If up until now, all you have ever used to visualize data is Excel, once you try Tableau you may just start dancing. Having just discovered Tableau a couple of months ago, I feel that I have come a long way in a short space of time, which I feel is partly due to getting involved with a number of #Tableau community projects.

One of my favourite projects so far has been #IronQuest run by the wonderful Sarah Bartlett (@sarahlovesdata). Unlike other community projects, #IronQuest allows you to take your visualization in whichever direction you chose, increasing your data knowledge in the process; Sourcing your data, analysing your data and finding your narrative are just some of the skills that #IronQuest encourages you to develop!

This month’s epic theme picked by Sarah was sea creatures – however by the time I had joined the project, all the cool sea creatures had already been taken! And so, I decided to focus on a wider issue threatening all sea creatures – plastic waste.

This was the first time I had really tried to use Tableau to take the user on a journey with the data. One of the main challenges I faced on this project therefore was avoiding the temptation of going down a data rabbit hole – there is so much data out there on plastic waste! I found that drawing out my viz on paper beforehand really helped me to organise my thoughts and allow me to map out my narrative. Once I had my plan in place, it was easier to source my data.

One of my main aims in this project was to really help the user understand the plight our sea creatures are facing. To do this I tried to keep my narrative as simple as possible whilst still sparking curiosity. By keeping interaction to a minimum, as well as sticking to just 4 colours I feel that the message was clearly relayed, delivering the desired level of impact.

As ever any feedback is more than welcome! Here is my finished viz:

#IronQuest tips:

~ Don’t be afraid to get involved! When I first joined in, I had only been using Tableau for a few weeks. All levels are welcome!

~ Ask for feedback from the community! Not only does the project give you the opportunity to be part of a fun community, it also offers you a safe space to receive feedback to help you improve.

~ Pick a topic you are passionate about! Though Sarah sets the theme, you can take your research in any direction you choose – find what your passionate about and start vizzing – not only will you have more fun creating your story, but others will buy more into your narrative too!

Join in the fun here: https://sarahlovesdata.co.uk/category/iron-quest/

What is history without data? …

‘My heart will go on’ is 1 of only 3 pieces that I can still play on the piano to a half-decent level and is therefore still the headlining performance at all family gatherings. Titanic is one of those films that has had a lasting impact, in that it was probably the film that first sparked my interest in history.

Learning Tableau over the past couple of weeks, I have come to realize that the skills you need for both data analysis and historical research are actually very similar.

Historians provide context and narrative of how events/places/things have changed over time. Often as historians we find ourselves having to consider how our work ‘fits in’ with the already existing work/research done by others within our field, as well as having to justify our findings when conflicting with previous research.

When it comes to visualizing data we are also providing context and narrative. When analyzing trends and variations in data over time, if there is a discrepancy you have to ask why and investigate possible causes – it is much the same when it comes to historical research.

History teaches us many lessons…

“Those that fail to learn from history, are doomed to repeat it” ~ Winston Churchill

The Titanic disaster was met with worldwide shock and outrage at the huge loss of life and the regulatory and operational failures that led to it. Of the estimated 2,224 passengers and crew aboard more than 1,500 died (around 68%), making it one of modern history’s deadliest peacetime commercial disasters. Given the number of lifeboat spaces available, the total percentage of passengers who could have survived was 53.4%, but due in part to lifeboats being filled to only half capacity, only 32% of passengers survived.

Public inquiries in Britain and the United States led to major improvements in maritime safety, which included the establishment of the International Convention for the Safety of Life at Sea (SOLAS) in 1914, which still governs maritime safety today.

Now let’s talk Data.

If like me you are just learning Tableau, then the Tableau public resources page is a great place to start! To create this visualisation I have used the known list of passengers on the Titanic which can be found on the Tableau public resources page here: https://public.tableau.com/en-us/s/resources. It should be noted that this data set doesn’t include any data on the crew members, therefore all given percentages do not take into consideration crew survival rates (of the estimated 913 crew members on board, only 215 survived).

So I started out creating this Viz to see to if social class and gender really made a difference when it came to surviving the Titanic, and which weighed more heavily when it came down to the final numbers.

Did social class make a difference?

As a female passenger travelling first class, you had a survival rate of 97% – compare that to if you were travelling 2^nd class, your chance of survival has now decreased to 89%. If you were travelling 3^rd class, your chance of getting off the titanic alive has now decreased by a whopping further 40%, now sitting at a 49% chance of survival. In 1912, social class made the difference between getting on a lifeboat or swimming.

So what about gender?

As a male passenger travelling first class, your chance of survival was 34%. Compare that to female first class passengers, that is a staggering difference of 63%. As a male passenger, travelling either second or third class would have given you an estimated chance of survival of just 15%. Compare that to the female passenger equivalents that is a difference of 74% and 34% respectively.

So why is the difference in survival rate between genders so high?

This is an interesting question to answer, and it all comes down to a society that was ruled by honor and shame. In 1912 society, gentlemen were expected to be honorable; To survive the titanic but live in shame was a risk that many men were unwilling to take. This is evidenced by several known cases of the wealthiest male passengers refusing to board lifeboats on the grounds of honor, even after it was clear that the Titanic was going down. These included John Jacob Astor, George Widener, and Isodor Strauss.

To conclude, if you were a male passenger on the titanic, you had a 19% chance of surviving; If you were female passenger on the titanic you had a 73% chance of Surviving. However overall as a first-class passenger you had a 62% chance of survival, compared to third class passenger at 25.5%. Looking at the data therefore, it could be concluded that a rule of First Class First, outweighed the principle of Women and Children First.

Tableau Takeaways…

• I have now mastered the art of using groups – yes I know this is probably quite simple but this is a great lesson learned as it has simply made life so much easier! Mastering this meant that I could have some fun with shapes which I think really added to this dashboard
• Really making the most out of tooltips – I feel that tooltips are very underrated. They are great when trying to tell a story, or just highlighting specific pieces of information that you really want the user to digest.
• LOD Calcs are your friends. These guys are proving difficult to master but I am trying my best to love them. So far, I have been learning them through trial and error and downloading the workbooks of others just to pick apart the LOD Calcs. This is something I am going to be trying to improve on for the foreseeable future.
• It’s okay asking the Tableau community for help. Obviously, I first try (usually for a few hours!) to figure something out on my own but then I get to a point where I realise that seeing fellow Tableau enthusiasts as valuable resources as well as friends can only help me to improve my own Tableau skills. Added to that I genuinely believe that you have never really mastered something until you can teach it to others, therefore by asking others to teach me, I am helping them to build on their tableau skills too!

Any feedback on the viz as always is more than welcome. For this viz I took inspiration from Chris Love’s work which can be found here:   https://public.tableau.com/profile/chrisluv#!/vizhome/Titanic_265/Titanic – check out his page – it’s epic!