Parallelization in R (tutorial)

Question

I've got a question kinda basic. It's the first time I use parallel package in R

I'm running a function you could find here. Well, this is not exactly the function but is very similar.

So my function is running on the WTI oil price dataset (see down below) and since p_max and q_max are 12 it takes like 3 min on my PC.

So I am trying to parallelize this process:

this is my code so far:

n.cores <- round(detectCores()/2)
cl <- makeCluster(n.cores)

clusterEvalQ(cl, {library(dplyr); library(forecast)})
clusterExport(cl, c("wti_", "which_arima"))

start_time <- Sys.time()
a <- parApply(cl = cl,
              wti_,
              function(x)
              wti_models <- which_arima(x,p_max = 12, d_max = 1, q_max = 12, expl = F))
end_time <- Sys.time()
stopCluster(cl)

end_time - start_time

this gets me an error

So this is what I know:

• I am creating a new environment only with forecast and dplyrpackages
• In this environment I am using the wti_ data frame and the which_arima function.

The desired result as you could see in the link is a dataframe, so my question is: how can I apply the function over a dataset, but using the parallelization.

Like how do I say to R, "apply this function, with this data, these libraries, and return my dataframe"

---

data:

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