Wrappers for using recipes::bake and recipes::juice to process data
returning data in either data frame or matrix format (Common formats needed
for machine learning algorithms).
Examples
library(dplyr)
library(timetk)
library(recipes)
library(lubridate)
predictors <- m4_monthly %>%
filter(id == "M750") %>%
select(-value) %>%
mutate(month = month(date, label = TRUE))
predictors
#> # A tibble: 306 × 3
#> id date month
#> <fct> <date> <ord>
#> 1 M750 1990-01-01 Jan
#> 2 M750 1990-02-01 Feb
#> 3 M750 1990-03-01 Mar
#> 4 M750 1990-04-01 Apr
#> 5 M750 1990-05-01 May
#> 6 M750 1990-06-01 Jun
#> 7 M750 1990-07-01 Jul
#> 8 M750 1990-08-01 Aug
#> 9 M750 1990-09-01 Sep
#> 10 M750 1990-10-01 Oct
#> # ℹ 296 more rows
# Create default recipe
xreg_recipe_spec <- create_xreg_recipe(predictors, prepare = TRUE)
# Extracts the preprocessed training data from the recipe (used in your fit function)
juice_xreg_recipe(xreg_recipe_spec)
#> # A tibble: 306 × 11
#> month_Feb month_Mar month_Apr month_May month_Jun month_Jul month_Aug
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0 0 0 0 0
#> 2 1 0 0 0 0 0 0
#> 3 0 1 0 0 0 0 0
#> 4 0 0 1 0 0 0 0
#> 5 0 0 0 1 0 0 0
#> 6 0 0 0 0 1 0 0
#> 7 0 0 0 0 0 1 0
#> 8 0 0 0 0 0 0 1
#> 9 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0
#> # ℹ 296 more rows
#> # ℹ 4 more variables: month_Sep <dbl>, month_Oct <dbl>, month_Nov <dbl>,
#> # month_Dec <dbl>
# Applies the prepared recipe to new data (used in your predict function)
bake_xreg_recipe(xreg_recipe_spec, new_data = predictors)
#> # A tibble: 306 × 11
#> month_Feb month_Mar month_Apr month_May month_Jun month_Jul month_Aug
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0 0 0 0 0
#> 2 1 0 0 0 0 0 0
#> 3 0 1 0 0 0 0 0
#> 4 0 0 1 0 0 0 0
#> 5 0 0 0 1 0 0 0
#> 6 0 0 0 0 1 0 0
#> 7 0 0 0 0 0 1 0
#> 8 0 0 0 0 0 0 1
#> 9 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0
#> # ℹ 296 more rows
#> # ℹ 4 more variables: month_Sep <dbl>, month_Oct <dbl>, month_Nov <dbl>,
#> # month_Dec <dbl>