How to Annotate a plot with P-value in ggplot2

Last updated on August 14, 2025

Adding statistical significance indicators to your data visualizations is crucial for communicating research findings effectively. When working with ggplot2 in R, displaying p-values directly on scatter plots helps viewers immediately understand the statistical relationship between variables.
In this comprehensive tutorial, you’ll learn how to annotate ggplot2 plots with p-values from linear regression analysis. We’ll cover two powerful methods: using the standard geom_text() function and the enhanced geom_richtext() from the ggtext package for customized annotations

Required R packages: tidyverse, palmerpenguins, ggtext, glue, and broom for statistical modeling.

What You’ll Learn

How to add p-values to ggplot2 scatter plots
Performing linear regression analysis and extracting statistical results
Using geom_text() for basic p-value annotations
Customizing p-value displays with geom_richtext() and styling options
Best practices for statistical significance visualization in R

library(tidyverse)
library(palmerpenguins)
library(ggtext)
library(glue)
theme_set(theme_bw(16))

We will use Palmer penguin dataset to make the scatter plot.

penguins |> head()

# A tibble: 6 × 8
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
1 Adelie  Torgersen           39.1          18.7               181        3750
2 Adelie  Torgersen           39.5          17.4               186        3800
3 Adelie  Torgersen           40.3          18                 195        3250
4 Adelie  Torgersen           NA            NA                  NA          NA
5 Adelie  Torgersen           36.7          19.3               193        3450
6 Adelie  Torgersen           39.3          20.6               190        3650
# ℹ 2 more variables: sex <fct>, year <int>

How to Add P-value to a plot in ggplot2

Here is a scatter plot between two numerical variables from penguins dataset.

p1 <- penguins |>
  ggplot(aes(flipper_length_mm, body_mass_g))+
  geom_point(aes(color=species))+
  geom_smooth(method = "lm", formula = y ~ x) +
  theme(legend.position = "none")+
  labs(title="How to annotate the plot with p-value")

p1
ggsave("how_to_annotate_with_p_value_ggplot2.png")

How to annotate a plot with p-value in ggplot2

In order to annotate the plot with p-value, let us first perform the statistical test using linear regression model and save p-value in a dataframe. In addition to the p-value, we also create variables to help annotate the plot with the p-value.

Do statistical test and save result in dataframe

In the code below, we use tidyverse framework to perform linear regression and store the results in a dataframe.

pval_df <- penguins |>
  summarize(lm_mod = list(lm(flipper_length_mm~ body_mass_g)),
            lm_res = map(lm_mod, broom::tidy)) |>
  unnest(lm_res) |>
  filter(term=="body_mass_g") |>
  select(p.value) |>
  mutate(flipper_length_mm=200,
         body_mass_g=6000,
         label=glue("p-val: {signif(p.value,3)}"))

This is how the dataframe with p-value looks like this.

pval_df
# A tibble: 1 × 4
    p.value flipper_length_mm body_mass_g label           
      <dbl>             <dbl>       <dbl> <glue>          
1 4.37e-107               200        6000 p-val: 4.37e-107

Adding statistical significance as annotation with geom_text()

Now we can use geom_text() function available in ggplot2 to add the p-value as annotation to the plot.

p1 +
  geom_text(
    data = pval_df,
    aes(label = label),
    hjust = 1, vjust = 1,
    size=6
  )+
  labs(title="Annotating a plot with p-value")
ggsave("annotate_plot_with_p_value_ggplot2.png")

Annotate a plot with p-value from linear regression model in ggplot2

Annotating a plot with p-value using ggtext’s geom_richtext()

We can further customize the annotation using geom_richtext() function from ggtext package to add color and a box around the annotation text.

p1+
   geom_richtext(
    data = pval_df,
    aes(
      label = label#,
      #fill = after_scale(alpha(colour, .2))
    ),
    text.colour = "purple",
    hjust = 1, vjust = 1,
    size=6
  )+
  labs(title="Annotating a plot with p-value")
ggsave("annotate_plot_with_p_value_ggplot2_example2.png")