Put Stars on Ggplot Barplots and Boxplots - to Indicate the Level of Significance (P-Value)

Put stars on ggplot barplots and boxplots - to indicate the level of significance (p-value)

Please find my attempt below.

First, I created some dummy data and a barplot which can be modified as we wish.

windows(4,4)

dat <- data.frame(Group = c("S1", "S1", "S2", "S2"),
                  Sub   = c("A", "B", "A", "B"),
                  Value = c(3,5,7,8))  

## Define base plot
p <-
ggplot(dat, aes(Group, Value)) +
    theme_bw() + theme(panel.grid = element_blank()) +
    coord_cartesian(ylim = c(0, 15)) +
    scale_fill_manual(values = c("grey80", "grey20")) +
    geom_bar(aes(fill = Sub), stat="identity", position="dodge", width=.5)

Adding asterisks above a column is easy, as baptiste already mentioned. Just create a data.frame with the coordinates.

label.df <- data.frame(Group = c("S1", "S2"),
                       Value = c(6, 9))

p + geom_text(data = label.df, label = "***")

To add the arcs that indicate a subgroup comparison, I computed parametric coordinates of a half circle and added them connected with geom_line. Asterisks need new coordinates, too.

label.df <- data.frame(Group = c(1,1,1, 2,2,2),
                       Value = c(6.5,6.8,7.1, 9.5,9.8,10.1))

# Define arc coordinates
r <- 0.15
t <- seq(0, 180, by = 1) * pi / 180
x <- r * cos(t)
y <- r*5 * sin(t)

arc.df <- data.frame(Group = x, Value = y)

p2 <-
p + geom_text(data = label.df, label = "*") +
    geom_line(data = arc.df, aes(Group+1, Value+5.5), lty = 2) +
    geom_line(data = arc.df, aes(Group+2, Value+8.5), lty = 2)

Lastly, to indicate comparison between groups, I built a larger circle and flattened it at the top.

r <- .5
x <- r * cos(t)
y <- r*4 * sin(t)
y[20:162] <- y[20] # Flattens the arc

arc.df <- data.frame(Group = x, Value = y)

p2 + geom_line(data = arc.df, aes(Group+1.5, Value+11), lty = 2) +
     geom_text(x = 1.5, y = 12, label = "***")

Add p value or asterisk to ggplot

An easy way could be to add manually the notations:

  p + annotate("text", x = 0.5, y = 23, label = "ns") +
  annotate("text", x = 1.5, y = 30, label = "**") +
  annotate("text", x = 2.5, y = 48, label = "***") 

Add statistical significance to ggplot with geom_bar by bar

An option is defining the y position of the significant signs by creating a vector. You can use geom_text and label to assign the text on top of your bars like this:

library(tidyverse)
library(ggpubr)
stats <- compare_means(value ~ B, group.by = c("A", "C"), data = dataplot, method = "t.test")

ggplot(dataplot, ) +
  geom_bar(aes(A, value, fill = B, color = B),
    position = "identity",
    stat = "summary",
    alpha = .5,
    fun = mean
  ) +
  geom_point(
    aes(x = A, y = value, fill = B, color = B),
    size = 2,
    stroke = 0.5,
    position = "jitter"
  )+ 
  geom_text(data = stats, aes(x = A, y = c(9, 16, 9, 16), label = p.signif), size = 10) +
  facet_wrap(~C) 

Output:

Add stars to p-value

You can build your own function. Note however that this is not the conventional star system (it's totally okay if you mention the scale somewhere though). See e.g. here.

stars.pval <- function(x){
  stars <- c("***", "**", "*", "n.s.")
  var <- c(0, 0.01, 0.05, 0.10, 1)
  i <- findInterval(x, var, left.open = T, rightmost.closed = T)
  stars[i]
}

transform(dat, stars = stars.pval(dat$`Anova F-Test p-Value`))

  Variables Anova.F.Test.p.Value stars
1         A                5e-02    **
2         B                5e-04   ***
3         C                5e-01  n.s.
4         D                5e-02    **
5         E                1e-02   ***

add boxplot significance indicator lines and asterisks in R plot_ly

Horrible hacky solution which gives the desired output

• Adding the brackets as a separate line trace
• Adding the significance markers as labels on top of hidden bar plots
• Hiding the helper categorical values via layout

The problem with using annotations is that there is no way of putting the asterisk in the right place, three boxplots means three categorical x-values. The new x-values are added via the bar plot.

