Unleashing the Power of Sankey Charts: Visualizing Complex Systems and Identifying Key Influencers

Sankey charts, also known as directed acyclic graphs (DAGs), are an excellent way to visualize complex systems and identify key influencers. DAGs are particularly useful for representing the flows of energy, materials, or information between different components of a system.

In this article, we will explore how to create Sankey charts using the popular data visualization library, ggplot2, and how to apply them to different types of systems.

Creating Sankey Charts with ggplot2

Sankey charts can be created using the ggplot2 package in R. Here’s an example of how to create a Sankey chart showing the flow of energy between different sectors:
“`perl
library(ggplot2)

Create data

energy_flows <- data.frame(
reactors = c(100, 50, 75),
electricity = c(100, 75, 50),
steel = c(50, 75, 100)
)

Create Sankey chart

gg Sankey(data = energyflows, direction = “both”) +
# Add labels
geomnode() +
geomarc() +
geomedge() +
scalexdiscrete(limits = c(100, 50, 75)) +
scaleydiscrete(limits = c(100, 75, 50))
“`
This code creates a Sankey chart showing the flow of energy between three sectors: reactors, electricity, and steel. The chart shows the amount of energy generated by each sector and how it is transformed into other forms of energy.

Applications of Sankey Charts

Sankey charts have a wide range of applications, from energy systems and supply chains to financial networks and social networks. Here are a few examples:

1. Energy Systems: Sankey charts can be used to visualize the flow of energy between different energy sources, such as renewable energy sources like solar, wind, and hydroelectric power.
2. Supply Chain: Sankey charts can be used to visualize the flow of materials between different stages of a supply chain, such as raw materials, intermediate goods, and finished goods.
3. Financial Networks: Sankey charts can be used to visualize the flow of financial assets between different players in a financial network, such as banks, corporations, and wealthy individuals.
4. Social Networks: Sankey charts can be used to visualize the flow of information or influence between different individuals or groups in a social network, such as Twitter, Facebook, or email.

Identifying Key Influencers

Sankey charts also offer a unique way to identify key influencers in a given system. For example, in a financial network, it might be interesting to identify the individuals or groups who control the majority of the assets or influence the decisions of other players in the network.

In R, we can use the ggplot2 package to analyze Sankey charts and identify key influencers. Here’s an example code that shows how to identify the top 10 individuals or groups in a financial network with the most influence:
“`scss
library(ggplot2)

Create a sample financial network Sankey chart

network <- data.frame(
node = c(“A”, “B”, “C”, “D”, “E”, “F”, “G”, “H”, “I”, “J”),
弧len = c(0, 10, 20, 15, 3, 20, 10, 5, 8, 6),
弧cap = c(10, 3, 2, 15, 50, 10, 20, 8, 4, 10)
)

Convert the network data into a directed acyclic graph (DAG)

dag <- as.DAG(network, direction = “both”)

Use ggplot2 to visualize the DAG

gg Sankey(data = dag, direction = “both”) +
scalexdiscrete(limits = names(dag$nodes$data)) +
scaleydiscrete(limits = names(dag$nodes$data)) +
geomnode() +
geomarc() +
geomedge() +
geomtext(aes(label = index), hjust = -0.2)

Loop through the DAG and identify the top 10 individuals with the most influence

“`

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.