R/RStudio for the Total Beginner
Dr. Ning-ning ZHOU, Yunnan university
2023-09-19
安装 R and Rstudio
下载R软件国内镜像：https://mirrors.tuna.tsinghua.edu.cn/CRAN/
下载Rstudio：https://posit.co/download/rstudio-desktop/
注意：安装完R和Rstudio之后，我们使用的软件是Rstudio而不是R。RStudio是一个集成开发环境（IDE），旨在提供一个更友好和便捷的开发环境来使用R语言，它基于R来运行，所以R不可以卸载。
Rstudio 界面介绍
1 左上角用于写代码和保存代码。
2 左下角用于运行代码。
3
右上角的enviroment用于记录运行代码产生的数据集，history用于记录历史代码。
4
右下角file处可设置工作路径，plots显示运行代码产生的图片，packages用于安装R包，help可用于查看包和函数的使用方法。
设置 Global options
e.g.:
1 点开options后，选择packages,可对Primary
SRAN的路径进行修改，选择国内镜像，在下载packages时可能会比国外快点。
2 Appearance中可以更改Rstudio的背景和代码颜色，可根据喜好修改。
创建一个new project和一个new script
1 创建new project 并设置路径：
工具栏File下选择New
project,弹框选择New Directory –> New project –>
为project命名并选择保存的路径。
2 创建new script：
工具栏File下选择new script,
编写代码并保存。
设置工作目录
可在右下角的file/more中设置工作路径，也可以使用setwd()函数 setwd(“path”)
假如在你目前的工作目录下创建了week1、week2，下级目录可直接切换
setwd("week1") 思考：同级目录下是否可以直接切换？
setwd("week2") Error in setwd(“DAY2”) : cannot change working directory
安装和加载R包(packages)
1 安装R包
install.packages("vcd") # 包含各种类型的数据和函数，可用于《R语言之数据统计分析》的自学练习。
install.packages("plyr") # 数据处理和操作的包
install.packages("dplyr") # 数据处理和操作的包
install.packages("tidyverse") #The easiest way to get ggplot2 is to install the whole tidyverse
install.packages("ggplot2") # 高级绘图2 加载 R package
library(vcd)## 载入需要的程辑包：grid如果在加载R包时提示出现冲突，怎么办？
e.g.
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors解决方案
install.packages("conflicted")library(conflicted)
#查看所有冲突
conflicted::conflict_scout()## 2 conflicts## • `filter()`: dplyr and stats## • `lag()`: dplyr and stats#解除冲突
conflict_prefer("filter", "dplyr") #[conflicted] Will prefer dplyr::filter over any other package.## [conflicted] Will prefer dplyr::filter over any other package.conflict_prefer("lag", "dplyr") #[conflicted] Will prefer dplyr::lag over any other package.## [conflicted] Will prefer dplyr::lag over any other package.library(tidyverse)一个简单的例子感受R包和函数的使用
1 查看R包”vcd”的帮助
library(vcd)
help(package="vcd") 2 Arthritis为vcd包中的数据之一，显示该数据的前5行
head(Arthritis,10)## ID Treatment Sex Age Improved
## 1 57 Treated Male 27 Some
## 2 46 Treated Male 29 None
## 3 77 Treated Male 30 None
## 4 17 Treated Male 32 Marked
## 5 36 Treated Male 46 Marked
## 6 23 Treated Male 58 Marked
## 7 75 Treated Male 59 None
## 8 39 Treated Male 59 Marked
## 9 33 Treated Male 63 None
## 10 55 Treated Male 63 None3 在environment中创建Arthritis的数据框
data("Arthritis") 4 使用summary()函数对Arthritis数据进行基本统计分析
summary(Arthritis)## ID Treatment Sex Age Improved
## Min. : 1.00 Placebo:43 Female:59 Min. :23.00 None :42
## 1st Qu.:21.75 Treated:41 Male :25 1st Qu.:46.00 Some :14
## Median :42.50 Median :57.00 Marked:28
## Mean :42.50 Mean :53.36
## 3rd Qu.:63.25 3rd Qu.:63.00
## Max. :84.00 Max. :74.005 思考：
1）Arthritis
的数据排列方式是怎样的？行和列分别是什么？根据该数据格式替换成自己的数据。
2）执行data(“Arthritis”)
后，会在Environment中显示和存储Arthritis，其中int 和
factor是什么意思？
3）每次重新启动Rstudio时，是否需要重新加载需要的R包？
4）如果你的数据名称为“SHJLO”，请根据以上代码进行相应的替换
5）如何解读summary（）计算给出的结果？
R 赋值，通过简单的计算认识两种符号 <- , =
把5赋值给a
a=5
a+2## [1] 7把6赋值给b
b <- 6
b+3## [1] 9思考：==是什么呢？
5==5## [1] TRUE5==6## [1] FALSER中的快捷键
常用的查找、保存、撤回等与word中的类似。
查找：ctrl + f
保存：ctrl + s
撤回：ctrl + z
运行命令：ctrl + enter
常见错误
1 中英文字符号，代码中只能用英文字符。
2 括号少一半。
3
没有加载所需的R包。
不论哪种错误，学会看左下角运行运行结束时的ERROR的提示，理解错误在哪里。
两类错误warning 和 error的区别：warning
通常不是致命的，依然可以运行；error是致命的，终止运行。
创建数据集
1 向量
用于存储数值型、字符型或逻辑性数据的一维数据，执行组合功能的函数c()可用来创建向量。
age <- c(25, 30, 35, 40, 45) #数值型
age## [1] 25 30 35 40 45sex <- c("male","female","male","female") #字符型
sex## [1] "male" "female" "male" "female"logi <- c("TURE","FALSE","TURE","FALSE") #逻辑型
logi## [1] "TURE" "FALSE" "TURE" "FALSE"2 矩阵
二维数据组，可通过函数matrix()创建。
