Stat Compare Means: An In-depth Guide for Statistical Comparisons in ggplot2

Stat_compare_means is a powerful function in the ggplot2 R package that enhances data visualization by adding statistical significance comparisons directly onto your plots. Specifically designed for comparing means of different groups within your dataset, stat_compare_means automatically performs statistical tests and displays the results in an easily interpretable format on your ggplot2 graphs. This guide provides a comprehensive overview of stat_compare_means, detailing its parameters and how to effectively use it to elevate your data analysis and presentation.

Understanding the Functionality of stat_compare_means

The primary purpose of stat_compare_means is to streamline the process of comparing groups and visualizing the statistical significance of those comparisons. Instead of manually performing tests and then annotating your plots, stat_compare_means automates this workflow. It leverages various statistical methods to compare means and then elegantly displays p-values and significance levels directly on your ggplot2 visualizations, making your findings more accessible and impactful.

Key Arguments and Customization Options in stat_compare_means

To harness the full potential of stat_compare_means, understanding its arguments is crucial. These arguments allow for extensive customization of both the statistical tests performed and the visual representation of the results.

Data and Aesthetic Mapping

• mapping: This argument defines the set of aesthetic mappings created using aes(). It dictates how variables in your dataset are mapped to visual properties of the plot. If a mapping is already defined in the main ggplot() call, and inherit.aes = TRUE (which is the default), the mappings are combined. You must specify mapping if no plot-level mapping exists.
• data: This argument specifies the dataset to be used for this layer. You have several options:
  • NULL (default): Inherits the dataset from the ggplot() call.
  • data.frame or other object: Overrides the plot data with the provided dataset.
  • function: A function that takes the plot data as input and returns a data.frame to be used for the layer.

Statistical Test Configuration

• method: This is a character string that specifies the statistical method used for comparing means. Common methods include “t.test” for t-tests and “wilcox.test” for Wilcoxon rank-sum tests. The choice of method depends on the nature of your data and the assumptions of each test.
• paired: A logical argument used in conjunction with t.test and wilcox.test. Set to TRUE if you are performing a paired test, appropriate for comparing measurements from the same subjects under different conditions.
• method.args: A list of additional arguments to be passed to the chosen method function. For instance, you might use method.args = list(alternative = "greater") to specify a one-sided Wilcoxon test.
• ref.group: This argument allows you to specify a reference group for comparisons. If defined, each group level is compared against this reference group. You can also use ref.group = ".all." to compare each group level to the overall mean (basemean).
• comparisons: A list of length-2 vectors defining the specific groups you want to compare. You can specify groups by their names on the x-axis or by their numerical index. For example, comparisons = list(c("Group A", "Group B"), c("Group B", "Group C")) would compare Group A vs Group B, and Group B vs Group C.

Customizing Labels and Appearance

• hide.ns: A logical value. If TRUE, it hides the “ns” (not significant) symbol when displaying significance levels, cleaning up the plot when non-significant comparisons are not of primary interest.
• label.sep: A character string used to separate elements within the label. The default is “, “, which separates the correlation coefficient and p-value if both are displayed.
• label: A character string specifying the type of label to display. Key options include:
  • "p.signif": Shows significance levels using symbols (ns, *, **, ***, ****).
  • "p.format": Shows the formatted p-value.
• label.x.npc, label.y.npc: These arguments control the label position using “normalized parent coordinates”. Values are between 0 and 1, representing the fraction across the plot panel. Character values like “right”, “left”, “center” for x-axis, and “bottom”, “top”, “center” for y-axis are also accepted for simpler positioning.
• label.x, label.y: These arguments specify the label position using absolute coordinates in data units, offering precise placement of labels on the plot.
• vjust: Adjusts the vertical position of the text label relative to the bracket, allowing for fine-tuning of label placement.
• tip.length: A numeric vector indicating the fraction of the total height that the bracket tip extends down to the column. Adjusting this can improve visual clarity, especially with multiple comparisons.
• bracket.size: Controls the width of the bracket lines, allowing you to modify the visual prominence of the brackets.
• step.increase: A numeric vector that increases the fraction of total height for each additional comparison bracket, helping to prevent brackets from overlapping when multiple comparisons are made.
• symnum.args: A list of arguments passed to the symnum() function, which is used for symbolic coding of p-values. This allows for customization of the significance symbols and cutoffs. The default convention is:
  • ns: p > 0.05
  • *: p <= 0.05
  • **: p <= 0.01
  • ***: p <= 0.001
  • ****: p <= 0.0001

Graphical Elements and Layer Properties

• geom: Specifies the geometric object used to display the data. By default, stat_compare_means uses geom_text to display labels. You can also use geom = "label" for labels with backgrounds.
• position: Determines the position adjustment. Useful for avoiding overplotting, for example using position_jitter() to slightly offset points.
• na.rm: A logical value. If FALSE (default), missing values are removed with a warning. If TRUE, missing values are silently removed.
• show.legend: A logical value indicating whether this layer should be included in the plot legend.
• inherit.aes: If FALSE, it overrides default aesthetics rather than combining them, useful for helper functions that define data and aesthetics independently.
• ...: Allows passing other arguments directly to geom_text or geom_label, providing further customization of the text or label appearance (e.g., color, size, fontface).

By carefully considering and customizing these arguments, you can effectively use stat_compare_means to add insightful statistical comparisons to your ggplot2 visualizations, enhancing the clarity and impact of your data-driven stories.