R/tm_t_events.R

In teal.modules.clinical: 'teal' Modules for Standard Clinical Outputs

#' Template: Events by Term
#'
#' Creates a valid expression to generate a table of events by term.
#'
#' @inheritParams template_arguments
#' @param sort_freq_col (`character`)\cr column to sort by frequency on if `sort_criteria` is set to `freq_desc`.
#' @param incl_overall_sum (`flag`)\cr  whether two rows which summarize the overall number of adverse events
#'   should be included at the top of the table.
#'
#' @inherit template_arguments return
#'
#' @seealso [tm_t_events()]
#'
#' @keywords internal
template_events <- function(dataname,
                            parentname,
                            arm_var,
                            hlt,
                            llt,
                            label_hlt = NULL,
                            label_llt = NULL,
                            add_total = TRUE,
                            total_label = default_total_label(),
                            na_level = default_na_str(),
                            event_type = "event",
                            sort_criteria = c("freq_desc", "alpha"),
                            sort_freq_col = total_label,
                            prune_freq = 0,
                            prune_diff = 0,
                            drop_arm_levels = TRUE,
                            incl_overall_sum = TRUE,
                            basic_table_args = teal.widgets::basic_table_args()) {
  checkmate::assert_string(dataname)
  checkmate::assert_string(parentname)
  checkmate::assert_character(arm_var, min.len = 1, max.len = 2)
  checkmate::assert_string(hlt, null.ok = TRUE)
  checkmate::assert_string(llt, null.ok = TRUE)
  checkmate::assert_string(label_hlt, null.ok = TRUE)
  checkmate::assert_string(label_llt, null.ok = TRUE)
  checkmate::assert_character(c(llt, hlt))
  checkmate::assert_flag(add_total)
  checkmate::assert_string(total_label)
  checkmate::assert_string(na_level)
  checkmate::assert_string(event_type)
  checkmate::assert_flag(drop_arm_levels)
  checkmate::assert_scalar(prune_freq)
  checkmate::assert_scalar(prune_diff)

  sort_criteria <- match.arg(sort_criteria)

  y <- list()

  # Start data steps.
  data_list <- list()

  data_list <- add_expr(
    data_list,
    substitute(
      expr = anl <- df,
      env = list(df = as.name(dataname))
    )
  )

  data_list <- add_expr(
    data_list,
    prepare_arm_levels(
      dataname = "anl",
      parentname = parentname,
      arm_var = arm_var[[1]],
      drop_arm_levels = drop_arm_levels
    )
  )
  if (length(arm_var) == 2) {
    data_list <- add_expr(
      data_list,
      prepare_arm_levels(
        dataname = "anl",
        parentname = parentname,
        arm_var = arm_var[[2]],
        drop_arm_levels = drop_arm_levels
      )
    )
  }

  data_list <- add_expr(
    data_list,
    substitute(
      expr = parentname <- df_explicit_na(parentname, na_level = na_lvl),
      env = list(parentname = as.name(parentname), na_lvl = na_level)
    )
  )

  if (sort_criteria == "alpha") {
    if (!is.null(hlt)) {
      data_list <- add_expr(
        data_list,
        substitute(
          expr = anl[[hlt]] <- as.character(anl[[hlt]]),
          env = list(hlt = hlt)
        )
      )
    }

    if (!is.null(llt)) {
      data_list <- add_expr(
        data_list,
        substitute(
          expr = anl[[llt]] <- as.character(anl[[llt]]),
          env = list(llt = llt)
        )
      )
    }
  }

  term_vars <- c(hlt, llt)

  data_list <- add_expr(
    data_list,
    substitute(
      expr = anl <- anl %>%
        df_explicit_na(omit_columns = setdiff(names(anl), term_vars)),
      env = list(
        term_vars = term_vars
      )
    )
  )
  y$data <- bracket_expr(data_list)

