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

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,
vjust = 0,
tip.length = 0.03,
bracket.size = 0.3,
step.increase = 0,
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. 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. A function can be created from a formula (e.g. ~ head(.x, 10)). a character string indicating which method to be used for comparing means. a logical indicating whether you want a paired test. Used only in t.test and in wilcox.test. a list of additional arguments used for the test method. For example one might use method.args = list(alternative = "greater") for wilcoxon test. 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). 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. logical value. If TRUE, hide ns symbol when displaying significance levels. a character string to separate the terms. Default is ", ", to separate the correlation coefficient and the p.value. character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value). 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. numeric Coordinates (in data units) to be used for absolute positioning of the label. If too short they will be recycled. move the text up or down relative to the bracket. 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. Width of the lines of the bracket. numeric vector with the increase in fraction of total height for every additional comparison to minimize overlap. 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 The geometric object to use display the data Position adjustment, either as a string, or the result of a call to a position adjustment function. If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values. 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. 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.

compare_means

## Examples

# Load data
data("ToothGrowth")
#> 1  4.2   VC  0.5
#> 2 11.5   VC  0.5
#> 3  7.3   VC  0.5
#> 4  5.8   VC  0.5
#> 5  6.4   VC  0.5
#> 6 10.0   VC  0.5
# Two independent groups
#:::::::::::::::::::::::::::::::::::::::::::::::::
p <- ggboxplot(ToothGrowth, x = "supp", y = "len",
color = "supp", palette = "npg", add = "jitter")

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")+
stat_compare_means(comparisons = my_comparisons, label.y = c(29, 35, 40))+
stat_compare_means(label.y = 45)     # Add global Anova p-value#> Warning: cannot compute exact p-value with ties#> Warning: cannot compute exact p-value with ties#> Warning: cannot compute exact p-value with ties
# 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",