# lag_vec: Lag Transformation In timetk: A Tool Kit for Working with Time Series

 lag_vec R Documentation

## Lag Transformation

### Description

`lag_vec()` applies a Lag Transformation.

### Usage

``````lag_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.

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

• `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)

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

# --- MUTATE ----

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

``````

timetk documentation built on Sept. 22, 2023, 5:11 p.m.