date-count-between {clock}R Documentation

Counting: date

Description

This is a Date method for the date_count_between() generic.

date_count_between() counts the number of precision units between start and end (i.e., the number of years or months). This count corresponds to the whole number of units, and will never return a fractional value.

This is suitable for, say, computing the whole number of years or months between two dates, accounting for the day of the month.

Calendrical based counting:

These precisions convert to a year-month-day calendar and count while in that type.

Time point based counting:

These precisions convert to a time point and count while in that type.

For dates, whether a calendar or time point is used is not all that important, but is is fairly important for date-times.

Usage

## S3 method for class 'Date'
date_count_between(start, end, precision, ..., n = 1L)

Arguments

start, end

⁠[Date]⁠

A pair of date vectors. These will be recycled to their common size.

precision

⁠[character(1)]⁠

One of:

  • "year"

  • "quarter"

  • "month"

  • "week"

  • "day"

...

These dots are for future extensions and must be empty.

n

⁠[positive integer(1)]⁠

A single positive integer specifying a multiple of precision to use.

Details

"quarter" is equivalent to "month" precision with n set to n * 3L.

Value

An integer representing the number of precision units between start and end.

Comparison Direction

The computed count has the property that if start <= end, then ⁠start + <count> <= end⁠. Similarly, if start >= end, then ⁠start + <count> >= end⁠. In other words, the comparison direction between start and end will never change after adding the count to start. This makes this function useful for repeated count computations at increasingly fine precisions.

Examples

start <- date_parse("2000-05-05")
end <- date_parse(c("2020-05-04", "2020-05-06"))

# Age in years
date_count_between(start, end, "year")

# Number of "whole" months between these dates. i.e.
# `2000-05-05 -> 2020-04-05` is 239 months
# `2000-05-05 -> 2020-05-05` is 240 months
# Since 2020-05-04 occurs before the 5th of that month,
# it gets a count of 239
date_count_between(start, end, "month")

# Number of "whole" quarters between (same as `"month"` with `n * 3`)
date_count_between(start, end, "quarter")
date_count_between(start, end, "month", n = 3)

# Number of days between
date_count_between(start, end, "day")

# Number of full 3 day periods between these two dates
date_count_between(start, end, "day", n = 3)

# Essentially the truncated value of this
date_count_between(start, end, "day") / 3

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

# Breakdown into full years, months, and days between
x <- start

years <- date_count_between(x, end, "year")
x <- add_years(x, years)

months <- date_count_between(x, end, "month")
x <- add_months(x, months)

days <- date_count_between(x, end, "day")
x <- add_days(x, days)

data.frame(
  start = start,
  end = end,
  years = years,
  months = months,
  days = days
)

# Note that when breaking down a date like that, you may need to
# set `invalid` during intermediate calculations
start <- date_build(2019, c(3, 3, 4), c(30, 31, 1))
end <- date_build(2019, 5, 05)

# These are 1 month apart (plus a few days)
months <- date_count_between(start, end, "month")

# But adding that 1 month to `start` results in an invalid date
try(add_months(start, months))

# You can choose various ways to resolve this
start_previous <- add_months(start, months, invalid = "previous")
start_next <- add_months(start, months, invalid = "next")

days_previous <- date_count_between(start_previous, end, "day")
days_next <- date_count_between(start_next, end, "day")

# Resulting in slightly different day values.
# No result is "perfect". Choosing "previous" or "next" both result
# in multiple `start` dates having the same month/day breakdown values.
data.frame(
  start = start,
  end = end,
  months = months,
  days_previous = days_previous,
  days_next = days_next
)

[Package clock version 0.6.0 Index]