Multicore generation of plots

I have a for loop which generates via png() and dev.off() a plot and saves it the working directory.

The loop I have is similar to the following example

test.df<-data.frame(id=1:25000, x=rnorm(25000),y=rnorm(25000))

for (i in test.df$id){
  plot(test.df$x[test.df$id==i], test.df$y[test.df$id==i], xlab="chi",ylab="psi")
}

The for loop will run and generate thousands of plots. Is it possible to make it run parallel on all 8 cores of my system so that I can get the plots faster?

PS. The code is an example. My original problem and plots are much more complicated. Don't go viral on the example.

Solution

Provided you are using a new version of R, then this should be straightforward. The trick is to create a function that can be run on any core in any order. First we create our data frame:

test.df = data.frame(id=1:250, x=rnorm(250),y=rnorm(250))

Next we create the function that runs on each core:

#I could also pass the row or the entire data frame
myplot = function(id) {
  fname = paste0("/tmp/plot", id, ".png")
  png(fname)
  plot(test.df$x[id], test.df$y[id], 
      xlab="chi",ylab="psi")
  dev.off()
  return(fname)
}

Then I load the parallel package (this comes with base R)

library(parallel)

and then use mclapply

no_of_cores = 8
##Non windows
mclapply(1:nrow(test.df), myplot, 
         mc.cores = no_of_cores)

##All OS's
cl = makeCluster(no_of_cores)
clusterExport(cl, "test.df")
parSapply(cl, 1:nrow(test.df), myplot)
stopCluster(cl)

There are two advantages here:

The package parallel comes with R, so we don't need to install anything extra
We can switch off the "parallel" part:
```
sapply(1:nrow(test.df), myplot)
```