end=max(ID) - number_empty_bar) %>% rowwise() %>% mutate(title=mean(c(start, end))) #绘图 p2+geom_segment(data=base_data, aes(x = start, y = -5, xend = end, yend = -5), color = "red", alpha=0.8, size=0.8 , inherit.aes = F)+#添加分组线 geom_text(...
barplot()第一个向量用来设定条形的高度,第二个向量用来设定每个条形对应的标签(可选)。 变量值条形图: 两个输入变量,x为分类变量,y表示变量值 频数条形图:一个输入变量,需要注意连续x轴和离散x轴的差异。 直方图 与条形图不同的地方在于,x为连续型变量 箱线图 需要传递两个向量:x和y 在x轴上引入两变量的...
ggbarplot(df2,x="name",y="mpg_z",fill = "mpg_grp",color = "white",palette="jco",sort.val = "asc",sort.by.groups = FALSE,x.text.angle=60,ylab="MPG z-score",xlab = FALSE,legend.title="MPG Group", rotate=TRUE) 1 g...
AI代码解释 ggplot(frame,aes(group,num,fill=group))+geom_col()+geom_errorbar(aes(group,ymin=mean-sd,ymax=mean+sd,color=group),width=0.6,size=1)+xlab("Group")+ylab("OR")+theme(legend.position="none",axis.title=element_text(size=15),axis.text=element_text(size=15))+annotate("text"...
(ToothGrowth, x="dose", y="len", color = "dose", palette = "jco", binwidth = 1) #An ordered Bar plot(bp) bp <- ggbarplot(mtcars, x="name", y="mpg", fill="cyl", #change fill color by cyl color="white", #Set bar border colors to white palette = "jco", #jco jourbal...
barplot(means$x,main="Mean", names.arg =means$Group.1, col=c('#BBFFFF','#AEEEEE','#96CDCD','#668B8B'), ylim=c(0,2)) lines(means$x,type = "b",pch=17,lty=2,col="red") #添加线条 #lines(x,y),相当于plot(x,y,type="1")?
Before trying to build one, check how to make a basic barplot with R and ggplot2. A few explanation about the code below: input dataset must provide 3 columns: the numeric value (value), and 2 categorical variables for the group (specie) and the subgroup (condition) levels. in the ...
p<-p+labs(title="Frequency Distribution Stacked Barplot",x="Sepal Length Group",y="Frequency")+theme_bw() 1. 2. 3. 4. 在上述代码中,我们使用labs()函数分别指定了标题、x轴标签和y轴标签的内容;使用theme_bw()函数将图形的背景设置为白色。
ha) ## # 添加barplot注释信息 ha = HeatmapAnnotation(barplot = anno_barplot(1:217, whic...
barplot + geom_bar() # 箱线图 boxplot <- ggplot(mtcars, aes(factor(gear), mpg)) boxplot + geom_boxplot() # 折线图 newdata <- mtcars %>% group_by(gear) %>% summarise(meanmpg = mean(mpg)) lineplot <- ggplot(newdata, aes(gear, meanmpg)) ...