Part 5:R语言作图

1.常用可视化R包

作图
- base
- ggplot2
- ggpubr
拼图
- par里mfrow
- grid.arrage
- cowplot
- customLayout
- patchwork
导出
- pdf（）等三段论
- ggsave
- eoffice
  - topptx

2.基础包-绘图函数

高级绘图函数

plot()      #绘制散点图等多种图形，根据数据的类型，调用相应的函数绘图
hist()      #频率直方图
boxplot()       #箱线图
stripchart()        #点图
barplot()       #柱状图
dotplot()       #点图
piechart()      #饼图
matplot()       #数字图形

低级绘图函数

lines()     #添加线
curve()     #添加曲线
abline()        #添加给定斜率的线
points()        #添加点
segments()      #折线
arrows()        #箭头
axis()          #坐标轴
box()       #外框
title()     #标题
text()      #文字
mtext()     #图边文字

绘图参数

#参数用在函数内部，在没有设定值时使用默认值。
font=字体
lty=线类型
lwd=线宽度
pch=点的类型
xlab=横坐标
ylab=纵坐标
xlim = 横坐标范围
ylim=纵坐标范围
也可以对整个要绘制图形的各种参数进行设定
参见par()

3.gglot2语法

1.入门级绘图模板

ggplot(data = <DATA>) +
<GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

ggplot(data=iris)+
    geom_point(mapping=aes(x=Sepal.Length,
                            y=Petal.Length))

2.映射-颜色、大小、透明度、形状

属性	参数
x轴	x
y轴	y
颜色	color
大小	size
形状	shape
透明度	alpha
填充颜色	fill

图中加粗为可手动设置的参数
手动设置需要设为有意义的值
- 颜色：字符串，blue，red等
- 大小：单位mm
- 形状：数字编号表示
  - 空心形状 0-14 color边框
  - 实心形状 15-20 color填充
  - 填充形状 21-24 color边框，和fill填充

image-20201124110810962.png

ggplot(data = mpg) +
    geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

映射vs手动设置

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy,color = class))
ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy),color = "blue")

3.分面

ggplot(data = iris) +
    geom_point(mapping = aes(x = Sepal.Length, y = Petal.Length)) +
    facet_wrap(~ Species)

image-20201124111102936.png

双分面

test$group = sample(letters[1:5],150,replace = T)
ggplot(data = test) +
  geom_point(mapping = aes(x = Sepal.Length, y = Petal.Length)) +
  facet_grid(group ~ Species)

image-20201124111222469.png

4.几何对象

理解分组

ggplot(data = test) +
  geom_smooth(aes(x = Sepal.Length,
                  y = Petal.Length,group = Species))

image-20201124111556014.png

ggplot(data = test) +
  geom_smooth(aes(x = Sepal.Length,
                  y = Petal.Length,color = Species))

image-20201124111713570.png

几何对象可以叠加

#局部映射
ggplot(data = test) +
  geom_smooth(mapping = aes(x = Sepal.Length, y = Petal.Length))+
  geom_point (mapping = aes(x = Sepal.Length, y = Petal.Length))

#全局映射
ggplot(data = test, mapping = aes(x = Sepal.Length, y = Petal.Length)) +
  geom_smooth()+
  geom_point ()

image-20201124111841885.png

映射
- 局部映射
  - 仅对当前图层有效
- 全局映射
  - 对所有图层有效
- 当局部映射和全局映射冲突时，以局部为准

5.统计变换

ggplot(data = diamonds) +
    geom_bar(mapping = aes(x = cut))
    
ggplot(data = diamonds) +
    stat_count(mapping = aes(x = cut))

使用场景1：使用表中数据直接做图，而不是统计

ggplot(data = fre) +
  geom_bar(mapping = aes(x = Var1, y = Freq), stat = "identity")

image-20201124114710331.png

使用场景2：不统计count，统计prop(比例)

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))

