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R绘图模板——箱线图+点线图+显著性+分组！！！
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图片灵感来源：
1、设置工作环境并加载所需R包：
rm(list=ls())#clear Global Environmentsetwd('D:/桌面/箱线图+点线图+显著性+分组')#设置工作路径
#加载R包library(ggplot2) # Create Elegant Data Visualisations Using the Grammar of Graphicslibrary(reshape2) # Flexibly Reshape Data: A Reboot of the Reshape Packagelibrary(dplyr) # A Grammar of Data Manipulation2、加载绘图数据（根据原文图片随机编写数据，无实际意义）：
#加载数据data1 <- read.table("data1.txt",header = 1,check.names = F,sep = "\t")data2 <- read.table("data2.txt",header = 1,check.names = F,sep = "\t")3、数据清洗及格式转换：
#数据清洗及处理df1 <- melt(data1,id.vars = c("sample","group"))df1$sample <- factor(df1$sample,levels = rev(data1$sample))df1$group <- factor(df1$group,levels = c("A","B","C","D","E","F"))df1$facet <- rep("Community-weighted trait means",times=132)df2 <- melt(data2,id.vars = "sample")df2$sample <- factor(df2$sample,levels = rev(data1$sample))df2$group <- rep(c("Control","Warming"),each=66)#添加一列数据以区分Control和Warming组4、绘图：
###绘制第一张箱线图#绘制基础箱线图p <- ggplot(df1,aes(sample,value))+ stat_boxplot(aes(color=group),geom = "errorbar", width=0.3,size=0.5)+#添加误差线 geom_boxplot(aes(fill=group,color=group),outlier.shape = 18,size=0.5)#绘制箱线图p#计算均值位置并根据基础箱线图确定位置df1 %>% group_by(sample) %>% summarise(mean_value=mean(value)) %>% cbind(ggplot_build(p)$data[[1]]) -> mean#绘图p1 <- ggplot(df1,aes(sample,value))+ #在y轴为0的位置添加辅助线 geom_hline(yintercept = 0, linetype = 2, color = "grey60",linewidth=0.8)+ stat_boxplot(aes(color=group),geom = "errorbar", width=0.3,size=0.6)+#添加误差线 geom_boxplot(aes(fill=group,color=group),outlier.shape = 18,size=0.6)+#绘制箱线图 geom_segment(mean, mapping=aes(x=xmin-0.25,xend=xmax+0.25,y=mean_value,yend=mean_value), color="white",size=0.5)+ #转变x轴与y轴位置 coord_flip()+ #自定义颜色 scale_fill_manual(values = c("#e3ac6d","#9d7bb8","#6caf83","#d9586e","#3c74bb","#f85b2b"))+ scale_color_manual(values = c("#e3ac6d","#9d7bb8","#6caf83","#d9586e","#3c74bb","#f85b2b"))+ #y轴范围设置 scale_y_continuous(limits = c(-100, 100))+ #主题设置 theme_bw()+ theme(legend.position = "none", panel.grid = element_blank(), axis.text = element_text(color = "black",size=10), strip.background = element_rect(fill = "grey", color = "transparent"), strip.text = element_text(color="black",size=10))+ #标题位置 labs(y="Response to warming (%)",x=NULL)+ #添加分组矩形 annotate("rect", xmin = 0, xmax = 6.5, ymin = -Inf, ymax = Inf, alpha = 0.2,fill="#d7ebce") + annotate("rect", xmin = 6.5, xmax = 20.5, ymin = -Inf, ymax = Inf, alpha = 0.2,fill="#bcced6") + annotate("rect", xmin = 20.5, xmax = 23, ymin = -Inf, ymax = Inf, alpha = 0.2,fill="#ffdc80")+ #手动添加显著性标记，图中列出部分，具体根据个人数据进行调整 annotate('text', label = '**', x =21, y =5, angle=-90, size =5,color="black")+ annotate('text', label = '***', x =2, y =55, angle=-90, size =5,color="black")+ annotate('text', label = '***', x =19, y =65, angle=-90, size =5,color="black")+ annotate('text', label = '***', x =16, y =35, angle=-90, size =5,color="black")+ annotate('text', label = '***', x =9, y =35, angle=-90, size =5,color="black")+ annotate('text', label = '***', x =5, y =100, angle=-90, size =5,color="black")+ facet_grid(~ facet)#基于分面函数添加图顶部标题p1
###绘制第二张点线图#计算均值library(Rmisc) # Ryan Miscellaneousmean2 <- summarySE(df2, measurevar = "value", groupvars = c("sample", "group"))mean2mean2$sample <- factor(mean2$sample,levels = rev(data2$sample))mean2$facet <- rep("Functional diversity",times=44)#绘图p2 <- ggplot(mean2, aes(sample,value, color = group)) + geom_errorbar(aes(ymin = value- se, ymax = value + se), width = 0,position = position_dodge(0.8),linewidth=0.5) + geom_point(position = position_dodge(0.8),shape=18,size=3)+ #转变x轴与y轴位置 coord_flip()+ #y轴范围设置 scale_y_continuous(limits = c(30, 110))+ #主题设置 theme_bw()+ theme(legend.position = c(0.8,0.95), legend.background = element_blank(), legend.key = element_blank(), panel.grid = element_blank(), axis.text.x = element_text(color = "black",size=10), axis.text.y = element_blank(), axis.ticks.y = element_blank(), strip.background = element_rect(fill = "grey", color = "transparent"), strip.text = element_text(color="black",size=10))+ #标题位置 labs(y="Functional dispersion values",x=NULL,color=NULL)+ scale_color_manual(values = c("#7fc190","#efb684"))+ #添加分组矩形 annotate("rect", xmin = 0, xmax = 6.5, ymin = -Inf, ymax = Inf, alpha = 0.2,fill="#d7ebce") + annotate("rect", xmin = 6.5, xmax = 20.5, ymin = -Inf, ymax = Inf, alpha = 0.2,fill="#bcced6") + annotate("rect", xmin = 20.5, xmax = 23, ymin = -Inf, ymax = Inf, alpha = 0.2,fill="#ffdc80")+ #手动添加显著性标记，图中列出部分，具体根据个人数据进行调整 annotate('text', label = '**', x =11, y =80, angle=-90, size =5,color="black")+ geom_segment(x = 10.5, xend = 11.5, y = 78, yend = 78,color = "black", size = 0.8)+ facet_grid(~ facet)#基于分面函数添加图顶部标题p2#拼图library(aplot) # Decorate a 'ggplot' with Associated Informationp1%>%insert_right(p2,width = 1)###最后用AI进行微调即可
PS: 以上内容是小编个人学习代码笔记分享，仅供参考学习，欢迎大家一起交流学习。
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