LTG01

Lattice Plot of Laboratory Tests by Treatment Group Over Time

Lattice plots are natively handled by R, the examples below rely mostly on the package ggplot2.

Output

Basic Plot

Preview
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Code

# General mapping and "lattice" ("facet" in ggplot2 nomenclature).
g1 <- {
  ggplot(
    data = adlb,
    mapping = aes(x = AVISIT, y = AVAL, colour = SUBJID, shape = SUBJID)
  ) +
    facet_grid(LBTESTCD ~ ARM, scales = "free_y") +
    scale_shape_manual(values = pch)
}

# Add points and lines.
g1 <- g1 + geom_point()
g1 <- g1 + geom_line()
plot <- g1
plot

Experimental use!

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The units describing rows of panes and the number of patients under each arm is specified by modifying facet_grid():

Code

# Include the units and the sample size N.
g2 <- g1 + facet_grid(
  paste0(LBTESTCD, "\n(", AVALU, ")") ~ ARM_N,
  scales = "free_y"
)

plot <- g2
plot

Experimental use!

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Modifying X-Axis

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The graphic elements are modified through usual ggplot2 functions. For instance, the x-axis could be improved as follows:

Code

g3 <- g2 + theme(
  axis.text.x = element_text(angle = 45, hjust = 1),
  axis.title = element_blank()
) + scale_x_continuous(breaks = adlb$AVISIT, labels = adlb$AVISIT_txt)

plot <- g3
plot

Experimental use!

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The functions stat_mean_ci and stat_median_ci from the tern package allow the addition of mean and/or median confidence intervals. The example below suggests a larger dataset, where the individual subject legend may not be relevant but the mean or the median are of special interest.

Pre-Processing

Code

# Datasets
adsl <- random.cdisc.data::cadsl %>% slice(1:40)
adlb <- random.cdisc.data::cadlb %>% filter(USUBJID %in% adsl$USUBJID)

# Pre-processing
adlb$AVISIT_txt <- adlb$AVISIT
adlb$AVISIT <- as.numeric(adlb$AVISIT)
adlb$ARM_N <- adlb$ARM
levels(adlb$ARM_N) <- with(
  data = adlb,
  paste0(
    levels(ARM_N), " (N = ",
    tapply(SUBJID, ARM_N, function(x) length(unique(x))), ")"
  )
)

# Plot utils
npch <- 1:25
npatients <- length(unique(adlb$SUBJID))
pch <- c(
  rep(npch, times = npatients %/% length(npch)),
  npch[1:(npatients %% length(npch))]
)

Basic Plot

Preview
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Code

# General mapping and "lattice" ("facet" in ggplot2 nomenclature)
g4 <- {
  ggplot(
    data = adlb,
    mapping = aes(x = AVISIT, y = AVAL, colour = SUBJID, shape = SUBJID)
  ) +
    facet_grid(LBTESTCD ~ ARM_N, scales = "free_y") +
    scale_shape_manual(values = pch) +
    scale_color_manual(values = rep(getOption("ggplot2.discrete.colour"), 2))
}

# Add points and lines.
# Note that with so many patients, legend might not be useful and transparency
#   is advisable.
g4 <- g4 + geom_point(alpha = .3)
g4 <- g4 + geom_line(alpha = .3)
g4 <- g4 + guides(colour = "none", shape = "none")
g4 <- g4 + theme(
  axis.text.x = element_text(angle = 45, hjust = 1),
  axis.title  = element_blank()
)
g4 <- g4 + scale_x_continuous(breaks = adlb$AVISIT, labels = adlb$AVISIT_txt)
plot <- g4
plot

Experimental use!

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Adding Mean

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Code

# Add the mean along with the 95% CI at every visit.
g51 <- g4 + stat_summary(
  fun = mean, linewidth = 1, geom = "line",
  aes(group = 1, linetype = "Mean +/- 95% CI")
)
g51 <- g51 + stat_summary(
  fun.data = tern::stat_mean_ci, geom = "errorbar",
  aes(group = 1, linetype = "Mean +/- 95% CI")
)
plot <- g51 + guides(linetype = guide_legend(title = NULL))
plot

Experimental use!

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Adding Median

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Code

# Instead of a mean, the median could be more relevant.
g52 <- g51 + stat_summary(
  fun = median, linewidth = 1, geom = "line",
  aes(group = 1, linetype = "Median +/- 95% CI")
)
g52 <- g52 + stat_summary(
  fun.data = tern::stat_median_ci, geom = "errorbar",
  aes(group = 1, linetype = "Median +/- 95% CI")
)
plot <- g52 + guides(linetype = guide_legend(title = "Aggregate"))
plot

Experimental use!

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Changing Confidence Level

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Code

# Change the confidence level of interval for the median.
# Note: check `?stat_mean_ci()` and `?stat_median_ci()` for further fine tuning.
g53 <- g4 + stat_summary(
  fun = median, linewidth = 1, geom = "line",
  aes(group = 1, linetype = "Median +/- 80% CI")
)
g53 <- g53 + stat_summary(
  fun.data = function(x) tern::stat_median_ci(x, conf_level = 0.8),
  geom = "errorbar", aes(group = 1, linetype = "Median +/- 80% CI")
)
plot <- g53 + guides(linetype = guide_legend(title = NULL))
plot

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Code

library(tern)
library(teal.modules.clinical)
library(ggplot2)
library(dplyr)
library(nestcolor)

# Datasets
adsl <- random.cdisc.data::cadsl %>% slice(1:8)
adlb <- random.cdisc.data::cadlb %>% filter(USUBJID %in% adsl$USUBJID)

# Pre-processing
adlb$AVISIT_txt <- adlb$AVISIT
adlb$AVISIT <- as.numeric(adlb$AVISIT)
adlb$ARM_N <- adlb$ARM
levels(adlb$ARM_N) <- with(
  data = adlb,
  paste0(
    levels(ARM_N), " (N = ",
    tapply(SUBJID, ARM_N, function(x) length(unique(x))), ")"
  )
)

# Plot utils
npch <- 1:25
npatients <- length(unique(adlb$SUBJID))
pch <- c(
  rep(npch, times = npatients %/% length(npch)),
  npch[1:(npatients %% length(npch))]
)

Reproducibility

Timestamp

[1] "2024-11-26 06:07:35 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14)
 os       Ubuntu 22.04.5 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2024-11-26
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
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 teal.code             * 0.5.0.9018  2024-11-26 [1] https://p~
 teal.data             * 0.6.0.9021  2024-11-26 [1] https://p~
 teal.logger             0.3.0.9002  2024-11-26 [1] https://p~
 teal.modules.clinical * 0.9.1.9031  2024-11-26 [1] https://p~
 teal.slice            * 0.5.1.9016  2024-11-26 [1] https://p~
 teal.transform        * 0.5.0.9016  2024-11-26 [1] https://p~
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 tern.gee                0.1.5.9004  2024-11-26 [1] https://p~
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

──────────────────────────────────────────────────────────────────────────────

`.lock` file

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Output

Basic Plot

Modifying Facets

Modifying X-Axis

Pre-Processing

Basic Plot

Adding Mean

Adding Median

Changing Confidence Level

Reproducibility

Timestamp

Session Info

.lock file

`.lock` file