## ---- echo=FALSE, include=FALSE-----------------------------------------------
library(knitr)
opts_chunk$set(fig.width = 5, fig.height = 5, fig.cap='',  collapse = TRUE)
library(dismo)


## ---- sdm15a------------------------------------------------------------------
library(dismo)
# get the file names 
files <- list.files(path=paste(system.file(package="dismo"), '/ex', 
                       sep=''),  pattern='grd',  full.names=TRUE )

# we use the first file to create a RasterLayer
mask <- raster(files[1])

# select 500 random points
# set seed to assure that the examples will always
# have the same random sample.
set.seed(1963)
bg <- randomPoints(mask, 500 )


## ---- sdm15, fig.width=9, fig.height=6----------------------------------------
# set up the plotting area for two maps
par(mfrow=c(1,2))
plot(!is.na(mask), legend=FALSE)
points(bg, cex=0.5)

# now we repeat the sampling, but limit 
# the area of sampling using a spatial extent
e <- extent(-80, -53, -39, -22)
bg2 <- randomPoints(mask, 50, ext=e)
plot(!is.na(mask), legend=FALSE)
plot(e, add=TRUE, col='red')
points(bg2, cex=0.5)


## ---- sdm16a------------------------------------------------------------------
file <- paste(system.file(package="dismo"), '/ex/acaule.csv', sep='')
ac <- read.csv(file)


## ---- sdm16b------------------------------------------------------------------
coordinates(ac) <- ~lon+lat
projection(ac) <- CRS('+proj=longlat +datum=WGS84')


## ---- sdm17-------------------------------------------------------------------
# circles with a radius of 50 km
x <- circles(ac, d=50000, lonlat=TRUE)
pol <- polygons(x)


## ---- sdm19-------------------------------------------------------------------
# sample randomly from all circles
samp1 <- spsample(pol, 250, type='random', iter=25)
# get unique cells
cells <- cellFromXY(mask, samp1)
length(cells)
cells <- unique(cells)
length(cells)
xy <- xyFromCell(mask, cells)


## ---- sdm20, fig.width=9, fig.height=6----------------------------------------
plot(pol, axes=TRUE)
points(xy, cex=0.75, pch=20, col='blue')


## ---- sdm21a------------------------------------------------------------------
spxy <- SpatialPoints(xy, proj4string=CRS('+proj=longlat +datum=WGS84'))
o <- over(spxy, geometry(x))
xyInside <- xy[!is.na(o), ]


## ---- sdm21b, fig.width=9, fig.height=6---------------------------------------
# extract cell numbers for the circles
v <- extract(mask, x@polygons, cellnumbers=T)
# use rbind to combine the elements in list v
v <- do.call(rbind, v)

# get unique cell numbers from which you could sample
v <- unique(v[,1])
head(v)

# to display the results
m <- mask
m[] <- NA
m[v] <- 1
plot(m, ext=extent(x@polygons)+1)
plot(x@polygons, add=T)