Scatter Plot Teal Module For Biomarker Analysis

Scatter Plot Teal Module For Biomarker Analysis

Usage

tm_g_gh_correlationplot(
  label,
  dataname = "ADLB",
  param_var = lifecycle::deprecated(),
  xaxis_param = teal.picks::picks(teal.picks::variables("PARAMCD", "PARAMCD"),
    teal.picks::values(selected = "ALT", multiple = FALSE), check_dataset = FALSE),
  xaxis_var = teal.picks::variables(c("AVAL", "BASE", "CHG", "PCHG"), "BASE"),
  yaxis_param = teal.picks::picks(teal.picks::variables("PARAMCD", "PARAMCD"),
    teal.picks::values(selected = "CRP", multiple = FALSE), check_dataset = FALSE),
  yaxis_var = teal.picks::variables(c("AVAL", "BASE", "CHG", "PCHG"), "AVAL"),
  trt_group = teal.picks::variables(dplyr::starts_with("ARM"), selected = "ARM"),
  color_manual = NULL,
  shape_manual = NULL,
  facet_ncol = 2,
  visit_facet = TRUE,
  trt_facet = FALSE,
  reg_line = FALSE,
  loq_legend = TRUE,
  rotate_xlab = FALSE,
  hline_arb = numeric(0),
  hline_arb_color = "red",
  hline_arb_label = "Horizontal line",
  hline_vars = character(0),
  hline_vars_colors = "green",
  hline_vars_labels = hline_vars,
  vline_arb = numeric(0),
  vline_arb_color = "red",
  vline_arb_label = "Vertical line",
  vline_vars = character(0),
  vline_vars_colors = "green",
  vline_vars_labels = vline_vars,
  plot_height = c(500, 200, 2000),
  plot_width = NULL,
  font_size = c(12, 8, 20),
  dot_size = c(1, 1, 12),
  reg_text_size = c(3, 3, 10),
  pre_output = NULL,
  post_output = NULL,
  transformators = list(),
  decorators = list()
)

Arguments

label

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

dataname

(character(1)) analysis data passed to the data argument of teal::init(). E.g. ADaM structured laboratory data frame ADLB.

param_var

(character(1)) name of variable containing biomarker codes e.g. PARAMCD.

xaxis_param

(teal.picks::picks() or legacy teal.transform::choices_selected()) biomarker selected for x-axis.

xaxis_var

(teal.picks::variables() or legacy teal.transform::choices_selected()) name of variable containing biomarker results displayed on x-axis e.g. BASE.

yaxis_param

(teal.picks::picks() or legacy teal.transform::choices_selected()) biomarker selected for y-axis.

yaxis_var

(teal.picks::variables() or legacy teal.transform::choices_selected()) name of variable containing biomarker results displayed on y-axis e.g. AVAL.

trt_group

(teal.picks::variables() or legacy teal.transform::choices_selected()) object with available choices and pre-selected option for variable names representing treatment group e.g. ARM.

color_manual

(named character, optional) vector of colors applied to treatment values.

shape_manual

(named numeric, optional) vector of symbols applied to LOQ values.

facet_ncol

(integer(1)) numeric value indicating number of facets per row.

visit_facet

(logical(1)) visit facet toggle.

trt_facet

(logical(1)) facet by treatment group trt_group.

reg_line

(logical(1)) include regression line and annotations for slope and coefficient in visualization. Use with facet TRUE.

loq_legend

(logical(1)) loq legend toggle.

rotate_xlab

(logical(1)) 45 degree rotation of x-axis values.

hline_arb

(numeric) vector of at most 2 values identifying intercepts for arbitrary horizontal lines.

hline_arb_color

(character) a character vector of at most length of hline_arb. naming the color for the arbitrary horizontal lines.

hline_arb_label

(character) a character vector of at most length of hline_arb. naming the label for the arbitrary horizontal lines.

hline_vars

(character) a character vector to name the columns that will define additional horizontal lines.

