## Not run:
# # Define an integer dimension
# dimState <- ncdim_def( "StateNo", "count", 1:50 )
#
# # Make an integer variable. Note that an integer variable can have
# # a double precision dimension, or vice versa; there is no fixed
# # relationship between the precision of the dimension and that of the
# # associated variable. We just make an integer variable here for
# # illustration purposes.
# varPop <- ncvar_def("Pop", "count", dimState, -1,
# longname="Population", prec="integer")
#
# # Create a netCDF file with this variable
# ncnew <- nc_create( "states_population.nc", varPop )
#
# # Write some values to this variable on disk.
# popAlabama <- 4447100
# ncvar_put( ncnew, varPop, popAlabama, start=1, count=1 )
#
# # Add source info metadata to file
# ncatt_put( ncnew, 0, "source", "Census 2000 from census bureau web site")
#
# nc_close(ncnew)
#
# # Now illustrate some manipulations of the var.ncdf object
# filename <- "states_population.nc"
# nc <- nc_open(filename)
# print(paste("File",nc$filename,"contains",nc$nvars,"variables"))
# for( i in 1:nc$nvars ) {
# v <- nc$var[[i]]
# print(paste("Here is information on variable number",i))
# print(paste(" Name: ",v$name))
# print(paste(" Units:",v$units))
# print(paste(" Missing value:",v$missval))
# print(paste(" # dimensions :",v$ndims))
# print(paste(" Variable size:",v$varsize))
# }
#
# # Illustrate creating variables of various types. You will find
# # that the type of the missing_value attribute automatically follows
# # the type of the variable.
# dimt <- ncdim_def( "Time", "days", 1:3 )
# missval <- -1
# varShort <- ncvar_def( "varShort", "meters", dimt, missval, prec="short")
# varInt <- ncvar_def( "varInt", "meters", dimt, missval, prec="integer")
# varFloat <- ncvar_def( "varFloat", "meters", dimt, missval, prec="single")
# varDouble<- ncvar_def( "varDouble","meters", dimt, missval, prec="double")
# nctypes <- nc_create("vartypes.nc", list(varShort,varInt,varFloat,varDouble) )
# nc_close(nctypes)
# ## End(Not run)
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