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I'm downloading the protected areas using the wdpar package which uses sf to deal with spatial data. … # load packages library(wdpar) library(dplyr) library(ggmap) library(sf) # download protected area data # (excluding areas represented as point localities) sa_raw_pa_data <- wdpa_fetch(c("South Africa …

Here's my code: # load packages library(tidyverse) library(amt) library(sf) library(spex) #' load in the protected areas merged_Africa = read_sf("shape//eswatini//WDPA_Apr2020_SWZ-shapefile-polygons.shp …

For this code to run you'll need to download the zip from protected planet and extract the three individual files for either UK or Ireland. library(sf) library(tidyverse) spath <- "data" (shps <- list.files …

You can see that when combined the area for D8 is around 30km2 but is ~21km2 when modelled alone. library(move) library(sf) library(amt) muleys <- read.csv( "https://raw.githubusercontent.com/walterASEL …

E.g. in the following simple code, it'd be handy to plug in 102022 in place of 4326. library(sf) library(spData) Kenya <- world[world$name_long == "Kenya", 0] rpoints <- st_sample(Kenya, size = 100, type …

I have tracking data from multiple animals and I have created kernel density estimates of their home ranges. I want to find out the proportion of each home range area that falls within protected areas …

I'm using R and the sf package. … Here's my code: #' load packages library(tidyverse) library(move) library(sf) library(amt) #' create a move object loc <- move( x = track$x_, y = track$y_, time = track$t_, proj = …

The comment put me on the right track. This worked for me: ID1_parks <- st_intersection(hr_isopleths(dbbmm$ID1, level = 0.95),merged_Africa_tranform) And then I can extract the names etc. of the vari …

Full code and data below: #' load packages library(tidyverse) library(move) library(sf) library(amt) #' create a move object loc <- move( x = track$ …