Using Show Me without using Show Me

At The Information Lab we are rather lucky to have so many certified Tableau trainers. This means that we get the chance to teach either complete newbies the FUNdamentals of Tableau or how to leverage some of the more advanced features of Tableau very often as there are not that many trainers out there. We also get the chance to teach each other so much that we won’t otherwise come across. Why is this important you rightly ask? Read on…

This week I the chance to teach some very bright analysts how to make the most of their skills with Tableau. Whilst teaching the attendees about the Marks Card through a technique that I call ‘Whiteboard Tableau’ (more on this soon), one attendee Natasha corrected me about the way to create a Stacked Bar chart without using the Show Me panel.

Normally I would say add your discrete field (blue pill) that you want to be your bars on to the Column or Row shelf (depending on whether you want a vertical or horizontal bar chart respectively) and add the measure (green pill to the opposite Shelf you placed the discrete pill. To create a stacked bar chart you can then drop what you are dividing each of those bars by on to the Colour Shelf of the Marks Card. Easy.

But I heard… “Just drag the new discrete field on to the bars and it colours the chart”. I froze, not wanting to say “No Tableau doesn’t work that way” as I have learnt you never say that as someone has always found a way. Despite using Tableau heavily for the last three years, I never had come across this technique before. I got Natasha to talk me through the technique and it worked a treat.

Ever noticed @tableau doing things you didn't expect but is actually better than expected? Blog article coming soon pic.twitter.com/mX6uAjydmj

— Carl Allchin (@Datajedininja) November 13, 2015

I posted it on Twitter and got eight favourites and a retweet. The tweet even got a reply from my favourite English Zen in America:

@Datajedininja @tableau never ever done that before, nice!

— Peter Gilks (@pgilks) November 10, 2015

Most people I have showed didn’t even know of this technique until I showed this guy:

It's almost keynote time! @Datajedininja and I are getting ready to be amazed #data14 pic.twitter.com/4xUlTPqxMf

— Robin Kennedy (@Odbin) September 9, 2014

(note the ‘guy’ in question is that clever one on the right not that other floppy haired fella on the left)

For those who haven’t come across Robin. I’m sorry. The man is a Tableau legend and always knows a trick or two to get you round that surreal blending issue or why that Table Calc won’t add itself to your Filter Shelf.

Robin instinctively knew the solution, “well, Tableau’s using Show Me”. Uhh? No, it’s not? I’m dropping it in to the view. Robin has an amazing teaching patience and didn’t call me the imbecile that I deserved to be regarded as. Watch the gif closely as the mouse reaches the View, it changes to have the Show Me logo pop-up.

What Tableau is doing here is using the Show Me logic that decides what is the best way to visualise the data you have selected. What I would have expected Tableau to do is treat this drag-and-drop in the same way that it would if you would double click this new discrete field (in the gif example ‘Category’). I would expect Category to be added to the right of Region (the discrete value dictating the bars). But no, Show Me is assessing that the best way to visualise this data when dropping this new discrete pill in to the view is to use it on colour.

How else can you make use of this?

Well understanding what Tableau is going to do in certain situations is key so the table below details what else happens when the Show Me logo pops up as you drag something in to the view

Starting Point	Type of new pill	Result
Basic bar – one discrete pill on columns and one continuous pill on rows	Discrete	Stacked bar – new pill used as colour
Basic bar – one discrete pill on columns and one continuous pill on rows	Continuous	Shaded bar chart – new pill used as colour as well but as it is continuous the colour is a scale rather than categorised
Basic Line Chart – Date on Columns, Continuous pill on rows	Discrete	Depends – if there are less than 20 Discrete Values then Table picks colour, if there are more then it just add the new field to Detail and created multiple lines
Basic Line Chart – Date on Columns, Continuous pill on rows	Continuous	Colours the existing line by the new data field
Part to Whole Chart (Treemap, Packed Bubble or Pie Chart)	Discrete	Creates additional rows using the new data field but retains the marks type
Part to Whole Chart (Treemap, Packed Bubble or Pie Chart)	Continuous	Creates additional rows retaining the marks type also but uses Measure Names and Measure Values to form the new rows

So next time you use Tableau, go a little slower and see what Tableau is doing is you might uncover something that you think is normal behaviour but isn’t, it could lead you to other time savings!  

