Using foreach() in R to speed up loop for ggplot2 - r

Question

I would like to create a PDF file containing hundreds of plots in a certain order.
My strategy was using foreach() and storing each ggplot2 object into the output list, and then printing each ggplot2 object to the output file.
For example, I would like to plot a histogram of prices for every factor "carat" in the diamonds dataset:
library(ggplot2)
library(plyr)
library(foreach) # for parallelization
library(doParallel) # for parallelization
#setup parallel backend to use 4 processors
cl<-makeCluster(4)
registerDoParallel(cl)
# use diamonds dataset
carats.summary <- ddply(diamonds, .(carat), summarise, count = length(carat))
m.list <- foreach(i = 1:length(carats.summary$carat),
.packages = "ggplot2") %dopar% {
jcarat = carats.summary$carat[i]
m <- ggplot(subset(diamonds, carat == jcarat), aes(x = price)) +
geom_histogram()
print(m)
}
With this code, I am hoping to create a list of ggplot2 objects which I can then save into a single pdf file (for example using pdf()) in an ordered manner (for example, in ascending carats).
However, running this results in an error message:
Error in serialize(data, node$con) : error writing to connection
I suspect this is due to the fact that if I tried to append the ggplot2 object to a list, I would get a warning message like this:
lst <- vector(mode = "list")
lst[1] <- m
Warning message:
In lst[1] <- m :
number of items to replace is not a multiple of replacement length
Although this is pure speculation and I could be wrong.
Does anybody have an idea how to use foreach() to save ggplot2 objects onto a list? Or some way to parallelize for loops involving ggplot2?
Thanks in advance.

Answer 1

You shouldn't be printing the object inside the loop, just create the ggplot object. Only print when you have the graphic device open that you want.
m.list <- foreach(i = 1:length(carats.summary$carat),
.packages = "ggplot2") %dopar% {
jcarat = carats.summary$carat[i]
ggplot(subset(diamonds, carat == jcarat), aes(x = price)) +
geom_histogram()
}
then you can get at them with
m.list[[1]]
etc...