Accumulate intermediate results of a vector reduction

accumulate() sequentially applies a 2-argument function to elements of a vector. Each application of the function uses the initial value or result of the previous application as the first argument. The second argument is the next value of the vector. The results of each application are returned in a list. The accumulation can optionally terminate before processing the whole vector in response to a done() signal returned by the accumulation function.

By contrast to accumulate(), reduce() applies a 2-argument function in the same way, but discards all results except that of the final function application.

accumulate2() sequentially applies a function to elements of two lists, .x and .y.

accumulate(.x, .f, ..., .init, .dir = c("forward", "backward"))

accumulate2(.x, .y, .f, ..., .init)

Arguments

.x	A list or atomic vector.
.f	For `accumulate()` `.f` is 2-argument function. The function will be passed the accumulated result or initial value as the first argument. The next value in sequence is passed as the second argument. For `accumulate2()`, a 3-argument function. The function will be passed the accumulated result as the first argument. The next value in sequence from `.x` is passed as the second argument. The next value in sequence from `.y` is passed as the third argument. The accumulation terminates early if `.f` returns a value wrapped in a `done()`.
...	Additional arguments passed on to the mapped function.
.init	If supplied, will be used as the first value to start the accumulation, rather than using `.x[[1]]`. This is useful if you want to ensure that `reduce` returns a correct value when `.x` is empty. If missing, and `.x` is empty, will throw an error.
.dir	The direction of accumulation as a string, one of `"forward"` (the default) or `"backward"`. See the section about direction below.
.y	For `accumulate2()` `.y` is the second argument of the pair. It needs to be 1 element shorter than the vector to be accumulated (`.x`). If `.init` is set, `.y` needs to be one element shorted than the concatenation of the initial value and `.x`.

Value

A vector the same length of .x with the same names as .x.

If .init is supplied, the length is extended by 1. If .x has names, the initial value is given the name ".init", otherwise the returned vector is kept unnamed.

If .dir is "forward" (the default), the first element is the initial value (.init if supplied, or the first element of .x) and the last element is the final reduced value. In case of a right accumulation, this order is reversed.

The accumulation terminates early if .f returns a value wrapped in a done(). If the done box is empty, the last value is used instead and the result is one element shorter (but always includes the initial value, even when terminating at the first iteration).

Life cycle

accumulate_right() is soft-deprecated in favour of the .dir argument as of rlang 0.3.0. Note that the algorithm has slightly changed: the accumulated value is passed to the right rather than the left, which is consistent with a right reduction.

Direction

When .f is an associative operation like + or c(), the direction of reduction does not matter. For instance, reducing the vector 1:3 with the binary function + computes the sum ((1 + 2) + 3) from the left, and the same sum (1 + (2 + 3)) from the right.

In other cases, the direction has important consequences on the reduced value. For instance, reducing a vector with list() from the left produces a left-leaning nested list (or tree), while reducing list() from the right produces a right-leaning list.

Examples

# With an associative operation, the final value is always the
# same, no matter the direction. You'll find it in the last element for a
# backward (left) accumulation, and in the first element for forward
# (right) one:
1:5 %>% accumulate(`+`)
#> [1]  1  3  6 10 15
1:5 %>% accumulate(`+`, .dir = "backward")
#> [1] 15 14 12  9  5

# The final value is always equal to the equivalent reduction:
1:5 %>% reduce(`+`)
#> [1] 15

# It is easier to understand the details of the reduction with
# `paste()`.
accumulate(letters[1:5], paste, sep = ".")
#> [1] "a"         "a.b"       "a.b.c"     "a.b.c.d"   "a.b.c.d.e"

# Note how the intermediary reduced values are passed to the left
# with a left reduction, and to the right otherwise:
accumulate(letters[1:5], paste, sep = ".", .dir = "backward")
#> [1] "a.b.c.d.e" "b.c.d.e"   "c.d.e"     "d.e"       "e"        

# `accumulate2()` is a version of `accumulate()` that works with
# 3-argument functions and one additional vector:
paste2 <- function(x, y, sep = ".") paste(x, y, sep = sep)
letters[1:4] %>% accumulate(paste2)
#> [1] "a"       "a.b"     "a.b.c"   "a.b.c.d"
letters[1:4] %>% accumulate2(c("-", ".", "-"), paste2)
#> [[1]]
#> [1] "a"
#> 
#> [[2]]
#> [1] "a-b"
#> 
#> [[3]]
#> [1] "a-b.c"
#> 
#> [[4]]
#> [1] "a-b.c-d"
#> 


# You can shortcircuit an accumulation and terminate it early by
# returning a value wrapped in a done(). In the following example
# we return early if the result-so-far, which is passed on the LHS,
# meets a condition:
paste3 <- function(out, input, sep = ".") {
  if (nchar(out) > 4) {
    return(done(out))
  }
  paste(out, input, sep = sep)
}
letters %>% accumulate(paste3)
#> [1] "a"     "a.b"   "a.b.c" "a.b.c"

# Note how we get twice the same value in the accumulation. That's
# because we have returned it twice. To prevent this, return an empty
# done box to signal to accumulate() that it should terminate with the
# value of the last iteration:
paste3 <- function(out, input, sep = ".") {
  if (nchar(out) > 4) {
    return(done())
  }
  paste(out, input, sep = sep)
}
letters %>% accumulate(paste3)
#> [1] "a"     "a.b"   "a.b.c"

# Here the early return branch checks the incoming inputs passed on
# the RHS:
paste4 <- function(out, input, sep = ".") {
  if (input == "f") {
    return(done())
  }
  paste(out, input, sep = sep)
}
letters %>% accumulate(paste4)
#> [1] "a"         "a.b"       "a.b.c"     "a.b.c.d"   "a.b.c.d.e"


# Simulating stochastic processes with drift
if (FALSE) {
library(dplyr)
library(ggplot2)

rerun(5, rnorm(100)) %>%
  set_names(paste0("sim", 1:5)) %>%
  map(~ accumulate(., ~ .05 + .x + .y)) %>%
  map_dfr(~ tibble(value = .x, step = 1:100), .id = "simulation") %>%
  ggplot(aes(x = step, y = value)) +
    geom_line(aes(color = simulation)) +
    ggtitle("Simulations of a random walk with drift")
}