More music trends: am I binge listening to artists or albums?

Following on from yesterday’s post, I wanted to look at my listening binges. When do I listen to one artist or album for a long time?

Let’s look at album streaks.

In these charts, each dot represents a track. the dots go up each time it’s a consecutive track from the same album. The higher the trail, the bigger the album listening streak.

Binge listening (albums)
Go check out the interactive version – it’s more fun!

Wow! Pink Floyd’s Endless River has been my biggest listening streak of last 3 years. Fans of Floyd will know that album has lots of short tracks on it. Easy to have a listening streak, sure, but I did obsess on that album for that period.

Below I’ve highlighted other times I listened to Endless River – clearly I listen to that one in bursts.

Binge listening (albums) FLOYD

You can see there’s another peak for Frozen, the curse of all parents over the last 3 years. Look at how many listening streaks I’ve had with that:

So much Frozen. Let it go, Andy!
So much Frozen. Let it go, Andy!

How about artist streaks?

Binge listening (artists)

Yes, I listened to Gabby Young 108 consecutive times in November last year!

Notice also the binges I’ve had on Pink Floyd. I guess I sometimes go back and listen to the old favourites in big binges:

Binge listening (artists) FLOYD
The Pink Floyd binges

Go check out the interactive versions, and, more importantly, plug your own data into this workbook! I want to know what other people’s data looks like.

How did I do the listening streaks? I recreated an idea from this prehistoric post on commuting in the rain. I updated that idea with some tricks from this post on Previous Values by Jonathan Drummey

Music listening trends: 2015. The death of the album?

Note: if you’re interested in doing something like this, get scrobbling to When you have a big enough corpus of music, download it using Ben Foxall’s lastfmtocsv converter.

Also I want feedback on the chart below. I want to show when I first and last listened to all bands in my data. The chart below works for me, but took me ages to explain to someone else. How might you show that data?

First and last
I want feedback on this: I can’t work out how to show the data. Click here for interactive version.

What’re the trends in my music habits for 2015? Last year it was all about Gabby Young, Frozen and Heavy Metal. Has anything changed this year? Yes. This year was about listening to new bands/artists. 1,237 of them!

new artists

In 2012, however, I only listened to 596 new artists, less than half the amount in 2015. Not only was 2012 a bit of a drought, I’ve also not listened to those new bands very much since:

More 2014 than 2012

So what happened in 2014? How come that year has cumulatively accounted for so many listens? One band: Gabby Young & Other Animals. We saw them at Just So Festival in 2014 and they became a family favourite.

I also wondered, of all these new bands, which ones did I stop listening to, and when? That’s the chart at the top and repeated again below. I’ve shown when I first and last listened to an artist. For an interactive view, click here.

What do you think? How would you show data like this? I think having time on x AND y is confusing.

First and last
I want feedback on this: I can’t work out how to show the data. Click here for interactive version

Finally, I wondered how many different bands/artists I listen to each month. The results were fascinating. It turns out I am listening to more artists each month than ever before. I need to do further analysis on this: does it represent the death of the album? Spotify’s Discover Weekly playlist has been a revelation this year, but this chart shows it’s led to fewer complete album listens.

I'm listening to more artists

Who knows what next year will bring!

You can do this too! Just use Ben’s lastfmtocsv tool. Then download my Tableau workbook and replace the extract with your data.




Tableau quick tip: turn your column headers into filters

nobody looks over here

A perennial problem with filters: how do you make your users aware they are there ? How do you wrench their eyes away from the marks and headers and over to the filters on the right?

To fix this, I stumbled across another potential method: replace the Column Headers with Filters. I say stumbled because I wasn’t consciously trying to solve the problem when I implemented it. It’s only afterwards that I realised what I’d done!


What’s the advantage of doing it this way?

  • People are looking at the top left of your chart, so they are more likely to register that they can filter the dimensions.
  • It’s a cleaner view. The top banner is freed to have just my dashboard title. The right hand side doesn’t have that unusual blank space beneath the filters.

To implement this, it was simply a case of floating the filters over the column headers. For a design aspect, I ensured they were the same width as the columns in the view itself. I also tweaked the colours to bring attention to the dropdown itself.

BTW – go check the interactive Script Wars viz here. Or go check out what others have done with #StarWarsData.

Star Wars: who has the longest lines?

Who gets the longest lines
Click the image to explore the data

Matt Francis showed me a work in progress version of his excellent Star Wars script dashboard. I ended up playing with the dataset myself, and explored who had the longest lines. Did you know Obiwan Kinobi has the longest line in all the films*? Princess Leia has the longest line in Star Wars: A New Hope.

To see an interactive version, click here.

I also came up with a novel idea to make Tableau Filters more obvious to end-users. Check out how, here: “Turn your column headers into filters

Lots of people are doing stuff with #StarWarsData – go check it out.