  # Start layout steps.
  layout_list <- list()

  basic_title <- if (is.null(hlt) && !is.null(llt)) {
    paste0("Event Summary by Term : ", label_llt)
  } else if (!is.null(hlt) && is.null(llt)) {
    paste0("Event Summary by Term : ", label_hlt)
  } else if (!is.null(hlt) && !is.null(llt)) {
    paste0("Event Summary by Term : ", label_hlt, " and ", label_llt)
  } else {
    "Event Summary by Term"
  }

  parsed_basic_table_args <- teal.widgets::parse_basic_table_args(
    teal.widgets::resolve_basic_table_args(
      user_table = basic_table_args,
      module_table = teal.widgets::basic_table_args(show_colcounts = TRUE, title = basic_title)
    )
  )

  layout_list <- add_expr(layout_list, parsed_basic_table_args)
  layout_list <- add_expr(
    layout_list,
    substitute(
      expr = rtables::split_cols_by(var = arm_var),
      env = list(arm_var = arm_var[[1]])
    )
  )
  if (length(arm_var) == 2) {
    layout_list <- add_expr(
      layout_list,
      if (drop_arm_levels) {
        substitute(
          expr = rtables::split_cols_by(nested_col, split_fun = drop_split_levels),
          env = list(nested_col = arm_var[[2]])
        )
      } else {
        substitute(
          expr = rtables::split_cols_by(nested_col),
          env = list(nested_col = arm_var[[2]])
        )
      }
    )
  }

  if (add_total) {
    layout_list <- add_expr(
      layout_list,
      substitute(
        expr = rtables::add_overall_col(label = total_label),
        env = list(total_label = total_label)
      )
    )
  }

  unique_label <- paste0("Total number of patients with at least one ", event_type)
  nonunique_label <- paste0("Overall total number of ", event_type, "s")

  if (incl_overall_sum) {
    layout_list <- add_expr(
      layout_list,
      substitute(
        summarize_num_patients(
          var = "USUBJID",
          .stats = c("unique", "nonunique"),
          .labels = c(
            unique = unique_label,
            nonunique = nonunique_label
          ),
          na_str = na_str
        ),
        env = list(unique_label = unique_label, nonunique_label = nonunique_label, na_str = na_level)
      )
    )
  }


  one_term <- is.null(hlt) || is.null(llt)

  if (one_term) {
    term_var <- ifelse(is.null(hlt), llt, hlt)

    layout_list <- add_expr(
      layout_list,
      substitute(
        expr = count_occurrences(vars = term_var, .indent_mods = -1L) %>%
          append_varlabels(dataname, term_var),
        env = list(
          term_var = term_var,
          dataname = as.name(dataname)
        )
      )
    )
  } else {
    # Case when both hlt and llt are used.

    y$layout_prep <- quote(split_fun <- drop_split_levels)

    layout_list <- add_expr(
      layout_list,
      substitute(
        expr = rtables::split_rows_by(
          hlt,
          child_labels = "visible",
          nested = FALSE,
          indent_mod = -1L,
          split_fun = split_fun,
          label_pos = "topleft",
          split_label = teal.data::col_labels(dataname[hlt])
        ) %>%
          summarize_num_patients(
            var = "USUBJID",
            .stats = c("unique", "nonunique"),
            .labels = c(
              unique = unique_label,
              nonunique = nonunique_label
            ),
            na_str = na_str
          ) %>%
          count_occurrences(vars = llt, .indent_mods = c(count_fraction = 1L)) %>%
          append_varlabels(dataname, llt, indent = 1L),
        env = list(
          dataname = as.name(dataname),
          hlt = hlt,
          llt = llt,
          unique_label = unique_label,
          nonunique_label = nonunique_label,
          na_str = na_level
        )
      )
    )
  }

  y$layout <- substitute(
    expr = lyt <- layout_pipe,
    env = list(layout_pipe = pipe_expr(layout_list))
  )

  # Full table.
  y$table <- substitute(
    expr = table <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = parent),
    env = list(parent = as.name(parentname))
  )