my_matrix <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), nrow = 3, ncol = 3)
my_matrix## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 93 数组
维度>2，可通过函数array()创建
my_array <- array(1:27, dim = c(3, 3, 3))
my_array## , , 1
##
## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9
##
## , , 2
##
## [,1] [,2] [,3]
## [1,] 10 13 16
## [2,] 11 14 17
## [3,] 12 15 18
##
## , , 3
##
## [,1] [,2] [,3]
## [1,] 19 22 25
## [2,] 20 23 26
## [3,] 21 24 274 数据框
处理数据时最常见的结构，不同的列可以包含不同类型的数据，较矩阵来说更为常用，可用data.frame()函数创建
student_data <- data.frame(
name = c("Alice", "Bob", "Charlie"),
age = c(20, 22, 21),
grade = c("A", "B", "C"),
stringsAsFactors = FALSE
)
student_data## name age grade
## 1 Alice 20 A
## 2 Bob 22 B
## 3 Charlie 21 C5 列表
一种灵活的数据结构，可以包含不同类型的对象，例如向量、矩阵、数据框等。可用list()函数创建
my_list <- list(
name = "John",
age = 25,
scores = c(85, 90, 92),
student_data = data.frame(
name = c("Alice", "Bob", "Charlie"),
age = c(20, 22, 21),
grade = c("A", "B", "C"),
stringsAsFactors = FALSE
)
)
my_list## $name
## [1] "John"
##
## $age
## [1] 25
##
## $scores
## [1] 85 90 92
##
## $student_data
## name age grade
## 1 Alice 20 A
## 2 Bob 22 B
## 3 Charlie 21 C读取数据
第一次读取数据可以用右上角environment下的import dataset –> From
text –> 选择要读取的文件，导入到environment中，这时
在右下角的命令符>后面会生成相应的代码，将它复制在这里保存，下次读取可以直接运行代码,例如:
读取csv：
studC <- read.csv("F:/9.courses/R/studentsInf.csv") #此处请替换自己的数据读取txt:
studT <- read.delim("F:/9.courses/R/studentsInf.txt")输出数据
输出csv:
write.table(studC, file = "stuinf.csv")输出txt:
write.table(studT, file = "stuinf.txt")我个人比较喜欢使用csv格式
玩转数据
1 数据框行列反转
#显示studC前5行
head(studC,5)## sample sex age height weight from
## 1 student1 male 23 173 65.0 shandong
## 2 student2 female 22 170 56.0 shanxi
## 3 student3 female 22 160 52.0 yunnan
## 4 student4 male 24 172 57.5 yunnan
## 5 student5 female 22 165 49.0 henan#行列反转
studC_R <- t(studC)
#显示studC_R前5行前4列
head(studC_R, 5)[, 1:4]## [,1] [,2] [,3] [,4]
## sample "student1" "student2" "student3" "student4"
## sex "male" "female" "female" "male"
## age "23" "22" "22" "24"
## height "173.0" "170.0" "160.0" "172.0"
## weight "65.0" "56.0" "52.0" "57.5"2 提取列
提取第6列
column_6 <- studC[, 6]提取前3列
column_h3 <- studC[, 1:3]提取第一列和第三列
column_13 <- studC[,c(1, 3)]3 提取行
提取前5行
Row_h5 <- studC[1:5, ]
print(Row_h5)## sample sex age height weight from
## 1 student1 male 23 173 65.0 shandong
## 2 student2 female 22 170 56.0 shanxi
## 3 student3 female 22 160 52.0 yunnan
## 4 student4 male 24 172 57.5 yunnan
## 5 student5 female 22 165 49.0 henan提取末尾5行
Row_t5 <- tail(studC, 5)
print(Row_t5)## sample sex age height weight from
## 41 student41 female 21 172 55 yunnan
## 42 student42 female 26 158 43 yunnan
## 43 student43 female 22 165 52 yunnan
## 44 student44 female 26 164 50 yunnan
## 45 student45 female 22 156 49 guizhou提取第1，4，6，8行
Row_r1468 <- studC[c(1, 4, 6, 8), ]
print(Row_r1468)## sample sex age height weight from
## 1 student1 male 23 173 65.0 shandong
## 4 student4 male 24 172 57.5 yunnan
## 6 student6 male 25 172 62.0 henan
## 8 student8 female 21 163 65.0 liaoning4 删除列
DelCol1 = Row_h5[, -3]
DelCol2 = Row_h5[, -(2:4)]
print(DelCol1)## sample sex height weight from
## 1 student1 male 173 65.0 shandong
## 2 student2 female 170 56.0 shanxi
## 3 student3 female 160 52.0 yunnan
## 4 student4 male 172 57.5 yunnan
## 5 student5 female 165 49.0 henanprint(DelCol2)## sample weight from
## 1 student1 65.0 shandong
## 2 student2 56.0 shanxi
## 3 student3 52.0 yunnan
## 4 student4 57.5 yunnan
## 5 student5 49.0 henan5 删除行
DelRow1 = Row_h5[-3,]
DelRow2 = Row_h5[-(2:4), ]
DelRow3 = Row_h5[-c(1,5), ]
print(DelRow1)## sample sex age height weight from
