R visualization: boxplot with points labeled in percentage by ggplot2

Boxplot with percentage per group

Last updated on Jun 14, 2023 5 min read Visualization, ggplot2

Introduction

The simple boxplot only shows the distribution of values per group. However, more information should be displayed in one figure. Here, using points determined by proportion and we could observe the percentage of index per group.

Loading required packages

knitr::opts_chunk$set(message = FALSE, warning = FALSE)
library(tidyverse)

rm(list = ls())
options(stringsAsFactors = F)

# group & color
group_names <- c("setosa", "versicolor", "virginica")
group_colors <- c("#0073C2FF", "#EFC000FF", "#CD534CFF")

Data preparation

  • Loading iris dataset

  • Calculating ratio per species (Occurrence)

data("iris")

plotdata <- iris |>
  dplyr::select(Sepal.Length, Species) |>
  dplyr::mutate(Species = factor(Species, levels = group_names)) |>
  dplyr::rename(Group = Species,
                Index = Sepal.Length) 

# threshold for prevalence
occ_cutoff <- 5.2
occ_fun <- function(x) {
  return(round(length(x[x > occ_cutoff])/length(x), 4))
}

plotOcc <- plotdata |>
  dplyr::group_by(Group) |>
  dplyr::summarise(occ = occ_fun(Index)) |>
  dplyr::mutate(occ_lab = paste0(occ * 100, "%")) |>
  dplyr::mutate(position = min(plotdata$Index) - min(plotdata$Index) * 0.1)

head(plotOcc)

## # A tibble: 3 × 4
##   Group        occ occ_lab position
##   <fct>      <dbl> <chr>      <dbl>
## 1 setosa      0.22 22%         3.87
## 2 versicolor  0.9  90%         3.87
## 3 virginica   0.98 98%         3.87

Plotting

  • Create boxplot with errorbar using key functions: stat_boxplot, geom_boxplot .

  • Add points: geom_point for values and percentages, respectively.

  • Apply geom_text to display the text of Ratio.

pl <- ggplot(data = plotdata, aes(x = Group, y = Index, color = Group)) +
  stat_boxplot(geom = "errorbar", width = 0.15) +
  geom_boxplot(width = .4, outlier.shape = NA) +
  geom_point(size = 2, shape = 5) +
  labs(x = "") + 
  scale_y_continuous(expand = expansion(mult = c(0.1, 0.1))) +
  geom_point(data = plotOcc, aes(x = Group, y = position, size = occ), 
             show.legend = FALSE, shape = 1, stroke = 1) +
  geom_text(data = plotOcc, aes(x = Group, y = position, label = occ_lab),
            show.legend = FALSE) +
  scale_size_continuous(range = c(10, 12)) +
  scale_color_manual(values = group_colors) +
  coord_flip() +
  guides(color = "none") +
  theme_classic() +
  theme(axis.title = element_text(size = 12, color = "black", face = "bold"),
        axis.text = element_text(size = 10, color = "black"),
        text = element_text(size = 9, color = "black"))

pl

Conclusion

We not only observed the differences of Index between three groups but also found the prevalence of Index between three groups from the aforementioned figure.

Systemic information

devtools::session_info()

