stat_welch_anova_test: Add Welch One-Way ANOVA Test P-values to a GGPlot

Description

Add Welch one-way ANOVA test p-values to a ggplot, such as box blots, dot plots and stripcharts.

Usage

stat_welch_anova_test(
  mapping = NULL,
  data = NULL,
  group.by = NULL,
  label = "{method}, p = {p.format}",
  label.x.npc = "left",
  label.y.npc = "top",
  label.x = NULL,
  label.y = NULL,
  step.increase = 0.1,
  p.adjust.method = "holm",
  significance = list(),
  geom = "text",
  position = "identity",
  na.rm = FALSE,
  show.legend = FALSE,
  inherit.aes = TRUE,
  parse = FALSE,
  ...
)

Arguments

mapping

Set of aesthetic mappings created by 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. A function can be created from a formula (e.g. ~ head(.x, 10)).

group.by

(optional) character vector specifying the grouping variable; it should be used only for grouped plots. Possible values are :

"x.var": Group by the x-axis variable and perform the test between legend groups. In other words, the p-value is compute between legend groups at each x position
"legend.var": Group by the legend variable and perform the test between x-axis groups. In other words, the test is performed between the x-groups for each legend level.

label

the column containing the label (e.g.: label = "p" or label = "p.adj"), where p is the p-value. Can be also an expression that can be formatted by the glue() package. For example, when specifying label = "t-test, p = {p}", the expression {p} will be replaced by its value.

label.x.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.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

numeric Coordinates (in data units) to be used for absolute positioning of the label. If too short they will be recycled.

label.y

numeric Coordinates (in data units) to be used for absolute positioning of the label. If too short they will be recycled.

step.increase

numeric vector with the increase in fraction of total height for every additional comparison to minimize overlap.

p.adjust.method

method for adjusting p values (see p.adjust). Has impact only in a situation, where multiple pairwise tests are performed; or when there are multiple grouping variables. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not recommended), use p.adjust.method = "none".

significance

a list of arguments specifying the signifcance cutpoints and symbols. For example, significance <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, Inf), 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 to display the data, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

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().

parse

If TRUE, the labels will be parsed into expressions and displayed as described in ?plotmath.

...

other arguments passed to the function geom_bracket() or geom_text()

Computed variables

statistic: the value of the test statistic (F-value)
DFn: Degrees of Freedom in the numerator (i.e. DF effect)
DFd: Degrees of Freedom in the denominator (i.e., DF error)
p: p-value.
p.adj: Adjusted p-values.
p.signif: P-value significance.
p.adj.signif: Adjusted p-value significance.
p.format: Formated p-value.
p.adj.format: Formated adjusted p-value.
n: number of samples.

Examples

Run this code

# Data preparation
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Transform `dose` into factor variable
df <- ToothGrowth
df$dose <- as.factor(df$dose)
# Add a random grouping variable
set.seed(123)
df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20)))
df$len <- ifelse(df$group == "grp2", df$len+2, df$len)
df$len <- ifelse(df$group == "grp3", df$len+7, df$len)
head(df, 3)


# Basic boxplot
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Create a basic boxplot
# Add 5% and 10% space to the plot bottom and the top, respectively
bxp <- ggboxplot(df, x = "dose", y = "len") +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.1)))

# Add the p-value to the boxplot
bxp + stat_welch_anova_test()

# Change the label position
# Using coordinates in data units
bxp + stat_welch_anova_test(label.x = "1", label.y = 10, hjust = 0)

# Format the p-value differently
custom_p_format <- function(p) {
  rstatix::p_format(p, accuracy = 0.0001, digits = 3, leading.zero = FALSE)
}
bxp + stat_welch_anova_test(
  label = "Welch Anova, italic(p) = {custom_p_format(p)}{p.signif}"
)

# Show a detailed label in italic
bxp + stat_welch_anova_test(label = "as_detailed_italic")


# Faceted plots
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Create a ggplot facet
bxp <- ggboxplot(df, x = "dose", y = "len", facet.by = "supp") +
 scale_y_continuous(expand = expansion(mult = c(0.05, 0.1)))
# Add p-values
bxp + stat_welch_anova_test()


# Grouped plots
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
bxp2 <- ggboxplot(df, x = "group", y = "len", color = "dose", palette = "npg")

# For each x-position, computes tests between legend groups
bxp2 + stat_welch_anova_test(aes(group = dose), label = "p = {p.format}{p.signif}")

#  For each legend group, computes tests between x variable groups
bxp2 + stat_welch_anova_test(aes(group = dose, color = dose), group.by = "legend.var")

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