Awesome

landscapetools

landscapetools provides utility functions for some of the less-glamorous tasks involved in landscape analysis:

Utilities:

util_binarize: Binarize continuous raster values, if > 1 breaks are given, return a RasterBrick.
util_classify: Classify a raster into proportions based upon a vector of class weightings.
util_merge: Merge a primary raster with other rasters weighted by scaling factors.
util_raster2tibble, util_tibble2raster: Coerce raster* objects to tibbles and vice versa.
util_rescale: Linearly rescale element values in a raster to a range between 0 and 1.
util_writeESRI: Export raster objects as ESRI asciis (with Windows linebreaks).

Visualization

show_landscape: Plot a Raster* object with the landscapetools default theme (as ggplot) or multiple raster (RasterStack, -brick or list of raster) side by side as facets.
show_shareplot: Plot the landscape share in subsequential buffers around a/multiple point(s) of interest

Themes:

theme_nlm, theme_nlm_grey: Opinionated ggplot2 theme to visualize raster (continuous data).
theme_nlm_discrete, theme_nlm_grey_discrete: Opinionated ggplot2 theme to visualize raster (discrete data).
theme_faceplot: Opinionated ggplot2 theme to visualize raster in a facet wrap.

Installation

You can install the released version from CRAN with:

install.packages("landscapetools")

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("ropensci/landscapetools")

Utilities

Classify

# Classify the landscape into land uses
classified_landscape <- util_classify(fractal_landscape,
                                      n = 3,
                                      level_names = c("Land Use 1", 
                                                      "Land Use 2",
                                                      "Land Use 3"))

show_landscape(classified_landscape, discrete = TRUE)

Merge

# Merge all landscapes into one
merged_landscape <- util_merge(fractal_landscape,
                               c(gradient_landscape, random_landscape),
                               scalingfactor = 1)

# Plot an overview
merge_vis <- list(
    "1) Primary" = fractal_landscape,
    "2) Secondary 1" = gradient_landscape,
    "3) Secondary 2" = random_landscape,
    "4) Result" = merged_landscape
)

show_landscape(merge_vis)
#> Warning: Removed 1196 rows containing missing values (geom_raster).