🌐 BiVariate_Maps

This repository provides a set of flexible and simple R functions to generate bivariate raster and polygon maps using custom color palettes. Designed with ecological modeling and spatial analysis in mind, the toolkit enables users to explore and visualize spatial covariation between two continuous variables.

🚀 Features

• BiColor(): Generate a perceptually blended bivariate color scale using two user-defined gradients and a neutral center.
• VarClass(): Classify continuous raster or vector values into quantile-based discrete classes.
• biRaster(): Combine two SpatRaster layers into a three-band bivariate map, encoding their spatial relationship into RGB color combinations.
• biPol(): Assign bivariate colors to polygon features (sf) based on attribute values, ideal for choropleth-style displays.
• biLegend(): Render a clean, intuitive color legend showing directional strength of each variable and their interaction.

🧭 Applications

These tools are well-suited for:

• Visualizing habitat suitability surfaces
  
• Mapping joint patterns of biodiversity and environmental gradients
  
• Communicating spatial overlap of two phenomena in conservation planning, land management, or epidemiology

:exclamation: Requirements

Most of the code relies on basic base-R functionality for processing and handling colour and spatial data. However, it is necessary to install the terra and sf libraries for the raster processing and spatial representation.

:computer: Code access

The main functions can be downloaded directly from this repository alongside the working example, or using the following script:

```{r}

0. Load/install the needed packages

list.of.packages<-c("httr","tidyverse")

new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])] if(length(new.packages)) install.packages(new.packages)

lapply(list.of.packages,require,character.only=TRUE) # conflicts_prefer(dplyr::filter) rm(list.of.packages,new.packages)

1. Connect to the AutoMaxent GitHub repository

github <- "https://api.github.com/repos/BioDivHealth/BiVariateMaps/git/trees/main?recursive=1" MaxRepo <- GET(git_hub) # Extract the repo information MaxRepo

2. Get the route to the functions

filepath <- data.frame(unlist(lapply(content(MaxRepo)$tree, function(x) x$path))) colnames(filepath) = c('Path') head(file_path)

Extract routes

filepath <- filepath %>% separate(Path,c('folder','filename'),'/') %>% filter(folder == 'Functions') %>% filter(str_detect(filename,'.R'))

3. Configure the routes, download, and export scripts

rawroute <- "https://raw.githubusercontent.com/BioDivHealth/BiVariateMaps/refs/heads/main" #This is the raw route to the gitHub repository MyRoute <- paste(getwd(),"BiVariate_Maps",sep="/")

for(i in 1:nrow(filepath)){ writelines(content(GET(paste(rawroute,filepath$folder[i],filepath$filename[i],sep="/"))), paste(MyRoute,filepath$filename[i],sep="/")) }

4. Load the functions

functions <- MyRoute %>% list.files(recursive = FALSE,pattern = ".R$",full.names = TRUE) lapply(functions,function(x) source(x)) `` In this case, you would need to specify the working environment or working directory before running the code. The **BiVariate_Maps** functions will be stored in theBiVariate_Mapsfolder created in your working environment. Be aware that this script would also install thehttrandtidyverselibraries on your computer unless you modify the code to use onlyhttr`.

:mag_right: Some output examples

All the figures are generated by the Example.R script using open-sourced data.

Created with ❤️ and spatial insight.