These are variants of slide() that iterate over multiple inputs in
parallel. They are parallel in the sense that each input is processed in
parallel with the others, not in the sense of multicore computing. These
functions work similarly to map2() and pmap() from purrr.
slide2(
.x,
.y,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE
)slide2_vec(
.x,
.y,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE,
.ptype = NULL
)
slide2_dbl(
.x,
.y,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE
)
slide2_int(
.x,
.y,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE
)
slide2_lgl(
.x,
.y,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE
)
slide2_chr(
.x,
.y,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE
)
slide2_dfr(
.x,
.y,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE,
.names_to = rlang::zap(),
.name_repair = c("unique", "universal", "check_unique")
)
slide2_dfc(
.x,
.y,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE,
.size = NULL,
.name_repair = c("unique", "universal", "check_unique", "minimal")
)
pslide(.l, .f, ..., .before = 0L, .after = 0L, .step = 1L, .complete = FALSE)
pslide_vec(
.l,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE,
.ptype = NULL
)
pslide_dbl(
.l,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE
)
pslide_int(
.l,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE
)
pslide_lgl(
.l,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE
)
pslide_chr(
.l,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE
)
pslide_dfr(
.l,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE,
.names_to = rlang::zap(),
.name_repair = c("unique", "universal", "check_unique")
)
pslide_dfc(
.l,
.f,
...,
.before = 0L,
.after = 0L,
.step = 1L,
.complete = FALSE,
.size = NULL,
.name_repair = c("unique", "universal", "check_unique", "minimal")
)
A vector fulfilling the following invariants:
slide2()vec_size(slide2(.x, .y)) == vec_size_common(.x, .y)
vec_ptype(slide2(.x, .y)) == list()
slide2_vec() and slide2_*() variantsvec_size(slide2_vec(.x, .y)) == vec_size_common(.x, .y)
vec_size(slide2_vec(.x, .y)[[1]]) == 1L
vec_ptype(slide2_vec(.x, .y, .ptype = ptype)) == ptype
pslide()vec_size(pslide(.l)) == vec_size_common(!!! .l)
vec_ptype(pslide(.l)) == list()
pslide_vec() and pslide_*() variantsvec_size(pslide_vec(.l)) == vec_size_common(!!! .l)
vec_size(pslide_vec(.l)[[1]]) == 1L
vec_ptype(pslide_vec(.l, .ptype = ptype)) == ptype
[vector]
Vectors to iterate over. Vectors of size 1 will be recycled.
[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.
[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.
[positive integer(1)]
The number of elements to shift the window forward between function calls.
[logical(1)]
Should the function be evaluated on complete windows only? If FALSE,
the default, then partial computations will be allowed.
[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.
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.
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.
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.
[list]
A list of vectors. The length of .l determines the
number of arguments that .f will be called with. If .l has names,
they will be used as named arguments to .f. Elements of .l with size
1 will be recycled.
slide(), slide_index2(), hop_index2()
# Slide along two inputs at once
slide2(1:4, 5:8, ~list(.x, .y), .before = 2)
# Or, for more than two, use `pslide()`
pslide(list(1:4, 5:8, 9:12), ~list(.x, .y, ..3), .before = 2)
# You can even slide along the rows of multiple data frames of
# equal size at once
set.seed(16)
x <- data.frame(a = rnorm(5), b = rnorm(5))
y <- data.frame(c = letters[1:5], d = letters[6:10])
row_return <- function(x_rows, y_rows) {
if (sum(x_rows$a) < 0) {
x_rows
} else {
y_rows
}
}
slide2(x, y, row_return, .before = 1, .after = 2)
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