* by “All the films” I’m excluding episodes 1, 2 and 3 because they just don’t count, do they?

Design Tips for Functional and Beautiful Dashboards

before after portrait

Over on the Tableau blog, we published a post on how easy it is to quickly format a key metrics dashboard. We then redesigned it in response to some valid criticisms of the original (click here). The changes made are subtle but effective. In this post I wanted to describe them to show how to maintain functionality when going minimal with their dashboards.

Example 1: the line charts

before and after line charts

What was wrong with the original? The lack of labels make things clean, but stop you learning anything about the values:

why label

I changed the following:

  • Added the x-axis headers. I’m all for hiding headers (see “How to design an axis for maximum impact”), but I think it’s wrong to hide the x-axis.
  • Dual axes provide better design control in Tableau:
    • The Sales chart is an area/line chart. This gives more definition to the top of the area.
    • The profit chart is a line/circle chart. I find this gives more definition to the points than just choosing the “All markers” on the Colour shelf:
      Dual or all Markers
  • I wanted to avoid showing a y-axis but there still needs to be some way of seeing the magnitude of the measure. For this I added a maximum reference line, with the text aligned to the right. If a user filters the dashboard, the max will always be accurate
  • All titles were changed to be left-aligned and single line. This creates better consistency across the dashboard. I aligned the Titles to the left and the Maximums to the right in order to prevent visual clutter and confusion. If they were closer together, it’s harder to tell which is which:
  • Note also that the zero line on the profit chart above is a Constant Reference Line, rather than an actual axis. I did this because it’s easier to control the formatting on a minimal chart.

Example 2: the Profit Charts


I don’t object to donut charts in all circumstances, but they are simply a bad choice when you make a donut for each Year (see p11 of Stephen Few’s classic, “Save the pies for dessert”). It’s nigh on impossible to see changes over time. I switched to a stacked area chart instead. This took up less space and allowed me to label the marks, too (removing the need for a y-axis).

Notice how the colour for Furniture is a really light salmon pink. That can be a problem against a white background, but I added borders to the marks to make them more obvious:

BTW, I didn’t address a key risk with this kind of chart: what happens if there is negative profit?

Example 3: the highlight table

highlight table

I love highlight tables. However, I didn’t think this one worked for 2 reasons. First, the lack of labels creates a real problem here. Without them, it’s just a mosaic. I also was not convinced that you’d ever really want to see Sales by Week and Weekday. I changed it to Month and Day. The increase in sales towards the end of each year is now clear.

Example 4: General changes

  • Colour changes
    • I used extensive use of grey in the text in order to soften the labelling and accentuate the data marks.
    • I used more distinct colour palettes to differentiate Sales and Categories.
    • I switched from a floating layout to a grid layout. This way, Tableau controls the size of the grey borders between the charts.
    • I have to say, changing the colour was the single hardest thing on this. All the other decisions were kind of straightforward but every time I changed the colour, I felt like I’d made one thing better but something else worse. Also – the image on the Tableau blog is from before I added the borders around the marks. 
  • Layout
    • I changed the layout so that Sales-related charts are on the left, and Profits ones are on the right.


Would you have done something different? The dashboard is still not perfect. For example, the blue highlight table and the blue map are problematic. However, I hope this exercise has shown that it is possible to have both beauty and functionality in a Tableau dashboard.

The key lesson for me is that it’s not hard in Tableau to acknowledge feedback and then iterate fast to fix the problems.

Feel free to download the workbook and share your ideas.

Before and after...
Before and after…

“Match mark colour” allows for some nice effects

Coloured labels

Tableau 9.2 was released this week, along with some amazing new features. One nice feature is “Match Mark Colour.” I believe it has all sorts of creative possibilities for your views. For example, above you can see I’ve got two labels on a bar chart. One shows the numbers, while the other, aligned left, shows the categories themselves, nicely using Tableau’s algorithm for matching useful colours.

"Match Mark Colour" is found on the Font drop down in the Label menu.
“Match Mark Colour” is found on the Font drop down in the Label menu.

How do you make the labelled chart above?

To make this labelled bar example, I created a dual axis bar chart, duplicating the SUM([Number]) measure, and synchronizing the axes.

Both measures were labelled. One Measure was labelled with [Colour], aligned to the left. The other was labelled with [Number], aligned to the right. Finally, right click on the [Colour] dimension on the Row shelf and untick “Show Header”

Setting up the dual axis
Setting up the dual axis

Simple! What other nice design tricks can you do with colour matching?


Carlisle Flood Defences: the one chart I’d like to see

Carlisle flood

Carlisle, in the north of England, has been hit, again, by severe floods. I feel huge sympathy for those whose homes, again, have been damaged by floodwater. The £38 million spent on flood defences by the Environment Agency since the last major floods in 2005 is being described as ineffective. “How could all that money not have protected the houses?” they are being asked.

