Colouring outside the box


After a long internal monologue on whether “I should or shouldn’t”; I decided to just get over myself so here I am with my very first blogpost. There will be more to come about myself and how I got started on my Tableau journey but for now I wanted to write a few words about one of my recent publications and give back to the awesome Tableau community that has been such an inspiration to me in the last couple of weeks & months. (I know I should probably say that more often but ask for your patience and that you keep in mind that I’m Belgian; most people I know will say we are a bit more reserved at the outset.)

For Makeover Monday 2018 WK42 we were asked to recreate a visualization showing how women are being represented in the US House of Representatives and I’ll be honest, that particular week I really struggled with finding my viz mojo. After seeing some of the great work that was being posted out early on, (note to self: need to stop doing that) I knew I wanted to challenge myself to do something different but didn’t really know where to start. I was also more interested in finding out how some of the other countries (including my own) compared to the US, so I decided to colour outside the box and looked for data on all OECD countries to get a more global view.

Next step was finding some inspiration for how to visualize the data; and then I remembered reading a blogpost by Kevin Flerlage on how to create polygon-like vizzes without the actual polygons. Before I get into some of the specifics of how my Viz came to be I should mention that I asked for Kevin’s permission to reference some of the content from his blogpost ‘No Polygons‘. My intention is certainly not to rewrite the entire blog but merely give you the highlights of where my approach is the same/different from Kevin’s original post.

No Polygons’ with a twist

If you haven’t read the blog post yet I recommend you do before continuing as that’ll make it easier to follow.

(1) Image prep

Following the original post, I started with using Powerpoint & Paint:net to create 4 individual shapes (petals) for each quadrant (each quadrant represents a separate measure).

image prep

In my original idea each set of shapes was going to represent a country and each quadrant would show one of the measures below.

  • % of total population that is female
  • % of total population that is male
  • % seats in national parliaments that are taken by women
  • % Seats in national parliaments that are taken by men

(2) Data prep

Then as per the original blog, I added X & Y values to the dataset to lay out the shapes/petals per quadrant and included predefined columns & rows to create the small multiples.

data prep

(3) Layering the shapes

Round 1

My first go at this was following Kevin’s idea and putting one shape & measure in each quadrant but felt the result wasn’t very compelling in showing how a gender was over/under represented. And what I really wanted to highlight with this viz was the inequality between men and women.

The image below is a mock-up of how the data for Japan would have been visualized.

  1. % of total population that is female
  2. % seats in national parliaments that are taken by women
  3. % of total population that is male
  4. % seats in national parliaments that are taken by men
first design

Round 2

For my second try I only used quadrants 1 and 4 and let the shapes overlap based on gender.

layering shapes

The result again was not what I was looking for as you couldn’t distinguish between the overlapping shapes.

  1. % of total population that is female + % seats in national parliaments that are taken by women
  2. ~
  3. ~
  4. % of total population that is male + % seats in national parliaments that are taken by men
second design

Round 3

And this is where the ah-ha moment comes in – using opacity on the colours. I went through several combinations to get the right blend and found you’ll get the best results when using use a pale colour on one set of measures (population) and blend it with a dark striking colour (seats in parliament) for the second set. As you can see it creates a nice overlap and you can still make out 4 distinct shapes.

round 3

Adding the tooltip

And finally, I used pretty much the same approach for adding all 4 measures to a single tool tip by adding an overlapping blank shape on a dual axis (on Rows/Y). Instead of creating formulas I had already added the values to the data set for each individual record. In hindsight it’s probably not the most efficient solution but it works.

(4) The result

Click the image below to view the interactive version of my viz on Tableau Public.

Gender Inequality in Political Representation

The data

In case you’re interested in creating your own visualization, I’m sharing the data sources I used and the dataset I created for Tableau.

Final thoughts … #VizSpiration

Ever since I read Neil Richards post ‘Haven’t I seen this somewhere before, I make a very conscious effort to give credit where credit is due but sometimes even I forget about these unconscious sparks that intentionally creep into our brains and sometimes weeks or even months later trigger an idea without you being aware. So even though the initial idea came from Kevin’s post, I would be remiss if I didn’t mention the awesome viz by Ivett Kovács that I’m sure has been the inspiration for many visualizations since. Thanks for reminding me Neil.

Click the image below to view the interactive version of Ivett’s viz on Tableau Public.

Gender&Ethnic disparities in Tech Companies

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