Scatter Plot Teal Module For Biomarker Analysis

Usage

tm_g_gh_correlationplot(
  label,
  dataname,
  param_var = "PARAMCD",
  xaxis_param = "ALT",
  xaxis_var = "BASE",
  yaxis_param = "CRP",
  yaxis_var = "AVAL",
  trt_group,
  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
)

Arguments

label: menu item label of the module in the teal app.
dataname: analysis data passed to the data argument of init. E.g. ADaM structured laboratory data frame ADLB.
param_var: name of variable containing biomarker codes e.g. PARAMCD.
xaxis_param: biomarker selected for x-axis.
xaxis_var: name of variable containing biomarker results displayed on x-axis e.g. BASE.
yaxis_param: biomarker selected for y-axis.
yaxis_var: name of variable containing biomarker results displayed on y-axis e.g. AVAL.
trt_group: choices_selected object with available choices and pre-selected option for variable names representing treatment group e.g. ARM.
color_manual: vector of colors applied to treatment values.
shape_manual: vector of symbols applied to LOQ values.
facet_ncol: numeric value indicating number of facets per row.
visit_facet: visit facet toggle.
trt_facet: facet by treatment group trt_group.
reg_line: include regression line and annotations for slope and coefficient in visualization. Use with facet TRUE.
loq_legend: loq legend toggle.
rotate_xlab: 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: a character vector of at most length of hline_arb. naming the color for the arbitrary horizontal lines.
hline_arb_label: a character vector of at most length of hline_arb. naming the label for the arbitrary horizontal lines.
hline_vars: a character vector to name the columns that will define additional horizontal lines.
hline_vars_colors: a character vector naming the colors for the additional horizontal lines.
hline_vars_labels: a character vector naming the labels for the additional horizontal lines that will appear
vline_arb: numeric vector of at most 2 values identifying intercepts for arbitrary horizontal lines.
vline_arb_color: a character vector of at most length of vline_arb. naming the color for the arbitrary horizontal lines.
vline_arb_label: a character vector of at most length of vline_arb. naming the label for the arbitrary horizontal lines.
vline_vars: a character vector to name the columns that will define additional vertical lines.
vline_vars_colors: a character vector naming the colors for the additional vertical lines.
vline_vars_labels: a character vector naming the labels for the additional vertical lines that will appear
plot_height: controls plot height.
plot_width: optional, controls plot width.
font_size: font size control for title, x-axis label, y-axis label and legend.
dot_size: plot dot size.
reg_text_size: 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.

Author

Nick Paszty (npaszty) paszty.nicholas@gene.com

Balazs Toth (tothb2) toth.balazs@gene.com

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)

  set.seed(1)
  ADSL <- rADSL
  ADLB <- 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",
      param_var = "PARAMCD",
      xaxis_param = choices_selected(c("ALT", "CRP", "IGA"), "ALT"),
      yaxis_param = choices_selected(c("ALT", "CRP", "IGA"), "CRP"),
      xaxis_var = choices_selected(c("AVAL", "BASE", "CHG", "PCHG"), "BASE"),
      yaxis_var = choices_selected(c("AVAL", "BASE", "CHG", "PCHG"), "AVAL"),
      trt_group = choices_selected(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
#> Initializing reporter_previewer_module
if (interactive()) {
  shinyApp(app$ui, app$server)
}