  # Start pruning table.
  prune_list <- list()
  prune_list <- add_expr(
    prune_list,
    quote(
      pruned_result <- rtables::prune_table(table)
    )
  )

  if (prune_freq > 0 || prune_diff > 0) {
    # Do not use "All Patients" column for pruning conditions.
    prune_list <- add_expr(
      prune_list,
      substitute(
        expr = col_indices <- 1:(ncol(table) - add_total),
        env = list(add_total = add_total)
      )
    )

    if (prune_freq > 0 && prune_diff == 0) {
      prune_list <- add_expr(
        prune_list,
        substitute(
          expr = row_condition <- has_fraction_in_any_col(atleast = prune_freq, col_indices = col_indices),
          env = list(prune_freq = prune_freq)
        )
      )
    } else if (prune_freq == 0 && prune_diff > 0) {
      prune_list <- add_expr(
        prune_list,
        substitute(
          expr = row_condition <- has_fractions_difference(atleast = prune_diff, col_indices = col_indices),
          env = list(prune_diff = prune_diff)
        )
      )
    } else if (prune_freq > 0 && prune_diff > 0) {
      prune_list <- add_expr(
        prune_list,
        substitute(
          expr = row_condition <- has_fraction_in_any_col(atleast = prune_freq, col_indices = col_indices) &
            has_fractions_difference(atleast = prune_diff, col_indices = col_indices),
          env = list(prune_freq = prune_freq, prune_diff = prune_diff)
        )
      )
    }

    # Apply pruning conditions.
    prune_list <- add_expr(
      prune_list,
      substitute(
        expr = pruned_result <- pruned_result %>% rtables::prune_table(keep_rows(row_condition))
      )
    )
  }

  y$prune <- bracket_expr(prune_list)

  # Start sorting pruned table.
  sort_list <- list()

  if (sort_criteria == "alpha") {
    if (prune_freq == 0 && prune_diff == 0) {
      # This is just a dummy step to get the right variable result.
      # No additional sorting is needed because during the data pre-processing step,
      # llt and/or hlt are converted to factors with alphabetically sorted levels.
      # So the order in y$table table is already alphabetically sorted.
      sort_list <- add_expr(
        sort_list,
        quote({
          pruned_and_sorted_result <- pruned_result
          pruned_and_sorted_result
        })
      )
    } else {
      sort_list <- add_expr(
        sort_list,
        quote(
          criteria_fun <- function(tr) {
            inherits(tr, "ContentRow")
          }
        )
      )

      sort_list <- add_expr(
        sort_list,
        quote({
          pruned_and_sorted_result <- rtables::trim_rows(pruned_result, criteria = criteria_fun)
          pruned_and_sorted_result
        })
      )
    }
  } else {
    # Sort by decreasing frequency.
    sort_list <- add_expr(
      sort_list,
      substitute(
        expr = idx_split_col <- which(sapply(col_paths(table), tail, 1) == sort_freq_col),
        env = list(sort_freq_col = sort_freq_col)
      )
    )

    # When the "All Patients" column is present we only use that for scoring.
    scorefun_hlt <- if (add_total) {
      quote(cont_n_onecol(idx_split_col))
    } else {
      quote(cont_n_allcols)
    }
    scorefun_llt <- if (add_total) {
      quote(score_occurrences_cols(col_indices = seq(1, ncol(table))))
    } else {
      quote(score_occurrences)
    }

    if (one_term) {
      term_var <- ifelse(is.null(hlt), llt, hlt)

      sort_list <- add_expr(
        sort_list,
        substitute(
          expr = {
            pruned_and_sorted_result <- pruned_result %>%
              sort_at_path(path = c(term_var), scorefun = scorefun_llt)
            pruned_and_sorted_result
          },
          env = list(
            term_var = term_var,
            scorefun_llt = scorefun_llt
          )
        )
      )
    } else {
      sort_list <- add_expr(
        sort_list,
        substitute(
          expr = {
            pruned_and_sorted_result <- pruned_result %>%
              sort_at_path(path = c(hlt), scorefun = scorefun_hlt) %>%
              sort_at_path(path = c(hlt, "*", llt), scorefun = scorefun_llt)
          },
          env = list(
            llt = llt,
            hlt = hlt,
            scorefun_hlt = scorefun_hlt,
            scorefun_llt = scorefun_llt
          )
        )
      )

