This family of functions is for working with invalid calendar dates. These represent dates that don't exist, such as year_month_day(2019, 02, 31).

Invalid dates are allowed in clock, provided that they are eventually resolved by using invalid_resolve() or by manually resolving them through arithmetic or setter functions.

invalid_detect(x)

invalid_any(x)

invalid_count(x)

invalid_resolve(x, ..., invalid = NULL)

## Arguments

x [calendar] A calendar vector. These dots are for future extensions and must be empty. [character(1) / NULL] One of the following invalid date resolution strategies: "previous": The previous valid instant in time. "previous-day": The previous valid day in time, keeping the time of day. "next": The next valid instant in time. "next-day": The next valid day in time, keeping the time of day. "overflow": Overflow by the number of days that the input is invalid by. Time of day is dropped. "overflow-day": Overflow by the number of days that the input is invalid by. Time of day is kept. "NA": Replace invalid dates with NA. "error": Error on invalid dates. Using either "previous" or "next" is generally recommended, as these two strategies maintain the relative ordering between elements of the input. If NULL, defaults to "error". If getOption("clock.strict") is TRUE, invalid must be supplied and cannot be NULL. This is a convenient way to make production code robust to invalid dates.

## Value

• invalid_detect(): Returns a logical vector detecting invalid dates.

• invalid_any(): Returns TRUE if any invalid dates are detected.

• invalid_count(): Returns a single integer containing the number of invalid dates.

• invalid_resolve(): Returns x with invalid dates resolved using the invalid strategy.

## Details

Invalid dates must be resolved before converting them to a time point.

It is recommended to use "previous" or "next" for resolving invalid dates, as these ensure that relative ordering among x is maintained. This is a often a very important property to maintain when doing time series data analysis. See the examples for more information.

## Examples

# Invalid date
x <- year_month_day(2019, 04, 30:31, c(3, 2), 30, 00)
x
#> <year_month_day<second>[2]>
#> [1] "2019-04-30 03:30:00" "2019-04-31 02:30:00"
invalid_detect(x)
#> [1] FALSE  TRUE
# Previous valid moment in time
x_previous <- invalid_resolve(x, invalid = "previous")
x_previous
#> <year_month_day<second>[2]>
#> [1] "2019-04-30 03:30:00" "2019-04-30 23:59:59"
# Previous valid day, retaining time of day
x_previous_day <- invalid_resolve(x, invalid = "previous-day")
x_previous_day
#> <year_month_day<second>[2]>
#> [1] "2019-04-30 03:30:00" "2019-04-30 02:30:00"
# Note that "previous" retains the relative ordering in x
x[1] < x[2]
#> [1] TRUEx_previous[1] < x_previous[2]
#> [1] TRUE
# But "previous-day" here does not!
x_previous_day[1] < x_previous_day[2]
#> [1] FALSE
y <- year_quarter_day(2019, 1, 90:92)
y
#> <year_quarter_day<January><day>[3]>
#> [1] "2019-Q1-90" "2019-Q1-91" "2019-Q1-92"
# Overflow rolls forward by the number of days between y and the previous
# valid date
invalid_resolve(y, invalid = "overflow")
#> <year_quarter_day<January><day>[3]>
#> [1] "2019-Q1-90" "2019-Q2-01" "2019-Q2-02"