create a ggplot from the output of summariseLargeScaleCharacteristics.

Usage

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

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

Named list that specifies how to style the different parts of the table generated. It can either be a pre-defined style ("default" or "darwin" - the latter just for gt and flextable), NULL to get the table default style, or custom. Keep in mind that styling code is different for all table styles. To see the different styles see visOmopResults::tableStyle().

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)
} # }