tk_augment_differences: Add many differenced columns to the data

View source: R/augment-tk_augment_differences.R

tk_augment_differencesR Documentation

Add many differenced columns to the data


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


  .lags = 1,
  .differences = 1,
  .log = FALSE,
  .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)


The number of differences to apply.


If TRUE, applies log-differences.


A vector of names for the new columns. Must be of same length as the number of output columns. Use "auto" to automatically rename the columns.



This is a scalable function that is:

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

  • Add multiple differences by adding a sequence of differences 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:

  • diff_vec() - Underlying function that powers tk_augment_differences()



m4_monthly %>%
    group_by(id) %>%
    tk_augment_differences(value, .lags = 1:20)

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