Meet the Penguins!

makingof

 

OK here we go. Iron Viz competition time. I don’t viz that much so pleased to dust off Tableau Desktop and have a go. This competition is all about the natural world. A very interesting theme for me being a massive nature fan.

1. The Idea

I love nature. Thinking of a theme I reached back into childhood memories and for some reason I thought of long afternoons with my family at the zoo. Aside from the usual animals we always used to make a beeline for the penguins, something that still happens when I take my own family to the zoo. Everyone loves penguins!

However, I don’t think that many people know just how many different flavours of penguin there are. They live in varied locations, come in a host of different sizes and looks and not all of them live in cold countries. They do all stink though.

So I thought I’d use Penguins of the World as my subject for this viz. And here it is.

Go take a look at the viz!

 

2. Data

I got the data from a single source- https://seaworld.org/en/animal-info/animal-infobooks/penguin/

References to sited data is always good to see

Although I didn’t conduct any lengthy data validation exercise I was given some degree of confidence that the website has a detailed references section, siting the data sources. That’s always good to see and something that is mandatory in scientific papers and such like.

Now there’s tons of penguin data available out there. But I really didn’t have the time to spend days looking for that perfect data source. I also didn’t want to spend days transforming the data before I started vizzing so I settled on this one pretty quickly.  Then it was a copy and paste into Excel and I was off.

Always be on the lookout for good images to incorporate into your viz

One thing I did like was the fact that this page had some cool drawings of each penguin species. That instantly got me thinking of using them as Tableau shapes. It’s always a good idea to be on the lookout for images and drawings that can you can incorporate into your visualisations.

 

3. Viz Design

Now I’m probably not the only person in this competition to be heavily influenced by the master of these kind of visualisations, Sir Jonni of Walker. (@jonni_walker). And with that I thought I’d steal like an artist and try to emulate him.

Key design choices were to use a black background, with plenty of large images and the use of BANs (Big Assed Numbers) as callouts. Things that Jonni does all the time and that really create a visual impact. I also wanted to utilise the penguin images as a “penguin picker” to create some interactivity.

I also wanted a map to be a main feature of the viz as maps are not only informative, but visually striking, highly customisable and also act as a canvas on which to overlay images (in oceans etc.). I had a problem at first with the fact that Antarctica was one of the main locations, and that meant the bottom of the map had a straight edge, which looked ugly. This meant the map would have to meet the bottom of the viz to draw attention away from the abrupt edge.

The IUCN scale

I was pretty pleased with the highlighted IUCN status of each species. The icons looked nice and almost acted as a traffic light theme. Jonni thought they should be greyscale but I overruled him. Pfft – what does he know anyway?

In terms of Tableau content, only a couple of charts to show population, location, height and weight; but that was fine. It didn’t really need anything else.

I also wanted to include a section on famous penguins but it ended up overloading the viz and spoiling the theme. Although it did make me smile. Bonus question – can you name all of these famous penguins?

How many of these famous penguins can you name?

I also considered the use of an embedded YouTube video but decided against it.

I had fun choosing the title font, something that I think can make a huge difference to the viewer if chosen well. Regular fonts were somehow boring, and fonts with penguin characters looked too cluttered. I finally managed to settle on an Austin Powers style font, and then had the idea of alternating the colours to give that penguiny feel. I like it!

In the end I think the final result was ok. However this wasn’t one that I enjoyed. See below.

 

4. Challenges

This was my first viz in a while. I’ve spent the last 3 years knee-deep in Tableau Server and have a crazy busy job building a Tableau Centre of Excellence, supporting thousands of demanding users so I’m the first one to admit I don’t have the Tableau Desktop skills of people like Adam Crahen, Neil Richards, Pooja Gandhi et al.

The standard of skill out there in the community is crazy good. And that really was the main challenge. I found this viz a fairly stressful experience, it made me feel like a newbie all over again, simply because I’d be putting this out there against some stunning competition. I even considered not entering for a while. But hey, that’s not what the Tableau community is all about so I thought I’d have a go at it.

As mentioned earlier, I was deliberately trying to emulate Jonni’s style. Now that proved to be pretty difficult. I managed to create something reasonable, and fairly quickly, and began thinking to myself that hey this is a piece of cake, Jonni who?? But then it got harder. My ideas began to dry up and I found myself staring at an okay-ish viz but being unable to take that next step to make it better. Felt like vizzers block.

And that’s when I realised that the people who create these REALLY good vizzes have a lot of inherent natural skills and imagination that folks like me lack. So I gave Jonni a call and asked his advice. He came back with a number of suggestions, none of them earth-shattering, but much more subtle and delicate. Making the map larger, bringing highlight colours out from the penguin plumage were a couple of suggestions that made a huge difference to the impact of the viz. My point here is that the real geniuses of visual design have these thoughts occur naturally and without significant effort, folks like me have to learn them, or at least work a little harder than some others.

But hey, IronViz (and any vizzing) is all about learning. So I’m good with that.

Another challenge was that the Tableau part of this was pretty easy. That’s obviously great and what we want from our favourite application, but in terms of this viz I spent more time in image manipulation tools than in Tableau. And that really did detract from enjoyment. Come on Tableau! Make it harder for us to complete our vizzes!

 

5. Analysis & Story

So what can we take from this story? Here are some of the key observations that Tableau has allowed me to glean from the dataset.

