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Empirical distribution of the reporting or revision delay

Source: R/plot_effects.R
plot_delay_distribution.Rd

[Stable]

The "delay_distribution" panel of autoplot(), drawn on its own: a case-count weighted histogram of .delay. For count-cumulative data it becomes the cumulative growth by delay instead — boxplots, on a log scale, of the ratio of each event date's cumulative count at a delay to its count at the previous delay.

axis = "revision" draws the same histogram of .revision_delay, the time from a report to its resolution, in the revision process's colours. A case still "pending" has no resolution, and so no revision delay, and does not appear.

Usage

plot_delay_distribution(
  x,
  axis = c("report", "revision"),
  by_revision_type = TRUE,
  ...
)

Arguments

x

A tbl_now() object.

axis

Which delay to draw: "report" (default), the time from the event to the report, or "revision", the time from the report to its resolution. "revision" needs a revision process (see add_revision_date()).

by_revision_type

Logical (default TRUE). Split the histogram by how each case eventually resolved — confirmed, pending, retracted and unknown, stacked, in the palette's outcome colours (see tbl_now_palette()). Whether a negative result comes back faster than a positive one is the question the split exists to answer, and diagnose_revision_delay() is the test of it. Ignored on an object with no revision axis, and when by_strata = TRUE, which already uses the fill for the strata.

...

Further arguments passed to autoplot.tbl_now(), e.g. by_strata, strata, delay_distribution_xlim, plotly or palette.

Value

A ggplot2 object (or a plotly widget when plotly = TRUE).

See also

autoplot.tbl_now(), plot_delay_profiles(), plot_delay_drift(); diagnose_revision_delay() for the test behind the outcome split.

Examples

data(denguedat)
dengue_now <- tbl_now(denguedat, onset_week, report_week, verbose = FALSE)
plot_delay_distribution(dengue_now)


# On the revision axis, split by how each case resolved.
cases <- data.frame(
  onset = as.Date("2021-01-04") + rep(0:9, each = 4),
  visit = as.Date("2021-01-05") + rep(0:9, each = 4),
  result = as.Date("2021-01-05") + rep(0:9, each = 4) +
    rep(c(1, 1, 5, 6), times = 10),
  outcome = rep(c("confirmed", "confirmed", "retracted", "retracted"), times = 10)
)
flu <- tbl_now(cases,
  event_date = onset, report_date = visit,
  revision_date = result, revision_type = outcome,
  data_type = "linelist", verbose = FALSE
)
plot_delay_distribution(flu, axis = "revision")