slide(.x, .f, ..., .before = 0L, .after = 0L, .step = 1L, .complete = FALSE)

slide_vec(
  .x,
  .f,
  ...,
  .before = 0L,
  .after = 0L,
  .step = 1L,
  .complete = FALSE,
  .ptype = NULL
)

slide_dbl(
  .x,
  .f,
  ...,
  .before = 0L,
  .after = 0L,
  .step = 1L,
  .complete = FALSE
)

slide_int(
  .x,
  .f,
  ...,
  .before = 0L,
  .after = 0L,
  .step = 1L,
  .complete = FALSE
)

slide_lgl(
  .x,
  .f,
  ...,
  .before = 0L,
  .after = 0L,
  .step = 1L,
  .complete = FALSE
)

slide_chr(
  .x,
  .f,
  ...,
  .before = 0L,
  .after = 0L,
  .step = 1L,
  .complete = FALSE
)

slide_dfr(
  .x,
  .f,
  ...,
  .before = 0L,
  .after = 0L,
  .step = 1L,
  .complete = FALSE,
  .names_to = rlang::zap(),
  .name_repair = c("unique", "universal", "check_unique")
)

slide_dfc(
  .x,
  .f,
  ...,
  .before = 0L,
  .after = 0L,
  .step = 1L,
  .complete = FALSE,
  .size = NULL,
  .name_repair = c("unique", "universal", "check_unique", "minimal")
)

.f(.x[[i]], ...)

.f(vctrs::vec_slice(.x, i), ...)

.f(.x[i], ...)

# The defaults work similarly to `map()`
slide(1:5, ~.x)

# Use `.before`, `.after`, and `.step` to control the window
slide(1:5, ~.x, .before = 1)

# This can be used for rolling means
slide_dbl(rnorm(5), mean, .before = 2)

# Or more flexible rolling operations
slide(rnorm(5), ~ .x - mean(.x), .before = 2)

# `.after` allows you to "align to the left" rather than the right
slide(1:5, ~.x, .after = 2)

# And a mixture of `.before` and `.after`
# allows you complete control over the exact alignment.
# Below, "center alignment" is used.
slide(1:5, ~.x, .before = 1, .after = 1)

# The `.step` controls how the window is shifted along `.x`,
# allowing you to "skip" iterations if you only need a less granular result
slide(1:10, ~.x, .before = 2, .step = 3)

# `.complete` controls whether or not partial results are computed.
# By default, they are, but setting `.complete = TRUE` restricts
# `slide()` to only evaluate the function where a complete window exists.
slide(1:5, ~.x, .before = 2, .after = 1)
slide(1:5, ~.x, .before = 2, .after = 1, .complete = TRUE)

# ---------------------------------------------------------------------------
# Data frames

# Data frames are iterated over rowwise
mtcars_rowwise <- slide(mtcars, ~.x)
mtcars_rowwise[1:3]

# This means that any column name is easily accessible
slide_dbl(mtcars, ~.x$mpg + .x$cyl)

# More advanced rowwise iteration is available as well by using the
# other arguments
mtcars_rowwise_window <- slide(mtcars, ~.x, .before = 1, .after = 1)
mtcars_rowwise_window[1:3]

# ---------------------------------------------------------------------------
# Cumulative sliding

# Using the special cased value, `Inf`, you can ask `slide()` to pin the
# start of the sliding window to the first element, effectively creating
# a cumulative window
slide(1:5, ~.x, .before = Inf)

# Same with `.after`, this creates a window where you start with all of the
# elements, but decrease the total number over each iteration
slide(1:5, ~.x, .after = Inf)

# ---------------------------------------------------------------------------
# Negative `.before` / `.after`

# `.before` is allowed to be negative, allowing you to "look forward" in
# your vector. Note that `abs(.before) <= .after` must hold if `.before` is
# negative. In this example, we look forward to elements in locations 2 and 3
# but place the result in position 1 in the output.
slide(1:5, ~.x, .before = -1, .after = 2)

# `.after` can be negative as well to "look backwards"
slide(1:5, ~.x, .before = 2, .after = -1)

# ---------------------------------------------------------------------------
# Removing padding

# If you are looking for a way to remove the `NA` values from something like
# this, then it doesn't exist as a built in option.
x <- rnorm(10)
slide_dbl(x, mean, .before = 3, .step = 2, .complete = TRUE)

# Adding an option to `slide_dbl()` to remove the `NA` values would destroy
# its size stability. Instead, you can use a combination of `slide_dfr()`
# to get the start/stop indices with `hop_index_vec()`.
i <- seq_along(x)
idx <- slide_dfr(
  i,
  ~data.frame(start = .x[1], stop = .x[length(.x)]),
  .before = 3,
  .step = 2,
  .complete = TRUE
)

idx

hop_index_vec(x, i, idx$start, idx$stop, mean, .ptype = double())