  • All penguins live in the southern hemisphere, and some in hot countries
  • There are some seriously big populations, although some are endangered
  • They range from massive to teeny tiny
  • Main predators are Leopard Seals & Sea Lions
  • There are not one, but two penguin days in the calendar

So that’s it. I hope you enjoy the visualisation. If you do then please consider voting for me in the IronViz competition. And thanks to Jonni Walker for providing advice for this viz. Top man.

Good luck to all the other entries this year. Especially blinders like this from Ken Flerlage. – The Killing Fields – Viz / Blog.

Hmm. After all that writing I could do with a chocolate biscuit. Now which one……

Regards, Paul

The Svalbard Global Seed Vault

makingof

Hi all,

OK here we go. Iron Viz competition time. My first viz in a long time, so it’s good to get back using Desktop again. The first competition this year is the Food Viz contest!

1. The Idea

So this one’s all about food. Plenty of potential ideas here but I love to deviate from the norm and go a little bit off the wall, a little bit unusual.

I got thinking about food. But then I thought what would we do if there was NO food? If we had nothing to grow. If all the crops in the world failed overnight. What would we do? That would be a pretty bad situation for sure and someone must have a backup plan. I’m in IT as you might know so I do love a good backup plan.

And it turns out there is one. The Svarlbad Global Seed Vault. Buried 130m into the Norwegian permafrost, this building looks more like a Bond villain’s hideout than a critical storage facility. Once I saw this website my mind started racing with questions and that’s a good sign that you’ve got a decent subject for a viz.

 

Screen Shot 2016-04-19 at 08.36.51

The Svalbard Global Seed Vault

Go take a look at the viz!

 

2. Data

I got the data from 3 main sources.

The main seed stocks data

Plenty of detail in the data which gives some good potential for analysis. The main seed stats xls was pretty tricky to work with. There were a lot of nulls and gaps which I had to exclude from the dataset, and the file was pretty untidy. There were also close to a million rows in the file and that meant my pc struggled at times. All of this made manipulating the data tricker than I would have liked.

 

3. Viz Design

As with last year’s entry I thought I’d use Story Points again. This format has limitations but I think it works well for visualisations that answer multiple questions. In terms of formatting, I’ll be honest. I just didn’t have the time to mess about so I pretty much went with the same style that I used for my Evolution of the Speed Record viz last year.

Screen Shot 2016-04-18 at 22.35.49

Construction stats

I also thought I’d use a lot of images with this viz. The seed vault is an impressive construction and had a load of really good quality images available for use. I found it was useful to use a text box to provide additional commentary on each slide.

 

 

 

Screen Shot 2016-04-18 at 22.39.29

Seed vault funding

Most of the information about the seed vault made a big deal about how this was a big global project. This led me to question who was contributing and supporting the project and who was pretending to? I was pretty sure there would be a big difference in contributions, both in terms of stock and also finance.

 

 

Screen Shot 2016-04-18 at 22.40.21

Embedded Wikipedia page

A technique I learned last year was embedding a contextual Wikipedia page into the viz. This provides more detail for anyone wanting to know more about the data points.  A good tip is to append “?printable=yes” to the URL to display a more cut down page, as well as using the mobile URL (thanks to David Pires for that tip). Some of the links didn’t work as there wasn’t a direct Wiki page – no big deal.

 

So there you go. An interesting story for sure and one that was pretty enjoyable to put together.

 

4. Challenges

This was my first viz in a while. I’ve spent the last year knee-deep in Tableau Server and have a crazy busy job building a Tableau Centre of Excellence, supporting thousands of demanding users.

So my biggest challenge wasn’t data, or thinking of a subject, it was my own lack of ability with Tableau Desktop. I was shocked at how rusty I’d become and even some basic tasks took way longer than they should have. On the plus side it was great to be back on the vizzing horse again! I’m now inspired to get stuck into some of the online training and boost my skills.

Another challenge was actually deciding to have a go. The standards in the Tableau Community have gone through the roof in the last year, and the level of quality out there is absolutely amazing. So for the first time ever I was nervous about even getting my entry out there.

 

5. Analysis & Story

So what can we take from this story? Here are some of the key observations that Tableau has allowed me to glean from the dataset.

  • The Svalbard Global Seed Vault was a decent build. Didn’t cost too much and also only took 20 months. Pretty impressive going.
  • Some unusual crops stored in the seed vault. Rice at the top, and mostly concentrated around the Triticeae tribe of crop – wheat, maize etc. Surprisingly few fruit. I like blueberries so I’d be stuffed without them for my doomsday breakfast.
  • Probably not a surprise to see India top the seed donations chart but it was curious to see several African nations amongst the top donators.
  • I was surprised to see seed donation amounts tailing off big time in recent years. I wonder if that’s down to project apathy or maybe we’ve just got all the samples we need for now?

Wanna know even more? Go check out this Interactive 360 tool.

So that’s it. I hope you enjoy the visualisation. If you do then please consider voting for me in the IronViz competition.

Regards, Paul

The Evolution of the Speed Record

makingof

Hi all,

Oh dear – it’s that time of year again. Time for the Iron Viz competition. The first challenge this year is the Wikipedia challenge. Create a viz, any viz, so long as the data comes from Wikipedia.