## 1 student1 male 23 173 65.0 shandong
## 2 student2 female 22 170 56.0 shanxi
## 4 student4 male 24 172 57.5 yunnan
## 5 student5 female 22 165 49.0 henanprint(DelRow2)## sample sex age height weight from
## 1 student1 male 23 173 65 shandong
## 5 student5 female 22 165 49 henanprint(DelRow3)## sample sex age height weight from
## 2 student2 female 22 170 56.0 shanxi
## 3 student3 female 22 160 52.0 yunnan
## 4 student4 male 24 172 57.5 yunnan总结：对于行列的处理，提取或删除行就在逗号前操作，提取或删除列就在逗号后处理。
6 合并数据框
1）rbind()函数
#合并行
merged1 <- rbind(DelRow2, DelRow3)
print(merged1)## sample sex age height weight from
## 1 student1 male 23 173 65.0 shandong
## 5 student5 female 22 165 49.0 henan
## 2 student2 female 22 170 56.0 shanxi
## 3 student3 female 22 160 52.0 yunnan
## 4 student4 male 24 172 57.5 yunnanmerged2 <- rbind(DelRow1, DelRow2)
print(merged2)## sample sex age height weight from
## 1 student1 male 23 173 65.0 shandong
## 2 student2 female 22 170 56.0 shanxi
## 4 student4 male 24 172 57.5 yunnan
## 5 student5 female 22 165 49.0 henan
## 11 student1 male 23 173 65.0 shandong
## 51 student5 female 22 165 49.0 henan#合并列
merged3<- cbind(DelCol1, DelCol2)
print(merged3)## sample sex height weight from sample weight from
## 1 student1 male 173 65.0 shandong student1 65.0 shandong
## 2 student2 female 170 56.0 shanxi student2 56.0 shanxi
## 3 student3 female 160 52.0 yunnan student3 52.0 yunnan
## 4 student4 male 172 57.5 yunnan student4 57.5 yunnan
## 5 student5 female 165 49.0 henan student5 49.0 henan思考：为什么在merged2的第一列中出现11和51的序列号？
2）inner_join()函数，合并列
library(tidyverse)
inner_join(DelCol1, DelCol2, by = 'sample')## sample sex height weight.x from.x weight.y from.y
## 1 student1 male 173 65.0 shandong 65.0 shandong
## 2 student2 female 170 56.0 shanxi 56.0 shanxi
## 3 student3 female 160 52.0 yunnan 52.0 yunnan
## 4 student4 male 172 57.5 yunnan 57.5 yunnan
## 5 student5 female 165 49.0 henan 49.0 henan思考：对比inner_join()和cbind()函数的输出结果，有什么区别。
7 去重复
#去重复前
column_6## [1] "shandong" "shanxi" "yunnan" "yunnan" "henan"
## [6] "henan" "yunnan" "liaoning" "heilongjiang" "hubei"
## [11] "yunnan" "sichuan" "shanxi" "guizhou" "yunnan"
## [16] "chongqing" "yunnan" "sichuan" "hubei" "yunnan"
## [21] "guizhou" "sichuan" "hainan" "yunnan" "yunnan"
## [26] "neimenggu" "jiangsu" "anhui" "yunnan" "guangxi"
## [31] "yunnan" "yunnan" "yunnan" "hubei" "shandong"
## [36] "yunnan" "guizhou" "feijian" "guizhou" "hubei"
## [41] "yunnan" "yunnan" "yunnan" "yunnan" "guizhou"#去重复后
DelDup <- unique(column_6)
print(DelDup)## [1] "shandong" "shanxi" "yunnan" "henan" "liaoning"
## [6] "heilongjiang" "hubei" "sichuan" "guizhou" "chongqing"
## [11] "hainan" "neimenggu" "jiangsu" "anhui" "guangxi"
## [16] "feijian"将字符型chr转换factor
factor在R中非常重要，它决定了数据的分析方式以及如何进行视觉呈现，例如：
输入数据
studC <- read.csv("F:/9.courses/R/studentsInf.csv")1 转换前的summary
summary(studC)## sample sex age height
## Length:45 Length:45 Min. :21.00 Min. :147.0
## Class :character Class :character 1st Qu.:22.00 1st Qu.:158.0
## Mode :character Mode :character Median :23.00 Median :163.0
## Mean :23.04 Mean :163.7
## 3rd Qu.:24.00 3rd Qu.:170.0
## Max. :27.00 Max. :180.0
## weight from
## Min. :42.00 Length:45
## 1st Qu.:50.00 Class :character
## Median :56.00 Mode :character
## Mean :58.08
## 3rd Qu.:66.00
## Max. :88.002 用as.foctor()函数转换
studC$sex <- as.factor(studC$sex)
studC$from <- as.factor(studC$from)3 转换后的summary
summary(studC)## sample sex age height weight
