| SpatialGridDataFrame-class {sp} | R Documentation |
Class for spatial attributes that have spatial locations on a (full) regular grid.
Objects can be created by calls of the form as(x,
"SpatialGridDataFrame"), where x is of class
SpatialPixelsDataFrame-class, or by importing through rgdal.
Ordered full grids are stored instead or unordered non-NA cells;
points:grid:grid.index:bbox:"matrix"; bounding box proj4string:"CRS"; projection data:
Class "SpatialGrid", directly.
Class "Spatial", by class "SpatialGrid".
signature(x = "SpatialGridDataFrame"): retrieves (and calculates!) coordinates signature(x = "SpatialGridDataFrame"): selects rows, columns, and attributes; returns an
object of class SpatialGridDataFramesignature(x = "SpatialGridDataFrame"): retrieves an attribute, dropping everything
else (topology) signature(x = "SpatialGridDataFrame"): assigns or replaces an attribute signature(x = "SpatialGridDataFrame"): coerce to matrix signature(...): if arguments have identical topology, combine their
attribute valuesEdzer J. Pebesma, e.pebesma@geo.uu.nl
SpatialGrid-class, which does not contain the attribute data,
and SpatialPixelsDataFrame-class which holds possibly incomplete
grids
data(meuse.grid) # only the non-missing valued cells
coordinates(meuse.grid) = c("x", "y") # promote to SpatialPointsDataFrame
gridded(meuse.grid) <- TRUE # promote to SpatialPixelsDataFrame
x = as(meuse.grid, "SpatialGridDataFrame") # creates the full grid
x[["idist"]] = 1 - x[["dist"]] # assigns new attribute
image(x["idist"]) # note the single [ for attribute selection
# toy example:
df = data.frame(z = c(1:6,NA,8,9),
xc = c(1,1,1,2,2,2,3,3,3),
yc = c(rep(c(0, 1.5, 3),3)))
coordinates(df) = ~xc+yc
gridded(df) = TRUE
df = as(df, "SpatialGridDataFrame") # to full grid
image(df["z"])
# draw labels to verify:
cc = coordinates(df)
z=df[["z"]]
zc=as.character(z)
zc[is.na(zc)]="NA"
text(cc[,1],cc[,2],zc)
# the following is weird, but illustrates the concept of row/col selection:
fullgrid(meuse.grid) = TRUE
image(meuse.grid)
image(meuse.grid[20:70, 10:70, "dist"], add = TRUE, col = bpy.colors())