future_by: Apply a Function to a Data Frame Split by Factors via Futures

In future.apply: Apply Function to Elements in Parallel using Futures

Apply a Function to a Data Frame Split by Factors via Futures

Description

Apply a Function to a Data Frame Split by Factors via Futures

Usage

future_by(
  data,
  INDICES,
  FUN,
  ...,
  simplify = TRUE,
  future.envir = parent.frame()
)

Arguments

data	An R object, normally a data frame, possibly a matrix.
INDICES	A factor or a list of factors, each of length nrow(data).
FUN	a function to be applied to (usually data-frame) subsets of data.
simplify	logical: see base::tapply.
future.envir	An environment passed as argument envir to future::future() as-is.
...	Additional arguments pass to future_lapply() and then to FUN().

Details

Internally, data is grouped by INDICES into a list of data subset elements which is then processed by future_lapply(). When the groups differ significantly in size, the processing time may differ significantly between the groups. To correct for processing-time imbalances, adjust the amount of chunking via arguments future.scheduling and future.chunk.size.

Value

An object of class "by", giving the results for each subset. This is always a list if simplify is false, otherwise a list or array (see base::tapply). See also base::by() for details.

Note on 'stringsAsFactors'

The future_by() is modeled as closely as possible to the behavior of base::by(). Both functions have "default" S3 methods that calls data <- as.data.frame(data) internally. This call may in turn call an S3 method for as.data.frame() that coerces strings to factors or not depending on whether it has a stringsAsFactors argument and what its default is. For example, the S3 method of as.data.frame() for lists changed its (effective) default from stringsAsFactors = TRUE to stringsAsFactors = TRUE in R 4.0.0.

Examples

## ---------------------------------------------------------
## by()
## ---------------------------------------------------------
library(datasets) ## warpbreaks
library(stats)    ## lm()

y0 <- by(warpbreaks, warpbreaks[,"tension"],
         function(x) lm(breaks ~ wool, data = x))

plan(multisession)
y1 <- future_by(warpbreaks, warpbreaks[,"tension"],
                function(x) lm(breaks ~ wool, data = x))

plan(sequential)
y2 <- future_by(warpbreaks, warpbreaks[,"tension"],
                function(x) lm(breaks ~ wool, data = x))

future.apply documentation built on Oct. 28, 2024, 1:06 a.m.