R折线图（自备）

目录

折线图基础

创建散点和折线图

复杂折现加图例

折线图+柱状图

数据处理

进行差异检验

基础绘图折线

基础绘图箱线

进行合并

双轴柱状与折线图

数据

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折线图基础

创建散点和折线图

rm(list = ls()) 
opar <-par(no.readonly=TRUE)##自带orange数据集
par(mfrow=c(1,2))#拼图
t1 <-subset(Orange,Tree==1)##提取使用数据集
head(t1)

head(t1)Tree  age circumference
1    1  118            30
2    1  484            58
3    1  664            87
4    1 1004           115
5    1 1231           120
6    1 1372           142

##散点图
p1 <- plot(t1$age,t1$circumference,xlab="Age(days)",ylab="Circumference(mm)",main="Orange Tree 1 Growth")#标题
p1
##折线图
p2 <-plot(t1$age,t1$circumference,xlab="Age(days)",ylab="Circumference(mm)",main="Orange Tree 1 Growth",type = "b")#标题
p2

折线图绘制参数：plot(x,y,type=)         lines(x,y,type=)

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复杂折现加图例

#使用ggplot 绘制折线图#
data <- Orangehead(Orange)
Tree  age circumference
1    2  118            30
2    2  484            58
3    2  664            87
4    2 1004           115
5    2 1231           120
6    2 1372           142

p <- ggplot(data = Orange, aes(x=age, y=circumference, linetype=Tree, color=Tree)) +geom_point() + geom_line(lwd = 1) + scale_color_brewer(palette = "Set1") + ##颜色配色https://zhuanlan.zhihu.com/p/657907907labs(title = "Orange Tree Growth", x = "Age(days)", y = "Circumference(mm)") + theme_bw() +theme(legend.position = c(0.2,0.8)) ##"right" 图例位置p

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折线图+柱状图

感谢R语言ggplot2 | 学习Nature文章精美配图 | 折线图 | 柱状图 | 误差棒 | 灰色背景 | 图片叠加 - 知乎 (zhihu.com)

数据：https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-021-04194-8/MediaObjects/41586_2021_4194_MOESM11_ESM.xlsx

接受不同处理的HFD喂养小鼠的体重变化：非SMK（n = 37），SMK（n = 38），非SMK + abx（n = 40）和SMK + abx（n = 39）。从四个独立的重复序列中汇总的结果。最后一天，进行单因素方差分析（ANOVA）和Sidak校正;插图显示了烟雾暴露（暴露）或停止烟雾暴露（停止）会话、单因素方差分析和 Sidak 校正时的曲线下面积 （iAUC） 权重变化增量。

下载数据进行处理：R语言长款数据转换（自备）_r语言宽数据变成长数据-CSDN博客

数据处理

rm(list = ls()) 
library(tidyverse) 
library(reshape2)
data <- read.csv('Nature data.CSV',header = T)
head(data) 
##折线图转换为长数据：[1] "treat"  "Day"    "value1" "value2" "value3"
#根据"treat"分组  "Day"（也是X轴内容）转换为长数据，将value合并为1列
data1 <- data[,c(1:5)] %>% melt(id=c('treat','Day'))##折线误差棒errorbar上下误差位置
topbar <- function(x){      return(mean(x)+sd(x)/sqrt(length(x))) #误差采用了mean+-sem
}
bottombar <- function(x){return(mean(x)-sd(x)/sqrt(length(x)))
}

head(data1)treat Day variable value
1 Non_SMK   0   value1   0.0
2 Non_SMK   7   value1   6.7
3 Non_SMK  14   value1  14.0
4 Non_SMK  21   value1  15.6
5 Non_SMK  28   value1  24.0
6 Non_SMK  35   value1  30.0

进行差异检验

连续性变量的组间差异分析_连续变量和连续变量差异性分析-CSDN博客

##检验测试##
tdat <- data[,c(1:5)]
tdat$mean <- rowMeans(select(tdat,c(3:5)))##计算第3-5行的均值，可以用于计算相关性
#单因素方差分析#
race.aov <-aov(mean ~treat,data = tdat)
summary(race.aov)
#Df Sum Sq Mean Sq F value Pr(>F)
#treat        3  751.2   250.4    2.09  0.134
#Residuals   20 2396.6   119.8   ##两组间最后一组数据t检验## 
group1 <- tdat[tdat$treat =="Non_SMK+abx"& tdat$Day=="35",c(3:5)]
group2 <- tdat[tdat$treat =="SMK+abx" & tdat$Day=="35",c(3:5)]
t.test(group1,group2,var.equal =TRUE)
#t = 12.267, df = 4, p-value = 0.0002536  ****group3 <- tdat[tdat$treat =="SMK"& tdat$Day=="35",c(3:5)]
t.test(group2,group3,var.equal =TRUE)#一样的
#t = -7.4158, df = 4, p-value = 0.001765 ***