1. The Idea

There are tons of data on Wikipedia. Trouble is, much of it is in a nightmare format and takes a lot of tidying up. I wasn’t cool with doing much of that this time so reasonably tidy data was a must. I also wanted something with depth, and an element of competition, danger and heroism. And I love technology so wanted that as well. All in all a tough ask.

But then I stumbled across the perfect topic – how speed records have evolved over time. Ticks all the boxes and could be a nice use of Story Points.

So that was it – “The Evolution of the Speed Record” was GO!

kings

The Evolution of the Speed Record!

 

2. Data

I got the data from 3 Wikipedia pages.

kand

An example of the land speed record data

The data has enough variety and richness to satisfy my requirements. It is also pretty consistent between pages so makes consolidation into Excel a lot easier. I did have to remove entries that referred to record attempts that were not ratified, and I also had to standardise on mph vs kph as well as distance miles vs kilometers. But with those caveats, I’d gotten me a pretty decent dataset.

It was also cool that most rows linked off to pages about the pilot and the craft used, each with some neat images for use in the viz. Plenty of room to supplement this data set should that be required. I also managed to find some clips of some of the drivers on YouTube.

 

3. Viz Design

The evolution of the record featured trials and tribulations, joy and pain, heroes and villains. So all in all this was a great opportunity to try Story Points for the first time.

axisThe overall look and feel took some arriving at and I’d like to thank Kelly & Chris for assisting with the peer review process. My original version made use of custom “speed-style” fonts to give the impression of speed, but we eventually decided that the real ethos of the whole story was the nostalgia and ‘Pathe’ News‘ style of flat capped heroes with handlebar mustaches pushing the boundaries of technology. So we switched to a style that sort of represented a 1930’s newspaper. I was really pleased with the final look and feel of it. Deciding the style really helped the story design of the charts. I tried to be as minimal as possible, removing unnecessary chart ink and distractions.

paper

Operation Paperclip

I wanted to give a feeling of progressing along a chronological timeline, whilst interspersing with ‘infographic’ style information pages. In particular there was a great story to tell about Germany and Operation Paperclip, that made a great infographic.

 

 

wiki

Embedded minimal Wikipedia page

I obviously wanted to use some advanced techniques so used the individual Wikipedia pages for some of the pilots to link off to an embedded web page. A masterstroke was working out that if I added “?printable=yes” to the URL it would give me a stripped back render of the page, that almost looked like a 1930’s newspaper, fitting the theme perfectly. I was really happy with that.

 

 

Screen Shot 2015-03-14 at 12.02.30

Embedded YouTube page

There’s also a page that links off to a YouTube video of the driver. I like that one, as the links are all monochromatic grainy film with appropriately stiff-upper-lipped voiceover. Excellent. I did worry a little about the ethics of including these videos as some of them show the final moments of the driver’s life. I think I’ve been respectful enough in my overall viz to justify inclusion though. I also added an old-school TV border to give a little bit more visual appeal.

So overall I was really pleased with this. A nice style, several good stories and a use of some advanced multimedia techniques.

 

4. Challenges

As mentioned this was my first use of Story Points. Unfortunately it turned out to be a frustrating experience. The feature, whilst undoubtedly useful, is in need of customisation and doesn’t provide a smooth user experience. One for the Tableau dev team to look at for sure.

Another challenge was the fact that the new Tableau Public site has sneakily been changed to https. That only becomes apparent when accessing a published viz using Chrome. Make sure your links to embedded content are https or they won’t work.

 

5. Analysis & Story

So what can we take from this story? Here are some of the key observations that Tableau has allowed me to glean from the dataset.

  • Records are dominated by only 3 nations, with France killing it in early years with their brilliant aviators.
  • It took a while for airspeed to get going, in fact land speeds were higher for a long time.
  • Germany’s poor record really didn’t tell the full story, their brilliant scientists being key to the USA’s great NASA missions in later years. Interesting how their previous misdemeanours were overlooked though…
  • Most record-breaking attempts advanced the speed slightly, with the occasional big jump.
  • The incredible Malcolm Campbell and his son Donald held an amazing 21 records.
  • Oddly, no-one seems to be bothered about records anymore, there hasn’t been a new record since 1997. Or is it too hard / dangerous now?

So that’s it. I hope you enjoy the visualisation. If you do then please consider voting for me in the IronViz competition, should this make the Elite8 twitter vote-off thing.

Regards, Paul

Stack-Attack! A Visualisation of World Records in Sport Stacking

makingof

Hi all,

You know I’ve been feeling a bit vizzed out lately. All I can see when I close my eyes at night is data. And the Tableau splash screen. A bit like when you overdose on Tetris, or Plants vs Zombies. So I was just getting ready for a little rest when they only went and announced the Iron Viz Competition for 2014! Damn you @DataRemixed!

So I’m straight back on the vizzing horse… When Iron Viz calls, you answer.

Bit of a nightmare picking a subject for Competition 1 (Sports). Sure I could grab loads of data for (real) Football, or Baseball or American Football, but that’s just not coooool enough. I wanted something with a bit of originality. It would have to be good to beat the competition and I’m hoping that originality will count for a lot in the selection of the “Elite 8”.

Cue a week-long thought process. A few ideas came and went, and even a few vizzes made it to the development table (step forward Athletics World Records & Driver Deaths in Motorsport), but they were too mainstream or a bit too macabre, so in the end I settled on this….

