block() breaks up the i-ndex by period, and then uses that to define the indices to chop x with.

For example, it can split x into monthly or yearly blocks. Combined with purrr::map(), it is a way to iterate over a vector in "time blocks".

block(x, i, period, every = 1L, origin = NULL)

## Arguments

x [vector] The vector to block. [Date / POSIXct / POSIXlt] The datetime index to block by. There are 3 restrictions on the index: The size of the index must match the size of x, they will not be recycled to their common size. The index must be an increasing vector, but duplicate values are allowed. The index cannot have missing values. [character(1)] A string defining the period to group by. Valid inputs can be roughly broken into: "year", "quarter", "month", "week", "day" "hour", "minute", "second", "millisecond" "yweek", "mweek" "yday", "mday" [positive integer(1)] The number of periods to group together. For example, if the period was set to "year" with an every value of 2, then the years 1970 and 1971 would be placed in the same group. [Date(1) / POSIXct(1) / POSIXlt(1) / NULL] The reference date time value. The default when left as NULL is the epoch time of 1970-01-01 00:00:00, in the time zone of the index. This is generally used to define the anchor time to count from, which is relevant when the every value is > 1.

## Value

A vector fulfilling the following invariants:

• vec_size(block(x)) == vec_size(unique(warp::warp_boundary(i)))

• vec_ptype(block(x)) == list()

• vec_ptype(block(x)[[1]]) == vec_ptype(x)

## Details

block() determines the indices to block by with warp::warp_boundary(), and splits x by those indices using vctrs::vec_chop().

Like slide(), block() splits data frame x values row wise.

slide_period(), slide(), slide_index()

## Examples

x <- 1:6
i <- as.Date("2019-01-01") + c(-2:2, 31)

block(i, i, period = "year")
#> [[1]]
#> [1] "2018-12-30" "2018-12-31"
#>
#> [[2]]
#> [1] "2019-01-01" "2019-01-02" "2019-01-03" "2019-02-01"
#>
# Data frames are split row wise
df <- data.frame(x = x, i = i)
block(df, i, period = "month")
#> [[1]]
#>   x          i
#> 1 1 2018-12-30
#> 2 2 2018-12-31
#>
#> [[2]]
#>   x          i
#> 1 3 2019-01-01
#> 2 4 2019-01-02
#> 3 5 2019-01-03
#>
#> [[3]]
#>   x          i
#> 1 6 2019-02-01
#>
# Iterate over these blocks to apply a function over
# non-overlapping period blocks. For example, to compute a
# mean over yearly or monthly blocks.
vapply(block(x, i, "year"), mean, numeric(1))
#> [1] 1.5 4.5vapply(block(x, i, "month"), mean, numeric(1))
#> [1] 1.5 4.0 6.0
# block by every 2 months, ensuring that we start counting
# the 1st of the 2 months from 2019-01-01
block(i, i, period = "month", every = 2, origin = as.Date("2019-01-01"))
#> [[1]]
#> [1] "2018-12-30" "2018-12-31"
#>
#> [[2]]
#> [1] "2019-01-01" "2019-01-02" "2019-01-03" "2019-02-01"
#>
# Use the origin to instead start counting from 2018-12-01, meaning
# that [2018-12, 2019-01] gets bucketed together.
block(i, i, period = "month", every = 2, origin = as.Date("2018-12-01"))
#> [[1]]
#> [1] "2018-12-30" "2018-12-31" "2019-01-01" "2019-01-02" "2019-01-03"
#>
#> [[2]]
#> [1] "2019-02-01"
#>