First of all, the EA never said the defences would protects against all future flooding.

What I’d like to see is a chart like the one above, but with real rain data. All the labelled years were genuine flooding years, but I do not know the actual rain values (apart from the 34.1cm-in-one-hour being reported for 2015). For the purposes of this, I’m using that value of 34 as the index value for 2015.

When setting a flood defence, an agency has to balance cost against the predicted maximum future level of flooding. If the EA predicted in 2005 that there would never be a 34cm deluge, then they wouldn’t waste public money building flood defences that high.

With real data in the above chart, we’d be able to see:

  1. How unprecedented is 2015? (in my chart, it’s 5cm higher than 2005)
  2. What level of rainfall are the flood defences set to protect against? (in my chart it’s higher than 2005 and 1822 flood events)
  3. What predictions do they have for future precipitation levels and how regularly will they be higher than the flood defence level?

If 2015 truly is unprecedented and wholly exceptional, then blame shouldn’t be put on the flood defences.

This post was inspired by memories of the Fukushima disaster. Then, the tsunami which hit the power station was higher than the station’s sea wall.

What do you think? Would this chart help asses the success of the Environment Agencies flood defences? What chart would help you answer the question?

NOTE: don’t forget, the data in the chart is not real. It’s for illustrative purposes only.

Is information visualisation research flawed?

From "Beyond Memorability...."
From “Beyond Memorability….

Stephen Few’s latest newsletter, “Information Visualization Research as Pseudo-Science” is a critique of the academic process in visualisation research. In it, he savages one paper in particular: “Beyond Memorability: Visualization Recognition and Recall.” He uses this as an example of what he thinks is a problem widespread in this field.

I agree there are problems in this paper. I agree with his suggestions for fixes.

However, I think it’s unfair to say this is a problem with visualisation research: it’s a problem with all research. In all fields, there are great studies and there are bad studies.

In this post, I’ll explain my own thoughts on the flaws of the paper, then the areas where I think Stephen is being unfair.

Here are my own thoughts on reading the paper (which I noted before I read Stephen’s article, as he instructed):

1. Stop publishing 2-column academic papers online!

Not the way to read papers online!
Not the way to read papers online!

Why are academic papers STILL written in two columns? This is ridiculous in a time when most consumption is on screen. To read a 2-column PDF on my phone or tablet I need to do ridiculous down-up-right-down-left scrolling to follow the text. Come on academia: design for mobile!

2. Why are they measuring memorability?

I agreed with Stephen on the key problem: why are they measuring memorability? Isn’t it more important to understand the message of a visualisation?

3. Hang on, Steve! Problems with experimental technique are not unique to visualisation research

Stephen goes to town dismantling the study’s approach. For example, he criticises the small sample size and much of its methodology. I am not as expert as Stephen in this, but I find myself agreeing with most of this.

But where I differ is how he damns visualisation research as if the rest of research doesn’t have the same problems.

Let’s look at some:

i. Statistical unreliability

There are no shortage of academics papers with statistical problems caused by small samples. Here’s one on fish oil, dismantled by Ben Goldacre. Incidentally, the study he refers to also used 33 subjects.

He also outlines a statistical anomaly so extreme, that half of all neuroscience studies are statistically wrong.

Conclusion? Statistical problems are not unique to visualisation research.

ii. Methodological misdirection

How many of the 53 landmark studies in cancer had results that could be replicated? 6.

Yes, 89% of landmark cancer studies have results which cannot be replicated. (source: this great article “When Science Goes Wrong” from The Economist)

Conclusion? Methodological problems exist in all science.

iii. Logical fallacies

Logical fallacies are hardly unique to visualisation research. For example, this list of the top 20 logical fallacies is a good example of how this is a problem in all science, not just visualisation research.

Part of this critique is surely just part of scientific rigor?

For a conclusion, I acknowledge that I’m not an academic and I don’t read many academic papers, so I am naive.

Part of me thinks that surely lots of this critique is just part of scientific research? Researchers publish papers and the world responds, positively and negatively. Future research then improves.

I assume Stephen’s frustration stems from the fact that many of these problems are perpetual and should have been fixed before the study started. I can’t disagree with that. But I don’t think the paper is “fundamentally flawed” as Stephen describes. Maybe memorability of the view is important? If so, this is a first step in the iterative, slow advance of academic research. The paper at the very least makes us consider the question of what it’s important to remember from looking at a visualisation. Having read it critically, I have considered the question and formed an opinion. That’s of value, surely?

I found it very interesting to sit and really read an academic paper in detail. I don’t do it often, and I respect people who can wade through the dense formulaic wording to get to the meaning.

[Updated 5pm 3 Dec to expand my summary]


What’s YOUR elevator pitch for data visualisation?

If you had to pitch dataviz, what would you say?

