Welcome to the second in the #FastFriday series.
For context, this is a weekly challenge that Ben Moss, Simona Loffredo and myself will be participating in. The concept is as follows - We have to create a visualisation created from a dataset which we see for the first time when we connect to Tableau. We're starting with a time limit of SIX (6) minutes to create a visualisation with meaning.
The objective? To learn about interpreting data better. To understand each other's approach when faced with a new data set.
And how well we viz under pressure.
We will screen record and then blog about our experiences.
Make sense? Check out my video below (complete with my mutterings while I build - again)
God. This one was tough! First of all, huge shout out to Chris Love for providing this week's dataset - It's been a hectic week for me, so I've only just been able to finish and post my effort! (the time is 10:44pm)
TIME: 6m 5s
Beginnings
Once more, the first thing I did was scroll across the data - this is a university data set, showing the rankings worldwide across different metrics. Given the time limit, I was already semi-panicking! I thought about measure names and measure values, as Ben and Simo did last week - my intentions were to create something simple and spend more time on dashboard design... Woops!
Looking at the number of universities gave me an idea of the amount of detail (as each row was one uni) but also adding in the year (changing it to a dimension first) - Then the heatmap was good to spot how the data was structured... Were all the universities always in the top 100? Were there others who didn't make it?
In my head, I was thinking of what I was going to do. A bump chart, a Top N filter, a slope chart with a highlight parameter... All crossed my mind. But I thought ranking was boring, so ended up using a map - cos maps are cool.
What shall I do now?
Given there was no geographical hierarchy, I thought I'd actually create one myself - using Groups. This was what really slowed me down.. The groups were (as I then realised) assigned by the data - in this case the raw latitude and longitudes. This meant that I couldn't simply intuitively add outliers into a group (as I found with the University of Sussex, which was a Null) - After grouping, renaming and being happy, I had not much time left to create something using these groups.
So I went to my trusted small multiple scatter! I mapped each region by year, and looked at how the teaching scores and research scores a) correlated and b) how they'd changed. This was quite interesting I thought, and the viz itself didn't look awful.
Thoughts
I did end up spending the last minute or two lamenting my approach to groups, while absentmindedly adding a title and doing some formatting things.
I did end up spending the last minute or two lamenting my approach to groups, while absentmindedly adding a title and doing some formatting things.
I attribute this slowness to a semi-frustrating end of the week, as well as the time being half past 10 at night etc etc, excuses excuses. I really enjoy this project, the panic is real - especially when you're running against the clock. It makes me think how much I love the iterative process of Tableau - but in order to work efficiently AND effectively, I think this exercise is a fantastic way of working toward that end.
Check out Ben & Simo's efforts here:
Ben Moss: https://benjnmoss.wordpress.com/2016/06/03/fastfriday-6-minute-vizzes-episode-2/
Simona Loffredo: https://thevizconnoisseur.wordpress.com/2016/06/03/fastfriday-6-minute-vizzes-2nd-episode/
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