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Parallel Coordinates Plot
This type of visualisation is used for plotting multivariate, numerical data. Parallel Coordinates Plots are ideal for comparing many variables together and seeing the relationships between them. For example, if you had to compare an array of products with the same attributes (comparing computer or cars specs across different models).
In a Parallel Coordinates Plot, each variable is given its own axis and all the axes are placed in parallel to each other. Each axis can have a different scale, as each variable works off a different unit of measurement, or all the axes can be normalised to keep all the scales uniform. Values are plotted as a series of lines that connected across all the axes. This means that each line is a collection of points placed on each axis, that have all been connected together.
The order the axes are arranged in can impact the way how the reader understands the data. One reason for this is that the relationships between adjacent variables are easier to perceive, then for non-adjacent variables. So re-ordering the axes can help in discovering patterns or correlations across variables.
The downside to Parallel Coordinates Plots, is that they can become over-cluttered and therefore, illegible when they’re very data-dense. The best way to remedy this problem is through interactivity and a technique known as “Brushing”. Brushing highlights a selected line or collection of lines while fading out all the others. This allows you to isolate sections of the plot you’re interested in while filtering out the noise.
Tools to Generate Visualisation
Apache ECharts (JS)
D3.js Graph Gallery (D3.js)
Jason Davies’s Block (D3.js)
Mike Bostock’s Block (D3.js)
Python Graph Gallery (Python: Pandas, plotly)
R Graph Gallery (R + MASS)
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