## Length:45 female:33 Min. :21.00 Min. :147.0 Min. :42.00
## Class :character male :12 1st Qu.:22.00 1st Qu.:158.0 1st Qu.:50.00
## Mode :character Median :23.00 Median :163.0 Median :56.00
## Mean :23.04 Mean :163.7 Mean :58.08
## 3rd Qu.:24.00 3rd Qu.:170.0 3rd Qu.:66.00
## Max. :27.00 Max. :180.0 Max. :88.00
##
## from
## yunnan :18
## guizhou : 5
## hubei : 4
## sichuan : 3
## henan : 2
## shandong: 2
## (Other) :114 思考：转换前和转换后summary给出结果的差异在哪？
图形初阶
1 饼图 pie函数画
sexRatio <- c(12, 33)
# 创建标签
labels <- c("male","female")# 每个部分的标签
# 绘制饼图
pie(sexRatio, labels = labels, main = "2023届专硕班男女比例") 
2 柱状分布图 hist()函数
hist(studC$height,nclass=10, ylab="Frequency", xlab = 'height',col ='blue' )
hist(studC$weight,nclass=10, ylab="Frequency", xlab = 'weight',col ='green' )
3 把两个图合并到一个图上，便于比较
par(mfrow = c(1,2))
hist(studC$height,nclass=10, ylab="Frequency", xlab = 'height',col ='blue' )
hist(studC$weight,nclass=10, ylab="Frequency", xlab = 'weight',col ='green' )
##dev.off()函数或者重新设置图形参数为默认值
dev.off()## null device
## 1思考：图形与summary数据摘要进行对比理解。
summary(studC)## sample sex age height weight
## Length:45 female:33 Min. :21.00 Min. :147.0 Min. :42.00
## Class :character male :12 1st Qu.:22.00 1st Qu.:158.0 1st Qu.:50.00
## Mode :character Median :23.00 Median :163.0 Median :56.00
## Mean :23.04 Mean :163.7 Mean :58.08
## 3rd Qu.:24.00 3rd Qu.:170.0 3rd Qu.:66.00
## Max. :27.00 Max. :180.0 Max. :88.00
##
## from
## yunnan :18
## guizhou : 5
## hubei : 4
## sichuan : 3
## henan : 2
## shandong: 2
## (Other) :11图形进阶
4 相关性分析
1）使用全部数据比较体重和身高的相关性
library(ggpubr)
p1 <- ggscatter(studC, x = "height", y = "weight",
add = "reg.line", #添加线性回归线
conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "height", ylab = "Weight", col ='pink')
print(p1)
2）仅使用female的值观察体重和身高的关系
#提取female data
femaleinf <- subset(studC, sex == "female")
head(femaleinf,5)## sample sex age height weight from
## 2 student2 female 22 170 56 shanxi
## 3 student3 female 22 160 52 yunnan
## 5 student5 female 22 165 49 henan
## 7 student7 female 21 160 50 yunnan
## 8 student8 female 21 163 65 liaoning#相关性
p2 <- ggscatter(femaleinf, x = "height", y = "weight",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "height", ylab = "Weight", col ='pink')
print(p2)
思考：
1）身高和体重是否具有相关性？
2）两部分结果中身高和体重的相关性哪个更高？
提示：
R值衡量了两个变量之间的线性相关程度，取值范围为-1到1。当R值接近1时，表示两个变量之间存在强正相关关系；
当R值接近-1时，表示两个变量之间存在强负相关关系；
当R值接近0时，表示两个变量之间存在较弱或无相关关系。
可以通过观察散点图上的数据点分布和回归线的趋势来初步判断相关性的强度和方向。
5 violin plot 小提琴图
library(ggplot2)
# 创建示例数据
group <- rep(c("Group 1", "Group 2"), each = 100)
value <- c(rnorm(100, mean = 0, sd = 1), rnorm(100, mean = 2, sd = 1))
data <- data.frame(group, value)
# 创建小提琴图
p3 <- ggplot(data, aes(x = group, y = value, fill = group)) +
geom_violin(trim = FALSE) +
geom_boxplot(width = 0.1, fill = "white", color = "black") +
theme_minimal()
#p
print(p3)
在上述代码中，首先加载了 ggplot2 包。然后，创建了一个示例数据框 data，其中包含两个组别 “Group 1” 和 “Group 2”，以及对应的数值数据。接下来，使用 ggplot() 函数创建一个绘图对象，并通过 aes() 函数指定 x 轴为组别，y 轴为数值，以及填充颜色。然后，使用 geom_violin() 函数创建小提琴图层，并设置 trim = FALSE 参数来展示完整的小提琴形状。同时，还使用 geom_boxplot() 函数添加盒图层，并设置 width 参数调整盒图的宽度，以及 fill 和 color 参数设置盒图的填充和边框颜色。最后，使用 theme_minimal() 函数来设置绘图的主题风格。
6 Sankey diagram 桑基图
from: https://r-graph-gallery.com/321-introduction-to-interactive-sankey-diagram-2.html
install.packages("networkD3")# Library
library(networkD3)
library(dplyr)
# A connection data frame is a list of flows with intensity for each flow
links <- data.frame(
source=c("group_A","group_A", "group_B", "group_C", "group_C", "group_E"),
target=c("group_C","group_D", "group_E", "group_F", "group_G", "group_H"),
value=c(2,3, 2, 3, 1, 3)
)
# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodes <- data.frame(
name=c(as.character(links$source),
as.character(links$target)) %>% unique()
)