基础绘图折线

#绘图赋值为p
p0 <- ggplot(data1,aes(x=Day,y=value,color=treat))+#Day为X，value值为Y，分组颜色treatgeom_rect(aes(xmin=21,xmax=40,ymin=(-Inf),ymax=Inf),##后半部分色块设置fill='grey90',color='grey90')+geom_vline(xintercept =21,linetype=2,cex=1.2)+            #添加虚线stat_summary(geom = 'line',fun='mean',cex=2.5)+           #折线stat_summary(geom = 'errorbar',                           #误差磅fun.min = bottombar,fun.max = topbar,width=1,cex=0.8,aes(color=treat))+stat_summary(geom = 'point',fun='mean',aes(fill=treat),   #点size=5,pch=21,color='black')+theme_classic(base_size = 15)+theme(legend.position = 'none')#top
p0

##点的颜色修改以及显著性标记的添加##
p1 <- p0+scale_color_manual(values = c('#5494cc','#0d898a','#e18283','#f9cc52'))+##线条的颜色scale_fill_manual(values = c('red','#0d898a','#e18283','white'))+ ##点的颜色scale_y_continuous(breaks = seq(0,60,20),expand = c(0,0))+              #Y轴范围scale_x_continuous(breaks = seq(0,40,10),expand = c(0,0))+              #X轴范围labs(y='Weight change(%)')+theme(axis.line = element_line(size = 1),                               #线条设置axis.text = element_text(color = 'black'),axis.ticks = element_line(size = 1,color='black'))+annotate(geom = 'segment',x=36.2,xend=36.2,y=18,yend=26,cex=1.2)+       ##显著性标注设置-线段annotate(geom = 'text',label='***',x=37.5,y=22,size=7,angle=90)+        ##显著性标注设置-数值或***annotate(geom = 'segment',x=38,xend = 38,y=18,yend = 40,cex=1.2)+annotate(geom = 'text',label='****',x=39.5,y=29,size=7,angle=90)
p1##折线图图列的添加并且修改了图例形状##
linechart <- p1+coord_cartesian(clip = 'off',ylim = c(0,60),xlim = c(0,40))+theme(plot.margin = margin(1.5,0.5,0.5,0.5,'cm'))+geom_rect(aes(xmin=0,xmax=3,ymin=63,ymax=65),fill='#5494cc',color='black')+geom_rect(aes(xmin=10,xmax=13,ymin=63,ymax=65),fill='#0d898a',color='black')+geom_rect(aes(xmin=20,xmax=23,ymin=63,ymax=65),fill='#e18283',color='black')+geom_rect(aes(xmin=30,xmax=33,ymin=63,ymax=65),fill='#f9cc52',color='black')+annotate('text',x=6,y=64,label='Non-SMK',size=4)+annotate('text',x=15.2,y=64,label='SMK',size=4)+annotate('text',x=26.4,y=64,label='Non-SMK+\nabx',size=4)+annotate('text',x=36,y=64,label='SMK+abx',size=4)
linechart

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基础绘图箱线

##提取箱线图数据
data2 <- data[,6:9] %>% gather(key = treat)
head(data2)
data3 <- data[,10:13] %>% gather(key = treat)#左边柱状图：
leftchart <- ggplot(data2,aes(factor(treat,levels = c('SMK.abx','Non_SMK.abx','SMK','Non_SMK')),value))+stat_summary(geom = 'bar',fun = 'mean',fill='white',color='black',width=0.7,cex=1)+stat_summary(geom = 'errorbar',fun.min = bottombar,fun.max = topbar,width=0.3,cex=0.8,color='black')+geom_jitter(aes(color=factor(treat,levels = c('SMK.abx','Non_SMK.abx','SMK','Non_SMK'))),width = 0.1,size=1.5)+scale_color_manual(values = c('#f9cc52','#0d898a','#e18283','#5494cc'))+labs(x=NULL,y=NULL)+scale_y_continuous(limits = c(-40,600),expand = c(0,0))+geom_hline(yintercept =0,cex=1)+theme_classic(base_size = 15)+theme(axis.ticks.y = element_blank(),axis.text.y= element_blank(),legend.position = 'none',axis.line = element_line(size = 1),axis.text = element_text(color = 'black'),axis.ticks = element_line(size = 1,color='black'))+coord_flip()+annotate(geom = 'segment',x=1,xend=2,y=530,yend=530,cex=1.2)+annotate(geom = 'text',label='****',x=1.5,y=570,size=6,angle=90)+annotate(geom = 'segment',x=3,xend =4,y=370,yend =370,cex=1.2)+annotate(geom = 'text',label='****',x=3.5,y=410,size=6,angle=90))
leftchart#右边柱状图：
rightchart <- ggplot(data3,aes(factor(treat,levels = c('SMK.abx.1','Non_SMK.abx.1','SMK.1','Non_SMK.1')),value))+stat_summary(geom = 'bar',fun = 'mean',fill='white',color='black',width=0.7,cex=1)+stat_summary(geom = 'errorbar',fun.min = bottombar,fun.max = topbar,width=0.3,cex=0.8,color='black')+geom_jitter(aes(color=factor(treat,levels = c('SMK.abx.1','Non_SMK.abx.1','SMK.1','Non_SMK.1'))),width = 0.1,size=1.5)+scale_color_manual(values = c('#f9cc52','#0d898a','#e18283','#5494cc'))+labs(x=NULL,y=NULL)+scale_y_continuous(limits = c(-40,500),expand = c(0,0))+geom_hline(yintercept =0,cex=1)+theme_classic(base_size = 15)+theme(axis.ticks.y = element_blank(),axis.text.y= element_blank(),legend.position = 'none',axis.line = element_line(size = 1),axis.text = element_text(color = 'black'),axis.ticks = element_line(size = 1,color='black'),plot.background = element_rect(fill = "transparent",colour = NA),panel.background = element_rect(fill = "transparent",colour = NA))+coord_flip()+annotate(geom = 'segment',x=1,xend=2,y=420,yend=420,cex=1.2)+annotate(geom = 'text',label='****',x=1.5,y=460,size=6,angle=90)+annotate(geom = 'segment',x=3,xend =4,y=430,yend =430,cex=1.2)+annotate(geom = 'text',label='****',x=3.5,y=470,size=6,angle=90)
rightchart

