可先阅读文章：R绘图笔记 | R语言绘图系统与常见绘图函数及参数
1.单数据系列柱状图
代码语言：javascript复制###绘图数据
data <- "Sample1;Sample2;Sample3;Sample4;Sample5
gene1;2.6;2.9;2.1;4.5;2.2
gene2;20.8;9.8;7.0;3.7;19.2
gene3;10.0;11.0;9.2;12.4;9.6
gene4;9;3.3;10.3;11.1;10"
data <- read.table(text=data, header=T, row.names=1, sep=";", quote="")
data
##gene1的在不同样本中的表达
data1 <- as.data.frame(t(data)[,1])
names(data1) <- "gene1"
data1$sample <- rownames(data1)> data1
gene1 sample
Sample1 2.6 Sample1
Sample2 2.9 Sample2
Sample3 2.1 Sample3
Sample4 4.5 Sample4
Sample5 2.2 Sample5绘图：geom_bar用于绘制柱状图，ylim设置纵轴值范围，them设置主题，axis.title设置坐标轴名称参数，axis.text设置坐标轴参数。
代码语言：javascript复制ggplot(data=data1,aes(x=sample,y=gene1))
geom_bar(stat = "identity",
width = 0.8,colour="black",size=0.25,
fill="#FC4E07",alpha=1)
ylim(0,max(data1$gene1))
theme(
axis.title=element_text(size=15,face="plain",color="blue"),
axis.text = element_text(size=12,face="plain",color="red")
)
可将数据进行排序后绘图。
代码语言：javascript复制#排序方法1：基于数据框data.frame
library(dplyr)
data1.a<-arrange(data1,desc(gene1))
data1.a$sample <- factor(data1.a$sample, levels = data1.a$sample)
ggplot(data=data1.a,aes(x=sample,y=gene1))
geom_bar(stat = "identity", width = 0.8,
colour="black",size=0.25,fill="#FC4E07",alpha=1)
#排序方法2：基于向量vector
data1.b <- data1
order<-sort(data1.b$gene1,index.return=TRUE,decreasing = TRUE)
data1.b$sample <- factor(data1.b$sample , levels = data1.b$sample [order$ix])
ggplot(data=data1.b,aes(x=sample,y=gene1))
geom_bar(stat = "identity", width = 0.8,
colour="black",size=0.25,fill="black",alpha=1)
将所有样本的基因表达值都绘制出来，position=position_dodge()表示柱子并排放置。也可以通过position_dodge()函数来改变数据序列间的间隔。
代码语言：javascript复制data2 <- data.frame(gene = rownames(data),data)
data2 <- melt(data2, id.vars=c("gene"))
ggplot(data2, aes(x=gene, y=value))
geom_bar(stat="identity", position=position_dodge(), aes(fill=variable))
但是，通常我们是不这样作图的，而是取均值，加上误差线。
代码语言：javascript复制# 获取平均值和标准差
data3 <- data2 %>% group_by(gene) %>% dplyr::summarise(sd=sd(value), value=mean(value))
data3 <- as.data.frame(data3)> data3
gene sd value
1 gene1 0.9710819 2.86
2 gene2 7.5491721 12.10
3 gene3 1.2837445 10.44
4 gene4 3.1325708 8.74ggplot(data3, aes(x=gene, y=value))
geom_bar(stat="identity", aes(fill=gene))
geom_errorbar(aes(ymin=value-sd, ymax=value sd), width=0.2, position=position_dodge(width=0.75))
theme(
axis.title=element_text(size=15,face="plain",color="black"),
axis.text = element_text(size=12,face="plain",color="black")
)
2.双序列图的绘制
代码语言：javascript复制library(reshape2)
data4 <- data.frame(Gene = c("gene1","gene2","gene3"),
CTRL = c(7.67,4.02,3.95),
Drug = c(5.84,6.45,6.76),stringsAsFactors=FALSE)
#colnames(data4) <- c("Gene","CTRL","Drug")
data4<-melt(data4,id.vars="Gene")
data4> data4
Gene variable value
1 gene1 CTRL 7.67
2 gene2 CTRL 4.02
3 gene3 CTRL 3.95
4 gene1 Drug 5.84
5 gene2 Drug 6.45
6 gene3 Drug 6.76ggplot(data=data4,aes(Gene,value,fill=variable))
geom_bar(stat="identity",position=position_dodge(),
color="black",width=0.7,size=0.25)
scale_fill_manual(values=c("#A61CE6", "#E81CA4"))
ylim(0, 10)
theme(
axis.title=element_text(size=15,face="plain",color="black"),
axis.text = element_text(size=12,face="plain",color="black"),
