目录
数据
计算四分位值
作图
数据
rm(list = ls())
library(ggplot2)
library(dplyr)
library(ggpubr)
library(reshape2)
library(tidyverse)
data <- iris##鸢尾花数据集
dat <- data[,c(5,1)]#单个数据进行分析计算四分位值
#根据分组计算四分位及中位数
dat1 <- dat %>% Species_by(Species) %>% mutate(upper = quantile(Sepal.Length, 0.75),
lower = quantile(Sepal.Length, 0.25),
mean = mean(Sepal.Length),
median = median(Sepal.Length))
> head(dat1) # A tibble: 6 × 6 # Groups: Species [1] Species Sepal.Length upper lower mean median <fct> <dbl> <dbl> <dbl> <dbl> <dbl> 1 setosa 5.1 5.2 4.8 5.01 5 2 setosa 4.9 5.2 4.8 5.01 5 3 setosa 4.7 5.2 4.8 5.01 5
作图
#比较分组
my_comparisons =list( c("setosa","versicolor"),c("versicolor","virginica"),
c("setosa","virginica"))
P <- ggplot(dat1,aes(x=Species,y=Sepal.Length)) + #ggplot作图
geom_jitter(shape = 21,aes(fill=Species),width = 0.25) +
stat_summary(fun=mean, geom="point", color="grey60") +
theme_cowplot() +
theme(axis.text = element_text(size = 10),axis.title = element_text(size = 10),legend.text = element_text(size = 10),
legend.title = element_text(size = 10),plot.title = element_text(size = 10,face = 'plain'),legend.position = 'none') +
labs(title = "Species",y='Sepal.Lengthage') +
geom_errorbar(aes(ymin = lower, ymax = upper),col = "grey60",width = 1)+#误差棒
#差异检验
stat_compare_means(comparisons=my_comparisons,
label.y = c(7.5, 8, 8.5),
method="t.test",#wilcox.test
label="p.signif")
P
dev.off()
ggplot2画各种误差线和森林图 - 知乎 (zhihu.com)
R进阶绘图--散点箱线图+显著性 / 组间差异比较 / ggpubr包 - 知乎 (zhihu.com)
