How to make maps in R with parallel processing?

Question

How to make maps in R with parallel processing?

Navigation

#1 by (1 votes)

3

I have tried to make multiple maps at the same time in R using plot and parallel processing using the snowfall package. I have done the following function mapas :

mapas <- function (tx,
                  limite)
    {
    # Open o png
    png(filename=paste0("mapas_mpc/",tx,".png"),width = 1365, height = 1024)
    plot.new()
    par(mar=c(0,0,0,0),oma=c(0,0,0,0))

    # Plot South America border
    #map(regions = SA, fill = TRUE, col="gray", border="black")
    plot(limite, col="gray", border="black")

    # Load species shapefile
    tx_shp <- shapefile (paste0("MPC/",tx,".shp"))

    # Plot species shapefile
    plot(tx_shp,col=rgb(0/255,139/255,0/255,alpha=0.5), border="darkgreen" ,lwd=3,add=T)

    # Species name
    nome_sp <- gsub(pattern="_",replacement=" ",tx)

    # Plot species name
    text(x=-97,y=-27, nome_sp,font=4,cex=1.5)

    # Close PNG
    dev.off()
    cat(paste("Mapa de ",tx),"\n")
}

After that I start the parallel processing:

library(snowfall)

# Strat parallel processing
sfInit(parallel=T,cpus=3)

And then I run the mapas function in parallel:

sfLapply(x = lista_sp,fun = mapas,limite = am_sul )

"list_p": is a vector the characters which contains the names of species. "am_sul": is the shapefile of South America boundaries.

Then I have the following error:

Error in checkForRemoteErrors(val) : 3 nodes produced errors; first error: cannot coerce type 'S4' to vector of type 'double'

I do not want my maps to be on the same plot page, even because they are thousands. I need to make maps as fast as possible, so I chose parallel processing. How can I solve this problem?

r

asked by anonymous 21.07.2016 / 22:03

1 answer

Is it a bad practice to leave an entire Cache Model in IIS? What does 'redacts password' mean?

score 1 · Answer 1

You probably have some map that is giving trouble when it comes to running. But then you end up losing what you already did. I recommend you use a function that does not stop execution when you have an error.

When I use parallel codes I use the following workflow:

On Windows:

library(doSNOW)
library(foreach)
cl <- makeCluster(2)
registerDoSNOW(cl)

On Linux:

library(doMC)
registerDoMC(2)

plyr::l_ply(lista_sp, plyr::failwith(NULL, function(i, limite){
  mapas(i, limite = am_sul)
}), .parallel = T, limite = am_sul)

The failwith function modifies the function so that it returns a pre-defined value (in the case NULL ) in case any error occurs.

That way, at the end of the loop you can see in detail why some cases did not run.

However, seeing that your three nodes are giving error, it pays to check if the maps function is working for the first element of lista_sp .