library(plotly)
p <- plot_ly()
p <- add_bars(p, 
              x = c('setosa', 'setosa0', 'versicolor', 'versicolor0', 'virginica'),
              y = c(3.5, 4.6, 2.5, 4.1, 3),
              opacity=1,
              showlegend = F,
              marker=list(line = list(color='rgba(0,0,0,0'),
                          color = 'rgba(0,0,0,0'),
              text = c('', '**', '', '*', ''),
              textposition = 'outside',
              legendgroup = "1"
)
p <- add_lines(p, 
               x = c('setosa', 'setosa', 'versicolor', 'versicolor'),
               y = c(4.5, 4.6, 4.6, 4.5),
               showlegend = F,
               line = list(color = 'black'),
               legendgroup = "1",
               hoverinfo = 'none'
)

p <- add_lines(p, 
               x = c('versicolor', 'versicolor', 'virginica', 'virginica'),
               y = c(4.0, 4.1, 4.1, 4.0),
               showlegend = F,
               line = list(color = 'black'),
               legendgroup = "1",
               hoverinfo = 'none'
)


p <- add_boxplot(p, data = iris, x = ~Species, y = ~Sepal.Width, 
                 color = ~Species, boxpoints = "all", jitter = 0.3, pointpos = 0,
                 legendgroup="1")


p <- layout(p,
            xaxis = list(tickmode = 'array',
                         tickvals = c('setosa', 'sf', 'versicolor', 'vet', 'virginica'),
                         ticktext = c('setosa', '', 'versicolor', '', 'virginica')),
            yaxis = list(range = c(0, 5))
            )
p

The graph below shows all the hidden traces used to get the graph right:

Indicating significance with ggplot2, in a boxplot with multiple groups

The solution given above by @dww (use label = "p.signif") is the correct one:

cmpr <- list(c("VaD+","HC"), c("AD","HC"))
myplot + stat_compare_means(comparisons = cmpr, tip.length=0.01,
         label = "p.signif", 
         symnum.args = list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1), 
         symbols = c("****", "***", "**", "*", "ns")))

EDIT: I modified stat_compare_means because this function seems to ignore symnum.args:

my_stat_compare_means  <- function (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, tip.length = 0.03, 
    symnum.args = list(), geom = "text", position = "identity", 
    na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...) 
{
    if (!is.null(comparisons)) {
        method.info <- ggpubr:::.method_info(method)
        method <- method.info$method
        method.args <- ggpubr:::.add_item(method.args, paired = paired)
        if (method == "wilcox.test") 
            method.args$exact <- FALSE
        pms <- list(...)
        size <- ifelse(is.null(pms$size), 0.3, pms$size)
        color <- ifelse(is.null(pms$color), "black", pms$color)
        map_signif_level <- FALSE
        if (is.null(label)) 
            label <- "p.format"
        if (ggpubr:::.is_p.signif_in_mapping(mapping) | (label %in% "p.signif")) {
            if (ggpubr:::.is_empty(symnum.args)) {
                map_signif_level <- c(`****` = 1e-04, `***` = 0.001, 
                  `**` = 0.01, `*` = 0.05, ns = 1)
            } else {
               map_signif_level <- symnum.args
            } 
            if (hide.ns) 
                names(map_signif_level)[5] <- " "
        }
        step_increase <- ifelse(is.null(label.y), 0.12, 0)
        ggsignif::geom_signif(comparisons = comparisons, y_position = label.y, 
            test = method, test.args = method.args, step_increase = step_increase, 
            size = size, color = color, map_signif_level = map_signif_level, 
            tip_length = tip.length, data = data)
    } else {
        mapping <- ggpubr:::.update_mapping(mapping, label)
        layer(stat = StatCompareMeans, data = data, mapping = mapping, 
            geom = geom, position = position, show.legend = show.legend, 
            inherit.aes = inherit.aes, params = list(label.x.npc = label.x.npc, 
                label.y.npc = label.y.npc, label.x = label.x, 
                label.y = label.y, label.sep = label.sep, method = method, 
                method.args = method.args, paired = paired, ref.group = ref.group, 
                symnum.args = symnum.args, hide.ns = hide.ns, 
                na.rm = na.rm, ...))
    }
}

symnum.args <- c("**"=0.0025,"*"=0.05,ns=1)
myplot + my_stat_compare_means(comparisons = cmpr, tip.length=0.01, 
           label = "p.signif", symnum.args = symnum.args)

Need to put asterisk on the top of ggplot barplot to flag the level of significance (pvalue)?

Technically, you can always add an appropriate geom with its independent dataset (that would be your data filtered to exclude pval > .05):

df_filtered <- res_all_s2 %>% filter(...)

## ggplot(...) +
      geom_point(data = df_filtered, pch = 8)
      ## pch = point character, no. 8 = asterisk

or

## ... +
      geom_text(data = df_filtered, aes(label = '*'), nudge_y = .05)
      ## nudge_y = vertical offset

or color only significant columns:

## ... +
   geom_col(aes(fill = c('grey','red')[1 + pval <= .05]))

So, yes, technically that's feasible. But before throwing the results of 13 x 7 x 5 = 455 linear models at your audience, please consider the issues of p-hacking, the benefits of multivariate analysis and the viewers' ressources ;-)

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