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value
##  version  R version 4.1.3 (2022-03-10)
##  os       macOS Big Sur/Monterey 10.16
##  system   x86_64, darwin17.0
##  ui       X11
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       Asia/Shanghai
##  date     2023-07-24
##  pandoc   3.1.3 @ /Users/zouhua/opt/anaconda3/bin/ (via rmarkdown)
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package     * version date (UTC) lib source
##  blogdown      1.18    2023-06-19 [2] CRAN (R 4.1.3)
##  bookdown      0.34    2023-05-09 [2] CRAN (R 4.1.2)
##  bslib         0.5.0   2023-06-09 [2] CRAN (R 4.1.3)
##  cachem        1.0.8   2023-05-01 [2] CRAN (R 4.1.2)
##  callr         3.7.3   2022-11-02 [2] CRAN (R 4.1.2)
##  cli           3.6.1   2023-03-23 [2] CRAN (R 4.1.2)
##  colorspace    2.1-0   2023-01-23 [2] CRAN (R 4.1.2)
##  crayon        1.5.2   2022-09-29 [2] CRAN (R 4.1.2)
##  devtools      2.4.5   2022-10-11 [2] CRAN (R 4.1.2)
##  digest        0.6.33  2023-07-07 [1] CRAN (R 4.1.3)
##  dplyr       * 1.1.2   2023-04-20 [2] CRAN (R 4.1.2)
##  ellipsis      0.3.2   2021-04-29 [2] CRAN (R 4.1.0)
##  evaluate      0.21    2023-05-05 [2] CRAN (R 4.1.2)
##  fansi         1.0.4   2023-01-22 [2] CRAN (R 4.1.2)
##  farver        2.1.1   2022-07-06 [2] CRAN (R 4.1.2)
##  fastmap       1.1.1   2023-02-24 [2] CRAN (R 4.1.2)
##  forcats     * 1.0.0   2023-01-29 [2] CRAN (R 4.1.2)
##  fs            1.6.2   2023-04-25 [2] CRAN (R 4.1.2)
##  generics      0.1.3   2022-07-05 [2] CRAN (R 4.1.2)
##  ggplot2     * 3.4.2   2023-04-03 [2] CRAN (R 4.1.2)
##  glue          1.6.2   2022-02-24 [2] CRAN (R 4.1.2)
##  gtable        0.3.3   2023-03-21 [2] CRAN (R 4.1.2)
##  highr         0.10    2022-12-22 [2] CRAN (R 4.1.2)
##  hms           1.1.3   2023-03-21 [2] CRAN (R 4.1.2)
##  htmltools     0.5.5   2023-03-23 [2] CRAN (R 4.1.2)
##  htmlwidgets   1.6.2   2023-03-17 [2] CRAN (R 4.1.2)
##  httpuv        1.6.11  2023-05-11 [2] CRAN (R 4.1.3)
##  jquerylib     0.1.4   2021-04-26 [2] CRAN (R 4.1.0)
##  jsonlite      1.8.7   2023-06-29 [2] CRAN (R 4.1.3)
##  knitr         1.43    2023-05-25 [2] CRAN (R 4.1.3)
##  labeling      0.4.2   2020-10-20 [2] CRAN (R 4.1.0)
##  later         1.3.1   2023-05-02 [2] CRAN (R 4.1.2)
##  lifecycle     1.0.3   2022-10-07 [2] CRAN (R 4.1.2)
##  lubridate   * 1.9.2   2023-02-10 [2] CRAN (R 4.1.2)
##  magrittr      2.0.3   2022-03-30 [2] CRAN (R 4.1.2)
##  memoise       2.0.1   2021-11-26 [2] CRAN (R 4.1.0)
##  mime          0.12    2021-09-28 [2] CRAN (R 4.1.0)
##  miniUI        0.1.1.1 2018-05-18 [2] CRAN (R 4.1.0)
##  munsell       0.5.0   2018-06-12 [2] CRAN (R 4.1.0)
##  pillar        1.9.0   2023-03-22 [2] CRAN (R 4.1.2)
##  pkgbuild      1.4.2   2023-06-26 [2] CRAN (R 4.1.3)
##  pkgconfig     2.0.3   2019-09-22 [2] CRAN (R 4.1.0)
##  pkgload       1.3.2.1 2023-07-08 [2] CRAN (R 4.1.3)
##  prettyunits   1.1.1   2020-01-24 [2] CRAN (R 4.1.0)
##  processx      3.8.2   2023-06-30 [2] CRAN (R 4.1.3)
##  profvis       0.3.8   2023-05-02 [2] CRAN (R 4.1.2)
##  promises      1.2.0.1 2021-02-11 [2] CRAN (R 4.1.0)
##  ps            1.7.5   2023-04-18 [2] CRAN (R 4.1.2)
##  purrr       * 1.0.1   2023-01-10 [2] CRAN (R 4.1.2)
##  R6            2.5.1   2021-08-19 [2] CRAN (R 4.1.0)
##  Rcpp          1.0.11  2023-07-06 [1] CRAN (R 4.1.3)
##  readr       * 2.1.4   2023-02-10 [2] CRAN (R 4.1.2)
##  remotes       2.4.2   2021-11-30 [2] CRAN (R 4.1.0)
##  rlang         1.1.1   2023-04-28 [2] CRAN (R 4.1.2)
##  rmarkdown     2.23    2023-07-01 [2] CRAN (R 4.1.3)
##  rstudioapi    0.15.0  2023-07-07 [2] CRAN (R 4.1.3)
##  sass          0.4.6   2023-05-03 [2] CRAN (R 4.1.2)
##  scales        1.2.1   2022-08-20 [2] CRAN (R 4.1.2)
##  sessioninfo   1.2.2   2021-12-06 [2] CRAN (R 4.1.0)
##  shiny         1.7.4.1 2023-07-06 [2] CRAN (R 4.1.3)
##  stringi       1.7.12  2023-01-11 [2] CRAN (R 4.1.2)
##  stringr     * 1.5.0   2022-12-02 [2] CRAN (R 4.1.2)
##  tibble      * 3.2.1   2023-03-20 [2] CRAN (R 4.1.2)
##  tidyr       * 1.3.0   2023-01-24 [2] CRAN (R 4.1.2)
##  tidyselect    1.2.0   2022-10-10 [2] CRAN (R 4.1.2)
##  tidyverse   * 2.0.0   2023-02-22 [1] CRAN (R 4.1.2)
##  timechange    0.2.0   2023-01-11 [2] CRAN (R 4.1.2)
##  tzdb          0.4.0   2023-05-12 [2] CRAN (R 4.1.3)
##  urlchecker    1.0.1   2021-11-30 [2] CRAN (R 4.1.0)
##  usethis       2.2.2   2023-07-06 [2] CRAN (R 4.1.3)
##  utf8          1.2.3   2023-01-31 [2] CRAN (R 4.1.2)
##  vctrs         0.6.3   2023-06-14 [1] CRAN (R 4.1.3)
##  withr         2.5.0   2022-03-03 [2] CRAN (R 4.1.2)
##  xfun          0.39    2023-04-20 [2] CRAN (R 4.1.2)
##  xtable        1.8-4   2019-04-21 [2] CRAN (R 4.1.0)
##  yaml          2.3.7   2023-01-23 [2] CRAN (R 4.1.2)
## 
##  [1] /Users/zouhua/Library/R/x86_64/4.1/library
##  [2] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
## 
## ──────────────────────────────────────────────────────────────────────────────
Visualization
Hua Zou
Hua Zou
Senior Bioinformatic Analyst

My research interests include host-microbiota intersection, machine learning and multi-omics data integration.