View source: R/future-modify.R
future_modify | R Documentation |
These functions work exactly the same as purrr::modify()
functions, but
allow you to modify in parallel.
future_modify( .x, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_modify_at( .x, .at, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_modify_if( .x, .p, .f, ..., .else = NULL, .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE )
.x |
A list or atomic vector. |
.f |
A function, formula, or vector (not necessarily atomic). If a function, it is used as is. If a formula, e.g.
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. If a component is not
present, the value of |
... |
Additional arguments passed on to the mapped function. |
.options |
The |
.env_globals |
The environment to look for globals required by |
.progress |
A single logical. Should a progress bar be displayed? Only works with multisession, multicore, and multiprocess futures. Note that if a multicore/multisession future falls back to sequential, then a progress bar will not be displayed. Warning: The |
.at |
A character vector of names, positive numeric vector of
positions to include, or a negative numeric vector of positions to
exlude. Only those elements corresponding to |
.p |
A single predicate function, a formula describing such a
predicate function, or a logical vector of the same length as |
.else |
A function applied to elements of |
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.
An object the same class as .x
library(magrittr) plan(multisession, workers = 2) # 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()
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