hline_vars_colors

(character) a character vector naming the colors for the additional horizontal lines.

hline_vars_labels

(character) a character vector naming the labels for the additional horizontal lines that will appear in the plot.

vline_arb

(numeric) vector of at most 2 values identifying intercepts for arbitrary vertical lines.

vline_arb_color

(character) a character vector of at most length of vline_arb. naming the color for the arbitrary vertical lines.

vline_arb_label

(character) a character vector of at most length of vline_arb. naming the label for the arbitrary vertical lines.

vline_vars

(character) a character vector to name the columns that will define additional vertical lines.

vline_vars_colors

(character) a character vector naming the colors for the additional vertical lines.

vline_vars_labels

(character) a character vector naming the labels for the additional vertical lines that will appear in the plot.

plot_height

(numeric(3)) controls plot height.

plot_width

(numeric(3), optional) controls plot width.

font_size

(numeric(3)) font size control for title, x-axis label, y-axis label and legend.

dot_size

(numeric(3)) plot dot size.

reg_text_size

(numeric(3)) font size control for regression line annotations.

pre_output

(shiny.tag) optional,
with text placed before the output to put the output into context. For example a title.

post_output

(shiny.tag) optional, with text placed after the output to put the output into context. For example the shiny::helpText() elements are useful.

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 lists of teal_transform_module) optional, decorator for tables or plots included in the module output reported. The decorators are applied to the respective output objects.

See section "Decorating Module" below for more details.

Value

A teal::module() object that can be used in a teal::init() call.

Decorating Module

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

• plot (ggplot)

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

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

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")

Author

Nick Paszty

Balazs Toth

Examples

# Example using ADaM structure analysis dataset.
data <- teal_data()
data <- within(data, {
  library(dplyr)
  library(stringr)

  # use non-exported function from goshawk
  .h_identify_loq_values <- getFromNamespace("h_identify_loq_values", "goshawk")

  # original ARM value = dose value
  .arm_mapping <- list(
    "A: Drug X" = "150mg QD",
    "B: Placebo" = "Placebo",
    "C: Combination" = "Combination"
  )
  .color_manual <- c("150mg QD" = "#000000", "Placebo" = "#3498DB", "Combination" = "#E74C3C")
  # assign LOQ flag symbols: circles for "N" and triangles for "Y", squares for "NA"
  .shape_manual <- c("N" = 1, "Y" = 2, "NA" = 0)

  ADSL <- teal.data::rADSL
  ADLB <- teal.data::rADLB
  .var_labels <- lapply(ADLB, function(x) attributes(x)$label)
  ADLB <- ADLB %>%
    mutate(AVISITCD = case_when(
      AVISIT == "SCREENING" ~ "SCR",
      AVISIT == "BASELINE" ~ "BL",
      grepl("WEEK", AVISIT) ~
        paste(
          "W",
          trimws(
            substr(
              AVISIT,
              start = 6,
              stop = str_locate(AVISIT, "DAY") - 1
            )
          )
        ),
      TRUE ~ NA_character_
    )) %>%
    mutate(AVISITCDN = case_when(
      AVISITCD == "SCR" ~ -2,
      AVISITCD == "BL" ~ 0,
      grepl("W", AVISITCD) ~ as.numeric(gsub("[^0-9]*", "", AVISITCD)),
      TRUE ~ NA_real_
    )) %>%
    # use ARMCD values to order treatment in visualization legend
    mutate(TRTORD = ifelse(grepl("C", ARMCD), 1,
      ifelse(grepl("B", ARMCD), 2,
        ifelse(grepl("A", ARMCD), 3, NA)
      )
    )) %>%
    mutate(ARM = as.character(.arm_mapping[match(ARM, names(.arm_mapping))])) %>%
    mutate(ARM = factor(ARM) %>%
      reorder(TRTORD)) %>%
    mutate(
      ANRHI = case_when(
        PARAMCD == "ALT" ~ 60,
        PARAMCD == "CRP" ~ 70,
        PARAMCD == "IGA" ~ 80,
        TRUE ~ NA_real_
      ),
      ANRLO = case_when(
        PARAMCD == "ALT" ~ 20,
        PARAMCD == "CRP" ~ 30,
        PARAMCD == "IGA" ~ 40,
        TRUE ~ NA_real_
      )
    ) %>%
    rowwise() %>%
    group_by(PARAMCD) %>%
    mutate(LBSTRESC = ifelse(
      USUBJID %in% sample(USUBJID, 1, replace = TRUE),
      paste("<", round(runif(1, min = 25, max = 30))), LBSTRESC
    )) %>%
    mutate(LBSTRESC = ifelse(
      USUBJID %in% sample(USUBJID, 1, replace = TRUE),
      paste(">", round(runif(1, min = 70, max = 75))), LBSTRESC
    )) %>%
    ungroup()
  attr(ADLB[["ARM"]], "label") <- .var_labels[["ARM"]]
  attr(ADLB[["ANRHI"]], "label") <- "Analysis Normal Range Upper Limit"
  attr(ADLB[["ANRLO"]], "label") <- "Analysis Normal Range Lower Limit"

