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.
The highlight table above has white borders around each mark. Why did I make this change to the defaults?
Tableau’s default mark border is None. in this post I will explain why I often find myself changing the defaults. Here’s what the chart looks like with the default border setting:
As I mentioned in my first post about these charts, there’s nothing inherently wrong with having no border. It largely comes down to personal intuition. I think that changing the border to white brings the marks into more focus.
What do you think? Is my highlight table improved by using white borders?
Should you add borders to your marks at all times? No. Should you add them sometimes? Yes. How should you decide? The good news is that it is so easy to do this in Tableau you can easily try it with our without borders and go with how you feel.
It’s no accident that the tick marks are like they are in the above chart. Check out how they’d have looked if I’d left them at their defaults:
I’d rather my tick marks were at round numbers (1975, 1980, etc) than at odd numbers like 1983. As I’ve said throughout this series of Design Month blogs, it’s not a big problem but it’s a nice one to solve.
Let’s look first at the time series:
Getting the tick marks to start at 1975 is acheived by using the “Tick Mark Origin” feature that appears when editing time Date dimension axes. Set it to the value of the first major tick mark you want as below:
Fixing axes on a slope chart
My slope chart has a couple more tricks up its sleeve.
You’ve spent hours crafting the perfect dashboard. It has some time based data on it and you’ve used Tableau’s amazing date hierarchy to build it. Every pixel is perfect. You publish it, proud and delighted.
And then someone interacts. They see a plus button. They click it.
Boom! What happened? The view’s broken.
The viewer leaves your view confused and somewhat disappointed.
Have no fear, this needn’t happen to you.
The plus button is designed so you can drill into and out of hierarchies (find out more here). It’s an amazing feature for exploration and works well on many dashboards.
Sometimes you don’t want this behaviour. In my highlight table above, for example, I don’t want my users to be able to drill into the date any further.
To get rid of the plus buttons when using dates, you can use custom dates. These are date fields focussed on just one level of a date hierarchy and cannot be expanded.
Right-click on your date field and choose Create Custom Date:
Then choose the date level you want. You can create discrete (“Date Part”) or continuous (“Date Value”) fields at any level from day to year.
You now have a new dimension available. Put the new date dimension onto your view instead of the original date and your view no longer has the plus symbol.
Well done. You’ve just got control of your guided analytic dashboard back!
In order to emphasise the seasonality, I made 7 decisions, each of which is explained below.
Draw one line for each year
Use moving average
Add an Average reference line
Use a gradient colour for a subtle indication of years
Add annotations for clarity
Create a custom legend
1. Draw one line for each year
I have written about seasonality before (click here). In this case I wanted Months on the x-axis with a line for each year. That’s very simple to do – just move the dimensions into the correct place:
This gives us a line for each year in the dataset. It’s a nice clear display of which months see the most fatalities.
2. Use moving average
The peaks and troughs are a little bumpy. To smooth out the experience for the end user, I used a moving average. You can see the differences below. The left hand side is easier on the eye and thus the story is easier to digest.
I don’t state anywhere on the view that I am using a moving average: I acknowledge that my view is slightly misleading. The tooltip, however, shows the actual value, not the moving average.
3. Add an Average reference line
The average reference line adds more context to the view. In this single view, you can see each individual year and also get the overall picture of fatalities during each month.
4. Use a gradient colour for a subtle indication of years
There are too many colours in the chart above. You could put [Year] on the Detail shelf to have just one colour:
This is ok but it’s hard to see any change between years. I chose to add a subtle colour palette. This indicates there’s a difference between each line but one that’s subtle.
To show the changes I:
Put Year on the colour shelf and chose a red palette
Reduced the size and increased the transparency:
5. Add annotations for clarity
Now that I have a line for every year and for the average fatalities for each month, I needed to clarify things a little. I annotated values in the upper and lower area of the chart. These provide context for the user.
6. Create a custom legend
Tableau won’t create a legend to show that the thin lines are single years and the thick line is an average reference line. I created that in Powerpoint (hat tip to Mark Jackson) and floated the image on the dashboard.
7. Add dots
The final piece was to put dots on the average line. This gives an extra indication that the average line isn’t the same as the fine lines.
