The Friday project – week 15

This week’s lesson: they can’t all be winners

bad santa

This week’s lesson comes from Bad Santa, as once again I was playing catch up with Makeover Monday, looking at how Gold and Crude Oil prices change over time.

Normally I try and avoid #MakeoverMonday on twitter but this week I had already seen several entries, including this fantastic effort from Pooja Gandhi:

pooja

My problem? I couldn’t think of any other way of showing the data…area charts seemed like the obvious choice, and it seemed perverse not to use gold and black as colours.

Given my limitations (technical and self-imposed – I try and complete every Makeover Monday in an hour) I ended up creating the candy corn in the calendar, a kind of Pooja-lite:

Gold and Oil prices

Acceptable on its own terms, but lacking in comparison with the superior original, I had made my own Bad Santa 2.

The Friday project – week 14

This week’s lesson: the power of learning by doing

This week I passed the half way mark of my six month Friday project and two pieces of good news made me review my approach: I found out that my secondment at work is being made permanent, and I that I had secured Tableau Desktop licences for my team.

So why does this change things?

Firstly, developing a portfolio was fundamentally a form of insurance for the job not being made permanent, a way to showcase myself to potential employers. Secondly, I have taken a fairly punk approach to getting up to speed with Tableau, learning by doing: is this the best foundation for training other people?

Take my Makeover Monday on automation from earlier this week (I am playing catch-up after a week off). I came up with the robot arm / lollipop chart approach more or less immediately, sketched it out, and found an appropriate icon from the brilliant Noun Project, ending up with the viz below:

Will a robot take your job.

But you know what was the best news of all? I had played around with making lollipop charts before, using Andy Kriebel’s excellent step-by-step instructions. And I had remembered how to do it. By doing, I had learned.

So instead of changing things completely, I am just going to tweak my approach. I am going to close a few technical gaps by completing the excellent Tableau 10 for Data Scientists course by Matt Francis, I am going to carry on with Makeover Monday, I am going to carry on developing a portfolio, and I am going to continue with the blog.

After all, when a robot takes my job, I might be looking again…

The Friday project – week 13

This week’s lesson: persevere!

A counter-intuitive lesson from the first Friday where I did absolutely no vizzing whatsoever (although I didn’t spend all day in my pants).

This week I received confirmation that I have finally secured Tableau Desktop licences for my team at work, only four years after my first attempt!

So will this change the focus of my Fridays? Check the blog next week to find out!

 

The Friday project – week 12

This week’s lesson: steal like an artist

This week’s Makeover Monday subject was basketball’s March Madness, and it didn’t take me long to sketch out my design: small multiple slope charts showing the results for each year’s winner, with colour denoting the difference in seeding between the two teams.

However Ann Jackson’s entry from last week had lodged in my mind. Would area charts look better, with the horizontal line aiding comparison of the margins of victory?

aj_wk11

In short, yes. My final version is a blatant steal – and I credited Ann in my tweet. The overall viz needs some work (I want to revisit to add conditional text showing the overall winner, loser and score), but I feel the area charts work reasonably well, and highlight those rare years where the underdog triumphed.

March madness tweet

So why the blog title “Steal like an artist”? Well I stole it from Alberto Cairo, who stole is from Austin Kleon. And the blog subject? Stolen from Neil Richard’s Is it OK to steal?, and Ryan Sleeper’s Data Visualization: The Stolen Art.

So in that spirit I will steal Neil’s last sentence: “if anyone ever wants to steal from me, then I know, unlikely as it seems, that I will have made it!”.

 

The Friday project – week 11

This week’s lesson: use the right data!

Before I began the Friday project at the start of the year I asked Andy Kirk and Andy Cotgreave’s advice on where to start. Andy C’s suggestion was to participate in Makeover Monday and Andy K’s was to be disciplined, to think like a Data Journalist, and to add constraints.

As my Tableau skills have improved I have sought to add constraints by completing Makeover Monday within an hour on Sunday, leaving Fridays to work on thinking like a Data journalist: finding an interesting story, sourcing appropriate data, and creating a visualisation.