Stack-Attack!A Visualisation of the World Records in Sport Stacking

For the uninitiated, Sport Stacking is where competitors attempt to stack, and then de-stack a pre-determined configuration of plastic cups in the fastest possible time.

Like this –

Mental eh? It’s a proper sport with a governing body, World Championships and everything. The best players even have their own sponsorship deals with equipment manufacturers. Like these William Polly branded stacking cups.

And here it is! Click the image to open the viz. 

stackdash

So how’d I do this?

1. Data

Pretty easy this. All the World Record times for Sport Stacking are tracked by the World Sport Stacking Association. Better still there are a number of main game types (3-3-3, 3-6-3 & cycle), as well as multiple age groups and a gender classification. So lots of nice measures all ready to visualise. Lovely.

I used ScraperWiki to extract the tables from the website, but had to manually add the competitor nationalities to the xls. Didn’t take too long though.

That gave me a decent dataset to work with.

2. Viz design

Charts –I wanted to tell a few different stories, hence the following chart selections

Which age is fastest? – Could have done this several ways but decided to use a line chart. Allows me to see the trend in fastest time across the age groups.

Male vs Female – Tried a bar chart for this initially but it was cluttered and hard to read. So settled on a line chart with cups as the marks. This makes it really easy to compare male vs female for each game type (using filter) and age group division.

Best Nations – I wanted an at-a-glance visual to get an idea of which countries dominated. And that was easy with this bar chart. Simple enough using colours to represent the nations.

Images & Video – Now I really wanted some cups there somewhere. So I went for a worksheet of images as the game filter on the left. I also really wanted a video of each world record attempt, selected when the user changes the game filter. That was pretty easy, thanks to this handy tip from @dataremixed.

Look and Feel – Took a risk and broke from my usual safety-blanket of an all white background to go with a rebellious @pgilks style black background. I think it actually works better on this occasion, especially with the cup marks on the Male vs Female comparison as they look all luminous and glowing.

Information – Important to have an info sheet with tooltip to provide context here as I was sure viewers would want to find out more. So I provided some text as to the game types and also a link to Wikipedia.

3. Analysis

So what’s this telling me about the world of Sport Stacking? Well there are several interesting things that jump out at me from the data.

It’s a young persons game! You can see the U-shaped trend from the line chart as the young kids start off slow, but then the times improve and peak around the 12 years old mark, before tailing off again with advancing age. To the point that a seniors time is slower than an under-6’s.

Males are faster – until the male reflexes cave in at senior age! Amazing this. You can see that males are always faster, until we get to the seniors division, where it all changes. What a great story. That’s one up for the Grandmas!

USA have a strong youth policy, but Germany have the experience. The sport is dominated by USA & Germany. You can see USA have almost all of the records from youth to early teenage level. It then evens out at 17-18 years, and swaps around totally when you get to the oldies. Again exactly the sort of story I was looking for. Vorsprung durch Technik (or something like that).

So that’s my entry for Iron Viz 2014 (Part 1 – Sports). If you like it then do cast your vote via twitter, provided I make the Elite 8 of course.

Now if you’ll excuse me I’m off to stack my wife’s finest china cups….. How hard can it be?

Cheers, Paul

tablogowl

Hello Tablovers,

It’s time for another edition of the Tableaudown. A casual look back at the BI and Tableau related posts, comments and vizzes that have impressed or enlightened us at VN towers. Hell they might even have made a grown ninja cry. This one looks back a couple of months so you might have seen some of the info, but maybe not.

Twitterati

  • @ChrisLuv – On a mission to show the power of Alteryx. Thanks for all the help with my Winter Olympics viz.
  • @Biff_Bruise & @pgilks – More great support and chat offline from you guys
  • @emily1852 – Love your blog Emily. Keep it going.
  • @MonaChalabi & @GuardianData – thanks for featuring my viz on the Guardian Data Blog!

Interesting Viz & Articles

Top Tips & Content

  • Cheers, Paul

How to: To Boldly Go – A History of the Space Shuttle

makingof

Hi all,

Space: the final frontier. These are the voyages of the starship Enterprise. Its five-year mission: to explore strange new worlds, to seek out new life and new civilizations, to boldly go where no man has gone before. DA DA DA, DA DA DA DA DAAAAAAAAA!

Now I’m no Trekkie, but I do love a bit of space action. And I remember rushing home from school early as a misty-eyed 9-year-old to watch the first launch of the Space Shuttle.

space-shuttle-atlantis_1223_600x450The Space Shuttle programme should rank as one of mankind’s greatest technological achievements. Over 130 missions across 20 years, to the point where a launch was completely routine and barely a newsworthy item. Incredible. And even more so when you look at the technology itself. Old and creaky by the time the programme ended, but it still worked.

I was pretty sad when the programme came to an end a couple of years ago. So I thought I’d relive the excitement by vizzing up some of the mission data.

And here it is!

shuttleviz

So how’d I do this?

1. Data

wikiI got the data from 2 sources. First off was a nice list of all the missions, dates and other data from Wikipedia. Columnar, consistent and easy to chuck into Excel.

The only issue with this data was that there were surely a load more measures of interest for each mission. Duration of mission was interesting and would make a good measure to viz, but what else could I find?

nasapdfThat brings me to the second data source. This excellent pdf from NASA details all the shuttle missions and crucially adds a couple more cool measures – namely distance travelled in miles and total orbits of the Earth.