I’ve always liked this little feature. It emphasises the marks for the average line, allowing the individual years to further blend into the background.
A time series focussing on seasonality
My end result? A chart, I hope, emphasises not only the seasonality of fatalities in the US, but also gives us a better sense of the data by showing individual years, too.
What do you think? Were my design decisions appropriate?
The full dashboard is available by clicking on the image below:
See the thick line in the chart above? That shows the average number of fatalities in a month for all years in the dataset.
It’s the equivalent of drawing an average reference line for each cell in the view, which you can see below:
The reference lines are quick and work, but they’re not attractive and are a little difficult to interpret.
To draw the line you need some table calculations.
I’ve recreated the chart using the sample Superstore data and you can see the workbook by clicking on the image below:
Making the average line
To make this chart, I start with a basic seasonality view: [Month] on Columns, [Year] on Colour/Detail shelf.
I then need to work out the window average in each month. here’s the calculation:
Drop that onto the Row shelf and set the Compute Using settings as follows:
Now you should have a chart with the regular measure and the average calculation on the Row shelf. Right click one of the measures and choose Dual Axis. Then right-click on one of the axis and synchronise them.
That’s the basic chart done. I had to tweak the Colour, Transparency and Size a little to get a result I was happy with.
An anti-aliasing problem
This is pretty much complete. But there’s one thing that’s niggling. The average line is jagged:
What’s going on here? Well our average line is actually multiple lines, one for each year in the dataset, drawn on top of each other.
How do we solve that? I created a new calculation:
If you set this with the same Compute Using… setings as the calculation above, you get a True/False value. The True value is applied to just one of the lines being drawn on the chart.
Drop this field on the size shelf, right-click on the False mark in the legend, and choose Hide.
This solves the anti-aliasing problem and makes the view that small but significant bit nicer to look at.
As you can see in the image below, what I’ve achieved is the equivalent of doing a per cell reference line, but made it much more attractive:
What do you think? Is this the best way of showing seasonality? Is there a more efficient way of doing this using simpler calculations?
Click the image below to see and play with the complete workbook:
Do you need “Year of accident” or “Year” on an axis when it’s obvious what it shows? I’d say no. Do you need to label your y-axis with “Fatalities” when you’ve got it in the title? I’d also say no. You can see how it looks with and without the titles below. Which do you prefer?
Show the fewest tick marks you can.
In the image below, you can see the difference between lots and few tick marks. Neither of these are bad but I felt fewer tick marks helped focus on the trend in the data
Set the axis range so that relevant, round values are shown.
Check out the difference between the default axis on the time chart (lower chart below) and my edited version (upper chart) It’s easier to start your eye at 1975 than it is at 1983:
In Tableau, getting round values in your default dates is a little trickier than you might expect and for full control you need to master the Edit Axis box. I will blog that separately.
Those were my design choices in this case. Was I successful? Let me know. Remember: none of these are mandatory changes and I don’t even suspect everyone would agree these are improvements to the originals – it’s partly subjective and partly depends on the goal of your viz.
Here are two dashboards. The bottom one uses all Tableau’s default formatting settings. The top one has at least 25 formatting changes or design decisions: these changes take a few hours to implement. Why bother? What’s wrong with Tableau’s default formatting?
The short answer is: nothing. The longer answer is more nuanced.
It’s Tableau Public’s Design Month so I wanted to do a series of related posts. In these posts, I’ll be focusing on the dashboard above (click here for bigger version). This was my entry into Tableau’s annual internal “VizWhiz” competition. In this round, we were given data was about US road traffic accidents.
There are at least 25 things design decisions I’ve made to produce that viz. That’s 25 changes I have made to the default Tableau formatting: some small, some large.
But first: why change the default formatting? What’s wrong with Tableau’s defaults?
Let’s look again at the default dashboard:
Let me repeat: There is nothing inherently wrong with Tableau’s defaults.
My story can be understood from the default dashboard above. In fact, I am sure some people reading this will think the defaults are better than my design. If so, let me know in the comments below.
So why bother? Why would I spend hours tweaking what is already a perfectly-fine dashboard?
I take my inspiration from ex-British Cycling performance director Dave Brailsford. He led the British Cycling team to huge success through his “marginal gains” (click here to find out more). The principle is that you make all the small and large changes you make. Even the small changes, when aggregated, make a difference.
