Name: landscapetools
Owner: rOpenSci
Description: ? R package for some of the less-glamorous tasks involved in landscape analysis ?
Created: 2018-03-15 13:41:56.0
Updated: 2018-03-26 13:19:56.0
Pushed: 2018-03-26 13:59:13.0
Homepage: https://ropensci.github.io/landscapetools/
Size: 13015
Language: R
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landscapetools provides utility functions to work with landscape data (raster* Objects).
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_plot: Plot a Raster* object with the landscapetools default theme (as ggplot).
util_facetplot: Plot multiple raster (RasterStack, -brick or list of raster) side by side as facets.
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.
util_import_roboto_condensed: Import Roboto Condensed font for theme_nlm.
You can install the development version from GitHub with:
stall.packages("devtools")
ools::install_github("ropensci/landscapetools")
ary(NLMR)
ary(landscapetools)
eate an artificial landscape
raster <- nlm_fbm(ncol = 200, nrow = 200, fract_dim = 0.8)
_plot(nlm_raster)
narize the map into habitat and matrix
rized_raster <- util_binarize(nlm_raster, breaks = 0.31415)
_plot(binarized_raster, discrete = TRUE)
assify the map into land uses
sified_raster <- util_classify(nlm_raster,
c(0.5, 0.25, 0.25),
level_names = c("Land Use 1", "Land Use 2", "Land Use 3"))
_plot(classified_raster, discrete = TRUE)
eate a primary and two secondary maps
<- nlm_edgegradient(ncol = 100, nrow = 100)
<- nlm_distancegradient(ncol = 100, nrow = 100,
origin = c(10, 10, 10, 10))
<- nlm_random(ncol = 100, nrow = 100)
rge all maps into one
<- util_merge(prim, c(sec1, sec2), scalingfactor = 1)
ot an overview
e_vis <- list(
"1) Primary" = prim,
"2) Secondary 1" = sec1,
"3) Secondary 2" = sec2,
"4) Result" = merg
_facetplot(merge_vis)
In the examples above we make heavy use of the NLMR package. Both packages were developed together until we split them into pure landscape functionality and utility tools. If you are interested in generating neutral landscapes via a multitude of available algorithms take a closer look at the NLMR package.
landscapetools in R doing citation(package = 'landscapetools')