R visualization: errorbar by ggplot2
errorbar with point
Introduction
Errorbar with jitter gives intuitive sense to observe the differences among groups.
Loading required packages
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
library(tidyverse)
library(ggpubr)
rm(list = ls())
options(stringsAsFactors = F)
# group & color
group_names <- c("setosa", "versicolor", "virginica")
group_colors <- c("#0073C2FF", "#EFC000FF", "#CD534CFF")
Data preparation
Loading iris and ToothGrowth dataset
Factorizing Species
data("iris")
plotdata <- iris |>
dplyr::select(Sepal.Length, Species) |>
dplyr::mutate(Species = factor(Species, levels = group_names)) |>
dplyr::rename(Group = Species,
Index = Sepal.Length)
head(plotdata)
## Index Group
## 1 5.1 setosa
## 2 4.9 setosa
## 3 4.7 setosa
## 4 4.6 setosa
## 5 5.0 setosa
## 6 5.4 setosa
# other dataset
data("ToothGrowth")
errorbar with points
create error bar by
stat_summaryusing errorbaradd point by
geom_point
pl <- ggplot(data = plotdata, aes(x = Group, y = Index, shape = Group)) +
stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1),
geom = "errorbar", width = 0.2) +
stat_summary(fun = median, fun.min = median, fun.max = median,
geom = "crossbar", width = 0.3) +
geom_point(aes(color = Group), position = position_jitter(width = 0.1),
size = 1.5, stroke = 1) +
stat_compare_means(comparisons = list(c("setosa", "versicolor"),
c("setosa", "virginica")),
method = "t.test",
label = "p.signif") +
labs(x = "") +
scale_y_continuous(expand = expansion(mult = c(0.1, 0.1))) +
scale_color_manual(values = group_colors) +
scale_shape_manual(values = c(1, 16, 17)) +
guides(color = "none", shape = "none", fill = "none") +
theme_classic() +
theme(axis.title = element_text(size = 12, color = "black", face = "bold"),
axis.text = element_text(size = 10, color = "black"),
text = element_text(size = 9, color = "black"))
pl
Errorbar with line
- Using ToothGrowth dataset with groups Dose and supp
ToothGrowth$dose <- factor(ToothGrowth$dose)
data_summary <- function(data, varname, groupnames){
require(plyr)
summary_func <- function(x, col) {
mean_value <- mean(x[[col]], na.rm = TRUE)
sd_value <- sd(x[[col]], na.rm = TRUE)
length_n <- length(x[[col]])
# standard error= standard deviation/squareroot(n)
se_value <- sd_value / sqrt(length_n)
return(c(mean = mean_value, sd = sd_value, se = se_value))
}
data_sum <- ddply(data, groupnames, .fun=summary_func, varname)
data_sum <- rename(data_sum, c("mean" = varname))
return(data_sum)
}
plotdata2 <- data_summary(
ToothGrowth,
varname = "len",
groupnames = c("supp", "dose"))
pl2 <- ggplot(data = plotdata2, aes(x = dose, y = len, group = supp, color = supp)) +
geom_line() +
geom_point(size = 1.5, stroke = 1) +
# geom_errorbar(aes(ymin = len - sd, ymax = len + sd), width = .2,
# position = position_dodge(0.05)) +
geom_errorbar(aes(ymin = len - se, ymax = len + se), width = .2,
position = position_dodge(0.05)) +
stat_compare_means(data = ToothGrowth,
aes(x = dose, y = len, group = supp),
label = "p.signif",
label.y = c(21, 28, 32)) +
labs(x = "") +
scale_y_continuous(expand = expansion(mult = c(0.1, 0.1))) +
scale_color_manual(values = c("#999999","#E69F00")) +
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"))
pl2
Conclusion
Compared to single boxplot, Errorbar with jitter provides more information.
Systemic information
devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.1.3 (2022-03-10)
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## 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)
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Reference
Hua Zou
Senior Bioinformatic Analyst
My research interests include host-microbiota intersection, machine learning and multi-omics data integration.