Diagnostic plots of the reporting process

Lays out a gallery of complementary views of a tbl_now's reporting process, all aimed at spotting reporting artefacts – especially batch reporting. Each view is also available on its own (see See also); diagnostic_plot() picks the ones named in panels and combines them with patchwork. Selecting a single panel returns it as a plain plot. Every view is facetted by stratum when the tbl_now declares strata.

Usage

diagnostic_plot(
  x,
  panels = "all",
  by = c("report", "event"),
  max_delay = NULL,
  ...,
  plotly = FALSE,
  axis = c("report", "validation"),
  palette = .tbl_now_palette()
)

Arguments

x

A tbl_now() object.

panels

Which panels, "all" (default) or any subset of "reporting", "triangle", "profiles", "delay_drift" and "transport".

by

For the "profiles" panel, one mark per "report" date (default) or per "event" date.

max_delay

Largest delay on the delay-based panels. NULL (default) caps at the delay covering 99% of reported mass.

...

Batch controls (lookback, period, alpha) routed to the "transport" panel.

plotly

If TRUE, return an interactive plotly widget (the panels stacked) instead of a static patchwork. Default FALSE.

axis

Which time axis the delay is measured to: "report" (default) or "validation". Both are measured from the event, so the two are directly comparable – run each in turn and the gap between them is the time the laboratory adds. (This is not the same quantity as the .validation_delay column, which is the laboratory's own turnaround, measured from the report.) Needs a validation process (see add_validation_date()); cases still "pending" are left out.

palette

A named colour palette. Defaults to the package palette.

Value

A patchwork object, or a single plot when one panel is selected (or a plotly widget when plotly = TRUE).

See also

Every panel is also a function of its own: plot_reporting_process() and plot_epidemic_process() (when reports arrived, versus when cases happened), plot_reporting_triangle() (the full event-by-delay grid), plot_delay_profiles() (each date's delay curve), plot_delay_drift() (whether delays are getting longer), plot_transport_discriminant(), plot_scalogram().

Examples

data(denguedat)
dn <- tbl_now(denguedat, onset_week, report_week, verbose = FALSE)
diagnostic_plot(dn, panels = c("triangle", "transport"))
#> Warning: ! `transport_discriminant()` is experimental: results are not guaranteed and
#>   the interface may change.
#> ℹ Treat a flagged report date as a potential batch, not a confirmed one.
#> This warning is displayed once every 8 hours.