6.位置调整

位置关系
- geom_point()

image-20201124115022619.png

geom_jitter()

image-20201124115112693.png

堆叠直方图

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x =cut,fill=clarity))

image-20201124115403891.png

并列直方图

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill =clarity), position = "dodge")

image-20201124115451819.png

7.坐标系

翻转coord_flip()

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
  geom_boxplot() +
  coord_flip()

image-20201124115622573.png

极坐标系coord_polar()

bar <- ggplot(data = diamonds) + 
  geom_bar(
    mapping = aes(x = cut, fill = cut), 
    show.legend = FALSE,
    width = 1
  ) + 
  theme(aspect.ratio = 1) +
  labs(x = NULL, y = NULL)
bar + coord_flip()
bar + coord_polar()
bar + theme_classic()
bar + theme_dark()

8.完整绘图模板

ggplot(data = <DATA>) +
    <GEOM_FUNCTION>(
        mapping = aes(<MAPPINGS>),
        stat = <STAT>,
        position = <POSITION>
) +
    <COORDINATE_FUNCTION> +
    <FACET_FUNCTION>

4.ggpubr

ggscatter(iris,x="Sepal.Length",y="Petal.Length",color="Species")

ggboxplot(iris, x = "Species", 
                y = "Sepal.Length",
                color = "Species",
                shape = "Species",
                add = "jitter")                 #ggpubr去掉了映射和图层的概念
p

my_comparisons <- list( c("setosa", "versicolor"), c("setosa", "virginica"),
c("versicolor", "virginica") )
p + stat_compare_means(comparisons = my_comparisons)+
    stat_compare_means(label.y = 9)

5.图片保存

ggplot2系列：

ggsave("iris_box_ggpubr.png")
ggsave(p,filename = "iris_box_ggpubr2.png")

通用：三段论

保存的格式及文件名     pdf(“test.pdf”)
作图代码              ..........
                      .........
画完了，关闭画板      dev.off()

神奇eoffice

library(eoffice)
topptx(p,"iris_box_ggpubr.pptx")

6.拼图

R包patchwork
语法简单，完美兼容ggplot2
拼图比例设置简单
- （1）支持直接p1+p2拼图，比任何一个包都简单
- （2）复杂的布局代码易读性更强
- （3）可以给子图添加标记（例如ABCD， I II III IV 这样）
- （4）可以统一修改所有子图
- （5）可以将子图的图例移到一起，整体性特别好

ibrary(patchwork)
p1 = ggscatter(iris,x="Sepal.Length",
          y="Petal.Length",
          color="Species")

p2 <- ggboxplot(iris, x = "Species", 
               y = "Sepal.Length",
               color = "Species", 
               shape = "Species",
               add = "jitter")
p3 = ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
  geom_boxplot()
p4 = ggplot(data = diamonds) + 
  geom_bar(
    mapping = aes(x = cut, fill = cut), 
    show.legend = FALSE,
    width = 1
  )
p1 + p2 + p3 + p4 + plot_annotation(tag_level = "A")
p1/p2

代码可运行却不出图——因为画板被占用

dev.off()         #表示关闭画板
#多次运行dev.off(),到null device为止，在运行出图代码或dev.new(y)

7.进阶

1.tidyr 核心函数

tidyr的扁和长

### 原始数据

test <- data.frame(geneid = paste0("gene",1:4),
                 sample1 = c(1,4,7,10),
                 sample2 = c(2,5,0.8,11),
                 sample3 = c(0.3,6,9,12))
test

### 扁变长

test_gather <- gather(data = test,
                    key = sample_nm,
                    value = exp,
                    - geneid)
head(test_gather)

### 长变扁

test_re <- spread(data = test_gather,
                key = sample_nm,
                value = exp)
head(test_re)

tidyr的分与合

### 原始数据
test <- data.frame(x = c( "a,b", "a,d", "b,c"));test
### 分割
test_seprate <- separate(test,x, c("X", "Y"),sep = ",");test_seprate
### 合并
test_re <- unite(test_seprate,"x",X,Y,sep = ",")

