tk_augment_lags: Add many lags to the data

In timetk: A Tool Kit for Working with Time Series

Add many lags to the data

Description

A handy function for adding multiple lagged columns to a data frame. Works with dplyr groups too.

Usage

tk_augment_lags(.data, .value, .lags = 1, .names = "auto")

tk_augment_leads(.data, .value, .lags = -1, .names = "auto")

Arguments

.data	A tibble.
.value	One or more column(s) to have a transformation applied. Usage of tidyselect functions (e.g. contains()) can be used to select multiple columns.
.lags	One or more lags for the difference(s)
.names	A vector of names for the new columns. Must be of same length as .lags.

Details

Lags vs Leads

A negative lag is considered a lead. The tk_augment_leads() function is identical to tk_augment_lags() with the exception that the automatic naming convetion (.names = 'auto') will convert column names with negative lags to leads.

Benefits

This is a scalable function that is:

• Designed to work with grouped data using dplyr::group_by()

• Add multiple lags by adding a sequence of lags using the .lags argument (e.g. .lags = 1:20)

Value

Returns a tibble object describing the timeseries.

See Also

Augment Operations:

• tk_augment_timeseries_signature() - Group-wise augmentation of timestamp features

• tk_augment_holiday_signature() - Group-wise augmentation of holiday features

• tk_augment_slidify() - Group-wise augmentation of rolling functions

• tk_augment_lags() - Group-wise augmentation of lagged data

• tk_augment_differences() - Group-wise augmentation of differenced data

• tk_augment_fourier() - Group-wise augmentation of fourier series

Underlying Function:

• lag_vec() - Underlying function that powers tk_augment_lags()

Examples

library(dplyr)

# Lags
m4_monthly %>%
    group_by(id) %>%
    tk_augment_lags(contains("value"), .lags = 1:20)

# Leads
m4_monthly %>%
    group_by(id) %>%
    tk_augment_leads(value, .lags = 1:-20)

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