Format a summariseCohortTiming result into a visual table.

Usage

tableCohortTiming(
  result,
  timeScale = "days",
  uniqueCombinations = TRUE,
  type = NULL,
  header = strataColumns(result),
  groupColumn = c("cdm_name"),
  hide = c("variable_level", settingsColumns(result)),
  style = NULL,
  .options = list()
)

Arguments

result

A summarised_result object.

timeScale

Time scale to show, it can be "days" or "years".

uniqueCombinations

Whether to restrict to unique reference and comparator comparisons.

type

Character string specifying the desired output table format. See visOmopResults::tableType() for supported table types. If type = NULL, global options (set via visOmopResults::setGlobalTableOptions()) will be used if available; otherwise, a default 'gt' table is created.

header

Columns to use as header. See options with availableTableColumns(result).

groupColumn

Columns to group by. See options with availableTableColumns(result).

hide

Columns to hide from the visualisation. See options with availableTableColumns(result).

style

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

1. Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.

2. YAML file path: Provide the path to an existing .yml file defining a new style.

3. 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.

.options

A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.

Value

A formatted table.

Examples

if (FALSE) { # \dontrun{
library(CohortCharacteristics)
library(omock)
library(DrugUtilisation)

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

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

timings <- summariseCohortTiming(cdm$my_cohort)

tableCohortTiming(timings, timeScale = "years")

cdmDisconnect(cdm)
} # }