Function to create a correlation plot. — g

Default plot displays correlation facetted by visit with color attributed treatment arms and symbol attributed LOQ values.

Usage

g_correlationplot(
  label = "Correlation Plot",
  data,
  param_var = "PARAMCD",
  xaxis_param = "CRP",
  xaxis_var = "BASE",
  xvar,
  yaxis_param = "IGG",
  yaxis_var = "AVAL",
  yvar,
  trt_group = "ARM",
  visit = "AVISITCD",
  loq_flag_var = "LOQFL_COMB",
  visit_facet = TRUE,
  loq_legend = TRUE,
  unit = "AVALU",
  xlim = c(NA, NA),
  ylim = c(NA, NA),
  title_text = title_text,
  xaxis_lab = xaxis_lab,
  yaxis_lab = yaxis_lab,
  color_manual = NULL,
  shape_manual = NULL,
  facet_ncol = 2,
  facet = FALSE,
  facet_var = "ARM",
  reg_line = 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,
  rotate_xlab = FALSE,
  font_size = 12,
  dot_size = 2,
  reg_text_size = 3
)

Arguments

label: text string to used to identify plot.
data: ADaM structured analysis laboratory data frame e.g. ADLB.
param_var: name of variable containing biomarker codes e.g. PARAMCD.
xaxis_param: x-axis biomarker to visualize e.g. IGG.
xaxis_var: name of variable containing biomarker results displayed on X-axis e.g. BASE.
xvar: x-axis analysis variable from transposed data set.
yaxis_param: y-axis biomarker to visualize e.g. IGG.
yaxis_var: name of variable containing biomarker results displayed on Y-axis.g. AVAL.
yvar: y-axis analysis variable from transposed data set.
trt_group: name of variable representing treatment group e.g. ARM.
visit: name of variable containing nominal visits e.g. AVISITCD.
loq_flag_var: name of variable containing LOQ flag e.g. LOQFL_COMB.
visit_facet: visit facet toggle.
loq_legend: logical whether to include LoQ legend.
unit: name of variable containing biomarker unit e.g. AVALU.
xlim: ('numeric vector') optional, a vector of length 2 to specify the minimum and maximum of the x-axis if the default limits are not suitable.
ylim: ('numeric vector') optional, a vector of length 2 to specify the minimum and maximum of the y-axis if the default limits are not suitable.
title_text: plot title.
xaxis_lab: x-axis label.
yaxis_lab: y-axis label.
color_manual: vector of colors applied to treatment values.
shape_manual: vector of symbols applied to LOQ values. (used with loq_flag_var).
facet_ncol: number of facets per row.
facet: set layout to use treatment facetting.
facet_var: variable to use for treatment facetting.
reg_line: include regression line and annotations for slope and coefficient. Use with facet = TRUE.
hline_arb: ('numeric vector') value identifying intercept for arbitrary horizontal lines.
hline_arb_color: ('character vector') optional, color for the arbitrary horizontal lines.
hline_arb_label: ('character vector') optional, label for the legend to the arbitrary horizontal lines.
hline_vars: ('character vector'), names of variables (ANR*) or values (*LOQ) identifying intercept values. The data inside of the ggplot2 object must also contain the columns with these variable names
hline_vars_colors: ('character vector') colors for the horizontal lines defined by variables.
hline_vars_labels: ('character vector') labels for the legend to the horizontal lines defined by variables.
vline_arb: ('numeric vector') value identifying intercept for arbitrary vertical lines.
vline_arb_color: ('character vector') optional, color for the arbitrary vertical lines.
vline_arb_label: ('character vector') optional, label for the legend to the arbitrary vertical lines.
vline_vars: ('character vector'), names of variables (ANR*) or values (*LOQ) identifying intercept values. The data inside of the ggplot2 object must also contain the columns with these variable names
vline_vars_colors: ('character vector') colors for the vertical lines defined by variables.
vline_vars_labels: ('character vector') labels for the legend to the vertical lines defined by variables.
rotate_xlab: 45 degree rotation of x-axis label values.
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.

