R: Add Mean Comparison P-values to a ggplot

stat_compare_means {ggpubr} | R Documentation

## Add Mean Comparison P-values to a ggplot

### Description

Add mean comparison p-values to a ggplot, such as box blots, dot plots and stripcharts.

### Usage

    stat_compare_means(mapping = NULL, data = NULL, method = NULL,
      paired = FALSE, method.args = list(), ref.group = NULL,
      comparisons = NULL, hide.ns = FALSE, label.sep = ", ", label = NULL,
      label.x.npc = "left", label.y.npc = "top", label.x = NULL,
      label.y = NULL, tip.length = 0.03, symnum.args = list(),
      geom = "text", position = "identity", na.rm = FALSE, show.legend = NA,
      inherit.aes = TRUE, ...)

### Arguments

`mapping` | Set of aesthetic mappings created by `aes()` or `aes_()`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply `mapping` if there is no plot mapping.
`data` | The data to be displayed in this layer. There are three options: If `NULL`, the default, the data is inherited from the plot data as specified in the call to `ggplot()`. A `data.frame`, or other object, will override the plot data. All objects will be fortified to produce a data frame. See `fortify()` for which variables will be created. A `function` will be called with a single argument, the plot data. The return value must be a `data.frame.`, and will be used as the layer data.
`method` | a character string indicating which method to be used for comparing means.
`paired` | a logical indicating whether you want a paired test. Used only in `t.test` and in wilcox.test.
`method.args` | a list of additional arguments used for the test method. For example one might use `method.args = list(alternative = "greater")` for wilcoxon test.
`ref.group` | a character string specifying the reference group. If specified, for a given grouping variable, each of the group levels will be compared to the reference group (i.e. control group). `ref.group` can be also `".all."`. In this case, each of the grouping variable levels is compared to all (i.e. basemean).
`comparisons` | A list of length-2 vectors. The entries in the vector are either the names of 2 values on the x-axis or the 2 integers that correspond to the index of the groups of interest, to be compared.
`hide.ns` | logical value. If TRUE, hide ns symbol when displaying significance levels.
`label.sep` | a character string to separate the terms. Default is ", ", to separate the correlation coefficient and the p.value.
`label` | character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value).
`label.x.npc, label.y.npc` | can be `numeric` or `character` vector of the same length as the number of groups and/or panels. If too short they will be recycled. If `numeric`, value should be between 0 and 1. Coordinates to be used for positioning the label, expressed in "normalized parent coordinates". If `character`, allowed values include: i) one of c('right', 'left', 'center', 'centre', 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', 'middle') for y-axis.
`label.x, label.y` | `numeric` Coordinates (in data units) to be used for absolute positioning of the label. If too short they will be recycled.
`tip.length` | numeric vector with the fraction of total height that the bar goes down to indicate the precise column. Default is 0.03. Can be of same length as the number of comparisons to adjust specifically the tip lenth of each comparison. For example tip.length = c(0.01, 0.03). If too short they will be recycled.
`symnum.args` | a list of arguments to pass to the function `symnum` for symbolic number coding of p-values. For example, `symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1), symbols = c("****", "***", "**", "*", "ns"))`. In other words, we use the following convention for symbols indicating statistical significance: `ns`: p > 0.05 `*`: p <= 0.05 `**`: p <= 0.01 `***`: p <= 0.001 `****`: p <= 0.0001
`geom` | The geometric object to use display the data
`position` | Position adjustment, either as a string, or the result of a call to a position adjustment function.
`na.rm` | If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.
`show.legend` | logical. Should this layer be included in the legends? `NA`, the default, includes if any aesthetics are mapped. `FALSE` never includes, and `TRUE` always includes. It can also be a named logical vector to finely select the aesthetics to display.
`inherit.aes` | If `FALSE`, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. `borders()`.
`...` | other arguments to pass to `geom_text` or `geom_label`.

### See Also

`compare_means`

### Examples

    # Load data
    data("ToothGrowth")
    head(ToothGrowth)

    # Two independent groups
    #:::::::::::::::::::::::::::::::::::::::::::::::::
    p <- ggboxplot(ToothGrowth, x = "supp", y = "len",
      color = "supp", palette = "npg", add = "jitter")

    #  Add p-value
    p + stat_compare_means()
    # Change method
    p + stat_compare_means(method = "t.test")

     # Paired samples
     #:::::::::::::::::::::::::::::::::::::::::::::::::
     ggpaired(ToothGrowth, x = "supp", y = "len",
       color = "supp", line.color = "gray", line.size = 0.4,
       palette = "npg")+
     stat_compare_means(paired = TRUE)

    # More than two groups
    #:::::::::::::::::::::::::::::::::::::::::::::::::
    # Pairwise comparisons: Specify the comparisons you want
    my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
    ggboxplot(ToothGrowth, x = "dose", y = "len",
              color = "dose", palette = "npg")+
    # Add pairwise comparisons p-value
    stat_compare_means(comparisons = my_comparisons, label.y = c(29, 35, 40))+
    stat_compare_means(label.y = 45)     # Add global Anova p-value

    # Multiple pairwise test against a reference group
    ggboxplot(ToothGrowth, x = "dose", y = "len",
        color = "dose", palette = "npg")+
    stat_compare_means(method = "anova", label.y = 40)+ # Add global p-value
    stat_compare_means(aes(label = ..p.signif..),
                      method = "t.test", ref.group = "0.5")

    # Multiple grouping variables
    #:::::::::::::::::::::::::::::::::::::::::::::::::
    # Box plot facetted by "dose"
    p <- ggboxplot(ToothGrowth, x = "supp", y = "len",
                  color = "supp", palette = "npg",
                  add = "jitter",
                  facet.by = "dose", short.panel.labs = FALSE)
    # Use only p.format as label. Remove method name.
    p + stat_compare_means(
     aes(label = paste0("p = ", ..p.format..))
    )

[Package ggpubr version 0.1.6 Index]