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Summarise a line list

Source: R/simulation-utils.R
sim_summarise_linelist.Rd

This function converts a line list into a daily count of incident cases, plus infections, admissions, deaths, test samples, test results if present. Censoring of these counts can also be defined. Whilst summarising various network measures such as the forward looking case reproduction number are also calculated.

Usage

sim_summarise_linelist(
  df = i_sim_linelist,
  ...,
  censoring = list(),
  max_time = max(df$time)
)

Arguments

df

a line list dataframe arising from e.g. sim_branching_process() - a dataframe with columns:

  • id (unique_id) - Patient level unique id

  • time (ggoutbreak::time_period) - Time of infection. A `time_period`

Any grouping allowed.

...

the grouping to include in the summarisation.

censoring

a named list of column names (without the _time suffix) of the kind created by sim_delay() or sim_apply_delay(), and an associated function defining the delay of reporting that the column experiences. For this function t (or .x for a purrr lambda) will refer to the XX_time column, i.e. whenever the event that is being reported happened and time the simulation infection time. N.B. since infection is not observed you can't censor it.

max_time

the censoring time for this observation.

Value

a count data frame with additional statistics.

A dataframe containing the following columns:

  • statistic (character) - An identifier for the statistic, whether that be infections, admissions, deaths

  • 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`

Minimally grouped by: statistic (and other groupings may be present).

Examples


sim = sim_branching_process(
  changes = tibble::tibble(t = c(0,40), R = c(1.7,0.8)),
  max_time = 120,
  seed = 100,
  fn_imports = ~ ifelse(.x==0,100,0)
)
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tmp = sim %>% sim_summarise_linelist()

p1 = plot_counts(tmp)

p2 = ggplot2::ggplot(tmp, ggplot2::aes(x=as.Date(time)))+
  ggplot2::geom_point(ggplot2::aes(y=rt.case,colour="case"))+
  ggplot2::geom_point(ggplot2::aes(y=rt.inst,colour="instantaneous"))+
  ggplot2::geom_line(ggplot2::aes(y=rt.weighted))+
  ggplot2::coord_cartesian(ylim=c(0,3.5))+
  ggplot2::xlab(NULL)

patchwork::wrap_plots(p1,p2,ncol=1,axes="collect")