USA and Russia dominate the Olympic Medal tables but is that simply because they are large countries? Are there countries who get more medals per million people in their country? Yes. Check out the dashboard below:
Over the past month, I’ve deconstructed a dashboard I made for Tableau’s internal VizWhiz competition. Below is an attempt to quantify the Impact and Difficulty of each of the design choices I made. You can click on each one to go read the post describing the design choice:
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Why did I bother changing a dashboard that looked pretty fine in the first place? Why did I write 14 posts about this single dashboard?
Simple: I wanted to really think about the design choices and share the process of taking a perfectly functional dashboard and trying to make it into a thing of beauty.
Stepping back made me realise how many and what type of choices I make. Each choice has a highor low impact and is easy or hard. I quantified that in the viz above. As Michael Mixon pointed out in a comment, as we become experts we take this stuff for granted.
You are fighting for viewers’ attention with any visualization you produce. One of the few things you can guarantee they will look at and process is the main title. Therefore you better be 100% sure this is both:
Explains the chart or asks a question
Your title is your one chance to grab the reader and convince them it is worth focusing on. This is not a new lesson: Willard Brinton was talking about this 100 years ago:
Subtitles are just as important. Consider the titles on each view:
I’ve numbered them. This helps guide the user through the view. Sure, the left-to-right nature determines the flow but numbering the titles helps.
I bolded the pertinent data-related facts. I’m using the title to communicate the data itself.
I’ve also added contextual text. Notice the “All” in the second view title. If you filter down to a single year in the highlight table (you can do this in the interactive version), “All” changes to the relevant year: To learn how to make dynamic titles in Tableau,click here to see a post on The Information Lab’s blog.
Annotate marks to add context
Imagine I’d left the annotations off the charts. You can see the effect above. What are you left with? A chart with a really interesting trend that gives no clues as to what causes it. The viewer is left with their interest piqued but with no answer. This is a serious failure. Adding the annotations answers those initial questions.
My first iterations of this dashboard did not have any annotations. It was only when I got feedback from people that they pointed out the problem. That’s the importance of getting feedback!
Below is the annotated time series: it’s much more useful:
My final word on these annotations – I deliberately aligned the annotation arrows to be vertical to give them consistency
Tooltips – ALWAYS be customizing your tooltips
Followers of this blog will not be surprised that all the tooltips are customized. If you want to know more about the importance of tooltips, and techniques to use them in Tableau, check out this series of posts. Summary: ALWAYS customise your tooltips; it is the easiest way to improve your dashboard.
Finally, don’t forget to turn off the command buttons: these are great for exploration in Tableau but a distraction for end users.
I’ve only considered a tiny set of considerations when using text in your visualizations. What else do you consider important for text on your visualizations?
Come back tomorrow for the final post in the Tableau Design Month series – a big wrap up of everything I’ve covered.
What is your process when doing visual design? This was asked at David McCandless’ talk this week. It’s also something I and others get asked a lot.
David McCandless starts with a question and then finds the data to answer the question. Often he ends up finding many other questions to answer along the way!
In my job, I more often start with the data and then need to find the story.
Both are valid ways to start. Sometimes the approach is decided for you.
I wanted to go through my process, based on reflections of my US Fatalities dashboard. In this case, I’m explaining a situation where I have a ready-made dataset that I have not seen before. It’s my job to find and communicate anything interesting I can find.
The first thing I always do is look at the number of records and what they look like over time:
Why? So I can see how much data I have and what the trends are. Looks like things are trending downward and something odd happened in 2009/9. Economic crash, perhaps? Missing data?
I’ll delve into seasonality, at this stage. If you’ve been reading my Design Month posts, you’ll know that I ultimately focussed on seasonality.
What dimensions do I have?
Second step is to break out the bar charts. I look for the ones that are interesting and have good data in them. “Light Conditions” was a pretty clean Dimension:
I need to focus on a few things at this stage:
Is there a lot of Unknown/Null data? If so, it’s unlikely to be of interest
Does the data need me to go find myself more data? Looks like most accidents happen during the day. But I would guess most driving is done during the day, so it’s risky just reporting the raw numbers
Am I interested in the Dimension? If I’m not, I’m unlikely to care much about exploring it further
Do dimensions and measures compare interestingly?
Next up, I’ll begin comparing dimensions. Is there a relationship between Light Conditions and Road Type and fatalities, for example? (answer: no)
At this stage, I could not do without Show Me. If there’s one thing that puts Tableau above everything else, it’s this part of the process. Even by now, I’ve probably drawn 50 or so views, most of which I have looked at for less than 3 seconds. Each one gives me a sense of the data at virtually no cost of effort or time. For example, the chart below is a bit of a shocker but was a valuable part of my exploration. It existed only fleetingly.
As I begin to really dive into the data to find the links, the ability to cycle through views, drop dimensions in new places is just amazing. It doesn’t matter if 99% of the views I make reveal nothing or are visually awful, I’m getting a sense of the data and I will eventually find the gems that form the story.
Stories that answer where questions are engaging so I’ll check out the quality of the geographic data. In this dataset, we had State available but it was pretty clear it reflected population.
What level of detail is there?
I need to explore the level of detail. Is the story more interesting at aggregate level of down in the detail? In this dataset I didn’t find anything I wanted to proceed with. An example where the granular detail was of great interest was my Gooaaaal! viz during the world cup.
By this stage I might have cycled through 250+ views of the data in maybe 30 minutes or so. I’ll now have a good sense of the data and will begin to find the story.
I kept some of the charts as you can see below.
Early on I had noticed that seasonality had a great story. The only dimension I’d find that really interested me was about road type (rural/urban).
