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


## ---- sdm1--------------------------------------------------------------------
# loads the dismo library
library(dismo)
file <- paste0(system.file(package="dismo"), "/ex/bradypus.csv")
# this is the file we will use:
file


## ---- sdm1b-------------------------------------------------------------------
bradypus <- read.table(file,  header=TRUE,  sep=",")
# or do:  bradypus <- read.csv(file)
# first rows
head(bradypus)
# we only need columns 2 and 3:
bradypus <- bradypus[,2:3]
head(bradypus)


## ---- sdm2--------------------------------------------------------------------
# load the saved S. acaule data
data(acaule)

# 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[1:4, c(1:5,7:10)]


## ---- sdm3--------------------------------------------------------------------
library(maptools)
data(wrld_simpl)
plot(wrld_simpl, xlim=c(-80,70), ylim=c(-60,10), axes=TRUE, col="light yellow")
# restore the box around the map
box()

# add the points
points(acgeo$lon, acgeo$lat, col='orange', pch=20, cex=0.75)
# plot points again to add a border, for better visibility 
points(acgeo$lon, acgeo$lat, col='red', cex=0.75)


## ---- sdm4a-------------------------------------------------------------------
acaule[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$lon < -50 & acg$lat > -50, ]


## ---- sdm6a-------------------------------------------------------------------
library(sp)
coordinates(acg) <- ~lon+lat
crs(acg) <- crs(wrld_simpl)
class(acg)


## ---- sdm6b-------------------------------------------------------------------
class(wrld_simpl)
ovr <- over(acg, wrld_simpl)


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


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


## ---- sdm6e-------------------------------------------------------------------
plot(acg)
plot(wrld_simpl, add=T, border='blue', lwd=2)
points(acg[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--------------------------------------------------------------------
georef$cloc[4]
#b <- geocode(georef$cloc[4], geo_key="abcdef" )
#b


## ---- sdm10-------------------------------------------------------------------
# create a RasterLayer with the extent of acgeo
r <- raster(acg)
# set the resolution of the cells to (for example) 1 degree
res(r) <- 1

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

# sample:
acsel <- gridSample(acg, r, n=1) 

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


## ---- sdm12-------------------------------------------------------------------
file <- paste(system.file(package="dismo"), '/ex/acaule.csv', sep='')
acsel <- read.csv(file)