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

future_by(data, INDICES, FUN, ..., simplify = TRUE)

data | An R object, normally a data frame, possibly a matrix. |
---|---|

INDICES | A factor or a list of factors, each of length |

FUN | a function to be applied to (usually data-frame) subsets of |

simplify | logical: see base::tapply. |

... | Additional arguments pass to |

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.

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`

.

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.

## --------------------------------------------------------- ## 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))