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Create a differenced predictor

Source: R/recipes-step_diff.R
step_diff.Rd

step_diff creates a specification of a recipe step that will add new columns of differenced data. Differenced data will include NA values where a difference was induced. These can be removed with step_naomit().

Usage

step_diff(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  lag = 1,
  difference = 1,
  log = FALSE,
  prefix = "diff_",
  columns = NULL,
  skip = FALSE,
  id = rand_id("diff")
)

# S3 method for step_diff
tidy(x, ...)

Arguments

recipe

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

...

One or more selector functions to choose which variables are affected by the step. See selections() for more details.

role

Defaults to "predictor"

trained

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

lag

A vector of positive integers identifying which lags (how far back) to be included in the differencing calculation.

difference

The number of differences to perform.

log

Calculates log differences instead of differences.

prefix

A prefix for generated column names, default to "diff_".

columns

A character string of variable names that will be populated (eventually) by the terms argument.

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_diff object.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

Details

The step assumes that the data are already in the proper sequential order for lagging.

See also

Time Series Analysis:

Remove NA Values:

Main Recipe Functions:

Examples

library(recipes)


FANG_wide <- FANG %>%
    dplyr::select(symbol, date, adjusted) %>%
    tidyr::pivot_wider(names_from = symbol, values_from = adjusted)


# Make and apply recipe ----

recipe_diff <- recipe(~ ., data = FANG_wide) %>%
  step_diff(FB, AMZN, NFLX, GOOG, lag = 1:3, difference = 1) %>%
  prep()

recipe_diff %>% bake(FANG_wide)
#> # A tibble: 1,008 × 17
#>    date          FB  AMZN  NFLX  GOOG diff_1_1_FB diff_1_1_AMZN diff_1_1_NFLX
#>    <date>     <dbl> <dbl> <dbl> <dbl>       <dbl>         <dbl>         <dbl>
#>  1 2013-01-02  28    257.  13.1  361.      NA           NA            NA     
#>  2 2013-01-03  27.8  258.  13.8  361.      -0.230        1.17          0.654 
#>  3 2013-01-04  28.8  259.  13.7  369.       0.990        0.670        -0.0871
#>  4 2013-01-07  29.4  268.  14.2  367.       0.66         9.31          0.460 
#>  5 2013-01-08  29.1  266.  13.9  366.      -0.360       -2.08         -0.291 
#>  6 2013-01-09  30.6  266.  13.7  369.       1.53        -0.0300       -0.179 
#>  7 2013-01-10  31.3  265.  14    370.       0.710       -1.01          0.299 
#>  8 2013-01-11  31.7  268.  14.5  370.       0.420        2.60          0.470 
#>  9 2013-01-14  31.0  273.  14.8  361.      -0.770        4.79          0.309 
#> 10 2013-01-15  30.1  272.  14.5  362.      -0.850       -0.830        -0.251 
#> # ℹ 998 more rows
#> # ℹ 9 more variables: diff_1_1_GOOG <dbl>, diff_2_1_FB <dbl>,
#> #   diff_2_1_AMZN <dbl>, diff_2_1_NFLX <dbl>, diff_2_1_GOOG <dbl>,
#> #   diff_3_1_FB <dbl>, diff_3_1_AMZN <dbl>, diff_3_1_NFLX <dbl>,
#> #   diff_3_1_GOOG <dbl>


# Get information with tidy ----

recipe_diff %>% tidy()
#> # A tibble: 1 × 6
#>   number operation type  trained skip  id        
#>    <int> <chr>     <chr> <lgl>   <lgl> <chr>     
#> 1      1 step      diff  TRUE    FALSE diff_dXnep

recipe_diff %>% tidy(1)
#> # A tibble: 12 × 5
#>    terms           lag  diff log   id        
#>    <chr>         <int> <dbl> <lgl> <chr>     
#>  1 diff_FB_1_1       1     1 FALSE diff_dXnep
#>  2 diff_AMZN_1_1     1     1 FALSE diff_dXnep
#>  3 diff_NFLX_1_1     1     1 FALSE diff_dXnep
#>  4 diff_GOOG_1_1     1     1 FALSE diff_dXnep
#>  5 diff_FB_2_1       2     1 FALSE diff_dXnep
#>  6 diff_AMZN_2_1     2     1 FALSE diff_dXnep
#>  7 diff_NFLX_2_1     2     1 FALSE diff_dXnep
#>  8 diff_GOOG_2_1     2     1 FALSE diff_dXnep
#>  9 diff_FB_3_1       3     1 FALSE diff_dXnep
#> 10 diff_AMZN_3_1     3     1 FALSE diff_dXnep
#> 11 diff_NFLX_3_1     3     1 FALSE diff_dXnep
#> 12 diff_GOOG_3_1     3     1 FALSE diff_dXnep