Teal module for the heatmap by grade — tm_g_heat

Display the heatmap by grade as a shiny module

Usage

tm_g_heat_bygrade(
  label,
  sl_dataname,
  ex_dataname,
  ae_dataname,
  id_var = teal.picks::variables(choices = teal.picks::is_categorical(), selected = 1L),
  visit_var = teal.picks::variables(choices = dplyr::starts_with("AVISIT"), selected =
    1L),
  ongo_var = teal.picks::variables(choices = dplyr::starts_with("ongo"), selected = 1L),
  anno_var = teal.picks::variables(choices = teal.picks::is_categorical(min.len = 2),
    selected = 1L, multiple = TRUE),
  heat_var = teal.picks::variables(choices = dplyr::starts_with("AET0"), selected = 1L),
  cm_dataname = NULL,
  conmed_var = NULL,
  fontsize = c(5, 3, 7),
  plot_height = c(600L, 200L, 2000L),
  plot_width = NULL,
  transformators = list(),
  decorators = list()
)

Arguments

label: (character(1)) Label shown in the navigation item for the module or module group. For modules() defaults to "root". See Details.
sl_dataname: (character) subject level dataset name, needs to be available in the list passed to the data argument of teal::init()
ex_dataname: (character) exposures dataset name, needs to be available in the list passed to the data argument of teal::init()
ae_dataname: (character) adverse events dataset name, needs to be available in the list passed to the data argument of teal::init()
specify to NA if no concomitant medications data is available
id_var: Either a (teal.picks::variables()) object or a (teal.transform::choices_selected()) object. choices_selected() is being deprecated as an argument type and will be removed in the future. Unique subject ID variable.
visit_var: Either a (teal.picks::variables()) object or a (teal.transform::choices_selected()) object. choices_selected() is being deprecated as an argument type and will be removed in the future. Analysis visit variable.
ongo_var: Either a (teal.picks::variables()) object or a (teal.transform::choices_selected()) object. choices_selected() is being deprecated as an argument type and will be removed in the future. Study ongoing status variable. This variable is a derived logical variable. Usually it can be derived from EOSSTT.
anno_var: Either a (teal.picks::variables()) object or a (teal.transform::choices_selected()) object. choices_selected() is being deprecated as an argument type and will be removed in the future. Annotation variable.
heat_var: Either a (teal.picks::variables()) object or a (teal.transform::choices_selected()) object. choices_selected() is being deprecated as an argument type and will be removed in the future. Heatmap variable.
cm_dataname: (character) concomitant medications dataset name,
conmed_var: Either a (teal.picks::variables()) object or a (teal.transform::choices_selected()) object. choices_selected() is being deprecated as an argument type and will be removed in the future. Concomitant medications variable, specify to NA if no concomitant medications data is available
fontsize: (numeric(1) or numeric(3))
Defines initial possible range of font-size. fontsize is set for teal.widgets::optionalSliderInputValMinMax() which controls font-size in the output plot.
plot_height: (numeric(3))
vector to indicate default value, minimum and maximum values.
plot_width: (numeric(3))
vector to indicate default value, minimum and maximum values.
transformators: (list of teal_transform_module) that will be applied to transform module's data input. To learn more check vignette("transform-input-data", package = "teal").
decorators: (named list of teal_transform_module) optional, decorators for the module plot output.

Value

the teal::module() object.

Details

`data“ object is only used for checks

Decorating Module

This module generates the following objects, which can be modified in place using decorators:

plot (grob, gtable)

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_g_heat_bygrade(
   ..., # arguments for module
   decorators = list(
     plot = teal_transform_module(...), # applied to the `plot` output
   )
)

For additional details and examples of decorators, refer to the vignette vignette("decorate-module-output", package = "teal.modules.general").

To learn more please refer to the vignette vignette("transform-module-output", package = "teal") or the teal::teal_transform_module() documentation.

Reporting

This module returns an object of class teal_module, that contains a server function. Since the server function returns a teal_report object, this makes this module reportable, which means that the reporting functionality will be turned on automatically by the teal framework.

For more information on reporting in teal, see the vignettes:

vignette("reportable-shiny-application", package = "teal.reporter")
vignette("adding-support-for-reporting-to-custom-modules", package = "teal")

Examples


data <- within(teal_data(), {
  library(dplyr)
  library(nestcolor)
  ADSL <- teal.data::rADSL %>% slice(1:30)
  ADEX <- teal.data::rADEX %>% filter(USUBJID %in% ADSL$USUBJID)
  ADAE <- teal.data::rADAE %>% filter(USUBJID %in% ADSL$USUBJID)
  ADCM <- teal.data::rADCM %>% filter(USUBJID %in% ADSL$USUBJID)
  # This preprocess is only to force legacy standard on ADCM
  ADCM <- ADCM %>%
    select(-starts_with("ATC")) %>%
    unique()
  # function to derive AVISIT from ADEX
  .add_visit <- function(data_need_visit) {
    visit_dates <- ADEX %>%
      filter(PARAMCD == "DOSE") %>%
      distinct(USUBJID, AVISIT, ASTDTM) %>%
      group_by(USUBJID) %>%
      arrange(ASTDTM) %>%
      mutate(next_vis = lead(ASTDTM), is_last = ifelse(is.na(next_vis), TRUE, FALSE)) %>%
      rename(this_vis = ASTDTM)
    data_visit <- data_need_visit %>%
      select(USUBJID, ASTDTM) %>%
      left_join(visit_dates, by = "USUBJID") %>%
      filter(ASTDTM > this_vis & (ASTDTM < next_vis | is_last == TRUE)) %>%
      left_join(data_need_visit) %>%
      distinct()
    return(data_visit)
  }
  # derive AVISIT for ADAE and ADCM
  ADAE <- .add_visit(ADAE)
  ADCM <- .add_visit(ADCM)
  # derive ongoing status variable for ADEX
  ADEX <- ADEX %>%
    filter(PARCAT1 == "INDIVIDUAL") %>%
    mutate(ongo_status = (EOSSTT == "ONGOING"))
})

join_keys(data) <- default_cdisc_join_keys[names(data)]

app <- init(
  data = data,
  modules = modules(
    tm_g_heat_bygrade(
      label = "Heatmap by grade",
      sl_dataname = "ADSL",
      ex_dataname = "ADEX",
      ae_dataname = "ADAE",
      cm_dataname = "ADCM",
      id_var = variables(
        choices = is_categorical(min.len = 2),
        selected = 1L
      ),
      visit_var = variables(
        choices = dplyr::starts_with("AVISIT"),
        selected = 1L
      ),
      ongo_var = variables(
        choices = dplyr::starts_with("ongo"),
        selected = 1L
      ),
      anno_var = variables(
        choices = is_categorical(min.len = 2),
        selected = c("SEX", "COUNTRY"),
        multiple = TRUE
      ),
      heat_var = variables(
        choices = dplyr::starts_with("AETO"),
        selected = 1L
      ),
      conmed_var = variables(
        choices = dplyr::starts_with("CMDECOD"),
        selected = 1L
      )
    )
  )
)
#> Initializing tm_g_heat_bygrade
#> Warning: variables(choices = is_categorical(min.len = 2), selected = c("SEX", "COUNTRY"), multiple = TRUE)
#>  - Setting explicit `selected` while `choices` are delayed (set using `tidyselect`) doesn't guarantee that `selected` is a subset of `choices`.
if (interactive()) {
  shinyApp(app$ui, app$server)
}