偶然间找到了一份教程利用ggplot2绘制环状柱形图,个人感觉非常适合用来展示叶绿体基因组蛋白编码基因的dn/ds值,因为不仅能够通过柱状图的高低来比较dn/ds值的大小,还能够通过环状展示蛋白编码基因在叶绿体基因组上所处的位置
ggplot(data_ggp, aes(x=group, y=values))+# Create barchart with ggplot2geom_bar(stat="identity") Figure 7: Barchart Created with ggplot2 Package. Figure 7 shows bars with the same values as in Examples 1-4. However, this time the bargraph is shown in the typical ggplot2 design. E...
ggplot(data,aes(Year,Thousands,fill = AgeGroup)) +geom_area() 接下来我们来画一个饼图吧 在R语言的自带base作图系统中,有一个内置的绘制饼图的函数,叫做pie()函数 它的参数非常简单,需要给出第一个参数就是:数值,也就是我们这个数据中的Value一列 第二个参数就是:数值的标签,也就是我们数据当中的Gro...
barplot(table(mtcars$cyl)) 对于ggplot2系统,可以使用qplot()函数得到类似的绘图结果(见图2-6)。绘制变量值的条形图时需将参数设定为geom="bar"和stat="identity"。注意变量x分别为连续取值和离散取值时输出结果的差异。 1. library(ggplot2) qplot(BOD$Time, BOD$demand, geom="bar", stat="identity") 将...
This post explains how to build grouped, stacked and percent stacked barplot with R and ggplot2. It provides a reproducible example with code for each type.
function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ... ... other arguments to be passed to be passed to ggpar(). Details...
#改变柱形图柱子的宽度,width为宽度参数 ggplot(data=df, aes(x=group, y=ORR)) + geom_bar(stat="identity", width=0.5) 3.3填充颜色 #改变柱形图的填充颜色,color为柱子线条颜色,fill为柱子内部填充颜色 ggplot(data=df, aes(x=group, y=ORR)) + geom_bar(stat="identity", color="blue", fill="...
ggplot2.barplot is a function, to plot easily bar graphs using R software and ggplot2 plotting methods. This function is from easyGgplot2 package. An R script is available in the next section to install the package. The aim of this tutorial is to show you step by step, how to plot an...
GGPlot2 Essentials for Great Data Visualization in R Key R functions Key function:geom_col()for creating bar plots. The heights of the bars represent values in the data. Key arguments to customize the plot: color,fill: bar border and fill color ...
R语言中如何创建百分比堆叠条形图? 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # library library(ggplot2) # create a dataset specie <- c(rep("sorgho" , 3) , rep("poacee" , 3) , rep("banana" , 3) , rep("triticum" , 3) ) condition <- rep(c("normal" , "stress" , "...