Decorate Module Output

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

library(teal.modules.clinical)

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)
}

code <- paste0(c(
  "interactive <- function() TRUE",
  knitr::knit_code$get("setup"),
  knitr::knit_code$get("decorate_listing_df")
), collapse = "\n")

url <- roxy.shinylive::create_shinylive_url(code)
knitr::include_url(url, height = "800px")

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)
}

code <- paste0(c(
  "interactive <- function() TRUE",
  knitr::knit_code$get("setup"),
  knitr::knit_code$get("decorate_ggplot")
), collapse = "\n")

url <- roxy.shinylive::create_shinylive_url(code)
knitr::include_url(url, height = "800px")

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())
    )
  )
)

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

code <- paste0(c(
  "interactive <- function() TRUE",
  knitr::knit_code$get("setup"),
  knitr::knit_code$get("decorate_datatable")
), collapse = "\n")

url <- roxy.shinylive::create_shinylive_url(code)
knitr::include_url(url, height = "800px")

teal.modules.clinical documentation built on April 4, 2025, 12:35 a.m.