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
links$IDsource <- match(links$source, nodes$name)-1
links$IDtarget <- match(links$target, nodes$name)-1
# Make the Network
p4 <- sankeyNetwork(Links = links, Nodes = nodes,
Source = "IDsource", Target = "IDtarget",
Value = "value", NodeID = "name",
sinksRight=FALSE)
p4# save the widget
# library(htmlwidgets)
# saveWidget(p, file=paste0( getwd(), "/HtmlWidget/sankeyBasic1.html"))7. Circular dendrogram 圆形树状图
from: https://r-graph-gallery.com/339-circular-dendrogram-with-ggraph.html
install.packages("ggraph")
install.packages("igraph")
install.packages("RColorBrewer")# Libraries
library(ggraph)
library(igraph)
library(tidyverse)
library(RColorBrewer)
# create a data frame giving the hierarchical structure of your individuals
d1=data.frame(from="origin", to=paste("group", seq(1,10), sep=""))
d2=data.frame(from=rep(d1$to, each=10), to=paste("subgroup", seq(1,100), sep="_"))
edges=rbind(d1, d2)
# create a vertices data.frame. One line per object of our hierarchy
vertices = data.frame(
name = unique(c(as.character(edges$from), as.character(edges$to))) ,
value = runif(111)
)
# Let's add a column with the group of each name. It will be useful later to color points
vertices$group = edges$from[ match( vertices$name, edges$to ) ]
#Let's add information concerning the label we are going to add: angle, horizontal adjustement and potential flip
#calculate the ANGLE of the labels
vertices$id=NA
myleaves=which(is.na( match(vertices$name, edges$from) ))
nleaves=length(myleaves)
vertices$id[ myleaves ] = seq(1:nleaves)
vertices$angle= 90 - 360 * vertices$id / nleaves
# calculate the alignment of labels: right or left
# If I am on the left part of the plot, my labels have currently an angle < -90
vertices$hjust<-ifelse( vertices$angle < -90, 1, 0)
# flip angle BY to make them readable
vertices$angle<-ifelse(vertices$angle < -90, vertices$angle+180, vertices$angle)
# Create a graph object
mygraph <- graph_from_data_frame( edges, vertices=vertices )
# Make the plot
p5 <- ggraph(mygraph, layout = 'dendrogram', circular = TRUE) +
geom_edge_diagonal(colour="grey") +
scale_edge_colour_distiller(palette = "RdPu") +
geom_node_text(aes(x = x*1.15, y=y*1.15, filter = leaf, label=name, angle = angle, hjust=hjust, colour=group), size=2, alpha=1) +
geom_node_point(aes(filter = leaf, x = x*1.07, y=y*1.07, colour=group, size=value, alpha=0.2)) +
scale_colour_manual(values= rep( brewer.pal(9,"Paired") , 30)) +
scale_size_continuous( range = c(0.1,10) ) +
theme_void() +
theme(
legend.position="none",
plot.margin=unit(c(0,0,0,0),"cm"),
) +
expand_limits(x = c(-1.3, 1.3), y = c(-1.3, 1.3))
p5## Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
思考：如何用自己的数据去套用现成的R模板代码？
举例说明如何套用上面的Circular dendrogram 圆形树状图代码，然后请同学进行举一反三。
Step1 理解该套代码中哪些字符名称表示数据？
最简单的方法：运行整套代码后查看右上角environment中的Data框（前提是你要把data框先清空），框中的一级标题就是该套代码中的所有数据名称。
Step2 使用print() 或 view()了解数据结构，思考哪几个数据对应你的试验元数据？
print(d1)## from to
## 1 origin group1
## 2 origin group2
## 3 origin group3
## 4 origin group4
## 5 origin group5
## 6 origin group6
## 7 origin group7
## 8 origin group8
## 9 origin group9
## 10 origin group10print(d2)## from to
## 1 group1 subgroup_1
## 2 group1 subgroup_2
## 3 group1 subgroup_3
## 4 group1 subgroup_4
## 5 group1 subgroup_5
## 6 group1 subgroup_6
## 7 group1 subgroup_7
## 8 group1 subgroup_8
## 9 group1 subgroup_9
## 10 group1 subgroup_10
## 11 group2 subgroup_11
## 12 group2 subgroup_12
## 13 group2 subgroup_13
## 14 group2 subgroup_14
## 15 group2 subgroup_15
## 16 group2 subgroup_16
## 17 group2 subgroup_17
## 18 group2 subgroup_18
## 19 group2 subgroup_19
## 