  # add LLOQ and ULOQ variables
  ADLB_LOQS <- .h_identify_loq_values(ADLB, "LOQFL")
  ADLB <- left_join(ADLB, ADLB_LOQS, by = "PARAM")
})

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

app <- init(
  data = data,
  modules = modules(
    tm_g_gh_correlationplot(
      label = "Correlation Plot",
      dataname = "ADLB",
      xaxis_param = picks(
        variables("PARAMCD", "PARAMCD"),
        values(selected = "ALT", multiple = FALSE),
        check_dataset = FALSE
      ),
      yaxis_param = picks(
        variables("PARAMCD", "PARAMCD"),
        values(selected = "CRP", multiple = FALSE),
        check_dataset = FALSE
      ),
      xaxis_var = variables(c("AVAL", "BASE", "CHG", "PCHG"), "BASE"),
      yaxis_var = variables(c("AVAL", "BASE", "CHG", "PCHG"), "AVAL"),
      trt_group = variables(c("ARM", "ACTARM"), "ARM"),
      color_manual = c(
        "Drug X 100mg" = "#000000",
        "Placebo" = "#3498DB",
        "Combination 100mg" = "#E74C3C"
      ),
      shape_manual = c("N" = 1, "Y" = 2, "NA" = 0),
      plot_height = c(500, 200, 2000),
      facet_ncol = 2,
      visit_facet = TRUE,
      reg_line = FALSE,
      loq_legend = TRUE,
      font_size = c(12, 8, 20),
      dot_size = c(1, 1, 12),
      reg_text_size = c(3, 3, 10),
      hline_arb = c(40, 50),
      hline_arb_label = "arb hori label",
      hline_arb_color = c("red", "blue"),
      hline_vars = c("ANRHI", "ANRLO", "ULOQN", "LLOQN"),
      hline_vars_colors = c("green", "blue", "purple", "cyan"),
      hline_vars_labels = c("ANRHI Label", "ANRLO Label", "ULOQN Label", "LLOQN Label"),
      vline_vars = c("ANRHI", "ANRLO", "ULOQN", "LLOQN"),
      vline_vars_colors = c("yellow", "orange", "brown", "gold"),
      vline_vars_labels = c("ANRHI Label", "ANRLO Label", "ULOQN Label", "LLOQN Label"),
      vline_arb = c(50, 70),
      vline_arb_label = "arb vert A",
      vline_arb_color = c("green", "orange")
    )
  )
)
#> Initializing tm_g_gh_correlationplot
#> Warning: rlang::dots_list(..., .ignore_empty = "trailing")
#>  - Setting explicit `selected` while `choices` are delayed (set using `tidyselect`) doesn't guarantee that `selected` is a subset of `choices`.
#> Warning: rlang::dots_list(..., .ignore_empty = "trailing")
#>  - 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)
}