|
| 1 | +test_that("autoplot snapshots", { |
| 2 | + jhu <- case_death_rate_subset %>% |
| 3 | + filter(time_value >= as.Date("2021-11-01")) |
| 4 | + |
| 5 | + r <- epi_recipe(jhu) %>% |
| 6 | + step_epi_lag(death_rate, lag = c(0, 7, 14)) %>% |
| 7 | + step_epi_ahead(death_rate, ahead = 7) %>% |
| 8 | + step_epi_lag(case_rate, lag = c(0, 7, 14)) %>% |
| 9 | + step_epi_naomit() |
| 10 | + |
| 11 | + f <- frosting() %>% |
| 12 | + layer_residual_quantiles( |
| 13 | + quantile_levels = c(.025, .1, .25, .75, .9, .975) |
| 14 | + ) %>% |
| 15 | + layer_threshold(dplyr::starts_with(".pred")) %>% |
| 16 | + layer_add_target_date() |
| 17 | + |
| 18 | + wf <- epi_workflow(r, parsnip::linear_reg(), f) %>% fit(jhu) |
| 19 | + p <- autoplot(wf) |
| 20 | + withr::with_file("autoplot.png", { |
| 21 | + ggsave("autoplot.png", p) |
| 22 | + expect_snapshot_file("autoplot.png") |
| 23 | + }) |
| 24 | + |
| 25 | + latest <- jhu %>% dplyr::filter(time_value >= max(time_value) - 14) |
| 26 | + preds <- predict(wf, latest) |
| 27 | + p <- autoplot(wf, preds, .max_facets = 4) |
| 28 | + withr::with_file("autoplot2.png", { |
| 29 | + ggsave("autoplot2.png", p) |
| 30 | + expect_snapshot_file("autoplot2.png") |
| 31 | + }) |
| 32 | +}) |
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