R: Lag Transformation

lag_vec {timetk} | R Documentation

## Lag Transformation

### Description

`lag_vec()` applies a Lag Transformation.

### Usage

    lag_vec(x, lag = 1)

    lead_vec(x, lag = -1)

### Arguments

`x` | A numeric 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)
    library(timetk)

    # --- 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))

[Package timetk version 2.6.1 Index]