furrr (version 0.1.0)

future_modify: Modify elements selectively via futures

Description

These functions work exactly the same as purrr::modify() functions, but allow you to modify in parallel.

Usage

future_modify(.x, .f, ..., .progress = FALSE, .options = future_options())
future_modify_at(.x, .at, .f, ..., .progress = FALSE,
  .options = future_options())
future_modify_if(.x, .p, .f, ..., .progress = FALSE,
  .options = future_options())

Arguments

.x

A list or atomic vector.

.f

A function, formula, or atomic vector.

If a function, it is used as is.

If a formula, e.g. ~ .x + 2, it is converted to a function. There are three ways to refer to the arguments:

• For a single argument function, use .

• For a two argument function, use .x and .y

• For more arguments, use ..1, ..2, ..3 etc

This syntax allows you to create very compact anonymous functions.

If character vector, numeric vector, or list, it is converted to an extractor function. Character vectors index by name and numeric vectors index by position; use a list to index by position and name at different levels. Within a list, wrap strings in get-attr() to extract named attributes. If a component is not present, the value of .default will be returned.

...

Additional arguments passed on to .f.

.progress

A logical, for whether or not to print a progress bar for multiprocess, multisession, and multicore plans.

.options

The future specific options to use with the workers. This must be the result from a call to future_options().

.at

A character vector of names or a numeric vector of positions. Only those elements corresponding to .at will be modified.

.p

A single predicate function, a formula describing such a predicate function, or a logical vector of the same length as .x. Alternatively, if the elements of .x are themselves lists of objects, a string indicating the name of a logical element in the inner lists. Only those elements where .p evaluates to TRUE will be modified.

Value

An object the same class as .x

Details

From purrr) Since the transformation can alter the structure of the input; it's your responsibility to ensure that the transformation produces a valid output. For example, if you're modifying a data frame, .f must preserve the length of the input.

Examples

# NOT RUN {
library(furrr)
library(dplyr) # for the pipe

# }
# NOT RUN {
plan(multiprocess)
# }
# NOT RUN {
# Convert each col to character, in parallel
future_modify(mtcars, as.character)

iris %>%
 future_modify_if(is.factor, as.character) %>%
 str()

mtcars %>% future_modify_at(c(1, 4, 5), as.character) %>% str()

# }