Sankey diagrams, named after William Sankey, an engineer at Battersea Power Station who first used them in the late 19th century to visualize energy flows in steam engines, have evolved into a powerful tool for visualizing complex data flows and interconnections. These charts are not merely artistic representations but are deeply functional in dissecting the movement and transformation of data across systems and networks. This article explores how Sankey charts can be created and how they unveil the hidden streams of data, offering insights into various applications across different fields.
The Basics of Sankey Chart Creation
Creating a Sankey chart involves a few key steps:
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Data Collection: Gathering the necessary data is the first step. This typically includes the starting points, intermediate flows, and final destinations or outcomes of the data flow. For example, in an energy system, this might involve inputs (such as coal or natural gas), outputs (such as electricity), and losses or conversion efficiencies along the way.
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Data Wrangling: Once collected, the data must be organized to fit into a format that represents both magnitude and directionality. Each category (or ‘flow’) should have associated values indicating quantity or volume passing through it. This step also involves ensuring consistency in units across different flows.
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Sankey Diagram Construction: With your data ready, you can begin constructing your diagram using software like Tableau, Python libraries (like matplotlib), R packages (like
ggplot2), or online tools like RAWGraphs or AGL Graph Builder. The construction involves: laying out source nodes at one edge of your canvas; drawing path segments between these nodes representing flow sizes; allocating widths according to quantities flowing through each segment; placing destination nodes at opposite end with connections back to source segments if needed for feedback loops; adjusting node positions to ensure clarity without overlap where possible; labeling all nodes with relevant names/descriptors for ease of interpretation by viewer(s).- Software platforms offer varying levels of customization options such as colors schemes which highlight specific trends visually; text formatting so labels don’t get lost among lines; slicing capabilities allow users selectively focus their visualization around particular segments/nodes depending upon their analysis objectives etcetera etcetera !
- For users who prefer manual tweaking over automated settings due perhaps simplicity preference reasons but still desire professional look then leveraging HTML5 based solutions offer more control over element positioning via CSS3(CSS Stylesheet)+DOM manipulation programming languages such JavaScript provide greater flexibility since raw pixels can be manipulated without relying solely on predefined templates offered by most charting libraries at present time frame point discussed here today February 09th 2023 UTC+0* *Note that this does come with added complexity requirement level though)!
SankeyMaster
SankeyMaster is your go-to tool for creating complex Sankey charts . Easily enter data and create Sankey charts that accurately reveal intricate data relationships.
