accumulate applies a function recursively over a list from the left, while accumulate_right applies the function from the right. Unlike reduce both functions keep the intermediate results.

accumulate(.x, .f, ..., .init)

accumulate_right(.x, .f, ..., .init)

Arguments

.x

A list or atomic vector.

.f

For reduce(), a 2-argument function. The function will be passed the accumulated value as the first argument and the "next" value as the second argument.

For reduce2(), a 3-argument function. The function will be passed the accumulated value as the first argument, the next value of .x as the second argument, and the next value of .y as the third argument.

...

Additional arguments passed on to .f.

.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.

Examples

1:3 %>% accumulate(`+`) 1:10 %>% accumulate_right(`*`) # From Haskell's scanl documentation 1:10 %>% accumulate(max, .init = 5) # Understanding the arguments .x and .y when .f # is a lambda function # .x is the accumulating value 1:10 %>% accumulate(~ .x) # .y is element in the list 1:10 %>% accumulate(~ .y) # Simulating stochastic processes with drift not_run({ library(dplyr) library(ggplot2) rerun(5, rnorm(100)) %>% set_names(paste0("sim", 1:5)) %>% map(~ accumulate(., ~ .05 + .x + .y)) %>% map_df(~ data_frame(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") })
#> Error: <text>:16:19: unexpected symbol #> 15: not_run({ #> 16: library(dplyr) library #> ^