summarise_by_time: Summarise (for Time Series Data)

View source: R/dplyr-summarise_by_time.R

summarise_by_timeR Documentation

Summarise (for Time Series Data)


summarise_by_time() is a time-based variant of the popular dplyr::summarise() function that uses .date_var to specify a date or date-time column and .by to group the calculation by groups like "5 seconds", "week", or "3 months".

summarise_by_time() and summarize_by_time() are synonyms.


  .by = "day",
  .type = c("floor", "ceiling", "round"),
  .week_start = NULL

  .by = "day",
  .type = c("floor", "ceiling", "round"),
  .week_start = NULL



A tbl object or data.frame


A column containing date or date-time values to summarize. If missing, attempts to auto-detect date column.


A time unit to summarise by. Time units are collapsed using lubridate::floor_date() or lubridate::ceiling_date().

The value can be:

  • second

  • minute

  • hour

  • day

  • week

  • month

  • bimonth

  • quarter

  • season

  • halfyear

  • year

Arbitrary unique English abbreviations as in the lubridate::period() constructor are allowed.


Name-value pairs of summary functions. The name will be the name of the variable in the result.

The value can be:

  • A vector of length 1, e.g. min(x), n(), or sum(

  • A vector of length n, e.g. quantile().

  • A data frame, to add multiple columns from a single expression.


One of "floor", "ceiling", or "round. Defaults to "floor". See lubridate::round_date.


when unit is weeks, specify the reference day. 7 represents Sunday and 1 represents Monday.


A tibble or data.frame

Useful summary functions

  • Sum: sum()

  • Center: mean(), median()

  • Spread: sd(), var()

  • Range: min(), max()

  • Count: dplyr::n(), dplyr::n_distinct()

  • Position: dplyr::first(), dplyr::last(), dplyr::nth()

  • Correlation: cor(), cov()

See Also

Time-Based dplyr functions:

  • summarise_by_time() - Easily summarise using a date column.

  • mutate_by_time() - Simplifies applying mutations by time windows.

  • filter_by_time() - Quickly filter using date ranges.

  • filter_period() - Apply filtering expressions inside periods (windows)

  • between_time() - Range detection for date or date-time sequences.

  • pad_by_time() - Insert time series rows with regularly spaced timestamps

  • condense_period() - Convert to a different periodicity

  • slidify() - Turn any function into a sliding (rolling) function


# Libraries

# First value in each month
m4_daily %>%
    group_by(id) %>%
        .date_var = date,
        .by       = "month", # Setup for monthly aggregation
        # Summarization
        value  = first(value)

# Last value in each month (day is first day of next month with ceiling option)
m4_daily %>%
    group_by(id) %>%
        .by        = "month",
        value      = last(value),
        .type      = "ceiling"
    ) %>%
    # Shift to the last day of the month
    mutate(date = date %-time% "1 day")

# Total each year (.by is set to "year" now)
m4_daily %>%
    group_by(id) %>%
        .by        = "year",
        value      = sum(value)

timetk documentation built on June 1, 2022, 1:07 a.m.