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Tips on Using the 'sp' classes in RSubmitted by dylan on Wed, 2008-10-08 20:13.
In addition, there are still some minor bugs to be worked out in terms of reading/writing vector data using methods in the rgdal package. This is currently being worked on by members of the sp and GRASS development community. # use to read in some vector data library(rgdal) # read something in, rows are identified by a column called 'id' spatial.data <- readOGR(...) # read in some tabular data, rows are identified by a column called 'id' new_table <- read.csv(...) # 'join' the new data with merge() # all.x=TRUE is used to ensure we have the same number of rows after the join # in case that the new table has fewer merged <- merge(x=spatial.data@data, y=new_table, by.x='id', by.y='id', all.x=TRUE) # generate a vector that represents the original ordering of rows in the sp object correct.ordering <- match(spatial.data@data$id, merged$id) # overwrite the original dataframe with the new merged dataframe, in the correct order spatial.data@data <- merged[correct.ordering, ] # check the ordering of the merged data, with the original spatial data cbind(spatial.data@data$id, merged$id[correct.ordering]) # libraries we need require(rgdal) require(foreign) # pass 1: write the shapefile writeOGR(spatial.data, dsn='new_folder', driver='ESRI Shapefile', layer='new_layer') # re-make the DBF: write.dbf(spatial.data@data, file='new_folder/new_layer.dbf') # need this to read in GRASS data require(spgrass6) # read in several rasters into a new sp object: x <- readRAST6(c('r1','r2','r3','r4')) # we want to do some analysis that will not work when all rasters contain NA # save a vector of positions where all of the rasters have a value of NA x.nas <- which(is.na(x@data$r1) & is.na(x@data$r2) & is.na(x@data$r3) & is.na(x@data$r4)) # save a vector of positions that have no NA in any raster x.vals <- which( !is.na(x@data$r1) & !is.na(x@data$r2) & !is.na(x@data$r3) & !is.na(x@data$r4)) # extract the records that only have complete data (no NA) x.no.na <- x@data[x.vals, ] # do something with the data require(cluster) x.clara <- clara(x.no.na, k=6, stand=TRUE) # add a new column to the original sp object to contain the results of the analysis x@data$terrain_group <- NA # fill in the locations where there were complete records with the results # note that we use the original locations of the non-NA values x@data$terrain_group[x.vals] <- x.clara$clustering # save result back to GRASS writeRAST6(x, zcol='cluster', vname='x_groups') # this one is from R. Bivand (thanks) # assume that we are working with the 'data' slot on an sp opject: # generate some data; sprinkle in some NA ; convert to DF x <- rnorm(100) x[sample(1:100, 5)] <- NA x.mat <- round(matrix(x, ncol=5), 2) x.df <- as.data.frame(x.mat) # extract an NA mask x.na_mask <- complete.cases(x.df) # just the 'rows' with complete data (across bands) x.df[x.na_mask,] V1 V2 V3 V4 V5 1 1.32 -1.27 0.00 -0.87 1.52 2 -0.44 1.33 0.78 -1.27 0.00 5 2.70 -0.08 -0.77 0.38 0.32 6 -0.52 -0.07 0.13 -0.43 1.12 7 -0.63 2.02 0.45 0.48 -0.59 8 -0.52 -0.78 -0.59 -0.07 -0.08 11 -1.37 -1.15 0.23 0.73 0.07 12 -0.11 -0.66 0.50 -1.14 0.71 13 -0.38 -0.08 1.00 -0.88 -0.19 14 -1.14 -0.45 -1.37 -0.43 -2.18 15 1.92 0.46 -0.72 0.12 0.27 16 0.51 0.22 -0.39 -1.54 -1.04 17 1.14 -1.11 1.04 0.00 -1.11 18 0.53 0.62 -0.10 -0.17 0.15 19 0.84 0.27 -1.01 0.87 0.31 20 0.82 0.87 0.46 1.35 0.13 # all the data x.df V1 V2 V3 V4 V5 1 1.32 -1.27 0.00 -0.87 1.52 2 -0.44 1.33 0.78 -1.27 0.00 3 -0.31 -0.13 -1.14 2.08 NA 4 0.76 NA -1.20 -0.54 -0.85 5 2.70 -0.08 -0.77 0.38 0.32 6 -0.52 -0.07 0.13 -0.43 1.12 7 -0.63 2.02 0.45 0.48 -0.59 8 -0.52 -0.78 -0.59 -0.07 -0.08 9 -0.38 -0.51 0.44 NA -0.81 10 -1.38 NA -0.65 -0.50 NA 11 -1.37 -1.15 0.23 0.73 0.07 12 -0.11 -0.66 0.50 -1.14 0.71 13 -0.38 -0.08 1.00 -0.88 -0.19 14 -1.14 -0.45 -1.37 -0.43 -2.18 15 1.92 0.46 -0.72 0.12 0.27 16 0.51 0.22 -0.39 -1.54 -1.04 17 1.14 -1.11 1.04 0.00 -1.11 18 0.53 0.62 -0.10 -0.17 0.15 19 0.84 0.27 -1.01 0.87 0.31 20 0.82 0.87 0.46 1.35 0.13 # generate something cool from complete data library(MASS) p <- princomp(~ . , data=x.df[x.na_mask,]) # assign back to original data x.df$pca_1[x.na_mask] <- predict(p)[,1] ( categories: )
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