warp_distance: Compute distances from a date time origin

Description Usage Arguments Details Value Period Precision Examples

View source: R/distance.R

Description

warp_distance() is a low level engine for computing date time distances.

It returns the distance from x to the origin in units defined by the period.

For example, period = "year" would return the number of years from the origin. Setting every = 2 would return the number of 2 year groups from the origin.

Usage

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warp_distance(x, period, ..., every = 1L, origin = NULL)

Arguments

x

[Date / POSIXct / POSIXlt]

A date time vector.

period

[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"

...

[dots]

These dots are for future extensions and must be empty.

every

[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.

origin

[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.

Details

The return value of warp_distance() has a variety of uses. It can be used for:

When the time zone of x differs from the time zone of origin, a warning is issued, and x is coerced to the time zone of origin without changing the number of seconds of x from the epoch. In other words, the time zone of x is directly changed to the time zone of origin without changing the underlying numeric representation. It is highly advised to specify an origin value with the same time zone as x. If a Date is used for x, its time zone is assumed to be "UTC".

Value

A double vector containing the distances.

Period

For period values of "year", "month", and "day", the information provided in origin is truncated. Practically this means that if you specify:

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warp_distance(period = "month", origin = as.Date("1970-01-15"))

then only 1970-01 will be used, and not the fact that the origin starts on the 15th of the month.

The period value of "quarter" is internally period = "month", every = every * 3. This means that for "quarter" the month specified for the origin will be used as the month to start counting from to generate the 3 month quarter.

To mimic the behavior of lubridate::floor_date(), use period = "week". Internally this is just period = "day", every = every * 7. To mimic the week_start argument of floor_date(), set origin to a date with a week day identical to the one you want the week to start from. For example, the default origin of 1970-01-01 is a Thursday, so this would be generate groups identical to floor_date(week_start = 4).

The period value of "yday" is computed as complete every-day periods from the origin, with a forced reset of the every-day counter every time you hit the month-day value of the origin. "yweek" is built on top of this internally as period = "yday", every = every * 7. This ends up using an algorithm very similar to lubridate::week(), with the added benefit of being able to control the origin date.

The period value of "mday" is computed as every-day periods within each month, with a forced reset of the every-day counter on the first day of each month. The most useful application of this is "mweek", which is implemented as period = "mday", every = every * 7. This allows you to group by the "week of the month". For "mday" and "mweek", only the year and month parts of the origin value are used. Because of this, the origin argument is not that interesting for these periods.

The "hour" period (and more granular frequencies) can produce results that might be surprising, even if they are technically correct. See the vignette at vignette("hour", package = "warp") for more information.

Precision

With POSIXct, the limit of precision is approximately the microsecond level. Only dates that are very close to the unix origin of 1970-01-01 can possibly represent microsecond resolution correctly (close being within about 40 years on either side). Otherwise, the values past the microsecond resolution are essentially random, and can cause problems for the distance calculations. Because of this, decimal digits past the microsecond range are zeroed out, so please do not attempt to rely on them. It should still be safe to work with microseconds, by, say, bucketing them by millisecond distances.

Examples

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x <- as.Date("1970-01-01") + -4:4
x

# Compute monthly distances (really, year + month)
warp_distance(x, "month")

# Compute distances every 2 days, relative to "1970-01-01"
warp_distance(x, "day", every = 2)

# Compute distances every 2 days, this time relative to "1970-01-02"
warp_distance(x, "day", every = 2, origin = as.Date("1970-01-02"))

y <- as.POSIXct("1970-01-01 00:00:01", "UTC") + c(0, 2, 3, 4, 5, 6, 10)

# Compute distances every 5 seconds, starting from the unix epoch of
# 1970-01-01 00:00:00
# So this buckets:
# [1970-01-01 00:00:00, 1970-01-01 00:00:05) = 0
# [1970-01-01 00:00:05, 1970-01-01 00:00:10) = 1
# [1970-01-01 00:00:10, 1970-01-01 00:00:15) = 2
warp_distance(y, "second", every = 5)

# Compute distances every 5 seconds, starting from the minimum of `x`
# 1970-01-01 00:00:01
# So this buckets:
# [1970-01-01 00:00:01, 1970-01-01 00:00:06) = 0
# [1970-01-01 00:00:06, 1970-01-01 00:00:11) = 1
# [1970-01-01 00:00:11, 1970-01-01 00:00:16) = 2
origin <- as.POSIXct("1970-01-01 00:00:01", "UTC")
warp_distance(y, "second", every = 5, origin = origin)

# ---------------------------------------------------------------------------
# Time zones

# When `x` is not UTC and `origin` is left as `NULL`, the origin is set as
# 1970-01-01 00:00:00 in the time zone of `x`. This seems to be the most
# practically useful default.
z <- as.POSIXct("1969-12-31 23:00:00", "UTC")
z_in_nyc <- as.POSIXct("1969-12-31 23:00:00", "America/New_York")

# Practically this means that these give the same result, because their
# `origin` values are defined in their respective time zones.
warp_distance(z, "year")
warp_distance(z_in_nyc, "year")

# Compare that to what would happen if we used a static `origin` of
# 1970-01-01 00:00:00 UTC.
# America/New_York is 5 hours behind UTC, so when `z_in_nyc` is converted to
# UTC the value becomes `1970-01-01 04:00:00 UTC`, a different year. Because
# this is generally surprising, a warning is thrown.
origin <- as.POSIXct("1970-01-01 00:00:00", tz = "UTC")
warp_distance(z, "year", origin = origin)
warp_distance(z_in_nyc, "year", origin = origin)

# ---------------------------------------------------------------------------
# `period = "yweek"`

x <- as.Date("2019-12-23") + 0:16
origin <- as.Date("1970-01-01")

# `"week"` counts the number of 7 day periods from the `origin`
# `"yweek"` restarts the 7 day counter every time you hit the month-day
# value of the `origin`. Notice how, for the `yweek` column, only 1 day was
# in the week starting with `2019-12-31`. This is because the next day is
# `2020-01-01`, which aligns with the month-day value of the `origin`.
data.frame(
  x = x,
  week = warp_distance(x, "week", origin = origin),
  yweek = warp_distance(x, "yweek", origin = origin)
)

# ---------------------------------------------------------------------------
# `period = "mweek"`

x <- as.Date("2019-12-23") + 0:16

# `"mweek"` breaks `x` up into weeks of the month. Notice how days 1-7
# of 2020-01 all have the same distance value. A forced reset of the 7 day
# counter is done at the 1st of every month. This results in the 3 day
# week of the month at the end of 2019-12, from 29-31.
data.frame(
  x = x,
  mweek = warp_distance(x, "mweek")
)

warp documentation built on Oct. 23, 2020, 7:23 p.m.