Author: NEST CoreDev

# Decorate Module Output

#### NEST CoreDev

## Introduction

The outputs produced by `teal` modules, like graphs or tables, are created by the module developer and look a certain way. It is hard to design an output that will satisfy every possible user, so the form of the output should be considered a default value that can be customized. Here we describe the concept of decoration, enabling the app developer to tailor outputs to their specific requirements without rewriting the original module code.

The decoration process is build upon transformation procedures, introduced in `teal`. While `transformators` are meant to edit module’s input, decorators are meant to adjust the module’s output. To distinguish the difference, modules in `teal.modules.clinical` have 2 separate parameters: `transformators` and `decorators`.

To get a complete understanding refer the following vignettes:

- Transforming the input data in this vignette.
- Transforming module output in this vignette.

## Outputs that can be decorated

It is important to note which output objects from a given module can be decorated. The module function documentation’s Decorating Module section has this information.

You can also refer the table shown below to know which module outputs can be decorated.

Module | Outputs (Class)
--- | ---
`tm_a_gee` | table (ElementaryTable)
`tm_a_mmrm` | lsmeans_table (TableTree), lsmeans_plot (ggplot), covariance_table (ElementaryTable), fixed_effects_table (ElementaryTable), diagnostic_table (ElementaryTable), diagnostic_plot (ggplot)
`tm_g_barchart_simple` | plot (ggplot)
`tm_g_ci` | plot (ggplot)
`tm_g_forest_rsp` | plot (ggplot)
`tm_g_forest_tte` | plot (ggplot)
`tm_g_ipp` | plot (ggplot)
`tm_g_km` | plot (ggplot)
`tm_g_lineplot` | plot (ggplot)
`tm_g_pp_adverse_events` | table (datatables), plot (ggplot)
`tm_g_pp_patient_timeline` | plot (ggplot)
`tm_g_pp_therapy` | plot (ggplot), table (datatables)
`tm_g_pp_vitals` | plot (ggplot)
`tm_t_abnormality` | table (TableTree)
`tm_t_abnormality_by_worst_grade` | table (TableTree)
`tm_t_ancova` | table (TableTree)
`tm_t_binary_outcome` | table (TableTree)
`tm_t_coxreg` | table (TableTree)
`tm_t_events` | table (TableTree)
`tm_t_events_by_grade` | table (TableTree)
`tm_t_events_patyear` | table (ElementaryTable)
`tm_t_events_summary` | table (TableTree)
`tm_t_exposure` | table (ElementaryTable)
`tm_t_logistic` | table (TableTree)
`tm_t_mult_events` | table (TableTree)
`tm_t_pp_basic_info` | table (datatables)
`tm_t_pp_laboratory` | table (datatables)
`tm_t_pp_medical_history` | table (TableTree)
`tm_t_pp_prior_medication` | table (datatables)
`tm_t_shift_by_arm` | table (TableTree)
`tm_t_shift_by_arm_by_worst` | table (TableTree)
`tm_t_shift_by_grade` | table (TableTree)
`tm_t_smq` | table (TableTree)
`tm_t_summary` | table (TableTree)
`tm_t_summary_by` | table (TableTree)
`tm_t_tte` | table (TableTree)

Also, note that there are three different types of objects that can be decorated:

1. `listing_df`, `ElementaryTable`, `TableTree`
2. `ggplot`
3. `datatables`

Tip: A general tip before trying to decorate the output from the module is to copy the reproducible code and running them in a separate R session to quickly iterate the decoration you want.

## Decorating `listing_df`, `ElementaryTable`, `TableTree`

Here’s an example to showcase how you can edit an output of class `listing_df`, `ElementaryTable`, or `TableTree`. All these classes are extension of objects created using `rtables` and can be modified with the help of `rtables` modifiers like `rtables::insert_rrow`.

    library(teal.modules.clinical)

    data <- within(teal_data(), {
      library(dplyr)
      ADSL <- tmc_ex_adsl |>
        mutate(
          ITTFL = factor("Y") |> with_label("Intent-To-Treat Population Flag")
        ) |>
        mutate(DTHFL = case_when(!is.na(DTHDT) ~ "Y", TRUE ~ "") |> with_label("Subject Death Flag"))

      ADLB <- tmc_ex_adlb |>
        mutate(AVISIT == forcats::fct_reorder(AVISIT, AVISITN, min)) |>
        mutate(
          ONTRTFL = case_when(
            AVISIT %in% c("SCREENING", "BASELINE") ~ "",
            TRUE ~ "Y"
          ) |> with_label("On Treatment Record Flag")
        )
    })
    join_keys(data) <- default_cdisc_join_keys[names(data)]

    insert_rrow_decorator <- function(default_caption = "I am a good new row") {
      teal_transform_module(
        label = "New row",
        ui = function(id) {
          shiny::textInput(shiny::NS(id, "new_row"), "New row", value = default_caption)
        },
        server = function(id, data) {
          moduleServer(id, function(input, output, session) {
            reactive({
              data() |>
                within(
                  {
                    table <- rtables::insert_rrow(table, rtables::rrow(new_row))
                  },
                  new_row = input$new_row
                )
            })
          })
        }
      )
    }

    app <- init(
      data = data,
      modules = modules(
        tm_t_abnormality(
          label = "tm_t_abnormality",
          dataname = "ADLB",
          arm_var = choices_selected(
            choices = variable_choices("ADSL", subset = c("ARM", "ARMCD")),
            selected = "ARM"
          ),
          add_total = FALSE,
          by_vars = choices_selected(
            choices = variable_choices("ADLB", subset = c("LBCAT", "PARAM", "AVISIT")),
            selected = c("LBCAT", "PARAM"),
            keep_order = TRUE
          ),
          baseline_var = choices_selected(
            variable_choices("ADLB", subset = "BNRIND"),
            selected = "BNRIND", fixed = TRUE
          ),
          grade = choices_selected(
            choices = variable_choices("ADLB", subset = "ANRIND"),
            selected = "ANRIND",
            fixed = TRUE
          ),
          abnormal = list(low = "LOW", high = "HIGH"),
          exclude_base_abn = FALSE,
          decorators = list(table = insert_rrow_decorator("I am a good new row"))
        )
      )
    )

    if (interactive()) {
      shinyApp(app$ui, app$server)
    }

## Decorating `ggplot`

Here’s an example to showcase how you can edit an output of class `ggplot`. You can extend them using `ggplot2` functions.