Problem was that this was pdf and I couldn’t get to the text easily. To get around that issue I used this Mac automator guide to extract the raw text from the pdf into a text file. Then I used a quick bash script to return just the 2 measures I was interested in, in a columnar format that I could paste into excel alongside the existing measures from Wikipedia. I could have used Alteryx for that also.

So how’d I do this?

2. Viz design

Timeline – I wanted to show a year on year timeline of the mission launches, broken down by shuttle and also to have one of the measures on there as well so I could pull some trends. That was easy enough, and I wanted to use a shuttle shape to act as one of the points.

shuttlelcipI had to download a simple shape and then make the background transparent so that I could further differentiate using colour. The end result looks a bit cluttered when all missions/years/shuttles are selected, but is still okay for analysis. 

E.g. I can see from the timeline how missions started short, then gradually got longer as the programme progressed. You can also see the gaps in the programme after the 2 shuttle crashes in 1986 & 2003. You can also see how each Shuttle had a different usage profile, something that I expanded on with the box & whisker plot.

Images – Yet again I followed Shawn Wallwork’s tip for dynamically assigning images. I downloaded each mission patch image and then assigned it to the appropriate point on the timeline. The image is then displayed when the user hovers the mouse over the shuttle shape. Took me ages to assign them all so you’d better appreciate it.

Shuttle Stats – Simple bar chart view of the key measures. I’ve used the average of the values to colour the bars.

shuttlestatsYou can see from this that although Discovery and Atlantis got the most missions, Endeavour and Colombia did more miles per mission on average than the others.

Box & Whisker Plot – First time I’ve used one of these. Seems to work nicely, giving an indication of the different usage profiles of each shuttle and the spread of mission distances. You can see how Endeavour was used primarily as a long haul shuttle, whereas Columbia’s mission distance spread is much greater, being used as a total all-rounder. A really effective view. Let me know what you think.

Look and Feel – Now I really wanted this to look Nasa-esque, and I managed to find the NASA font for download. Here it is. 

I’m sure you agree it makes the viz look good. But there’s a problem. For anyone to get the same effect they need to install the font or it will default to one of the regular fonts and the effect will be lost. To get around this all the titles in this viz are actually small images. A bit of a pain to do but I can see that technique becoming something I’ll use a lot.

3. Final Thoughts

Hope you like this viz. I had a lot of fun doing it, in particular browsing through the NASA catalogue of images from various missions. Some spectacular photos out there.

But we all know that all this progress wasn’t without pain. So I’d like to end with the following dedication.

This viz is dedicated to the astronauts that lost their lives during the Space Shuttle programme.

STS-151-L – 28th Jan 1986
Greg Jarvis,, Christa MCauliffe, Ronald Mcnair, Ellison Onizuka, Judith Resnik, Michael J. Smith, Dick Scobee

Challenger_flight_51-l_crew

STS-107 – 1st Feb 2003

Rick D. Husband, William MCcool, Michael P. Anderson, David M. Brown, Kalpana Chawla, Laurel B. Clark, Ilan Ramon

030201-F-9999G-001

Regards, Paul

Quizzical Vizzical

makingof

Let’s get vizzical, vizzical! I wanna get vizzical! I think that’s how the song goes anyway.

logo_optaSome of you may know a company called Opta. They’re the leading provider of sport data in Britain and provide feeds to just about everyone, including major broadcasters, sporting clubs and governing bodies. What they don’t know about data isn’t worth knowing.

optaquizAnyway, I know what goes on there as my brother Simon is their Director of Marketing. And one of their main industry events is a twice a year football quiz event where they invite all the big players from media, online, broadcast and other experts to get together and battle it out. The questions are ultra-fiendish and the competition fiercely fought. It’s a very popular event.

So my task was to viz up the results from the last quiz and see if we can infer some trends and have a bit of fun.

And here it is. The 4th OptaJoe Football Quiz results viz.

optaviz

So how’d I do this?

1. Data

For a change this was the easy bit! A spreadsheet of questions, teams and the scores for each team. I used a very simple Alteryx module to transpose the data into the correct format, but that was a 2 minute job.

That gave me a data structure like this. Perfect for Tableau.

datasheet

2. Viz design

This was the hard bit. I needed this to be clear, accurate and look smart. Opta plan on using this viz as part of the promotional material for their next quiz so it needed to be spot on.

Top Panel – I usually put the focus of the viz in this area. And the focus is obviously the result. I used one of my favourite views, the dual axis bar chart with shape terminator to show the results.

Bottom Left – I wanted to show how the standings were fluctuating as the rounds went on. For example, did Sky Sports run away with it, or did the lead change hands multiple times? Who finished strongly and who faded at the finish line? Did that with a simple line chart. Not so effective with all the teams selected but as with many views, very effective when only a subset is selected.

Bottom Right – Now I love this view. This is a matrix using the rank calculation to show how each team ranked per round. It works great with the colour coding and allows you to easily see the teams that were constantly good or bad. It also adds an element of colour to the viz.

rankTake The Guardian for example, who clearly would have won the quiz but for a terrible round midway though the event where they ranked 17th.

You can also see how some teams actually won a couple of rounds, like the Mirror, but also had some shockers that cost them a decent overall finish.

