Forestplots

1. Introduction

This vignette documents and illustrates workflows for producing forest plots using the function plotForest from the ospsuite.plots package. Forest plots are a useful way to visualize the results of multiple studies or datasets, showing estimates of effect sizes along with their confidence intervals.

1.1 Setup

This vignette uses the {ospsuite.plots} and {tidyr} libraries. We will use the default settings of {ospsuite.plots} (see vignette("ospsuite.plots", package = "ospsuite.plots")) but will adjust the legend position for better visibility.

options(rmarkdown.html_vignette.check_title = FALSE)

library(ospsuite.plots)
library(tidyr)
library(data.table)
#> 
#> Attaching package: 'data.table'
#> The following object is masked from 'package:base':
#> 
#>     %notin%

# Set Defaults
oldDefaults <- ospsuite.plots::setDefaults()

# Place default legend position above the plot
theme_update(legend.position = "top")
theme_update(legend.direction = "horizontal")
theme_update(legend.title = element_blank())

1.2 Example Data

This vignette uses the following datasets:

  • Data Set 1: A simulated dataset containing various covariates such as ID, Country, Age, AgeBin, Observations, and Predictions. The dataset will be filtered and reshaped to prepare it for plotting.
histData <- exampleDataCovariates |>
  dplyr::filter(SetID == "DataSet1") |>
  dplyr::select(c("ID", "Country", "Age", "AgeBin", "Obs", "Pred")) |>
  melt(
    id.vars = c("ID", "Country", "Age", "AgeBin"),
    value.name = "value",
    variable.name = "DataType"
  )

# Prepare plot data by calculating mean and standard deviation
plotData <- histData[, .(
  Mean = mean(value),
  SD = sd(value)
),
by = c("Country", "AgeBin", "DataType")
]

2. Generating Forest Plots

2.1 Basic Example

In this example, we will create a basic forest plot using the mean and standard deviation of the data, faceted by Country. The plot will display the mean on the x-axis and the AgeBin on the y-axis.

plotObject <-
  plotForest(
    plotData = plotData,
    mapping = aes(x = Mean, error = SD, y = AgeBin, groupby = DataType),
    xLabel = "Mean",
    yFacetColumns = "Country",
    tableColumns = c("Mean", "SD"),
    tableLabels = c("Mean", "SD")
  )

print(plotObject)

A basic forest plot showing mean values of different age bins faceted by country.

Explanation:

  • plotData: The data prepared in the previous section.
  • mapping: Specifies the aesthetics for the plot, including x and y axes, and grouping.
  • xLabel: The label for the x-axis.
  • yFacetColumns: Column used for faceting on the y-axis.

2.2 Example Without Table

In this example, we will create a forest plot similar to the previous one but without including a summary table below the plot.

plotObject <-
  plotForest(
    plotData = plotData,
    mapping = aes(x = Mean, error = SD, y = AgeBin, groupby = DataType),
    xLabel = "Mean",
    yFacetColumns = "Country",
    tableColumns = c("Mean", "SD"),
    tableLabels = c("Mean", "SD"),
    withTable = FALSE
  )

print(plotObject)

A forest plot showing mean values of different age bins faceted by country, without a summary table.

Explanation:

  • Setting withTable = FALSE excludes the summary table from the output, focusing solely on the graphical representation.

2.3 Faceting by Data Type

In this example, we will facet the plot by DataType while still using Country for the y-axis. This allows for a more detailed view of the data categorized by both Country and DataType.

plotObject <-
  plotForest(
    plotData = plotData,
    mapping = aes(x = Mean, error = SD, y = AgeBin),
    xLabel = "Mean",
    yFacetColumns = "Country",
    xFacetColumn = "DataType",
    tableColumns = c("Mean", "SD"),
    tableLabels = c("Mean", "SD"),
    withTable = FALSE
  )

print(plotObject)

A forest plot showing mean values of different age bins, faceted by country and data type.

Explanation:

  • xFacetColumn: By specifying this parameter, we create separate plots for each DataType, allowing us to compare them side by side.