Keynote 4 – Data & Me - Hannah Fry

Maths is clean, data is not Mark J (a Wikipedia game) – you keep clicking on the 1st link on a Wikipedia page. Everything gets you back to ‘philosophy’. 95% of Wikipedia searches will take you through to Philosophy.

Mathematicians see 2 separate worlds – the real world and the mathematical world that can describe what is going on around you. Data provides the link between the two worlds. OK Cupid intentionally build in data collection elements. Men’s rating of women is a nice bell curve. Only 1 in 6 women rate men as above average HF started to focus on data with Google Trends – mistyping Google as Googlw is increasing massively. But why are people Google, Google anyway?

London Cycle Hire – spikes found in usage as people cycle downhill but won’t cycle as much back uphill Visualisation of transport mapping for every transport type in London is fantastic. Analysis done on worst place to have an issue and it was found to be Highbury & Islington. There are very few options of a different route if there is an issue there.

Data isn’t the end of the story it’s only the beginning Team collected gelocated tweets to see the second language in London and where they are located. A French community actually turned out to be one Frenchman tweeting a lot in just French. This led HF to think about the ‘Trough of Disillusionment’ and about what data you leave in or takeaway. Can lead to misleading conclusions.

Austerity issue – economic theory – you don’t grow when you have high debt as a country. New Zealand had only one year (1951) of high debt but didn’t weight the data so the New Zealand result skewed the results hugely. With mathematics you can only have absolute truths where data can be cut in different ways.

‘Street Bump’ app to detect when your car goes over a pot hole so the local government can find road quality issues. But this twisted the results as only the more affluent had smartphones and the inclination to download and use the app (or have a car to ride in). The challenge of encryption is tough but geolocation on your apps show your normal behaviour. Ie when you leave home and posting any gelocational tweets or messages.

The data revolution can give you new insights. A 17 year old boy wearing an Apple Watch showed his heartbeat was very high and remained high after exercise. He went to the doctors only to discover he was having heart and liver failure.

Prediction is the holy grail. Humans have a prediction addiction. “We can predict everything but the future”. HF doesn’t think anyone has made good predictions about the future. Likely is possible but exactly is not possible. Probability is the only way we can really predict what is likely to happen. 

Serial killer, Dr Harold Shipman chart of his patients’ deaths showed a massive increase in deaths in the afternoon in comparison to all other doctors. Police want to be able to search through events and find potential suspects. You are looking for trends that rely on very few assumptions. This can be used on infectious disease or bomb factories.

Web Data Connectors

Craig Bloodworth

There are many data sources that would help us understand the world around us that are still only exporting .csv so how do you automate these? Web Data Connector

There are resources out there. You need the Simulator and SDK. Don’t be afraid to google and use other people’s code.

The WDC allows you to use a connection that is hosted on a webpage (and been written by someone else) and works just like a Driver for a database.

HTML – the building blocks of the page

CSS – the styling of the page

Javascript – the engine that is running in the background.

In Javascript there are Variables (like a Parameter in Tableau), Objects (a Variable that has many properties) and Functions (the elements that do the work)

HTML DOM – native set of functions to Javascript

J Query – a bit quicker to write

Event Listeners – waiting for certain user actions like a click

Arrays – like a shopping list

AJAX – how to connect to an external web service

JSON – hopefully the data will be in JSON as it is made for Javascript

Loops & Logic – need to be able to loop through those

Recommended read: Javasccript & Jquery by Jon Duckett

Major components: 1. User interface, 2. API interactions & config, 3. Decalre column names and data types, 4. Build data table

Major Compnents in coded order: 1. Declare column  names and data types, 2. build data table, 3. API interactions & config, 4. User interface.

Build through adjusting an existing WDC to help you see the changes you are making to see the effects.

50 Shades of Data

Matt Francis

1666 Isaac Newton was looking at Optics when he discovered the Spectrum. Split out 7 colours, he chose 7 as there are 7 musical notes.

This led to the idea of the colour wheel. This developed in to colour theory.