      if (prune_freq > 0 || prune_diff > 0) {
        sort_list <- add_expr(
          sort_list,
          quote(
            criteria_fun <- function(tr) {
              inherits(tr, "ContentRow")
            }
          )
        )

        sort_list <- add_expr(
          sort_list,
          quote(
            pruned_and_sorted_result <- rtables::trim_rows(pruned_and_sorted_result, criteria = criteria_fun)
          )
        )
      }

      sort_list <- add_expr(
        sort_list,
        quote(pruned_and_sorted_result)
      )
    }
  }
  y$sort <- bracket_expr(sort_list)

  y
}

#' teal Module: Events by Term
#'
#' This module produces a table of events by term.
#'
#' @inheritParams module_arguments
#' @inheritParams teal::module
#' @inheritParams template_events
#' @param arm_var ([teal.transform::choices_selected()])\cr object with all
#'   available choices and preselected option for variable names that can be used as `arm_var`.
#'   It defines the grouping variable(s) in the results table.
#'   If there are two elements selected for `arm_var`,
#'   second variable will be nested under the first variable.
#'
#' @inherit module_arguments return seealso
#'
#' @section Decorating Module:
#'
#' This module generates the following objects, which can be modified in place using decorators:
#' - `table` (`TableTree` as created from `rtables::build_table`)
#'
#' A Decorator is applied to the specific output using a named list of `teal_transform_module` objects.
#' The name of this list corresponds to the name of the output to which the decorator is applied.
#' See code snippet below:
#'
#' ```
#' tm_t_events(
#'    ..., # arguments for module
#'    decorators = list(
#'      table = teal_transform_module(...) # applied only to `table` output
#'    )
#' )
#' ```
#'
#' For additional details and examples of decorators, refer to the vignette
#' `vignette("decorate-module-output", package = "teal.modules.clinical")`.
#'
#' To learn more please refer to the vignette
#' `vignette("transform-module-output", package = "teal")` or the [`teal::teal_transform_module()`] documentation.
#'
#' @examplesShinylive
#' library(teal.modules.clinical)
#' interactive <- function() TRUE
#' {{ next_example }}
#'
#' @examples
#' data <- teal_data()
#' data <- within(data, {
#'   ADSL <- tmc_ex_adsl
#'   ADAE <- tmc_ex_adae
#' })
#' join_keys(data) <- default_cdisc_join_keys[names(data)]
#'
#' ADSL <- data[["ADSL"]]
#' ADAE <- data[["ADAE"]]
#'
#' app <- init(
#'   data = data,
#'   modules = modules(
#'     tm_t_events(
#'       label = "Adverse Event Table",
#'       dataname = "ADAE",
#'       arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
#'       llt = choices_selected(
#'         choices = variable_choices(ADAE, c("AETERM", "AEDECOD")),
#'         selected = c("AEDECOD")
#'       ),
#'       hlt = choices_selected(
#'         choices = variable_choices(ADAE, c("AEBODSYS", "AESOC")),
#'         selected = "AEBODSYS"
#'       ),
#'       add_total = TRUE,
#'       event_type = "adverse event"
#'     )
#'   )
#' )
#' if (interactive()) {
#'   shinyApp(app$ui, app$server)
#' }
#'
#' @export
tm_t_events <- function(label,
                        dataname,
                        parentname = ifelse(
                          inherits(arm_var, "data_extract_spec"),
                          teal.transform::datanames_input(arm_var),
                          "ADSL"
                        ),
                        arm_var,
                        hlt,
                        llt,
                        add_total = TRUE,
                        total_label = default_total_label(),
                        na_level = default_na_str(),
                        event_type = "event",
                        sort_criteria = c("freq_desc", "alpha"),
                        sort_freq_col = total_label,
                        prune_freq = 0,
                        prune_diff = 0,
                        drop_arm_levels = TRUE,
                        incl_overall_sum = TRUE,
                        pre_output = NULL,
                        post_output = NULL,
                        basic_table_args = teal.widgets::basic_table_args(),
                        transformators = list(),
                        decorators = list()) {
  message("Initializing tm_t_events")
  checkmate::assert_string(label)
  checkmate::assert_string(dataname)
  checkmate::assert_string(parentname)
  checkmate::assert_class(arm_var, "choices_selected")
  checkmate::assert_class(hlt, "choices_selected")
  checkmate::assert_class(llt, "choices_selected")
  checkmate::assert_string(event_type)
  checkmate::assert_flag(add_total)
  checkmate::assert_string(total_label)
  checkmate::assert_string(na_level)
  checkmate::assert_string(sort_freq_col)
  checkmate::assert_scalar(prune_freq)
  checkmate::assert_scalar(prune_diff)
  checkmate::assert_flag(drop_arm_levels)
  checkmate::assert_flag(incl_overall_sum)
  sort_criteria <- match.arg(sort_criteria)
  checkmate::assert_class(pre_output, classes = "shiny.tag", null.ok = TRUE)
  checkmate::assert_class(post_output, classes = "shiny.tag", null.ok = TRUE)
  checkmate::assert_class(basic_table_args, "basic_table_args")
  assert_decorators(decorators, "table")

