I want to do something relatively simply but it seems to be very difficult to do, though I am very new to using R for spatial data so apologies if I've missed something obvious.
I have a list containing 161 shapefiles of bird distributions in the world, they are each in the format of a SpatialPolygonsDataFrame. I also have world climate data in a raster stack format for 8 variables. Both sets of files are in the same projection (lat and long WGS84). I want to create new columns for the climate data with 1's where the species polygon overlaps with the climate data and 0 where it doesn't. I tried various functions to do this, quite a few initial options didn't work, the best option appeared to be:
pnt.in.poly(XY coordinates of climate data points, XY coordinates of species polygon)
I changed my raster of climate data into points to get the XY coordinates. I unified the polygons for each species shapefile and then used:
species@polygons[[1]]@Polygons[[1]]@cords
to get the coordinates for my species polygons as a matrix. However, I discovered that:
species@polygons[[1]]@Polygons[[1]]@coords
wasn't actually getting all the coordinates of the polygons only some of them.
I tried using fortify from the ggplot package, and it gives me a dataframe with coordinates but some of them are for holes in the polygons. I ran that with the pnt.in.poly function excluding the holes but it took an hour to run and when I looked at the output maps after it matched for some of the range but not all of it- there were holes in the distribution where there shouldn't be holes.
I also tried this method that I found somewhere else:
lonlat <- lapply(slot(species, "polygons"), function(x) lapply(slot(x,"Polygons"), function(y) slot(y, "coords")))
which gives me a list with 436 items- but I'm still not sure how to get the coordinates into a matrix form that will work in pnt.in.poly.
I'm sure this should be easier to do! Does anyone know a better way?
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