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.
tk_augment_differences( .data, .value, .lags = 1, .differences = 1, .log = FALSE, .names = "auto" )
.data |
A tibble. |
.value |
One or more column(s) to have a transformation applied. Usage
of |
.lags |
One or more lags for the difference(s) |
.differences |
The number of differences to apply. |
.log |
If TRUE, applies log-differences. |
.names |
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. |
Benefits
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.
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()
library(tidyverse)
library(timetk)
m4_monthly %>%
group_by(id) %>%
tk_augment_differences(value, .lags = 1:20)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.