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The following chunk of code works only when ddply is non-parallelized; i.e., ddply(..., .parallel = FALSE). Why doesn't it work when .parallel=TRUE? I have a calculation to perform that requires parallelization and ddply is perfect for it, but I can't seem to figure out how to parallelize a function that includes a tryCatch() statement using ddply. It is as if ddply disregards the fact that the code is within a tryCatch().
# tryCatch in ddply
library(plyr)
library(dplyr)
library(reshape2)
library(parallel)
library(doParallel)
theFunc <- function(df)
m <- df$a
m <- tryCatch(
if(m>1)
# do something normal
m+1
else
# do something that throws an error
m+"mehwhatever"
,
warning=function(war)
message(war)
m <- df$a
return(m)
,
error=function(cond)
message(cond)
m <- df$a
return(m)
,
finally=
print("Does this even work?")
print(m)
)
df$a <- m
return(df)
df <- data.frame(a=1:10)
print(df)
nodes <- detectCores(logical = FALSE)
cl <- makeCluster(nodes)
registerDoParallel(cl)
df <- ddply(.data = df,.variables = c("a"),.fun = function(x)return(theFunc(x)),.parallel = TRUE,.paropts = list(.export=c(as.vector(lsf.str()))))
parallel::stopCluster(cl)
print(df)
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