  args <- as.list(environment())

  data_extract_list <- list(
    arm_var = cs_to_des_select(arm_var, dataname = parentname, multiple = TRUE, ordered = TRUE),
    hlt = cs_to_des_select(hlt, dataname = dataname),
    llt = cs_to_des_select(llt, dataname = dataname)
  )

  module(
    label = label,
    ui = ui_t_events_byterm,
    server = srv_t_events_byterm,
    ui_args = c(data_extract_list, args),
    server_args = c(
      data_extract_list,
      list(
        dataname = dataname,
        parentname = parentname,
        event_type = event_type,
        label = label,
        total_label = total_label,
        na_level = na_level,
        sort_freq_col = sort_freq_col,
        incl_overall_sum = incl_overall_sum,
        basic_table_args = basic_table_args,
        decorators = decorators
      )
    ),
    transformators = transformators,
    datanames = teal.transform::get_extract_datanames(data_extract_list)
  )
}

#' @keywords internal
ui_t_events_byterm <- function(id, ...) {
  ns <- NS(id)
  a <- list(...)
  is_single_dataset_value <- teal.transform::is_single_dataset(a$arm_var, a$hlt, a$llt)

  teal.widgets::standard_layout(
    output = teal.widgets::white_small_well(
      teal.widgets::table_with_settings_ui(ns("table"))
    ),
    encoding = tags$div(
      ### Reporter
      teal.reporter::simple_reporter_ui(ns("simple_reporter")),
      ###
      tags$label("Encodings", class = "text-primary"),
      teal.transform::datanames_input(a[c("arm_var", "hlt", "llt")]),
      teal.transform::data_extract_ui(
        id = ns("arm_var"),
        label = "Select Treatment Variable",
        data_extract_spec = a$arm_var,
        is_single_dataset = is_single_dataset_value
      ),
      teal.transform::data_extract_ui(
        id = ns("hlt"),
        label = "Event High Level Term",
        data_extract_spec = a$hlt,
        is_single_dataset = is_single_dataset_value
      ),
      teal.transform::data_extract_ui(
        id = ns("llt"),
        label = "Event Low Level Term",
        data_extract_spec = a$llt,
        is_single_dataset = is_single_dataset_value
      ),
      checkboxInput(ns("add_total"), "Add All Patients columns", value = a$add_total),
      ui_decorate_teal_data(ns("decorator"), decorators = select_decorators(a$decorators, "table")),
      teal.widgets::panel_item(
        "Additional table settings",
        checkboxInput(
          ns("drop_arm_levels"),
          label = "Drop columns not in filtered analysis dataset",
          value = a$drop_arm_levels
        ),
        selectInput(
          inputId = ns("sort_criteria"),
          label = "Sort Criteria",
          choices = c(
            "Decreasing frequency" = "freq_desc",
            "Alphabetically" = "alpha"
          ),
          selected = a$sort_criteria,
          multiple = FALSE
        ),
        helpText(tags$strong("Pruning Options:")),
        numericInput(
          inputId = ns("prune_freq"),
          label = "Minimum Incidence Rate(%) in any of the treatment groups",
          value = a$prune_freq,
          min = 0,
          max = 100,
          step = 1,
          width = "100%"
        ),
        numericInput(
          inputId = ns("prune_diff"),
          label = "Minimum Difference Rate(%) between any of the treatment groups",
          value = a$prune_diff,
          min = 0,
          max = 100,
          step = 1,
          width = "100%"
        )
      )
    ),
    forms = tagList(
      teal.widgets::verbatim_popup_ui(ns("rcode"), button_label = "Show R code")
    ),
    pre_output = a$pre_output,
    post_output = a$post_output
  )
}

