Chopping

• vec_chop() provides an efficient method to repeatedly slice a vector. It captures the pattern of map(indices, vec_slice, x = x). When no indices are supplied, it is generally equivalent to as.list().

• list_unchop() combines a list of vectors into a single vector, placing elements in the output according to the locations specified by indices. It is similar to vec_c(), but gives greater control over how the elements are combined. When no indices are supplied, it is identical to vec_c(), but typically a little faster.

If indices selects every value in x exactly once, in any order, then list_unchop() is the inverse of vec_chop() and the following invariant holds:

list_unchop(vec_chop(x, indices = indices), indices = indices) == x

Usage

vec_chop(x, ..., indices = NULL, sizes = NULL)

list_unchop(
  x,
  ...,
  indices = NULL,
  ptype = NULL,
  name_spec = NULL,
  name_repair = c("minimal", "unique", "check_unique", "universal", "unique_quiet",
    "universal_quiet"),
  error_arg = "x",
  error_call = current_env()
)

Arguments

x

A vector

...

These dots are for future extensions and must be empty.

indices

For vec_chop(), a list of positive integer vectors to slice x with, or NULL. Can't be used if sizes is already specified. If both indices and sizes are NULL, x is split into its individual elements, equivalent to using an indices of as.list(vec_seq_along(x)).

For list_unchop(), a list of positive integer vectors specifying the locations to place elements of x in. Each element of x is recycled to the size of the corresponding index vector. The size of indices must match the size of x. If NULL, x is combined in the order it is provided in, which is equivalent to using vec_c().

sizes

An integer vector of non-negative sizes representing sequential indices to slice x with, or NULL. Can't be used if indices is already specified.

For example, sizes = c(2, 4) is equivalent to indices = list(1:2, 3:6), but is typically faster.

sum(sizes) must be equal to vec_size(x), i.e. sizes must completely partition x, but an individual size is allowed to be 0.

ptype

If NULL, the default, the output type is determined by computing the common type across all elements of x. Alternatively, you can supply ptype to give the output a known type.

name_spec

A name specification for combining inner and outer names. This is relevant for inputs passed with a name, when these inputs are themselves named, like outer = c(inner = 1), or when they have length greater than 1: outer = 1:2. By default, these cases trigger an error. You can resolve the error by providing a specification that describes how to combine the names or the indices of the inner vector with the name of the input. This specification can be:

• A function of two arguments. The outer name is passed as a string to the first argument, and the inner names or positions are passed as second argument.

• An anonymous function as a purrr-style formula.

• A glue specification of the form "{outer}_{inner}".

• An rlang::zap() object, in which case both outer and inner names are ignored and the result is unnamed.

See the name specification topic.

name_repair

How to repair names, see repair options in vec_as_names().

error_arg

An argument name as a string. This argument will be mentioned in error messages as the input that is at the origin of a problem.

error_call

The execution environment of a currently running function, e.g. caller_env(). The function will be mentioned in error messages as the source of the error. See the call argument of abort() for more information.

Value

• vec_chop(): A list where each element has the same type as x. The size of the list is equal to vec_size(indices), vec_size(sizes), or vec_size(x) depending on whether or not indices or sizes is provided.

• list_unchop(): A vector of type vec_ptype_common(!!!x), or ptype, if specified. The size is computed as vec_size_common(!!!indices) unless the indices are NULL, in which case the size is vec_size_common(!!!x).

Dependencies of vec_chop()

• vec_slice()

Dependencies of list_unchop()

• vec_c()

Examples

vec_chop(1:5)
#> [[1]]
#> [1] 1
#> 
#> [[2]]
#> [1] 2
#> 
#> [[3]]
#> [1] 3
#> 
#> [[4]]
#> [1] 4
#> 
#> [[5]]
#> [1] 5
#> 

# These two are equivalent
vec_chop(1:5, indices = list(1:2, 3:5))
#> [[1]]
#> [1] 1 2
#> 
#> [[2]]
#> [1] 3 4 5
#> 
vec_chop(1:5, sizes = c(2, 3))
#> [[1]]
#> [1] 1 2
#> 
#> [[2]]
#> [1] 3 4 5
#> 

# Can also be used on data frames
vec_chop(mtcars, indices = list(1:3, 4:6))
#> [[1]]
#>                mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4     21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
#> Datsun 710    22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
#> 
#> [[2]]
#>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
#> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
#> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
#> 

# If `indices` selects every value in `x` exactly once,
# in any order, then `list_unchop()` inverts `vec_chop()`
x <- c("a", "b", "c", "d")
indices <- list(2, c(3, 1), 4)
vec_chop(x, indices = indices)
#> [[1]]
#> [1] "b"
#> 
#> [[2]]
#> [1] "c" "a"
#> 
#> [[3]]
#> [1] "d"
#> 
list_unchop(vec_chop(x, indices = indices), indices = indices)
#> [1] "a" "b" "c" "d"

# When unchopping, size 1 elements of `x` are recycled
# to the size of the corresponding index
list_unchop(list(1, 2:3), indices = list(c(1, 3, 5), c(2, 4)))
#> [1] 1 2 1 3 1

# Names are retained, and outer names can be combined with inner
# names through the use of a `name_spec`
lst <- list(x = c(a = 1, b = 2), y = 1)
list_unchop(lst, indices = list(c(3, 2), c(1, 4)), name_spec = "{outer}_{inner}")
#> y_1 x_b x_a y_2 
#>   1   2   1   1 

# An alternative implementation of `ave()` can be constructed using
# `vec_chop()` and `list_unchop()` in combination with `vec_group_loc()`
ave2 <- function(.x, .by, .f, ...) {
  indices <- vec_group_loc(.by)$loc
  chopped <- vec_chop(.x, indices = indices)
  out <- lapply(chopped, .f, ...)
  list_unchop(out, indices = indices)
}

breaks <- warpbreaks$breaks
wool <- warpbreaks$wool

ave2(breaks, wool, mean)
#>  [1] 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704
#>  [8] 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704
#> [15] 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704
#> [22] 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704 25.25926
#> [29] 25.25926 25.25926 25.25926 25.25926 25.25926 25.25926 25.25926
#> [36] 25.25926 25.25926 25.25926 25.25926 25.25926 25.25926 25.25926
#> [43] 25.25926 25.25926 25.25926 25.25926 25.25926 25.25926 25.25926
#> [50] 25.25926 25.25926 25.25926 25.25926 25.25926

identical(
  ave2(breaks, wool, mean),
  ave(breaks, wool, FUN = mean)
)
#> [1] TRUE

# If you know your input is sorted and you'd like to split on the groups,
# `vec_run_sizes()` can be efficiently combined with `sizes`
df <- data_frame(
  g = c(2, 5, 5, 6, 6, 6, 6, 8, 9, 9),
  x = 1:10
)
#> Warning: `data_frame()` was deprecated in tibble 1.1.0.
#> ℹ Please use `tibble()` instead.
vec_chop(df, sizes = vec_run_sizes(df$g))
#> [[1]]
#> # A tibble: 1 × 2
#>       g     x
#>   <dbl> <int>
#> 1     2     1
#> 
#> [[2]]
#> # A tibble: 2 × 2
#>       g     x
#>   <dbl> <int>
#> 1     5     2
#> 2     5     3
#> 
#> [[3]]
#> # A tibble: 4 × 2
#>       g     x
#>   <dbl> <int>
#> 1     6     4
#> 2     6     5
#> 3     6     6
#> 4     6     7
#> 
#> [[4]]
#> # A tibble: 1 × 2
#>       g     x
#>   <dbl> <int>
#> 1     8     8
#> 
#> [[5]]
#> # A tibble: 2 × 2
#>       g     x
#>   <dbl> <int>
#> 1     9     9
#> 2     9    10
#> 

# If you have a list of homogeneous vectors, sometimes it can be useful to
# unchop, apply a function to the flattened vector, and then rechop according
# to the original indices. This can be done efficiently with `list_sizes()`.
x <- list(c(1, 2, 1), c(3, 1), 5, double())
x_flat <- list_unchop(x)
x_flat <- x_flat + max(x_flat)
vec_chop(x_flat, sizes = list_sizes(x))
#> [[1]]
#> [1] 6 7 6
#> 
#> [[2]]
#> [1] 8 6
#> 
#> [[3]]
#> [1] 10
#> 
#> [[4]]
#> numeric(0)
#>