# install.packages("remotes")
remotes::install_github("harithmorgadinho/wege_ind")

library(WEGE) 
library(sp)
#> Warning: package 'sp' was built under R version 3.5.2
library(sf)
#> Linking to GEOS 3.6.1, GDAL 2.1.3, PROJ 4.9.3

species <- letters[1:26]
range_list <- list()
for (i in seq_along(species)) {
  temp0 <- cbind(runif(3,1,50),runif(3,1,50))
  temp <- Polygon(rbind(temp0,temp0[1,]))
  range_list[[i]] <- Polygons(list(temp), ID = c(species[i]))
}
input <- st_as_sf(SpatialPolygons(range_list))
categories <- c('LC','NT','VU','EN','CR')
input$binomial <- species
input$category <- sample(size = nrow(input),x = categories,replace = TRUE)
input$ED <- runif(nrow(input),1,30)
temp0 <- cbind(runif(3,1,50),runif(3,1,50))
target_area <- Polygon(rbind(temp0,temp0[1,]))
target_area <- Polygons(list(target_area), ID = 'Target area')
target_area <- st_as_sf(SpatialPolygons(list(target_area)))

get_edge(target_area = target_area,input = input,species = 'binomial',category = 'category')
#> [[1]]
#> [1] 23.60822

library(sp)
library(sf)
library(WEGE)

species <- letters[1:26]
range_list <- list()
for (i in seq_along(species)){
  temp  <-  Polygon(cbind(runif(4,1,50),runif(4,1,50)))
  range_list[[i]] <- Polygons(list(temp), ID = c(species[i]))}
input <- st_as_sf(SpatialPolygons(range_list))
categories <- c('LC','NT','VU','EN','CR')
input$binomial <- species
input$category <- sample(size = nrow(input),x = categories,replace = TRUE)

target_area <- Polygon(cbind(runif(4,1,50),runif(4,1,50)))
target_area <- Polygons(list(target_area), ID = 'Target area')
target_area <- st_as_sf(SpatialPolygons(list(target_area)))
get_wege(target_area,input,species = 'binomial',category = 'category')
#> [[1]]
#> [1] 312.7381

library(WEGE)
library(sp)
library(sf)

species <- letters[1:26]
range_list <- list()
for (i in seq_along(species)){
  temp0 <- cbind(runif(3,1,50),runif(3,1,50))
  temp  <-  Polygon(rbind(temp0,temp0[1,]))
  range_list[[i]] <- Polygons(list(temp), ID = c(species[i]))
}
input <- st_as_sf(SpatialPolygons(range_list))
categories <- c('LC','NT','VU','EN','CR')
input$binomial <- species
input$category <- sample(size = nrow(input),x = categories,replace = TRUE)

temp0 <- cbind(runif(3,1,50),runif(3,1,50))
target_area <- Polygon(rbind(temp0,temp0[1,]))
target_area <- Polygons(list(target_area), ID = 'Target area')
target_area <- st_as_sf(SpatialPolygons(list(target_area)))
get_kba_criteria(target_area,input)
#>    species         area category     area_kba  perc_kba A1a A1b A1e  B1
#> 1        a 2.947391e-05       CR 1.786149e-06  6.060102 yes  no  no  no
#> 2        b 2.768724e-04       EN 7.429253e-05 26.832764 yes  no  no yes
#> 3        e 2.434958e-04       CR 1.244859e-04 51.124459 yes  no  no yes
#> 4        f 3.128119e-04       CR 3.224454e-05 10.307965 yes  no  no yes
#> 5        g 4.479853e-04       NT 1.918042e-04 42.814836  no  no  no yes
#> 6        h 2.102851e-04       NT 1.337322e-04 63.595688  no  no  no yes
#> 7        j 1.013406e-04       EN 3.655669e-05 36.073100 yes  no  no yes
#> 8        k 4.449901e-04       VU 1.025432e-04 23.043938  no yes  no yes
#> 9        m 3.871863e-04       CR 1.013974e-04 26.188272 yes  no  no yes
#> 10       n 4.809546e-04       NT 2.858035e-04 59.424224  no  no  no yes
#> 11       o 9.440874e-05       NT 2.502407e-05 26.506091  no  no  no yes
#> 12       q 5.152714e-04       CR 2.376042e-04 46.112449 yes  no  no yes
#> 13       r 5.082501e-05       EN 3.078056e-05 60.561836 yes  no  no yes
#> 14       s 2.523799e-04       VU 1.587406e-04 62.897513  no yes  no yes
#> 15       t 2.591122e-04       LC 8.592068e-05 33.159645  no  no  no yes
#> 16       u 5.776036e-05       LC 5.589551e-05 96.771400  no  no  no yes
#> 17       v 6.707679e-05       NT 1.720175e-05 25.644862  no  no  no yes
#> 18       w 2.207896e-04       NT 9.193474e-05 41.639073  no  no  no yes
#> 19       x 6.142535e-04       VU 2.235084e-04 36.386998  no yes  no yes
#> 20       y 9.276385e-05       CR 4.771320e-05 51.435119 yes  no  no yes
#> 21       z 3.607598e-04       EN 9.905803e-05 27.458169 yes  no  no yes

library(WEGE)
library(sp)
library(sf)
library(raster)
#> Warning: package 'raster' was built under R version 3.5.2

 species <- sample(letters, 10)
 range_list <- list()
 for (i in seq_along(species)) {
   temp0 <- cbind(runif(3,1,50),runif(3,1,50))
   temp <- Polygon(rbind(temp0,temp0[1,]))
   range_list[[i]] <- Polygons(list(temp), ID = c(species[i]))
   }
 input <- st_as_sf(SpatialPolygons(range_list))
 categories <- c('LC','NT','VU','EN','CR')
 input$binomial <- species
 input$category <- sample(size = nrow(input),x = categories,replace = TRUE)
 
 input$ed <- runif(runif(10,1,50))
 temp0 <- cbind(runif(3,1,50),runif(3,1,50))
 target_area <- Polygon(rbind(temp0,temp0[1,]))
 target_area <- Polygons(list(target_area), ID = 'Target area')
 target_area <- st_as_sf(SpatialPolygons(list(target_area)))
 spat_ras(target_area,input,species = 'binomial',ed='ed', res = 1)
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