R包安装
install.packages(“包”)
BiocManager::install(“包”)
R包加载
library(包)
dplyr包
示例数据
test <- iris[c(1:2,51:52,101:102),]
mutate() ：新增列
mutate(test, new = Sepal.Length * Sepal.Width)
select()：按列筛选
可按列号、列名筛选
> 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 5.8
> select(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
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
arrange()：按某1列或某几列对整个表格进行排序
> arrange(test,Sepal.Length) # 默认为从小到大
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)) #使用desc使排序为从大到小
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
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
> group_by(test, Species) #按Species分组
# 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))
# 先按照Species分组，计算每组Sepal.Length的平均值和标准差
# A tibble: 3 x 3
Species `mean(Sepal.Length)` `sd(Sepal.Length)`
<fct> <dbl> <dbl>
1 setosa 5 0.141
2 versicolor 6.7 0.424
3 virginica 6.05 0.354
dplyr的两个实用技能
管道操作 %>%（快键键：cmd+shift+M）
管道操作可以连续传参，省去中间变量。左侧作为参数传入右侧函数内部
> test %>% group_by(Species) %>%
+ summarise(mean(Sepal.Length), sd(Sepal.Length))
# A tibble: 3 x 3
Species `mean(Sepal.Length)` `sd(Sepal.Length)`
<fct> <dbl> <dbl>
1 setosa 5 0.141
2 versicolor 6.7 0.424
3 virginica 6.05 0.354
count统计某列的unique值
> count(test,Species)
# A tibble: 3 x 2
Species n
<fct> <int>
1 setosa 2
2 versicolor 2
3 virginica 2
dplyr处理关系数据
演示数据
> test1 <- data.frame(x = c('b','e','f','x'),
+ z = c("A","B","C",'D'),
+ stringsAsFactors = F)
> 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),
+ stringsAsFactors = F)
> test2
x y
1 a 1
2 b 2
3 c 3
4 d 4
5 e 5
6 f 6
inner_join：取交集
> inner_join(test1,test2,by="x") #取x的交集
x z y
1 b A 2
2 e B 5
3 f C 6
left_jion：左连
> 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
full_join：全连
> 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
semi_join：半连接-返回能够与y表匹配的x表所有记录
> semi_join(x = test1, y = test2,by="x") # x=,y=可以省略
x z
1 b A
2 e B
3 f C
anti_join：反连接—返回无法与y表匹配的x表的所记录
> anti_join(x = test2, y = test1, by = 'x') # x=,y=可以省略
x y
1 a 1
2 c 3
3 d 4
bind_rows,bind_cols：简单合并
相当于base包里的cbind()函数和rbind()函数
bind_rows()函数需要两个数据框列数相同
bind_cols()函数需要两个数据框行数相同
示例数据
> test1 <- data.frame(x = c(1,2,3,4), y = c(10,20,30,40))
> test2 <- data.frame(x = c(5,6), y = c(50,60))
> test3 <- data.frame(z = c(100,200,300,400))
> test1
x y
1 1 10
2 2 20
3 3 30
4 4 40
> test2
x y
1 5 50
2 6 60
> test3
z
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
网友评论