R visualization: barplot by ggplot2
barplot with point
Introduction
Barplot with jitter shows the mean and standard deviation or standard error between groups.
Loading required packages
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
library(tidyverse)
library(ggpubr)
rm(list = ls())
options(stringsAsFactors = F)
# group & color
dose_names <- c("0.5", "1", "2")
dose_colors <- c("#0073C2FF", "#EFC000FF", "#CD534CFF")
supp_names <- c("OJ", "VC")
supp_colors <- c('#999999','#E69F00')
Data preparation
Loading ToothGrowth dataset
Factorizing categorical variables
data("ToothGrowth")
plotdata <- ToothGrowth |>
dplyr::mutate(dose = factor(as.character(dose), levels = dose_names),
supp = factor(supp, levels = supp_names)) |>
dplyr::rename(Group = dose,
Subgroup = supp,
Index = len) |>
dplyr::select(Group, Subgroup, Index)
head(plotdata)
## Group Subgroup Index
## 1 0.5 VC 4.2
## 2 0.5 VC 11.5
## 3 0.5 VC 7.3
## 4 0.5 VC 5.8
## 5 0.5 VC 6.4
## 6 0.5 VC 10.0
- Calculating mean & sd & se
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)
}
dat_cal_one <- data_summary(
plotdata,
varname = "Index",
groupnames = "Group")
head(dat_cal_one)
## Group Index sd se
## 1 0.5 10.605 4.499763 1.0061776
## 2 1 19.735 4.415436 0.9873216
## 3 2 26.100 3.774150 0.8439257
dat_cal_two <- data_summary(
plotdata,
varname = "Index",
groupnames = c("Group", "Subgroup"))
head(dat_cal_two)
## Group Subgroup Index sd se
## 1 0.5 OJ 13.23 4.459709 1.4102837
## 2 0.5 VC 7.98 2.746634 0.8685620
## 3 1 OJ 22.70 3.910953 1.2367520
## 4 1 VC 16.77 2.515309 0.7954104
## 5 2 OJ 26.06 2.655058 0.8396031
## 6 2 VC 26.14 4.797731 1.5171757
barplot with single group
create bar by
geom_baradd point by
geom_jitter
pl <- ggplot(data = dat_cal_one, aes(x = Group, y = Index, fill = Group, color = Group)) +
geom_bar(stat = "identity", color = "white", position = position_dodge()) +
geom_errorbar(aes(ymin = Index, ymax = Index + sd),
width = 0.2, position = position_dodge(width = 0.9),
linewidth = 1) +
stat_compare_means(data = plotdata,
aes(x = Group, y = Index),
comparisons = list(c("0.5", "1"),
c("1", "2")),
label = "p.signif") +
geom_jitter(data = plotdata,
aes(x = Group, y = Index),
size = 2, color = "gray10", show.legend = FALSE, pch = 21) +
labs(x = "") +
scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
scale_fill_manual(values = dose_colors) +
scale_color_manual(values = dose_colors) +
guides(color = "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
barplot with two groups
create bar by
geom_baradd point by
geom_jitter
pl2 <- ggplot(data = dat_cal_two, aes(x = Group, y = Index, fill = Subgroup, color = Subgroup)) +
geom_bar(stat = "identity", color = "white", position = position_dodge()) +
geom_errorbar(aes(ymin = Index - sd, ymax = Index + sd),
width = 0.2, position = position_dodge(width = 0.9),
size = 1) +
stat_compare_means(data = plotdata,
aes(x = Group, y = Index, group = Subgroup),
label = "p.signif",
label.y = c(21, 28, 36)) +
geom_point(data = plotdata,
aes(x = Group, y = Index, shape = Subgroup),
position = position_jitterdodge(jitter.width = 0.5,
dodge.width = 0.7),
size = 2, color = "gray50", show.legend = FALSE) +
labs(x = "") +
scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
scale_fill_manual(values = supp_colors) +
scale_color_manual(values = supp_colors) +
scale_shape_manual(values = c(1, 21)) +
guides(color = "none", shape = "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, barplot focus on the mean and variation of data per group.
Systemic information
devtools::session_info()
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## date 2023-07-25
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Reference
Hua Zou
Senior Bioinformatic Analyst
My research interests include host-microbiota intersection, machine learning and multi-omics data integration.