Font – Now Opta have a very well defined brand identity, and part of that brand is the typeface they use. I managed to identify it as “Chalet Paris NineteenSeventy“, which was as close as I could get to their font. I managed to download this font and install, which allowed me to select it as one of the usable fonts in Tableau. Unfortunately I got inconsistent behaviour when publishing to Tableau Public so the end result isnt as good as I would have liked. Any report consumer would also have to have that font installed to get the full effect.

You can go grab the font from here if you want.

Filtering – The chosen filtering was determined by the intended use of this viz. For example, this viz is likely to be shared amongst the teams to generate banter and a bit of a laugh at how well / badly one team has performed against one of their industry rivals. So I decided to group up the teams by industry sector, to allow a direct comparison within a couple of clicks.

filteredviz

Take this example of the viz filtered to show just the TV companies taking place. You can see the final outcome, round by round progress and comparative rankings per round. It’s even better when you clear the round selector and iterate through the rounds, effectively replaying the quiz from the start. Very cool.

So there you have it. I’m well pleased with this one. I’ve never tried to vizualise quiz results before and I think this format works well. It’s a good example of the viz design being heavily influenced by the audience and what I imagined they’d do with the viz in terms of generating banter, chat and competition in the pub.

For more information on the OptaJoe Football Quiz, here’s their live blog from the event. And here’s some more info on the participants.

Oh and if you think you can do better than some of the teams then here are the questions. Good luck!

Questions 1st half

Questions 2nd half

Regards, Paul

Snowy Sports – Winter Olympics Dashboard

makingof

Hey viz fans.

I’m baaaaack. Thanks to everyone that send good wishes for my shoulder surgery last week. It was a frustrating few days so I thought I’d get vizzical as quickly as possible.

I’m gonna start this post with a caveat – Don’t treat this viz as authoritative! Please! I had some massive challenges with the data and tried a few things for the first time so I just can’t guarantee that it is as accurate as some would like. As with most of these vizzes, they are as much to showcase the capabilities of Tableau / Alteryx as they are intended to be informative. If you’ve got an issue with that then proceed no further.

I love sport. All sport. Especially the Olympics. And as you will know we are coming up to the start of the 22nd Winter Olympic Games in Sochi, Russia.  So I thought I’d viz up the medal history going back to the first competition in 1924.

And here it is!

sochidash

So how’d I do this?

1. Data

Now this was *almost* a great success story. Let me explain.
All the medal history was held at http://www.databaseolympics.com, see the Winter Olympics section. The trouble is, you have to navigate through a couple of levels to get to the sports you need, there’s no way to view all the results in one go. When you do get to the data it’s in a nice format for copying into Excel, see below.

data

If only I could get it all into one place. Then I had an idea.

First off I managed to get a list of all the base level URLs I needed to cover each sport for each games. There were about 90 of them. e.g. www.databaseolympics.com/sport/sportevent.htm?sp=ALP&enum=110

Then it was dadda!! – Alteryx time. Let’s see what you got eh. Erm… Well I was pretty confident that this could be done but I’m far too inexperienced with the tool to get anywhere. Luckily a good friend of us at VN is the current Alteryx Grand Prix Champion – Chris Love (@chrisluv) of The Information Lab. I thought I’d see if he could solve the issue.

This is what he sent back to me… Turned out to be fairly tricky.

lovemail

And here’s the Alteryx module that cracked it. Happy to send it to anyone if you want to take a closer look.

alteryx

Absolutely awesome. I’ll be honest, I don’t yet fully understand how it works but I’ll be looking into that in more detail. I’m a little disappointed that Alteryx doesn’t seem to do web queries a little easier, nor can it process html files (even though it’s ok with xml).

So there we are – almost. I now had good data for all competitions up to 2006. Trouble is there was no data on that website for the Vancouver 2010 event (argh!). I did manage to locate the information from Wikipedia but it was a totally different format and had a number of other variances from the other data.

Another caveat is that I’ve treated every team medal won by an individual as an individual medal. For example, the bronze medal won by the GBR Ice Hockey team in 1924 counts as 10 bronze medals – not strictly accurate I know.

I managed to use Alteryx again to transform some of the 2010 data into a better format but I couldn’t get away from the fact that I had to do a lot of manual crap to get the info into the format I needed. With that, there are sure to be some mistakes – hence the above caveats.

Anyway….

UPDATE: Due to some pretty bad data quality issues with the source detailed above (missing whole events etc), I switched to this alternative. It looks to be a lot more accurate but again I can’t vouch for the quality. Let me know if you see any issues.

http://www.olympic.org/medallists-results

2. Viz design

Timeline – Quite like this view. Simple plot using an Olympic Ring image as the mark, plotting the year of the competition.

saraOne thing I really wanted to do was to represent the official flyer of the competitions and have that change with user selection. I’ve been meaning to try out Shawn Wallwork’s tip on how to do this. http://community.tableausoftware.com/thread/119079.

In summary it’s a tip for creating a dynamic background map, based on user selection. Check it out. Very smart.

Filters – I then created a couple of worksheets to act as filters for Sport and Medal type. Simple enough, using shapes. Needed to make sure the medals were consistent in size and style.

pictoFor the sport selector I used the official pictograms for the Sochi games. I think they’re awesome and look perfect for this style of viz. I positioned them centrally to act as a central selection panel. Love the look and feel of the images themselves. Give it a bit of use as filter action and job done.

medalsMedal Table & Individual Records – These were simple enough. The only cool bit was the use of small medal shapes for the gold, silver and bronze medals. Looks smarter than a basic text or highlight table.

