raster

I have been using the extract function from the raster package to extract data from raster files using an area defined by shapefiles. However, I am having problems with the amount of memory that this process is now requiring. I do have a large number of shapefiles (~1000). The raster files are large (~1.6gb) My process is: shp <- mclapply(list.files(pattern="*.shp",full.names=TRUE), readShapePoly,mc.cores=6) ndvi <- raster("NDVI.dat") mc<- function(y) { temp <- gUnionCascaded(y) extract <- extract(ndvi,temp) mean <- range(extract, na.rm=T )[1:2] leng <- length(output) } output <- lapply(shp,

There are several raster files that I want to manipulate and then write them again. rasterfiles <- list.files("C:\\data", "*.envi", full.names = TRUE) d1 <- overlay(stack(rasterfiles ), fun=function(x) movingFun(x, fun=mean, n=3, na.rm=TRUE)) d2=unstack(d1) I am grateful to any idea on how we write d2 (rasters) writeRaster(d1, file="d1.nc") #other file formats such as .envi work as well works since d1 is one single raster and not a list of rasters: indeed the result of overlay is one single raster (see ?overlay ). Additionally the concept of stack is precisely to take several rasters having

I have two dataframes which I will like to map. The dfs have the same xy coordinates and I need a single colorbar with a visible discrete color scale for both dfs like the one shown here. I would like the colors in the colorkey to match the self-defined breaks. a more general solution that can be applied outside this example is much appreciated The RdYIBu color palette from the RcolorBrewer package is what I am after. My code so far: library(rasterVis) ras1 <- raster(nrow=10,ncol=10) set.seed(1) ras1[] <- rchisq(df=10,n=10*10) ras2=ras1*(-1)/2 s <- stack(ras1,ras2) Uniques <- cellStats(s,stat

Can a raster object (in R) have layers of different mode (data type)? On the face of it it seems we are always forced to one type: library(raster) ## create a SpatialPixelsDataFrame with (trivially) two different "layer" types d <- data.frame(expand.grid(x = 1:10, y = 2:11), z = 1:100, a = sample(letters, 100, replace = TRUE), stringsAsFactors = FALSE) coordinates(d) <- 1:2 gridded(d) <- TRUE ## now coerce this to a raster brick or stack and our "a" is crushed to numeric NA all(is.na(getValues(brick(d)[[2]]))) [1] TRUE Is there anything like a rasterDataFrame? Also, note that we presumably

I'd like to crop GeoTiff Raster Files using the two mentioned packages, "rgdal" and "raster". Everything works fine, except that the quality of the resulting output tif is very poor and in greyscale rather than colour. The original data are high quality raster maps from the swiss federal office of Topography, example files can be downloaded here . This is my code: ## install.packages("rgdal") ## install.packages("raster") library("rgdal") library("raster") tobecroped <- raster("C:/files/krel_1129_2012_254dpi_LZW.tif") ex <- raster(xmn=648000, xmx=649000, ymn=224000, ymx=225000) projection(ex)

I need to replace the NA 's in the raster object ( r ) from the example below. library(raster) filename <- system.file("external/test.grd", package="raster") r <- raster(filename) I also tried to remove these these (and place the result in a data.frame ), but to no avail. dfr <- as.data.frame(r, na.rm=T) summary(dfr) # test # Min. : 128.4 # 1st Qu.: 293.2 # Median : 371.4 # Mean : 423.2 # 3rd Qu.: 499.8 # Max. :1805.8 # NA's :6097 I'm not sure it makes sense to remove NA values from a raster object, but you can easily replace it. For example: oldpar <- par(mfrow=c(1, 2)) plot(r) r[is.na(r)] <-