Each formatting tweak might only improve my viz a tiny bit.
The default formatting gives you a bronze-medal dashboard. There’s no shame in getting a bronze medal for something. If you’re in a business environment, producing dashboards for fast, iterative consumption, it is perfectly fine to leave the defaults as they are.
However, what if you want the Gold medal? In this case, you make all the changes you can. Even if they are small, the aggregate effect is significant. For that reason, I am happy to go the distance and make grab every “marginal gain” that I can.
Over the next month, I’ll be describing most of the formatting and design decisions I made.
Humblebrag alert: I am pleased with my dashboard but am wide open to criticisms about it. I am sure that what I think are good decisions might well seem like bad ones to you)
You’ve built a nice chart using synchronised dual axes (maybe it’s a lollipop chart – as described in my previous posts!). It looks great, but you’re just left with a niggling little design flaw: those dual axis sure take up a lot of space, at the top and the bottom of your viz:
What can we do about this? The first thing I thought of was to format all the Tick Marks and Titles away from the top axis:
That’s no good – there’s a huge useless white banner of dead space at the top of the chart now.
Instead, why not just format each axis to show one of the important parts: one to show the title and one to show the tick marks? This is the end result:
Now I have the best of both worlds – the user’s not aware of any dead space, and nothing gets repeated. To format the axis, right-click anywhere on the axis on the worksheet and choose Edit axis…
On the top axis, leave the Title as it is, and show just the Minor tick marks, making sure they are the same interval as the Major tick marks on the bottom axis:
On the bottom axis, leave the tick marks, and delete the Title:
Job done! You’ve maximised your available space and made your users’ lives a bit easier.
Here’s the problem: I am visualising satisfaction rates over multiple dimensions. In almost all cases, satisfaction rates are high (between 70% and 100%). I want a visualisation that allows comparison over multiple dimensions that is also nice on the eye. Below is the result: a lollipop chart. Although I stumbled across this design by trial and error in Tableau, it is a chart type found elsewhere, eg on Chandoo’s excellent Excel blog. What I thought I would do in this post is explain why I think it’s a great chart in this situation and how to do it in Tableau. Note: in this post, I’m using the Superstore data, not my real dataset. In my next post, I’ll explain how to build a lollipop in Tableau. If you can’t wait that long, you could try it yourself as your homework 🙂
To me, it’s a great way to reduce the data-ink ratio while retaining readability. What do you think? Here’s how I arrived at this design.
Tableau’s default visualisation is the bar. What’s the problem with this? Well, when the bars are all very long (as is the case with my data), there’s just too much ink, and it creates an unpleasant Moire effect:
How can we solve this? Well, we can reduce the bars to wafer thin ones, but this looks, well, flakey:
Maybe we should push the size slider to the max (and add a border). This is what I would normally do in this situation. It removes the Moire effect, and isn’t too bad, but boy, there’s now a lot of ink being used:
Given there’s too much ink, maybe the bar itself is the problem. So how does a circle work? Well, the problem is that the circle is a long long way away from the label. When we try and foist this kind of thing on our users, they tell us it’s too hard to relate the circle to the name, even using shaded lines:
We can get round this distance problem in a couple of ways. One is to fix the axis so that it’s range is only as wide as the min/max values:
But we all know that an axis that doesn’t start at zero is a bad thing, right? Well, sometimes it isn’t a bad thing, but it sure makes the states at the bottom of the list look like poor performers, even though they’re actually only 0.6% lower than the top of the list. Best in this case to keep the axis starting at zero. Maybe we could label the circle directly instead:
This still isn’t right: all that white space at the left of the chart seems wrong.
And this was when I had my brainwave. Thick bars are no good and lonely circles are no good. How about making a combination of them both? And that’s how I came up with a lollipop.I think it has the following benefits:
Can be used when all dimension members have high values (i.e. long/tall bars in a bar chart)
Greatly reduces the data-ink ratio while maintaining a clear link to axis labels
All the users I’ve shown it to so far have really engaged with it – they think it’s both pretty and easy to read
I also like the fact that it works if you add more dimensions to make small multiples:
Next time we’ll look at how to build it in Tableau. In the meantime, let me know your thoughts.