R/tableCohortTiming.R

In CohortCharacteristics: Summarise and Visualise Characteristics of Patients in the OMOP CDM

# Copyright 2024 DARWIN EU (C)
#
# This file is part of CohortCharacteristics
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

#' Format a summariseCohortTiming result into a visual table.
#'
#' `r lifecycle::badge("experimental")`
#'
#' @param result A summarised_result object. Output of summariseCohortTiming().
#' @param timeScale Time scale to plot results. Can be days or years.
#' @param uniqueCombinations Whether to restrict to unique reference and
#' comparator comparisons.
#' @param type Type of table. Check supported types with
#' `visOmopResults::tableType()`.
#' @param header Columns to use as header. See options with
#' `tidyColumns(result)`.
#' @param groupColumn Columns to group by. See options with
#' `tidyColumns(result)`.
#' @param hide Columns to hide from the visualisation. See options with
#' `tidyColumns(result)`.
#'
#' @return A formatted table of the summariseCohortTiming result.
#'
#' @export
#'
#' @examples
#' \dontrun{
#' library(CohortCharacteristics)
#' library(duckdb)
#' library(CDMConnector)
#' library(DrugUtilisation)
#'
#' con <- dbConnect(duckdb(), eunomiaDir())
#' cdm <- cdmFromCon(con, cdmSchem = "main", writeSchema = "main")
#'
#' cdm <- generateIngredientCohortSet(
#'   cdm = cdm,
#'   name = "my_cohort",
#'   ingredient = c("acetaminophen", "morphine", "warfarin")
#' )
#'
#' timings <- summariseCohortTiming(cdm$my_cohort)
#'
#' plotCohortTiming(
#'   timings,
#'   timeScale = "years",
#'   facet = c("cdm_name", "cohort_name_reference"),
#'   colour = c("cohort_name_comparator")
#' )
#'
#' cdmDisconnect(cdm)
#' }
#'
tableCohortTiming <- function(result,
                              timeScale = "days",
                              uniqueCombinations = TRUE,
                              type = "gt",
                              header = visOmopResults::strataColumns(result),
                              groupColumn = NULL,
                              hide = "variable_level") {
  # initial checks
  result <- omopgenerics::validateResultArgument(result)
  omopgenerics::assertChoice(timeScale, c("days", "years"))
  omopgenerics::assertChoice(type, c("gt", "flextable", "tibble"))
  omopgenerics::assertLogical(uniqueCombinations, length = 1)

  # check settings
  result <- result |>
    visOmopResults::filterSettings(
      .data$result_type == "summarise_cohort_timing"
    ) |>
    dplyr::filter(!.data$estimate_name %in% c("density_x", "density_y"))

  if (nrow(result) == 0) {
    cli::cli_warn("`result` object does not contain any `result_type == 'summarise_cohort_timing'` information.")
    return(emptyResultTable(type))
  }

  if (timeScale == "years") {
    result <- changeDaysToYears(result)
  }

  if (uniqueCombinations) result <- getUniqueCombinationsSr(result)

  # format table
  tab <- visOmopResults::visOmopTable(
    result = result,
    estimateName = c(
      "N" = "<count>",
      "Mean (SD)" = "<mean> (<sd>)",
      "Median [Q25 - Q75]" = "<median> [<q25> - <q75>]",
      "Range" = "<min> to <max>"
    ),
    header = header,
    groupColumn = groupColumn,
    type = type,
    hide = hide
  )

  return(tab)
}