R: Add Pairwise Comparisons P-values to a GGPlot

stat_pwc {ggpubr}

R Documentation

Add Pairwise Comparisons P-values to a GGPlot

Description

add pairwise comparison p-values to a ggplot such as box plots, dot plots and stripcharts.

Usage

stat_pwc(
  mapping = NULL,
  data = NULL,
  method = "wilcox_test",
  method.args = list(),
  ref.group = NULL,
  label = "p.format",
  y.position = NULL,
  group.by = NULL,
  dodge = 0.8,
  bracket.nudge.y = 0.05,
  bracket.shorten = 0,
  bracket.group.by = c("x.var", "legend.var"),
  step.increase = 0.12,
  tip.length = 0.03,
  size = 0.3,
  label.size = 3.88,
  family = "",
  vjust = 0,
  hjust = 0.5,
  p.adjust.method = "holm",
  p.adjust.by = c("group", "panel"),
  symnum.args = list(),
  hide.ns = FALSE,
  remove.bracket = FALSE,
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  parse = FALSE,
  ...
)

geom_pwc(
  mapping = NULL,
  data = NULL,
  stat = "pwc",
  method = "wilcox_test",
  method.args = list(),
  ref.group = NULL,
  label = "p.format",
  y.position = NULL,
  group.by = NULL,
  dodge = 0.8,
  stack = FALSE,
  step.increase = 0.12,
  tip.length = 0.03,
  bracket.nudge.y = 0.05,
  bracket.shorten = 0,
  bracket.group.by = c("x.var", "legend.var"),
  size = 0.3,
  label.size = 3.88,
  family = "",
  vjust = 0,
  hjust = 0.5,
  p.adjust.method = "holm",
  p.adjust.by = c("group", "panel"),
  symnum.args = list(),
  hide.ns = FALSE,
  remove.bracket = FALSE,
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  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)`).
`method`	a character string indicating which method to be used for pairwise comparisons. Default is `"wilcox_test"`. Allowed methods include pairwise comparisons methods implemented in the `rstatix` R package. These methods are: `"wilcox_test", "t_test", "sign_test", "dunn_test", "emmeans_test", "tukey_hsd", "games_howell_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 or a numeric value 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). Allowed values can be: numeric value: specifying the rank of the reference group. For example, use `ref.group = 1` when the first group is the reference; use `ref.group = 2` when the second group is the reference, and so on. This works for all situations, including i) when comparisons are performed between x-axis groups and ii) when comparisons are performed between legend groups. character value: For example, you can use `ref.group = "ctrl"` instead of using the numeric rank value of the "ctrl" group. "all": In this case, each of the grouping variable levels is compared to all (i.e. basemean).
`label`	character string specifying label. Can be: the column containing the label (e.g.: `label = "p"` or `label = "p.adj"`), where `p` is the p-value. Other possible values are `"p.signif", "p.adj.signif", "p.format", "p.adj.format"`. an expression that can be formatted by the `glue()` package. For example, when specifying `label = "Wilcoxon, p = \{p\}"`, the expression {p} will be replaced by its value. a combination of plotmath expressions and glue expressions. You may want some of the statistical parameter in italic; for example:`label = "Wilcoxon, italic(p)= {p}"` .
`y.position`	numeric vector with the y positions of the brackets
`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.
`dodge`	dodge width for grouped ggplot/test. Default is 0.8. It's used to dodge the brackets position when `group.by = "legend.var"`.
`bracket.nudge.y`	Vertical adjustment to nudge brackets by (in fraction of the total height). Useful to move up or move down the bracket. If positive value, brackets will be moved up; if negative value, brackets are moved down.
`bracket.shorten`	a small numeric value in [0-1] for shortening the width of bracket.
`bracket.group.by`	(optional); a variable name for grouping brackets before adding step.increase. Useful for grouped plots. Possible values include `"x.var"` and `"legend.var"`.
`step.increase`	numeric vector with the increase in fraction of total height for every additional comparison to minimize overlap.
`tip.length`	numeric vector with the fraction of total height that the bar goes down to indicate the precise column/
`size`	change the width of the lines of the bracket
`label.size`	change the size of the label text
`family`	change the font used for the text
`vjust`	move the text up or down relative to the bracket.
`hjust`	move the text left or right relative to the bracket.
`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. Ignored when the specified method is `"tukey_hsd"` or `"games_howell_test"` because they come with internal p adjustment method. 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".
`p.adjust.by`	possible value is one of `c("group", "panel")`. Default is `"group"`: for a grouped data, if pairwise test is performed, then the p-values are adjusted for each group level independently. P-values are adjusted by panel when `p.adjust.by = "panel"`.
`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, 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
`hide.ns`	can be logical value (`TRUE` or `FALSE`) or a character vector (`"p.adj"` or `"p"`).
`remove.bracket`	logical, if `TRUE`, brackets are removed from the plot. Case when logical value. If TRUE, hide ns symbol when displaying significance levels. Filter is done by checking the column `p.adj.signif`, `p.signif`, `p.adj` and `p`. Case when character value. Possible values are "p" or "p.adj", for filtering out non significant.
`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`	logical for parsing plotmath expression.
`...`	other arguments passed on to `layer`. These are often aesthetics, used to set an aesthetic to a fixed value, like `color = "red"` or `size = 3`. They may also be parameters to the paired geom/stat.
`stat`	The statistical transformation to use on the data for this layer, either as a `ggproto` `Geom` subclass or as a string naming the stat stripped of the `stat_` prefix (e.g. `"count"` rather than `"stat_count"`)
`stack`	logical value. Default is FALSE; should be set to TRUE for stacked bar plots or line plots. If TRUE, then the brackets are automatically removed and the `dodge` value is set to zero.