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进行合并

#要叠加的图片先用ggplotGrob()函数处理：
leftchart <- ggplotGrob(leftchart)
rightchart <- ggplotGrob(rightchart)#叠加绘图：
linechart+annotation_custom(leftchart,xmin=0,xmax=20.5,ymin=40,ymax=57)+annotation_custom(rightchart,xmin=21,xmax=39.5,ymin=40,ymax=57)+annotate('text',label='iAUC: Exposure',x=10.5,y=58.5,size=7)+annotate('text',label='iAUC: Cessation',x=31,y=58.5,size=7)

感谢：“R语言ggplot2科研绘图”的代码，本文主要是复现流程和差异计算补充！！

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双轴柱状与折线图

Nature图表复现｜双轴柱状与折线图 (qq.com)

数据

rm(list = ls()) 
library(tidyverse)
library(gapminder)
library(Hmisc)
library(gapminder)data <- gapminder
##提取continent=="Asia"的数据
df <- data %>% filter(continent=="Asia") %>% select(1,3,4,6) %>% mutate(year=as.character(year))p <- ggplot() +stat_summary(data=df, aes(year, lifeExp), fun = "mean", geom = "bar", alpha = 0.7, fill="#00A08A") +# 添加预期寿命（lifeExp）的误差条stat_summary(data=df, aes(year, lifeExp), fun.data = "mean_cl_normal", geom = "errorbar", width = .2, color="#00A08A") +# 添加人均GDP（gdpPercap）的误差条stat_summary(data=df %>% mutate(gdpPercap=gdpPercap/20), aes(year, gdpPercap), fun = mean, geom = "errorbar", width=.2, color="#F98400",fun.max = function(x) mean(x) + sd(x) / sqrt(length(x)),fun.min = function(x) mean(x) - sd(x) / sqrt(length(x))) +# 添加人均GDP（gdpPercap）的平均值点stat_summary(data=df %>% mutate(gdpPercap=gdpPercap/20), aes(year, gdpPercap), fun = "mean", geom = "point", size=3, color="#F98400") +# 添加人均GDP（gdpPercap）的平均值线stat_summary(data=df %>% mutate(gdpPercap=gdpPercap/20), aes(year, gdpPercap, group=1), fun = "mean", geom = "line", color="#F98400") +# 设置y轴的比例和标签scale_y_continuous(expand = c(0, 1), breaks = scales::pretty_breaks(n = 12),sec.axis = sec_axis(~. * 20, breaks = scales::pretty_breaks(n = 12),name = "gdpPercap")) +# 应用主题theme_test() +# 自定义主题设置theme(panel.background = element_blank(),axis.ticks.length.x.bottom = unit(-0.05, "in"),axis.ticks.length.y.left = unit(-0.05, "in"),axis.ticks.length.y.right = unit(-0.05, "in"),axis.line.y.left = element_line(color="#00A08A"),axis.line.y.right = element_line(color="#F98400"),axis.line.x.bottom = element_line(color="black"),axis.line.x.top = element_line(color="grey80"),axis.text.y.right = element_text(color="#F98400", margin = margin(l = 5, r = 10)),axis.text.y.left = element_text(color="#00A08A", margin = margin(l = 10, r = 5)),axis.title.y.left = element_text(color="#00A08A", face="bold"),axis.title.y.right = element_text(color="#F98400", face="bold"),axis.title.x.bottom = element_blank())
p
dev.off()

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参考：

1：《R语言实战手册》

2：Gut microbiota modulates weight gain in mice after discontinued smoke exposure