This week it was this article that caught my eye: Man found guilty of killing one of Britain’s rarest butterflies.

large blue

Now butterflies are something I know almost nothing about but the article triggered a few questions for me, e.g.how many Large Blues are there in the UK, how have numbers changed over the years, and how do they compare to the other 25 protected species?

And here is the kicker: I couldn’t find the information I wanted. The best data I could find was these Occurrence (Distribution) and Abundance (Population) Trends for 1976-2014 and 2015-2014, for 60 species of UK butterflies, including the 25 protected ones (top 5 only shown):

Butterfly data

For the Large Blue there was insufficient data for Occurrence change for both periods, a 1,440% increase in Abundance change for 1976-2014, and a 20% decrease for 2005-2014. So why the fuss if numbers had increased so much since 1976?

Without the underlying numbers behind the increase, I had no coherent story. But instead of looking for another topic and wasting a morning, I pressed ahead and wasted the day, creating, then deleting, a dashboard which was devoid of interest. I had less to show from the day than I had from spending an hour on my Makeover Monday viz:

Joy of sex

So what were those key lessons?

  1. Add time constraints to data gathering as well as to data visualisation
  2. Be wary of the sunk cost fallacy: know when to move on
  3. Be thankful for the work done to create those Makeover Monday datasets – it is harder than it looks!

The Friday project – week 10

This week’s lessons: embrace serendipity, and use the right tools!

While checking twitter this morning, I came across the following tweet from my ex-colleague Ferg:

ferg tweet

Lucky Ferg, I thought! (Or something like that). Then I checked out Raw Graphs.

Last week I blogged about how complex it was to create a Sankey diagram in Tableau, and how the process felt more like a test of how well I could follow instructions than a creative exercise.

So could I create a Sankey using Raw Graphs? Two minutes later I had my answer: you bet!

The Sankey on the left took me about two hours to do in Tableau (about the same as a flight from the UK to Milan), the Sankey on the right took me two minutes with Raw Graphs:

All I had to do was paste in the data and choose my graph type. Another lesson learned!

Using the right tool also meant that I could revisit my original intention from last Friday to redesign the diagram below:

original

Here is a screenshot of my first attempt at a redesign, Visualising UK Labour Market Flows. Although I lost some interactivity by pasting the Raw Graphs Sankey as an image, I think it works as part of a larger dashboard:

Labour Market Flows

 

The other visualisation I created this week was about the Top 500 YouTube Games Channels for Makeover Monday. For this one I wanted to embed videos of the two highest performing channels within the dashboard.

Sounds like it could be tricky, right? Not at all. I just had to chose Web Page for the dashboard object, drag, enter the URL, and resize. Another example of using the right tools for the job!

YouTube Games Channels

The Friday project – week 9

Simplicity v Complexity

This week I created two vizzes, and I am not quite sure I quite nailed either one. With the benefit of hindsight, one was too simple, and one was too complex.

Makeover Monday

The first was my redesign for Makeover Monday to show Andy Kriebel’s Amex spend in 2016. I went for a simple, stripped-back approach, showing cumulative spend over the year as an area graph.

How much did Andy spend on his Amex in 2016?

 

amex-spend

So why do I think it is too simple? Because there are a number of questions which could be answered from the data which aren’t addressed, e.g.

  • how many transactions did Andy make?
  • what type of spend was it?
  • what days of the week did Andy spend the most?

If I get the time I will look to expand the viz to incorporate answers to these questions: to keep the design simple, but not the message.

Sankey experiment

The other viz I created this week was this Sankey diagram, using guidance from Chris Love.

Sankey diagram (technical experiment)

 

sankey

So why do I think it is too complex?

Firstly the graph type is not a standard one in Tableau, and the method Chris has used is very complex – I would never have worked out how to do it on my own.

Secondly for this type of Sankey, where there are distinct categories on the left and the right, there are better, simpler ways of showing the breakdown in volume, as Chris himself explains in his excellent talk on embracing simplicity.

The other lesson for me is I lost sight of my original intention, which was to redesign this diagram:

original

I do still think a Sankey could still work for this, with Q3 on the left and Q4 on the right, but because the instructions I found didn’t cater for change in volume over time, I just built a Sankey using dummy data.

So I proved that I could follow and adapt instructions, but in terms of the ultimate objective of data visualisation – to communicate – it was a hollow exercise.

