Adjust p-values Displayed on a GGPlot

Source: R/ggadjust_pvalue.R

Adjust p-values produced by geom_pwc() on a ggplot. This is mainly useful when using facet, where p-values are generally computed and adjusted by panel without taking into account the other panels. In this case, one might want to adjust after the p-values of all panels together.

ggadjust_pvalue(
  p,
  layer = NULL,
  p.adjust.method = "holm",
  label = "p.adj",
  hide.ns = NULL,
  symnum.args = list(),
  output = c("plot", "stat_test")
)

Arguments

p

a ggplot

layer

An integer indicating the statistical layer rank in the ggplot (in the order added to the plot).

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".

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}"

.

hide.ns

can be logical value (TRUE or FALSE) or a character vector ("p.adj" or "p").

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

output

character. Possible values are one of c("plot", "stat_test"). Default is "plot".

Examples

# Data preparation
#:::::::::::::::::::::::::::::::::::::::
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

# Boxplot: Two groups by panel
#:::::::::::::::::::::::::::::::::::::::
# Create a box plot
bxp <- ggboxplot(
  df, x = "supp", y = "len", fill = "#00AFBB",
  facet.by = "dose"
)
# Make facet and add p-values
bxp <- bxp + geom_pwc(method = "t_test")
bxp

# Adjust all p-values together after
ggadjust_pvalue(
  bxp, p.adjust.method = "bonferroni",
  label = "{p.adj.format}{p.adj.signif}", hide.ns = TRUE
)



# Boxplot: Three groups by panel
#:::::::::::::::::::::::::::::::::::::::
# Create a box plot
bxp <- ggboxplot(
  df, x = "dose", y = "len", fill = "#00AFBB",
  facet.by = "supp"
)
# Make facet and add p-values
bxp <- bxp + geom_pwc(method = "t_test")
bxp

# Adjust all p-values together after
ggadjust_pvalue(
  bxp, p.adjust.method = "bonferroni",
  label = "{p.adj.format}{p.adj.signif}"
)