Details

Regression uses deming model.

Author

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

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

Examples

# Example using ADaM structure analysis dataset.

library(stringr)
library(tidyr)

# 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)

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("\\D+", "", 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 <- goshawk:::h_identify_loq_values(ADLB, flag_var = "LOQFL")
ADLB <- left_join(ADLB, ADLB_LOQS, by = "PARAM")

# given the 2 param and 2 analysis vars we need to transform the data
plot_data_t1 <- ADLB %>%
  gather(
    ANLVARS, ANLVALS, PARAM, LBSTRESC, BASE2, BASE, AVAL, BASE, LOQFL,
    ANRHI, ANRLO, ULOQN, LLOQN
  ) %>%
  mutate(ANL.PARAM = ifelse(ANLVARS %in% c("PARAM", "LBSTRESC", "LOQFL"),
    paste0(ANLVARS, "_", PARAMCD),
    paste0(ANLVARS, ".", PARAMCD)
  )) %>%
  select(USUBJID, ARM, ARMCD, AVISITN, AVISITCD, ANL.PARAM, ANLVALS) %>%
  spread(ANL.PARAM, ANLVALS)
#> Warning: attributes are not identical across measure variables; they will be dropped

# the transformed analysis value variables are character and need to be converted to numeric for
# ggplot
# remove records where either of the analysis variables are NA since they will not appear on the
# plot and will ensure that LOQFL = NA level is removed
plot_data_t2 <- plot_data_t1 %>%
  filter(!is.na(BASE.CRP) & !is.na(AVAL.ALT)) %>%
  mutate_at(vars(contains(".")), as.numeric) %>%
  mutate(
    LOQFL_COMB = ifelse(LOQFL_CRP == "Y" | LOQFL_ALT == "Y", "Y", "N")
  )

g_correlationplot(
  label = "Correlation Plot",
  data = plot_data_t2,
  param_var = "PARAMCD",
  xaxis_param = c("CRP"),
  xaxis_var = "AVAL",
  xvar = "AVAL.CRP",
  yaxis_param = c("ALT"),
  yaxis_var = "BASE",
  yvar = "BASE.ALT",
  trt_group = "ARM",
  visit = "AVISITCD",
  visit_facet = TRUE,
  loq_legend = TRUE,
  unit = "AVALU",
  title_text = "Correlation of ALT to CRP",
  xaxis_lab = "CRP",
  yaxis_lab = "ALT",
  color_manual = color_manual,
  shape_manual = shape_manual,
  facet_ncol = 4,
  facet = FALSE,
  facet_var = "ARM",
  reg_line = FALSE,
  hline_arb = c(15, 25),
  hline_arb_color = c("gray", "green"),
  hline_arb_label = "Hori_line_label",
  vline_arb = c(.5, 1),
  vline_arb_color = c("red", "black"),
  vline_arb_label = c("Vertical Line A", "Vertical Line B"),
  hline_vars = c("ANRHI.ALT", "ANRLO.ALT", "ULOQN.ALT", "LLOQN.ALT"),
  hline_vars_colors = c("green", "blue", "purple", "cyan"),
  hline_vars_labels = c("ANRHI ALT Label", "ANRLO ALT Label", "ULOQN ALT Label", "LLOQN ALT Label"),
  vline_vars = c("ANRHI.CRP", "ANRLO.CRP", "ULOQN.CRP", "LLOQN.CRP"),
  vline_vars_colors = c("yellow", "orange", "brown", "gold"),
  vline_vars_labels = c("ANRHI CRP Label", "ANRLO CRP Label", "ULOQN CRP Label", "LLOQN CRP Label"),
  rotate_xlab = FALSE,
  font_size = 14,
  dot_size = 2,
  reg_text_size = 3
)