R初学者必知图形添加显著性标记小细节
R初学者必知图形添加显著性标记小细节
R语言数据分析指南
发布于 2022-12-20 19:50:28
发布于 2022-12-20 19:50:28
❝本节来介绍如何对图形做显著性标记，介绍两种方法第一种通过代码来自动进行标记，另一种通过手动的方式来添加标记；两种方法各有其独特的用处，各位观众老爷细细品味。
加载R包
library(tidyverse)
library(readxl)
library(ggsignif)
library(rstatix)
library(ggpubr)
library(ggsci)
统计分析
stat.test <- read_excel("41588_2022_1184_MOESM6_ESM.xlsx",sheet = 3) %>%
mutate(value=as.numeric(DTT)) %>%
drop_na() %>%
group_by(Loc) %>%
t_test(value ~ Type) %>%
adjust_pvalue() %>% add_significance("p.adj") %>%
add_xy_position(x="Loc",scales="free",fun = "max")
添加显子性标记
read_excel("41588_2022_1184_MOESM6_ESM.xlsx",sheet = 3) %>%
mutate(value=as.numeric(DTT)) %>%
drop_na() %>%
ggplot(.,aes(Loc,value))+
stat_summary(geom = "bar",position = "dodge",aes(fill=Type),width=0.4) +
stat_pvalue_manual(stat.test,label = "p.adj.signif",label.size=5,hide.ns = T,
tip.length = 0.01)+
stat_summary(geom = "errorbar",fun.data = "mean_sdl",
fun.args = list(mult = 1),aes(fill=Type),
position=position_dodge(0.4),width=0.2,color="black") +
labs(x=NULL,y=NULL)+
scale_fill_jama()+
theme_test()+
theme(legend.position = "top",
legend.title = element_blank())
❝上述图形是通过代码自动判定了显著性的位置信息进而进行添加，如果数据分组较为复杂并且存在分面操作的情况；那么需要对上述代码的位置信息做过多的调整，因此第一种方法对初学者不太友好；接下来介绍如何使用「ggsignif」包来手动添加显著性标记 ❞
构建数据
dataf<- data.frame(Group<- c("G1","G1","G2","G2"),
Subject<- c("A","B","A","B"),
Score<- c(5,7,20,25))
案例一
ggplot(dataf, aes(Group,Score))+
geom_bar(aes(fill = Subject), stat = "identity",
position = "dodge", width = .5)+
geom_signif(y_position = c(10,26), xmin = c(0.8,1.8),
xmax = c(1.2,2.2), annotation = c("NS","**"),
tip_length = 0)+
geom_signif(comparisons = list(c("G1","G2")), y_position = 30,
tip_length = 0, vjust = .1)+
theme_light(base_size=10)
❝可以看到非常直观，简单明了但是此种方法只适用于数据量较小的情况 ❞
案例二
ggplot(dataf, aes(Group,Score))+
geom_bar(aes(fill = Subject), stat = "identity",
position = "dodge", width = .5)+
geom_signif(y_position = c(7.3,25.3), xmin = c(0.8,1.8),
xmax = c(1.2,2.2), annotation = c("Annotation","How i want it"),
tip_length = 0)+
geom_signif(comparisons = list(c("G1","G2")), y_position = 28,
tip_length = 0, vjust = .1)+
theme_light(base_size=13)
annotation参数还可以使用文本来进行注释
案例三
❝此外geom_signif还支持tip_length参数控制每一条竖线的的长度，的确很是贴心 ❞
ggplot(dataf, aes(Group,Score))+
geom_bar(aes(fill = Subject), stat = "identity",
position = "dodge", width = .5)+
geom_signif(y_position = c(7.3,25.3), xmin = c(0.8,1.8),
xmax = c(1.2,2.2), annotation = c("NS","**"),
tip_length = 0.01)+
geom_signif(comparisons = list(c("G1","G2")), y_position = 28,
tip_length = c(1,0.1), vjust = .1)+
theme_light(base_size=13)
本节介绍到此结束
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原始发表：2022-11-13，如有侵权请联系 cloudcommunity@tencent.com 删除
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