Speed up API calls in R - r

Question

I am querying Freebase to get the genre information for some 10000 movies.
After reading How to optimise scraping with getURL() in R, I tried to execute the requests in parallel. However, I failed - see below. Besides parallelization, I also read that httr might be a better alternative to RCurl.
My questions are:
Is it possible to speed up the API calls by using
a) a parallel version of the loop below (using a WINDOWS machine)?
b) alternatives to getURL such as GET in the httr-package?
library(RCurl)
library(jsonlite)
library(foreach)
library(doSNOW)
df <- data.frame(film=c("Terminator", "Die Hard", "Philadelphia", "A Perfect World", "The Parade", "ParaNorman", "Passengers", "Pink Cadillac", "Pleasantville", "Police Academy", "The Polar Express", "Platoon"), genre=NA)
f_query_freebase <- function(film.title){
request <- paste0("https://www.googleapis.com/freebase/v1/search?",
"filter=", paste0("(all alias{full}:", "\"", film.title, "\"", " type:\"/film/film\")"),
"&indent=TRUE",
"&limit=1",
"&output=(/film/film/genre)")
temp <- getURL(URLencode(request), ssl.verifypeer = FALSE)
data <- fromJSON(temp, simplifyVector=FALSE)
genre <- paste(sapply(data$result[[1]]$output$`/film/film/genre`[[1]], function(x){as.character(x$name)}), collapse=" | ")
return(genre)
}
# Non-parallel version
# ----------------------------------
for (i in df$film){
df$genre[which(df$film==i)] <- f_query_freebase(i)
}
# Parallel version - Does not work
# ----------------------------------
# Set up parallel computing
cl<-makeCluster(2)
registerDoSNOW(cl)
foreach(i=df$film) %dopar% {
df$genre[which(df$film==i)] <- f_query_freebase(i)
}
stopCluster(cl)
# --> I get the following error: "Error in { : task 1 failed", further saying that it cannot find the function "getURL".

Answer 1

This doesn't achieve parallel requests within a single R session, however, it's something I've used to achieve >1 simultaneous requests (e.g. in parallel) across multiple R sessions, so it may be useful.
At a high level
You'll want to break the process into a few parts:
Get a list of the URLs/API calls you need to make and store as a csv/text file
Use the code below as a template for starting multiple R processes and dividing the work among them
Note: this happened to run on windows, so I used powershell. On mac this could be written in bash.
Powershell/bash script
Use a single powershell script to start off multiple instances R processes (here we divide the work between 3 processes):
e.g. save a plain text file with .ps1 file extension, you can double click on it to run it, or schedule it with task scheduler/cron:
start powershell { cd C:\Users\Administrator\Desktop; Rscript extract.R 1; TIMEOUT 20000 }
start powershell { cd C:\Users\Administrator\Desktop; Rscript extract.R 2; TIMEOUT 20000 }
start powershell { cd C:\Users\Administrator\Desktop; Rscript extract.R 3; TIMEOUT 20000 }
What's it doing? It will:
Go the the Desktop, start a script it finds called extract.R, and provide an argument to the R script (1, 2, and 3).
The R processes
Each R process can look like this
# Get command line argument
arguments <- commandArgs(trailingOnly = TRUE)
process_number <- as.numeric(arguments[1])
api_calls <- read.csv("api_calls.csv")
# work out which API calls each R script should make (e.g.
indicies <- seq(process_number, nrow(api_calls), 3)
api_calls_for_this_process_only <- api_calls[indicies, ] # this subsets for 1/3 of the API calls
# (the other two processes will take care of the remaining calls)
# Now, make API calls as usual using rvest/jsonlite or whatever you use for that