Skip to contents

Add color palettes to bioregion cluster objects

Source: R/bioregion_colors.R
bioregion_colors.Rd

This function assigns colors to clusters in a bioregion.clusters object using color palettes from the rcartocolor package. It handles large numbers of clusters by assigning vivid colors to the most important clusters (based on size), grey shades to less important clusters, and optionally black to insignificant clusters.

Usage

bioregion_colors(
  clusters,
  palette = "Vivid",
  cluster_ordering = "n_sites",
  cutoff_insignificant = NULL
)

Arguments

clusters

An object of class bioregion.clusters, typically output from clustering functions such as netclu_infomap(), hclu_hierarclust(), or nhclu_pam().

palette

A character string indicating which color palette from rcartocolor to use. Default is "Vivid". Other qualitative palettes include "Bold", "Prism", "Safe", "Antique", and "Pastel".

cluster_ordering

A character string indicating the criterion for ranking clusters to determine color assignment priority. Options are:

  • "n_sites" (default): Rank by number of sites in each cluster

  • "n_species": Rank by number of species (bipartite networks only)

  • "n_both": Rank by combined sites + species (bipartite networks only)

Larger clusters (by the chosen criterion) receive vivid colors first.

cutoff_insignificant

A numeric value or NULL (default). When specified, clusters with values at or below this threshold (based on the cluster_ordering criterion) are considered insignificant and colored black, reducing visual clutter on maps. If NULL, all clusters receive distinct colors.

Value

A modified bioregion.clusters object with two additional elements:

  • colors: A list where each element corresponds to a partition (bioregionalization). Each list element is a data.frame with two columns:

    • cluster (character): Cluster identifier for that partition

    • color (character): Hex color code (e.g., "#FF5733")

  • clusters_colors: A data.frame with the same structure as the clusters element, but with cluster IDs replaced by their corresponding hex color codes for direct use in plotting functions.

Details

The function uses a two-step algorithm to assign colors:

Step 1: Identify insignificant clusters (if cutoff_insignificant is specified)

Insignificant clusters are those with a marginal size compared to others. This is a subjective threshold set by the user. All such clusters are assigned the color black (#000000) to minimize their visual impact. Clusters with values at or below the threshold are assigned black (#000000).

Step 2: Assign colors to significant clusters

Remaining clusters are ranked by the cluster_ordering criterion:

  • Top clusters (up to 12): Receive distinct colors from the chosen palette. This limit is because above 12 the human eye struggles to distinguish between colors.

  • Remaining clusters (beyond top 12): Receive shades of grey from light (#CCCCCC) to dark (#404040), maintaining visual distinction but with less prominence.

Multiple partitions: If the cluster object contains multiple partitions (e.g., from hierarchical clustering with different k values), colors are assigned independently for each partition. Each partition gets its own color scale optimized for the number of clusters in that partition.

Note

The colored cluster object can be directly used with map_bioregions(), which will automatically detect and apply the color scheme when present.

References

Color palettes from the rcartocolor package: Nowosad J (2018). "CARTOColors: color palettes inspired by CARTO." https://github.com/Nowosad/rcartocolor

See also

map_bioregions() for visualizing colored clusters on maps

Author

Boris Leroy (leroy.boris@gmail.com)
Maxime Lenormand (maxime.lenormand@inrae.fr)
Pierre Denelle (pierre.denelle@gmail.com)

Examples

data(fishmat)
data(fishsf)

# Basic example with few clusters
sim <- similarity(fishmat, metric = "Simpson")
clust <- netclu_greedy(sim)
clust_colored <- bioregion_colors(clust)
print(clust_colored)
#> Clustering results for algorithm : netclu_greedy 
#>  - Number of sites:  338 
#> Clustering results:
#>  - Number of partitions:  1 
#>  - Number of clusters:  4 
#>  - Color palette assigned:
#>    *  K_4 : 4 vivid colors 

if (FALSE) { # \dontrun{
# Map with automatic colors
map_bioregions(clust_colored, fishsf)

# Example with many clusters and cutoff
dissim <- similarity_to_dissimilarity(sim)
clust <- hclu_hierarclust(dissim,
                          optimal_tree_method = "best",
                          n_clust = 15)
clust_colored2 <- bioregion_colors(clust, 
                                   cluster_ordering = "n_sites",
                                   cutoff_insignificant = 1)
map_bioregions(clust_colored2, fishsf)

# Example with different palette
clust_colored3 <- bioregion_colors(clust, palette = "Bold")
map_bioregions(clust_colored3, fishsf)


# Example with bipartite network
clust_bip <- netclu_greedy(fishdf, bipartite = TRUE)
clust_bip_colored <- bioregion_colors(clust_bip, 
                                      cluster_ordering = "n_both")
map_bioregions(clust_bip_colored, fishsf)
                                      
} # }