MakeoverMonday: an introduction

Savings

I initially decided to get join forces with Andy Kriebel on MakeoverMonday because I wanted to refresh my dataviz skills. What I didn’t expect was how quickly the project would evolve.

It’s no longer about just making charts. It’s about using a tool to debate data. It’s about improving people’s data literacy.

We must improve society’s data literacy if we are to debate and understand the sometimes deliberately deceptive charts produced by media, business and political organisations.

Each week, everyone involved brings a new perspective to the data. As the day progresses, people challenge and discuss what each version of the chart reveals.

People getting involved later in the day adapt their entries according to the debate that’s already gone ahead. This one from week 5 was an excellent example. Luke made an intentional choice to do something deceptive, even though it falls in the realms of “good practice”, by changing the measures.

This project is not about criticising others. It’s about continually asking new questions of a dataset. It’s about revealing messages in new ways. It’s about improving people’s data literacy. 

Here’s some links so you can get involved:

  • The datasets are posted here each Sunday evening or Monday morning.
  • Here’s a Pinterest board showing all the incredible work by the community.
  • Add to and follow the conversation on Twitter (#MakeoverMonday). You need to ignore the tweets about make up and furniture up-cycling!
  • Check out all my makeovers with my MakeoverMonday tag on this blog.

Good luck and thank you for being involved!

 

Storytelling and data: resources

Which of these has a more powerful message?
Which of these has a more powerful message?

I did a Webinar for Brighttalk today entitled, “How Data Storytelling Can Enhance the Way You Communicate

There were lots of resources and links I mentioned in the post. Here they are:

 

MakeoverMonday: US Police Violence

 

Police Violence with labelled circles

This week we chose data about police violence. It’s not a happy dataset but it is a very important one. The organisation Mapping Police Violence has data on every shooting in the US in 2015. The original article and charts can be found here.

Things I liked about the original charts:

  1. I found the formatting simple and striking
  2. I liked the simple approach to showing the data.
  3. They callout the key departments they are telling the story about (departments which shot only black people last year)

Things I didn’t like:

  • The bar chart has vertical labels – I don’t like turning my head!
  • They split the number of deaths and the rate of deaths into 2 separate charts.
  • The warning symbol for departments that killed only black people didn’t appeal to me as a clean visual indicator.

For my makeover, I wanted to stay true to their intention: a clean list, showing just the numbers. There’s power in these numbers which don’t need elaboration. I turned the rate of killings into a bar, as that’s simply the best way to show magnitude of a measure.

Labelled circles? Just the numbers?
Labelled circles? Just the numbers?

I added the number of deaths as a sized circle at the left of the bar. I toyed with just adding the number, but it was hard to identify what that number meant. I also put the labels inside the circle. I wanted to imply the magnitude of what these big numbers mean: people killed.

I took the same colour scheme as Mapping Police Violence for my own chart.

In the original, the commentary to the chart is placed on the right hand side of the chart. Since I chose to use a sorted bar chart, I have lots of white space on the middle and lower right of the chart. I added the commentary there in order to make good use of this blank space.

Download the workbook from Tableau Public here.

Do you have a world class dashboard? Want to share it?

BBD image
I’m delighted to announce I am working on a book: The Big Book of Dashboards will be published by Wiley later this year. I’m co-authoring with Steve Wexler and Jeff Shaffer.

The book will be a compendium of real-world dashboards, each a valuable solution to real-world problems. There will be sections on the real world challenges faced when building and maintaining dashboards.

So where do we get these amazing examples from? Why, the community, of course! Do YOU have an amazing, world-class, beautiful, high impact, dashboard? If so, we want to hear from you.

Please use the form below to tell us about it. If we like what we see, we’ll get in touch to discuss the dashboard in more details with you. If it has sensitive data, you will need to be prepared to share it with anonymised data.

Your dashboards don’t have to be in Tableau!