legend.title=element_text(size=14,face="plain",color="black"),
legend.background =element_blank(),
legend.position = c(0.88,0.88)
) ylab("Expression values")
按CTRL组排序。
代码语言：javascript复制data5 <- data.frame(Gene = c("gene1","gene2","gene3"),
CTRL = c(8.67,4.02,6.95),
Drug = c(5.84,6.45,6.76),stringsAsFactors=FALSE)
data5$Gene <- factor(data5$Gene, levels = data5$Gene[order(data5[,"CTRL"],decreasing = TRUE)])
data5 <- melt(data5,id.vars='Gene')
ggplot(data=data5,aes(Gene,value,fill=variable))
geom_bar(stat="identity", color="black", position=position_dodge(),width=0.7,size=0.25)
scale_fill_manual(values=c("#00AFBB", "#E7B800"))
ylim(0, 10) ylab("Expression values")
theme(
axis.title=element_text(size=15,face="plain",color="black"),
axis.text = element_text(size=12,face="plain",color="black"),
legend.title=element_text(size=14,face="plain",color="black"),
legend.background =element_blank(),
legend.position = c(0.88,0.88)
)
3.堆积柱状图
代码语言：javascript复制data6 <- data.frame(Gene = c("gene1","gene2","gene3","gene4","gene5"),
sam1 = c(150,1200,1300,2800,2000),
sam2 =c(400,1100,2300,2900,2700),
sam3 = c(390,1700,3300,3500,4200),
sam4 = c(300,900,1900,2800,3300),
sam5 = c(130,790,1800,3000,4200),
sam6 = c(100,1300,1900,1800,2700),
sam7 = c(100,1200,1700,1600,2100),
sam8 = c(150,1100,1300,1280,1300),stringsAsFactors=FALSE)
data6 <- melt(data6,id.vars='Gene')
ggplot(data=data6,aes(variable,value,fill=Gene))
geom_bar(stat="identity",position="stack", color="black", width=0.7,size=0.25)
scale_fill_manual(values=brewer.pal(9,"YlOrRd")[c(6:2)])
ylim(0, 15000) xlab("Sample") ylab("Expression values")
theme(
axis.title=element_text(size=15,face="plain",color="black"),
axis.text = element_text(size=12,face="plain",color="black"),
legend.title=element_text(size=14,face="plain",color="black"),
legend.background =element_blank(),
legend.position = c(0.85,0.82)
)
data7 <- data.frame(Gene = c("gene1","gene2","gene3","gene4","gene5"),
sam1 = c(150,1200,1300,2800,2000),
sam2 =c(400,1100,2300,2900,2700),
sam3 = c(390,1700,3300,3500,4200),
sam4 = c(300,900,1900,2800,3300),
sam5 = c(130,790,1800,3000,4200),
sam6 = c(100,1300,1900,1800,2700),
sam7 = c(100,1200,1700,1600,2100),
sam8 = c(150,1100,1300,1280,1300),stringsAsFactors=FALSE)> data7
Gene sam1 sam2 sam3 sam4 sam5 sam6 sam7 sam8
1 gene1 150 400 390 300 130 100 100 150
2 gene2 1200 1100 1700 900 790 1300 1200 1100
3 gene3 1300 2300 3300 1900 1800 1900 1700 1300
4 gene4 2800 2900 3500 2800 3000 1800 1600 1280
5 gene5 2000 2700 4200 3300 4200 2700 2100 1300##按行求和，排序
sum <- sort(rowSums(data7[,2:ncol(data7)]),index.return=TRUE)
#按列求和,排序
colsum<-sort(colSums(data7[,2:ncol(data7)]),index.return=TRUE,decreasing = TRUE)
data7 <- data7[,c(1,colsum$ix 1)]> data7
Gene sam3 sam5 sam2 sam4 sam6 sam1 sam7 sam8
1 gene1 390 130 400 300 100 150 100 150
2 gene2 1700 790 1100 900 1300 1200 1200 1100
3 gene3 3300 1800 2300 1900 1900 1300 1700 1300
4 gene4 3500 3000 2900 2800 1800 2800 1600 1280
5 gene5 4200 4200 2700 3300 2700 2000 2100 1300data7$Gene <- factor(data7$Gene, levels = data7$Gene[order(sum$ix)])
data7<-melt(data7,id.vars='Gene')
ggplot(data=data7,aes(variable,value,fill=Gene))
geom_bar(stat="identity",position="stack", color="black", width=0.7,size=0.25)
