R包的安装和使用（以dplyr为例）
1.安装和加载R包
（1）镜像设置
options("repos" = c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/")) #对应清华源
options(BioC_mirror="https://mirrors.ustc.edu.cn/bioc/") #对应中科大源
（2）安装
install.packages("包"）#安装包存在于CRAN网站用这个命令安装
BioManager::install("包"）#Biocductor网站的安装包（3）加载
library和require两个函数均可以加载，需要先安装后加载options("repos" = c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
options(BioC_mirror="https://mirrors.ustc.edu.cn/bioc/")
install.packages("dplyr")
library(dplyr)2.dplyr包的使用（五个基础函数）
（1）mutate()#新增列
mutate(test, new = Sepal.Length * Sepal.Width)Sepal.Length Sepal.Width Petal.Length Petal.Width Species new
1 5.1 3.5 1.4 0.2 setosa 17.85
2 4.9 3.0 1.4 0.2 setosa 14.70
3 7.0 3.2 4.7 1.4 versicolor 22.40
4 6.4 3.2 4.5 1.5 versicolor 20.48
5 6.3 3.3 6.0 2.5 virginica 20.79
6 5.8 2.7 5.1 1.9 virginica 15.66
（2）select()#按列筛选
- 按列号筛选select(test,1)
Sepal.Length
1 5.1
2 4.9
51 7.0
52 6.4
101 6.3
102 5.8
select(test,c(1,5))
Sepal.Length Species
1 5.1 setosa
2 4.9 setosa
51 7.0 versicolor
52 6.4 versicolor
101 6.3 virginica
102 5.8 virginica
select(test,Sepal.Length)
Sepal.Length
1 5.1
2 4.9
51 7.0
52 6.4
101 6.3
102 5.8select(test, Petal.Length, Petal.Width)
Petal.Length Petal.Width
1 1.4 0.2
2 1.4 0.2
51 4.7 1.4
52 4.5 1.5
101 6.0 2.5
102 5.1 1.9
vars <- c("Petal.Length", "Petal.Width")
select(test, one_of(vars))Petal.Length Petal.Width
1 1.4 0.2
2 1.4 0.2
51 4.7 1.4
52 4.5 1.5
101 6.0 2.5
102 5.1 1.9（3）filter（）筛选行filter(test, Species == "setosa")
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
filter(test, Species == "setosa"&Sepal.Length > 5 )
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
filter(test, Species %in% c("setosa","versicolor"))
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 7.0 3.2 4.7 1.4 versicolor
4 6.4 3.2 4.5 1.5 versicolor（4）arrange(),按某1列或某几列对整个表格进行排序arrange(test, Sepal.Length)#默认从小到大排序
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.9 3.0 1.4 0.2 setosa
2 5.1 3.5 1.4 0.2 setosa
3 5.8 2.7 5.1 1.9 virginica
4 6.3 3.3 6.0 2.5 virginica
5 6.4 3.2 4.5 1.5 versicolor
6 7.0 3.2 4.7 1.4 versicolor
arrange(test, desc(Sepal.Length))#用desc从大到小
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 7.0 3.2 4.7 1.4 versicolor
2 6.4 3.2 4.5 1.5 versicolor
3 6.3 3.3 6.0 2.5 virginica
4 5.8 2.7 5.1 1.9 virginica
5 5.1 3.5 1.4 0.2 setosa
6 4.9 3.0 1.4 0.2 setosa（5）summarise():汇总summarise(test, mean(Sepal.Length), sd(Sepal.Length))# 计算Sepal.Length的平均值和标准差
mean(Sepal.Length) sd(Sepal.Length)
1 5.916667 0.8084965
先按照Species分组，计算每组Sepal.Length的平均值和标准差
group_by(test, Species)
# A tibble: 6 x 5
# Groups: Species [3]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
* <dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 7 3.2 4.7 1.4 versicolor
4 6.4 3.2 4.5 1.5 versicolor
5 6.3 3.3 6 2.5 virginica
6 5.8 2.7 5.1 1.9 virginica
summarise(group_by(test, Species),mean(Sepal.Length), sd(Sepal.Length))
# A tibble: 3 x 3
Species
mean(Sepal.Length)sd(Sepal.Length)1 setosa 5 0.141
2 versicolor 6.7 0.424
3 virginica 6.05 0.354dplyr两个实用技能（1）管道操作 %>% (快捷键：ctr+shift+M)test %>%
group_by(Species) %>%
summarise(mean(Sepal.Length), sd(Sepal.Length))# A tibble: 3 x 3
Species
mean(Sepal.Length)sd(Sepal.Length)1 setosa 5 0.141
2 versicolor 6.7 0.424
3 virginica 6.05 0.354%>%向右依次执行命令
- 按列名筛选
（2）count统计某列的unique值
count(test,Species)# A tibble: 3 x 2
Species n
1 setosa 2
2 versicolor 2
3 virginica 2
dplyr处理关系数据
test1 <- data.frame(x = c('b','e','f','x'),z = c("A","B","C",'D'))test1
x z
1 b A
2 e B
3 f C
4 x D
test2 <- data.frame(x = c('a','b','c','d','e','f'),
y = c(1,2,3,4,5,6))
test2x y
1 a 1
2 b 2
3 c 3
4 d 4
5 e 5
6 f 6
（1）内连inner_join，取交集
inner_join(test1, test2, by = "x")x z y
1 b A 2
2 e B 5
3 f C 6
（2）左连left_join
left_join(test1, test2, by = 'x')x z y
1 b A 2
2 e B 5
3 f C 6
4 x D NA
left_join(test2, test1, by = 'x')
x y z
1 a 1 NA
2 b 2 A
3 c 3 NA
4 d 4 NA
5 e 5 B
6 f 6 C
（3）全连full_join
full_join( test1, test2, by = 'x')x z y
1 b A 2
2 e B 5
3 f C 6
4 x D NA
5 a NA 1
6 c NA 3
7 d NA 4
（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 = 'x')x z
1 b A
2 e B
3 f C
（5）反连接：返回无法与y表的所记录anti_join
anti_join(x = test2, y = test1, by = 'x')x y
1 a 1
2 c 3
3 d 4
（6）简单合并
在相当于base包里的cbind()函数和rbind()函数;注意，bind_rows()函数需要两个表格列数相同，而bind_cols()函数则需要两个数据框有相同的行数
test1 <- data.frame(x = c(1,2,3,4), y = c(10,20,30,40))test1
x y
1 1 10
2 2 20
3 3 30
4 4 40
test2 <- data.frame(x = c(5,6), y = c(50,60))
test2x y
1 5 50
2 6 60
test3 <- data.frame(z = c(100,200,300,400))
test3z
1 100
2 200
3 300
4 400
bind_rows(test1, test2)
x y
1 1 10
2 2 20
3 3 30
4 4 40
5 5 50
6 6 60
bind_cols(test1, test3)
x y z
1 1 10 100
2 2 20 200
3 3 30 300
4 4 40 400
