Calendar effects on the case counts or on the reporting delay

One panel of autoplot(), drawn on its own. Each function shows the same boxplots the corresponding autoplot() panel does, for one calendar grouping:

plot_day_of_week_effects() — by day of week (daily data only).
plot_week_of_year_effects() — by epidemiological week.
plot_month_of_year_effects() — by month (monthly data only).
plot_holiday_effects() — by day type (Weekday / Weekend / Holiday, following the attached temporal_effects() spec).
plot_weekend_effects() — the same panel, on an object that may not carry a spec yet: it attaches temporal_effects(weekend = TRUE) when there is no weekend effect already, so the weekend boxes appear without a separate add_temporal_effects() call. The spec goes on a copy — your object is not modified — and a calendar already attached still contributes its Holiday box. Daily data only, since a weekend is a property of the day.
plot_holiday_lag_effects() — by position relative to the nearest holiday ("1 before", "Holiday", "1 after", ..., plus "Other").

type picks which process to describe: "epidemic" (green — how the cases vary by calendar group), "report" (red — how the reporting does), or "revision" (ochre — how resolved cases arrive on revision dates).

The three day-type / holiday-lag functions have no measure argument: they are always normalized. Their categories are not equal-sized parts of a calendar block — the weekend is two days in seven — so a percentage share would mostly restate the calendar rather than the data ("29% of the cases at the weekend" is average, not low). The day-of-week, week-of-year and month-of-year functions keep both measures.

Use these when you want one effect, in its own figure, at its own size; use autoplot() when you want the diagnostic grid in one call. Everything else is the same: autoplot(x, panels = "calendar_weekday") and plot_day_of_week_effects(x) return the identical plot.

Usage

plot_day_of_week_effects(
  x,
  type = c("epidemic", "report", "revision"),
  measure = c("percent", "normalized"),
  ...
)

plot_week_of_year_effects(
  x,
  type = c("epidemic", "report", "revision"),
  measure = c("percent", "normalized"),
  ...
)

plot_month_of_year_effects(
  x,
  type = c("epidemic", "report", "revision"),
  measure = c("percent", "normalized"),
  ...
)

plot_holiday_effects(x, type = c("epidemic", "report", "revision"), ...)

plot_weekend_effects(
  x,
  type = c("epidemic", "report", "revision"),
  weekend_days = c("Sat", "Sun"),
  ...
)

plot_holiday_lag_effects(x, type = c("epidemic", "report", "revision"), ...)

Arguments

x: A tbl_now() object.
type: "epidemic" (default) for the case-count effect, "report" for the reporting-delay one, or "revision" for revision-date arrivals.
measure: "percent" (default) for the share of cases in each group — "10% of cases in week 1 versus 3% in week 2" — with the IQR around it, or "normalized" for the value divided by its overall mean (1 = average). See autoplot.tbl_now() for the blocks the percentages are taken over. The day-type and holiday-lag functions do not take it; they are always normalized.
...: Further arguments passed to autoplot.tbl_now(), e.g. by_strata, strata, plotly or palette.
weekend_days: Character vector naming the weekend days (default c("Sat", "Sun")), as in is_weekday(). Used only when plot_weekend_effects() has to attach the effect itself; an object that already carries a weekend effect keeps the definition it was given.

Value

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

Examples

data(denguedat)
# First few years only, to keep the example quick; the full data works the same.
dengue_now <- tbl_now(denguedat[1:2500, ], onset_week, report_week, verbose = FALSE)

# How the cases vary by epidemiological week
plot_week_of_year_effects(dengue_now)


# The weekend on its own, on daily data, with no spec to attach first
days <- seq(as.Date("2021-01-01"), as.Date("2021-06-30"), by = "day")
daily_now <- tbl_now(
  data.frame(event_date = days, report_date = days + 1),
  event_date, report_date, verbose = FALSE
)
plot_weekend_effects(daily_now)


# By month, on monthly-unit data. `type` picks the process and `measure`
# picks the scale; both compose, and work the same way on every calendar
# function here. (The day-type and holiday-lag panels are always
# normalized, so they take `type` but not `measure`.)
monthly_now <- tbl_now(
  data.frame(
    event_date  = seq(as.Date("2018-01-01"), as.Date("2021-12-01"), by = "month"),
    report_date = seq(as.Date("2018-02-01"), as.Date("2022-01-01"), by = "month")
  ),
  event_date, report_date,
  event_units = "months", report_units = "months", verbose = FALSE
)
plot_month_of_year_effects(monthly_now, type = "report", measure = "normalized")


if (requireNamespace("almanac", quietly = TRUE)){

  ## By day type (weekday / weekend / holiday), once a holiday calendar is attached
  holiday_now <- dengue_now |>
   add_temporal_effects(temporal_effects(weekend = TRUE, holidays = almanac::cal_us_federal()))
  plot_holiday_effects(holiday_now)

  # By position relative to the nearest holiday
  holiday_lag_now <- dengue_now |>
    add_temporal_effects(temporal_effects(holidays = almanac::cal_us_federal(), holiday_lags = 2))
  plot_holiday_lag_effects(holiday_lag_now)

}

Calendar effects on the case counts or on the reporting delay

Usage

Arguments

Value

See also

Examples