I’m researching chart types for my data works.
Sankey Diagrams display flows and their quantities in proportion to one another. The width of the arrows or lines are used to show their magnitudes, so the bigger the arrow, the larger the quantity of flow. Flow arrows or lines can combine together or split through their paths on each stage of a process. Colour can be used to divide the diagram into different categories or to show the transition from one state of the process to another.
Typically, Sankey Diagrams are used to visually show the transfer of energy, money or materials, but they can be used to show the flow of any isolated system process.
Parallel Set charts are similar to Sankey Diagrams in the way they show flow and proportions. However, Parallel Sets don’t use arrows and they divide the flow-path at each displayed line-set.
Each line-set corresponds to a dimension/dataset, which its values/categories are represented in each line divide in that line-set. The width of each line and the flow-path that stems from it is determined by the proportional fraction of the category total. Each flow-path can be coloured to show and compare the distribution between different categories.
Parallel Coordinates Plots
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.
As known as: Spider Chart, Web Chart, Polar Chart, Star Plots.
Radar Charts are a way of comparing multiple quantitative variables. This makes them useful for seeing which variables have similar values or if there are any outliers amongst each variable. Radar Charts are also useful for seeing which variables are scoring high or low within a dataset, making them ideal for displaying performance.
Each variable is provided with an axis that starts from the centre. All axes are arranged radially, with equal distances between each other, while maintaining the same scale between all axes. Grid lines that connect from axis-to-axis are often used as a guide. Each variable value is plotted along its individual axis and all the variables in a dataset and connected together to form a polygon.
However, there are some major flaws with Radar Charts:
Having multiple polygons in one Radar Chart makes it hard to read, confusing and too cluttered. Especially if the polygons are filled in, as the top polygon covers all the other polygons underneath it.
Having too many variables creates too many axes and can also make the chart hard to read and complicated. So it’s good practice to keep Radar Charts simple and limit the number of variables used.
Another flaw with Radar Charts is that they’re not so good for comparing values across each variable. Even with the aid of the spiderweb-like grid guide. Comparing values all on a single straight axis is much easier.