#' @keywords internal
srv_t_events_byterm <- function(id,
                                data,
                                filter_panel_api,
                                reporter,
                                dataname,
                                parentname,
                                event_type,
                                arm_var,
                                hlt,
                                llt,
                                drop_arm_levels,
                                incl_overall_sum,
                                label,
                                total_label,
                                na_level,
                                sort_freq_col,
                                basic_table_args,
                                decorators) {
  with_reporter <- !missing(reporter) && inherits(reporter, "Reporter")
  with_filter <- !missing(filter_panel_api) && inherits(filter_panel_api, "FilterPanelAPI")
  checkmate::assert_class(data, "reactive")
  checkmate::assert_class(shiny::isolate(data()), "teal_data")

  moduleServer(id, function(input, output, session) {
    teal.logger::log_shiny_input_changes(input, namespace = "teal.modules.clinical")
    selector_list <- teal.transform::data_extract_multiple_srv(
      data_extract = list(arm_var = arm_var, hlt = hlt, llt = llt),
      datasets = data,
      select_validation_rule = list(
        arm_var = ~ if (length(.) != 1 && length(.) != 2) {
          "Please select 1 or 2 treatment variable values"
        },
        hlt = ~ if (length(selector_list()$llt()$select) + length(.) == 0) {
          "Please select at least one of \"LOW LEVEL TERM\" or \"HIGH LEVEL TERM\" variables."
        },
        llt = ~ if (length(selector_list()$hlt()$select) + length(.) == 0) {
          "Please select at least one of \"LOW LEVEL TERM\" or \"HIGH LEVEL TERM\" variables."
        }
      )
    )

    iv_r <- reactive({
      iv <- shinyvalidate::InputValidator$new()
      iv$add_rule("prune_freq", shinyvalidate::sv_required("Please provide an Incidence Rate between 0 and 100 (%)."))
      iv$add_rule(
        "prune_freq",
        shinyvalidate::sv_between(0, 100, message_fmt = "Please provide an Incidence Rate between 0 and 100 (%).")
      )
      iv$add_rule("prune_diff", shinyvalidate::sv_required("Please provide a Difference Rate between 0 and 100 (%)."))
      iv$add_rule(
        "prune_diff",
        shinyvalidate::sv_between(0, 100, message_fmt = "Please provide a Difference Rate between 0 and 100 (%).")
      )
      teal.transform::compose_and_enable_validators(iv, selector_list)
    })

    anl_inputs <- teal.transform::merge_expression_srv(
      datasets = data,
      selector_list = selector_list,
      merge_function = "dplyr::inner_join"
    )

    adsl_inputs <- teal.transform::merge_expression_module(
      datasets = data,
      data_extract = list(arm_var = arm_var),
      anl_name = "ANL_ADSL"
    )

    anl_q <- reactive({
      data() %>%
        teal.code::eval_code(as.expression(anl_inputs()$expr)) %>%
        teal.code::eval_code(as.expression(adsl_inputs()$expr))
    })

    merged <- list(
      anl_input_r = anl_inputs,
      adsl_input_r = adsl_inputs,
      anl_q = anl_q
    )