scale_fill_manual(values=brewer.pal(9,"YlOrRd")[c(6:2)])
ylim(0, 15000) xlab("Sample") ylab("Expression values")
theme(
axis.title=element_text(size=15,face="plain",color="black"),
axis.text = element_text(size=12,face="plain",color="black"),
legend.title=element_text(size=14,face="plain",color="black"),
legend.background =element_blank(),
legend.position = c(0.85,0.82)
)
4.百分比堆积柱形图
scale_fill_manual用于修改填充色。
代码语言：javascript复制ggplot(data=data7,aes(variable,value,fill=Gene))
geom_bar(stat="identity", position="fill",color="black", width=0.8,size=0.25)
scale_fill_manual(values=brewer.pal(9,"GnBu")[c(7:2)])
xlab("Sample") ylab("Expression values")
theme(
axis.title=element_text(size=15,face="plain",color="black"),
axis.text = element_text(size=12,face="plain",color="black"),
legend.title=element_text(size=14,face="plain",color="black"),
legend.position = "right"
)
5.不等宽柱形图
代码语言：javascript复制library(ggplot2)
#install.packages("Cairo")
library(Cairo)
#install.packages("showtext")
library(showtext)data8<-data.frame(Name=paste0("Group",1:5),Scale=c(35,30,20,25,15),Count=c(56,37,63,57,59))
data8$xmin<-0
for (i in 2:5){
data8$xmin[i]<-sum(data8$Scale[1:i-1])
}
#构造矩形X轴的终点（最大点）
for (i in 1:5){
data8$xmax[i]<-sum(data8$Scale[1:i])
}
#构造数据标签的横坐标：
for (i in 1:5){
data8$label[i]<-sum(data8$Scale[1:i])-data8$Scale[i]/2
}
data8> data8
Name Scale Count xmin xmax label
1 Group1 35 56 0 35 17.5
2 Group2 30 37 35 65 50.0
3 Group3 20 63 65 85 75.0
4 Group4 25 57 85 110 97.5
5 Group5 15 59 110 125 117.5#windowsFonts(myFont = windowsFont("微软雅黑"))
#颜色的映射设定是在 aes() 内部完成的，而颜色的重新设定是在 aes() 外部完成的
ggplot(data8)
geom_rect(aes(xmin=xmin,xmax=xmax,ymin=0,ymax=Count,fill=Name),colour="black",size=0.25)
geom_text(aes(x=label,y=Count 3,label=Count),size=4,col="black")
geom_text(aes(x=label,y=-2.5,label=Name),size=4,col="black")
ylab("Count")
xlab("Group")
ylim(-5,80)
theme(panel.background=element_rect(fill="white",colour=NA),
panel.grid.major = element_line(colour = "grey60",size=.25,linetype ="dotted" ),
panel.grid.minor = element_line(colour = "grey60",size=.25,linetype ="dotted" ),
text=element_text(size=15),
plot.title=element_text(size=15,hjust=.5),#family="myfont",
legend.position="none"
)
5.径向柱形图
代码语言：javascript复制data9 <- data.frame(species=rep(paste0("specie",c(1:10)), 5),
gene=rep(paste0("gene",c(1:5)), each=10),
value=rep((1:5), each=10) rnorm(50, 0,.5))
head(data9)> head(data9)
species gene value
1 specie1 gene1 0.8178002
2 specie2 gene1 0.5365643
3 specie3 gene1 0.7836265
4 specie4 gene1 0.9158748
5 specie5 gene1 0.8929767
6 specie6 gene1 1.9134189myAng <- seq(-20,-340,length.out = 10)
ggplot(data=data9,aes(species,value,fill=gene))
geom_bar(stat="identity", color="black", position=position_dodge(),width=0.7,size=0.25)
coord_polar(theta = "x",start=0)
ylim(c(-3,6))
scale_fill_brewer()
theme_light()
theme( panel.background = element_blank(),
panel.grid.major = element_line(colour = "grey80",size=.25),
axis.text.y = element_text(size = 12,colour="black"),
axis.line.y = element_line(size=0.25),
axis.text.x=element_text(size = 13,colour="black",angle = myAng))
coord_polar将直角坐标转化为极坐标。
参考资料：
1.R语言数据可视化之美，张杰/著