20 group2 subgroup_20
## 21 group3 subgroup_21
## 22 group3 subgroup_22
## 23 group3 subgroup_23
## 24 group3 subgroup_24
## 25 group3 subgroup_25
## 26 group3 subgroup_26
## 27 group3 subgroup_27
## 28 group3 subgroup_28
## 29 group3 subgroup_29
## 30 group3 subgroup_30
## 31 group4 subgroup_31
## 32 group4 subgroup_32
## 33 group4 subgroup_33
## 34 group4 subgroup_34
## 35 group4 subgroup_35
## 36 group4 subgroup_36
## 37 group4 subgroup_37
## 38 group4 subgroup_38
## 39 group4 subgroup_39
## 40 group4 subgroup_40
## 41 group5 subgroup_41
## 42 group5 subgroup_42
## 43 group5 subgroup_43
## 44 group5 subgroup_44
## 45 group5 subgroup_45
## 46 group5 subgroup_46
## 47 group5 subgroup_47
## 48 group5 subgroup_48
## 49 group5 subgroup_49
## 50 group5 subgroup_50
## 51 group6 subgroup_51
## 52 group6 subgroup_52
## 53 group6 subgroup_53
## 54 group6 subgroup_54
## 55 group6 subgroup_55
## 56 group6 subgroup_56
## 57 group6 subgroup_57
## 58 group6 subgroup_58
## 59 group6 subgroup_59
## 60 group6 subgroup_60
## 61 group7 subgroup_61
## 62 group7 subgroup_62
## 63 group7 subgroup_63
## 64 group7 subgroup_64
## 65 group7 subgroup_65
## 66 group7 subgroup_66
## 67 group7 subgroup_67
## 68 group7 subgroup_68
## 69 group7 subgroup_69
## 70 group7 subgroup_70
## 71 group8 subgroup_71
## 72 group8 subgroup_72
## 73 group8 subgroup_73
## 74 group8 subgroup_74
## 75 group8 subgroup_75
## 76 group8 subgroup_76
## 77 group8 subgroup_77
## 78 group8 subgroup_78
## 79 group8 subgroup_79
## 80 group8 subgroup_80
## 81 group9 subgroup_81
## 82 group9 subgroup_82
## 83 group9 subgroup_83
## 84 group9 subgroup_84
## 85 group9 subgroup_85
## 86 group9 subgroup_86
## 87 group9 subgroup_87
## 88 group9 subgroup_88
## 89 group9 subgroup_89
## 90 group9 subgroup_90
## 91 group10 subgroup_91
## 92 group10 subgroup_92
## 93 group10 subgroup_93
## 94 group10 subgroup_94
## 95 group10 subgroup_95
## 96 group10 subgroup_96
## 97 group10 subgroup_97
## 98 group10 subgroup_98
## 99 group10 subgroup_99
## 100 group10 subgroup_100print(edges)## from to
## 1 origin group1
## 2 origin group2
## 3 origin group3
## 4 origin group4
## 5 origin group5
## 6 origin group6
## 7 origin group7
## 8 origin group8
## 9 origin group9
## 10 origin group10
## 11 group1 subgroup_1
## 12 group1 subgroup_2
## 13 group1 subgroup_3
## 14 group1 subgroup_4
## 15 group1 subgroup_5
## 16 group1 subgroup_6
## 17 group1 subgroup_7
## 18 group1 subgroup_8
## 19 group1 subgroup_9
## 20 group1 subgroup_10
## 21 group2 subgroup_11
## 22 group2 subgroup_12
## 23 group2 subgroup_13
## 24 group2 subgroup_14
## 25 group2 subgroup_15
## 26 group2 subgroup_16
## 27 group2 subgroup_17
## 28 group2 subgroup_18
## 29 group2 subgroup_19
## 30 group2 subgroup_20
## 31 group3 subgroup_21
## 32 group3 subgroup_22
## 33 group3 subgroup_23
## 34 group3 subgroup_24
## 35 group3 subgroup_25
## 36 group3 subgroup_26
## 37 group3 subgroup_27
## 38 group3 subgroup_28
## 39 group3 subgroup_29
## 40 group3 subgroup_30
## 41 group4 subgroup_31
## 42 group4 subgroup_32
## 43 group4 subgroup_33
## 44 group4 subgroup_34
## 45 group4 subgroup_35
## 46 group4 subgroup_36
## 47 group4 subgroup_37
## 48 group4 subgroup_38
## 49 group4 subgroup_39
## 50 group4 subgroup_40
## 51 group5 subgroup_41
## 52 group5 subgroup_42
## 53 group5 subgroup_43
## 54 group5 subgroup_44
## 55 group5 subgroup_45
## 56 group5 subgroup_46
## 57 group5 subgroup_47
## 58 group5 subgroup_48
## 59 group5 subgroup_49
## 60 group5 subgroup_50
## 61 group6 subgroup_51
## 62 group6 subgroup_52
## 63 group6 subgroup_53
## 64 group6 subgroup_54
## 65 group6 subgroup_55
