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

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

- 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()`

.

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))
```