Generate a plot visualisation (ggplot2) from the output of summariseIndication

Usage

plotIndication(
  result,
  facet = cdm_name + cohort_name ~ window_name,
  colour = "variable_level",
  style = "default"
)

Arguments

result: A summarised_result object.
facet: Columns to facet by. See options with availablePlotColumns(result). Formula is also allowed to specify rows and columns.
colour: Columns to color by. See options with availablePlotColumns(result).
style: Which style to apply to the plot, options are: "default", "darwin" and NULL (default ggplot style). Customised styles can be achieved by modifying the returned ggplot object.

Value

A ggplot2 object

Examples

# \donttest{
library(DrugUtilisation)
library(CDMConnector)

cdm <- mockDrugUtilisation()

indications <- list(headache = 378253, asthma = 317009)
cdm <- generateConceptCohortSet(cdm = cdm,
                                conceptSet = indications,
                                name = "indication_cohorts")

cdm <- generateIngredientCohortSet(cdm = cdm,
                                   name = "drug_cohort",
                                   ingredient = "acetaminophen")
#> ℹ Subsetting drug_exposure table
#> ℹ Checking whether any record needs to be dropped.
#> ℹ Collapsing overlaping records.
#> ℹ Collapsing records with gapEra = 1 days.

result <- cdm$drug_cohort |>
  summariseIndication(
    indicationCohortName = "indication_cohorts",
    unknownIndicationTable = "condition_occurrence",
    indicationWindow = list(c(-Inf, 0), c(-365, 0))
  )
#> ℹ Intersect with indications table (indication_cohorts)
#> ℹ Summarising indications.

plotIndication(result)
#> Warning: Ignoring empty aesthetic: `width`.

# }