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Automatically plot an epi_workflow or canned_epipred object

Source: R/autoplot.R
autoplot-epipred.Rd

For a fit workflow, the training data will be displayed, the response by default. If predictions is not NULL then point and interval forecasts will be shown as well. Unfit workflows will result in an error, (you can simply call autoplot() on the original epi_df).

Usage

# S3 method for class 'epi_workflow'
autoplot(
  object,
  predictions = NULL,
  .levels = c(0.5, 0.8, 0.95),
  ...,
  .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
  .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
  .base_color = "dodgerblue4",
  .point_pred_color = "orange",
  .max_facets = Inf
)

# S3 method for class 'canned_epipred'
autoplot(
  object,
  ...,
  .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
  .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
  .base_color = "dodgerblue4",
  .point_pred_color = "orange",
  .max_facets = Inf
)

Arguments

object

An epi_workflow

predictions

A data frame with predictions. If NULL, only the original data is shown.

.levels

A numeric vector of levels to plot for any prediction bands. More than 3 levels begins to be difficult to see.

...

Ignored

.color_by

Which variables should determine the color(s) used to plot lines. Options include:

  • all_keys - the default uses the interaction of any key variables including the geo_value

  • geo_value - geo_value only

  • other_keys - any available keys that are not geo_value

  • .response - the numeric variables (same as the y-axis)

  • all - uses the interaction of all keys and numeric variables

  • none - no coloring aesthetic is applied

.facet_by

Similar to .color_by except that the default is to display the response.

.base_color

If available, prediction bands will be shown with this color.

.point_pred_color

If available, point forecasts will be shown with this color.

.max_facets

Cut down of the number of facets displayed. Especially useful for testing when there are many geo_value's or keys.

Examples

library(dplyr)
jhu <- case_death_rate_subset %>%
  filter(time_value >= as.Date("2021-11-01"))

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_epi_naomit()

f <- frosting() %>%
  layer_residual_quantiles(
    quantile_levels = c(.025, .1, .25, .75, .9, .975)
  ) %>%
  layer_threshold(starts_with(".pred")) %>%
  layer_add_target_date()

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

autoplot(wf)


latest <- jhu %>% filter(time_value >= max(time_value) - 14)
preds <- predict(wf, latest)
autoplot(wf, preds, .max_facets = 4)


# ------- Show multiple horizons

p <- lapply(c(7, 14, 21, 28), function(h) {
  r <- epi_recipe(jhu) %>%
    step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
    step_epi_ahead(death_rate, ahead = h) %>%
    step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
    step_epi_naomit()
  ewf <- epi_workflow(r, linear_reg(), f) %>% fit(jhu)
  forecast(ewf)
})

p <- do.call(rbind, p)
autoplot(wf, p, .max_facets = 4)


# ------- Plotting canned forecaster output

jhu <- case_death_rate_subset %>%
  filter(time_value >= as.Date("2021-11-01"))
flat <- flatline_forecaster(jhu, "death_rate")
autoplot(flat, .max_facets = 4)
#> Warning: Removed 7 rows containing missing values or values outside the scale range
#> (`geom_line()`).


arx <- arx_forecaster(jhu, "death_rate", c("case_rate", "death_rate"),
  args_list = arx_args_list(ahead = 14L)
)
autoplot(arx, .max_facets = 6)