Customizing Forest Plots in R with forestploter: Coloring by Significance Levels
Forest plots are a powerful way to visualize the results of meta-analyses or studies with multiple subgroups. The forestploter package in R provides a flexible way to create these plots. This article will explore how to customize the colors of estimates in a forest plot based on their significance levels, drawing on examples and insights from Stack Overflow.
Understanding the Problem
When analyzing data with multiple groups and outcomes, it's crucial to highlight the significance of findings. A color-coded forest plot can effectively communicate which estimates are statistically significant and which are not.
Stack Overflow Solution: Leveraging forestploter Customization
The Stack Overflow question you referenced seeks a way to color-code estimates based on significance columns (e.g., significance_trt1_result1, significance_trt1_result2). While forestploter doesn't offer direct coloring based on these columns, we can exploit its customization options to achieve this.
Code Breakdown and Explanation
Let's break down the process of creating a custom-colored forest plot using the provided dataset (dt) and forestploter.
library(forestploter)
# Define color palettes for significant and non-significant estimates
sig_colors <- c("green4", "blue4")
non_sig_colors <- c("lightgreen", "lightskyblue")
# Define a function to map significance to colors
get_color <- function(sig) {
if (sig == "*") {
return(sig_colors[1])
} else if (sig == "***") {
return(sig_colors[2])
} else {
return(non_sig_colors[1])
}
}
# Create a list of colors for each estimate
est_colors <- list(
`est_gp1` = sapply(dt
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Forestploter in r : how to customize the colors according to the significance levels in the forest plot
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DORSETRIGS
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Forestploter in r : how to customize the colors according to the significance levels in the forest plot
DORSETRIGS
2 min read
·
01-09-2024
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significance_trt1_result1`, get_color),
`est_gp2` = sapply(dt
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Forestploter in r : how to customize the colors according to the significance levels in the forest plot
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DORSETRIGS
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Forestploter in r : how to customize the colors according to the significance levels in the forest plot
DORSETRIGS
2 min read
·
01-09-2024
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significance_trt1_result2`, get_color),
`est_gp3` = sapply(dt
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Forestploter in r : how to customize the colors according to the significance levels in the forest plot
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DORSETRIGS
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Forestploter in r : how to customize the colors according to the significance levels in the forest plot
DORSETRIGS
2 min read
·
01-09-2024
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significance_trt2_result1`, get_color),
`est_gp4` = sapply(dt
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Forestploter in r : how to customize the colors according to the significance levels in the forest plot
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DORSETRIGS
Home
Forestploter in r : how to customize the colors according to the significance levels in the forest plot
DORSETRIGS
2 min read
·
01-09-2024
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atOptions = {
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significance_trt2_result2`, get_color)
)
# Generate the forest plot with custom colors
forestplot(
dt,
est = c("est_gp1", "est_gp2", "est_gp3", "est_gp4"),
ci = c("low_gp1", "low_gp2", "low_gp3", "low_gp4", "hi_gp1", "hi_gp2", "hi_gp3", "hi_gp4"),
title = "Forest Plot with Significance Coloring",
col_est = est_colors,
xlab = "Estimate"
)
Explanation:
- Define Color Palettes: We establish two color palettes:
sig_colors for significant estimates and non_sig_colors for non-significant ones.
- Create
get_color Function: This function maps a significance symbol ("", "**", "ns") to its corresponding color from the palettes.
- Generate Estimate Colors: We use
sapply to apply the get_color function to each significance column, creating a list (est_colors) containing colors for each estimate.
- Forest Plot Customization: The
forestplot function is used to create the plot. The col_est argument is set to the est_colors list, applying the custom color scheme to each estimate.
Additional Considerations:
- Alpha Transparency: To achieve the "lower alpha for 'ns'" effect, you can use the
col_alpha argument in forestplot. Instead of using a separate palette, define a single palette with varying alpha values for significant and non-significant estimates.
- Clarity and Aesthetics: Consider the overall aesthetics of your plot. Choose colors that are distinct and visually appealing, ensuring the plot remains clear and informative.
- Alternative Visualization: Explore alternative visualization techniques to showcase significance beyond color. Consider using symbols (e.g., asterisks, stars) or distinct shapes for estimates.
Conclusion
Customizing forest plots with forestploter allows you to highlight significant findings effectively. By leveraging the package's flexibility and creating custom color mappings, you can create more informative and visually engaging visualizations that communicate the importance of your research findings. Remember to prioritize clarity, aesthetics, and choose a method that best suits your data and research questions.
