time_expand: A time based extension to 'tidyr::complete()'.

In timeplyr: Fast Tidy Tools for Date and Date-Time Manipulation

A time based extension to tidyr::complete().

Description

A time based extension to tidyr::complete().

Usage

time_expand(
  data,
  time = NULL,
  ...,
  .by = NULL,
  time_by = NULL,
  from = NULL,
  to = NULL,
  sort = TRUE
)

time_complete(
  data,
  time = NULL,
  ...,
  .by = NULL,
  time_by = NULL,
  from = NULL,
  to = NULL,
  sort = TRUE,
  fill = NULL
)

Arguments

data	A data frame.
time	Time variable.
...	Groups to expand.
.by	(Optional). A selection of columns to group by for this operation. Columns are specified using tidy-select.
time_by	A timespan.
from	Time series start date.
to	Time series end date.
sort	Logical. If TRUE expanded/completed variables are sorted.
fill	A named list containing value-name pairs to fill the named implicit missing values.

Details

This works much the same as tidyr::complete(), except that you can supply an additional time argument to allow for completing implicit time gaps and creating time sequences by group.

Value

A data.frame of expanded time by or across groups.

Examples

library(timeplyr)
library(dplyr)
library(lubridate)
library(nycflights13)

x <- flights$time_hour

time_num_gaps(x) # Missing hours

flights_count <- flights %>%
  fastplyr::f_count(time_hour)

# Fill in missing hours
flights_count %>%
  time_complete(time = time_hour)

# You can specify units too
flights_count %>%
  time_complete(time = time_hour, time_by = "hours")
flights_count %>%
  time_complete(time = as_date(time_hour), time_by = "days") #  Nothing to complete here

# Where time_expand() and time_complete() really shine is how fast they are with groups
flights %>%
  group_by(origin, dest) %>%
  time_expand(time = time_hour, time_by = dweeks(1))

timeplyr documentation built on April 3, 2025, 6:15 p.m.