Colour is a fantastic tool.

Las Vegas uses colour to draw your attention to suck the money out of your pockets.

Colour theory: 3 primary colours (fundamental). The colour wheel helps us pick complimentary colours. Complimentary colours are those that are on the opposite side of the colour wheel. Orange & Blue contrast nicely. Used in film posters a lot sun / sky.

When picking colours you have to be careful about the perception of colour. Colour should always enhance the visualisation.

People see colours differently. Should we use Red / Green? We understand Red is Bad and Green is Good. MF’s says yes you can use it. If you use it for yourself but if it goes public then you should avoid it. You can use high contrasting colour.

Use vischeck.com to check your visualisations for colour blind tests. Stepped colour makes it easier to use as tone can be distinguished.

Colour has associations and so can act as a short cut. Colour is one of the first things we see so those associations happen before we have read the content.

Colour highlighting has two types: 1 Biased highlighting (something is wrong) and 2 Impartial highlighting (interesting)

Colour can be used to bring emotions out. Downward bar chart to show gun deaths. Make it red and it adds the emotional element.

Colour Themes – Matt’s viz about fast food calorific content was perfected through colour choice. 

The colour matches the theme. Chart colours need to fit the theme and relate well to the theme of the overall dashboard.

Tabpal.co – upload an image and it lets you select a colour palette. Add these to your custom colour palettes in you preferences file (My Docs > My tableau Repository > Preference.tps)

Colour theory gets us 90% of the way there but we should play with colour too

Using the medium default colour palette is a nice tip to avoid overly contrasting colours.   

Data15 Keynote 2 – Daniel Pink

New style of work – free, independent from the Corporate fixed roles where you are passionate for what you do

Books include: “A whole new mind”, “Drive”

Today’s session we will look at “what motivates us” from a data driven perspective

Two types of knowledge: 1. Explicit knowledge (you know it and can show it) 2. Implicit knowledge (you know it but you don’t know you know it)

The laws of motivation are very evident. If you reward behaviour you get more of it. If you punish behaviour, you get less of it. You don’t need a hypothesis to test to understand this. If your unlying laws are a little off then you will misread situations. Punishing behaviour doesn’t always result in less of that behaviour.

4 economists did 9 tests in America and India. Everyone was treated the same way across a series of challenges except the reward they were given. Participants got 3 different levels of reward for good performance. India’s reward was a lot higher relatively. For mechanical tasks the highest reward group performed the best. “But once the task called for even rudimentary cognitive skill, a larger reward led to poorer performance”

Controlling contingent reward – if / then rewards – great for simple and short term work. Humans love rewards (it’s the definition of the word!). Rewards get our attention and focus. If / then rewards are not great for long term and complex tasks. Great for algorthymic tasks (ie follow a simple set of steps). If you are solving a creative task then you need an expansive view where you don’t have that laser-beam focus where you narrow your thinking. This contradicts our Implicit knowledge so this is why we don’t find this in every day society.

Animals are implicitly aware of fairness. If you have uneven pay levels, you will get rebellion. You have to pay people enough.

If you are getting people to do long term complex work, then you want them to stop thinking about the money

Autonomy, Mastery and Purpose – are the 3 key elements of work

1. Autonomy – let’s think about management – DP argues that management is a technology designed in the 1850s to produce improvements in task completion. You still need compliance but people don’t produce their great work when they are compliant, they do it when they are ‘engaged’. 2 in 10 people are actively disengaged in the workforce. You have to have sovereignty for your employees if you want engagement.

Zappos is the extremem version where there is no hierarchy or management.

Netflix is less extreme – their expense policy is “Act in Netflix’s best interest”

If you have autonomy on Time, Technique, Team and Task then this sovereignty gives you much higher engagement.

Atlassian – Australian Software company. Each week you have a ‘Ship It’ where devs work on what they want as long as they show it to the rest of company

Columbia Credit Union – one manager gives an hour each week to go and do something different than answer the phone. Called the ‘Genius Hour’

Manchester University – have ‘Friday Evening Experiments’ that “You’re allowed to do whatever you want as long as it is not boring” – no funding just try stuff. Led to Graphene discovery and a Nobel Prize.

