那就先从violin plot开始吧。 (假设你已经安装了R和RStudio) Step1.绘图数据的准备 首先要把你想要绘图的数据调整成R语言可以识别的格式。 一般我们的数据都是保存在excel文档里,这里建议大家在excel中保存成csv格式,读写的速度会比xlsx快很多。(小仙同学的亲身经验,我的有些数据有两万行,xlsx在我的耐心范围内读...
下面,小猿团队的小狐带您一步步使用R语言的ggplot函数或统计猿在线工具实现Kruskal-Wallis秩和检验及数据的可视化。本讲内容包括数据准备与整理、Kruskal-Wallis秩和检验和事后多重比较检验。 在学习教程之前,请同学们自行完成R语言GUI和RStudio两个软件的安装。 未安装“ggplot2”等R包的同学请先通过install.packages("...
在学习教程之前,请同学们自行完成R语言GUI和RStudio两个软件的安装。数据由代码生成,无需另外下载。 未安装“ggplot2”等R包的同学请先通过install.packages("包的名字")自行安装。 通过本教程,读者将学会: 生成、存储、读取模拟连续型变量数据; 使用ggplot2生成高质量的图表样式,并在小提琴图上添加显著性标记,以...
ggplot2基于Leland Wilkinson在Grammar of Graphics(图形的语法)中提出的理论,取首字母缩写再加上plot,于是得名ggplot。按照《图形的语法》一书中的观点,一张统计图形就是从数据到点、线或方块等几何对象的颜色、形状或大小等图形属性的一个映射,其中还可能包含对数据进行统计变换(如求均值或方差),最后将这个映射绘制...
A Violin Plot is used to visualise the distribution of the data and its probability density. This chart is a combination of a Box Plot and a Density Plot that is rotated and placed on each side (to show the distribution shape of the data). The white dot in the middle is the median...
This post is the third in a series of four on boxplots and closely related data visualization techniques for comparing subsets of a dataset, or comparing different datasets that we hope or expect to be similarly distributed. The previous two post...
小提琴图(Violin Plot)是一种用于展示和比较数据分布的可视化工具。它结合了箱形图(Box Plot)和密度图(KernelDensity Plot)的特点:中间有箱形图表示四分位数和中位数,外围是密度估计曲线,显示数据分布的密度。这种设计旨在提供关于数据分布形状、峰度和离散性的直观信息。
First we plot Sepal Length on its own: vioplot(Sepal.Length~Species,data=iris) An indirect comparison can be achieved with par: {par(mfrow=c(2,1))vioplot(Sepal.Length~Species,data=iris_small,col ="lightblue",plotCentre ="line")vioplot(Sepal.Length~Species,data=iris_large,col ="paleviol...
The plot is set mostly before Dorothy's arrival from Kansas, and includes several references to well-known scenes and dialogue in the 1939 film The Wizard of Oz. The musical debuted on Broadway on October 30, 2003. It is produced by Universal Pictures and directed by Joe Mantello, with ...
vioplot(rnorm(200,3,0.5),rpois(200,2.5),rbinom(100,10,0.4),rlnorm(200,0,0.5),rnbinom(200,10,0.9),rlogis(20,0,0.5),areaEqual =T,main="Equal Area",xlab="distribution",ylab="data value",names=c("normal","poisson","binomial","log-normal","neg-binomial","logistic"))...