# Slidify Rolling Window Transformation (Augmented Version)

Source:`R/recipes-step_slidify_augment.R`

`step_slidify_augment.Rd`

`step_slidify_augment`

creates a a *specification* of a recipe
step that will "augment" (add multiple new columns) that have had a sliding function applied.

## Arguments

- recipe
A recipe object. The step will be added to the sequence of operations for this recipe.

- ...
One or more numeric columns to be smoothed. See

`recipes::selections()`

for more details. For the`tidy`

method, these are not currently used.- period
The number of periods to include in the local rolling window. This is effectively the "window size".

- .f
A summary

**formula**in one of the following formats:`mean`

with no arguments`function(x) mean(x, na.rm = TRUE)`

`~ mean(.x, na.rm = TRUE)`

, it is converted to a function.

- align
Rolling functions generate

`period - 1`

fewer values than the incoming vector. Thus, the vector needs to be aligned. Alignment of the vector follows 3 types:**Center:**`NA`

or`.partial`

values are divided and added to the beginning and end of the series to "Center" the moving average. This is common for de-noising operations. See also`[smooth_vec()]`

for LOESS without NA values.**Left:**`NA`

or`.partial`

values are added to the end to shift the series to the Left.**Right:**`NA`

or`.partial`

values are added to the beginning to shif the series to the Right. This is common in Financial Applications such as moving average cross-overs.

- partial
Should the moving window be allowed to return partial (incomplete) windows instead of NA values. Set to FALSE by default, but can be switched to TRUE to remove NA's.

- prefix
A prefix for generated column names, default to "slidify_".

- role
For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new variable columns created by the original variables will be used as predictors in a model.

- trained
A logical to indicate if the quantities for preprocessing have been estimated.

- columns
A character string of variable names that will be populated (eventually) by the

`terms`

argument.- f_name
A character string for the function being applied. This field is a placeholder and will be populated during the

`tidy()`

step.- skip
A logical. Should the step be skipped when the recipe is baked by

`bake.recipe()`

? While all operations are baked when`prep.recipe()`

is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using`skip = TRUE`

as it may affect the computations for subsequent operations- id
A character string that is unique to this step to identify it.

- x
A

`step_slidify_augment`

object.

## Value

For `step_slidify_augment`

, an updated version of recipe with
the new step added to the sequence of existing steps (if any).
For the `tidy`

method, a tibble with columns `terms`

(the selectors or variables selected), `value`

(the feature
names).

## Details

**Alignment**

Rolling functions generate `period - 1`

fewer values than the incoming vector.
Thus, the vector needs to be aligned. Alignment of the vector follows 3 types:

**Center:**`NA`

or`partial`

values are divided and added to the beginning and end of the series to "Center" the moving average. This is common for de-noising operations. See also`[smooth_vec()]`

for LOESS without NA values.**Left:**`NA`

or`partial`

values are added to the end to shift the series to the Left.**Right:**`NA`

or`partial`

values are added to the beginning to shif the series to the Right. This is common in Financial Applications such as moving average cross-overs.

**Partial Values**

The advantage to using

`partial`

values vs`NA`

padding is that the series can be filled (good for time-series de-noising operations).The downside to partial values is that the partials can become less stable at the regions where incomplete windows are used.

If instability is not desirable for de-noising operations, a suitable alternative
is `step_smooth()`

, which implements local polynomial regression.

## See also

Time Series Analysis:

Engineered Features:

`step_timeseries_signature()`

,`step_holiday_signature()`

,`step_fourier()`

Diffs & Lags

`step_diff()`

,`recipes::step_lag()`

Smoothing:

`step_slidify()`

,`step_smooth()`

Variance Reduction:

`step_box_cox()`

Imputation:

`step_ts_impute()`

,`step_ts_clean()`

Padding:

`step_ts_pad()`

Main Recipe Functions:

## Examples

```
# library(tidymodels)
library(dplyr)
library(recipes)
library(parsnip)
m750 <- m4_monthly %>%
filter(id == "M750") %>%
mutate(value_2 = value / 2)
m750_splits <- time_series_split(m750, assess = "2 years", cumulative = TRUE)
#> Using date_var: date
# Make a recipe
recipe_spec <- recipe(value ~ date + value_2, rsample::training(m750_splits)) %>%
step_slidify_augment(
value, value_2,
period = c(6, 12, 24),
.f = ~ mean(.x),
align = "center",
partial = FALSE
)
recipe_spec %>% prep() %>% juice()
#> # A tibble: 282 × 9
#> date value_2 value slidify_6_value slidify_6_value_2 slidify_12_value
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1990-01-01 3185 6370 NA NA NA
#> 2 1990-02-01 3215 6430 NA NA NA
#> 3 1990-03-01 3260 6520 6535 3268. NA
#> 4 1990-04-01 3290 6580 6473. 3237. NA
#> 5 1990-05-01 3310 6620 6310 3155 NA
#> 6 1990-06-01 3345 6690 6303. 3152. 6481.
#> 7 1990-07-01 3000 6000 6343. 3172. 6527.
#> 8 1990-08-01 2725 5450 6383. 3192. 6567.
#> 9 1990-09-01 3240 6480 6427. 3213. 6603.
#> 10 1990-10-01 3410 6820 6580 3290 6638.
#> # ℹ 272 more rows
#> # ℹ 3 more variables: slidify_12_value_2 <dbl>, slidify_24_value <dbl>,
#> # slidify_24_value_2 <dbl>
bake(prep(recipe_spec), rsample::testing(m750_splits))
#> # A tibble: 24 × 9
#> date value_2 value slidify_6_value slidify_6_value_2 slidify_12_value
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2013-07-01 4515 9030 NA NA NA
#> 2 2013-08-01 4810 9620 NA NA NA
#> 3 2013-09-01 5025 10050 10107. 5053. NA
#> 4 2013-10-01 5305 10610 10390 5195 NA
#> 5 2013-11-01 5380 10760 10563. 5282. NA
#> 6 2013-12-01 5285 10570 10705 5352. 10472.
#> 7 2014-01-01 5365 10730 10773. 5387. 10498.
#> 8 2014-02-01 5330 10660 10803. 5402. 10521.
#> 9 2014-03-01 5450 10900 10837. 5418. 10547.
#> 10 2014-04-01 5510 11020 10605 5302. 10575
#> # ℹ 14 more rows
#> # ℹ 3 more variables: slidify_12_value_2 <dbl>, slidify_24_value <dbl>,
#> # slidify_24_value_2 <dbl>
```