Flowing Data laid down the challenge to visual the sexual health and behaviour dataset from Indiana University. Seemed like an interesting topic…. Here’s my attempt:
London 2012 published the Olympic ticket prices today. There’s lots of good value seats to be had. I took the data from the London 2012 site and built this viz that you can use to find out how much your favourite sport will cost you:
Note: this viz does not include the Opening or Closing ceremonies. Top price seats for those will set you back a max of £2,012 and £1,500 respectively! Ouch
I see that Interflora is suing M&S over the latter’s keyword piggy-back tactic. I notice that this is also happening in the Fast Analytics world:
Warning! This post discusses disc golf. If you’ve not heard of it, just imagine I’m referring to “normal” golf.
Here’s my leaderboards, made using their data. The first shows the ranks of the players, showing you how many birdies and bogies they got. You can click on a player, or multiple players, to see how they did in each round (this viz will be updated as round scores are available; you may only see Round 1 scores for a day or so):
The second viz shows how we can use the data to analyse things other than players. Because we know how everybody scored on every hole, we can adapt the viz to analyse each hole, and see which played the hardest or easiest:
Both of these dashboards can be seen in full screen. Click here for the player dashboard, and here for the hole-by-hole dashboard.
There’s a chart doing the rounds that shows who is suing whom in the telecoms industries (eg at the Guardian). David McCandelish has improved it nicely here, and asked the question of whether companies who’s revenue is going down are more litigious than those on the rise. Well, I took his data from here and created the charts below.
There are lots of caveats: I excluded Smartphone Technologiess LLC because the data didn’t say if their revenue was up or down. That’s a shame, because they’ve got the most lawsuits open. Also, we don’t know if the revenue was going up or down when the lawsuit was started.
PS – for Tableau geeks, the tooltips on the chart are using my Conditional Formatting trick (click here) to make the text colour change depending upon Revenue Direction.
[Update: in theory, v6.1.1 has made it easier to create the bars described in this post by using REPLACE() and SPACE(). However, at time of writing (19 Aug 2011) there is a problem: while the solution works fine in Tableau Desktop, the view won’t display on Tableau Public – have a look at the “Using REPLACE and SPACE() tab in the embedded workbook below. The calculations have also been optimised using parameters.]
Consider this map:
It highlights one of many situations when pie charts don’t work. The smallest pies are so small you have no chance of making out the size of the slices. And what if I wanted to show Profits on the viz, as well as Sales and Product Type?
The Tableau guys have come up with a way of using Gantt Bars to emulate a bar chart and maybe solve some of these problems. This works okay, but has its own flaws; mainly that it’s still hard to make out the individual bar segments.
Well, how about using a bar chart inside a tooltip? Surely you can’t do that? Well, yes you can:
The viz above solves several problems. The circle size is based on Sales, and Profit by colour. And the tooltip shows the proportion of sales of each Product Type. With just a mouse-hover, you’ve revealed a new dimension on the data. Three dimensions on one map viz – bingo!
How do we do this? Well, first we need to decide if our dataset is suitable. First of all, you need a known number of values in your tooltip-chart dimension, because we’ll be creating a calculated field each one. In the above, we only have four Product Types, so that’s a suitable candidate. Secondly, there is a maximum length that the bar can be. Too long and it will wrap around in the tooltip. As we’ll see, the bar is built using a text string, so we can’t just have an unlimited bar with a value in the 100s. Percentages work best as they are going to have a maximum value of 100.
For each Product Type, we need two calculated fields. One works out the percentage of sales of that product type:
and the other works out the length of the bar. How do you build a bar out of text? Simple: use the ALT+219 ASCII character. That’s this one: ?. You create it by pressing the ALT key and then 219 on the Number Pad (using a laptop? Copy and paste the one in this blog instead!). We need to create a parameter – [Bar] – which is one loooong string of 100 ? characters, and then create a calculated field that is a subset of that, based upon the % value in your other calculated field. Here’s the text of that field:
LEFT([Bar],ROUND([% coffee sales]))
Now we have two calculated fields for each Product Type. Drop them all onto the ever-so-useful Level of Detail shelf, and now you can build your tooltip as follows:
You’ve done it! You’ve now become a tooltip genius. Using the skills we’ve learned in our other tooltip posts (basics here, and conditional formatting here), your tooltips can be as jazzy as your vizzes themselves!
Those of you who are big Excel users may know where the inspiration comes from. This post gives a huge Tip o’ The Hat to the posts from Juice Analytics, Daily Dose of Excel, Jon Peltier, and Chandoo that showed us all how to do in-cell charts in Excel in the last few years. Also, thanks to Richard Leeke on the Tableau forum for giving me the last piece in the jigsaw for this idea: Excel has a REPT function that can be used to create the long string. Tableau doesn’t. Richard pointed out that the only way round this is to type the string out manually and take a substring of it. Nice!
I played in the Croydon Cyclone last weekend, a part of the British Disc Golf Association national tour. One hole’s layout was controversial. It’s short, a distance that almost any player can reach from the tee. The designers spiced things up by surrounding the basket and most of the fairway with Out-of-Bounds areas that incurred a one-shot penalty, and required a rethrow, from the tee. What had in previous years been a really easy hole became a cruel score card nightmare. I took the stats of all the scores from the score cards and did the viz below.
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