lachlanmacnish

An attempt to return the purpose and narrative to an important and beautifully-presented data-driven message, using solely R and ggplot.

Effective conveyance of a meaningful message through data visualisation first requires capturing a viewer’s limited attention. Design is a very important part of modern data visualisation, especially when trying to capture the attention of an audience who may not have a scientific understanding of data.

The following chart is incredible beautiful in design. It appears at first to convey a strong and meaningful message. It would look great on a page. It has purpose. However, in the designer’s attempt to design a visual masterpiece, have they lost the true meaning of their work, and failed to effectively convey their message?

It is indeed a beautiful work, but have the following considerations lead to a loss of meaning in their work?

• A Sankey diagram is not intended to be used to convey volumetric data, but rather to illustrate an activity flow. At first, this may seem a novel use of this chart type, but has it lead to further issues?
• Has the two-dimensional use of both height/width and length of the bar chart representing the “share of plastic inadequately managed” lost meaning, as the mind’s eye interprets only the length in absence of the bar width (a flow-on effect of the combined use with a Sankey diagram)?
• Has the normalised length of the bars lead to a perception that they are all equal, losing perspective of the true volume of waste produced and mismanaged?
• Has the use of a one-dimensional bubble plot to represent GDP lost precision and meaning as bubbles become difficult to interpret and exaggerated through bunching in an almost logarithmic pattern?
• Was there value in presenting so much data that it becomes difficult to read even the labels of the data categories, and the representation of data values so small that determining precision is impossible?

It is indeed a valiant and a visually beautiful attempt at conveying an important message, but something the focus on novel presentation can lead to a loss of the narrative and value of the designer’s work.

While far from as visually-appealing, could we re-arrange this data in a way that is easier to interpret, both by improving perceivable precision, and making it easier to correlate multivariate data? I have attempted to do so below, using only R and ggplot to create this data visualisation (without attempt at this stage to match the design beauty of its predecessor).

References

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