How to Add P-Values onto Horizontal GGPLOTS

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03 Jun

How to Add P-Values onto Horizontal GGPLOTS

Alboukadel

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This article describes how to add p-values onto horizontal ggplots using the R function stat_pvalue_manual() available in the ggpubr R package.

  • Horizontal plots can be created using the function coord_flip() [in ggplot2 package].
  • When adding the p-values to a horizontal ggplot, you need to specify the option coord.flip = TRUE in the function stat_pvalue_manual() [in ggpubr package]. The options vjust (vertical adjustment) and hjust (horizontal adjustment) can be also specified to adapt the position of the p-value labels.

Note that, in some situations, the p-value labels are partially hidden by the plot top border. In these cases, the ggplot2 function scale_y_continuous(expand = expansion(mult = c(0, 0.1))) can be used to add more spaces between labels and the plot top border. The option mult = c(0, 0.1) indicates that 0% and 10% spaces are respectively added at the bottom and the top of the plot.



Contents:

Prerequisites

Make sure you have installed the following R packages:

  • ggpubr for creating easily publication ready plots
  • rstatix provides pipe-friendly R functions for easy statistical analyses.

Start by loading the following required packages:

library(ggpubr)
library(rstatix)

Data preparation

# Transform `dose` into factor variable
df <- ToothGrowth
df$dose <- as.factor(df$dose)
head(df, 3)
##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5

Create simple plots

# Box plots
bxp <- ggboxplot(df, x = "dose", y = "len", fill = "dose", 
                 palette = c("#00AFBB", "#E7B800", "#FC4E07"))

# Bar plots showing mean +/- SD
bp <- ggbarplot(df, x = "dose", y = "len", add = "mean_sd", fill = "dose", 
                palette = c("#00AFBB", "#E7B800", "#FC4E07"))

Pairwise comparisons

Statistical test

In the following example, we’ll perform T-test using the function t_test() [rstatix package]. It’s also possible to use the function wilcox_test().

stat.test <- df %>% t_test(len ~ dose)
stat.test
## # A tibble: 3 x 10
##   .y.   group1 group2    n1    n2 statistic    df        p    p.adj p.adj.signif
## * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>    <dbl>    <dbl> <chr>       
## 1 len   0.5    1         20    20     -6.48  38.0 1.27e- 7 2.54e- 7 ****        
## 2 len   0.5    2         20    20    -11.8   36.9 4.40e-14 1.32e-13 ****        
## 3 len   1      2         20    20     -4.90  37.1 1.91e- 5 1.91e- 5 ****

Vertical plots with p-values

# Box plot
stat.test <- stat.test %>% add_xy_position(x = "dose")
bxp + stat_pvalue_manual(stat.test, label = "p.adj.signif", tip.length = 0.01)

# Bar plot
stat.test <- stat.test %>% add_xy_position(fun = "mean_sd", x = "dose")
bp + stat_pvalue_manual(stat.test, label = "p.adj.signif", tip.length = 0.01)

Horizontal plots with p-values

Using the adjusted p-value significance levels as labels

# Horizontal box plot with p-values
stat.test <- stat.test %>% add_xy_position(x = "dose")
bxp + 
  stat_pvalue_manual(
    stat.test, label = "p.adj.signif", tip.length = 0.01,
    coord.flip = TRUE
    ) +
  coord_flip()

# Horizontal bar plot with p-values
stat.test <- stat.test %>% add_xy_position(fun = "mean_sd", x = "dose")
bp + stat_pvalue_manual(
  stat.test, label = "p.adj.signif", tip.length = 0.01,
  coord.flip = TRUE
  ) +
  coord_flip()

Using the adjusted p-values as labels

# Using `p.adj` as labels
stat.test <- stat.test %>% add_xy_position(x = "dose")
bxp + 
  stat_pvalue_manual(
    stat.test, label = "p.adj", tip.length = 0.01,
    coord.flip = TRUE
    ) +
  coord_flip()