At this stage, I decided to go with seasonality.
Make a painting
By now, I know what my data feels like and I have a sense of the story I want to tell: I wanted to focus on fatalities during the holiday seasons.
The next stage is like painting. I start again with a blank canvas and begin to add elements here and there. For a while there’s a mix of analysis, exploration, making calculations, tweaking designs, choosing fonts, colours, and changing layout. They all happen simultaneously, intuitively. These decisions are fully documented in my Design Month posts.
Finally I’ll be almost there. Now to open the door and share with others. I asked for feedback on dataviz communities, on Twitter, from my wife and kids, from colleagues. You cannot get enough feedback! You don’t need to listen to all of it, but if 80% of people tell you the same thing, you know that thing is a problem.
Walkaway and come back
It’s important to walk away from your work for a day or so (as long as you afford). This allows you to return to it with fresh eyes.
Know when to stop
Sometimes you will just know. Other times you could keep on tweaking forever. There’s often deadline which will make the decision for you but in the end you need to know when to stop.
Have a gin and tonic
Publish it and reward yourself.
What’s your process like? I would love to know how you do things.
For this post, one of a series supporting Tableau Design Month, I’ll explain the colour choices made in design the dashboard above. There are three points I will highlight in this post:
Simplify the colour scheme as much as you can
Choose a colour that relates to your topic
Soften the darker tones
Simplify the colour scheme
Let’s see what Tableau’s default colour scheme would have been:
Tableau, or any visualisation tool, cannot know what the purpose of your vizualisation is. Therefore its choices should be appropriate to the chart being built. But in the above, the end result is overwhelming. There are colours everywhere.
It turns out that using just 2 colours: red and grey, you can tell the exact same story more clearly. You can even test your dashboard by trying it in greyscale: is the story still visible in the version below:
Choose a colour that represents your topic
I chose red to evoke the emotional aspect of this dataset. Red is powerful and emphasises the reality of fatalities. What if I’d have chosen a different colour? Blue, for example:
In this case the dashboard is much more neutral. It’s less provocative. It’s less opinionated.
Your colour choice should depend on your audience and your goal.
Soften the darker tones
You can choose palettes to emphasise just the parts of the data you want to. Tableau defaults to a perfectly serviceable green gradient palette.
My goal was to make the 3 most lethal seasons (Jan 1, Jul 4, Dec 25) pop out. A simple red palette didn’t do it so I tried red-black, but the black was too prominent. I settled on a red-white diverging as this really popped the days I wanted to focus on. All my choices can be seen below:
I went a lot further in this dashboard to soften the dark tones. For example, all the fonts are softened from black to a lighter grey. As I write this post, I’m unsure now whether that was a successful choice. Check out the image below. Which do you think is more successful – the dark font or the light font?
Colour isn’t easy. In this post I’ve covered just 3 choices. You also need to consider cultural implications, colour-blidness, publication type, and much much more. As always, I am very interested in your thoughts – let me know in the comments.
For my Design Month dashboard, I chose to go with a Menu action. I like the fact that when a user hovers their mouse over a mark, a nice customised tooltip with a call to action appears right where their eyes are looking.
I used a Menu action but a similar trick can be achieved with a Select action. Using a Select action gives you more control over the format of the Call to Action. I like this example from another recent Viz of the Day:
This technique is not perfect:
There isn’t a Hover equivalent on a mobile interface.
What if the user DOESN’T move their mouse over the viz?
Which actions do you prefer in your dashboards? What else do you consider when instructing people about interactivity?
When you design a chart, just how many borders and lines can you remove to maintain clarity? Do you improve clarity by removal?
Could I have gone any further? Sometimes I will hide the y-axis completely and just label the max value but I think that’s pushing it a little too far:
That’s what we’ll look at in this post. I’ll cover axis ranges and tick marks separately. In this post, I’m going to focus on what’s available from the formatting pane.
In the image above, you can see that my formatting approach is to reduce the lines as far as possible while retaining the meaning. Did I go too far? I think I got it about right.
Let’s look at how my end result compares with the defaults:
Default formats on the right, extreme reduction on the left. There’s nothing wrong with the defaults – the gridlines and borders are very sensible choices for a default setting. I do think I have emphasised the data more by reducing the lines.
Here’s how you can reduce the borders and grid lines in Tableau:
Remove all of the outer borders by selecting Format…Borders from the menu and then turning off all dividers at the sheet level:
I owe a hat-tip to Nelson Davis (@nelsondavis) for suggesting that it’s great to show only the horizontal grid lines in a view.
To achieve the effect, just go to the Format…Lines pane and set the Columns Grid Lines to None:
I like the end result, it’s very crisp. One bonus is that because there’s no border at the bottom, it makes it less likely someone will think the y-axes start at zero.
How did I choose the layout for my Fatalities dashboard, the focus of my Design Month posts?
The answer was I tried everything else and went with what I felt was best.
A vertical layout would have worked very well for a blog post:
How did I choose? I asked lots of people for their feedback. Some favoured horizontal, some favoured vertical. Their feedback was greatly appreciated. In the end I chose horizontal because left-to-right felt like a more comfortable way to read the story. Horizontal also allows you to compare across charts more easily.
You can make an okay decision about this on your own but it’s not until you share your work and get feedback that you can make an informed decision.
Having made the decision to go horizontal there was one more thing I needed to add – a vertical line between each chart. You can see them below.
The lines allow each view to stand alone. Without the lines, the focus of the dashboard was more blurred. I created these lines separately and imported them as images.
Which do you prefer? Horizontal or vertical?
Wherein Andy Cotgreave pours stories about data onto the web.