AbsenceBGData.Rmd

---
title: "Absence_presence_data"
author: "Hoang Le Xuan"
date: "2024-04-04"
output: html_document
---

Reference:https://rspatial.org/

## Absence and background points

first get the list of filenames with the predictor raster data

```{r}
library(dismo)
library(maptools)
data(wrld_simpl)
```

```{r}
files <- list.files(path="data/bioclim",  pattern='asc',  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 )
```
And inspect the results by plotting

```{r}
# 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(-180, 180, -90, 90)
bg2 <- randomPoints(mask, 50, ext=e)
plot(!is.na(mask), legend=FALSE)
plot(e, add=TRUE, col='red')
points(bg2, cex=0.5)
```
## Creating SpatialPointsDataFrame
```{r}
ct <- read.csv("data/ctsel.csv")

coordinates(ct) <- ~lon+lat
projection(ct) <- CRS('+proj=longlat +datum=WGS84')
```

## Environmental data
### Raster data

create a RasterStack of predictor variables.

```{r}
predictors <- stack(files)
predictors

#class      : RasterStack 
#dimensions : 3600, 8640, 31104000, 19  (nrow, ncol, ncell, nlayers)
#resolution : 0.04166667, 0.04166667  (x, y)
#extent     : -180, 180, -60, 90  (xmin, xmax, ymin, ymax)
#crs        : +proj=longlat +datum=WGS84 +no_defs 
#names      : bclim1, bclim10, bclim11, bclim12, bclim13, bclim14, bclim15, bclim16, bclim17, bclim18, bclim19, bclim2, bclim3, bclim4, bclim5, 

names(predictors)
#[1] "bclim1"  "bclim10" "bclim11" "bclim12" "bclim13" "bclim14" "bclim15" "bclim16" "bclim17" "bclim18"
#[11] "bclim19" "bclim2"  "bclim3"  "bclim4"  "bclim5"  "bclim6"  "bclim7"  "bclim8"  "bclim9" 
```
Overlay the data point to the bioclim1 map:
```{r}
# first layer of the RasterStack
plot(predictors, 1)
# note the "add=TRUE" argument with plot
plot(wrld_simpl, add=TRUE)
# with the points function, "add" is implicit
points(ct, col='blue')
```

## Extracting values from rasters

```{r}
presvals <- extract(predictors, ct)
# Predictor values
# setting random seed to always create the same
# random set of points for this example
set.seed(0)
backgr <- randomPoints(predictors, 500)
absvals <- extract(predictors, backgr)

#Presence Absence point
pb <- c(rep(1, nrow(presvals)), rep(0, nrow(absvals)))
sdmdata <- data.frame(cbind(pb, rbind(presvals, absvals)))

sdmdata

```

```{r}
summary(sdmdata)
pairs(sdmdata[,2:5], cex=0.1)

```

```{r}
saveRDS(sdmdata, "sdm.Rds")
saveRDS(presvals, "pvals.Rds")

#load again;
sdmdata <- readRDS("sdm.Rds")
presvals <- readRDS("pvals.Rds")
```