Visualise the top concepts per each cdm name, cohort, statification and window.

Usage

tableTopLargeScaleCharacteristics(
  result,
  topConcepts = 10,
  type = "gt",
  style = NULL
)

Arguments

result

A summarised_result object.

topConcepts

Number of concepts to restrict the table.

type

Type of table, it can be any of the supported visOmopResults::tableType() formats.

style

Defines the visual formatting of the table. This argument can be provided in one of the following ways:

Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.
YAML file path: Provide the path to an existing .yml file defining a new style.
List of custome R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type.

If style = NULL, the function will use global options (see visOmopResults::setGlobalTableOptions()) or an existing ⁠_brand.yml⁠ file (if found); otherwise, the default style is applied. For more details, see the Styles vignette in visOmopResults website.

Value

A formated table.

Examples

if (FALSE) { # \dontrun{
library(CohortCharacteristics)
library(omock)
libarry(CDMConnector)
library(dplyr, warn.conflicts = FALSE)

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

cdm <- generateConceptCohortSet(
  cdm = cdm,
  conceptSet = list(viral_pharyngitis = 4112343),
  name = "my_cohort"
)

result <- summariseLargeScaleCharacteristics(
  cohort = cdm$my_cohort,
  window = list(c(-Inf, -1), c(0, 0), c(1, Inf)),
  episodeInWindow = "drug_exposure"
)

tableTopLargeScaleCharacteristics(result)

cdmDisconnect(cdm)
} # }