Composing data visualisations

How does one create new data visualisations? Apart from the art, is there a science to it?

Let’s explore a few popular charts. We have the vertical bar graph small-vertical-bar or the horizontal bar graph small-horizontal-bar. The stacked bar small-stacked-bar. The variwide or Marimekko chart small-variwide. The waterfall small-waterfall. The scatterplot small-scatterplot. The treemap small-treemap. And so on.

The first thing you’ll observe is that all of these are a series of rectangles. (We’re treating the dots on the scatterplot as little squares.) The only thing that varies across these charts is the position and size of the rectangles – and the colour as well.

That gives us a hint. Perhaps there are many ways of creating visualisations just by changing the position, size and colour of rectangles. For example the horizontal bar graph small-horizontal-bar can be constructed as follows:

  • The x position is constant for each rectangle. It starts at zero.
  • The width is proportional to the value of the series
  • The y position is proportional to the index of the values (1,2,3,…)
  • The height is constant for each of the bars
  • The colour is constant too.

Whereas, if we look at a horizontal stacked bar small-horizontal-stack, then:

  • The x position is proportional to the cumulative value of the series.
  • The width is proportional to the value of the series
  • The y position is constant at zero
  • The height is constant for each of the bars
  • The colour is based on the index of the values (distinct colours labelled 1,2,3,…)

Generalising this, we can construct a table like this that shows the structure of various visualisations:

Chart x width y height colour
Vertical bar chart index constant constant value constant
Stacked bar index constant cumulative value index
Waterfall index constant cumulative value constant
Scatterplot value constant value constant index
Horizontal bar chart constant value index constant constant
Variwide cumulative value constant value constant

That leads to a line of thought: what if we tweaked this table? Would we get new visualisations that might be interesting?

Let’s experiment with a few.

waterfall-variwideWhat if we took the waterfall chart, and made the constant widths proportional to value, instead? The waterfall chart shows a cumulative series of values (e.g. percentages). This new chart – a cascade chart – allows us to depict each bar’s relative importance as well as value.

boxesWhat if we kept the width, height and y constant, and just let the x values vary as the index? It would just be a row of boxes. But we’d have the option of colouring them with a value. This could be useful when showing performance along a discrete series (e.g. attendance by weekday).

boxesWhat if we allowed the x, y, width, height and colour to vary with a different value? The graph looks like a scatterplot, but every dimension here – position, size,  colour, even aspect ratio – indicates some informational measure.

This chart can, for example, show the position and spread of two metrics. For example, if the X-axis were sales, and the Y-axis were price, each bar could be the distribution of price and sales in a branch, with the colour indicating growth of the branch.

Just using the combinations discussed above, there are 75 possible types of visualisations – many of which are meaningful in different circumstances. And this is just using rectangles.

What we’ve done here is mapped data to attributes of a visualisation. This is part of a generalised approach to graphics, similar to that covered by Leland Wilkinson’s Grammar of Graphics and implemented in libraries like ggplot2 or D3. Once we establish that basic concept – that a chart is a mapping of attributes to data – the variety of charts you’ll be able to create is unlimited, and you move from being a user of charts to a composer of data-driven visualisations.

Leave a Reply