```
## ---------------------------------------------------------
## lapply(), sapply(), tapply()
## ---------------------------------------------------------
x <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE, FALSE, FALSE, TRUE))
y0 <- lapply(x, FUN = quantile, probs = 1:3/4)
y1 <- future_lapply(x, FUN = quantile, probs = 1:3/4)
print(y1)
stopifnot(all.equal(y1, y0))
y0 <- sapply(x, FUN = quantile)
y1 <- future_sapply(x, FUN = quantile)
print(y1)
stopifnot(all.equal(y1, y0))
y0 <- vapply(x, FUN = quantile, FUN.VALUE = double(5L))
y1 <- future_vapply(x, FUN = quantile, FUN.VALUE = double(5L))
print(y1)
stopifnot(all.equal(y1, y0))
## ---------------------------------------------------------
## Parallel Random Number Generation
## ---------------------------------------------------------
# \donttest{
## Regardless of the future plan, the number of workers, and
## where they are, the random numbers produced are identical
plan(multisession)
set.seed(0xBEEF)
y1 <- future_lapply(1:5, FUN = rnorm, future.seed = TRUE)
str(y1)
plan(sequential)
set.seed(0xBEEF)
y2 <- future_lapply(1:5, FUN = rnorm, future.seed = TRUE)
str(y2)
stopifnot(all.equal(y1, y2))
# }
## ---------------------------------------------------------
## Process chunks of data.frame rows in parallel
## ---------------------------------------------------------
iris <- datasets::iris
chunks <- split(iris, seq(1, nrow(iris), length.out = 3L))
y0 <- lapply(chunks, FUN = function(iris) sum(iris$Sepal.Length))
y0 <- do.call(sum, y0)
y1 <- future_lapply(chunks, FUN = function(iris) sum(iris$Sepal.Length))
y1 <- do.call(sum, y1)
print(y1)
stopifnot(all.equal(y1, y0))
# \dontshow{
## R CMD check: make sure any open connections are closed afterward
if (!inherits(plan(), "sequential")) plan(sequential)
# }
```

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