Introduction to Alluvial Plots and ggalluvial

In the world of data visualization, alluvial plots have gained popularity in recent years due to their ability to effectively display complex sequences of events or activities. These plots are particularly useful for representing the flow of individuals through different stages or steps, which is a common scenario in various fields such as business process analysis, social network analysis, and more.

One popular R package used to create alluvial plots is ggalluvial, which provides an easy-to-use interface for generating these visualizations. In this article, we will delve into the world of ggalluvial and explore its capabilities in creating alluvial plots with nodes holding different values.

Understanding Alluvial Plots

An alluvial plot is a type of visualization that displays a flow or sequence of events through a series of stages or steps. Each step represents a node, and the flow between nodes represents the transition from one stage to another. The x-axis typically represents the input or starting point, while the y-axis represents the output or ending point.

Alluvial plots are useful for displaying complex sequences because they allow us to visualize the flow of individuals through different stages without having to create a separate plot for each step.

Understanding ggalluvial

ggalluvial is an R package that provides an interface for creating alluvial plots. It allows users to easily generate alluvial plots with various features and customization options, making it a popular choice among data analysts and visualization enthusiasts.

Some of the key features of ggalluvial include:

• Long-form data: ggalluvial requires long-form data, which means that each column represents a different stage or step. The x-axis represents the input or starting point, while the y-axis represents the output or ending point.
• Node values: Each node in the plot can have different values, making it useful for displaying complex sequences with multiple stages.

Creating Alluvial Plots with ggalluvial

To create an alluvial plot using ggalluvial, you will need to follow these steps:

Step 1: Prepare Your Data

First, you need to prepare your data in a long-form format. This means that each column should represent a different stage or step.

# Assuming df is the original dataset
df$acts_activity_id <- NULL  # Remove acts_activity_id column

# Convert the dataset to a long format using ggsankey::make_long
x <- df %>% 
  ggsankey::make_long(Step1, Step2, Step3, Step4, Step5)

Step 2: Create the Alluvial Plot

Next, you can create the alluvial plot using ggalluvial.

# Create the alluvial plot
ggplot(x, aes(x = x, next_x = next_x,
              node = node, next_node = next_node,
              fill = factor(node), label = node)) +
  geom_sankey(flow.alpha = 0.6, node.color = "gray30") +
  geom_sankey_label(size = 3, color = "white", fill = "gray40") +
  scale_fill_viridis_d() +
  theme_sankey(base_size = 18) +
  labs(x = NULL) +
  theme(legend.position = "none",
        plot.title = element_text(hjust = .5))

Tips and Variations

While creating alluvial plots with ggalluvial, there are a few things to keep in mind:

• Customization options: ggalluvial provides various customization options for the plot, including colors, node sizes, and labels.
• Node values: Each node can have different values, making it useful for displaying complex sequences with multiple stages.
• Flow alpha: The flow.alpha parameter controls the transparency of the arrows in the plot.

Conclusion

In conclusion, ggalluvial is a powerful R package that provides an easy-to-use interface for creating alluvial plots. By understanding the basics of alluvial plots and how to use ggalluvial, you can create complex visualizations that effectively display sequences of events or activities.

Whether you’re working with business process analysis, social network analysis, or other fields where complex sequences are common, ggalluvial is an excellent choice for creating informative and engaging alluvial plots.

Last modified on 2024-01-06