Plot a timeseries of disease prevalence — plot

Usage

plot_prevalence(
  modelled = i_prevalence_model,
  raw = i_proportion_data,
  ...,
  mapping = .check_for_aes(modelled, ...),
  events = i_events
)

Arguments

modelled

Prevalence estimates - a dataframe with columns:

time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a `time_period`
prevalence.0.025 (proportion) - lower confidence limit of prevalence (true scale)
prevalence.0.5 (proportion) - median estimate of prevalence (true scale)
prevalence.0.975 (proportion) - upper confidence limit of prevalence (true scale)

Any grouping allowed.

raw

Raw proportion data - a dataframe with columns:

denom (positive_integer) - Total test counts associated with the specified time frame
count (positive_integer) - Positive case counts associated with the specified time frame
time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a `time_period`

Any grouping allowed.

...

Named arguments passed on to geom_events

events

Significant events or time spans - a dataframe with columns:

label (character) - the event label
start (date) - the start date, or the date of the event
end (date) - the end date or NA if a single event

Any grouping allowed.

A default value is defined.

mapping

a ggplot2::aes mapping. Most importantly setting the colour to something if there are multiple incidence timeseries in the plot

events

Significant events or time spans - a dataframe with columns:

label (character) - the event label
start (date) - the start date, or the date of the event
end (date) - the end date or NA if a single event

Any grouping allowed.

A default value is defined.

Value

a ggplot object

Examples


if(interactive()) {

  plot_prevalence(
    ukc19::ons_infection_survey %>%
      dplyr::mutate(time = as.time_period(date,"1 day")),
    mapping = ggplot2::aes(colour=name))
}