# Change the orientation angle of the p-values
# Adjust the position of p-values using vjust and hjust
# Add more spaces between labels and the plot border
bxp + 
  stat_pvalue_manual(
    stat.test, label = "p.adj", tip.length = 0.01,
    coord.flip = TRUE, angle = 0, 
    hjust = 0, vjust = c(0, 1.2, 0)
    ) +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.3))) + 
  coord_flip()

Comparisons against reference groups

Statistical tests

stat.test <- df %>% t_test(len ~ dose, ref.group = "0.5") 
stat.test
## # A tibble: 2 x 10
##   .y.   group1 group2    n1    n2 statistic    df        p    p.adj p.adj.signif
## * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>    <dbl>    <dbl> <chr>       
## 1 len   0.5    1         20    20     -6.48  38.0 1.27e- 7 1.27e- 7 ****        
## 2 len   0.5    2         20    20    -11.8   36.9 4.40e-14 8.80e-14 ****

Vertical plots with p-values

# Vertical box plot with p-values
stat.test <- stat.test %>% add_xy_position(x = "dose")
bxp + 
  stat_pvalue_manual(stat.test, label = "p.adj", tip.length = 0.01) +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.1)))

# Vertical bar plot with p-values
stat.test <- stat.test %>% add_xy_position(fun = "mean_sd", x = "dose")
bp + 
  stat_pvalue_manual(stat.test, label = "p.adj.signif", tip.length = 0.01) +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.1)))

Horizontal plots with p-values

# Horizontal box plot with p-values
stat.test <- stat.test %>% add_xy_position(x = "dose")
bxp + 
  stat_pvalue_manual(
    stat.test, label = "p.adj", tip.length = 0.01, 
    coord.flip = TRUE
    ) +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) +
  coord_flip()

# Horizontal bar plot with p-values
stat.test <- stat.test %>% add_xy_position(fun = "mean_sd", x = "dose")
bp + 
  stat_pvalue_manual(
    stat.test, label = "p.adj", tip.length = 0.01,
    coord.flip = TRUE
    ) +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) +
  coord_flip()

# Show p-values at x = "group2"
stat.test <- stat.test %>% add_xy_position(x = "dose")
bxp + 
  stat_pvalue_manual(
    stat.test, label = "p = {p.adj}", tip.length = 0.01, 
    coord.flip = TRUE, x = "group2", hjust = 0.4
    ) +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.2))) +
  coord_flip()

# Make the p-values horizontal using the option angle
stat.test <- stat.test %>% add_xy_position(x = "dose")
bxp + 
  stat_pvalue_manual(
    stat.test, label = "p = {p.adj}", tip.length = 0.01, 
    coord.flip = TRUE, x = "group2", angle = 90
    ) +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) +
  scale_x_discrete(expand = expansion(mult = c(0.05, 0.3))) +
  coord_flip()

Conclusion

This article describes how to add p-values onto horizontal ggplots using the R function stat_pvalue_manual() available in the ggpubr R package. See other related frequently asked questions: ggpubr FAQ.



 
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Comments ( 4 )

  • Arkadiusz
    07 Jun 2020

    Thank you for another excellent guide.
    I would like to ask if it is possible to show only the upper standard deviation line (+ sd) on the barplot (ggbarplot) chart.
    Best wishes

    Reply
    • Kassambara
      07 Jun 2020

      Thank you for the positive feedback, highly appreciated.

      It’s possible to show only the upper error bars by specifying the option error.plot = "upper_errorbar":

      library(ggpubr)
ggbarplot(
  ToothGrowth, x = "dose", y = "len", add = "mean_sd",  
  error.plot = "upper_errorbar"
  )
      Reply
      • Arkadiusz
        07 Jun 2020

        Thank you for the quick reply.
        Works perfectly 🙂

        Reply
  • Kamau Lindhardt
    18 Aug 2021

    Thank you! Especially the part on how to rotate the angle of the p-values on the barplot was extremely helpful to me 🙂

    Reply

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