In this week’s #AskAndy Anything About Data, Chris Love (@chrisluv) asked myself and Andy Kirk the following:

A great question to ask, but tougher to answer than you’d think. Andy K and I both gave our answers (go check the webinar recording to hear them). I then threw it back to Chris. He gamely took on the challenge. Here it is:  

Nice work, Chris.

NOW IT’S OVER TO YOU: what is YOUR elevator pitch for data visualization. Give yourself 30 seconds to 1 minute. How do you pitch data visualization?  Tweet your efforts to me at @acotgreave. Hashtag: #datavizElevatorPitch

Image from LinkedIn Pulse

#AskAndy webinars: the director’s cut

Hello again. We didn’t manage to answer all the questions that were submitted in yesterday’s #AskAndy webinar. But don’t worry, we collected them all, and I’ve written some brief answers to each one.

Apurvaa Vijay Sharada: Do you think at some point the beauty of dataviz overtakes the actual intent behind it? If yes, how do we avoid that pitfall?

Can you have functionality and beauty in dataviz?
Can you have functionality and beauty in dataviz?

Yes – this can happen. You need to remember the purpose of your visualization. Always take a step back and ask if it’s actually possible to discern the meaning from the visualisation. Ask other people if they understand your viz. Adding beauty will make it more engaging, but unless you’re actually making data art, keep checking that the view actually works.

Melissa Black: What do you think are the most common mistakes made with visualisations?

Thinking of beauty before functionality! Many people get enticed by whizzy effects BEFORE they’ve learnt the basics about effective visual communication. On a practical note, I see many visualizations with disappointing titles. The title and caption are a chance to set out the exact purpose of the visualization. It takes moments to think about something appropriate and it can make a big difference to how it is interpreted.

Paul Banoub/Nicholas Bignell: Have you noticed any variations in dataviz trends / styles / usage across different global regions?

Great question. You know what, I don’t think I have. However, I do remember seeing a post somewhere on the blogosphere about this recently, but cannot find it. If anyone has that link, it was a useful read.

Imogen Robinson: Hi all, my question is: how can data visualisations be made more effective in informing decision-making?

The titles tell the storyThe titles tell the story

  1. Make the titles of your views questions. Eg “Should we invest in Singapore?” The question reflects the decision you’re trying to reach. It also forces you to look at your view and ask yourself – does the view help me decide. If not, you have the wrong view.
  2. Use reference lines to show where thresholds are being beaten – anything above a line, for example, needs attention. You can also use simple colour schemes for this. eg grey for most things, and red for those that need action.
  3. Use scatterplots as a way of comparing measures. This helps you determine if one thing causes another.

Peter Wallis: Could you point us towards some other good blogs to help newbies?

I use and recommend Feedly for blog reading. You can import this OPML file into feedly. It’s a list of all the Tableau and BI blogs I read.

Nicholas Bignell: Who are your favourite bloggers?

The Tableau blogging scene is huge (just check any month’s Best of Tableau Web roundups)

I’m more enthused by some of the amazing dataviz podcasts at the moment. Go check them out.

Suzanne Wilson: How do you define the relationship btw data, info, knowledge & intelligence?

I think there’s value in thinking of a wisdom funnel (this isn’t anything new – check out Google) but I have an aversion to funnels as metaphors: they suggest a linear process and a single direction. I’m more keen to think of data, info, knowledge and intelligence as part of a cycle of visual analysis. A bit of knowledge might make you seek out more data. More data gives you info, but only by sharing and acting with others do you get the intelligence

Anton Lokov: What is your favourite tool for rapid dataviz prototyping?

Why, Tableau Desktop, of course! But seriously, I don’t think anything is as good for exploring data quickly to prototype views.

Anil Mistry: Following on from Biel’s question, I am new to Tableau, are there any specific beginner books you’d reccomend?

A great new book is Storyelling With Data by Cole Nussbaumer. It covers the basics. I really enjoyed Information Dashboard Design by Stephen Few, it’s a fantastic introduction. For starting with Tableau, try Communicating Data With Tableau by Ben Jones.

Auzema Qureshi: What was/is the biggest challenge you have faced when using Tableau for data visulisation?

I remember doing a live “Analysts in the Hot Seat” at a Tableau conference in 2011. We were given a dataset, which we’d not seen before, and asked to explore it and build a dashboard in front of an audience. It wasn’t a great conference session. I spent 10 minutes semi-randomly dragging and dropping things around some worksheets, and duplicating lots of things. Then I realised that, uninspiring as it was to watch, this was actually EXACTLY why Tableau’s so amazing – I was throwing things around to see what stuck. What had so far felt embarrassing (“Really? This is how Andy Cotgreave plays with data?”) was actually the power of Tableau – drag, drop, fail fast, move on. After the 11th minute, all that exploration began to gel into something really powerful and I build a cool dashboard.