处理NA

### 原始数据
X<-data.frame(X1 = LETTERS[1:5],X2 = 1:5)
X[2,2] <- NA
X[4,1] <- NA

### 1.去掉含有NA的行,可以选择只根据某一列来去除

drop_na(X)
drop_na(X,X1)
drop_na(X,X2)
### 2.替换NA

replace_na(X$X2,0)

### 3.用上一行的值填充NA
X
fill(X,X2)

2.dplyr

1.mutate(),新增列

test <- iris[c(1:2,51:52,101:102),]
rownames(test) =NULL
mutate(test, new = Sepal.Length * Sepal.Width)

2.select(),按列筛选

####(1)按列号筛选
select(test,1)
select(test,c(1,5))

####(2)按列名筛选
select(test,Sepal.Length)
select(test, Petal.Length, Petal.Width)
vars <- c("Petal.Length", "Petal.Width")
select(test, one_of(vars))
#####一组来自tidyselect的有用函数
select(test, starts_with("Petal"))
select(test, ends_with("Width"))
select(test, contains("etal"))
select(test, matches(".t."))
select(test, everything())
select(test, last_col())
select(test, last_col(offset = 1))

####(4)利用everything()，列名可以重排序

select(test,Species,everything())

3.filter()筛选行

filter(test, Species == "setosa")
filter(test, Species == "setosa"&Sepal.Length > 5 )
filter(test, Species %in% c("setosa","versicolor"))

4.arrange(),按某一列对整个表格进行排序

arrange(test, Sepal.Length)#默认从小到大排序
arrange(test, desc(Sepal.Length))#用desc从大到小
arrange(test,  desc(Sepal.Width),Sepal.Length)

5.summarise()：汇总

#对数据进行汇总操作,结合group_by使用实用性强

summarise(test, mean(Sepal.Length), sd(Sepal.Length))# 计算Sepal.Length的平均值和标准差：

# 先按照Species分组，计算每组Sepal.Length的平均值和标准差
group_by(test, Species)
tmp = summarise(group_by(test, Species),mean(Sepal.Length), sd(Sepal.Length))

6.两个实用技能

1：管道操作 %>% (cmd/ctr + shift + M)

library(dplyr)
x1 = filter(iris,Sepal.Width>3)
x2 = select(x1,c("Sepal.Length","Sepal.Width" ))
x3 = arrange(x2,Sepal.Length)

colnames(iris)
iris %>% 
  filter(Sepal.Width>3) %>% 
  select(c("Sepal.Length","Sepal.Width" ))%>%
  arrange(Sepal.Length)

2：count统计某列的unique值

count(test,Species)

##处理关系数据:即将2个表进行连接，注意：不要引入factor
options(stringsAsFactors = F)

test1 <- data.frame(name = c('jimmy','nicker','doodle'), 
                    blood_type = c("A","B","O"))
test1
test2 <- data.frame(name = c('doodle','jimmy','nicker','tony'),
                    group = c("group1","group1","group2","group2"),
                    vision = c(4.2,4.3,4.9,4.5))
test2 

test3 <- data.frame(NAME = c('doodle','jimmy','lucy','nicker'),
                    weight = c(140,145,110,138))
merge(test1,test2,by="name")
merge(test1,test3,by.x = "name",by.y = "NAME")

###1.內连inner_join,取交集
inner_join(test1, test2, by = "name")
inner_join(test1,test3,by = c("name"="NAME"))

###2.左连left_join
left_join(test1, test2, by = 'name')

left_join(test2, test1, by = 'name')
###3.全连full_join
full_join(test1, test2, by = 'name')

###4.半连接：返回能够与y表匹配的x表所有记录semi_join
semi_join(x = test1, y = test2, by = 'name')

###5.反连接：返回无法与y表匹配的x表的所记录anti_join
anti_join(x = test2, y = test1, by = 'name')

3.stringr

1.检测字符串长度

library(stringr)

x <- "The birch canoe slid on the smooth planks."

x
length(x)
str_length(x)

2.字符串拆分与组合

str_split(x," ")
x2 = str_split(x," ")[[1]]
str_c(x2,collapse = " ")
str_c(x2,1234,sep = "+")