    library(teal.modules.clinical)

    data <- teal_data(join_keys = default_cdisc_join_keys[c("ADSL", "ADRS")])
    data <- within(data, {
      require(nestcolor)
      ADSL <- rADSL
      ADTTE <- tmc_ex_adtte
    })
    join_keys(data) <- default_cdisc_join_keys[names(data)]

    ggplot_caption_decorator <- function(default_caption = "I am a good decorator") {
      teal_transform_module(
        label = "Caption",
        ui = function(id) {
          shiny::textInput(shiny::NS(id, "title"), "Plot Title", value = default_caption)
        },
        server = function(id, data) {
          moduleServer(id, function(input, output, session) {
            reactive({
              data() |>
                within(
                  {
                    plot <- plot +
                      ggplot2::ggtitle(title) +
                      cowplot::theme_cowplot()
                  },
                  title = input$title
                )
            })
          })
        }
      )
    }

    app <- init(
      data = data,
      modules = modules(
        tm_g_km(
          label = "tm_g_km",
          dataname = "ADTTE",
          arm_var = choices_selected(
            variable_choices("ADSL", c("ARM", "ARMCD", "ACTARMCD")),
            "ARM"
          ),
          paramcd = choices_selected(
            value_choices("ADTTE", "PARAMCD", "PARAM"),
            "OS"
          ),
          arm_ref_comp = list(
            ACTARMCD = list(ref = "ARM B", comp = c("ARM A", "ARM C")),
            ARM = list(ref = "B: Placebo", comp = c("A: Drug X", "C: Combination"))
          ),
          strata_var = choices_selected(
            variable_choices("ADSL", c("SEX", "BMRKR2")),
            "SEX"
          ),
          facet_var = choices_selected(
            variable_choices("ADSL", c("SEX", "BMRKR2")),
            NULL
          ),
          decorators = list(plot = ggplot_caption_decorator())
        )
      )
    )

    if (interactive()) {
      shinyApp(app$ui, app$server)
    }

## Decorating `datatables`

Here’s an example to showcase how you can edit an output of class `datatables`. Please refer the helper functions of the `DT` package to learn more about extending the `datatables` objects.

    library(teal.modules.clinical)

    data <- teal_data(join_keys = default_cdisc_join_keys[c("ADSL", "ADRS")])
    data <- within(data, {
      ADSL <- rADSL
      ADLB <- tmc_ex_adlb |>
        mutate(AVISIT == forcats::fct_reorder(AVISIT, AVISITN, min)) |>
        mutate(
          ONTRTFL = case_when(
            AVISIT %in% c("SCREENING", "BASELINE") ~ "",
            TRUE ~ "Y"
          ) |> with_label("On Treatment Record Flag")
        )
    })
    join_keys(data) <- default_cdisc_join_keys[names(data)]

    dt_table_decorator <- function(color1 = "pink", color2 = "lightblue") {
      teal_transform_module(
        label = "Table color",
        ui = function(id) {
          selectInput(
            NS(id, "color"),
            "Table Color",
            choices = c("white", color1, color2),
            selected = "Default"
          )
        },
        server = function(id, data) {
          moduleServer(id, function(input, output, session) {
            reactive({
              data() |> within(
                {
                  table <- DT::formatStyle(
                    table,
                    columns = attr(table$x, "colnames")[-1],
                    target = "row",
                    backgroundColor = color
                  )
                },
                color = input$color
              )
            })
          })
        }
      )
    }

    app <- init(
      data = data,
      modules = modules(
        tm_t_pp_laboratory(
          label = "tm_t_pp_laboratory",
          dataname = "ADLB",
          patient_col = "USUBJID",
          paramcd = choices_selected(
            choices = variable_choices("ADLB", "PARAMCD"),
            selected = "PARAMCD"
          ),
          param = choices_selected(
            choices = variable_choices("ADLB", "PARAM"),
            selected = "PARAM"
          ),
          timepoints = choices_selected(
            choices = variable_choices("ADLB", "ADY"),
            selected = "ADY"
          ),
          anrind = choices_selected(
            choices = variable_choices("ADLB", "ANRIND"),
            selected = "ANRIND"
          ),
          aval_var = choices_selected(
            choices = variable_choices("ADLB", "AVAL"),
            selected = "AVAL"
          ),
          avalu_var = choices_selected(
            choices = variable_choices("ADLB", "AVALU"),
            selected = "AVALU"
          ),
          decorators = list(table = dt_table_decorator())
        )
      )
    )

    ## Warning: The `decorators` argument of `tm_t_pp_laboratory()` is deprecated as of
    ## teal.modules.clinical 0.11.0.
    ## ℹ Decorators functionality was removed from this module. The `decorators`
    ##   argument will be ignored.
    ## This warning is displayed once every 8 hours.
    ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
    ## generated.

    if (interactive()) {
      shinyApp(app$ui, app$server)
    }