    validate_checks <- reactive({
      teal::validate_inputs(iv_r())

      adsl_filtered <- merged$anl_q()[[parentname]]
      anl_filtered <- merged$anl_q()[[dataname]]

      input_arm_var <- as.vector(merged$anl_input_r()$columns_source$arm_var)
      input_level_term <- c(
        as.vector(merged$anl_input_r()$columns_source$hlt),
        as.vector(merged$anl_input_r()$columns_source$llt)
      )

      validate(
        if (length(input_arm_var) >= 1) {
          need(is.factor(adsl_filtered[[input_arm_var[[1]]]]), "Treatment variable is not a factor.")
        },
        if (length(input_arm_var) == 2) {
          need(
            is.factor(adsl_filtered[[input_arm_var[[2]]]]) & all(!adsl_filtered[[input_arm_var[[2]]]] %in% c(
              "", NA
            )),
            "Please check nested treatment variable which needs to be a factor without NA or empty strings."
          )
        }
      )

      # validate inputs
      validate_standard_inputs(
        adsl = adsl_filtered,
        adslvars = c("USUBJID", "STUDYID", input_arm_var),
        anl = anl_filtered,
        anlvars = c("USUBJID", "STUDYID", input_level_term),
        arm_var = input_arm_var[[1]]
      )
    })

    # The R-code corresponding to the analysis.
    table_q <- reactive({
      validate_checks()
      ANL <- merged$anl_q()[["ANL"]]

      input_hlt <- as.vector(merged$anl_input_r()$columns_source$hlt)
      input_llt <- as.vector(merged$anl_input_r()$columns_source$llt)
      label_hlt <- if (length(input_hlt) != 0) attributes(ANL[[input_hlt]])$label else NULL
      label_llt <- if (length(input_llt) != 0) attributes(ANL[[input_llt]])$label else NULL

      my_calls <- template_events(
        dataname = "ANL",
        parentname = "ANL_ADSL",
        arm_var = as.vector(merged$anl_input_r()$columns_source$arm_var),
        hlt = if (length(input_hlt) != 0) input_hlt else NULL,
        llt = if (length(input_llt) != 0) input_llt else NULL,
        label_hlt = label_hlt,
        label_llt = label_llt,
        add_total = input$add_total,
        total_label = total_label,
        na_level = na_level,
        event_type = event_type,
        sort_criteria = input$sort_criteria,
        sort_freq_col = sort_freq_col,
        prune_freq = input$prune_freq / 100,
        prune_diff = input$prune_diff / 100,
        drop_arm_levels = input$drop_arm_levels,
        incl_overall_sum = incl_overall_sum,
        basic_table_args = basic_table_args
      )

      teal.code::eval_code(merged$anl_q(), as.expression(unlist(my_calls)))
    })

    decorated_table_q <- srv_decorate_teal_data(
      id = "decorator",
      data = table_q,
      decorators = select_decorators(decorators, "table"),
      expr = table
    )

    # Outputs to render.
    table_r <- reactive(decorated_table_q()[["table"]])

    teal.widgets::table_with_settings_srv(
      id = "table",
      table_r = table_r
    )

    # Render R code.
    source_code_r <- reactive(teal.code::get_code(req(decorated_table_q())))
    teal.widgets::verbatim_popup_srv(
      id = "rcode",
      verbatim_content = source_code_r,
      title = label
    )

    ### REPORTER
    if (with_reporter) {
      card_fun <- function(comment, label) {
        card <- teal::report_card_template(
          title = "Events by Term Table",
          label = label,
          with_filter = with_filter,
          filter_panel_api = filter_panel_api
        )
        card$append_text("Table", "header3")
        card$append_table(table_r())
        if (!comment == "") {
          card$append_text("Comment", "header3")
          card$append_text(comment)
        }
        card$append_src(source_code_r())
        card
      }
      teal.reporter::simple_reporter_srv("simple_reporter", reporter = reporter, card_fun = card_fun)
    }
    ###
  })
}