R visualization: Raincloud plot by ggplot2
Raincloud plot
Introduction
Raincloud plot can be used to visualize raw data, the distribution of the data, and the key summary statistics at the same time.
Loading required packages
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
library(ggpubr)
library(ggdist)
library(gghalves)
library(tidyverse)
# rm(list = ls())
options(stringsAsFactors = F)
# group & color
sp_names <- c("setosa", "versicolor", "virginica")
sp_colors <- c("#0073C2FF", "#EFC000FF", "#CD534CFF")
Data preparation
- Data object iris
data("iris")
data_pre <- iris
head(data_pre)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
Plot function
get_raincloud <- function(
dat,
group,
group_names,
group_colors,
measures) {
# group for plot x-label
dat_cln2 <- dat
colnames(dat_cln2)[which(colnames(dat_cln2) == group)] <- "Group_new"
if (group_names[1] == "all") {
tempdata <- dat_cln2
} else {
tempdata <- dat_cln2 %>%
dplyr::filter(Group_new %in% group_names)
}
tempdata$Group_new <- factor(tempdata$Group_new, levels = group_names)
if (length(measures) > 1) {
plotdata <- tempdata %>%
dplyr::select(all_of(c("Group_new", measures))) %>%
tidyr::pivot_longer(cols = 2:(length(measures)+1),
names_to = "Index",
values_to = "Values") %>%
dplyr::mutate(Index = factor(Index, levels = measures))
} else {
plotdata <- tempdata %>%
dplyr::select(all_of(c("Group_new", measures)))
colnames(plotdata)[2] <- "Values"
}
cmp <- list()
num <- utils::combn(length(group_names), 2)
for (i in 1:ncol(num)) {
cmp[[i]] <- num[, i]
}
if (length(measures) > 1) {
pl <- ggplot(plotdata, aes(x = Group_new, y = Values, fill = Group_new)) +
ggdist::stat_halfeye(adjust = 0.5, width = 0.3,
.width = 0, justification = -0.3, point_colour = NA) +
stat_boxplot(aes(color = Group_new), geom = "errorbar", width = 0.1) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
gghalves::geom_half_point(side = "l", range_scale = 0.4, alpha = 0.5) +
stat_summary(geom = "crossbar", width = 0.08, fatten = 0, color = "white",
fun.data = function(x){c(y = median(x), ymin = median(x), ymax = median(x))}) +
labs(x = "", y = "Value") +
scale_fill_manual(values = group_colors) +
scale_color_manual(values = group_colors) +
guides(fill = "none", color = "none") +
scale_y_continuous(expand = expansion(mult = c(0.1, 0.1))) +
ggpubr::stat_compare_means(method = "wilcox.test",
comparisons = cmp) +
facet_wrap(.~ Index, scales = "free") +
theme_bw() +
theme(axis.title.y = element_text(size = 10, face = "bold"),
axis.text.y = element_text(size = 9),
axis.text.x = element_text(size = 10, hjust = .5, vjust = .5, angle = 30),
strip.text = element_text(size = 12, face = "bold", color = "black"))
} else {
pl <- ggplot(plotdata, aes(x = Group_new, y = Values, fill = Group_new)) +
ggdist::stat_halfeye(adjust = 0.5, width = 0.3,
.width = 0, justification = -0.3, point_colour = NA) +
stat_boxplot(aes(color = Group_new), geom = "errorbar", width = 0.1) +
geom_boxplot(width = 0.1, outlier.shape = NA) +
gghalves::geom_half_point(side = "l", range_scale = 0.4, alpha = 0.5) +
stat_summary(geom = "crossbar", width = 0.08, fatten = 0, color = "white",
fun.data = function(x){c(y = median(x), ymin = median(x), ymax = median(x))}) +
labs(x = "", y = measures) +
scale_fill_manual(values = group_colors) +
scale_color_manual(values = group_colors) +
guides(fill = "none", color = "none") +
scale_y_continuous(expand = expansion(mult = c(0.1, 0.1))) +
ggpubr::stat_compare_means(method = "wilcox.test",
comparisons = cmp) +
theme_bw() +
theme(axis.title.y = element_text(size = 10, face = "bold"),
axis.text.y = element_text(size = 9),
axis.text.x = element_text(size = 10, hjust = .5, vjust = .5, angle = 30),
strip.text = element_text(size = 12, face = "bold", color = "black"))
}
return(pl)
}
Raincloud plot with single measure
get_raincloud(
dat = data_pre,
group = "Species",
group_names = sp_names,
group_colors = sp_colors,
measures = "Sepal.Length")
Results:
- Raincloud plot shows the distribution of the data and the boxplot of data in three groups.
Raincloud plot with mulitple measures
get_raincloud(
dat = data_pre,
group = "Species",
group_names = sp_names,
group_colors = sp_colors,
measures = c("Sepal.Length", "Sepal.Width"))
Conclusion
RainCloud plots using Half Violin plot with jittered data points to show the distribution of the data.
Systemic information
devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
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## ctype en_US.UTF-8
## tz Asia/Shanghai
## date 2024-03-14
## pandoc 3.1.3 @ /Users/zouhua/opt/anaconda3/bin/ (via rmarkdown)
##
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Hua Zou
Senior Bioinformatic Analyst
My research interests include host-microbiota intersection, machine learning and multi-omics data integration.