## 66 group6 subgroup_56
## 67 group6 subgroup_57
## 68 group6 subgroup_58
## 69 group6 subgroup_59
## 70 group6 subgroup_60
## 71 group7 subgroup_61
## 72 group7 subgroup_62
## 73 group7 subgroup_63
## 74 group7 subgroup_64
## 75 group7 subgroup_65
## 76 group7 subgroup_66
## 77 group7 subgroup_67
## 78 group7 subgroup_68
## 79 group7 subgroup_69
## 80 group7 subgroup_70
## 81 group8 subgroup_71
## 82 group8 subgroup_72
## 83 group8 subgroup_73
## 84 group8 subgroup_74
## 85 group8 subgroup_75
## 86 group8 subgroup_76
## 87 group8 subgroup_77
## 88 group8 subgroup_78
## 89 group8 subgroup_79
## 90 group8 subgroup_80
## 91 group9 subgroup_81
## 92 group9 subgroup_82
## 93 group9 subgroup_83
## 94 group9 subgroup_84
## 95 group9 subgroup_85
## 96 group9 subgroup_86
## 97 group9 subgroup_87
## 98 group9 subgroup_88
## 99 group9 subgroup_89
## 100 group9 subgroup_90
## 101 group10 subgroup_91
## 102 group10 subgroup_92
## 103 group10 subgroup_93
## 104 group10 subgroup_94
## 105 group10 subgroup_95
## 106 group10 subgroup_96
## 107 group10 subgroup_97
## 108 group10 subgroup_98
## 109 group10 subgroup_99
## 110 group10 subgroup_100print(mygraph)## IGRAPH 92aeaaa DN-- 111 110 --
## + attr: name (v/c), value (v/n), group (v/c), id (v/n), angle (v/n),
## | hjust (v/n)
## + edges from 92aeaaa (vertex names):
## [1] origin->group1 origin->group2 origin->group3
## [4] origin->group4 origin->group5 origin->group6
## [7] origin->group7 origin->group8 origin->group9
## [10] origin->group10 group1->subgroup_1 group1->subgroup_2
## [13] group1->subgroup_3 group1->subgroup_4 group1->subgroup_5
## [16] group1->subgroup_6 group1->subgroup_7 group1->subgroup_8
## [19] group1->subgroup_9 group1->subgroup_10 group2->subgroup_11
## + ... omitted several edgesprint(vertices)## name value group id angle hjust
## 1 origin 0.44800742 <NA> NA NA NA
## 2 group1 0.26655036 origin NA NA NA
## 3 group2 0.31203776 origin NA NA NA
## 4 group3 0.79181592 origin NA NA NA
## 5 group4 0.05585058 origin NA NA NA
## 6 group5 0.32995128 origin NA NA NA
## 7 group6 0.02850016 origin NA NA NA
## 8 group7 0.85771688 origin NA NA NA
## 9 group8 0.81555825 origin NA NA NA
## 10 group9 0.45430256 origin NA NA NA
## 11 group10 0.20030234 origin NA NA NA
## 12 subgroup_1 0.98959081 group1 1 86.4 0
## 13 subgroup_2 0.74503827 group1 2 82.8 0
## 14 subgroup_3 0.60365412 group1 3 79.2 0
## 15 subgroup_4 0.77637777 group1 4 75.6 0
## 16 subgroup_5 0.71010442 group1 5 72.0 0
## 17 subgroup_6 0.46416162 group1 6 68.4 0
## 18 subgroup_7 0.57125994 group1 7 64.8 0
## 19 subgroup_8 0.94477534 group1 8 61.2 0
## 20 subgroup_9 0.44172403 group1 9 57.6 0
## 21 subgroup_10 0.69499809 group1 10 54.0 0
## 22 subgroup_11 0.12803949 group2 11 50.4 0
## 23 subgroup_12 0.88753814 group2 12 46.8 0
## 24 subgroup_13 0.08320777 group2 13 43.2 0
## 25 subgroup_14 0.30573049 group2 14 39.6 0
## 26 subgroup_15 0.51419146 group2 15 36.0 0
## 27 subgroup_16 0.72021910 group2 16 32.4 0
## 28 subgroup_17 0.42017897 group2 17 28.8 0
## 29 subgroup_18 0.09006888 group2 18 25.2 0
## 30 subgroup_19 0.73441077 group2 19 21.6 0
## 31 subgroup_20 0.77268533 group2 20 18.0 0
## 32 subgroup_21 0.49937881 group3 21 14.4 0
## 33 subgroup_22 0.44270437 group3 22 10.8 0
## 34 subgroup_23 0.79249107 group3 23 7.2 0
## 35 subgroup_24 0.45202810 group3 24 3.6 0
## 36 subgroup_25 0.54255351 group3 25 0.0 0
## 37 subgroup_26 0.08434688 group3 26 -3.6 0
## 38 subgroup_27 0.88726748 group3 27 -7.2 0
## 39 subgroup_28 0.18711969 group3 28 -10.8 0
## 40 subgroup_29 0.89674607 group3 29 -14.4 0
## 41 subgroup_30 0.46212743 group3 30 -18.0 