How to Create a Beautiful Plots in R with Summary Statistics Labels

beautiful-plot-in-r-with-summary-statistics-labels-boxplots-with-summary-statistics-1.png


How to Create a Beautiful Plots in R with Summary Statistics Labels

You will learn how to create beautiful plots in R and add summary summary statistics table such as sample size (n), median, mean and IQR onto the plot. We will also describes how to create multipanel graphics combined with the summary table. Examples of plots illustrated here, include: box plot, violin plot, bar plot, line plot; etc.

Contents:

Prerequisites

Load required R packages

library(tidyverse)
library(rstatix)
library(ggpubr)

Data preparation:

# Demo data
data("ToothGrowth")
df <- ToothGrowth
df$dose <- as.factor(df$dose)
# Add random QC column
set.seed(123)
qc <- rep(c("pass", "fail"), 30)
df$qc <- as.factor(sample(qc, 60))
# Inspect the data
head(df)
##    len supp dose   qc
## 1  4.2   VC  0.5 fail
## 2 11.5   VC  0.5 pass
## 3  7.3   VC  0.5 fail
## 4  5.8   VC  0.5 pass
## 5  6.4   VC  0.5 pass
## 6 10.0   VC  0.5 pass

Basic box plots with add summary statistics

In the following R code, possible values for the argument ggfunc are the ggpubr R package functions, including: ggboxplot, ggviolin, ggdotplot, ggbarplot, ggline, etc. It can be also any other ggplot function that accepts the following arguments: data, x, color, fill, palette, ggtheme, facet.by.

# Basic plot
ggsummarystats(
  df, x = "dose", y = "len", 
  ggfunc = ggboxplot, add = "jitter"
  )

# Color by groups
ggsummarystats(
  df, x = "dose", y = "len", 
  ggfunc = ggboxplot, add = "jitter",
  color = "dose", palette = "npg"
  )

Note that, you can create step by step your own graph and summary table. The following R code describes how to combine the main graph and the summary table into one figure.

# Compute summary statistics
summary.stats <- df %>%
  group_by(dose) %>%
  get_summary_stats() %>%
  select(dose, n, median, iqr)
summary.stats

# Create a boxplot
bxp <- ggboxplot(
  df, x = "dose", y = "len", add = "jitter", 
  ggtheme = theme_bw()
)

# Visualize the summary statistics
summary.plot <- ggsummarytable(
  summary.stats, x = "dose", y = c("n", "median", "iqr"),
  ggtheme = theme_bw()
  ) +
  clean_table_theme()

# Combine the boxplot and the summary statistics plot
ggarrange(
  bxp, summary.plot, ncol = 1, align = "v",
  heights = c(0.80, 0.20)
  )

Grouped plots with summary table

Grouped box plots and violin plots

# Grouped plots
ggsummarystats(
  df, x = "dose", y = "len", 
  ggfunc = ggboxplot, add = "jitter",
  color = "supp", palette = "npg"
  )

# Change plot type to violin
ggsummarystats(
  df, x = "dose", y = "len", 
  ggfunc = ggviolin, add = c("jitter", "median_iqr"),
  color = "supp", palette = "npg"
  )

Grouped bar plots and line plots

# Create barplot
ggsummarystats(
  df, x = "dose", y = "len", 
  ggfunc = ggbarplot, add = c("jitter", "median_iqr"), position = position_dodge(),
  color = "supp", palette = "npg"
  )

# Create line plots
ggsummarystats(
  df, x = "dose", y = "len", 
  ggfunc = ggline, add = c("jitter", "median_iqr"), 
  color = "supp", palette = "npg"
  )

Three groups on the x axis

ggsummarystats(
  df, x = "supp", y = "len", 
  ggfunc = ggboxplot, add = c("jitter"), 
  color = "dose", palette = "npg"
  )

Multipanel plots with summary table

Key arguments:

  • facet.by: character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.
  • labeller: Character vector. Possible values are one of label_both (panel labelled by both grouping variable names and levels) and label_value (panel labelled with only grouping levels).

Create panels according to one grouping variable:

ggsummarystats(
  df, x = "dose", y = "len", 
  ggfunc = ggboxplot, add = c("jitter"), 
  color = "dose", palette = "jco",
  facet.by = "supp", labeller = "label_value",
  ggtheme = theme_bw(), legend = "top"
)

Create panels according to two grouping variables

ggsummarystats(
  df, x = "dose", y = "len", 
  ggfunc = ggboxplot, add = c("jitter"), 
  color = "dose", palette = "jco",
  facet.by = c("supp", "qc"), labeller = "label_both", 
  ggtheme = theme_bw(), legend = "top"
)

Create independent panels using the argument free:

ggsummarystats(
  df, x = "dose", y = "len", 
  ggfunc = ggboxplot, add = c("jitter"), 
  color = "dose", palette = "jco",
  facet.by = c("supp", "qc"), labeller = "label_both", 
  free.panels = TRUE,
  ggtheme = theme_bw(), legend = "top"
)

Build step by step a custom multipanel plot

Create a multipanel box plot using one grouping variable (supp):

# Group the data by supp
# Apply the function ggsummarystats to each subset
df.grouped <- df %>%
  df_split_by(supp, label_col = "panel", labeller = df_label_both) %>%
  mutate(plot_list = map(
    data, ggsummarystats, x = "dose", y = "len",
    ggfunc = ggbarplot, 
    add = c("jitter", "median_iqr"), 
    facet.by = "panel"
    )
  )
df.grouped
## # A tibble: 2 x 4
##   supp  data              panel   plot_list 
##   <fct> <list>            <fct>   <list>    
## 1 VC    <tibble [30 × 4]> supp:OJ <ggsmmrys>
## 2 OJ    <tibble [30 × 4]> supp:VC <ggsmmrys>
# Print the plots
plot_list <- df.grouped$plot_list
class(plot_list) <- c("ggsummarystats_list", "list")
print(plot_list)

Version: Français





Comments ( 2 )

  • Jegor

    I the function ggsummarystats in the package that is available on CRAN? Because I dont find it.

    • Kassambara

      The ggsummarystats() is available in the ggpubr dev version. You can install it as follow:

      if(!require(devtools)) install.packages("devtools")
      devtools::install_github("kassambara/ggpubr")

      A CRAN released is planned for next week.

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