Lag Transformation
lag_vec() applies a Lag Transformation.
lag_vec(x, lag = 1) lead_vec(x, lag = -1)
x |
A numeric vector to be lagged. |
lag |
Which lag (how far back) to be included in the differencing calculation. Negative lags are leads. |
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.
A numeric vector
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()
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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.