Skip to contents

Add Pairwise Comparisons P-values to a GGPlot

Source: R/geom_pwc.R
geom_pwc.Rd

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"),
  p.adjust.n = NULL,
  symnum.args = list(),
  hide.ns = FALSE,
  remove.bracket = FALSE,
  p.format.style = "default",
  p.digits = NULL,
  p.leading.zero = NULL,
  p.min.threshold = NULL,
  p.decimal.mark = NULL,
  signif.cutoffs = NULL,
  signif.symbols = NULL,
  ns.symbol = "ns",
  use.four.stars = 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"),
  p.adjust.n = NULL,
  symnum.args = list(),
  hide.ns = FALSE,
  remove.bracket = FALSE,
  p.format.style = "default",
  p.digits = NULL,
  p.leading.zero = NULL,
  p.min.threshold = NULL,
  p.decimal.mark = NULL,
  signif.cutoffs = NULL,
  signif.symbols = NULL,
  ns.symbol = "ns",
  use.four.stars = 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.format.signif", "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".

p.adjust.n

optional single number giving the number of comparisons to use for the p-value adjustment, passed as n to p.adjust. Default is NULL, which uses the number of p-values actually computed (the standard behavior). Set it when the displayed comparisons are a subset of a larger family and the adjustment should reflect that larger family size. As required by p.adjust, p.adjust.n must be greater than or equal to the number of p-values being adjusted. It affects the adjustment that geom_pwc() performs itself: the panel-level adjustment (p.adjust.by = "panel") and cases where the test returns a single set of p-values (a single comparison, or grouped comparisons with two levels per group). It does not alter the adjustment that pairwise tests already perform internally per group.

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

Note: If symnum.args is provided, it takes precedence over signif.cutoffs.

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.

p.format.style

character string specifying the p-value formatting style. One of: "default" (backward compatible, uses scientific notation), "apa" (APA style, no leading zero), "nejm" (NEJM style), "lancet" (Lancet style), "ama" (AMA style), "graphpad" (GraphPad style), or "scientific" (scientific notation for GWAS). See list_p_format_styles for details.

p.digits

integer specifying the number of decimal places for p-values. If provided, overrides the style default.

p.leading.zero

logical indicating whether to include leading zero before decimal point (e.g., "0.05" vs ".05"). If provided, overrides the style default.

p.min.threshold

numeric specifying the minimum p-value to display exactly. Values below this threshold are shown as "< threshold". If provided, overrides the style default.

p.decimal.mark

character string to use as the decimal mark. If NULL, uses getOption("OutDec").

signif.cutoffs

numeric vector of p-value cutoffs in descending order for assigning significance symbols. For example, c(0.10, 0.05, 0.01) means p < 0.10 gets "*", p < 0.05 gets "**", p < 0.01 gets "***". Default is NULL, which uses the package defaults.

signif.symbols

character vector of symbols corresponding to signif.cutoffs. If NULL, auto-generated as "*", "**", "***" (and "****" if use.four.stars = TRUE).

ns.symbol

character string for non-significant results. Default is "ns". Use "" (empty string) to show nothing.

use.four.stars

logical. If TRUE, allows four stars (****) for the most significant level. Default is FALSE.

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

  • The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.

  • A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".

  • For more information and other ways to specify the position, see the layer position documentation.

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. To include legend keys for all levels, even when no data exists, use TRUE. If NA, all levels are shown in legend, but unobserved levels are omitted.

inherit.aes

If FALSE (the default for most ggpubr functions), 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. Set to TRUE to inherit aesthetics from the parent ggplot layer.

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. When using a geom_*() function to construct a layer, the stat argument can be used to override the default coupling between geoms and stats. The stat argument accepts the following:

  • A Stat ggproto subclass, for example StatCount.

  • A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".

  • For more information and other ways to specify the stat, see the layer stat documentation.

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.

For grouped plots with sparse data, if some subsets do not contain enough levels or observations for the requested test, those subsets are skipped while preserving valid inferential comparisons from comparable subsets. Informative messages list skipped grouped subsets and their skip reasons (for example, missing ref.group or fewer than two comparison levels).

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

See also

ggadjust_pvalue

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)
#>    len supp dose group
#> 1  4.2   VC  0.5  grp1
#> 2 11.5   VC  0.5  grp2
#> 3  7.3   VC  0.5  grp1


# 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)))