tk_augment_lags: Add many lags to the data

View source: R/augment-tk_augment_lags.R

tk_augment_lagsR Documentation

Add many lags to the data


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


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

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



A tibble.


One or more column(s) to have a transformation applied. Usage of tidyselect functions (e.g. contains()) can be used to select multiple columns.


One or more lags for the difference(s)


A vector of names for the new columns. Must be of same length as .lags.


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.


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)


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()



# 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.