Slide over multiple inputs simultaneously

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.

Usage

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")
)

Arguments

.x, .y

[vector]

Vectors to iterate over. Vectors of size 1 will be recycled.

.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.

.l

[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.

Value

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_*() variants

• vec_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_*() variants

• vec_size(pslide_vec(.l)) == vec_size_common(!!! .l)

• vec_size(pslide_vec(.l)[[1]]) == 1L

• vec_ptype(pslide_vec(.l, .ptype = ptype)) == ptype

See also

slide(), slide_index2(), hop_index2()

Examples

# Slide along two inputs at once
slide2(1:4, 5:8, ~list(.x, .y), .before = 2)
#> [[1]]
#> [[1]][[1]]
#> [1] 1
#> 
#> [[1]][[2]]
#> [1] 5
#> 
#> 
#> [[2]]
#> [[2]][[1]]
#> [1] 1 2
#> 
#> [[2]][[2]]
#> [1] 5 6
#> 
#> 
#> [[3]]
#> [[3]][[1]]
#> [1] 1 2 3
#> 
#> [[3]][[2]]
#> [1] 5 6 7
#> 
#> 
#> [[4]]
#> [[4]][[1]]
#> [1] 2 3 4
#> 
#> [[4]][[2]]
#> [1] 6 7 8
#> 
#> 

# Or, for more than two, use `pslide()`
pslide(list(1:4, 5:8, 9:12), ~list(.x, .y, ..3), .before = 2)
#> [[1]]
#> [[1]][[1]]
#> [1] 1
#> 
#> [[1]][[2]]
#> [1] 5
#> 
#> [[1]][[3]]
#> [1] 9
#> 
#> 
#> [[2]]
#> [[2]][[1]]
#> [1] 1 2
#> 
#> [[2]][[2]]
#> [1] 5 6
#> 
#> [[2]][[3]]
#> [1]  9 10
#> 
#> 
#> [[3]]
#> [[3]][[1]]
#> [1] 1 2 3
#> 
#> [[3]][[2]]
#> [1] 5 6 7
#> 
#> [[3]][[3]]
#> [1]  9 10 11
#> 
#> 
#> [[4]]
#> [[4]][[1]]
#> [1] 2 3 4
#> 
#> [[4]][[2]]
#> [1] 6 7 8
#> 
#> [[4]][[3]]
#> [1] 10 11 12
#> 
#> 

# 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)
#> [[1]]
#>   c d
#> 1 a f
#> 2 b g
#> 3 c h
#> 
#> [[2]]
#>   c d
#> 1 a f
#> 2 b g
#> 3 c h
#> 4 d i
#> 
#> [[3]]
#>   c d
#> 1 b g
#> 2 c h
#> 3 d i
#> 4 e j
#> 
#> [[4]]
#>   c d
#> 1 c h
#> 2 d i
#> 3 e j
#> 
#> [[5]]
#>           a        b
#> 1 -1.444229 1.024973
#> 2  1.147829 0.573142
#>