So the message is carve out a few ‘Islands of autonomy’ – create space to try something different.

2. Mastery – “making progress in meaningful work” has been found as the key element. Feedback is vital to showing the progress is being made. Millennials have grown up where they have information at their fingertips the whole time. In organisations, the feedback disappears and is done every 6 months.

Two ideas – 1. weekly one-on-ones with a twist. Every monthly meeting ask ‘Love and Loathe’ rather than what you are working on. Career long term or Removing Barriers. 2. Progress Rituals – Humans create rituals to understand the world. Write down 3 good things that happened each day.

3. Purpose – How / Why – if you are struggling and find out the How it gets focus. Why gives the focus as it creates a purpose to deliver against. Have 2 fewer conversations about How and have 2 conversations about Why.

We have the chance to run organisations that work with the grain of how humans work.

Minority Report in Tableau

Allan Walker, Anya Ahern and Jeffrey Schaffer

Preface: There are not enough words that I can use to describe this session. The experimental work done by these amazing Tableau Zens is phenomenal. I have given hints at what the content is below but until you see some of the techniques the team have come up with, you can only use your imaginations. That is what this session is about. Using the fundamentals in Tableau and setting up experiences to allow you to interact with the tool in an entirely new way.

_____________________________________________________________________________ 

Where it started: Tableau Reader was all there used to be until Tableau Server arrived. But that wasn’t enough!

The Javascript API allows you so much access to try to do anything.

The CSS set-up allows you to do a whole lot of fun interaction.

The team have created the ability to take the visualisations from Tableau Server and create your own webpage.

Reveal.js – can create slide transitions but loads the visualisations up front

The team have used Leap Motion to interact with visualisations by just hand movements (and not with a mouse!)

Anya has taken inspiration from the James Bond film Skyfall to create real time crime, traffic, fire and weather data. All pulled together and controlled using voice and Leap Motion control.

I wish there was a video of this session as there is so much possible and it really is the future!

What the team wanted to do was to get the Minority Report all built in Tableau. And the team actually has. Wanted to build parallel processing, animated, movable and resizable and they nailed it.

Fighting Ebola with Tableau

Peter Gilks, Nelson Davis and John Mathis

The Tableau Foundation deployed Zen Masters and software to help fight Ebola

Tableau foundation began in 2012 and 2013 IPO of Tableau created funding for the Foundation. Aim 'to encourage the use of facts and analytical reasoning to solve the World's problems'. Does Mission Grants, Community Grants, Disaster Response and Employee Service & Giving.

The President of Guinea said “the Tableau Foundation work helped to transform the fight against Ebola”.

Volunteer network of Tableau experts eager to help non-profits to do more with data.

Background: 1^st outbreak in Guinea in December 2013. There have been c.3800 cases and c. 2800 deaths. The GDP per capita is less than $500. There is a population of 10.5 million people

5 stage process: 1. Contact Identification, 2. Contact Tracing, 3. Diagnosis, 4. Treatment, 5. Safe burials

Electronic Data management and Contact Tracing was a big step forward. Used basic smartphones to capture the data. Data was sent straight back to the system so live updates were possible. It helps to increase transparency around the tracing.

CommCare created a simplistic app to capture data and create on the ground recommendations. Visualising the data was not the best so in stepped Tableau.

Data flow was a daily upload that goes in to SQL Server and the data was held on Tableau Server. 

Data was not always as clean as hoped. Training of colleagues was continual so this meant data quality was challenged here as well. Transactional data also posed challenges. It was also in French so that was added fun.

Cultural challenges was tough as trying to develop reporting for use of those that haven’t had high reporting exposure adds to the challenge.

Quick fire dashboards were the requirement rather than running extensive usability testing.

Level of Detail would have made the transactional data a lot easier to handle and measure but the team were using 8.2.

Scaffolding was used to get round date issues but adding a data filter to look at whether the data was less than today to make sure the records are reduced as much as possible.

Doctors used the dashboards to work out how to allocate resources and could make as many data led decisions as much as possible because if it wasn’t optimal more people would contract Ebola

You can volunteer at https://servicecorps.tableaufoundation.org