slide: Slide
In DavisVaughan/slurrr: Sliding Window Functions

slide

R Documentation

Slide

Description

slide() iterates through .x using a sliding window, applying .f to each sub-window of .x.

Usage

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")
)

Arguments

`.x`	`⁠[vector]⁠` The vector to iterate over and apply `.f` to.
`.f`	`⁠[function / formula]⁠` If a function, it is used as is. If a formula, e.g. `~ .x + 2`, it is converted to a function. There are three ways to refer to the arguments: For a single argument function, use `.` For a two argument function, use `.x` and `.y` For more arguments, use `..1`, `..2`, `..3` etc This syntax allows you to create very compact anonymous functions.
`...`	Additional arguments passed on to the mapped function.
`.before`, `.after`	`⁠[integer(1) / Inf]⁠` The number of values before or after the current element to include in the sliding window. Set to `Inf` to select all elements before or after the current element. Negative values are allowed, which allows you to "look forward" from the current element if used as the `.before` value, or "look backwards" if used as `.after`.
`.step`	`⁠[positive integer(1)]⁠` The number of elements to shift the window forward between function calls.
`.complete`	`⁠[logical(1)]⁠` Should the function be evaluated on complete windows only? If `FALSE`, the default, then partial computations will be allowed.
`.ptype`	`⁠[vector(0) / NULL]⁠` A prototype corresponding to the type of the output. If `NULL`, the default, the output type is determined by computing the common type across the results of the calls to `.f`. If supplied, the result of each call to `.f` will be cast to that type, and the final output will have that type. If `getOption("vctrs.no_guessing")` is `TRUE`, the `.ptype` must be supplied. This is a way to make production code demand fixed types.
`.names_to`	This controls what to do with input names supplied in `...`. By default, input names are zapped. If a string, specifies a column where the input names will be copied. These names are often useful to identify rows with their original input. If a column name is supplied and `...` is not named, an integer column is used instead. If `NULL`, the input names are used as row names.
`.name_repair`	One of `"unique"`, `"universal"`, `"check_unique"`, `"unique_quiet"`, or `"universal_quiet"`. See `vec_as_names()` for the meaning of these options. With `vec_rbind()`, the repair function is applied to all inputs separately. This is because `vec_rbind()` needs to align their columns before binding the rows, and thus needs all inputs to have unique names. On the other hand, `vec_cbind()` applies the repair function after all inputs have been concatenated together in a final data frame. Hence `vec_cbind()` allows the more permissive minimal names repair.
`.size`	If, `NULL`, the default, will determine the number of rows in `vec_cbind()` output by using the tidyverse recycling rules. Alternatively, specify the desired number of rows, and any inputs of length 1 will be recycled appropriately.

Details

Unlike lapply() or purrr::map(), which construct calls like

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

the equivalent with slide() looks like

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

which is approximately

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

except in the case of data frames or arrays, which are iterated over row-wise.

If .x has names, then the output will preserve those names.

Using vctrs::vec_cast(), the output of .f will be automatically cast to the type required by the variant of ⁠slide_*()⁠ being used.

Value

A vector fulfilling the following invariants:

`slide()`

vec_size(slide(.x)) == vec_size(.x)
vec_ptype(slide(.x)) == list()

`slide_vec()` and `⁠slide_*()⁠` variants

vec_size(slide_vec(.x)) == vec_size(.x)
vec_size(slide_vec(.x)[[1]]) == 1L
vec_ptype(slide_vec(.x, .ptype = ptype)) == ptype

Examples

# 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())

DavisVaughan/slurrr documentation built on Oct. 29, 2024, 2:28 p.m.