lag_vec: Lag Transformation

In timetk: A Tool Kit for Working with Time Series

Lag Transformation

Description

lag_vec() applies a Lag Transformation.

Usage

lag_vec(x, lag = 1)

lead_vec(x, lag = -1)

Arguments

x	A vector to be lagged.
lag	Which lag (how far back) to be included in the differencing calculation. Negative lags are leads.

Details

Benefits:

This function is NA padded by default so it works well with dplyr::mutate() operations. The function allows both lags and leads (negative lags).

Lag Calculation

A lag is an offset of lag periods. NA values are returned for the number of lag periods.

Lead Calculation

A negative lag is considered a lead. The only difference between lead_vec() and lag_vec() is that the lead_vec() function contains a starting negative value.

Value

A numeric vector

See Also

Modeling and Advanced Lagging:

• recipes::step_lag() - Recipe for adding lags in tidymodels modeling

• tk_augment_lags() - Add many lags group-wise to a data.frame (tibble)

Vectorized Transformations:

• Box Cox Transformation: box_cox_vec()

• Lag Transformation: lag_vec()

• Differencing Transformation: diff_vec()

• Rolling Window Transformation: slidify_vec()

• Loess Smoothing Transformation: smooth_vec()

• Fourier Series: fourier_vec()

• Missing Value Imputation for Time Series: ts_impute_vec(), ts_clean_vec()

Examples

library(dplyr)

# --- VECTOR ----

# Lag
1:10 %>% lag_vec(lag = 1)

# Lead
1:10 %>% lag_vec(lag = -1)


# --- MUTATE ----

m4_daily %>%
    group_by(id) %>%
    mutate(lag_1 = lag_vec(value, lag = 1))

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