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Teal module for the heatmap by grade

Source: R/tm_g_heat_bygrade.R
tm_g_heat_bygrade.Rd

[Stable]

Display the heatmap by grade as a shiny module

Usage

tm_g_heat_bygrade(
  label,
  sl_dataname,
  ex_dataname,
  ae_dataname,
  cm_dataname = NA,
  id_var,
  visit_var,
  ongo_var,
  anno_var,
  heat_var,
  conmed_var = NULL,
  fontsize = c(5, 3, 7),
  plot_height = c(600L, 200L, 2000L),
  plot_width = NULL
)

Arguments

label

(character(1))
menu item label of the module in the teal app.

sl_dataname

(character) subject level dataset name, needs to be available in the list passed to the data argument of init

ex_dataname

(character) exposures dataset name, needs to be available in the list passed to the data argument of init

ae_dataname

(character) adverse events dataset name, needs to be available in the list passed to the data argument of init

cm_dataname

(character) concomitant medications dataset name, needs to be available in the list passed to the data argument of init
specify to NA if no concomitant medications data is available

id_var

(choices_seleced) unique subject ID variable

visit_var

(choices_seleced) analysis visit variable

ongo_var

(choices_seleced) study ongoing status variable, This variable is a derived logical variable. Usually it can be derived from EOSSTT.

anno_var

(choices_seleced) annotation variable

heat_var

(choices_seleced) heatmap variable

conmed_var

(choices_seleced) 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.

Value

the teal::module() object.

Examples

library(dplyr)
library(nestcolor)
ADSL <- osprey::rADSL %>% slice(1:30)
ADEX <- osprey::rADEX %>% filter(USUBJID %in% ADSL$USUBJID)
ADAE <- osprey::rADAE %>% filter(USUBJID %in% ADSL$USUBJID)
ADCM <- osprey::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)
#> Warning: Detected an unexpected many-to-many relationship between `x` and `y`.
#> ℹ Row 1 of `x` matches multiple rows in `y`.
#> ℹ Row 56 of `y` matches multiple rows in `x`.
#> ℹ If a many-to-many relationship is expected, set `relationship =
#>   "many-to-many"` to silence this warning.
#> Joining with `by = join_by(USUBJID, ASTDTM)`
ADCM <- add_visit(ADCM)
#> Warning: Detected an unexpected many-to-many relationship between `x` and `y`.
#> ℹ Row 1 of `x` matches multiple rows in `y`.
#> ℹ Row 56 of `y` matches multiple rows in `x`.
#> ℹ If a many-to-many relationship is expected, set `relationship =
#>   "many-to-many"` to silence this warning.
#> Joining with `by = join_by(USUBJID, ASTDTM)`
# derive ongoing status variable for ADEX
ADEX <- ADEX %>%
  filter(PARCAT1 == "INDIVIDUAL") %>%
  mutate(ongo_status = (EOSSTT == "ONGOING"))

app <- init(
  data = cdisc_data(
    cdisc_dataset("ADSL", ADSL),
    cdisc_dataset("ADEX", ADEX),
    cdisc_dataset("ADAE", ADAE),
    cdisc_dataset("ADCM", ADCM, keys = c("STUDYID", "USUBJID", "ASTDTM", "CMSEQ", "CMDECOD")),
    code = "
    ADSL <- osprey::rADSL %>% slice(1:30)
    ADEX <- osprey::rADEX %>% filter(USUBJID %in% ADSL$USUBJID)
    ADAE <- osprey::rADAE %>% filter(USUBJID %in% ADSL$USUBJID)
    ADCM <- osprey::rADCM %>% filter(USUBJID %in% ADSL$USUBJID)
    ADCM <- ADCM %>% select(-starts_with(\"ATC\")) %>% unique()
    ADEX  <- ADEX %>%
      filter(PARCAT1 == 'INDIVIDUAL') %>%
      mutate(ongo_status = (EOSSTT == 'ONGOING'))
    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)
    }
    ADAE <- add_visit(ADAE)
    ADCM <- add_visit(ADCM)
    ",
    check = TRUE
  ),
  modules = modules(
    tm_g_heat_bygrade(
      label = "Heatmap by grade",
      sl_dataname = "ADSL",
      ex_dataname = "ADEX",
      ae_dataname = "ADAE",
      cm_dataname = "ADCM",
      id_var = teal.transform::choices_selected(
        selected = "USUBJID",
        choices = c("USUBJID", "SUBJID")
      ),
      visit_var = teal.transform::choices_selected(
        selected = "AVISIT",
        choices = c("AVISIT")
      ),
      ongo_var = teal.transform::choices_selected(
        selected = "ongo_status",
        choices = c("ongo_status")
      ),
      anno_var = teal.transform::choices_selected(
        selected = c("SEX", "COUNTRY"),
        choices = c("SEX", "COUNTRY", "USUBJID")
      ),
      heat_var = teal.transform::choices_selected(
        selected = "AETOXGR",
        choices = c("AETOXGR")
      ),
      conmed_var = teal.transform::choices_selected(
        selected = "CMDECOD",
        choices = c("CMDECOD")
      ),
      plot_height = c(600, 200, 2000)
    )
  )
)
#> Joining with `by = join_by(USUBJID, ASTDTM)`
#> Joining with `by = join_by(USUBJID, ASTDTM)`
#> [INFO] 2023-08-14 13:53:46.2030 pid:1120 token:[] teal.osprey Initializing tm_g_heat_bygrade
if (interactive()) {
  shinyApp(app$ui, app$server)
}