Details

Notes on adjusted p-values and facet. When using the ggplot facet functions, the p-values are computed and adjusted by panel, without taking into account the other panels. This is by design in ggplot2.

In this case, when there is only one computed p-value by panel, then using 'label = "p"' or 'label = "p.adj"' will give the same results using 'geom_pwc()'. Again, p-value computation and adjustment in a given facet panel is done independently to the other panels.

One might want to adjust the p-values of all the facet panels together. There are two solutions for that:

Using ggadjust_pvalue(p) after creating the plot p
or adding the adjusted p-value manually using stat_pvalue_manual(). Read more at:
- How to Add P-values to GGPLOT Facets
- Add P-values to GGPLOT Facets with Different Scales

Examples

df <- ToothGrowth
df$dose <- factor(df$dose)

# Data preparation
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Transform `dose` into factor variable
df <- ToothGrowth
df$dose <- as.factor(df$dose)
# Add a random grouping variable
df$group <- factor(rep(c("grp1", "grp2"), 30))
head(df, 3)


# Two groups by x position
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

# Create a box plot
# Add 10% spaces between the p-value labels and the plot border
bxp <- ggboxplot(
  df, x = "dose", y = "len",
  color = "supp", palette = c("#00AFBB", "#E7B800")
) +
 scale_y_continuous(expand = expansion(mult = c(0.05, 0.10)))


# Add p-values onto the box plots
# label can be "p.format"  or "p.adj.format"
bxp + geom_pwc(
  aes(group = supp), tip.length = 0,
  method = "t_test", label = "p.format"
)

# Show adjusted p-values and significance levels
# Hide ns (non-significant)
bxp + geom_pwc(
  aes(group = supp), tip.length = 0,
  method = "t_test", label = "{p.adj.format}{p.adj.signif}",
  p.adjust.method = "bonferroni", p.adjust.by = "panel",
  hide.ns = TRUE
)

# Complex cases
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# 1. Add p-values of OJ vs VC at each dose group
bxp.complex <- bxp +
  geom_pwc(
    aes(group = supp), tip.length = 0,
    method = "t_test", label = "p.adj.format",
    p.adjust.method = "bonferroni", p.adjust.by = "panel"
  )
# 2. Add pairwise comparisons between dose levels
# Nudge up the brackets by 20% of the total height
bxp.complex <- bxp.complex +
  geom_pwc(
    method = "t_test", label = "p.adj.format",
    p.adjust.method = "bonferroni",
    bracket.nudge.y = 0.2
  )
# 3. Display the plot
bxp.complex


# Three groups by x position
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

# Simple plots
#_____________________________________

# Box plots with p-values
bxp <- ggboxplot(
  df, x = "supp", y = "len", fill = "dose",
  palette = "npg"
)
bxp +
  geom_pwc(
    aes(group = dose), tip.length = 0,
    method = "t_test", label = "p.adj.format",
    bracket.nudge.y = -0.08
  ) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))

# Bar plots with p-values
bp <- ggbarplot(
  df, x = "supp", y = "len", fill = "dose",
  palette = "npg", add = "mean_sd",
  position = position_dodge(0.8)
)
bp +
  geom_pwc(
    aes(group = dose), tip.length = 0,
    method = "t_test", label = "p.adj.format",
    bracket.nudge.y = -0.08
  ) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))