學習R包
特別鳴謝：@公眾號：生信星球 豆豆&花花
依照慣例构诚，先來張思維導圖~
Day6_針對R包的操作
下邊以內(nèi)置數(shù)據(jù)集iris中的部分數(shù)據(jù)為操作對象范嘱，練習今天學到的dplyr包里的幾個函數(shù)。
1. 讀取數(shù)據(jù)
#加載dplyr包
library(dplyr)
#取iris的指定行
test_data <- iris[seq(1,150,by=10),]
#查看行列數(shù)
dim(test_data)
2. mutate()
> mutate(test_data, ID=1:nrow(test_data))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species ID
1 5.1 3.5 1.4 0.2 setosa 1
2 5.4 3.7 1.5 0.2 setosa 2
3 5.4 3.4 1.7 0.2 setosa 3
4 4.8 3.1 1.6 0.2 setosa 4
5 5.0 3.5 1.3 0.3 setosa 5
6 7.0 3.2 4.7 1.4 versicolor 6
7 5.0 2.0 3.5 1.0 versicolor 7
8 5.9 3.2 4.8 1.8 versicolor 8
9 5.5 2.4 3.8 1.1 versicolor 9
10 5.5 2.6 4.4 1.2 versicolor 10
11 6.3 3.3 6.0 2.5 virginica 11
12 6.5 3.2 5.1 2.0 virginica 12
13 6.9 3.2 5.7 2.3 virginica 13
14 7.4 2.8 6.1 1.9 virginica 14
15 6.7 3.1 5.6 2.4 virginica 15
3. %>% + groupby() + arrange()
> test_data = mutate(test_data, ID=1:nrow(test_data))
> test_data %>%
+ group_by(Species) %>%
+ arrange(desc(Petal.Width))
# A tibble: 15 x 6
# Groups: Species [3]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species ID
<dbl> <dbl> <dbl> <dbl> <fct> <int>
1 6.3 3.3 6 2.5 virginica 11
2 6.7 3.1 5.6 2.4 virginica 15
3 6.9 3.2 5.7 2.3 virginica 13
4 6.5 3.2 5.1 2 virginica 12
5 7.4 2.8 6.1 1.9 virginica 14
6 5.9 3.2 4.8 1.8 versicolor 8
7 7 3.2 4.7 1.4 versicolor 6
8 5.5 2.6 4.4 1.2 versicolor 10
9 5.5 2.4 3.8 1.1 versicolor 9
10 5 2 3.5 1 versicolor 7
11 5 3.5 1.3 0.3 setosa 5
12 5.1 3.5 1.4 0.2 setosa 1
13 5.4 3.7 1.5 0.2 setosa 2
14 5.4 3.4 1.7 0.2 setosa 3
15 4.8 3.1 1.6 0.2 setosa 4
4. join
定義sub_test1和sub_test2
> test_data %>%
+ select(c("ID", "Species")) %>%
+ filter(Species %in% c("setosa","virginica")) -> sub_test1
> sub_test1
ID Species
1 1 setosa
2 2 setosa
3 3 setosa
4 4 setosa
5 5 setosa
6 11 virginica
7 12 virginica
8 13 virginica
9 14 virginica
10 15 virginica
> sub_test2 <- test_data[seq(1,nrow(test_data),2), c("ID", "Sepal.Length")]
> sub_test2
ID Sepal.Length
1 1 5.1
3 3 5.4
5 5 5.0
7 7 5.0
9 9 5.5
11 11 6.3
13 13 6.9
15 15 6.7
各種join
注意：實踐發(fā)現(xiàn)盏阶，join里的by參數(shù)所取的列，該列數(shù)值不能存在重復脑慧，否則結果不對……
# inner-join
> inner_join(sub_test1, sub_test2, by = "ID")
ID Species Sepal.Length
1 1 setosa 5.1
2 3 setosa 5.4
3 5 setosa 5.0
4 11 virginica 6.3
5 13 virginica 6.9
6 15 virginica 6.7
# left-join
> left_join(sub_test1, sub_test2, by = "ID")
ID Species Sepal.Length
1 1 setosa 5.1
2 2 setosa NA
3 3 setosa 5.4
4 4 setosa NA
5 5 setosa 5.0
6 11 virginica 6.3
7 12 virginica NA
8 13 virginica 6.9
9 14 virginica NA
10 15 virginica 6.7
# full-join
> full_join(sub_test1, sub_test2, by = "ID")
ID Species Sepal.Length
1 1 setosa 5.1
2 2 setosa NA
3 3 setosa 5.4
4 4 setosa NA
5 5 setosa 5.0
6 11 virginica 6.3
7 12 virginica NA
8 13 virginica 6.9
9 14 virginica NA
10 15 virginica 6.7
11 7 <NA> 5.0
12 9 <NA> 5.5
# semi-join
> semi_join(sub_test1, sub_test2, by = "ID")
ID Species
1 1 setosa
2 3 setosa
3 5 setosa
4 11 virginica
5 13 virginica
6 15 virginica
# anti-join
> anti_join(sub_test1, sub_test2, by = "ID")
ID Species
1 2 setosa
2 4 setosa
3 12 virginica
4 14 virginica
combine行/列
> test1 <- data.frame(ID = c(1,2,3), num = c(1,11,111))
> test1
ID num
1 1 1
2 2 11
3 3 111
>
> test2 <- data.frame(ID = c(4,5), num = c(1111,11111))
> test2
ID num
1 4 1111
2 5 11111
>
> test3 <- data.frame(level = c("A","AA","AAA"))
> test3
level
1 A
2 AA
3 AAA
>
> bind_rows(test1, test2)
ID num
1 1 1
2 2 11
3 3 111
4 4 1111
5 5 11111
>
> bind_cols(test1, test3)
ID num level
1 1 1 A
2 2 11 AA
3 3 111 AAA
以上，就是今天學到的內(nèi)容岩梳。
