Create a Python iterator from an R function

py_iterator(fn, completed = NULL, prefetch = 0L)

Arguments

fn: R function with no arguments.
completed: Special sentinel return value which indicates that iteration is complete (defaults to NULL).
prefetch: Number items to prefetch. Set this to a positive integer to avoid a deadlock in situations where the generator values are consumed by python background threads while the main thread is blocked.

Value

Python iterator which calls the R function for each iteration.

Details

Python generators are functions that implement the Python iterator protocol. In Python, values are returned using the yield keyword. In R, values are simply returned from the function.

In Python, the yield keyword enables successive iterations to use the state of previous iterations. In R, this can be done by returning a function that mutates its enclosing environment via the <<- operator. For example:

sequence_generator <- function(start) {
  value <- start
  function() {
    value <<- value + 1
    value
  }
}

Then create an iterator using py_iterator():

g <- py_iterator(sequence_generator(10))

Ending Iteration

In Python, returning from a function without calling yield indicates the end of the iteration. In R however, return is used to yield values, so the end of iteration is indicated by a special return value (NULL by default, however this can be changed using the completed parameter). For example:

sequence_generator <-function(start) {
  value <- start
  function() {
    value <<- value + 1
    if (value < 100)
      value
    else
      NULL
  }
}

Threading

Some Python APIs use generators to parallellize operations by calling the generator on a background thread and then consuming its results on the foreground thread. The py_iterator() function creates threadsafe iterators by ensuring that the R function is always called on the main thread (to be compatible with R's single-threaded runtime) even if the generator is run on a background thread.