 

Makeover Monday: Travel agents and online hotels

Animation=drama?
Animation=drama?

I had fun with this one, deciding to do a connected scatterplot. There is much debate about the efficacy of connected scatterplots. I agree they require an extra mental leap to comprehend, but once the meaning is grasped, I think they are powerful.

As Hans Rosling proved, they are especially effective when animated, hence the GIF. Following the animation is like following a story, the up/down and left/right movements adding drama and anticipation for the viewer. A static version of the chart is below.

Connected scatterplot anyone?
Connected scatterplot anyone?

The original this week, on first glance, wasn’t too bad. There’s nothing inherently wrong with a dual axis line chart. However, once you try and work out which axis is which and which colour is which, you realise the creator of this didn’t think through the positioning of the labels!

Which axis and colour is for which measure?

A simple makeover would have been to fix the labelling and make things much clearer using colour to help the reader. Here’s what that would look like:

dual axis

I’m happy to concede even that the simpler approach may be better than my connected scatterplot version!

MakeoverMonday: Are you saving enough?

A vertical unit chart
A vertical unit chart (click here for interactive version)

Boy, the Americans need to get saving. That’s what I discovered this week. The source chart was a stacked bar. This is an interesting choice by Andy: the chart wasn’t a total disaster, but there’s certainly room to play around with it.

Also, stacked bars have been much debated recently. Stephen Few has argued that, if a time series is involved, you should always use lines to represent the category instead of bars. Cole Nussbaumer and others have made the case that while the lines allow for clearer comparisons, they don’t show “part-to-whole” relationships as well as stacked bars. You should go and read the fascinating debate on Stephanie’s and Steve’s blogs.

Here’s this week’s original chart:

Multi-coloured stacked bar

What do I like about the original?

  1. It asks a clear question
  2. You can look up every single value, even if it takes a while

What could be improved?

  1. Double-labelling the category is overloading. I chose to just use the category name (Baby Boomers) and not show the ages. I acknowledge that means that if you don’t know the ages of those categories you’re stuck. However, the message is still clear, I believe
  2. The colour scheme is arbitrary
  3. Moving your eyes between the legend and back again is difficult.

So how did I go about my makeover?

I first tried a diverging stacked bar, a method developed by Steve Wexler. He places the “negative” categories to the left of the axis and the “positive” categories to the right. This allows you to see more easily the overall positive/negative leanings.

However, my diverging stacks didn’t thrill me, so I thought I’d try a unit chart instead. I like the way they simply represent percentages. I was always struck by the brilliant simplicity of the ONS’ “How well do you know your area project” and wanted to do something similar.

ONS: inspiring
ONS: inspiring

I confess I had to construct a separate dataset to make the unit, because in Tableau every dot needs a separate record (although I suspect Joe Mako could make it work with just two rows)

Once I had the data, I played with shapes and settled on blank/filled circles to show the threshold between adequare and inadequate savings. I chose a colour scheme which still showed the different inadequate saving levels (e.g. “no savings account” and “under $1.000”), but all within a red palette. I also added a parameter to allow you to set the level of “enough savings”. You can play with that by opening the interactive version.

I also created a horizontal and vertical version. The horizontal one works well in tweets, the vertical one is better in a blog. Font choice this week was inspired by Kelly Martin’s amazing Font Choice blog post.

The horizontal version
The horizontal version

Tableau confessions: you can move labels? Wow!

labels

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.

“Wait. What?”

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!

MakeoverMonday : Are consumers bored with technology? (Black and White Week)

with shape

This week’s source chart is a shocker. There’s very little right with this chart. The title is good, because it asks a question. After that it pretty much falls apart. The actual percentage value is very hard to see. The donut doesn’t help. The percent change circle is totally confusing. They also used a negative scale to size the circles which requires mental hurdles to overcome.

Do you think there’s anything right with this chart?

My makeover is at the top.

