Rich Country, Rich Citizens? | #MakeOverMonday 2020 Week 7

A trifecta of MakeOverMondays complete! This time I went back to the bar for a visual essay examining country wealth. The dataset showed 2019 wealth values per country and was visualised by Credit Suisse like so:

Which is actually a pretty inviting visual just on its own, though proves to be difficult to interpret if you’re interested in small countries.

After some playing around, I mixed in some population data and began to look at it from the point of view of a citizen. Does living in a rich country mean you might be rich? I then brought in data around Gini Coefficients to inform the viewer about income inequality (something I wasn’t totally familiar with before – so I definitely learned something new).

The result was a four bar chart  visual essay. Lots of text – maybe too much, but I don’t mind it that much.

A new take on international education student enrolment data by region: Experimenting with Tableau mapping

The Department of Education, Skills and Employment provide the international education sector in Australia with a large amount of very useful data on international students studying within our borders. Much of it is provided in a number of formats – from pivot tables made available through Austrade’s Market Information Package to a range of data visualisations on the Department’s website itself.

Being a data visualisation aficionado and and analytics working in this field for nearly nine years now, I have used these resources heavily. However, one area that I had not particularly visited often was the Department’s international student data at a sub-State level, where they break down international student numbers by local government area (LGA).

The interface looks like the following:


Regional Data Website Screen Shot


Now while this interface is all well and good if you want to examine regions one by one, it’s not super handy if you want to compare across regions at the same time. Fortunately, I spied a handy “Get the data” link in the top right.

I happened to have a Saturday where I had hours to kill, so I decided to see if I could teach myself a bit more about Tableau’s great mapping capability and breathe further life into the great regional data made available by the Department. The resulting basic data visualisation is embedded below.

Visually-wise, the above is nothing super special, but I like to think its a bit more accessible than the original data and makes it a bit more useful for the intended audience. I’ve already thought of about a million ways I can make this better (e.g dot density maps!) and if I get time, I’ll look to improve upon it.

You’ll note that the maps seemed to indicate concentration in certain LGAs. This is probably to be expected, because I believe enrolments will reflect the location of education institutions rather than individual students. I haven’t checked every metric, but the few I have checked seem to reflect the data available on the Department’s website.

Furthermore, I’ll be back soon with some information on the steps I went through to wrangle the data into the correct shape to build this visualisation along with the information on the geographical shapes file. Also there’s a few more advancements I can easily make in terms of filters and also the visualisation itself. So stay tuned.

Americans at Peace and War | #MakeOverMonday 2020 Week 6

Two weeks in a row now that I’ve managed to get something done for MakeOverMonday. This week’s challenge had us rearranging the visuals of this Washington Post article by Philip Bump titled “Nearly a quarter of Americans have never experienced the U.S. in peace time“.

I’d been procrastinating all week, checking out many great vizzes done by the Tableau community when all of a sudden an alternate design came to me like a bolt from the blue. How about doing something with the inverse of the provided data? 

The data provided simply a ‘birth year’ dimension and a ‘% at war’ type measure, but if I simply took that measure from 1, I’d get a ‘% at peace’ variable and I could then do some fun stuff comparing the two proportions. I also decided to conceptualise time in terms of age rather than birth year, as I think it’s more natural to think of oneself in terms of years on Earth.

The result is the below visualisation which uses stacked region charts. I picked up a great little tip from Sean Miller’s website where I learned a nice little LOD technique to colour between the two regions.

As usual, this viz is better looked at on a standard computer desktop rather than mobiles or tablets. I still haven’t mastered presenting in these formats. You can also look at this direct via my Tableau Public gallery.

Leave our Bond alone? | #MakeOverMonday 2020 Week 5

So I finally managed to grab some free time to contribute something to the MakeOverMonday social data project. I’ve been a fan for many years and have often wanted to contribute – and finally I’ve done something that I’m relatively happy with. This is my data story analysing potential attitudes to changes in the James Bond character in terms of Brexit affiliations.

You can also view this directly on my Tableau Public profile.

When I get a second later, I’ll update this post with some information on my design and insight choices.

Tableau tricks: Adding colour to geomaps by continent or region

Tableau is a great tool for data visualisation. One major selling point of the product is its excellent mapping tools which make building visualisation fun and interpreting data a hell of a lot easier than in a flat table.

Recently, I was attempting to replicate a neat visualisation I saw on the Guardian’s data blog. Simply put, I wanted to measure some data by country but colour code the data by region as well. A trip through Tableau’s detailed online help and forums only turned up solutions that were either way too complicated or not quite suited to what I was chasing.

Essentially, I just wanted to map a variable by country and then colour those variables by the continent or region the country belong to. So, for example, all data points that represented a country in Asia got a particular colour, whereas any data points in the Americas got a different colour again.

I’ll freely admit that my skills in Tableau are developing. I spent about a day researching the issue until I stumbled across obvious help in the Tableau mapping tutorial. The dominoes began falling in my head around the 13 minute mark of this Tableau help video and I could complete my task.

The solution is remarkably easy.

First bring across the variable you want to measure in Tableau on a geomap. Using Tableau’s example Superstore dataset, I’ve brought the state variable across into the main view.

Second, you just need to simply need to drag across the variable you want to colour by. Using the same Superstore example, I’ll drag region on to the colour mark.

Now, you should see the different regions of the United States distinguished by different colours.

From there, you can then do all sorts of fun things like adjust the size of the bubbles by other variables. I just have to move the appropriate variable from the data pane on to the size option in the marks pain.

Here’s me using the sales variable to control the size of the circles above (also I’ve put a border on the circles for aesthetic reasons).

Now, to do this in terms of global data, you simply need a way to link countries with regions. If you have a dataset with a variable like countries, all you need to do is map those countries to a region and include that data in your Tableau project. This can easily be done using a standard UN country/region dataset.

Philip Burger has handily made one suitable for Tableau available via his website (nice one Phil!).

Importing the dataset into my Tableau project means I can link it up with my source data and begin the process of making a cool looking geomap visualisation similar to the one used in The Guardian example above.

Linking my tableau output data to the UN data set based on country. I’ve used a LEFT JOIN meaning all data in my source table is outputted and only matching country names are returned in the UN dataset.

And voila, by using the technique above, I can make nice looking geographical maps like the image below.

Pretty neat, huh?