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.

# 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 <- goshawk::rADSL
ADLB <- goshawk::rADLB
var_labels <- lapply(ADLB, function(x) attributes(x)$label)
ADLB <- ADLB %>%
  dplyr::mutate(AVISITCD = dplyr::case_when(
    AVISIT == "SCREENING" ~ "SCR",
    AVISIT == "BASELINE" ~ "BL",
    grepl("WEEK", AVISIT) ~
      paste(
        "W",
        trimws(
          substr(
            AVISIT,
            start = 6,
            stop = stringr::str_locate(AVISIT, "DAY") - 1
          )
        )
      ),
    TRUE ~ NA_character_
  )) %>%
  dplyr::mutate(AVISITCDN = dplyr::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
  dplyr::mutate(TRTORD = ifelse(grepl("C", ARMCD), 1,
    ifelse(grepl("B", ARMCD), 2,
      ifelse(grepl("A", ARMCD), 3, NA)
    )
  )) %>%
  dplyr::mutate(ARM = as.character(arm_mapping[match(ARM, names(arm_mapping))])) %>%
  dplyr::mutate(ARM = factor(ARM) %>%
    reorder(TRTORD)) %>%
  dplyr::mutate(
    ANRHI = dplyr::case_when(
      PARAMCD == "ALT" ~ 60,
      PARAMCD == "CRP" ~ 70,
      PARAMCD == "IGA" ~ 80,
      TRUE ~ NA_real_
    ),
    ANRLO = dplyr::case_when(
      PARAMCD == "ALT" ~ 20,
      PARAMCD == "CRP" ~ 30,
      PARAMCD == "IGA" ~ 40,
      TRUE ~ NA_real_
    )
  ) %>%
  dplyr::rowwise() %>%
  dplyr::group_by(PARAMCD) %>%
  dplyr::mutate(LBSTRESC = ifelse(
    USUBJID %in% sample(USUBJID, 1, replace = TRUE),
    paste("<", round(runif(1, min = 25, max = 30))), LBSTRESC
  )) %>%
  dplyr::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 <- goshawk:::h_identify_loq_values(ADLB)
ADLB <- dplyr::left_join(ADLB, ADLB_LOQS, by = "PARAM")

app <- teal::init(
  data = teal.data::cdisc_data(
    teal.data::cdisc_dataset("ADSL", ADSL, code = "ADSL <- goshawk::rADSL"),
    teal.data::cdisc_dataset(
      "ADLB",
      ADLB,
      code = "set.seed(1)
              ADLB <- goshawk::rADLB
              var_labels <- lapply(ADLB, function(x) attributes(x)$label)
              ADLB <- ADLB %>%
                dplyr::mutate(AVISITCD = dplyr::case_when(
                  AVISIT == 'SCREENING' ~ 'SCR',
                  AVISIT == 'BASELINE' ~ 'BL',
                  grepl('WEEK', AVISIT) ~
                    paste(
                      'W',
                      trimws(
                        substr(
                          AVISIT,
                          start = 6,
                          stop = stringr::str_locate(AVISIT, 'DAY') - 1
                        )
                      )
                    ),
                  TRUE ~ NA_character_)) %>%
                dplyr::mutate(AVISITCDN = dplyr::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
                dplyr::mutate(TRTORD = ifelse(grepl('C', ARMCD), 1,
                                       ifelse(grepl('B', ARMCD), 2,
                                              ifelse(grepl('A', ARMCD), 3, NA)))) %>%
                dplyr::mutate(ARM = as.character(arm_mapping[match(ARM, names(arm_mapping))])) %>%
                dplyr::mutate(ARM = factor(ARM) %>%
                         reorder(TRTORD)) %>%
                dplyr::mutate(
                  ANRHI = dplyr::case_when(
                    PARAMCD == 'ALT' ~ 60,
                    PARAMCD == 'CRP' ~ 70,
                    PARAMCD == 'IGA' ~ 80,
                    TRUE ~ NA_real_
                  ),
                  ANRLO = dplyr::case_when(
                    PARAMCD == 'ALT' ~ 20,
                    PARAMCD == 'CRP' ~ 30,
                    PARAMCD == 'IGA' ~ 40,
                    TRUE ~ NA_real_
                  )) %>%
                dplyr::rowwise() %>%
                dplyr::group_by(PARAMCD) %>%
                dplyr::mutate(LBSTRESC = ifelse(
                  USUBJID %in% sample(USUBJID, 1, replace = TRUE),
                  paste('<', round(runif(1, min = 25, max = 30))), LBSTRESC)) %>%
                dplyr::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 <- goshawk:::h_identify_loq_values(ADLB)
              ADLB <- left_join(ADLB, ADLB_LOQS, by = 'PARAM')",
      vars = list(arm_mapping = arm_mapping)
    ),
    check = TRUE
  ),
  modules = teal::modules(
    teal.goshawk::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_label = 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")
    )
  )
)
#> [INFO] 2023-08-14 13:52:00.5965 pid:1122 token:[] teal.goshawk Initializing tm_g_gh_correlationplot
if (interactive()) {
  shinyApp(app$ui, app$server)
}