mutate_by_time: Mutate (for Time Series Data)

In timetk: A Tool Kit for Working with Time Series

Mutate (for Time Series Data)

Description

mutate_by_time() is a time-based variant of the popular dplyr::mutate() 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".

Usage

mutate_by_time(
  .data,
  .date_var,
  .by = "day",
  ...,
  .type = c("floor", "ceiling", "round")
)

Arguments

.data	A tbl object or data.frame
.date_var	A column containing date or date-time values to summarize. If missing, attempts to auto-detect date column.
.by	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. The name gives the name of the column in the output. The value can be: A vector of length 1, which will be recycled to the correct length. A vector the same length as the current group (or the whole data frame if ungrouped). NULL, to remove the column. A data frame or tibble, to create multiple columns in the output.
.type	One of "floor", "ceiling", or "round. Defaults to "floor". See lubridate::round_date.

Value

A tibble or data.frame

See Also

Time-Based dplyr functions:

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

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

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

• filter_by_time() - Quickly filter using date ranges.

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

• slice_period() - Apply slice inside periods (windows)

• condense_period() - Convert to a different periodicity

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

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

Examples

# Libraries
library(dplyr)

# First value in each month
m4_daily_first_by_month_tbl <- m4_daily %>%
    group_by(id) %>%
    mutate_by_time(
        .date_var = date,
        .by       = "month", # Setup for monthly aggregation
        # mutate recycles a single value
        first_value_by_month  = first(value)
    )
m4_daily_first_by_month_tbl

# Visualize Time Series vs 1st Value Each Month
m4_daily_first_by_month_tbl %>%
    tidyr::pivot_longer(value:first_value_by_month) %>%
    plot_time_series(date, value, name,
                     .facet_scale = "free", .facet_ncol = 2,
                     .smooth = FALSE, .interactive = FALSE)

timetk documentation built on Nov. 2, 2023, 6:18 p.m.