0
## 42 subgroup_31 0.22491721 group4 31 -21.6 0
## 43 subgroup_32 0.47266067 group4 32 -25.2 0
## 44 subgroup_33 0.81502083 group4 33 -28.8 0
## 45 subgroup_34 0.09302276 group4 34 -32.4 0
## 46 subgroup_35 0.25313071 group4 35 -36.0 0
## 47 subgroup_36 0.04565808 group4 36 -39.6 0
## 48 subgroup_37 0.75828602 group4 37 -43.2 0
## 49 subgroup_38 0.25401609 group4 38 -46.8 0
## 50 subgroup_39 0.25659480 group4 39 -50.4 0
## 51 subgroup_40 0.58664747 group4 40 -54.0 0
## 52 subgroup_41 0.59526738 group5 41 -57.6 0
## 53 subgroup_42 0.99029509 group5 42 -61.2 0
## 54 subgroup_43 0.16839686 group5 43 -64.8 0
## 55 subgroup_44 0.75773809 group5 44 -68.4 0
## 56 subgroup_45 0.25796999 group5 45 -72.0 0
## 57 subgroup_46 0.17682004 group5 46 -75.6 0
## 58 subgroup_47 0.24308785 group5 47 -79.2 0
## 59 subgroup_48 0.82644313 group5 48 -82.8 0
## 60 subgroup_49 0.13209491 group5 49 -86.4 0
## 61 subgroup_50 0.57465728 group5 50 -90.0 0
## 62 subgroup_51 0.87544752 group6 51 86.4 1
## 63 subgroup_52 0.91518356 group6 52 82.8 1
## 64 subgroup_53 0.52080183 group6 53 79.2 1
## 65 subgroup_54 0.66542379 group6 54 75.6 1
## 66 subgroup_55 0.58711622 group6 55 72.0 1
## 67 subgroup_56 0.19247736 group6 56 68.4 1
## 68 subgroup_57 0.46903629 group6 57 64.8 1
## 69 subgroup_58 0.90573718 group6 58 61.2 1
## 70 subgroup_59 0.82789350 group6 59 57.6 1
## 71 subgroup_60 0.65591924 group6 60 54.0 1
## 72 subgroup_61 0.91193616 group7 61 50.4 1
## 73 subgroup_62 0.53272488 group7 62 46.8 1
## 74 subgroup_63 0.95420129 group7 63 43.2 1
## 75 subgroup_64 0.44200329 group7 64 39.6 1
## 76 subgroup_65 0.76157813 group7 65 36.0 1
## 77 subgroup_66 0.19868759 group7 66 32.4 1
## 78 subgroup_67 0.28585503 group7 67 28.8 1
## 79 subgroup_68 0.10546475 group7 68 25.2 1
## 80 subgroup_69 0.21927622 group7 69 21.6 1
## 81 subgroup_70 0.38321552 group7 70 18.0 1
## 82 subgroup_71 0.81657712 group8 71 14.4 1
## 83 subgroup_72 0.82805822 group8 72 10.8 1
## 84 subgroup_73 0.80359772 group8 73 7.2 1
## 85 subgroup_74 0.02102864 group8 74 3.6 1
## 86 subgroup_75 0.34098824 group8 75 0.0 1
## 87 subgroup_76 0.11452250 group8 76 -3.6 1
## 88 subgroup_77 0.37177285 group8 77 -7.2 1
## 89 subgroup_78 0.27052051 group8 78 -10.8 1
## 90 subgroup_79 0.80620871 group8 79 -14.4 1
## 91 subgroup_80 0.66400577 group8 80 -18.0 1
## 92 subgroup_81 0.27976840 group9 81 -21.6 1
## 93 subgroup_82 0.57438941 group9 82 -25.2 1
## 94 subgroup_83 0.22449205 group9 83 -28.8 1
## 95 subgroup_84 0.78080396 group9 84 -32.4 1
## 96 subgroup_85 0.08164244 group9 85 -36.0 1
## 97 subgroup_86 0.59261693 group9 86 -39.6 1
## 98 subgroup_87 0.97517322 group9 87 -43.2 1
## 99 subgroup_88 0.20012700 group9 88 -46.8 1
## 100 subgroup_89 0.86214768 group9 89 -50.4 1
## 101 subgroup_90 0.22909202 group9 90 -54.0 1
## 102 subgroup_91 0.01786694 group10 91 -57.6 1
## 103 subgroup_92 0.48977770 group10 92 -61.2 1
## 104 subgroup_93 0.43194225 group10 93 -64.8 1
## 105 subgroup_94 0.47971528 group10 94 -68.4 1
## 106 subgroup_95 0.59724094 group10 95 -72.0 1
## 107 subgroup_96 0.44759819 group10 96 -75.6 1
## 108 subgroup_97 0.02367235 group10 97 -79.2 1
## 109 subgroup_98 0.29594446 group10 98 -82.8 1
## 110 subgroup_99 0.13900171 group10 99 -86.4 1
## 111 subgroup_100 0.16073637 group10 100 -90.0 1Step3 根据上面的数据格式调整你试验的元数据格式，输入数据。
Step4
在整套代码中，把你的元数据名称进行相应的替换。分析过程中产生的名称可换可不换，随各人喜欢。
总结
学习使用R语言进行数据分析和绘图其实很简单，因为已经有很多供你套用的代码，你不用全部读懂代码也可以无障碍的画出类似的图，只要需要明白如何替换数据就行。
如果你还想对图的大小、颜色、标题、排版等进行更改，可以用R中的help工具，去了解各参数的含义，或者更简单的去问ChatGPT。
模板代码推荐两个网站：
https://r-graph-gallery.com/
https://www.datanovia.com/en/
这两个网站提供的代码，几乎可以满足你所有的绘图需求。