Create a epi_recipe for preprocessing data — epi

A recipe is a description of the steps to be applied to a data set in order to prepare it for data analysis. This is a loose wrapper around recipes::recipe() to properly handle the additional columns present in an epi_df

Usage

epi_recipe(x, ...)

# S3 method for class 'epi_df'
epi_recipe(
  x,
  reference_date = NULL,
  formula = NULL,
  ...,
  vars = NULL,
  roles = NULL
)

# S3 method for class 'formula'
epi_recipe(formula, data, reference_date = NULL, ...)

Arguments

x, data: An epi_df of the template data set (see below).
...: Further arguments passed to or from other methods (not currently used).
reference_date: Either a date of the same class as the time_value column in the epi_df or NULL. If a date, it gives the date to which all operations are relative. Typically, in real-time tasks this is the date that the model is created (and presumably trained). In forecasting, this is often the same as the most recent date of data availability, but when data is "latent" (reported after the date to which it corresponds), or if performing a nowcast, the reference_date may be later than this. Setting reference_date to a value BEFORE the most recent data is not a true "forecast", because future data is being used to create the model, but this may be reasonable in model building, nowcasting (predicting finalized values from preliminary data), or if producing a backcast. If NULL, it will be set to the as_of date of the epi_df.
formula: A model formula. No in-line functions should be used here (e.g. log(x), x:y, etc.) and minus signs are not allowed. These types of transformations should be enacted using step functions in this package. Dots are allowed as are simple multivariate outcome terms (i.e. no need for cbind; see Examples).
vars: A character string of column names corresponding to variables that will be used in any context (see below)
roles: A character string (the same length of vars) that describes a single role that the variable will take. This value could be anything but common roles are "outcome", "predictor", "time_value", and "geo_value"

Value

An object of class recipe with sub-objects:

var_info: A tibble containing information about the original data set columns.
term_info: A tibble that contains the current set of terms in the data set. This initially defaults to the same data contained in var_info.
steps: A list of step or check objects that define the sequence of preprocessing operations that will be applied to data. The default value is NULL.
template: A tibble of the data. This is initialized to be the same as the data given in the data argument but can be different after the recipe is trained.

Examples

library(dplyr)
library(recipes)
jhu <- covid_case_death_rates %>%
  filter(time_value > "2021-08-01") %>%
  arrange(geo_value, time_value)

r <- epi_recipe(jhu) %>%
  step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
  step_epi_ahead(death_rate, ahead = 7) %>%
  step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
  step_naomit(all_predictors()) %>%
  # below, `skip` means we don't do this at predict time
  step_naomit(all_outcomes(), skip = TRUE)

r
#> 
#> ── Epi Recipe ──────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> raw:        2
#> geo_value:  1
#> time_value: 1
#> 
#> ── Operations 
#> 1. Lagging: death_rate by 0, 7, 14
#> 2. Leading: death_rate by 7
#> 3. Lagging: case_rate by 0, 7, 14
#> 4. • Removing rows with NA values in: all_predictors()
#> 5. • Removing rows with NA values in: all_outcomes()