## ----sdm10A-------------------------------------------------------------------
library(terra)
library(predicts)
filename <- file.path(system.file(package="predicts"), "ex/bradypus.csv")
# this is the file we will use:
basename(filename)


## ----sdm11A-------------------------------------------------------------------
bradypus <- read.csv(filename)
# first rows
head(bradypus)
# we only need columns 2 and 3:
bradypus <- bradypus[,2:3]
head(bradypus)


## ----sdm11B, eval=FALSE-------------------------------------------------------
## acaule <- geodata::sp_occurrence("solanum", "acaule*", geo=FALSE)
## ## Loading required namespace: jsonlite
## ## 7238 records found
## ## 0-300-600-900-1200-1500-1800-2100-2400-2700-3000-3300-3600-3900-4200


## ----sdm2---------------------------------------------------------------------
# load the saved S. acaule data
acfile <- file.path(system.file(package="predicts"), "ex/acaule.csv")
acaule <- read.csv(acfile)

# how many rows and colums?
dim(acaule)

#select the records that have longitude and latitude data
colnames(acaule)
acgeo <- subset(acaule, !is.na(lon) & !is.na(lat))
dim(acgeo)

# show some values
acgeo[1:5, c(1:5,7:10)]


## ----sdm3---------------------------------------------------------------------
library(geodata)
wrld <- world(path=".")
plot(wrld, xlim=c(-110,60), ylim=c(-80,40), col="light yellow", border="light gray")
# add the points
points(acgeo$lon, acgeo$lat, col='red', pch=20)


## ----sdm4a--------------------------------------------------------------------
acgeo[c(303,885),1:10]


## ----sdm4b--------------------------------------------------------------------
lonzero <- subset(acgeo, lon==0)
# show all records, only the first 13 columns
lonzero[, 1:13]


## ----sdm5a--------------------------------------------------------------------
# which records are duplicates (only for the first 10 columns)?
dups <- duplicated(lonzero[, 1:10])
# remove duplicates
lonzero  <-  lonzero[dups, ]
lonzero[,1:13]


## ----sdm5b--------------------------------------------------------------------
# differentiating by (sub) species
# dups2 <- duplicated(acgeo[, c('species', 'lon', 'lat')])
# ignoring (sub) species and other naming variation
dups2 <- duplicated(acgeo[, c('lon', 'lat')])
# number of duplicates
sum(dups2)
# keep the records that are _not_ duplicated
acg <- acgeo[!dups2, ]


## ----sdm5c--------------------------------------------------------------------
i <- acg$lon > 0 & acg$lat > 0
acg$lon[i] <- -1 * acg$lon[i]
acg$lat[i] <- -1 * acg$lat[i]
acg <- acg[acg$lon < -60 & acg$lat > -50, ]


## ----sdm6a--------------------------------------------------------------------
library(terra)
acv <- vect(acg, geom=c("lon", "lat"), crs="+proj=longlat +datum=WGS84")
class(acv)


## ----sdm6b--------------------------------------------------------------------
ovr <- extract(acv, wrld)


## ----sdm6c--------------------------------------------------------------------
head(ovr)
cntr <- ovr$NAME_0


## ----sdm6d--------------------------------------------------------------------
i <- which(is.na(cntr))
i
j <- which(cntr != acv$country)
# for the mismatches, bind the country names of the polygons and points
m <- cbind(cntr[j], acg$country[j])
colnames(m) <- c("polygons", "acaule")
m


## ----sdm6e--------------------------------------------------------------------
plot(acv)
lines(wrld, col='blue', lwd=2)
points(acv[j, ], col='red', pch=20, cex=2)


## ----sdm8---------------------------------------------------------------------
georef <- subset(acaule, (is.na(lon) | is.na(lat)) & ! is.na(locality) )
dim(georef)
georef[1:3,1:13]


## ----sdm9, eval=FALSE---------------------------------------------------------
## georef$cloc[4]
## #b <- geocode(georef$cloc[4], geo_key="abcdef" )
## #b


## ----sdm100A------------------------------------------------------------------
# create a SpatRaster with the extent of acgeo
r <- rast(acv)
# set the resolution of the cells to (for example) 1 degree
res(r) <- 1

# extend (expand) the extent of the SpatRaster a little
r <- extend(r, ext(r)+1)

# sample:
set.seed(13)
acsel <- spatSample(acv, size=1, "random", strata=r) 

# to illustrate the method and show the result
p <- as.polygons(r)
plot(p, border='gray')
points(acv)
# selected points in red
points(acsel, cex=1, col='red', pch='x')


## ----sdm12--------------------------------------------------------------------
file <- paste(system.file(package="predicts"), '/ex/acaule.rds', sep='')
acsel <- readRDS(file)