NB a prerequisite is that the netcdf libraries are already installed.
$ mkdir -p ~/.R/libs
$ export R_LIBS=~/.R/libs
$ echo 'R_LIBS=~/.R/libs/'
$ export R_LIBS=~/.R/libs/
$ echo 'R_LIBS=~/.R/libs/'
$ echo 'R_LIBS=~/.R/libs/' >> ~/.Renviron
#Download the ncdf package from CRAN
#All this can be done in a temporary area ... (?)
$ tar xvzf ncdf_1.6.tar.gz
$ R CMD INSTALL ncdf
$ history
...
...
# there are perhaps even easier ways to do this:
$ install.packages("gplots", dependencies = TRUE)
# no source was linked ... but the mirror choose automatically opens
# and one just selects a site and the install happens!
...
$
Making use of ncdf in R
$ R
Method='unspecified'
Shortname='outfile'
library(ncdf)
nd = open.ncdf("interpolations.nc")
x=get.var.ncdf(nd,"Original")
y=get.var.ncdf(nd,"Interpolations")
x[x==-1]=0
v <- array(x, length(x))
w <- array(y, length(y))
xx=x[!is.na(x) & x>=0 & x < 100]
yy=y[!is.na(x) & x>=0 & x < 100]
yyy=yy[!is.na(yy) & yy>=0 & yy < 100]
xxx=xx[!is.na(yy) & yy>=0 & yy < 100]
jpeg(filename=paste(Shortname,'jpg',sep='.'))
plot(xxx,yyy,pch=46,xlab='Original Value',ylab='Interpoltion',main=Method)
XC=cor(xxx,yyy)
ff <- lm(xxx ~ yyy)
ff$coefficients[1]
ff$coefficients[2]
z=ff$coefficients[1]*xxx + ff$coefficients[2]
#lines(xxx,z)
text(0,95,"Linear Regression",pos=4)
text(0,90,paste("Gradient =",format(ff$coefficients[1],digits=5)),pos=4)
text(0,85,paste("Intercept =",format(ff$coefficients[2],digits=5)),pos=4)
text(0,75,paste("Correlation coefficient =",format(XC,digits=5)),pos=4)
dev.off()
Alternatively, place the code in a file and run from the command line:
$ Rscript plot_netcdf.R "Test Plot" OUTPUT
that will produce the file OUTPUT.jpg with the plot title “Test Plot”.