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

It can be useful for, say, sliding over daily data in monthly chunks.

The underlying engine for breaking up .i is warp::warp_distance(). If you need more information about the .period types, that is the best place to look.

slide_period(
.x,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE
)

slide_period_vec(
.x,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE,
.ptype = NULL
)

slide_period_dbl(
.x,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE
)

slide_period_int(
.x,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE
)

slide_period_lgl(
.x,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE
)

slide_period_chr(
.x,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE
)

slide_period_dfr(
.x,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE,
.names_to = rlang::zap(),
.name_repair = c("unique", "universal", "check_unique")
)

slide_period_dfc(
.x,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE,
.size = NULL,
.name_repair = c("unique", "universal", "check_unique", "minimal")
)

## Arguments

.x [vector] The vector to iterate over and apply .f to. [Date / POSIXct / POSIXlt] A datetime index to break into periods. 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" [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. [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. [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. [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", or "check_unique". 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 standard recycling rules. Alternatively, specify the desired number of rows, and any inputs of length 1 will be recycled appropriately.

## Value

A vector fulfilling the following invariants:

### slide_period()

• vec_size(slide_period(.x)) == vec_size(unique(warp::warp_distance(.i)))

• vec_ptype(slide_period(.x)) == list()

### slide_period_vec() and slide_period_*() variants

• vec_size(slide_period_vec(.x)) == vec_size(unique(warp::warp_distance(.i)))

• vec_size(slide_period_vec(.x)[[1]]) == 1L

• vec_ptype(slide_period_vec(.x, .ptype = ptype)) == ptype

block(), slide(), slide_index()

## Examples

i <- as.Date("2019-01-28") + 0:5

# Split i into 2-day periods to apply .f to
slide_period(i, i, "day", identity, .every = 2)
#> [[1]]
#> [1] "2019-01-28" "2019-01-29"
#>
#> [[2]]
#> [1] "2019-01-30" "2019-01-31"
#>
#> [[3]]
#> [1] "2019-02-01" "2019-02-02"
#>
# Or into 1-month periods
slide_period(i, i, "month", identity)
#> [[1]]
#> [1] "2019-01-28" "2019-01-29" "2019-01-30" "2019-01-31"
#>
#> [[2]]
#> [1] "2019-02-01" "2019-02-02"
#>
# Now select:
# - The current 2-day period
# - Plus 1 2-day period before the current one
slide_period(i, i, "day", identity, .every = 2, .before = 1)
#> [[1]]
#> [1] "2019-01-28" "2019-01-29"
#>
#> [[2]]
#> [1] "2019-01-28" "2019-01-29" "2019-01-30" "2019-01-31"
#>
#> [[3]]
#> [1] "2019-01-30" "2019-01-31" "2019-02-01" "2019-02-02"
#>
# Alter the origin to control the reference date for
# how the 2-day groups are formed
origin <- as.Date("2019-01-29")
slide_period(i, i, "day", identity, .every = 2, .origin = origin)
#> [[1]]
#> [1] "2019-01-28"
#>
#> [[2]]
#> [1] "2019-01-29" "2019-01-30"
#>
#> [[3]]
#> [1] "2019-01-31" "2019-02-01"
#>
#> [[4]]
#> [1] "2019-02-02"
#>
# This can be useful for, say, monthly averages
daily_sales <- c(2, 5, 3, 6, 9, 4)
slide_period_dbl(daily_sales, i, "month", mean)
#> [1] 4.0 6.5
# If you need the index, slide over and return a data frame
sales_df <- data.frame(i = i, sales = daily_sales)

slide_period_dfr(
sales_df,
sales_df$i, "month", ~data.frame( i = max(.x$i),
sales = mean(.x\$sales)
)
)
#>            i sales
#> 1 2019-01-31   4.0
#> 2 2019-02-02   6.5
# One of the most unique features about slide_period() is that it is
# aware of how far apart elements of .i are in the .period you are
# interested in. For example, if you do a monthly slide with i2, selecting
# the current month and 1 month before it, then it will recognize that
# 2019-02-01 and 2019-04-01 are not beside each other, and it won't
# group them together.
i2 <- as.Date(c("2019-01-01", "2019-02-01", "2019-04-01", "2019-05-01"))

slide_period(i2, i2, "month", identity, .before = 1)
#> [[1]]
#> [1] "2019-01-01"
#>
#> [[2]]
#> [1] "2019-01-01" "2019-02-01"
#>
#> [[3]]
#> [1] "2019-04-01"
#>
#> [[4]]
#> [1] "2019-04-01" "2019-05-01"
#>