create a ggplot from the output of summariseLargeScaleCharacteristics.

Usage

plotComparedLargeScaleCharacteristics(
  result,
  colour,
  reference = NULL,
  facet = NULL,
  missings = 0,
  style = NULL
)

Arguments

result

A summarised_result object.

colour

Columns to color by. See options with availablePlotColumns(result).

reference

A named character to set up the reference. It must be one of the levels of reference.

facet

Columns to facet by. See options with availablePlotColumns(result). Formula is also allowed to specify rows and columns.

missings

Value to replace the missing value with. If NULL missing values will be eliminated.

style

Visual theme to apply. Character, or NULL. If a character, this may be either the name of a built-in style (see plotStyle()), or a path to a .yml file that defines a custom style. If NULL, the function will use the explicit default style, unless a global style option is set (see setGlobalPlotOptions()), or a ⁠_brand.yml⁠ file is present (in that order). Refer to the package vignette on styles to learn more.

Value

A ggplot.

Examples

if (FALSE) { # \dontrun{
library(CohortCharacteristics)
library(DrugUtilisation)
library(plotly, warn.conflicts = FALSE)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

cdm <- generateIngredientCohortSet(
  cdm = cdm, name = "my_cohort", ingredient = "acetaminophen"
)

resultsLsc <- cdm$my_cohort |>
  summariseLargeScaleCharacteristics(
    window = list(c(-365, -1), c(1, 365)),
    eventInWindow = "condition_occurrence"
  )

resultsLsc |>
  plotComparedLargeScaleCharacteristics(
    colour = "variable_level",
    reference = "-365 to -1",
    missings = NULL
  ) |>
  ggplotly()

cdmDisconnect(cdm)
} # }