- Great design, neat layout, uncluttered viz
- Admittedly it assumes an already informed audience, but I am confident that the viz is intriguing enough to make anyone who doesn’t immediately ‘get it’ go and find out the who and what and why behind those ‘scratched years’
- It makes you think!
What I like about it:
- Simple, clever and creative
- Great use of color and efficient use of space
- Easy to understand (although an audience with no knowledge about the topic or the data would benefit from a bit of context I imagine)
- The icons give you a sense of speed, so reflect the message of the viz
- I like that the fastest rider on the far right looks like he is breaking away from the bunch. This happens to be one of the ‘doping’ years, so a nice play on the story
- Works well as a static image
What I like about it:
- A great overview of Le Tour, providing the audience with simple summary figures before focusing on key statistics in more detail
- Beautiful clean design that looks simple and elegant
- Works really well as a static image, all the information is there
- It includes a pie chart that is actually really good 🙂
- Great use of color, especially for the labels. They are subtle enough to let the data stand out but still provide enough context
What I like about it:
- Very clean and simple design
- The question in the title is addressed at the ‘end’ of the viz after being guided there through a couple of key call-outs in the data
- Yellow and white on black works well
- The dotted reference lines are a great way to indicate the hypothetical 100% completion point and year in the data, the labels in rounded-corner boxes are a nice touch
What I like about it:
- Great design with a lot of information but enough guidance to not make me feel overwhelmed by it
- Breaking the data down into decades rather than individual years shows the impact of the two world wars and makes the information easier to take in. The addition of the line with marks for every single year is great for context
- Using Stage length rather than total distance works really well for the comparison and makes the races easier to compare
- The line chart at the bottom is a great way to show how the percentage of finishers has increased over the years and I appreciate the annotations and footnote