Category: Visualization

Cole Nussbaumer Knaflic takes a fairly simple table and creates eight visuals out of it:

STEP 1: When I encounter this table, I start reading and scanning down columns and across rows. In terms of specific observations, I might start by noticing that the majority of accounts are in Tiers B and C, while Tiers A and A+—though they don’t make up a huge number (or percentage) of accounts—do make up a meaningful amount of revenue. In terms of questions, I wonder if the tiers are in order: I would think A+ belongs above A and am confused that they don’t appear that way in the table (perhaps due to alphabetical sorting?).

This is a really nice practical exercise if you want to learn how to apply the right visuals to tell your story.

Adrian Colyer summarizes an interesting IEEE paper:

So far this week we’ve seen how to create all sorts of fantastic interactive visualisations, and taken a look at what data analysts actually do when they do ‘exploratory data analysis.’

To round off the week today’s choice is a recent paper on an age-old topic: what visualisation should I use?

No prizes for guessing “it depends!”

…the effectiveness of a visualization depends on several factors including task at the hand, and data attributes and datasets visualized.

Is this the paper to finally settle the age-old debate surrounding pie-charts??

The results were very interesting, though as an official Pie Chart Hater, I would point out that in none of their results was a pie chart ever better than a bar/column chart. There are cases where it works out okay, but if it’s never better and often worse than something, I’d rather use the alternative.

Elizabeth Ricks shows how to create shaded ranges in Excel:

We can see there’s clear seasonality in this business—overall volume is highest in the summer and each outing type generally follows the same monthly pattern. Let’s say you manage the Family rentals and you’d like to compare your monthly volume to what you’re seeing across the entire fleet. 

For the purpose of this tactical illustration, let’s assume the shape of the data—relative peaks and valleys—is more important than the specifics of each category individually. If that’s the case, I can simplify by showing a shaded region to depict the range of absolute passengers each month.

This technique is excellent when you have a large number of lines but only care about one versus the norm, and individual lines would be too distracting.