Omit NAs from predictions or other columns — layer

Omit NAs from predictions or other columns

Usage

layer_naomit(frosting, ..., id = rand_id("naomit"))

Arguments

frosting: a frosting postprocessor
...: <tidy-select> One or more unquoted expressions separated by commas. Variable names can be used as if they were positions in the data frame, so expressions like x:y can be used to select a range of variables. Typical usage is .pred to remove any rows with NA predictions.
id: a random id string

Value

an updated frosting postprocessor

Examples

library(dplyr)
jhu <- covid_case_death_rates %>%
  filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))

r <- epi_recipe(jhu) %>%
  step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
  step_epi_ahead(death_rate, ahead = 7)

wf <- epi_workflow(r, linear_reg()) %>% fit(jhu)

f <- frosting() %>%
  layer_predict() %>%
  layer_naomit(.pred)

wf1 <- wf %>% add_frosting(f)

p <- forecast(wf1)
p
#> An `epi_df` object, 3 x 3 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * other_keys = geo_value, time_value
#> * as_of     = 2022-05-31
#> 
#> # A tibble: 3 × 3
#>   geo_value time_value .pred
#> * <chr>     <date>     <dbl>
#> 1 ak        2021-12-31 0.245
#> 2 ca        2021-12-31 0.313
#> 3 ny        2021-12-31 0.295