Introduction

This tutorial shows how to plot samc analyses using ggplot2. It is based on the code in the basic tutorial.

Setup

# First step is to load the libraries. Not all of these libraries are stricly
# needed; some are used for convenience and visualization for this tutorial.
library("samc")
library("raster")
library("ggplot2")
library("viridis")


# "Load" the data. In this case we are using data built into the package.
# In practice, users will likely load raster data using the raster() function
# from the raster package.
res_data <- samc::ex_res_data
abs_data <- samc::ex_abs_data
occ_data <- samc::ex_occ_data


# Create a samc object using the resistance and absorption data. We use the 
# recipricol of the arithmetic mean for calculating the transition matrix. Note,
# the input data here are matrices, not RasterLayers. If using RasterLayers, the
# `latlon` parameter must be set.
samc_obj <- samc(res_data, abs_data, tr_fun = function(x) 1/mean(x))


# Convert the occupancy data to probability of occurrence
occ_prob_data <- occ_data / sum(occ_data, na.rm = TRUE)


# Calculate short- and long-term mortality metrics and long-term dispersal
short_mort <- mortality(samc_obj, occ_prob_data, time = 4800)
long_mort <- mortality(samc_obj, occ_prob_data)
long_disp <- dispersal(samc_obj, occ_prob_data)
#> Performing setup. This can take several minutes... Complete.
#> Calculating matrix inverse diagonal...
Calculating matrix inverse diagonal... Complete                                           
#> Performing final calculations. This may take a few minutes... Complete.


# Create rasters using the vector result data for plotting.
short_mort_map <- map(samc_obj, short_mort)
long_mort_map <- map(samc_obj, long_mort)
long_disp_map <- map(samc_obj, long_disp)

Visualization With ggplot2