Comparing growth and decline between two time periods immediately cried out “Slope Chart”. It could have been a side-by-side bar chart, but I am not a fan of those. A dumbbell chart might have worked too, but showing the direction of time is a challenge. I tried Comet Charts to show this, unsuccessfully, a few years ago.

I really wanted a pure slope to work, with all lines within one pane.

pure slope

However, this didn’t work for me. Too many of the lines were too close. I had to do callout labels for Television. The four lines along the bottom are all a little too close to each other for my liking.

Instead I resorted to a pane for each item. Here’s the version without shapes:

without shape

It’s still easy to compare the slopes of each line but not quite as easy. The cognitive load is a little higher this way. As you can see at the top, I chose to add icons. I did this to make it easier to identify each line without the requirement to move your eyes to the top of each pane. Icons are more appealing than labels. However, using icons can be a challenge: what’s a universally identifiable icon for “smart thermostat”? How do you tell the difference between a Smartwatch and Fitness Monitor icon?

We chose this week to stick to greyscale. Remaking this chart was straightforward. Because there are multiple panes, I don’t need to use colour to identify each product.

2016-01-17_22-41-31

I added the annotations to help users read the slopes, if they’re not familiar with them. Being in greyscale this week was a good reason to soften the text to a very light grey. This helps the annotation disappear when you want to focus on the marks. If the labels were as black as the lines, it all gets too much:

2016-01-17_22-43-08

David Bowie and me

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.

Dashboard 11

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 –

Dataviz criticism: know the author’s intentions first

Which do you prefer?
Which is most effective? Which do you prefer? Cole on the left, Steve on the right?

Stephen Few has been discussing 100% stacked bar charts. He’s opened a great debate with Cole Nussbaumer. I recommend you go and read the blog and the comments. In a nutshell, Steve prefers lines over stacked bars because they allow comparison of all data points at all places in the time series.

My problem is Steve’s assumption that Cole intended people to be able to compare all data points at all times in chart. In one of the comments, he says this:

“The information and eventual understanding that we get from a graph is more important than any impression that it provides unless that impression is all that matters.”

What if ‘that impression’ IS all that matters?

What impression do you get from this?
What impression do you get from this?

Sure, Steve’s lines allow you to do more accurate comparison of more data points. However, why should that be the only purpose of a chart? That purpose is one he imposed on Cole’s chart. It might not be the objective Cole had in mind when designing it.

Maybe Cole’s objective was to highlight the % missed only (ie the red marks on each chart). The other bar segments are secondary information, not vital to her key objective in making a 100% stacked bar. The “impression” I get from her stacked bar is that Missed Targets have increased to 42%. If her intention was that I get that impression, then it’s a success.

I might argue that if that was her intention, she could have drawn just the % missed data, and ignored the rest. However, this is at the expense of secondary information. Here’s what that could look like:

Show just the data necessary
Show just the data necessary

Steve also wanted other example of multi-segment stacked bars. I found one from my UK Election project:

% of total

I acknowledge the problems with this chart: it’s very very hard to see which organisation tweeted most about UKIP, or the Lib Dems, or any of the other central segments.

But my intention was NOT to allow comparison of every segment. My intention (as shown by the title of the view) was to highlight which organisations were tweeting most about the Conservative party. That was my prime goal. The Conservatives are the left-most segment and sorted in descending order: it’s easy to see which orgs tweeted most about the Conservatives.

Other information which can be learnt from this is secondary to my prime purpose and therefore was intentionally compromised by using stacked bars. An interactive version would add tooltips and more contextual data, allowing the curious to discover more in the chart.

I acknowledge the stacked bar isn’t perfect, but I don’t know how else I could have designed the chart so that it answered my prime intention (% of tweets about Conservatives) and allowed the viewer to see secondary information. How would you have redesigned it? If you wish, the data is here.

Remember, every visualisation is a compromise. And every visualisation has a prime intention which must be considered before critiquing it.

I wholly recommend the following posts for further reading: