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This family of functions revolves around splitting an iv on its endpoints, which results in a new iv that is entirely disjoint (i.e. non-overlapping). The intervals in the resulting iv are known as "splits".

  • iv_splits() computes the disjoint splits for x.

  • iv_identify_splits() identifies the splits that correspond to each interval in x. It replaces x with a list of the same size where each element of the list contains the splits that the corresponding interval in x overlaps. This is particularly useful alongside tidyr::unnest().

  • iv_locate_splits() returns a two column data frame with a key column containing the result of iv_splits() and a loc list-column containing integer vectors that map each interval in x to the splits that it overlaps.

Usage

iv_splits(x, ..., on = NULL)

iv_identify_splits(x, ..., on = NULL)

iv_locate_splits(x, ..., on = NULL)

Arguments

x

[iv]

An interval vector.

...

These dots are for future extensions and must be empty.

on

[vector / NULL]

An optional vector of additional values to split on.

This should have the same type as iv_start(x).

Value

  • For iv_splits(), an iv with the same type as x.

  • For iv_identify_splits(), a list-of containing ivs with the same size as x.

  • For iv_locate_splits(), a two column data frame with a key column of the same type as x and loc list-column containing integer vectors.

Graphical Representation

Graphically, generating splits looks like:

Examples

library(tidyr)
library(dplyr)

# Guests to a party and their arrival/departure times
guests <- tibble(
  arrive = as.POSIXct(
    c("2008-05-20 19:30:00", "2008-05-20 20:10:00", "2008-05-20 22:15:00"),
    tz = "UTC"
  ),
  depart = as.POSIXct(
    c("2008-05-20 23:00:00", "2008-05-21 00:00:00", "2008-05-21 00:30:00"),
    tz = "UTC"
  ),
  name = list(
    c("Mary", "Harry"),
    c("Diana", "Susan"),
    "Peter"
  )
)

guests <- unnest(guests, name) %>%
  mutate(iv = iv(arrive, depart), .keep = "unused")

guests
#> # A tibble: 5 × 2
#>   name                                          iv
#>   <chr>                                 <iv<dttm>>
#> 1 Mary  [2008-05-20 19:30:00, 2008-05-20 23:00:00)
#> 2 Harry [2008-05-20 19:30:00, 2008-05-20 23:00:00)
#> 3 Diana [2008-05-20 20:10:00, 2008-05-21 00:00:00)
#> 4 Susan [2008-05-20 20:10:00, 2008-05-21 00:00:00)
#> 5 Peter [2008-05-20 22:15:00, 2008-05-21 00:30:00)

# You can determine the disjoint intervals at which people
# arrived/departed with `iv_splits()`
iv_splits(guests$iv)
#> <iv<datetime<UTC>>[5]>
#> [1] [2008-05-20 19:30:00, 2008-05-20 20:10:00)
#> [2] [2008-05-20 20:10:00, 2008-05-20 22:15:00)
#> [3] [2008-05-20 22:15:00, 2008-05-20 23:00:00)
#> [4] [2008-05-20 23:00:00, 2008-05-21 00:00:00)
#> [5] [2008-05-21 00:00:00, 2008-05-21 00:30:00)

# Say you'd like to determine who was at the party at any given time
# throughout the night
guests <- mutate(guests, splits = iv_identify_splits(iv))
guests
#> # A tibble: 5 × 3
#>   name                                          iv           splits
#>   <chr>                                 <iv<dttm>> <list<iv<dttm>>>
#> 1 Mary  [2008-05-20 19:30:00, 2008-05-20 23:00:00)              [3]
#> 2 Harry [2008-05-20 19:30:00, 2008-05-20 23:00:00)              [3]
#> 3 Diana [2008-05-20 20:10:00, 2008-05-21 00:00:00)              [3]
#> 4 Susan [2008-05-20 20:10:00, 2008-05-21 00:00:00)              [3]
#> 5 Peter [2008-05-20 22:15:00, 2008-05-21 00:30:00)              [3]

# Unnest the splits to generate disjoint intervals for each guest
guests <- guests %>%
  unnest(splits) %>%
  select(name, splits)

guests
#> # A tibble: 15 × 2
#>    name                                      splits
#>    <chr>                                 <iv<dttm>>
#>  1 Mary  [2008-05-20 19:30:00, 2008-05-20 20:10:00)
#>  2 Mary  [2008-05-20 20:10:00, 2008-05-20 22:15:00)
#>  3 Mary  [2008-05-20 22:15:00, 2008-05-20 23:00:00)
#>  4 Harry [2008-05-20 19:30:00, 2008-05-20 20:10:00)
#>  5 Harry [2008-05-20 20:10:00, 2008-05-20 22:15:00)
#>  6 Harry [2008-05-20 22:15:00, 2008-05-20 23:00:00)
#>  7 Diana [2008-05-20 20:10:00, 2008-05-20 22:15:00)
#>  8 Diana [2008-05-20 22:15:00, 2008-05-20 23:00:00)
#>  9 Diana [2008-05-20 23:00:00, 2008-05-21 00:00:00)
#> 10 Susan [2008-05-20 20:10:00, 2008-05-20 22:15:00)
#> 11 Susan [2008-05-20 22:15:00, 2008-05-20 23:00:00)
#> 12 Susan [2008-05-20 23:00:00, 2008-05-21 00:00:00)
#> 13 Peter [2008-05-20 22:15:00, 2008-05-20 23:00:00)
#> 14 Peter [2008-05-20 23:00:00, 2008-05-21 00:00:00)
#> 15 Peter [2008-05-21 00:00:00, 2008-05-21 00:30:00)

# Tabulate who was there at any given time
guests %>%
  group_by(splits) %>%
  summarise(n = n(), who = list(name))
#> # A tibble: 5 × 3
#>                                       splits     n who      
#>                                   <iv<dttm>> <int> <list>   
#> 1 [2008-05-20 19:30:00, 2008-05-20 20:10:00)     2 <chr [2]>
#> 2 [2008-05-20 20:10:00, 2008-05-20 22:15:00)     4 <chr [4]>
#> 3 [2008-05-20 22:15:00, 2008-05-20 23:00:00)     5 <chr [5]>
#> 4 [2008-05-20 23:00:00, 2008-05-21 00:00:00)     3 <chr [3]>
#> 5 [2008-05-21 00:00:00, 2008-05-21 00:30:00)     1 <chr [1]>

# ---------------------------------------------------------------------------

x <- iv_pairs(c(1, 5), c(4, 9), c(12, 15))
x
#> <iv<double>[3]>
#> [1] [1, 5)   [4, 9)   [12, 15)

# You can provide additional singular values to split on with `on`
iv_splits(x, on = c(2, 13))
#> <iv<double>[6]>
#> [1] [1, 2)   [2, 4)   [4, 5)   [5, 9)   [12, 13) [13, 15)