3.提取字符串的一部分

str_sub(x,5,9)

4.大小写转换

str_to_upper(x2)
str_to_lower(x2)
str_to_title(x2)

5.字符串排序

str_sort(x2)

6.字符检测

str_detect(x2,"h")
str_starts(x2,"T")
str_ends(x2,"e")

###与sum和mean连用，可以统计匹配的个数和比例
sum(str_detect(x2,"h"))
mean(str_detect(x2,"h"))

7.提取匹配到的字符串

str_subset(x2,"h")

8.字符计数

str_count(x," ")
str_count(x2,"o")

9.字符串替换

str_replace(x2,"o","A")
str_replace_all(x2,"o","A")

8.条件语句和循环语句

一.条件语句

###1.if(){ }

#### (1)只有if没有else，那么条件是FALSE时就什么都不做


i = -1
if (i<0) print('up')
if (i>0) print('up')

#理解下面代码
if(!require(tidyr)) install.packages('tidyr')

#### (2)有else
i =1
if (i>0){
  cat('+')
} else {
  print("-")
}

ifelse(i>0,"+","-")

x=rnorm(10)
y=ifelse(x>0,"+","-")
y

#### (3)多个条件
i = 0
if (i>0){
  print('+')
} else if (i==0) {
  print('0')
} else if (i< 0){
  print('-')
}

ifelse(i>0,"+",ifelse((i<0),"-","0"))

### 2.switch()
cd = 3
foo <- switch(EXPR = cd, 
              #EXPR = "aa", 
              aa=c(3.4,1),
              bb=matrix(1:4,2,2),
              cc=matrix(c(T,T,F,T,F,F),3,2),
              dd="string here",
              ee=matrix(c("red","green","blue","yellow")))
foo

ifelse函数
- 三个参数
- ifelse(x,yes,no)
- x:逻辑值
- yes：逻辑值为TRUE时的返回值
- no:逻辑值为FALSE时的返回值

二、循环语句

### 1.for循环
#**顺便看一下next和break**
x <- c(5,6,0,3)
s=0
for (i in x){
  s=s+i
  #if(i == 0) next
  #if (i == 0) break
  print(c(which(x==i),i,1/i,s))
}

x <- c(5,6,0,3)
s = 0
for (i in 1:length(x)){
  s=s+x[[i]]
  #if(i == 3) next
  #if (i == 3) break
  print(c(i,x[[i]],1/i,s))
}
#如何将结果存下来?
s = 0
result = list()
for(i in 1:length(x)){
  s=s+x[[i]]
  result[[i]] = c(i,x[[i]],1/i,s)
}
do.call(cbind,result)

### 2.while 循环
i = 0

while (i < 5){
  print(c(i,i^2))
  i = i+1
}

### 3.repeat 语句

#注意：必须有break
i=0L
s=0L
repeat{
 i = i + 1
 s = s + i
 print(c(i,s))
 if(i==50) break
}

三、长脚本管理方式

1.分成多个脚本，每个脚本最后保存Rdata，下一个脚本开头清空再加载。

image-20201126170014008.png

if(F){…} ，则{ }里的脚本被跳过 if(T){…} ，则{ }里的脚本被运行凡是带有{ }的代码，均可以被折叠

image-20201126170110219.png

四、apply函数

apply(X, MARGIN, FUN, …) 

apply(test, 2, mean)
apply(test, 1, sum)

#其中X是数据框/矩阵名；MARGIN为1表示取行，为2表示取列，FUN是函数
#对X的每一行/列进行FUN这个函数

五、R语言遍历、创建、删除文件夹

dir()
file.create() file.exists(...)
file.remove(...)
file.rename(from, to)
file.append(file1, file2)
file.copy(from, to, overwrite = recursive, recursive = FALSE,
copy.mode = TRUE, copy.date = FALSE)
file.symlink(from, to)
file.link(from, to)
dir.create("doudou")
unlink("doudou",recursive = T)