Sankey charts are a type of data visualization that displays flows and connections between different entities or categories in a visually appealing manner. They are named after Captain Matthew自主Sankey, a British engineer who first used this type of chart in the 19th century to represent the flow of energy through different parts of a steam engine.

Sankey charts are a type of flow chart that displays the magnitude of a continuous variable between different categories or nodes. The width of each link (or arrow) in the chart represents the magnitude of the flow between the nodes. This makes it easy to see which categories or nodes are receiving or sending the most data, which can be very useful in a variety of applications.

Sankey charts are commonly used in fields such as energy, economics, and environmental science. For example, an energy analyst could use a Sankey chart to visualize the flow of energy through a power grid, with nodes representing different sources of energy (such as coal, hydroelectric, and wind) and the width of the links representing the amount of energy being sent from one source to another.

Another example is in the field of economics. An economist could use a Sankey chart to visualize the flow of goods and services between countries, with nodes representing the countries and the width of the links representing the volume of trade between them.

In this article, I will explore the creation and applications of Sankey charts in detail. Although it’s impossible for me to provide a visual representation here, I will discuss how you can create one using various tools like Microsoft Excel, R, Python, and other data visualization software.

Tools for Creating Sankey Charts

Microsoft Excel: Excel is a popular choice for creating simple Sankey charts, especially for those who are already familiar with the platform. You can add the “Sankey Diagram” tool in Excel add-ins or utilize the Sankey chart template available through Office Add-ins.

R: Data analysts often prefer R for its extensive library of visualization packages. Libraries like sankeydiag and ggraph with ggplot2 provide flexible and powerful tools for creating and customizing Sankey diagrams.

Python: For Python users, there is a variety of libraries like pySankey, sankeydiagram, pyFlowchart, and networkx with post-processing capabilities for tweaking the visual details. altair is another option that supports Sankey diagrams through declarative syntax.

Key Features of Sankey Charts

1. Flow Visualization: The width of each link represents the magnitude of the flow. This makes it easy to compare the volumes of data or materials being transferred between different nodes.

2. Customizability: Sankey charts offer a high degree of customization, allowing for adjustments in color, opacity, and other visual attributes to enhance the aesthetic and informational value of the diagram.

3. Multidirectional Flows: Unlike traditional flow charts, Sankey charts can represent flows in both directions, which is particularly useful when depicting bi-directional relationships between categories.

4. Complexity Management: Tools and libraries mentioned above help manage the complexity introduced by a large number of nodes and links, making large Sankey charts comprehensible.

Building a Sankey Chart

For a step-by-step guide, let’s consider a simplified Sankey chart with three nodes: Source A, Source B, and Destination C. Here’s how to create it using both R (using the sankeydiag package) and Python with pySankey:

1. Data Preparation: Create a dataframe with columns for from (sources), to (destinations), and value (magnitude of flow).

R
data <- data.frame(from = c("A", "B", "B"),
to = c("C", "C", "C"),
value = c(100, 200, 300))


“`python
import pandas as pd

data = pd.DataFrame({
‘from’: [‘A’, ‘B’, ‘B’],
‘to’: [‘C’, ‘C’, ‘C’],
‘value’: [100, 200, 300]
})
“`

2. Creating the Chart: Use the appropriate libraries to plot the Sankey chart.

R:

“`r
library(sankeydiag)

sankey <- Sankey(title = “Sankey Chart Example”,
start.txt = c(“From”), end.txt = c(“To”),
start.angle = 180, end.angle = -90,
text.angle = 270)
sankey
“`

Python:

“`python
from pySankey import Sankey
import matplotlib.pyplot as plt

s = Sankey(start=(‘From’), end=(‘To’), showText=True, textRotation=270)
s.addSankey(data[‘value’], data[‘from’], data[‘to’], s=10)
s.makeSankey()
plt.show()
“`

Applications and Benefits

• Business Intelligence: Identify major sources and destinations in supply chains or identify high-value data streams in a digital ecosystem.
• Environmental Analysis: Analyze flows of natural resources, such as water, energy, and emissions, across different regions or industries.
• Engineering and Energy: Visualize energy generation, consumption, and transmission in power grids or other systems.
• Sociological Research: Study migration patterns, relationship flows, or data transmission in networked information systems.

Conclusion

Sankey charts offer a unique and powerful way to visualize complex flows and connections, making them an indispensable tool in data analysis and presentation. Whether you’re working in energy analysis, economics, engineering, or other fields, a well-designed Sankey chart can provide valuable insights by highlighting the magnitude and direction of data flow. With a myriad of tools and libraries available, creating these charts has never been easier, allowing you to unlock new levels of detail and insight into your data.

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