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). This is also the weekend effect: attach temporal_effects(weekend = TRUE) and the weekend becomes one of the boxes.

• 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) or "report" (red — how the reporting does).

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"),
  measure = c("percent", "normalized"),
  ...
)

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

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

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

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

Arguments

x

A tbl_now() object.

type

"epidemic" (default) for the case-count effect, or "report" for the reporting-delay one.

measure

"normalized" (default) for the value divided by its overall mean (1 = average), or "percent" for the share of cases in each group — "10% of cases at the weekend versus 90% on weekdays" — with the IQR around it. See autoplot.tbl_now() for the blocks the percentages are taken over.

...

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

Value

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

See also

autoplot.tbl_now(), plot_cycles(), plot_delay_distribution(), plot_observed_cases().

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)


## 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)


# By month, on monthly-unit data
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)


# ... and how the reporting does, as a share of the year's cases rather than
# normalized. `type` and `measure` compose.
plot_week_of_year_effects(dengue_now, type = "report", measure = "percent")