Multidplyr and ggmap's mutate_geocode

Question

I am looking to geocode quickly a large number of addresses using Google's API accessed through ggmap (for which I am using ggmap version 2.7 which allows to specify a google API key through register_google() function).

The basic functionality works but is very slow when using dplyr in the following manner:

devtools::install_github("dkahle/ggmap") #needed for v 2.7, not yet on CRAN

library(dplyr)
library(ggmap)
DF %>%
  select(ID, full_address) %>%
  mutate_geocode(full_address)

I am getting at best 1-3 queries per second, which for a few thousand records will not be fast enough for my use. I would like to use multidplyr to run parallel queries and speed up the process.

I have seen a multidplyr example on SO here but I cannot seem to implement it for my case.

The solution I imagine could use multidplyr::partition() and either use:

1. the ggmap::mutate_geocode() function; or
2. the ggmap::geocode() function which returns a pair of columns (longitude and latitude) together with dplyr::do() function.

Based on the SO example linked above, I believe the code could look something like this:

  library(dplyr)
  library(ggmap)
  devtools::install_github("hadley/multidplyr")
  library(multidplyr)

  DF$split <- 1:4 # the column by which data will be split across 4 threads

  # For solution (1)
  DF %>%
  select(ID, full_address) %>%
  partition(split) %>%
  mutate_geocode(full_address)


  # For solution (2)
  DF %>%
  select(ID, full_address) %>%
  partition(split) %>%
  do(
  {ggmap::geo_code(full_address)}
     )

Can anyone help me get this in the correct shape? Any recommendations to make this even faster?