R语言基础之第五部分 总结数据信息
1、以R自带数据集airquality为例
> head(airquality) #默认给出数据集前6行
Ozone Solar.R Wind Temp Month Day
1 41 190 7.4 67 5 1
2 36 118 8.0 72 5 2
3 12 149 12.6 74 5 3
4 18 313 11.5 62 5 4
5 NA NA 14.3 56 5 5
6 28 NA 14.9 66 5 6
> head(airquality,10) #给出数据集前10行
> tail(airquality) #默认给出数据集最后6行
> summary(airquality)
Ozone Solar.R Wind Temp Month
Min. : 1.00 Min. : 7.0 Min. : 1.700 Min. :56.00 Min. :5.000
1st Qu.: 18.00 1st Qu.:115.8 1st Qu.: 7.400 1st Qu.:72.00 1st Qu.:6.000
Median : 31.50 Median :205.0 Median : 9.700 Median :79.00 Median :7.000
Mean : 42.13 Mean :185.9 Mean : 9.958 Mean :77.88 Mean :6.993
3rd Qu.: 63.25 3rd Qu.:258.8 3rd Qu.:11.500 3rd Qu.:85.00 3rd Qu.:8.000
Max. :168.00 Max. :334.0 Max. :20.700 Max. :97.00 Max. :9.000
NA's :37 NA's :7
Day
Min. : 1.0
1st Qu.: 8.0
Median :16.0
Mean :15.8
3rd Qu.:23.0
Max. :31.0
> str(airquality) #查看变量属性
'data.frame': 153 obs. of 6 variables:
$ Ozone : int 41 36 12 18 NA 28 23 19 8 NA ...
$ Solar.R: int 190 118 149 313 NA NA 299 99 19 194 ...
$ Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
$ Temp : int 67 72 74 62 56 66 65 59 61 69 ...
$ Month : int 5 5 5 5 5 5 5 5 5 5 ...
$ Day : int 1 2 3 4 5 6 7 8 9 10 ...
> table(airquality$Month) #统计月份的频数
5 6 7 8 9
31 30 31 31 30
> table(airquality$Ozone) #包含缺失值
1 4 6 7 8 9 10 11 12 13 14 16 18 19 20 21 22 23 24 27 28
1 1 1 3 1 3 1 3 2 4 4 4 4 1 4 4 1 6 2 1 3
29 30 31 32 34 35 36 37 39 40 41 44 45 46 47 48 49 50 52 59 61
1 2 1 3 1 2 2 2 2 1 1 3 2 1 1 1 1 1 1 2 1
63 64 65 66 71 73 76 77 78 79 80 82 84 85 89 91 96 97 108 110 115
1 2 1 1 1 2 1 1 2 1 1 1 1 2 1 1 1 2 1 1 1
118 122 135 168
1 1 1 1
> table(airquality$Month,airquality$Day)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
31
5 1
6 0
7 1
8 1
9 0
> any(is.na(airquality$Month)) #Month变量是否有缺失值
[1] FALSE
> sum(airquality$Month)
[1] 1070
> all(airquality$Month < 11) #是否所有的月份都是小于11月
[1] TRUE
2、以R自带数据集Titanic为例
> Titanic
> Titanic
, , Age = Child, Survived = No
Sex
Class Male Female
1st 0 0
2nd 0 0
3rd 35 17
Crew 0 0
, , Age = Adult, Survived = No
Sex
Class Male Female
1st 118 4
2nd 154 13
3rd 387 89
Crew 670 3
, , Age = Child, Survived = Yes
Sex
Class Male Female
1st 5 1
2nd 11 13
3rd 13 14
Crew 0 0
, , Age = Adult, Survived = Yes
Sex
Class Male Female
1st 57 140
2nd 14 80
3rd 75 76
Crew 192 20
> titanic = as.data.frame(Titanic)
> head(titanic,10)
Class Sex Age Survived Freq
1 1st Male Child No 0
2 2nd Male Child No 0
3 3rd Male Child No 35
4 Crew Male Child No 0
5 1st Female Child No 0
6 2nd Female Child No 0
7 3rd Female Child No 17
8 Crew Female Child No 0
9 1st Male Adult No 118
10 2nd Male Adult No 154
> dim(titanic)
[1] 32 5
> summary(titanic)
Class Sex Age Survived Freq
1st :8 Male :16 Child:16 No :16 Min. : 0.00
2nd :8 Female:16 Adult:16 Yes:16 1st Qu.: 0.75
3rd :8 Median : 13.50
Crew:8 Mean : 68.78
3rd Qu.: 77.00
Max. :670.00
> x= xtabs(Freq ~ Class + Age, data = titanic) #交叉表
> x
Age
Class Child Adult
1st 6 319
2nd 24 261
3rd 79 627
Crew 0 885
> ftable(x) #扁平化
Age Child Adult
Class
1st 6 319
2nd 24 261
3rd 79 627
Crew 0 885
后续请参考：
R语言基础之第一部分 : 5种数据对象类型
R语言基础之第二部分 : 操纵数据取子集
R语言基础之第三部分：重要函数apply族函数的使用
R语言基础之第四部分 : 排序
