step_lag {recipes} | R Documentation |
step_lag
creates a specification of a recipe step that
will add new columns of lagged data. Lagged data will
by default include NA values where the lag was induced.
These can be removed with step_naomit()
, or you may
specify an alternative filler value with the default
argument.
step_lag( recipe, ..., role = "predictor", trained = FALSE, lag = 1, prefix = "lag_", default = NA, columns = NULL, skip = FALSE, id = rand_id("lag") )
recipe |
A recipe object. The step will be added to the sequence of operations for this recipe. |
... |
One or more selector functions to choose variables
for this step. See |
role |
For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model. |
trained |
A logical to indicate if the quantities for preprocessing have been estimated. |
lag |
A vector of positive integers. Each specified column will be lagged for each value in the vector. |
prefix |
A prefix for generated column names, default to "lag_". |
default |
Passed to |
columns |
A character string of variable names that will
be populated (eventually) by the |
skip |
A logical. Should the step be skipped when the
recipe is baked by |
id |
A character string that is unique to this step to identify it. |
The step assumes that the data are already in the proper sequential order for lagging.
An updated version of recipe
with the new step added to the
sequence of any existing operations.
When you tidy()
this step, a tibble with column
terms
(the columns that will be affected) is returned.
The underlying operation does not allow for case weights.
Other row operation steps:
step_arrange()
,
step_filter()
,
step_impute_roll()
,
step_naomit()
,
step_sample()
,
step_shuffle()
,
step_slice()
n <- 10 start <- as.Date("1999/01/01") end <- as.Date("1999/01/10") df <- data.frame( x = runif(n), index = 1:n, day = seq(start, end, by = "day") ) recipe(~., data = df) %>% step_lag(index, day, lag = 2:3) %>% prep(df) %>% bake(df)