Colourful nonsense: what does your data visualisation actually say?

The democratisation of data visualisation tools brought us two major advancements: we can make great analytical products, faster. We can deceive easier, too.

Previously a domain ruled by statisticians and IT departments, analytics have now opened up to anyone with a laptop. For marketers and managers alone, BI apps such as Tableau, QlikView, or MS Excel have become a commodity. Tools have matured too: programming fluency was overruled by drag-and-drop interfaces. A visually stunning chart is literally a click away. The software intelligently picks the graph type and the colour scheme for us. For the more ambitious users the adjustment options are plenty, although within the range of pre-programmed configurations. While some of these visual endeavours lead to great analytical products, some result in colourful nonsense.

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Stories from a Bar (Chart). Data Visualisation from Scratch P.2

Summary: Intro | A Simple Bar Chart | A Multi-Series Bar Chart

If this post was a painting, it would probably be one of Mark Ryden’s works: it seems I have just gone and done a one detailed blog post. The funny thing is that it’s about bar charts, and everything has already been said about bar charts. In fact a bar chart is a graph so simple, this post should never have been written: yet, the simpleness of a bar chart is actually it’s most dangerous trap. It’s very easy to overdo, and with so few elements it’s tempting to tweak or enhance at least some of them. So this blog post is, above all, about resistance. I will look at what – and why – constitutes as a good bar chart, what are the best practices, and how to fight the horror vacui of a simple plot. We will use D3.js and the blank canvas we have built with zero coding skills in the last post to create a reusable template of a simple bar graph, and then of a multi-series bar graph. This is part of a data visualisation with D3 series, throughout which we will create a set of graphics that can be easily re-purposed for data visualisation projects.

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D3 Canvas Setup with 0 Coding Skills. Data Visualisation from Scratch P.1

Summary: Intro | About D3.js | Initial Setup & Python Server | Canvas Setup

In the following series I will cover the basics of data visualisation. There are many data visualisation tools available (free & paid versions) on the market, so for an everyday analyst the knowledge of how to build graphs from scratch is not essential. However, most (if not all) of these pre-built tools fall short as soon as any customisation is required: it could be a graph type that is not supported, or the design that cannot be adjusted to follow the company branding guidelines. Therefore, there are cases when the knowledge of how to build something yourself is essential.

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