1.dplyr包了解及其安装

dplyr了解引自网络

#安装加载：
install.packages("dplyr");library(dplyr)

2.mutate()新增列函数，在数据集的基础上新增列，不对原数据作更改

构建test数据框

> rm(list = ls())
> test <- iris[c(1:2,51:52,101:102),]
> library(dplyr)
> test
    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
51           7.0         3.2          4.7         1.4 versicolor
52           6.4         3.2          4.5         1.5 versicolor
101          6.3         3.3          6.0         2.5  virginica
102          5.8         2.7          5.1         1.9  virginica

新增一列

test_mutate<-mutate(test,new=test$Sepal.Length*test$Sepal.Width)
> test_mutate
  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
> 

3.select()按列筛选

按照列号

> test
    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
51           7.0         3.2          4.7         1.4 versicolor
52           6.4         3.2          4.5         1.5 versicolor
101          6.3         3.3          6.0         2.5  virginica
102          5.8         2.7          5.1         1.9  virginica
> test_select1<-select(test,1)
> test_select1
    Sepal.Length
1            5.1
2            4.9
51           7.0
52           6.4
101          6.3
102          5.8
> test_select2<-select(test,c(1,3,5))
> test_select2
    Sepal.Length Petal.Length    Species
1            5.1          1.4     setosa
2            4.9          1.4     setosa
51           7.0          4.7 versicolor
52           6.4          4.5 versicolor
101          6.3          6.0  virginica
102          5.8          5.1  virginica

按照列名筛选

> test
    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
51           7.0         3.2          4.7         1.4 versicolor
52           6.4         3.2          4.5         1.5 versicolor
101          6.3         3.3          6.0         2.5  virginica
102          5.8         2.7          5.1         1.9  virginica
> test_select3<-select(test,Sepal.Width,Species)
> test_select3
    Sepal.Width    Species
1           3.5     setosa
2           3.0     setosa
51          3.2 versicolor
52          3.2 versicolor
101         3.3  virginica
102         2.7  virginica

varss<-c("Sepal.Width","Species")
> test_select4<-select(test,varss)
Note: Using an external vector in selections is ambiguous.
i Use `all_of(varss)` instead of `varss` to silence this message.
i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
> test_select4
    Sepal.Width    Species
1           3.5     setosa
2           3.0     setosa
51          3.2 versicolor
52          3.2 versicolor
101         3.3  virginica
102         2.7  virginica
> test_select4<-select(test,one_of(varss)

> test_select4
    Sepal.Width    Species
1           3.5     setosa
2           3.0     setosa
51          3.2 versicolor
52          3.2 versicolor
101         3.3  virginica
102         2.7  virginica

4.filter()筛选行

filter() 返回行的子集,按照指定的条件筛选符合条件中逻辑判断要求的数据行。

> test
    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
51           7.0         3.2          4.7         1.4 versicolor
52           6.4         3.2          4.5         1.5 versicolor
101          6.3         3.3          6.0         2.5  virginica
102          5.8         2.7          5.1         1.9  virginica
#筛选物种为setosa的行
 test_filter1<-filter(test,Species=="setosa")
> test_filter1
  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

test_filter2<-filter(test,test$Species=="setosa"&test$Sepal.Length>5)
> test_filter2
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
> test_filter3<-filter(test,Species %in% c("setosa","versicolor"))

> test_filter3
  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：默认从小到大排序

> test_arrange1<-arrange(test,test$Sepal.Length)
> test_arrange1
  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

#desc()设置从大到小
test_arrange2<-arrange(test,desc(test$Sepal.Length))
> test_arrange2
  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

计算Sepal.Length的平均值和标准差

summarise(test,mean(test$Sepal.Length),sd(test$Sepal.Width))
  mean(test$Sepal.Length) sd(test$Sepal.Width)
1                5.916667            0.2738613

对数据进行汇总操作,结合group_by使用实用性强

test_summurize<-summarise(group_by(test,Species),mean(test$Sepal.Length),sd(test$Sepal.Length))
> test_summurize
# A tibble: 3 x 3
  Species    `mean(test$Sepal.Length)` `sd(test$Sepal.Length)`
  <fct>                          <dbl>                   <dbl>
1 setosa                          5.92                   0.808
2 versicolor                      5.92                   0.808
3 virginica                       5.92                   0.808
> 

dplyr两个实用技能

1.管道操作 %>% (cmd/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)`
  <fct>                     <dbl>              <dbl>
1 setosa                     5                 0.141
2 versicolor                 6.7               0.424
3 virginica                  6.05              0.354

2: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
> table(test$Species)

    setosa versicolor  virginica 
         2          2          2 

dplyr处理关系数据

构建数据集

> 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

1.內连inner_join取交集

> test_join1<-inner_join(test1,test2,by="x")
> test_join1 #取交集
  x z y
1 b A 2
2 e B 5
3 f C 6

2.左连left_join

>test_jion2<-left_join(test1,test2,by="x")
> test_jion2
  x z  y
1 b A  2
2 e B  5
3 f C  6
4 x D NA

3.全连full_join

> test_jion3<-full_join(test1,test2,by="x")
> test_jion3
  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semi_join

返回能够与y表匹配的x表所有记录

> test_jion4<-semi_join(test1,test2,by="x")
> test_jion4
  x z
1 b A
2 e B
3 f C

5.半连接semi_join

返回无法与y表匹配的x表的所记录anti_join

> test_jion5<-anti_join(test1,test2,by="x")
> test_jion5
  x z
1 x D

6.简单合并:bind_rows()，bind_cols()

类似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))
> test2
  x  y
1 5 50
2 6 60
> test3 <- data.frame(z = c(100,200,300,400))
> 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

总结

学习小组-Day6-Mingwei Guo R包.png