Final touch was to add an athlete wildcard search, and then a mouseover info sheet to highlight the caveats discussed in the post. I also added a country code custom list filter so you can compare how one nation has performed compared to another.

3. The Finished Product

sochidash

So there you go. A viz of the Winter Olympics. Fun to do that one and great to be back in vizzing action. Lots of ways this one could be improved but I didn’t have the time on this occasion.

Regards, Paul

FIFA Ballon d’Or 2013 Dashboard

makingof

Hello there everyone!

Thanks to everyone for the great feedback on my recent Pinball viz. Was a lot of fun doing it, and glad it brought back some youthful memories for a number of people. I wasn’t intending to produce another viz so soon, but this one is fairly timely.

See I love football. I mean LOVE it. For some reason I’m still playing every week in the Surrey Sunday leagues, my 21st season. The old bones creak a bit these days and I’m not as quick as I once was, but the enjoyment is still there. So like many I watched with interest as the recent World Footballer of the Year awards were announced. Sorry I’d better say the FIFA World Footballer of the Year awards in case Mr Blatter is watching.

The actual results weren’t too much of a surprise. Ronaldo, Messi & Ribery making up the top 3. But I wanted to see how those votes broke down and see if I could pull out any trends using Tableau. Especially as there has been much chat about whether these votes are a bit of a farce or otherwise.

And here it is!

dordash

A viz of all the votes, showing geographical distribution, points breakdown per player and voter type (coach, captain or media).

So how did I do this?

1. Data

Data was pretty easy to get hold of. In a rare display of process transparency FIFA publish it here. Each voter names their top 3 players from a short list with 5 points given for first place, 3 for second and one point for third. Total points wins the trophy. Simple.

dordataWell sort of simple. See this format has the player name as a measure, I want it as a dimension. Copying this data into Excel for analysis wasn’t a goer, it just didn’t seem to be manageable with Tableau in that format.

reshaperSo this gave me the chance to try out this Excel Tableau “data reshaper”. I’ve heard other people talk about it but never used it myself. Simple enough to install and bingo – worked first time.

formatteddataMy data now looked like this, with the player name as a dimension. Worked fine when connected to Tableau. Perfect.

2. Viz design

I really wanted this viz to show the following

  • Geographical distribution of votes
  • Is there a trend of votes between the 3 voter types
  • Any oddities in voting?

With that I made the focus of the viz a map with pies. Now I know this looks a bit cluttered when all the marks are visible, but it does seem to work when the set is restricted with filters or even highlights.

The player filter allows me to easily see where each player’s points came from. The points calculation was done with a simple calculated field to assign either 5, 3 or 1 point to each player’s SUM(Number of Records).

I like highlight tables so I thought I’d provide a full points breakdown. This is more interesting when combined with a filter.

Regarding the regional grouping, that was manually done. Could have split it into ‘official’ FIFA regions I suppose but didn’t have time.

3. The Finished Product

FIFA Ballon d’Or 2013 Dashboard

dordash

What’s this telling me?

There are a few interesting trends that this viz allows me to infer.

mediaFirst off, although Ronaldo won the award, you can see that he got a large portion of his votes from captains and coaches. When I select media as the filter then you can see that Ribery easily won the vote of media representatives, except in the Americas region – kind of expected given that Messi is Argentinian. That’s an interesting finding as FIFA controversially extended the voting deadline to allow Ronaldo’s awesome performance against Sweden to be considered, a move that many felt cost Ribery the award. Ronaldo only topped the vote for one region, Europe.

It’s also easy to see that there was a lot of national favouritism. I.e. assigning points to the player of your own nationality even thought they are clearly not one of the favourites. Examples include – Zlatan Ibrahimovic, Gareth Bale, Yaya Toure, Radamel Falcao, Andrea Pirlo, Robert Lewandowski & Luis Suarez – all of whom received significant points from their home nations.

persie

Lotta love for Robin van Persie in the Carribean (see left).

It was also interesting to see that neither Ronaldo nor Messi voted each other in their top 3. Understandable I suppose, if a little childish.

I can also see a surprising amount of countries that didn’t bother to send in all their votes. I’m looking at YOU Turkmenistan.

So this voting does seem a little contrived and a bit like the Eurovision Song Contest at times. Clear favouritism & agendas at play.

Ok that’s it. As always feedback appreciated. And I know it’s not perfect, so don’t take it too seriously.

“FIFA – For the game. For the world.” – Yeah whatever Sepp.

Regards, Paul

That deaf dumb and blind kid…

makingof

#He’s a pinball wizard there has to be a twist. A pinball wizard, got such a supple wrist.. do do de do… How do you think he does it? What makes him so goood?#

Good morning all. I don’t start every post with a song but at the moment I can’t get that tune out of my head. And it’s no surprise given the subject of the post.

Peter Gilks recent viz about video games consoles got me reminiscing back to the heady days of 8-bit gaming and all those days I spent in dodgy arcades in the North-East of England. And I got thinking about all the great pinball tables I used to play, both back then and later at University. So as with most thoughts us BI folk tend to have, it gravitated into a Tableau viz!

