Understanding Edge Colors in IG Graph Plots

In graph visualization, edges often require attention to differentiate them based on various attributes. For a biological transport network, where edge weights represent the width of connections between nodes, color-scaled edge plots can help convey this information effectively. In this article, we’ll explore how to achieve colorful edge plots using the cscale and viridis_pal functions from the scales and RColorBrewer packages in R.

Introduction to Edge Colors

When plotting edges with different attributes, such as width, color is a crucial aspect of visualization. Color-scaled edge plots can help convey this information effectively. In the case of biological transport networks, where edge weights represent connection widths, colorful edge plots can aid in understanding the network structure and connections between nodes.

Using cscale for Color Scaling

The cscale function from the scales package provides a convenient way to create color palettes based on continuous variables. This is particularly useful when working with edges in IG graph plots, where edge attributes like width need attention.

Creating a Custom Color Palette

To apply a custom color palette to your edges, you can use the seq_gradient_pal function from the scales package. Here’s an example:

# Load necessary libraries
library(igraph)
library(scales)

# Create a custom color palette for edge colors
color_palette <- seq_gradient_pal(low = "yellow", high = "red")

# Plot the graph with custom color palette
plot(graph, 
     vertex.label = V(graph)$name,
     edge.color = cscale(E(graph)$width, palette = color_palette))

In this code snippet:

1. We load the necessary libraries (igraph and scales) for graph plotting and creating color palettes.
2. We define a custom color palette using seq_gradient_pal. This function generates a sequence of colors based on specified values (in this case, “yellow” for low width and “red” for high width).
3. We apply the custom color palette to our edge plot using the cscale function.

Troubleshooting Common Issues

When working with cscale, it’s common to encounter two types of errors:

1. Warning about first element usage: This warning occurs when the first element of the length-out argument is used. In this case, the problem arises from passing a single value (like an edge width) directly to cscale. To resolve this issue, you should pass a sequence or range of values.

2. Error in palette(x): This error typically occurs when trying to apply a color palette to edges with different widths. The solution involves passing the count of all edges to the palette function to ensure consistent coloring for each edge width value.

Here’s how you can address these issues:

# Apply custom color palette to graph edges
plot(graph, 
     vertex.label = V(graph)$name,
     edge.color = cscale(E(graph)$width, palette = "viridis"),
     check.aesthetics = c("edge_color" = TRUE))

In this code snippet:

1. We use the check.aesthetics argument to ensure that all aesthetic names are valid (in this case, only "edge_color" is applied).
2. We fix the first element usage issue by specifying a range or sequence of values using seq. For example, seq(min(E(graph)$width), max(E(graph)$width), length.out = n), where n is the total number of edges in the graph.

Using viridis_pal

To apply the viridis_pal function to create a color palette for edge colors:

# Load necessary libraries
library(igraph)
library(RColorBrewer)

# Create a custom color palette for edge colors using viridis_pal
color_palette <- viridis_pal()

# Plot the graph with custom color palette
plot(graph, 
     vertex.label = V(graph)$name,
     edge.color = cscale(E(graph)$width, palette = color_palette))

In this code snippet:

1. We load necessary libraries (igraph and RColorBrewer) for graph plotting and creating a color palette.
2. We define a custom color palette using viridis_pal. This function generates a sequence of colors based on the specified palette.
3. We apply the custom color palette to our edge plot using the cscale function.

Alternative Solutions: Using colourvalues

Another alternative solution is using SymbolixAU’s colourvalues function:

# Load necessary libraries
library(igraph)
library(viridis)
library(colourvalues)

# Read in data frame with pairwise distances, 
# convert to lower triangular matrix, and stash the column names for later use.

g <- graph_from_adjacency_matrix(m, mode = "lower", weighted = TRUE, add.colnames = TRUE)
V(g)$name <- colnames(m)

# Assign color values to each edge
E(g)$color <- colour_values(E(g)$weight, palette = "viridis")

# Plot the graph with custom color palette
plot(graph, 
     vertex.label = V(graph)$name,
     edge.width = E(graph)$weight / 10,
     edge.label = E(graph)$weight,
     edge.color = E(graph)$color)

In this code snippet:

1. We load necessary libraries (igraph, viridis, and colourvalues) for graph plotting and creating a color palette.
2. We define a lower triangular matrix using pairwise distances.
3. We assign color values to each edge based on the specified colour_values function.

Conclusion

Color-scaled edge plots are an effective way to convey information about edges in graph visualizations, particularly for biological transport networks where edge widths represent connection widths. Using cscale and viridis_pal, you can create a custom color palette that suits your specific use case. By addressing common issues such as first element usage and palette application, we’ve provided practical solutions to help you achieve colorful edge plots with IG graph visualizations.

Remember to explore additional libraries and functions for more customization options in graph plotting, ensuring that your visualization effectively communicates the information you want to convey.

Last modified on 2024-11-03