定制小提琴图（Customising Violin Plots）

# vioplot包测试_20200210Monday

## 1.设置当前工作目录

setwd("./vioplot/")

## 2.安装和导入R包：vioplot

# install.packages(vioplot)

library(vioplot)

# 载入需要的程辑包：sm

# Package 'sm', version 2.2-5.6: type help(sm) for summary information

# 载入需要的程辑包：zoo

# 载入程辑包：‘zoo’

# The following objects are masked from ‘package:base’:

# as.Date, as.Date.numeric

## 3.R包：vioplot简介

### 3.1 Description

# Package: vioplot

# Title: Violin Plot

# Version: 0.3.4

# Date: 2019-11-30

# [email protected]: c(person("Daniel", "Adler", email = "[email protected]", role = c("aut", "cph")),

# person("S. Thomas", "Kelly", email = "[email protected]", role = c("aut", "cre")),

# person("Tom M.", "Elliott", email = "[email protected]", role = c("ctb")))

# Description: A violin plot is a combination of a box plot and a kernel density plot. This package allows extensive customisation of violin plots.

# Depends: sm, zoo

# License: BSD_3_clause + file LICENSE

# URL: https://github.com/TomKellyGenetics/vioplot

# BugReports: https://github.com/TomKellyGenetics/vioplot/issues

# LazyData: true

# RoxygenNote: 7.0.1

# Suggests: ggplot2, RColorBrewer, knitr, rmarkdown, testthat

# Language: en-GB

# VignetteBuilder: knitr

# Encoding: UTF-8

# NeedsCompilation: no

# Packaged: 2019-11-29 14:54:09 UTC; tom

# Author: Daniel Adler [aut, cph],

# S. Thomas Kelly [aut, cre],

# Tom M. Elliott [ctb]

# Maintainer: S. Thomas Kelly <[email protected]>

# Repository: CRAN

# Date/Publication: 2019-11-29 18:00:02 UTC

# Built: R 3.6.1; ; 2019-12-03 13:51:08 UTC; windows

### 3.2 Main function

ls(package:vioplot) # vioplot包中只有一个函数，说明这个函数中的参数可能有很多

# [1] "vioplot"

# Warning message:

# In ls(package:vioplot) : ‘package:vioplot’被转换成字符串

## 4.测试：Customising Violin Plots

#@ vioplot包是专门用来绘制小提琴图的R包，我们今天来测试一下它的功能和用法

#@ Therefore violin plots are a powerful tool to assist researchers to visualise data, particularly in the quality checking and exploratory parts of an analysis. Violin plots have many benefits:

# 1.Greater flexibility for plotting variation than boxplots

# 2.More familiarity to boxplot users than density plots

# 3.Easier to directly compare data types than existing plots

#@ 测试数据：鸢尾花数据iris

data("iris") # 载入R内置数据

class(iris) # 查看iris的数据集类型

# [1] "data.frame"

dim(iris) # 获取iris的数据维度信息

# [1] 150 5

head(iris) # 获取iris的前6行

# Sepal.Length Sepal.Width Petal.Length Petal.Width Species

# 1 5.1 3.5 1.4 0.2 setosa

# 2 4.9 3.0 1.4 0.2 setosa

# 3 4.7 3.2 1.3 0.2 setosa

# 4 4.6 3.1 1.5 0.2 setosa

# 5 5.0 3.6 1.4 0.2 setosa

# 6 5.4 3.9 1.7 0.4 setosa

str(iris) # 查看iris的数据集结构

# 'data.frame': 150 obs. of 5 variables:

# $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...

# $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...

# $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...

# $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...

# $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

### 4.1 对比箱线图和小提琴图

#### 4.1.1 绘制箱线图

boxplot(iris$Sepal.Length[iris$Species == "setosa"], iris$Sepal.Length[iris$Species == "versicolor"], iris$Sepal.Length[iris$Species == "virginica"], names = c("setosa", "versicolor", "virginica"))