 

The Friday project – week 8

I am a bit behind with the blog this week but I managed to add two more vizzes to my portfolio, and wanted to briefly highlight one element of each: the importance of preliminary sketches, and the importance of iteration.

Preliminary sketches

Makeover Monday this week used a detailed dataset on EU potato production, and I decided early on I wanted to take a more traditional, tiled, dashboard-style approach.

After some exploration of the data in its original form and in Tableau, I created a (very) rough sketch of my dashboard structure: an overall view (including tree map) in the top half , then the ability to drill down to see key metrics by country in the bottom half.

Here is the rough sketch and the finished viz side by side – it’s not going to win any prizes for draughtsmanship, but for a dashboard-style viz where a more disciplined approach is required, I find it an integral part of the development process.

 

Iteration, iteration, iteration

I also spent some time this week working on a viz showing the respective fortunes of Football League clubs over the last 16 seasons: The Ups and Downs of the 92.

Like the Makeover Monday dashboard, the main half of the viz shows the overall picture (a line for each of the 92 clubs, with the ability to filter by current league or club), with a more detailed view in the other half: in this case, individual graphs highlighting the respective fortunes of rival clubs.

the-ups-and-downs-of-the-92-club-rivalries-first-draft

 

But something was niggling away at me – the feeling that the top-left graph wasn’t telling the whole story. In the text I had referenced AFC Wimbledon’s six promotions in 13 seasons, but the graph only shows two promotions as the dataset I created didn’t go below the Conference.

Another iteration was required. I trawled Wikipedia for details of the previous seven seasons and added them to my dataset just for AFC Wimbledon. I wanted to keep the size of the axes in the right-hand graphs consistent to allow comparisons, so I dropped the bottom left graph.

It was more work, but the central story is much clearer for it: the steepness of the line and the inclusion of the additional four non-league segments brings home just how impressive AFC Wimbledon’s rise through the leagues has been.

the-ups-and-downs-of-the-92-club-rivalries

Tableau Public links:

Which EU-28 countries lead potato production?

The Ups and Downs of the 92

The Friday project – week 7

A really quick update today as I have been battling a heavy cold (just in time for my week off!)

Luckily the weather was so bad on Sunday that I had Makeover Monday in the bag before the week even started – here is the finished viz:

valentines-day

It is a simple viz but I feel that it brings out the two most interesting themes I found in the data: the unexpectedly large (for me – I must be a cheapskate!)  amount Americans spend, and the fact that they don’t just spend on their partners. Who knew!

The respective areas of the heart shapes are harder to compare than, say, a bar graph, but you can discern the more obvious trends, and the detail for each data point is available in the tooltips, including what percentage of people spent in each category.

And that is it for today – I did start compiling a dataset of English football league positions I hope to complete and use in the future, but it was a bit of a write-off as far as vizzing goes.

If I am feeling better I will probably do Makeover Monday early next week – after seeing the 100% club t-shirts for 2016 my aim is a full set for 2017!

 

The Friday project – week 6

A shorter blog today so I will start with my effort for Makeover Monday.

Cab usage in Chicago

Unfortunately I had to use the smaller dataset which only included 2015 and 2016 data for pick-ups from the Near North Side.

Here is the final viz below – my original idea was to create two: one showing the overall trends and one narrowing the focus to a specific angle, possibly cab usage on St Patrick’s Day.

In the end I only had time to show the overall view, and used a much simpler design than my original plan to showing multiple view of the data in a dashboard style.

I think it tells a clear story though, and it was another opportunity to experiment with design and colour in a way it would be hard to do in my day job.

I also learned how to add an info button which provides more information when you hover over it in Tableau Desktop, an approach I will definitely take again to keep my visualisations uncluttered.

cab-usage-in-chicago

Working on my CV

From Monday to Thursday I work for Lloyds Banking Group, and blog about Data Visualisation on their version of Jive.

The group has been a great way of connecting with people all across the Group who have an interest in Data Visualisation, and is the 25th most viewed out of over 4,000 groups.

One of the most popular threads has been on infographic CVs, and I spent the rest of the day turning mine into something a bit more visual.

Here is the result (I will update the featured visualisations as my portfolio improves) – let me know what you think!

data-visualisation-cv