extract(x, y, ...)SpatialPolygons, Sy is a SpatialPolygons* or SpatialLines* object or if a buffer argument is used (but not a fun argument).
focal values are returned as a n column matrix. The first column has the column numbers, the remaining has the focal values for each layer.fun. Function to summarize the values (e.g. mean). The function should
take a single numeric vector as argument and return a single values (e.g. mean, min or max), and accept a na.rm argument.
Thus, standard R functions not including an na.rm argument must be wrapped as in this example: fun=function(x,...)length(x)
If y represents points supplying a fun argument is only useful when a buffer is used (see below).
2) na.rm. Only useful when fun is supplied. If na.rm=TRUE (the default value), NA values are removed before fun is applied. This argument may be ignored if the function used has a ... argument and ignres an additional na.rm argument.
= points =
If y represents points, extract returns the values of a Raster* object for the cells in which a set of points fall.
Additional arguments that can be suplied to this function if y represents points:
1) method. If method='simple' (the default), values for the cell a point falls in are returned. The
alternative is method='bilinear', in which case the returned values are interpolated from the values of the four nearest raster cells.
2) buffer. The radius of a buffer around each point for which to extract cell values.
It can be a single value, or a vector of the length of the number of points.
If the data are not projected (latitude/longitude), the unit should be meters. Otherwise it should be in map-units (typically also meters).
3) cellnumbers. Logical. If cellnumbers=TRUE and buffer is not NULL, cellnumbers will also be returned. cellnumbers is set to FALSE when an argument fun is used.
= lines =
If no fun argument is supplied:
cellnumbers. Logical. If cellnumbers=TRUE is used, cell-numbers will also be returned.
= polygons =
If y represents polygons, the extract method returns the values of the cells of a Raster* object that is covered by a polygon.
A cell is covered if its center is inside the polygon (but see the weights option for considering partly covered cells; and argument small for getting values for small polygons anyway).
if y represents polygons are:
1) small. Logical. Default is FALSE. If TRUE a value is also returned for relatively small polygons (e.g. those smaller than a single cell of the Raster* object),
or polygons with an odd shape, for which otherwise no values are returned because they do not cover any Raster* object's cell centers. In some cases, you could use alternatively
use the centroids of such polygons, for example using extract(x, coordinates(y)) or extract(x, coordinates(y), method='bilinear').
Additional arguments that can be suplied to this function, if no fun argument is supplied:
2) weights. If TRUE, the function returns, for each polygon, a matrix with the cell values and the approximate fraction
of each cell that is covered by the polygon(rounded to 1/100). The weights can be used for averaging; see examples.
This option can be useful if the polygons are small relative to the cells size of the Raster* object.
3) cellnumbers. Logical. If cellnumbers=TRUE, cell-numbers will also be returned.
= focal values =
If y is missing, you can supply two arguments to get 'focal' values (i.e. the values in a neighborhood around each cell)
1) row. row number (this is a required argument).
2) ngb. The neighborhood to consider. Either a single integer or a vector of two integers. Default is 3. See focal
= all multi-layer objects =
If x is a RasterStack or RasterBrick object, extract accepts, in addition to the arguments listed above,
these two additional arguments:
1)layer. Integer. First layer for which you want values
2) nl. Integer. Number of layers for which you want valuesvaluesr <- raster(ncol=36, nrow=18)
r[] <- 1:ncell(r)
###############################
# extract values by cell number
###############################
extract(r, c(1:2, 10, 100))
s <- stack(r, sqrt(r), r/r)
extract(s, c(1, 10, 100), layer=2, n=2)
###############################
# extract values with points
###############################
xy <- cbind(-50, seq(-80, 80, by=20))
extract(r, xy)
sp <- SpatialPoints(xy)
extract(r, sp, method='bilinear')
# examples with a buffer
extract(r, xy[1:3,], buffer=1000000)
extract(r, xy[1:3,], buffer=1000000, fun=mean)
## illustrating the varying size of a buffer (expressed in meters) on a longitude/latitude raster
z <- extract(r, xy, buffer=1000000)
s <- raster(r)
for (i in 1:length(z)) { s[z[[i]]] <- i }
## compare with raster that is not longitude/latitude
projection(r) <- "+proj=UTM +zone=17"
xy[,1] <- 50
z <- extract(r, xy, buffer=8)
for (i in 1:length(z)) { s[z[[i]]] <- i }
plot(s)
# library(maptools)
# data(wrld_simpl)
# plot(wrld_simpl, add=TRUE)
###############################
# extract values with lines
###############################
cds1 <- rbind(c(-50,0), c(0,60), c(40,5), c(15,-45), c(-10,-25))
cds2 <- rbind(c(80,20), c(140,60), c(160,0), c(140,-55))
lines <- SpatialLines(list(Lines(list(Line(cds1)), "1"), Lines(list(Line(cds2)), "2") ))
extract(r, lines)
###############################
# extract values with polygons
###############################
cds1 <- rbind(c(-180,-20), c(-160,5), c(-60, 0), c(-160,-60), c(-180,-20))
cds2 <- rbind(c(80,0), c(100,60), c(120,0), c(120,-55), c(80,0))
polys <- SpatialPolygons(list(Polygons(list(Polygon(cds1)), 1), Polygons(list(Polygon(cds2)), 2)))
#plot(r)
#plot(polys, add=TRUE)
v <- extract(r, polys)
v
# mean for each polygon
unlist(lapply(v, function(x) if (!is.null(x)) mean(x, na.rm=TRUE) else NA ))
# v <- extract(r, polys, cellnumbers=TRUE)
# weighted mean
# v <- extract(r, polys, weights=TRUE, fun=mean)
# equivalent to:
# v <- extract(r, polys, weights=TRUE)
# sapply(v, function(x) if (!is.null(x)) {sum(apply(x, 1, prod)) / sum(x[,2])} else NA )
###############################
# extract values with an extent
###############################
e <- extent(150,170,-60,-40)
extract(r, e)
#plot(r)
#plot(e, add=T)
###############################
# focal values
###############################
f <- extract(r, row=5, ngb=3)
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