And here it is.

pinballviz

A viz of all pinball tables where more than 1000 units were manufactured. The timeline shows release date, coloured by manufacturer. I’ve also pulled a table rating and the exact number of units produced.

So how did I do this?

1. Data

pinsearchData is obviously the key to any viz. And luckily the folks at The Internet Pinball Database have a detailed repository, easy to query and returns the results in a format that I can easily get into Excel. So step 1 was to use their search functionality andsearchresult pull out a list of all tables with >1000 units produced and copy that into Excel. Simple enough. I was hoping to locate a really detailed datasource and given the fact loads of people take their flippering very seriously it wasn’t a surprise to find this resource.

2. Viz design

Now I knew from experience that pinball had a “golden age”, was hit by the advent of video games and then made a bit of a comeback. But would that be reflected in the data? So my first view was a timeline by year, coloured by manufacturer to show the big players like Williams and Gottlieb etc.

That was simple enough to create but I wanted to give it a kind of 8-bit graphics feel, like you’d get on a table display, and that was done with a stacked bar chart, carefully choosing the colours and border around each cell. I really like the look of it, especially from a distance. Works great when using the manufacturer highlight also.

The other 2 views were self explanatory enough, highlighting the ratings and units produced. I had planned on limiting them to the top 20 tables so I didn’t have annoying scrollbars (I hate those things), but a couple of people I showed it to said they’d like to see more detail. Let me know what you think.

#Aint got no distractions. Can’t hear those buzzers and bells. Don’t see lights a flashing. Plays by sense of smell. Always gets a replay, never seen him faaalll… # Sorry – there it goes again… Be gone Daltrey!

3. Filtering

I wanted to highlight and filter according to manufacturer. Highlights look great on the timeline with all those little squares lit up like a video game, but it would also be nice to filter the whole sheet by manufacturer to tell their own story through the years and view their top / most produced tables. This was achieved by allowing the tables by manufacturer chart to double up as a dashboard filter.

I also added a filter for machine types, so you can go back the really old-school days of Electro-Mechanical tables, before they all became Solid-State.

4. Graphical Grief

I really wanted to get some more graphics into the viz, pictures of tables etc, but ran into two issues.

Firstly I did manage to find all the images on ipdb.org, but there were far too many to use as custom shapes or icons. So I figured that I’d be able to have a web page object that linked back to the image at source, displaying the table whenever it was selected by the user. That would be ace.

All I needed was the URL path for one image for each table. Now I’m sure as hell not gonna manually copy the URLs into the datasource so managed to achieve this with the help of a colleague (@GlenRobinson72) who knocked up this VBScript.

ipdbscriptIt works by taking a text file of pinball table names, and for each one it connects to ipdb.org, searches for the table, finds an image file and parses the URL, which it returns back to me. I then manually copy that into the datasource csv as a dimension. Dead easy. It doesn’t always get the best image for each table but that’s less of a concern.

Then the next step was to create a dashboard URL action, that referenced the new photo URL dimension.

And finally a web page object that dynamically changed URL destination based on what table was selected.

imageproblemThat works great. BUT – the web page object has no control over the size of the image returned. I’d wanted each image to resize into the web page object but what ended up happening was that only a tiny portion of the image was displayed with huge scrollbars. I was unable to find a workaround to this.

For example, see the image in the bottom right corner of the screenshot (left).

I did find a compromise. I removed the line in the script that stripped the “tn_” from the URL and this meant that the thumbnail image was returned. Too small to be of real use but more workable than the large image.

If anyone knows a way of getting around this then let me know. It would have been the icing on the cake for this viz. I suppose I could have downloaded the images locally, resized them manually and then used them. I wonder if Alteryx could be used to solve this problem?

5. If I’d had more time

I managed to find a data set of world’s best pinball players and their ranking points totals from 2013. That would have been good to represent, and maybe correlate with their favourite tables. The data is at http://www.ifpapinball.com/ if anyone is interested.

I also wanted to represent the World Record score for each table, but wasn’t able to find that data.

The Finished Product

I’m well pleased with this. In particular the way the colour matrix works for the timeline chart. Looks funky.

http://public.tableausoftware.com/views/PinballTables/PinballWizardry#1

And there are a surprising number of trends to infer from the data. You can see the golden age of pinball from the mid 60’s to late 70’s, when the impact of Space Invaders and Pac-Man led to a crash in table production. And then the mini-revival in the early 90’s, which is when I ploughed loads of my student grant into them. Stern are still producing tables to the present day but these are in lower numbers than 1000 so are not included in this dataset.

Looking at ratings you can see Gottlieb have by far the biggest presence at the top of the ratings chart, even though they didn’t produce individual tables in huge numbers. But they clearly have a cult following as the ratings suggest. Williams and Bally seemed to go for large numbers, consistently across the golden age, with Williams continuing into the 90’s revival. Of course none of that would have been possible without the early pioneers like Chicago Coin and Genco.

No surprise to see The Addams Family as the top produced table. That thing was everywhere. I never got on with the magnetic madness on that table although everyone else seemed to love it. I’ve called out my favourite table in an annotation. As far as I’m concerned it was directly responsible for me not getting a 1st in my undergraduate degree so has a lot to answer for.

So there it is. Hope you like it. Feedback invited as always. And don’t buy that stuff about the deaf dumb and blind kid. I’ve tried playing blindfolded….

Regards, Tommy – agh sorry!