Sankey charts are a type of diagram that can be used to visualize complex data. Created by merging the flowchart and the bubble chart, it works well when representing connections between multiple data sources. As the name suggests, the main flow direction of data comes from a top to the bottom by going clockwise.

Before diving into the process of creating a Sankey chart, one must first define its structure. A Sankey diagram usually consists of five elements: the source node, each connected node, a target node, and the weight of the connections.

Creating and applying Sankey charts in R
Sankey Chart (R) is a powerful tool in R. It allows you to create Sankey charts easily. The main functions used in Sankey charts in R are gSankey in plyr and geom_sankey, geom_connected_circle.

Here’s an example of a Sankey chart created in R:

“` R
library(plyr)

Create a data frame

df_sankey <- data.frame(
source = c(“A”, “A”, “A”, “B”, “B”),
target = c(“B”, “B”, “A”, “C”, “C”),
weight = c(4, 3, 2, 1, 1)
)

Create a Sankey chart

gsankey <- gSankey(dfsankey)
“`

geom_sankey creates a Sankey chart, g_sankey is the output of gSankey, the data you inserted into the function, and the size of the node represents the source and target node.

The source is the location where data flows to, target is where it flows, and weight is the strength of the flow. As mentioned above, the direction of data flow is from top to bottom.

Sankey Chart (R) in Python
In Python, pandas’ read_csv, plot, and geopandas can be used to create Sankey charts. For example:

“` python
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt

Load the data

dfsankey = pd.readcsv(‘sankey_data.csv’)

Use geopandas to load and transform data

dfsankey[‘X’] = dfsankey[‘geometry’].x
dfsankey[‘Y’] = dfsankey[‘geometry’].y

Use geopandas’ MultiPolygon to plot data

gdf = gpd.GeoDataFrame(dfsankey, geometry=gpd.pointsfromxy(dfsankey[‘X’], df_sankey[‘Y’]))
gdf.plot(color=’blue’)

Use matplotlib.pyplot to plot the Sankey chart

gsankey = gpd.sankey(dfsankey)

g_sankey.plot()
“`

Here, g_sankey is returned from gpd.sankey, showing where the data flows in the direction of flow.

Sankey Chart in Julia
In Julia, we can use DataFrames and ggraph. For instance:

“` julia
import DataFrames, GGraph

Load the data

src = load(“@my-source-data@/data.csv”)
tgt = load(“@my-target-data@/data.csv”)

Create a ggraph object

g = GGraph(newgraph(true))

Create a Sankey graph

g.sankey(
src = src
.index,
tgt = tgt
.index,
weight = tgt
.value
)

Add links to a graph

for i in colindex(src)
for j in colindex(tgt)
g.add(s = i, t = j, w = src[i]
end
end
end
end
“`

In this example, the sankey method from DataFrames.Sankey adds the connections between the source and target based on the weights.

In conclusion, Sankey diagrams are an excellent choice for visualizing complex data. They facilitate an understanding of connections and flows through various categories, thus making it easier for the user to interpret the complex data. By using R, Python, and Julia, we can easily generate Sankey charts based on our data, helping us learn in a simpler and clear way.

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.