It got me thinking: what other pieces of gold (and not so gold) are languishing in our blogging past?
So I call on everyone to share their old stuff. Each Thursday, from this week, I’m going to share one of my really old posts. Some are still relevant, some are showing their age, and some are plain silly.
Join me! If you were blogging before 2013, what should we go back and read?
Anyone who follows my blog will immediately recognise my inspiration for this week’s makeover: Simon Scarr’s incredible Iraq’s Bloody Toll infographic from the South China Morning Post in 2011. I’ve written and spoken about this post many times in recent years.
The original source for this week’s makeover (“The Next to Die”) is a great project. It puts a new perspective on this topic, focusing not on the past but on the future. One thing it doesn’t emphasise, which I learnt by examining the dataset, is how the number of executions is dropping across the US.
Given I learnt that the number of executions is dropping, let’s go back to my makeover. I chose red bars and made them face down: evoking a smear of blood. But if I my biggest learning point was that numbers are going down, surely orienting the bars in a normal way (ie up) would make it clearer?
Well, here you go:
Regular readers will, of course, recognise this is what I also did with the Iraq’s Bloody Toll infographic. Here’s the Iraq’s Bloody Toll and my Capital Punishment story told in both ways.
This week MakeoverMonday is LIVE at Tableau Conference on Tour. Check out the hastags #makeovermonday and #data16 during Monday to follow things live.
For my makeover this week, I wanted to simplift the message. The differences between 2012 and 2015 weren’t that great. There are more women at each level, but the trends themselves haven’t changed. I decided to remove 2012 from my data to focus more clearly on the Pipeline story.
I liked the quote in the first paragraph of the original so lifted that for the title.
In our makeover about women in legislature, I extended the y-axis to 100% to emphasise the distance to parity with men. In this case, I decided to end the y-axis at 50%. To make it clear that the top of the chart is 50% I made the reference line stand out, and put the title beneath it. Did that succeed? Did you see the reference line?
The original chart wasn’t a great one this week.
What I liked:
There’s a table, so I can lookup the numbers
The colour scheme is very easy to distinguish
They attempted to use a visual metaphor for a pipe
What could have been improved:
The mix of line chart and pipeline renders the chart pretty meaningless: it’s not possible to see what’s actually being shown in the chart
The designers appear to have drawn a straight line in the chart, but the data doesn’t quite drop the way it’s shown.
Tableau labels the zero on axes. There’s nothing wrong with that, unless you’re showing Ranks, when zero is meaningless. What I did here was to drop a text object over the top of the 0.
I also added the axis label (“Rank”) above the 1 as it’s more likely to be seen and read by a viewer at the top than halfway down the left hand side of the axis, oriented on its side.
How do you label both ends of a line of a slope chart? Simple: you just turn on labels and choose Start/End of line? Well, no, because then you end up with your labels misaligned:
One option is to just label one end of the line. This is ok, but sometimes reduces the speed to insight. If you want to label both ends, you need to duplicate your measure onto a dual axis, and set each label differently. One measure is set to label the start of the line, and the other is set to label the end of the line.
There is a downside to this: the marks are all duplicated. This can lead to the edges of the lines looking jagged.
I was just on a call with Zen Masters Steve Wexler, Jeff Shaffer and Robert Rouse. We were talking about formatting labels, and Robert was saying “Well, of course, you can just drag the labels around.”
“Wait. What?” I said.
“Click on the label and drag it,” said Robert.
And thus I discovered a cool new trick. How many one-off charts have I struggled with because Tableau didn’t quite put the label where I expected it? (Answer: hundreds, at least). This trick is going to make MakeoverMonday much easier!
All you do is turn labels on, and to move a label, click on it once, then drag it.
EIGHT years I’ve been doing this Tableau thing, and there’s still new tricks to learn!
Like my friend Rob, I never got wildly into David Bowie. His passing, however, made me realise how he was one of those people who are fundamentally a part of my music history, even if they weren’t on regular rotation, or if I never got beyond his singles.
I thought I’d look at when I listened to Bowie over the last 6 years. It’s interesting – he was a real “binge” artists for me. The line shows cumulative listens over time. Look at how the line goes up in steps.
What’s happening? With Bowie, once or twice a year, I’d think “Bowie was amazing. I’ll put on his greatest hits.” And then I’d be listening on rotation for the best part of a day.
Favourite track? Before even looking at the data, my answer was Heroes. The data proves it –
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.
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.
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:
How about artist streaks?
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:
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?
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!
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:
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.
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.
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.
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.
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”
Simple! What other nice design tricks can you do with colour matching?